From 801582bf8b3b7ef6d245b18ceb0f6377a77f9afa Mon Sep 17 00:00:00 2001 From: zwang28 <70626450+zwang28@users.noreply.github.com> Date: Thu, 2 Nov 2023 16:19:19 +0800 Subject: [PATCH 1/8] refactor(meta): adapt metadata restore to metadata model V2 (#13197) --- proto/backup_service.proto | 1 + src/meta/src/backup_restore/mod.rs | 1 + src/meta/src/backup_restore/restore.rs | 192 +++++------------ .../src/backup_restore/restore_impl/mod.rs | 32 +++ .../src/backup_restore/restore_impl/v1.rs | 202 ++++++++++++++++++ .../src/backup_restore/restore_impl/v2.rs | 96 +++++++++ src/meta/src/backup_restore/utils.rs | 4 + src/storage/backup/src/lib.rs | 6 +- src/storage/backup/src/storage.rs | 1 + src/storage/src/hummock/backup_reader.rs | 1 + 10 files changed, 395 insertions(+), 141 deletions(-) create mode 100644 src/meta/src/backup_restore/restore_impl/mod.rs create mode 100644 src/meta/src/backup_restore/restore_impl/v1.rs create mode 100644 src/meta/src/backup_restore/restore_impl/v2.rs diff --git a/proto/backup_service.proto b/proto/backup_service.proto index feca5f17b7dc3..c84b628e58550 100644 --- a/proto/backup_service.proto +++ b/proto/backup_service.proto @@ -45,6 +45,7 @@ message MetaSnapshotMetadata { uint64 hummock_version_id = 2; uint64 max_committed_epoch = 3; uint64 safe_epoch = 4; + optional uint32 format_version = 5; } service BackupService { diff --git a/src/meta/src/backup_restore/mod.rs b/src/meta/src/backup_restore/mod.rs index 0dfe5b3442415..fc33af5ef4628 100644 --- a/src/meta/src/backup_restore/mod.rs +++ b/src/meta/src/backup_restore/mod.rs @@ -19,6 +19,7 @@ mod meta_snapshot_builder; mod meta_snapshot_builder_v2; mod metrics; mod restore; +mod restore_impl; mod utils; pub use restore::*; diff --git a/src/meta/src/backup_restore/restore.rs b/src/meta/src/backup_restore/restore.rs index 48dd6fc0cb664..5474b43654017 100644 --- a/src/meta/src/backup_restore/restore.rs +++ b/src/meta/src/backup_restore/restore.rs @@ -14,22 +14,20 @@ use std::sync::Arc; -use itertools::Itertools; use risingwave_backup::error::{BackupError, BackupResult}; -use risingwave_backup::meta_snapshot_v1::MetaSnapshotV1; +use risingwave_backup::meta_snapshot::Metadata; use risingwave_backup::storage::{MetaSnapshotStorage, MetaSnapshotStorageRef}; +use risingwave_backup::MetaSnapshotId; use risingwave_common::config::MetaBackend; use risingwave_hummock_sdk::version_checkpoint_path; use risingwave_object_store::object::object_metrics::ObjectStoreMetrics; use risingwave_object_store::object::parse_remote_object_store; use risingwave_pb::hummock::{HummockVersion, HummockVersionCheckpoint}; +use crate::backup_restore::restore_impl::v1::{LoaderV1, WriterModelV1ToMetaStoreV1}; +use crate::backup_restore::restore_impl::v2::{LoaderV2, WriterModelV2ToMetaStoreV2}; +use crate::backup_restore::restore_impl::{Loader, Writer}; use crate::backup_restore::utils::{get_backup_store, get_meta_store, MetaStoreBackendImpl}; -use crate::dispatch_meta_store; -use crate::hummock::model::CompactionGroup; -use crate::manager::model::SystemParamsModel; -use crate::model::{ClusterId, MetadataModel, TableFragments}; -use crate::storage::{MetaStore, DEFAULT_COLUMN_FAMILY}; /// Command-line arguments for restore. #[derive(clap::Args, Debug, Clone)] @@ -98,99 +96,6 @@ async fn restore_hummock_version( Ok(()) } -async fn restore_metadata_model( - meta_store: &S, - metadata: &[T], -) -> BackupResult<()> { - if !T::list(meta_store).await?.is_empty() { - return Err(BackupError::NonemptyMetaStorage); - } - for d in metadata { - d.insert(meta_store).await?; - } - Ok(()) -} - -async fn restore_system_param_model( - meta_store: &S, - metadata: &[T], -) -> BackupResult<()> { - if T::get(meta_store).await?.is_some() { - return Err(BackupError::NonemptyMetaStorage); - } - for d in metadata { - d.insert(meta_store).await?; - } - Ok(()) -} - -async fn restore_cluster_id( - meta_store: &S, - cluster_id: ClusterId, -) -> BackupResult<()> { - if ClusterId::from_meta_store(meta_store).await?.is_some() { - return Err(BackupError::NonemptyMetaStorage); - } - cluster_id.put_at_meta_store(meta_store).await?; - Ok(()) -} - -async fn restore_default_cf( - meta_store: &S, - snapshot: &MetaSnapshotV1, -) -> BackupResult<()> { - if !meta_store.list_cf(DEFAULT_COLUMN_FAMILY).await?.is_empty() { - return Err(BackupError::NonemptyMetaStorage); - } - for (k, v) in &snapshot.metadata.default_cf { - meta_store - .put_cf(DEFAULT_COLUMN_FAMILY, k.clone(), v.clone()) - .await?; - } - Ok(()) -} - -async fn restore_metadata( - meta_store: S, - snapshot: MetaSnapshotV1, -) -> BackupResult<()> { - restore_default_cf(&meta_store, &snapshot).await?; - restore_metadata_model(&meta_store, &[snapshot.metadata.version_stats]).await?; - restore_metadata_model( - &meta_store, - &snapshot - .metadata - .compaction_groups - .into_iter() - .map(CompactionGroup::from_protobuf) - .collect_vec(), - ) - .await?; - restore_metadata_model( - &meta_store, - &snapshot - .metadata - .table_fragments - .into_iter() - .map(TableFragments::from_protobuf) - .collect_vec(), - ) - .await?; - restore_metadata_model(&meta_store, &snapshot.metadata.user_info).await?; - restore_metadata_model(&meta_store, &snapshot.metadata.database).await?; - restore_metadata_model(&meta_store, &snapshot.metadata.schema).await?; - restore_metadata_model(&meta_store, &snapshot.metadata.table).await?; - restore_metadata_model(&meta_store, &snapshot.metadata.index).await?; - restore_metadata_model(&meta_store, &snapshot.metadata.sink).await?; - restore_metadata_model(&meta_store, &snapshot.metadata.view).await?; - restore_metadata_model(&meta_store, &snapshot.metadata.source).await?; - restore_metadata_model(&meta_store, &snapshot.metadata.function).await?; - restore_metadata_model(&meta_store, &snapshot.metadata.connection).await?; - restore_system_param_model(&meta_store, &[snapshot.metadata.system_param]).await?; - restore_cluster_id(&meta_store, snapshot.metadata.cluster_id.into()).await?; - Ok(()) -} - /// Restores a meta store. /// Uses `meta_store` and `backup_store` if provided. /// Otherwise creates them based on `opts`. @@ -212,62 +117,69 @@ async fn restore_impl( Some(b) => b, }; let target_id = opts.meta_snapshot_id; - let snapshot_list = backup_store.manifest().snapshot_metadata.clone(); + let snapshot_list = &backup_store.manifest().snapshot_metadata; if !snapshot_list.iter().any(|m| m.id == target_id) { return Err(BackupError::Other(anyhow::anyhow!( "snapshot id {} not found", target_id ))); } - let mut target_snapshot: MetaSnapshotV1 = backup_store.get(target_id).await?; - tracing::info!( - "snapshot {} before rewrite:\n{}", - target_id, - target_snapshot - ); - let newest_id = snapshot_list - .into_iter() - .map(|m| m.id) - .max() - .expect("should exist"); - assert!(newest_id >= target_id); - // Always use newest snapshot's `default_cf` during restoring, in order not to corrupt shared - // data of snapshots. Otherwise, for example if we restore a older SST id generator, an - // existent SST in object store is at risk of being overwrote by the restored cluster. - // All these risky metadata are in `default_cf`, e.g. id generator, epoch. They must satisfy: - // - Value is monotonically non-decreasing. - // - Value is memcomparable. - // - Keys of newest_snapshot is a superset of that of target_snapshot. - if newest_id > target_id { - let newest_snapshot: MetaSnapshotV1 = backup_store.get(newest_id).await?; - for (k, v) in &target_snapshot.metadata.default_cf { - let newest_v = newest_snapshot - .metadata - .default_cf - .get(k) - .unwrap_or_else(|| panic!("violate superset requirement. key {:x?}", k)); - assert!(newest_v >= v, "violate monotonicity requirement"); + + let format_version = match snapshot_list.iter().find(|m| m.id == target_id) { + None => { + return Err(BackupError::Other(anyhow::anyhow!( + "snapshot id {} not found", + target_id + ))); + } + Some(s) => s.format_version, + }; + match &meta_store { + MetaStoreBackendImpl::Sql(m) => { + if format_version < 2 { + todo!("write model V1 to meta store V2"); + } else { + dispatch( + target_id, + &opts, + LoaderV2::new(backup_store), + WriterModelV2ToMetaStoreV2::new(m.to_owned()), + ) + .await?; + } + } + _ => { + assert!(format_version < 2, "format_version {}", format_version); + dispatch( + target_id, + &opts, + LoaderV1::new(backup_store), + WriterModelV1ToMetaStoreV1::new(meta_store), + ) + .await?; } - target_snapshot.metadata.default_cf = newest_snapshot.metadata.default_cf; - tracing::info!( - "snapshot {} after rewrite by snapshot {}:\n{}", - target_id, - newest_id, - target_snapshot, - ); } + + Ok(()) +} + +async fn dispatch, W: Writer, S: Metadata>( + target_id: MetaSnapshotId, + opts: &RestoreOpts, + loader: L, + writer: W, +) -> BackupResult<()> { + let target_snapshot = loader.load(target_id).await?; if opts.dry_run { return Ok(()); } restore_hummock_version( &opts.hummock_storage_url, &opts.hummock_storage_directory, - &target_snapshot.metadata.hummock_version, + target_snapshot.metadata.hummock_version_ref(), ) .await?; - dispatch_meta_store!(meta_store.clone(), store, { - restore_metadata(store.clone(), target_snapshot.clone()).await?; - }); + writer.write(target_snapshot).await?; Ok(()) } diff --git a/src/meta/src/backup_restore/restore_impl/mod.rs b/src/meta/src/backup_restore/restore_impl/mod.rs new file mode 100644 index 0000000000000..0d997c7af7cab --- /dev/null +++ b/src/meta/src/backup_restore/restore_impl/mod.rs @@ -0,0 +1,32 @@ +// Copyright 2023 RisingWave Labs +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +use risingwave_backup::error::BackupResult; +use risingwave_backup::meta_snapshot::{MetaSnapshot, Metadata}; +use risingwave_backup::MetaSnapshotId; + +pub mod v1; +pub mod v2; + +/// `Loader` gets, validates and amends `MetaSnapshot`. +#[async_trait::async_trait] +pub trait Loader { + async fn load(&self, id: MetaSnapshotId) -> BackupResult>; +} + +/// `Writer` writes `MetaSnapshot` to meta store. +#[async_trait::async_trait] +pub trait Writer { + async fn write(&self, s: MetaSnapshot) -> BackupResult<()>; +} diff --git a/src/meta/src/backup_restore/restore_impl/v1.rs b/src/meta/src/backup_restore/restore_impl/v1.rs new file mode 100644 index 0000000000000..793ce411714c1 --- /dev/null +++ b/src/meta/src/backup_restore/restore_impl/v1.rs @@ -0,0 +1,202 @@ +// Copyright 2023 RisingWave Labs +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +use itertools::Itertools; +use risingwave_backup::error::{BackupError, BackupResult}; +use risingwave_backup::meta_snapshot::MetaSnapshot; +use risingwave_backup::meta_snapshot_v1::{ClusterMetadata, MetaSnapshotV1}; +use risingwave_backup::storage::{MetaSnapshotStorage, MetaSnapshotStorageRef}; +use risingwave_backup::MetaSnapshotId; + +use crate::backup_restore::restore_impl::{Loader, Writer}; +use crate::backup_restore::utils::MetaStoreBackendImpl; +use crate::dispatch_meta_store; +use crate::hummock::model::CompactionGroup; +use crate::manager::model::SystemParamsModel; +use crate::model::{ClusterId, MetadataModel, TableFragments}; +use crate::storage::{MetaStore, DEFAULT_COLUMN_FAMILY}; + +pub struct LoaderV1 { + backup_store: MetaSnapshotStorageRef, +} + +impl LoaderV1 { + pub fn new(backup_store: MetaSnapshotStorageRef) -> Self { + Self { backup_store } + } +} + +#[async_trait::async_trait] +impl Loader for LoaderV1 { + async fn load(&self, target_id: MetaSnapshotId) -> BackupResult> { + let backup_store = &self.backup_store; + let snapshot_list = &backup_store.manifest().snapshot_metadata; + let mut target_snapshot: MetaSnapshotV1 = backup_store.get(target_id).await?; + tracing::info!( + "snapshot {} before rewrite:\n{}", + target_id, + target_snapshot + ); + let newest_id = snapshot_list + .iter() + .map(|m| m.id) + .max() + .expect("should exist"); + assert!( + newest_id >= target_id, + "newest_id={}, target_id={}", + newest_id, + target_id + ); + // Always use newest snapshot's `default_cf` during restoring, in order not to corrupt shared + // data of snapshots. Otherwise, for example if we restore a older SST id generator, an + // existent SST in object store is at risk of being overwrote by the restored cluster. + // All these risky metadata are in `default_cf`, e.g. id generator, epoch. They must satisfy: + // - Value is monotonically non-decreasing. + // - Value is memcomparable. + // - Keys of newest_snapshot is a superset of that of target_snapshot. + if newest_id > target_id { + let newest_snapshot: MetaSnapshotV1 = backup_store.get(newest_id).await?; + for (k, v) in &target_snapshot.metadata.default_cf { + let newest_v = newest_snapshot + .metadata + .default_cf + .get(k) + .unwrap_or_else(|| panic!("violate superset requirement. key {:x?}", k)); + assert!(newest_v >= v, "violate monotonicity requirement"); + } + target_snapshot.metadata.default_cf = newest_snapshot.metadata.default_cf; + tracing::info!( + "snapshot {} after rewrite by snapshot {}:\n{}", + target_id, + newest_id, + target_snapshot, + ); + } + Ok(target_snapshot) + } +} + +pub struct WriterModelV1ToMetaStoreV1 { + meta_store: MetaStoreBackendImpl, +} + +impl WriterModelV1ToMetaStoreV1 { + pub fn new(meta_store: MetaStoreBackendImpl) -> Self { + Self { meta_store } + } +} + +#[async_trait::async_trait] +impl Writer for WriterModelV1ToMetaStoreV1 { + async fn write(&self, target_snapshot: MetaSnapshot) -> BackupResult<()> { + dispatch_meta_store!(&self.meta_store, store, { + restore_metadata(store.clone(), target_snapshot.clone()).await?; + }); + Ok(()) + } +} + +async fn restore_metadata_model( + meta_store: &S, + metadata: &[T], +) -> BackupResult<()> { + if !T::list(meta_store).await?.is_empty() { + return Err(BackupError::NonemptyMetaStorage); + } + for d in metadata { + d.insert(meta_store).await?; + } + Ok(()) +} + +async fn restore_system_param_model( + meta_store: &S, + metadata: &[T], +) -> BackupResult<()> { + if T::get(meta_store).await?.is_some() { + return Err(BackupError::NonemptyMetaStorage); + } + for d in metadata { + d.insert(meta_store).await?; + } + Ok(()) +} + +async fn restore_cluster_id( + meta_store: &S, + cluster_id: ClusterId, +) -> BackupResult<()> { + if ClusterId::from_meta_store(meta_store).await?.is_some() { + return Err(BackupError::NonemptyMetaStorage); + } + cluster_id.put_at_meta_store(meta_store).await?; + Ok(()) +} + +async fn restore_default_cf( + meta_store: &S, + snapshot: &MetaSnapshotV1, +) -> BackupResult<()> { + if !meta_store.list_cf(DEFAULT_COLUMN_FAMILY).await?.is_empty() { + return Err(BackupError::NonemptyMetaStorage); + } + for (k, v) in &snapshot.metadata.default_cf { + meta_store + .put_cf(DEFAULT_COLUMN_FAMILY, k.clone(), v.clone()) + .await?; + } + Ok(()) +} + +async fn restore_metadata( + meta_store: S, + snapshot: MetaSnapshotV1, +) -> BackupResult<()> { + restore_default_cf(&meta_store, &snapshot).await?; + restore_metadata_model(&meta_store, &[snapshot.metadata.version_stats]).await?; + restore_metadata_model( + &meta_store, + &snapshot + .metadata + .compaction_groups + .into_iter() + .map(CompactionGroup::from_protobuf) + .collect_vec(), + ) + .await?; + restore_metadata_model( + &meta_store, + &snapshot + .metadata + .table_fragments + .into_iter() + .map(TableFragments::from_protobuf) + .collect_vec(), + ) + .await?; + restore_metadata_model(&meta_store, &snapshot.metadata.user_info).await?; + restore_metadata_model(&meta_store, &snapshot.metadata.database).await?; + restore_metadata_model(&meta_store, &snapshot.metadata.schema).await?; + restore_metadata_model(&meta_store, &snapshot.metadata.table).await?; + restore_metadata_model(&meta_store, &snapshot.metadata.index).await?; + restore_metadata_model(&meta_store, &snapshot.metadata.sink).await?; + restore_metadata_model(&meta_store, &snapshot.metadata.view).await?; + restore_metadata_model(&meta_store, &snapshot.metadata.source).await?; + restore_metadata_model(&meta_store, &snapshot.metadata.function).await?; + restore_metadata_model(&meta_store, &snapshot.metadata.connection).await?; + restore_system_param_model(&meta_store, &[snapshot.metadata.system_param]).await?; + restore_cluster_id(&meta_store, snapshot.metadata.cluster_id.into()).await?; + Ok(()) +} diff --git a/src/meta/src/backup_restore/restore_impl/v2.rs b/src/meta/src/backup_restore/restore_impl/v2.rs new file mode 100644 index 0000000000000..086ad70f38437 --- /dev/null +++ b/src/meta/src/backup_restore/restore_impl/v2.rs @@ -0,0 +1,96 @@ +// Copyright 2023 RisingWave Labs +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +use std::iter; + +use risingwave_backup::error::{BackupError, BackupResult}; +use risingwave_backup::meta_snapshot::MetaSnapshot; +use risingwave_backup::meta_snapshot_v2::{MetaSnapshotV2, MetadataV2}; +use risingwave_backup::storage::{MetaSnapshotStorage, MetaSnapshotStorageRef}; +use risingwave_backup::MetaSnapshotId; + +use crate::backup_restore::restore_impl::{Loader, Writer}; +use crate::controller::SqlMetaStore; + +pub struct LoaderV2 { + backup_store: MetaSnapshotStorageRef, +} + +impl LoaderV2 { + pub fn new(backup_store: MetaSnapshotStorageRef) -> Self { + Self { backup_store } + } +} + +#[async_trait::async_trait] +impl Loader for LoaderV2 { + async fn load(&self, target_id: MetaSnapshotId) -> BackupResult> { + let target_snapshot: MetaSnapshotV2 = self.backup_store.get(target_id).await?; + tracing::info!( + "snapshot {} before rewrite:\n{}", + target_id, + target_snapshot + ); + todo!("validate and rewrite seq") + } +} + +pub struct WriterModelV2ToMetaStoreV2 { + meta_store: SqlMetaStore, +} + +impl WriterModelV2ToMetaStoreV2 { + pub fn new(meta_store: SqlMetaStore) -> Self { + Self { meta_store } + } +} + +#[async_trait::async_trait] +impl Writer for WriterModelV2ToMetaStoreV2 { + async fn write(&self, target_snapshot: MetaSnapshot) -> BackupResult<()> { + let metadata = target_snapshot.metadata; + let db = &self.meta_store.conn; + insert_models(iter::once(metadata.version_stats), db).await?; + insert_models(metadata.compaction_configs, db).await?; + todo!("write other metadata") + } +} + +async fn insert_models( + models: impl IntoIterator, + db: &impl sea_orm::ConnectionTrait, +) -> BackupResult<()> +where + S: sea_orm::ModelTrait + Sync + Send + Sized + sea_orm::IntoActiveModel, + A: sea_orm::ActiveModelTrait + sea_orm::ActiveModelBehavior + Send + Sync + From, + <::Entity as sea_orm::EntityTrait>::Model: + sea_orm::IntoActiveModel, +{ + use sea_orm::EntityTrait; + if ::Entity::find() + .one(db) + .await + .map_err(|e| BackupError::MetaStorage(e.into()))? + .is_some() + { + return Err(BackupError::NonemptyMetaStorage); + } + for m in models { + m.into_active_model() + .insert(db) + .await + .map_err(|e| BackupError::MetaStorage(e.into()))?; + } + Ok(()) +} diff --git a/src/meta/src/backup_restore/utils.rs b/src/meta/src/backup_restore/utils.rs index c17650fb2aead..83cd1095beb25 100644 --- a/src/meta/src/backup_restore/utils.rs +++ b/src/meta/src/backup_restore/utils.rs @@ -23,6 +23,7 @@ use risingwave_object_store::object::object_metrics::ObjectStoreMetrics; use risingwave_object_store::object::parse_remote_object_store; use crate::backup_restore::RestoreOpts; +use crate::controller::SqlMetaStore; use crate::storage::{EtcdMetaStore, MemStore, WrappedEtcdClient as EtcdClient}; use crate::MetaStoreBackend; @@ -30,6 +31,8 @@ use crate::MetaStoreBackend; pub enum MetaStoreBackendImpl { Etcd(EtcdMetaStore), Mem(MemStore), + #[expect(dead_code, reason = "WIP")] + Sql(SqlMetaStore), } #[macro_export] @@ -38,6 +41,7 @@ macro_rules! dispatch_meta_store { match $impl { MetaStoreBackendImpl::Etcd($store) => $body, MetaStoreBackendImpl::Mem($store) => $body, + MetaStoreBackendImpl::Sql(_) => panic!("not supported"), } }}; } diff --git a/src/storage/backup/src/lib.rs b/src/storage/backup/src/lib.rs index af3c989995b9a..a69c4a3479c51 100644 --- a/src/storage/backup/src/lib.rs +++ b/src/storage/backup/src/lib.rs @@ -56,10 +56,12 @@ pub struct MetaSnapshotMetadata { pub ssts: Vec, pub max_committed_epoch: u64, pub safe_epoch: u64, + #[serde(default)] + pub format_version: u32, } impl MetaSnapshotMetadata { - pub fn new(id: MetaSnapshotId, v: &HummockVersion) -> Self { + pub fn new(id: MetaSnapshotId, v: &HummockVersion, format_version: u32) -> Self { Self { id, hummock_version_id: v.id, @@ -68,6 +70,7 @@ impl MetaSnapshotMetadata { .collect_vec(), max_committed_epoch: v.max_committed_epoch, safe_epoch: v.safe_epoch, + format_version, } } } @@ -104,6 +107,7 @@ impl From<&MetaSnapshotMetadata> for PbMetaSnapshotMetadata { hummock_version_id: m.hummock_version_id, max_committed_epoch: m.max_committed_epoch, safe_epoch: m.safe_epoch, + format_version: Some(m.format_version), } } } diff --git a/src/storage/backup/src/storage.rs b/src/storage/backup/src/storage.rs index 5c325809f2650..3db89abfacf33 100644 --- a/src/storage/backup/src/storage.rs +++ b/src/storage/backup/src/storage.rs @@ -124,6 +124,7 @@ impl MetaSnapshotStorage for ObjectStoreMetaSnapshotStorage { .push(MetaSnapshotMetadata::new( snapshot.id, snapshot.metadata.hummock_version_ref(), + snapshot.format_version, )); self.update_manifest(new_manifest).await?; Ok(()) diff --git a/src/storage/src/hummock/backup_reader.rs b/src/storage/src/hummock/backup_reader.rs index b9fa4a6258bbe..acca6454c2fec 100644 --- a/src/storage/src/hummock/backup_reader.rs +++ b/src/storage/src/hummock/backup_reader.rs @@ -192,6 +192,7 @@ impl BackupReader { } else { let this = self.clone(); let f = async move { + // TODO: change to v2 let snapshot: meta_snapshot_v1::MetaSnapshotV1 = current_store.0.get(snapshot_id).await.map_err(|e| { format!("failed to get meta snapshot {}. {}", snapshot_id, e) From 1211f9d8d29ace628ca0baa9d92b9a6b30bd1d3a Mon Sep 17 00:00:00 2001 From: Eric Fu Date: Thu, 2 Nov 2023 16:53:09 +0800 Subject: [PATCH 2/8] refactor(metrics): clean & restructure some metrics (#13195) --- .../dashboards/risingwave-dev-dashboard.json | 2 +- grafana/risingwave-dev-dashboard.dashboard.py | 107 ++---------------- grafana/risingwave-dev-dashboard.json | 2 +- .../src/executor/monitor/streaming_stats.rs | 10 +- 4 files changed, 18 insertions(+), 103 deletions(-) diff --git a/docker/dashboards/risingwave-dev-dashboard.json b/docker/dashboards/risingwave-dev-dashboard.json index a10842aec9374..85534ee34e85e 100644 --- a/docker/dashboards/risingwave-dev-dashboard.json +++ b/docker/dashboards/risingwave-dev-dashboard.json @@ -1 +1 @@ -{"__inputs":[],"annotations":{"list":[]},"description":"RisingWave Dev Dashboard","editable":true,"gnetId":null,"graphTooltip":0,"hideControls":false,"id":null,"links":[],"panels":[{"cacheTimeout":null,"collapsed":true,"datasource":null,"description":null,"editable":true,"error":false,"fieldConfig":{"defaults":{"thresholds":{"mode":"absolute","steps":[]}}},"gridPos":{"h":1,"w":24,"x":0,"y":0},"height":null,"hideTimeOverride":false,"id":1,"interval":null,"links":[],"maxDataPoints":100,"maxPerRow":null,"minSpan":null,"panels":[{"cacheTimeout":null,"color":{"mode":"thresholds"},"columns":[],"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"description":"Mapping from actor id to fragment 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a/grafana/risingwave-dev-dashboard.dashboard.py b/grafana/risingwave-dev-dashboard.dashboard.py index a813a1109e3b1..3d635f8dc8490 100644 --- a/grafana/risingwave-dev-dashboard.dashboard.py +++ b/grafana/risingwave-dev-dashboard.dashboard.py @@ -971,36 +971,6 @@ def section_streaming_actors(outer_panels): ), ], ), - panels.timeseries_actor_latency( - "Actor Barrier Latency", - "", - [ - panels.target( - f"rate({metric('stream_actor_barrier_time')}[$__rate_interval]) > 0", - "{{actor_id}}", - ), - ], - ), - panels.timeseries_actor_latency( - "Actor Processing Time", - "", - [ - panels.target( - f"rate({metric('stream_actor_processing_time')}[$__rate_interval]) > 0", - "{{actor_id}}", - ), - ], - ), - panels.timeseries_actor_latency( - "Actor Execution Time", - "", - [ - panels.target( - f"rate({metric('stream_actor_actor_execution_time')}[$__rate_interval]) > 0", - "{{actor_id}}", - ), - ], - ), panels.timeseries_row( "Actor Input Row", "", @@ -1021,25 +991,6 @@ def section_streaming_actors(outer_panels): ), ], ), - panels.timeseries_actor_ops( - "Join Executor Cache", - "", - [ - panels.target( - f"rate({metric('stream_join_lookup_miss_count')}[$__rate_interval])", - "cache miss - {{side}} side, join_table_id {{join_table_id}} degree_table_id {{degree_table_id}} actor {{actor_id}} ", - ), - panels.target( - f"rate({metric('stream_join_lookup_total_count')}[$__rate_interval])", - "total lookups {{side}} side, join_table_id {{join_table_id}} degree_table_id {{degree_table_id}} actor {{actor_id}}", - ), - panels.target( - f"rate({metric('stream_join_insert_cache_miss_count')}[$__rate_interval])", - "cache miss when insert {{side}} side, join_table_id {{join_table_id}} degree_table_id {{degree_table_id}} actor {{actor_id}}", - ), - ], - ), - panels.timeseries_actor_ops( "Temporal Join Executor Cache", "", @@ -1183,54 +1134,6 @@ def section_streaming_actors(outer_panels): ), ], ), - panels.timeseries_actor_ops( - "Aggregation Executor Cache Statistics For Each Key/State", - "Lookup miss count counts the number of aggregation key's cache miss per second." - "Lookup total count counts the number of rows processed per second." - "By diving these two metrics, one can derive the cache miss rate per second.", - [ - panels.target( - f"rate({metric('stream_agg_lookup_miss_count')}[$__rate_interval])", - "stream agg cache miss - table {{table_id}} actor {{actor_id}}", - ), - panels.target( - f"rate({metric('stream_agg_lookup_total_count')}[$__rate_interval])", - "stream agg total lookups - table {{table_id}} actor {{actor_id}}", - ), - panels.target( - f"rate({metric('stream_agg_distinct_cache_miss_count')}[$__rate_interval])", - "distinct agg cache miss - table {{table_id}} actor {{actor_id}}", - ), - panels.target( - f"rate({metric('stream_agg_distinct_total_cache_count')}[$__rate_interval])", - "distinct agg total lookups - table {{table_id}} actor {{actor_id}}", - ), - panels.target( - f"rate({metric('stream_group_top_n_cache_miss_count')}[$__rate_interval])", - "group top n cache miss - table {{table_id}} actor {{actor_id}}", - ), - panels.target( - f"rate({metric('stream_group_top_n_total_query_cache_count')}[$__rate_interval])", - "group top n total lookups - table {{table_id}} actor {{actor_id}}", - ), - panels.target( - f"rate({metric('stream_group_top_n_appendonly_cache_miss_count')}[$__rate_interval])", - "group top n appendonly cache miss - table {{table_id}} actor {{actor_id}}", - ), - panels.target( - f"rate({metric('stream_group_top_n_appendonly_total_query_cache_count')}[$__rate_interval])", - "group top n appendonly total lookups - table {{table_id}} actor {{actor_id}}", - ), - panels.target( - f"rate({metric('stream_lookup_cache_miss_count')}[$__rate_interval])", - "lookup executor cache miss - table {{table_id}} actor {{actor_id}}", - ), - panels.target( - f"rate({metric('stream_lookup_total_query_cache_count')}[$__rate_interval])", - "lookup executor total lookups - table {{table_id}} actor {{actor_id}}", - ), - ], - ), panels.timeseries_actor_ops( "Aggregation Executor Cache Statistics For Each StreamChunk", "", @@ -1318,6 +1221,16 @@ def section_streaming_actors_tokio(outer_panels): outer_panels.row_collapsed( "Streaming Actors (Tokio)", [ + panels.timeseries_actor_latency( + "Actor Execution Time", + "", + [ + panels.target( + f"rate({metric('stream_actor_actor_execution_time')}[$__rate_interval]) > 0", + "{{actor_id}}", + ), + ], + ), panels.timeseries_actor_latency_small( "Tokio: Actor Fast Poll Time", "", diff --git a/grafana/risingwave-dev-dashboard.json b/grafana/risingwave-dev-dashboard.json index a10842aec9374..85534ee34e85e 100644 --- a/grafana/risingwave-dev-dashboard.json +++ b/grafana/risingwave-dev-dashboard.json @@ -1 +1 @@ -{"__inputs":[],"annotations":{"list":[]},"description":"RisingWave Dev 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a/src/stream/src/executor/monitor/streaming_stats.rs b/src/stream/src/executor/monitor/streaming_stats.rs index f2ae23060e552..75824549897eb 100644 --- a/src/stream/src/executor/monitor/streaming_stats.rs +++ b/src/stream/src/executor/monitor/streaming_stats.rs @@ -42,8 +42,6 @@ pub struct StreamingMetrics { // Streaming actor metrics from tokio (disabled by default) pub actor_execution_time: GenericGaugeVec, - pub actor_output_buffer_blocking_duration_ns: LabelGuardedIntCounterVec<3>, - pub actor_input_buffer_blocking_duration_ns: LabelGuardedIntCounterVec<3>, pub actor_scheduled_duration: GenericGaugeVec, pub actor_scheduled_cnt: GenericGaugeVec, pub actor_fast_poll_duration: GenericGaugeVec, @@ -72,6 +70,10 @@ pub struct StreamingMetrics { // Exchange (see also `compute::ExchangeServiceMetrics`) pub exchange_frag_recv_size: GenericCounterVec, + // Backpressure + pub actor_output_buffer_blocking_duration_ns: LabelGuardedIntCounterVec<3>, + pub actor_input_buffer_blocking_duration_ns: LabelGuardedIntCounterVec<3>, + // Streaming Join pub join_lookup_miss_count: LabelGuardedIntCounterVec<5>, pub join_lookup_total_count: LabelGuardedIntCounterVec<5>, @@ -903,8 +905,6 @@ impl StreamingMetrics { level, executor_row_count, actor_execution_time, - actor_output_buffer_blocking_duration_ns, - actor_input_buffer_blocking_duration_ns, actor_scheduled_duration, actor_scheduled_cnt, actor_fast_poll_duration, @@ -924,6 +924,8 @@ impl StreamingMetrics { sink_input_row_count, mview_input_row_count, exchange_frag_recv_size, + actor_output_buffer_blocking_duration_ns, + actor_input_buffer_blocking_duration_ns, join_lookup_miss_count, join_lookup_total_count, join_insert_cache_miss_count, From 4daf6424ac53b19bb461600a2d479aa43d3937d4 Mon Sep 17 00:00:00 2001 From: StrikeW Date: Thu, 2 Nov 2023 17:23:08 +0800 Subject: [PATCH 3/8] fix(cdc): avoid serde the `schema` field in the Debzium connector (#13212) --- .../source/core/DbzCdcEventConsumer.java | 7 +++- .../source/core/DbzJsonConverter.java | 39 +++++++++++++++++++ 2 files changed, 44 insertions(+), 2 deletions(-) create mode 100644 java/connector-node/risingwave-connector-service/src/main/java/com/risingwave/connector/source/core/DbzJsonConverter.java diff --git a/java/connector-node/risingwave-connector-service/src/main/java/com/risingwave/connector/source/core/DbzCdcEventConsumer.java b/java/connector-node/risingwave-connector-service/src/main/java/com/risingwave/connector/source/core/DbzCdcEventConsumer.java index d1d46d6924204..e45fdded17c71 100644 --- a/java/connector-node/risingwave-connector-service/src/main/java/com/risingwave/connector/source/core/DbzCdcEventConsumer.java +++ b/java/connector-node/risingwave-connector-service/src/main/java/com/risingwave/connector/source/core/DbzCdcEventConsumer.java @@ -50,11 +50,14 @@ public class DbzCdcEventConsumer this.outputChannel = store; this.heartbeatTopicPrefix = heartbeatTopicPrefix; - var jsonConverter = new JsonConverter(); + // The default JSON converter will output the schema field in the JSON which is unnecessary + // to source parser, we use a customized JSON converter to avoid outputting the `schema` + // field. + var jsonConverter = new DbzJsonConverter(); final HashMap configs = new HashMap<>(2); // only serialize the value part configs.put(ConverterConfig.TYPE_CONFIG, ConverterType.VALUE.getName()); - // include record schema + // include record schema to output JSON in { "schema": { ... }, "payload": { ... } } format configs.put(JsonConverterConfig.SCHEMAS_ENABLE_CONFIG, true); jsonConverter.configure(configs); this.converter = jsonConverter; diff --git a/java/connector-node/risingwave-connector-service/src/main/java/com/risingwave/connector/source/core/DbzJsonConverter.java b/java/connector-node/risingwave-connector-service/src/main/java/com/risingwave/connector/source/core/DbzJsonConverter.java new file mode 100644 index 0000000000000..11fcdcba55553 --- /dev/null +++ b/java/connector-node/risingwave-connector-service/src/main/java/com/risingwave/connector/source/core/DbzJsonConverter.java @@ -0,0 +1,39 @@ +// Copyright 2023 RisingWave Labs +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +package com.risingwave.connector.source.core; + +import com.fasterxml.jackson.databind.node.ObjectNode; +import org.apache.kafka.connect.data.Schema; +import org.apache.kafka.connect.json.JsonConverter; + +/** + * Customize the JSON converter to avoid outputting the `schema` field but retian `payload` field in + * the JSON output. e.g. + * + * 
+ * {
+ *     "schema": null,
+ *     "payload": {
+ *     	...
+ *     }
+ * }
+ *
+ */ +public class DbzJsonConverter extends JsonConverter { + @Override + public ObjectNode asJsonSchema(Schema schema) { + return null; + } +} From 2ce7c9f091b9ae787a484d8703217e6b6ed1b687 Mon Sep 17 00:00:00 2001 From: xiangjinwu <17769960+xiangjinwu@users.noreply.github.com> Date: Thu, 2 Nov 2023 18:39:46 +0800 Subject: [PATCH 4/8] fix(expr): `date_trunc` and `+/-` on `timestamptz` during DST jump-back (#13201) --- e2e_test/batch/functions/issue_12072.slt.part | 129 ++++++++++++++++++ src/expr/impl/src/scalar/date_trunc.rs | 96 ++++++++----- src/expr/impl/src/scalar/timestamptz.rs | 19 ++- 3 files changed, 206 insertions(+), 38 deletions(-) create mode 100644 e2e_test/batch/functions/issue_12072.slt.part diff --git a/e2e_test/batch/functions/issue_12072.slt.part b/e2e_test/batch/functions/issue_12072.slt.part new file mode 100644 index 0000000000000..3669a9c007136 --- /dev/null +++ b/e2e_test/batch/functions/issue_12072.slt.part @@ -0,0 +1,129 @@ +# Some exprs on `timestamptz` are implemented by a chain of 3 steps: +# * convert `timestamptz` into naive `timestamp` in the given zone +# * perform actual operation on the naive `timestamp` +# * convert naive `timestamp` in the given zone back to `timestamptz` +# +# This can lead to unintuitive results, because the first and last steps are not perfect inverse. + +# Two different `timestamptz` values collapse into the same naive `timestamp`. +query T +select + '2023-11-05 08:40:00Z'::timestamptz AT TIME ZONE 'US/Pacific', + '2023-11-05 09:40:00Z'::timestamptz AT TIME ZONE 'US/Pacific'; +---- +2023-11-05 01:40:00 2023-11-05 01:40:00 + +# When ambiguous, the `timestamptz` after a jump-back transition is returned. +query T +select '2023-11-05 01:40:00'::timestamp AT TIME ZONE 'US/Pacific'; +---- +2023-11-05 09:40:00+00:00 + +# Tests below are about exprs following the 3-step pattern: +# * `date_trunc(field, timestamptz, zone)` -> `timestamptz` +# * `timestamptz + interval` -> `timestamptz` +# * Also used by `interval + timestamptz` and `timestamptz - interval` + +# (A) intuitive cases without anomalies observed (but require special care behind the scenes) + +query R +with t(id, v) as (values + ('a', '2023-11-05 01:40:00-07:00'::timestamptz), + ('b', '2023-11-05 01:40:00-08:00'::timestamptz)) +select extract(epoch from date_trunc('hour', v, 'US/Pacific')) from t order by id; +---- +1699171200.000000 +1699174800.000000 + +statement ok +set timezone = 'US/Pacific'; + +query T +with t(id, v) as (values + ('a', '2023-11-05 01:40:00-07:00'::timestamptz), + ('b', '2023-11-05 01:40:00-08:00'::timestamptz)) +select v + interval '5' minute from t order by id; +---- +2023-11-05 01:45:00-07:00 +2023-11-05 01:45:00-08:00 + +statement ok +set timezone = 'UTC'; + +# (B) common 1-hour jump-back for whole-hour zones + +query R +with t(id, v) as (values + ('a', '2023-11-05 01:40:00-07:00'::timestamptz), + ('b', '2023-11-05 01:40:00-08:00'::timestamptz)) +select extract(epoch from date_trunc('day', v, 'US/Pacific')) from t order by id; +---- +1699167600.000000 +1699167600.000000 + +statement ok +set timezone = 'US/Pacific'; + +query T +with t(id, v) as (values + ('a', '2023-11-05 01:40:00-07:00'::timestamptz), + ('b', '2023-11-05 01:40:00-08:00'::timestamptz), + ('c', '2023-11-04 01:40:00-07:00'::timestamptz)) +select v + interval '1' day from t order by id; +---- +2023-11-06 01:40:00-08:00 +2023-11-06 01:40:00-08:00 +2023-11-05 01:40:00-08:00 + +statement ok +set timezone = 'UTC'; + +# (C) 1-hour jump-back for half-hour zones + +query T +with t(id, v) as (values + ('a', '2023-04-01 15:58:00Z'::timestamptz), + ('b', '2023-04-01 16:58:00Z'::timestamptz)) +select date_trunc('hour', v, 'Australia/South') from t order by id; +---- +2023-04-01 15:30:00+00:00 +2023-04-01 16:30:00+00:00 + +# (D) half-hour jump-back +# Note that `01:45:00+10:30` is truncated to non-existent `01:00:00+10:30` whose canonical form is `01:30:00+11:00` + +statement ok +set timezone = 'Australia/Lord_Howe'; + +query TTT +with t(id, v) as (values + ('a', '2023-04-01 14:45:00Z'::timestamptz), + ('b', '2023-04-01 15:15:00Z'::timestamptz)) +select date_trunc('hour', v, 'Australia/Lord_Howe'), 'truncated from', v from t order by id; +---- +2023-04-02 01:00:00+11:00 truncated from 2023-04-02 01:45:00+11:00 +2023-04-02 01:30:00+11:00 truncated from 2023-04-02 01:45:00+10:30 + +statement ok +set timezone = 'UTC'; + +# (E) jump-back to day boundary, making it ambiguous +# Due to the disambiguation rule selecting post-transition value, +# `date_trunc` effectively returns an instant in the future, rather than the past. + +query T +with t(id, v) as (values + ('a', '2023-11-05 04:40:00Z'::timestamptz), + ('b', '2023-11-05 05:40:00Z'::timestamptz)) +select date_trunc('day', v, 'America/Havana') from t order by id; +---- +2023-11-05 05:00:00+00:00 +2023-11-05 05:00:00+00:00 + +# (F) jump-forward from day boundary, making it invalid +# Here `2023-09-02 23:59:59-04:00` is followed by `2023-09-03 01:00:00-03:00`. +# There is no `2023-09-03 00:00:00-04:00` or `2023-09-03 00:00:00-03:00`. +# PostgreSQL returns `2023-09-03 01:00:00-03:00` but it is hard using `chrono` crate. + +statement error interpret +select date_trunc('day', '2023-09-03 12:00:00Z'::timestamptz, 'America/Santiago'); diff --git a/src/expr/impl/src/scalar/date_trunc.rs b/src/expr/impl/src/scalar/date_trunc.rs index ffc24fbfc8137..35ba48631bfc4 100644 --- a/src/expr/impl/src/scalar/date_trunc.rs +++ b/src/expr/impl/src/scalar/date_trunc.rs @@ -16,24 +16,39 @@ use risingwave_common::types::{Interval, Timestamp, Timestamptz}; use risingwave_expr::expr::BoxedExpression; use risingwave_expr::{build_function, function, ExprError, Result}; -use super::timestamptz::{timestamp_at_time_zone, timestamptz_at_time_zone}; +use super::timestamptz::timestamp_at_time_zone; + +// TODO(xiangjinwu): parse into an enum +const MICROSECONDS: &str = "microseconds"; +const MILLISECONDS: &str = "milliseconds"; +const SECOND: &str = "second"; +const MINUTE: &str = "minute"; +const HOUR: &str = "hour"; +const DAY: &str = "day"; +const WEEK: &str = "week"; +const MONTH: &str = "month"; +const QUARTER: &str = "quarter"; +const YEAR: &str = "year"; +const DECADE: &str = "decade"; +const CENTURY: &str = "century"; +const MILLENNIUM: &str = "millennium"; #[function("date_trunc(varchar, timestamp) -> timestamp")] pub fn date_trunc_timestamp(field: &str, ts: Timestamp) -> Result { Ok(match field.to_ascii_lowercase().as_str() { - "microseconds" => ts.truncate_micros(), - "milliseconds" => ts.truncate_millis(), - "second" => ts.truncate_second(), - "minute" => ts.truncate_minute(), - "hour" => ts.truncate_hour(), - "day" => ts.truncate_day(), - "week" => ts.truncate_week(), - "month" => ts.truncate_month(), - "quarter" => ts.truncate_quarter(), - "year" => ts.truncate_year(), - "decade" => ts.truncate_decade(), - "century" => ts.truncate_century(), - "millennium" => ts.truncate_millennium(), + MICROSECONDS => ts.truncate_micros(), + MILLISECONDS => ts.truncate_millis(), + SECOND => ts.truncate_second(), + MINUTE => ts.truncate_minute(), + HOUR => ts.truncate_hour(), + DAY => ts.truncate_day(), + WEEK => ts.truncate_week(), + MONTH => ts.truncate_month(), + QUARTER => ts.truncate_quarter(), + YEAR => ts.truncate_year(), + DECADE => ts.truncate_decade(), + CENTURY => ts.truncate_century(), + MILLENNIUM => ts.truncate_millennium(), _ => return Err(invalid_field_error(field)), }) } @@ -52,33 +67,52 @@ fn build_date_trunc_timestamptz_implicit_zone( #[function("date_trunc(varchar, timestamptz, varchar) -> timestamptz")] pub fn date_trunc_timestamptz_at_timezone( field: &str, - ts: Timestamptz, + tsz: Timestamptz, timezone: &str, ) -> Result { - let timestamp = timestamptz_at_time_zone(ts, timezone)?; - let truncated = date_trunc_timestamp(field, timestamp)?; - timestamp_at_time_zone(truncated, timezone) + use chrono::Offset as _; + + use super::timestamptz::time_zone_err; + + let tz = Timestamptz::lookup_time_zone(timezone).map_err(time_zone_err)?; + let instant_local = tsz.to_datetime_in_zone(tz); + + let truncated_naive = date_trunc_timestamp(field, Timestamp(instant_local.naive_local()))?; + + match field.to_ascii_lowercase().as_str() { + MICROSECONDS | MILLISECONDS | SECOND | MINUTE | HOUR => { + // When unit < day, follow PostgreSQL to use old timezone offset. + // rather than reinterpret it in the timezone. + // https://github.com/postgres/postgres/blob/REL_16_0/src/backend/utils/adt/timestamp.c#L4270 + // See `e2e_test/batch/functions/issue_12072.slt.part` for the difference. + let fixed = instant_local.offset().fix(); + // `unwrap` is okay because `FixedOffset` always returns single unique conversion result. + let truncated_local = truncated_naive.0.and_local_timezone(fixed).unwrap(); + Ok(Timestamptz::from_micros(truncated_local.timestamp_micros())) + } + _ => timestamp_at_time_zone(truncated_naive, timezone), + } } #[function("date_trunc(varchar, interval) -> interval")] pub fn date_trunc_interval(field: &str, interval: Interval) -> Result { Ok(match field.to_ascii_lowercase().as_str() { - "microseconds" => interval, - "milliseconds" => interval.truncate_millis(), - "second" => interval.truncate_second(), - "minute" => interval.truncate_minute(), - "hour" => interval.truncate_hour(), - "day" => interval.truncate_day(), - "week" => return Err(ExprError::UnsupportedFunction( + MICROSECONDS => interval, + MILLISECONDS => interval.truncate_millis(), + SECOND => interval.truncate_second(), + MINUTE => interval.truncate_minute(), + HOUR => interval.truncate_hour(), + DAY => interval.truncate_day(), + WEEK => return Err(ExprError::UnsupportedFunction( "interval units \"week\" not supported because months usually have fractional weeks" .into(), )), - "month" => interval.truncate_month(), - "quarter" => interval.truncate_quarter(), - "year" => interval.truncate_year(), - "decade" => interval.truncate_decade(), - "century" => interval.truncate_century(), - "millennium" => interval.truncate_millennium(), + MONTH => interval.truncate_month(), + QUARTER => interval.truncate_quarter(), + YEAR => interval.truncate_year(), + DECADE => interval.truncate_decade(), + CENTURY => interval.truncate_century(), + MILLENNIUM => interval.truncate_millennium(), _ => return Err(invalid_field_error(field)), }) } diff --git a/src/expr/impl/src/scalar/timestamptz.rs b/src/expr/impl/src/scalar/timestamptz.rs index a14eb0a36319b..002b0c773966e 100644 --- a/src/expr/impl/src/scalar/timestamptz.rs +++ b/src/expr/impl/src/scalar/timestamptz.rs @@ -133,19 +133,24 @@ pub fn timestamptz_interval_add( interval: Interval, time_zone: &str, ) -> Result { + use num_traits::Zero as _; + // A month may have 28-31 days, a day may have 23 or 25 hours during Daylight Saving switch. // So their interpretation depends on the local time of a specific zone. let qualitative = interval.truncate_day(); // Units smaller than `day` are zone agnostic. let quantitative = interval - qualitative; - // Note the current implementation simply follows the frontend-rewrite chains of exprs before - // refactor. There may be chances to improve. - let t = timestamptz_at_time_zone(input, time_zone)?; - let t = t - .checked_add(qualitative) - .ok_or(ExprError::NumericOverflow)?; - let t = timestamp_at_time_zone(t, time_zone)?; + let mut t = input; + if !qualitative.is_zero() { + // Only convert into and from naive local when necessary because it is lossy. + // See `e2e_test/batch/functions/issue_12072.slt.part` for the difference. + let naive = timestamptz_at_time_zone(t, time_zone)?; + let naive = naive + .checked_add(qualitative) + .ok_or(ExprError::NumericOverflow)?; + t = timestamp_at_time_zone(naive, time_zone)?; + } let t = timestamptz_interval_quantitative(t, quantitative, i64::checked_add)?; Ok(t) } From 537bbe2fa3b38466f555716b00a974462477a448 Mon Sep 17 00:00:00 2001 From: Eric Fu Date: Thu, 2 Nov 2023 20:42:13 +0800 Subject: [PATCH 5/8] feat(metrics): fragment level streaming metrics (part 2) (#13196) --- .../dashboards/risingwave-dev-dashboard.json | 2 +- grafana/common.py | 6 + grafana/risingwave-dev-dashboard.dashboard.py | 255 +++++++++++------- grafana/risingwave-dev-dashboard.json | 2 +- .../src/executor/monitor/streaming_stats.rs | 6 +- .../src/executor/over_window/general.rs | 7 +- 6 files changed, 179 insertions(+), 99 deletions(-) diff --git a/docker/dashboards/risingwave-dev-dashboard.json b/docker/dashboards/risingwave-dev-dashboard.json index 85534ee34e85e..18cc9021471e3 100644 --- a/docker/dashboards/risingwave-dev-dashboard.json +++ b/docker/dashboards/risingwave-dev-dashboard.json @@ -1 +1 @@ -{"__inputs":[],"annotations":{"list":[]},"description":"RisingWave Dev 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index 6f6de73e57058..34365d9278a86 100644 --- a/grafana/common.py +++ b/grafana/common.py @@ -129,6 +129,12 @@ def target(self, expr, legendFormat, hide=False): datasource=self.datasource, hide=hide) + def target_hidden(self, expr, legendFormat): + return Target(expr=expr, + legendFormat=legendFormat, + datasource=self.datasource, + hide=True) + def table_target(self, expr, hide=False): return Target(expr=expr, datasource=self.datasource, diff --git a/grafana/risingwave-dev-dashboard.dashboard.py b/grafana/risingwave-dev-dashboard.dashboard.py index 3d635f8dc8490..eab1ab3cbef0a 100644 --- a/grafana/risingwave-dev-dashboard.dashboard.py +++ b/grafana/risingwave-dev-dashboard.dashboard.py @@ -908,16 +908,6 @@ def section_streaming_actors(outer_panels): outer_panels.row_collapsed( "Streaming Actors", [ - panels.timeseries_actor_rowsps( - "Executor Throughput", - "When enabled, this metric shows the input throughput of each executor.", - [ - panels.target( - f"rate({metric('stream_executor_row_count')}[$__rate_interval])", - "{{executor_identity}} actor {{actor_id}}", - ), - ], - ), panels.timeseries_percentage( "Actor Output Blocking Time Ratio (Backpressure)", "We first record the total blocking duration(ns) of output buffer of each actor. It shows how " @@ -930,64 +920,65 @@ def section_streaming_actors(outer_panels): ), ], ), - panels.timeseries_bytes( - "Actor Memory Usage (TaskLocalAlloc)", + panels.timeseries_percentage( + "Actor Input Blocking Time Ratio", "", [ panels.target( - f"{metric('actor_memory_usage')}", - "{{actor_id}}", + f"avg(rate({metric('stream_actor_input_buffer_blocking_duration_ns')}[$__rate_interval])) by (fragment_id, upstream_fragment_id) / 1000000000", + "fragment {{fragment_id}}<-{{upstream_fragment_id}}", ), ], ), - panels.timeseries_bytes( - "Executor Memory Usage", + panels.timeseries_row( + "Actor Input Rows", "", [ panels.target( - f"{metric('stream_memory_usage')}", - "table {{table_id}} actor {{actor_id}} desc: {{desc}}", + f"sum(rate({metric('stream_actor_in_record_cnt')}[$__rate_interval])) by (fragment_id)", + "fragment {{fragment_id}}", ), - ], - ), - - panels.timeseries_bytes( - "Materialized View Memory Usage", - "Materialzed View Memory Usage", - [ - panels.target( - f"sum({metric('stream_memory_usage')} * on(table_id, actor_id) group_left(materialized_view_id) table_info) by (materialized_view_id)", - "materialized_view {{materialized_view_id}}", + panels.target_hidden( + f"rate({metric('stream_actor_in_record_cnt')}[$__rate_interval])", + "actor {{actor_id}}", ), ], ), - panels.timeseries_percentage( - "Actor Input Blocking Time Ratio", + panels.timeseries_row( + "Actor Output Rows", "", [ panels.target( - f"avg(rate({metric('stream_actor_input_buffer_blocking_duration_ns')}[$__rate_interval])) by (fragment_id, upstream_fragment_id) / 1000000000", - "fragment {{fragment_id}}<-{{upstream_fragment_id}}", + f"sum(rate({metric('stream_actor_out_record_cnt')}[$__rate_interval])) by (fragment_id)", + "fragment {{fragment_id}}", + ), + panels.target_hidden( + f"rate({metric('stream_actor_out_record_cnt')}[$__rate_interval])", + "actor {{actor_id}}", ), ], ), - panels.timeseries_row( - "Actor Input Row", - "", + panels.timeseries_bytes( + "Executor Cache Memory Usage", + "The operator-level memory usage statistics collected by each LRU cache", [ panels.target( - f"rate({metric('stream_actor_in_record_cnt')}[$__rate_interval]) > 0", - "{{actor_id}}", + f"sum({metric('stream_memory_usage')}) by (table_id, desc)", + "table {{table_id}} desc: {{desc}}", + ), + panels.target_hidden( + f"{metric('stream_memory_usage')}", + "table {{table_id}} actor {{actor_id}} desc: {{desc}}", ), ], ), - panels.timeseries_row( - "Actor Output Row", - "", + panels.timeseries_bytes( + "Executor Cache Memory Usage of Materialized Views", + "Memory usage aggregated by materialized views", [ panels.target( - f"rate({metric('stream_actor_out_record_cnt')}[$__rate_interval]) > 0", - "{{actor_id}}", + f"sum({metric('stream_memory_usage')} * on(table_id, actor_id) group_left(materialized_view_id) table_info) by (materialized_view_id)", + "materialized view {{materialized_view_id}}", ), ], ), @@ -996,6 +987,10 @@ def section_streaming_actors(outer_panels): "", [ panels.target( + f"sum(rate({metric('stream_temporal_join_cache_miss_count')}[$__rate_interval])) by (fragment_id)", + "temporal join cache miss, table_id {{table_id}} fragment {{fragment_id}}", + ), + panels.target_hidden( f"rate({metric('stream_temporal_join_cache_miss_count')}[$__rate_interval])", "temporal join cache miss, table_id {{table_id}} actor {{actor_id}}", ), @@ -1006,12 +1001,20 @@ def section_streaming_actors(outer_panels): "", [ panels.target( - f"rate({table_metric('stream_materialize_cache_hit_count')}[$__rate_interval])", - "cache hit count - table {{table_id}} - actor {{actor_id}} {{instance}}", + f"sum(rate({table_metric('stream_materialize_cache_hit_count')}[$__rate_interval])) by (table_id, fragment_id)", + "cache hit count - table {{table_id}} fragment {{fragment_id}}", ), panels.target( + f"sum(rate({table_metric('stream_materialize_cache_total_count')}[$__rate_interval])) by (table_id, fragment_id)", + "total cached count - table {{table_id}} fragment {{fragment_id}}", + ), + panels.target_hidden( + f"rate({table_metric('stream_materialize_cache_hit_count')}[$__rate_interval])", + "cache hit count - table {{table_id}} actor {{actor_id}}", + ), + panels.target_hidden( f"rate({table_metric('stream_materialize_cache_total_count')}[$__rate_interval])", - "total cached count - table {{table_id}} - actor {{actor_id}} {{instance}}", + "total cached count - table {{table_id}} actor {{actor_id}}", ), ], ), @@ -1020,12 +1023,20 @@ def section_streaming_actors(outer_panels): "", [ panels.target( - f"rate({table_metric('stream_over_window_cache_lookup_count')}[$__rate_interval])", - "cache lookup count - table {{table_id}} - actor {{actor_id}} {{instance}}", + f"sum(rate({table_metric('stream_over_window_cache_lookup_count')}[$__rate_interval])) by (table_id, fragment_id)", + "cache lookup count - table {{table_id}} fragment {{fragment_id}}", ), panels.target( + f"sum(rate({table_metric('stream_over_window_cache_miss_count')}[$__rate_interval])) by (table_id, fragment_id)", + "cache miss count - table {{table_id}} fragment {{fragment_id}}", + ), + panels.target_hidden( + f"rate({table_metric('stream_over_window_cache_lookup_count')}[$__rate_interval])", + "cache lookup count - table {{table_id}} actor {{actor_id}}", + ), + panels.target_hidden( f"rate({table_metric('stream_over_window_cache_miss_count')}[$__rate_interval])", - "cache miss count - table {{table_id}} - actor {{actor_id}} {{instance}}", + "cache miss count - table {{table_id}} actor {{actor_id}}", ), ] ), @@ -1034,40 +1045,40 @@ def section_streaming_actors(outer_panels): "", [ panels.target( - f"(sum(rate({metric('stream_join_lookup_miss_count')}[$__rate_interval])) by (side, join_table_id, degree_table_id, actor_id) ) / (sum(rate({metric('stream_join_lookup_total_count')}[$__rate_interval])) by (side, join_table_id, degree_table_id, actor_id))", - "join executor cache miss ratio - - {{side}} side, join_table_id {{join_table_id}} degree_table_id {{degree_table_id}} actor {{actor_id}}", + f"(sum(rate({metric('stream_join_lookup_miss_count')}[$__rate_interval])) by (side, join_table_id, degree_table_id, fragment_id) ) / (sum(rate({metric('stream_join_lookup_total_count')}[$__rate_interval])) by (side, join_table_id, degree_table_id, fragment_id))", + "join executor cache miss ratio - - {{side}} side, join_table_id {{join_table_id}} degree_table_id {{degree_table_id}} fragment {{fragment_id}}", ), panels.target( - f"(sum(rate({metric('stream_agg_lookup_miss_count')}[$__rate_interval])) by (table_id, actor_id) ) / (sum(rate({metric('stream_agg_lookup_total_count')}[$__rate_interval])) by (table_id, actor_id))", - "Agg cache miss ratio - table {{table_id}} actor {{actor_id}} ", + f"(sum(rate({metric('stream_agg_lookup_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_agg_lookup_total_count')}[$__rate_interval])) by (table_id, fragment_id))", + "Agg cache miss ratio - table {{table_id}} fragment {{fragment_id}} ", ), panels.target( - f"(sum(rate({metric('stream_agg_distinct_cache_miss_count')}[$__rate_interval])) by (table_id, actor_id) ) / (sum(rate({metric('stream_agg_distinct_total_cache_count')}[$__rate_interval])) by (table_id, actor_id))", - "Distinct agg cache miss ratio - table {{table_id}} actor {{actor_id}} ", + f"(sum(rate({metric('stream_agg_distinct_cache_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_agg_distinct_total_cache_count')}[$__rate_interval])) by (table_id, fragment_id))", + "Distinct agg cache miss ratio - table {{table_id}} fragment {{fragment_id}} ", ), panels.target( - f"(sum(rate({metric('stream_group_top_n_cache_miss_count')}[$__rate_interval])) by (table_id, actor_id) ) / (sum(rate({metric('stream_group_top_n_total_query_cache_count')}[$__rate_interval])) by (table_id, actor_id))", - "Stream group top n cache miss ratio - table {{table_id}} actor {{actor_id}} ", + f"(sum(rate({metric('stream_group_top_n_cache_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_group_top_n_total_query_cache_count')}[$__rate_interval])) by (table_id, fragment_id))", + "Stream group top n cache miss ratio - table {{table_id}} fragment {{fragment_id}} ", ), panels.target( - f"(sum(rate({metric('stream_group_top_n_appendonly_cache_miss_count')}[$__rate_interval])) by (table_id, actor_id) ) / (sum(rate({metric('stream_group_top_n_appendonly_total_query_cache_count')}[$__rate_interval])) by (table_id, actor_id))", - "Stream group top n appendonly cache miss ratio - table {{table_id}} actor {{actor_id}} ", + f"(sum(rate({metric('stream_group_top_n_appendonly_cache_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_group_top_n_appendonly_total_query_cache_count')}[$__rate_interval])) by (table_id, fragment_id))", + "Stream group top n appendonly cache miss ratio - table {{table_id}} fragment {{fragment_id}} ", ), panels.target( - f"(sum(rate({metric('stream_lookup_cache_miss_count')}[$__rate_interval])) by (table_id, actor_id) ) / (sum(rate({metric('stream_lookup_total_query_cache_count')}[$__rate_interval])) by (table_id, actor_id))", - "Stream lookup cache miss ratio - table {{table_id}} actor {{actor_id}} ", + f"(sum(rate({metric('stream_lookup_cache_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_lookup_total_query_cache_count')}[$__rate_interval])) by (table_id, fragment_id))", + "Stream lookup cache miss ratio - table {{table_id}} fragment {{fragment_id}} ", ), panels.target( - f"(sum(rate({metric('stream_temporal_join_cache_miss_count')}[$__rate_interval])) by (table_id, actor_id) ) / (sum(rate({metric('stream_temporal_join_total_query_cache_count')}[$__rate_interval])) by (table_id, actor_id))", - "Stream temporal join cache miss ratio - table {{table_id}} actor {{actor_id}} ", + f"(sum(rate({metric('stream_temporal_join_cache_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_temporal_join_total_query_cache_count')}[$__rate_interval])) by (table_id, fragment_id))", + "Stream temporal join cache miss ratio - table {{table_id}} fragment {{fragment_id}} ", ), panels.target( - f"1 - (sum(rate({metric('stream_materialize_cache_hit_count')}[$__rate_interval])) by (table_id, actor_id) ) / (sum(rate({metric('stream_materialize_cache_total_count')}[$__rate_interval])) by (table_id, actor_id))", - "materialize executor cache miss ratio - table {{table_id}} actor {{actor_id}} {{instance}}", + f"1 - (sum(rate({metric('stream_materialize_cache_hit_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_materialize_cache_total_count')}[$__rate_interval])) by (table_id, fragment_id))", + "materialize executor cache miss ratio - table {{table_id}} fragment {{fragment_id}} {{instance}}", ), panels.target( - f"(sum(rate({metric('stream_over_window_cache_miss_count')}[$__rate_interval])) by (table_id, actor_id) ) / (sum(rate({metric('stream_over_window_cache_lookup_count')}[$__rate_interval])) by (table_id, actor_id))", - "Over window cache miss ratio - table {{table_id}} actor {{actor_id}} ", + f"(sum(rate({metric('stream_over_window_cache_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_over_window_cache_lookup_count')}[$__rate_interval])) by (table_id, fragment_id))", + "Over window cache miss ratio - table {{table_id}} fragment {{fragment_id}} ", ), ], ), @@ -1077,14 +1088,14 @@ def section_streaming_actors(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('stream_join_barrier_align_duration_bucket')}[$__rate_interval])) by (le, fragment_id, wait_side, job, instance))", - f"p{legend} - fragment {{{{fragment_id}}}} {{{{wait_side}}}} - {{{{job}}}} @ {{{{instance}}}}", + f"histogram_quantile({quantile}, sum(rate({metric('stream_join_barrier_align_duration_bucket')}[$__rate_interval])) by (le, fragment_id, wait_side, job))", + f"p{legend} - fragment {{{{fragment_id}}}} {{{{wait_side}}}} - {{{{job}}}}", ), [90, 99, 999, "max"], ), panels.target( - f"sum by(le, fragment_id, wait_side, job, instance)(rate({metric('stream_join_barrier_align_duration_sum')}[$__rate_interval])) / sum by(le,fragment_id,wait_side,job,instance) (rate({metric('stream_join_barrier_align_duration_count')}[$__rate_interval]))", - "avg - fragment {{fragment_id}} {{wait_side}} - {{job}} @ {{instance}}", + f"sum by(le, fragment_id, wait_side, job)(rate({metric('stream_join_barrier_align_duration_sum')}[$__rate_interval])) / sum by(le,fragment_id,wait_side,job) (rate({metric('stream_join_barrier_align_duration_count')}[$__rate_interval]))", + "avg - fragment {{fragment_id}} {{wait_side}} - {{job}}", ), ], ), @@ -1093,8 +1104,12 @@ def section_streaming_actors(outer_panels): "", [ panels.target( + f"sum(rate({metric('stream_join_actor_input_waiting_duration_ns')}[$__rate_interval]) / 1000000000) by (fragment_id)", + "fragment {{fragment_id}}", + ), + panels.target_hidden( f"rate({metric('stream_join_actor_input_waiting_duration_ns')}[$__rate_interval]) / 1000000000", - "{{actor_id}}", + "actor {{actor_id}}", ), ], ), @@ -1103,8 +1118,12 @@ def section_streaming_actors(outer_panels): "", [ panels.target( + f"sum(rate({metric('stream_join_match_duration_ns')}[$__rate_interval]) / 1000000000) by (fragment_id)", + "fragment {{fragment_id}} {{side}}", + ), + panels.target_hidden( f"rate({metric('stream_join_match_duration_ns')}[$__rate_interval]) / 1000000000", - "{{actor_id}}.{{side}}", + "actor {{actor_id}} {{side}}", ), ], ), @@ -1113,8 +1132,10 @@ def section_streaming_actors(outer_panels): "Multiple rows with distinct primary keys may have the same join key. This metric counts the " "number of join keys in the executor cache.", [ - panels.target(f"{metric('stream_join_cached_entry_count')}", - "{{actor_id}} {{side}}"), + panels.target(f"sum({metric('stream_join_cached_entry_count')}) by (fragment_id, side)", + "fragment {{fragment_id}} {{side}}"), + panels.target_hidden(f"{metric('stream_join_cached_entry_count')}", + "actor {{actor_id}} {{side}}"), ], ), panels.timeseries_count( @@ -1123,14 +1144,14 @@ def section_streaming_actors(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('stream_join_matched_join_keys_bucket')}[$__rate_interval])) by (le, fragment_id, table_id, job, instance))", - f"p{legend} - fragment {{{{fragment_id}}}} table_id {{{{table_id}}}} - {{{{job}}}} @ {{{{instance}}}}", + f"histogram_quantile({quantile}, sum(rate({metric('stream_join_matched_join_keys_bucket')}[$__rate_interval])) by (le, fragment_id, table_id, job))", + f"p{legend} - fragment {{{{fragment_id}}}} table_id {{{{table_id}}}} - {{{{job}}}}", ), [90, 99, "max"], ), panels.target( - f"sum by(le, job, instance, actor_id, table_id) (rate({metric('stream_join_matched_join_keys_sum')}[$__rate_interval])) / sum by(le, job, instance, fragment_id, table_id) (rate({table_metric('stream_join_matched_join_keys_count')}[$__rate_interval]))", - "avg - fragment {{fragment_id}} table_id {{table_id}} - {{job}} @ {{instance}}", + f"sum by(le, job, actor_id, table_id) (rate({metric('stream_join_matched_join_keys_sum')}[$__rate_interval])) / sum by(le, job, fragment_id, table_id) (rate({table_metric('stream_join_matched_join_keys_count')}[$__rate_interval]))", + "avg - fragment {{fragment_id}} table_id {{table_id}} - {{job}}", ), ], ), @@ -1139,10 +1160,18 @@ def section_streaming_actors(outer_panels): "", [ panels.target( + f"sum(rate({metric('stream_agg_chunk_lookup_miss_count')}[$__rate_interval])) by (table_id, fragment_id)", + "chunk-level cache miss - table {{table_id}} fragment {{fragment_id}}", + ), + panels.target( + f"sum(rate({metric('stream_agg_chunk_lookup_total_count')}[$__rate_interval])) by (table_id, fragment_id)", + "chunk-level total lookups - table {{table_id}} fragment {{fragment_id}}", + ), + panels.target_hidden( f"rate({metric('stream_agg_chunk_lookup_miss_count')}[$__rate_interval])", "chunk-level cache miss - table {{table_id}} actor {{actor_id}}}", ), - panels.target( + panels.target_hidden( f"rate({metric('stream_agg_chunk_lookup_total_count')}[$__rate_interval])", "chunk-level total lookups - table {{table_id}} actor {{actor_id}}", ), @@ -1152,17 +1181,23 @@ def section_streaming_actors(outer_panels): "Aggregation Cached Keys", "The number of keys cached in each hash aggregation executor's executor cache.", [ - panels.target(f"{metric('stream_agg_cached_entry_count')}", + panels.target(f"sum({metric('stream_agg_cached_entry_count')}) by (table_id, fragment_id)", + "stream agg cached keys count | table {{table_id}} fragment {{fragment_id}}"), + panels.target(f"sum({metric('stream_agg_distinct_cached_entry_count')}) by (table_id, fragment_id)", + "stream agg distinct cached keys count | table {{table_id}} fragment {{fragment_id}}"), + panels.target_hidden(f"{metric('stream_agg_cached_entry_count')}", "stream agg cached keys count | table {{table_id}} actor {{actor_id}}"), - panels.target(f"{metric('stream_agg_distinct_cached_entry_count')}", - "stream agg distinct cached keys count |table {{table_id}} actor {{actor_id}}"), + panels.target_hidden(f"{metric('stream_agg_distinct_cached_entry_count')}", + "stream agg distinct cached keys count | table {{table_id}} actor {{actor_id}}"), ], ), panels.timeseries_count( "Aggregation Dirty Groups Count", "The number of dirty (unflushed) groups in each hash aggregation executor's executor cache.", [ - panels.target(f"{metric('stream_agg_dirty_groups_count')}", + panels.target(f"sum({metric('stream_agg_dirty_groups_count')}) by (table_id, fragment_id)", + "stream agg dirty groups count | table {{table_id}} fragment {{fragment_id}}"), + panels.target_hidden(f"{metric('stream_agg_dirty_groups_count')}", "stream agg dirty groups count | table {{table_id}} actor {{actor_id}}"), ], ), @@ -1170,7 +1205,9 @@ def section_streaming_actors(outer_panels): "Aggregation Dirty Groups Heap Size", "The total heap size of dirty (unflushed) groups in each hash aggregation executor's executor cache.", [ - panels.target(f"{metric('stream_agg_dirty_groups_heap_size')}", + panels.target(f"sum({metric('stream_agg_dirty_groups_heap_size')}) by (table_id, fragment_id)", + "stream agg dirty groups heap size | table {{table_id}} fragment {{fragment_id}}"), + panels.target_hidden(f"{metric('stream_agg_dirty_groups_heap_size')}", "stream agg dirty groups heap size | table {{table_id}} actor {{actor_id}}"), ], ), @@ -1178,9 +1215,13 @@ def section_streaming_actors(outer_panels): "TopN Cached Keys", "The number of keys cached in each top_n executor's executor cache.", [ - panels.target(f"{metric('stream_group_top_n_cached_entry_count')}", + panels.target(f"sum({metric('stream_group_top_n_cached_entry_count')}) by (table_id, fragment_id)", + "group top_n cached count | table {{table_id}} fragment {{fragment_id}}"), + panels.target(f"sum({metric('stream_group_top_n_appendonly_cached_entry_count')}) by (table_id, fragment_id)", + "group top_n appendonly cached count | table {{table_id}} fragment {{fragment_id}}"), + panels.target_hidden(f"{metric('stream_group_top_n_cached_entry_count')}", "group top_n cached count | table {{table_id}} actor {{actor_id}}"), - panels.target(f"{metric('stream_group_top_n_appendonly_cached_entry_count')}", + panels.target_hidden(f"{metric('stream_group_top_n_appendonly_cached_entry_count')}", "group top_n appendonly cached count | table {{table_id}} actor {{actor_id}}"), ], ), @@ -1188,28 +1229,60 @@ def section_streaming_actors(outer_panels): "Temporal Join Cache Keys", "The number of keys cached in temporal join executor's executor cache.", [ - panels.target(f"{metric('stream_temporal_join_cached_entry_count')}", + panels.target(f"sum({metric('stream_temporal_join_cached_entry_count')}) by (table_id, fragment_id)", + "Temporal Join cached count | table {{table_id}} fragment {{fragment_id}}"), + panels.target_hidden(f"{metric('stream_temporal_join_cached_entry_count')}", "Temporal Join cached count | table {{table_id}} actor {{actor_id}}"), - ], ), panels.timeseries_count( "Lookup Cached Keys", "The number of keys cached in lookup executor's executor cache.", [ - panels.target(f"{metric('stream_lookup_cached_entry_count')}", + panels.target(f"sum({metric('stream_lookup_cached_entry_count')}) by (table_id, fragment_id)", + "lookup cached count | table {{table_id}} fragment {{fragment_id}}"), + panels.target_hidden(f"{metric('stream_lookup_cached_entry_count')}", "lookup cached count | table {{table_id}} actor {{actor_id}}"), - ], ), panels.timeseries_count( "Over Window Cached Keys", "The number of keys cached in over window executor's executor cache.", [ - panels.target(f"{metric('stream_over_window_cached_entry_count')}", + panels.target(f"sum({metric('stream_over_window_cached_entry_count')}) by (table_id, fragment_id)", + "over window cached count | table {{table_id}} fragment {{fragment_id}}"), + panels.target_hidden(f"{metric('stream_over_window_cached_entry_count')}", "over window cached count | table {{table_id}} actor {{actor_id}}"), ], ), + panels.timeseries_actor_rowsps( + "Executor Throughput", + "When enabled, this metric shows the input throughput of each executor.", + [ + panels.target( + f"sum(rate({metric('stream_executor_row_count')}[$__rate_interval])) by (executor_identity, fragment_id)", + "{{executor_identity}} fragment {{fragment_id}}", + ), + panels.target_hidden( + f"rate({metric('stream_executor_row_count')}[$__rate_interval])", + "{{executor_identity}} actor {{actor_id}}", + ), + ], + ), + panels.timeseries_bytes( + "Actor Memory Usage (TaskLocalAlloc)", + "The actor-level memory usage statistics reported by TaskLocalAlloc. (Disabled by default)", + [ + panels.target( + f"sum({metric('actor_memory_usage')}) by (fragment_id)", + "fragment {{fragment_id}}", + ), + panels.target_hidden( + f"{metric('actor_memory_usage')}", + "actor {{actor_id}}", + ), + ], + ), ], ) ] diff --git a/grafana/risingwave-dev-dashboard.json b/grafana/risingwave-dev-dashboard.json index 85534ee34e85e..18cc9021471e3 100644 --- a/grafana/risingwave-dev-dashboard.json +++ b/grafana/risingwave-dev-dashboard.json @@ -1 +1 @@ -{"__inputs":[],"annotations":{"list":[]},"description":"RisingWave Dev Dashboard","editable":true,"gnetId":null,"graphTooltip":0,"hideControls":false,"id":null,"links":[],"panels":[{"cacheTimeout":null,"collapsed":true,"datasource":null,"description":null,"editable":true,"error":false,"fieldConfig":{"defaults":{"thresholds":{"mode":"absolute","steps":[]}}},"gridPos":{"h":1,"w":24,"x":0,"y":0},"height":null,"hideTimeOverride":false,"id":1,"interval":null,"links":[],"maxDataPoints":100,"maxPerRow":null,"minSpan":null,"panels":[{"cacheTimeout":null,"color":{"mode":"thresholds"},"columns":[],"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"description":"Mapping from actor id to fragment id","editable":true,"error":false,"fieldConfig":{"defaults":{"custom":{"align":"auto","displayMode":"auto","filterable":true},"thresholds":{"mode":"absolute","steps":[]}},"overrides":[]},"fontSize":"100%","gridPos":{"h":8,"w":12,"x":0,"y":0},"height":null,"hideTimeOverride":false,"id":2,"interval":null,"links":[],"mappings":[],"maxDataPoints":100,"maxPerRow":null,"minSpan":null,"options":{"showHeader":true},"repeat":null,"repeatDirection":null,"span":6,"targets":[{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"actor_info{job=~\"$job\",instance=~\"$node\"}","format":"table","hide":false,"instant":true,"interval":"","intervalFactor":2,"legendFormat":"","metric":"","query":"actor_info{job=~\"$job\",instance=~\"$node\"}","refId":"","step":10,"target":""}],"timeFrom":null,"timeShift":null,"title":"Actor Id 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a/src/stream/src/executor/monitor/streaming_stats.rs b/src/stream/src/executor/monitor/streaming_stats.rs index 75824549897eb..d2cae6ae19ac0 100644 --- a/src/stream/src/executor/monitor/streaming_stats.rs +++ b/src/stream/src/executor/monitor/streaming_stats.rs @@ -683,7 +683,7 @@ impl StreamingMetrics { let over_window_cached_entry_count = register_int_gauge_vec_with_registry!( "stream_over_window_cached_entry_count", "Total entry (partition) count in over window executor cache", - &["table_id", "actor_id"], + &["table_id", "actor_id", "fragment_id"], registry ) .unwrap(); @@ -691,7 +691,7 @@ impl StreamingMetrics { let over_window_cache_lookup_count = register_int_counter_vec_with_registry!( "stream_over_window_cache_lookup_count", "Over window executor cache lookup count", - &["table_id", "actor_id"], + &["table_id", "actor_id", "fragment_id"], registry ) .unwrap(); @@ -699,7 +699,7 @@ impl StreamingMetrics { let over_window_cache_miss_count = register_int_counter_vec_with_registry!( "stream_over_window_cache_miss_count", "Over window executor cache miss count", - &["table_id", "actor_id"], + &["table_id", "actor_id", "fragment_id"], registry ) .unwrap(); diff --git a/src/stream/src/executor/over_window/general.rs b/src/stream/src/executor/over_window/general.rs index 38c959039bf56..4b105e2994f65 100644 --- a/src/stream/src/executor/over_window/general.rs +++ b/src/stream/src/executor/over_window/general.rs @@ -613,18 +613,19 @@ impl OverWindowExecutor { { // update metrics let actor_id_str = this.actor_ctx.id.to_string(); + let fragment_id_str = this.actor_ctx.fragment_id.to_string(); let table_id_str = this.state_table.table_id().to_string(); this.metrics .over_window_cached_entry_count - .with_label_values(&[&table_id_str, &actor_id_str]) + .with_label_values(&[&table_id_str, &actor_id_str, &fragment_id_str]) .set(vars.cached_partitions.len() as _); this.metrics .over_window_cache_lookup_count - .with_label_values(&[&table_id_str, &actor_id_str]) + .with_label_values(&[&table_id_str, &actor_id_str, &fragment_id_str]) .inc_by(std::mem::take(&mut vars.stats.cache_lookup)); this.metrics .over_window_cache_miss_count - .with_label_values(&[&table_id_str, &actor_id_str]) + .with_label_values(&[&table_id_str, &actor_id_str, &fragment_id_str]) .inc_by(std::mem::take(&mut vars.stats.cache_miss)); } From c02cdc4a684efc1aa2ab8e9ef0b0ee52361df70a Mon Sep 17 00:00:00 2001 From: Bugen Zhao Date: Thu, 2 Nov 2023 20:58:59 +0800 Subject: [PATCH 6/8] refactor: introduce `thiserror-ext` for boxed error wrapper definition (#13200) Signed-off-by: Bugen Zhao --- Cargo.lock | 22 ++++++++++++++++ Cargo.toml | 1 + src/expr/core/src/expr/expr_udf.rs | 2 +- .../core/src/table_function/user_defined.rs | 2 +- src/udf/Cargo.toml | 1 + src/udf/src/error.rs | 25 ++++++------------- src/udf/src/external.rs | 11 +++++--- src/udf/src/lib.rs | 2 ++ 8 files changed, 43 insertions(+), 23 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index a0b3e08b46441..09f9d7070a414 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -8424,6 +8424,7 @@ dependencies = [ "madsim-tonic", "static_assertions", "thiserror", + "thiserror-ext", ] [[package]] @@ -9966,6 +9967,27 @@ dependencies = [ "thiserror-impl", ] +[[package]] +name = "thiserror-ext" +version = "0.0.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "5f28a4a7351f496662affc257826b85dd2a613406ce3cc2f07b849685e166d8c" +dependencies = [ + "thiserror", + "thiserror-ext-derive", +] + +[[package]] +name = "thiserror-ext-derive" +version = "0.0.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "67621f6d39449754da63668ddd2423ad0c81c27434c16090f8805ad1db59b621" +dependencies = [ + "proc-macro2", + "quote", + "syn 2.0.37", +] + [[package]] name = "thiserror-impl" version = "1.0.48" diff --git a/Cargo.toml b/Cargo.toml index dc38b19e237f4..919c990deb7d4 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -120,6 +120,7 @@ arrow-buffer = "48" arrow-flight = "48" arrow-select = "48" arrow-ord = "48" +thiserror-ext = "0.0.1" tikv-jemalloc-ctl = { git = "https://github.com/risingwavelabs/jemallocator.git", rev = "64a2d9" } tikv-jemallocator = { git = "https://github.com/risingwavelabs/jemallocator.git", features = [ "profiling", diff --git a/src/expr/core/src/expr/expr_udf.rs b/src/expr/core/src/expr/expr_udf.rs index a11af2434b4f9..8aae5beae60a4 100644 --- a/src/expr/core/src/expr/expr_udf.rs +++ b/src/expr/core/src/expr/expr_udf.rs @@ -150,7 +150,7 @@ impl Build for UdfExpression { "", DataType::from(t) .try_into() - .map_err(risingwave_udf::Error::Unsupported)?, + .map_err(risingwave_udf::Error::unsupported)?, true, )) }) diff --git a/src/expr/core/src/table_function/user_defined.rs b/src/expr/core/src/table_function/user_defined.rs index 60fde34f9df1f..cbb8682ba48a7 100644 --- a/src/expr/core/src/table_function/user_defined.rs +++ b/src/expr/core/src/table_function/user_defined.rs @@ -140,7 +140,7 @@ pub fn new_user_defined(prost: &PbTableFunction, chunk_size: usize) -> Result = std::result::Result; /// The error type for UDF operations. -#[derive(thiserror::Error, Debug)] -pub enum Error { +#[derive(Error, Debug, Box, Construct)] +#[thiserror_ext(type = Error)] +pub enum ErrorInner { #[error("failed to connect to UDF service: {0}")] Connect(#[from] tonic::transport::Error), #[error("failed to send requests to UDF service: {0}")] - Tonic(#[from] Box), + Tonic(#[from] tonic::Status), #[error("failed to call UDF: {0}")] - Flight(#[from] Box), + Flight(#[from] FlightError), #[error("type mismatch: {0}")] TypeMismatch(String), @@ -45,16 +48,4 @@ pub enum Error { ServiceError(String), } -static_assertions::const_assert_eq!(std::mem::size_of::(), 40); - -impl From for Error { - fn from(status: tonic::Status) -> Self { - Error::from(Box::new(status)) - } -} - -impl From for Error { - fn from(error: FlightError) -> Self { - Error::from(Box::new(error)) - } -} +static_assertions::const_assert_eq!(std::mem::size_of::(), 8); diff --git a/src/udf/src/external.rs b/src/udf/src/external.rs index e77b96f2bdab4..c5ed44850f1bf 100644 --- a/src/udf/src/external.rs +++ b/src/udf/src/external.rs @@ -59,7 +59,7 @@ impl ArrowFlightUdfClient { let full_schema = Schema::try_from(info) .map_err(|e| FlightError::DecodeError(format!("Error decoding schema: {e}")))?; if input_num > full_schema.fields.len() { - return Err(Error::ServiceError(format!( + return Err(Error::service_error(format!( "function {:?} schema info not consistency: input_num: {}, total_fields: {}", id, input_num, @@ -73,13 +73,13 @@ impl ArrowFlightUdfClient { let expect_input_types: Vec<_> = args.fields.iter().map(|f| f.data_type()).collect(); let expect_result_types: Vec<_> = returns.fields.iter().map(|f| f.data_type()).collect(); if !data_types_match(&expect_input_types, &actual_input_types) { - return Err(Error::TypeMismatch(format!( + return Err(Error::type_mismatch(format!( "function: {:?}, expect arguments: {:?}, actual: {:?}", id, expect_input_types, actual_input_types ))); } if !data_types_match(&expect_result_types, &actual_result_types) { - return Err(Error::TypeMismatch(format!( + return Err(Error::type_mismatch(format!( "function: {:?}, expect return: {:?}, actual: {:?}", id, expect_result_types, actual_result_types ))); @@ -91,7 +91,10 @@ impl ArrowFlightUdfClient { pub async fn call(&self, id: &str, input: RecordBatch) -> Result { let mut output_stream = self.call_stream(id, stream::once(async { input })).await?; // TODO: support no output - let head = output_stream.next().await.ok_or(Error::NoReturned)??; + let head = output_stream + .next() + .await + .ok_or_else(Error::no_returned)??; let mut remaining = vec![]; while let Some(batch) = output_stream.next().await { remaining.push(batch?); diff --git a/src/udf/src/lib.rs b/src/udf/src/lib.rs index 513551a9108af..25207c2c19edf 100644 --- a/src/udf/src/lib.rs +++ b/src/udf/src/lib.rs @@ -12,6 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. +#![feature(error_generic_member_access)] + mod error; mod external; From 0007064bc893142d74c2ecdb42f7a61cd8e90e86 Mon Sep 17 00:00:00 2001 From: xiangjinwu <17769960+xiangjinwu@users.noreply.github.com> Date: Thu, 2 Nov 2023 21:06:56 +0800 Subject: [PATCH 7/8] fix(optimizer): impl ExprRewritable for StreamValues (#13217) --- e2e_test/streaming/values.slt | 11 +++++++++++ .../tests/testdata/output/array.yaml | 4 ++-- .../tests/testdata/output/basic_query.yaml | 2 +- .../tests/testdata/output/expr.yaml | 2 +- .../tests/testdata/output/with_ordinality.yaml | 2 +- .../src/optimizer/plan_node/stream_values.rs | 17 ++++++++++++++++- 6 files changed, 32 insertions(+), 6 deletions(-) diff --git a/e2e_test/streaming/values.slt b/e2e_test/streaming/values.slt index 9f4f6e0a36261..2cc5c701a4cec 100644 --- a/e2e_test/streaming/values.slt +++ b/e2e_test/streaming/values.slt @@ -75,3 +75,14 @@ NULL statement ok drop materialized view mv; + +statement ok +create materialized view mv as select '2020-01-01'::timestamptz;; + +query I +select * from mv; +---- +2020-01-01 00:00:00+00:00 + +statement ok +drop materialized view mv; diff --git a/src/frontend/planner_test/tests/testdata/output/array.yaml b/src/frontend/planner_test/tests/testdata/output/array.yaml index be957047c6d1a..6631e9a7510b1 100644 --- a/src/frontend/planner_test/tests/testdata/output/array.yaml +++ b/src/frontend/planner_test/tests/testdata/output/array.yaml @@ -56,7 +56,7 @@ batch_plan: 'BatchValues { rows: [[ARRAY[]:List(Timestamptz)]] }' stream_plan: |- StreamMaterialize { columns: [array, _row_id(hidden)], stream_key: [_row_id], pk_columns: [_row_id], pk_conflict: NoCheck } - └─StreamValues { rows: [[Array, 0:Int64]] } + └─StreamValues { rows: [[ARRAY[]:List(Timestamptz), 0:Int64]] } - sql: | select ARRAY[]::STRUCT[]; logical_plan: |- @@ -65,7 +65,7 @@ batch_plan: 'BatchValues { rows: [[ARRAY[]:List(Struct(StructType { field_names: ["f1"], field_types: [Int32] }))]] }' stream_plan: |- StreamMaterialize { columns: [array, _row_id(hidden)], stream_key: [_row_id], pk_columns: [_row_id], pk_conflict: NoCheck } - └─StreamValues { rows: [[Array, 0:Int64]] } + └─StreamValues { rows: [[ARRAY[]:List(Struct(StructType { field_names: ["f1"], field_types: [Int32] })), 0:Int64]] } - sql: | select array_cat(array[66], array[123]); logical_plan: |- diff --git a/src/frontend/planner_test/tests/testdata/output/basic_query.yaml b/src/frontend/planner_test/tests/testdata/output/basic_query.yaml index ce6724dc91c37..ddddfdfbc8602 100644 --- a/src/frontend/planner_test/tests/testdata/output/basic_query.yaml +++ b/src/frontend/planner_test/tests/testdata/output/basic_query.yaml @@ -3,7 +3,7 @@ batch_plan: 'BatchValues { rows: [[11:Int32, 22:Int32], [36:Int32, 44:Int32]] }' stream_plan: |- StreamMaterialize { columns: [*VALUES*_0.column_0, *VALUES*_0.column_1, _row_id(hidden)], stream_key: [_row_id], pk_columns: [_row_id], pk_conflict: NoCheck } - └─StreamValues { rows: [[11:Int32, 22:Int32, 0:Int64], [(33:Int32 + (1:Int32 + 2:Int32)), 44:Int32, 1:Int64]] } + └─StreamValues { rows: [[11:Int32, 22:Int32, 0:Int64], [36:Int32, 44:Int32, 1:Int64]] } - sql: select * from t binder_error: 'Catalog error: table or source not found: t' - sql: | diff --git a/src/frontend/planner_test/tests/testdata/output/expr.yaml b/src/frontend/planner_test/tests/testdata/output/expr.yaml index 020c6a3548ef5..f16eae977a33c 100644 --- a/src/frontend/planner_test/tests/testdata/output/expr.yaml +++ b/src/frontend/planner_test/tests/testdata/output/expr.yaml @@ -600,7 +600,7 @@ batch_plan: 'BatchValues { rows: [[1:Int32]] }' stream_plan: |- StreamMaterialize { columns: [abs, _row_id(hidden)], stream_key: [_row_id], pk_columns: [_row_id], pk_conflict: NoCheck } - └─StreamValues { rows: [[Abs(-1:Int32), 0:Int64]] } + └─StreamValues { rows: [[1:Int32, 0:Int64]] } - sql: | select * from range(1,2); batch_plan: |- diff --git a/src/frontend/planner_test/tests/testdata/output/with_ordinality.yaml b/src/frontend/planner_test/tests/testdata/output/with_ordinality.yaml index e614b0c20a36b..5a56058765fbb 100644 --- a/src/frontend/planner_test/tests/testdata/output/with_ordinality.yaml +++ b/src/frontend/planner_test/tests/testdata/output/with_ordinality.yaml @@ -190,7 +190,7 @@ batch_plan: 'BatchValues { rows: [[1:Int32, 1:Int64]] }' stream_plan: |- StreamMaterialize { columns: [abs, ordinality, _row_id(hidden)], stream_key: [_row_id], pk_columns: [_row_id], pk_conflict: NoCheck } - └─StreamValues { rows: [[Abs(1:Int32), 1:Int64, 0:Int64]] } + └─StreamValues { rows: [[1:Int32, 1:Int64, 0:Int64]] } - sql: | create table t(x int , arr int[]); select * from t, abs(x) WITH ORDINALITY; diff --git a/src/frontend/src/optimizer/plan_node/stream_values.rs b/src/frontend/src/optimizer/plan_node/stream_values.rs index 3ae19a3112c5e..51cd507db260d 100644 --- a/src/frontend/src/optimizer/plan_node/stream_values.rs +++ b/src/frontend/src/optimizer/plan_node/stream_values.rs @@ -88,4 +88,19 @@ impl StreamNode for StreamValues { } } -impl ExprRewritable for StreamValues {} +impl ExprRewritable for StreamValues { + fn has_rewritable_expr(&self) -> bool { + true + } + + fn rewrite_exprs(&self, r: &mut dyn crate::expr::ExprRewriter) -> crate::PlanRef { + Self::new( + self.logical + .rewrite_exprs(r) + .as_logical_values() + .unwrap() + .clone(), + ) + .into() + } +} From 8b58861c8afc9005a6adc20e23f1bd7bf2a6a445 Mon Sep 17 00:00:00 2001 From: ArkBriar Date: Thu, 2 Nov 2023 22:27:41 +0800 Subject: [PATCH 8/8] feat: support k8s labels for dashboards (#13209) Signed-off-by: arkbriar --- .../dashboards/risingwave-dev-dashboard.json | 2 +- grafana/common.py | 185 +- grafana/risingwave-dev-dashboard.dashboard.py | 1492 +++++++++-------- grafana/risingwave-dev-dashboard.json | 2 +- .../risingwave-user-dashboard.dashboard.py | 250 +-- 5 files changed, 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index 34365d9278a86..fbc8e71a3f098 100644 --- a/grafana/common.py +++ b/grafana/common.py @@ -17,25 +17,40 @@ # We use DASHBOARD_VERSION env variable to indicate whether to use a variable as the datasource DASHBOARD_DYNAMIC_SOURCE = "DASHBOARD_DYNAMIC_SOURCE" -namespace_filter_enabled = os.environ.get( - NAMESPACE_FILTER_ENABLED, "") == "true" +namespace_filter_enabled = os.environ.get(NAMESPACE_FILTER_ENABLED, "") == "true" if namespace_filter_enabled: print("Enable filter for namespace field in the generated prometheus query") -risingwave_name_filter_enabled = os.environ.get( - RISINGWAVE_NAME_FILTER_ENABLED, "") == "true" +risingwave_name_filter_enabled = ( + os.environ.get(RISINGWAVE_NAME_FILTER_ENABLED, "") == "true" +) if risingwave_name_filter_enabled: print("Enable filter for namespace_filter field in the generated prometheus query") -dynamic_source_enabled = os.environ.get( - DASHBOARD_DYNAMIC_SOURCE, "") == "true" +dynamic_source_enabled = os.environ.get(DASHBOARD_DYNAMIC_SOURCE, "") == "true" if dynamic_source_enabled: print("Enable use the datasource variable as the dashboard datasource") +COMPONENT_LABEL = "job" +COMPONENT_VARIABLE_LABEL = "Job" +COMPONENT_VARIABLE = "job" +NODE_LABEL = "instance" +NODE_VARIABLE_LABEL = "Node" +NODE_VARIABLE = "node" + +# Use different labels for role and instance when namespace filter enabled. (Kubernetes) +if namespace_filter_enabled: + COMPONENT_LABEL = "risingwave_component" + COMPONENT_VARIABLE_LABEL = "Component" + COMPONENT_VARIABLE = "component" + NODE_LABEL = "pod" + NODE_VARIABLE_LABEL = "Pod" + NODE_VARIABLE = "pod" + templating = Templating() if namespace_filter_enabled: templating = Templating( list=[ { - "definition": "label_values(up{risingwave_name=~\".+\"}, namespace)", + "definition": 'label_values(up{risingwave_name=~".+"}, namespace)', "description": "Kubernetes namespace.", "hide": 0, "includeAll": False, @@ -44,21 +59,20 @@ "name": "namespace", "options": [], "query": { - "query": "label_values(up{risingwave_name=~\".+\"}, namespace)", - "refId": "StandardVariableQuery" + "query": 'label_values(up{risingwave_name=~".+"}, namespace)', + "refId": "StandardVariableQuery", }, "refresh": 2, "regex": "", "skipUrlSync": False, "sort": 0, - "type": "query" + "type": "query", } ] ) class Layout: - def __init__(self): self.x = 0 self.y = 0 @@ -110,24 +124,20 @@ def __init__(self, datasource): self.datasource = datasource def row( - self, - title, + self, + title, ): gridPos = self.layout.next_row() return RowPanel(title=title, gridPos=gridPos) def row_collapsed(self, title, panels): gridPos = self.layout.next_row() - return RowPanel(title=title, - gridPos=gridPos, - collapsed=True, - panels=panels) + return RowPanel(title=title, gridPos=gridPos, collapsed=True, panels=panels) def target(self, expr, legendFormat, hide=False): - return Target(expr=expr, - legendFormat=legendFormat, - datasource=self.datasource, - hide=hide) + return Target( + expr=expr, legendFormat=legendFormat, datasource=self.datasource, hide=hide + ) def target_hidden(self, expr, legendFormat): return Target(expr=expr, @@ -136,11 +146,13 @@ def target_hidden(self, expr, legendFormat): hide=True) def table_target(self, expr, hide=False): - return Target(expr=expr, - datasource=self.datasource, - hide=hide, - instant=True, - format='table') + return Target( + expr=expr, + datasource=self.datasource, + hide=hide, + instant=True, + format="table", + ) def timeseries(self, title, description, targets): gridPos = self.layout.next_half_width_graph() @@ -153,11 +165,7 @@ def timeseries(self, title, description, targets): **self.common_options, ) - def timeseries_count(self, - title, - description, - targets, - legendCols=["mean"]): + def timeseries_count(self, title, description, targets, legendCols=["mean"]): gridPos = self.layout.next_half_width_graph() return TimeSeries( title=title, @@ -169,11 +177,7 @@ def timeseries_count(self, **self.common_options, ) - def timeseries_percentage(self, - title, - description, - targets, - legendCols=["mean"]): + def timeseries_percentage(self, title, description, targets, legendCols=["mean"]): # Percentage should fall into 0.0-1.0 gridPos = self.layout.next_half_width_graph() return TimeSeries( @@ -187,11 +191,7 @@ def timeseries_percentage(self, **self.common_options, ) - def timeseries_latency(self, - title, - description, - targets, - legendCols=["mean"]): + def timeseries_latency(self, title, description, targets, legendCols=["mean"]): gridPos = self.layout.next_half_width_graph() return TimeSeries( title=title, @@ -204,11 +204,7 @@ def timeseries_latency(self, **self.common_options, ) - def timeseries_latency_ms(self, - title, - description, - targets, - legendCols=["mean"]): + def timeseries_latency_ms(self, title, description, targets, legendCols=["mean"]): gridPos = self.layout.next_half_width_graph() return TimeSeries( title=title, @@ -221,11 +217,9 @@ def timeseries_latency_ms(self, **self.common_options, ) - def timeseries_actor_latency(self, - title, - description, - targets, - legendCols=["mean"]): + def timeseries_actor_latency( + self, title, description, targets, legendCols=["mean"] + ): gridPos = self.layout.next_half_width_graph() return TimeSeries( title=title, @@ -238,11 +232,9 @@ def timeseries_actor_latency(self, **self.common_options, ) - def timeseries_actor_latency_small(self, - title, - description, - targets, - legendCols=["mean"]): + def timeseries_actor_latency_small( + self, title, description, targets, legendCols=["mean"] + ): gridPos = self.layout.next_one_third_width_graph() return TimeSeries( title=title, @@ -255,11 +247,9 @@ def timeseries_actor_latency_small(self, **self.common_options, ) - def timeseries_query_per_sec(self, - title, - description, - targets, - legendCols=["mean"]): + def timeseries_query_per_sec( + self, title, description, targets, legendCols=["mean"] + ): gridPos = self.layout.next_half_width_graph() return TimeSeries( title=title, @@ -272,11 +262,9 @@ def timeseries_query_per_sec(self, **self.common_options, ) - def timeseries_bytes_per_sec(self, - title, - description, - targets, - legendCols=["mean"]): + def timeseries_bytes_per_sec( + self, title, description, targets, legendCols=["mean"] + ): gridPos = self.layout.next_half_width_graph() return TimeSeries( title=title, @@ -289,11 +277,7 @@ def timeseries_bytes_per_sec(self, **self.common_options, ) - def timeseries_bytes(self, - title, - description, - targets, - legendCols=["mean"]): + def timeseries_bytes(self, title, description, targets, legendCols=["mean"]): gridPos = self.layout.next_half_width_graph() return TimeSeries( title=title, @@ -331,11 +315,7 @@ def timeseries_ms(self, title, description, targets, legendCols=["mean"]): **self.common_options, ) - def timeseries_kilobytes(self, - title, - description, - targets, - legendCols=["mean"]): + def timeseries_kilobytes(self, title, description, targets, legendCols=["mean"]): gridPos = self.layout.next_half_width_graph() return TimeSeries( title=title, @@ -348,11 +328,7 @@ def timeseries_kilobytes(self, **self.common_options, ) - def timeseries_dollar(self, - title, - description, - targets, - legendCols=["mean"]): + def timeseries_dollar(self, title, description, targets, legendCols=["mean"]): gridPos = self.layout.next_half_width_graph() return TimeSeries( title=title, @@ -378,11 +354,7 @@ def timeseries_ops(self, title, description, targets, legendCols=["mean"]): **self.common_options, ) - def timeseries_actor_ops(self, - title, - description, - targets, - legendCols=["mean"]): + def timeseries_actor_ops(self, title, description, targets, legendCols=["mean"]): gridPos = self.layout.next_half_width_graph() return TimeSeries( title=title, @@ -395,11 +367,9 @@ def timeseries_actor_ops(self, **self.common_options, ) - def timeseries_actor_ops_small(self, - title, - description, - targets, - legendCols=["mean"]): + def timeseries_actor_ops_small( + self, title, description, targets, legendCols=["mean"] + ): gridPos = self.layout.next_one_third_width_graph() return TimeSeries( title=title, @@ -412,11 +382,7 @@ def timeseries_actor_ops_small(self, **self.common_options, ) - def timeseries_rowsps(self, - title, - description, - targets, - legendCols=["mean"]): + def timeseries_rowsps(self, title, description, targets, legendCols=["mean"]): gridPos = self.layout.next_half_width_graph() return TimeSeries( title=title, @@ -429,11 +395,7 @@ def timeseries_rowsps(self, **self.common_options, ) - def timeseries_bytesps(self, - title, - description, - targets, - legendCols=["mean"]): + def timeseries_bytesps(self, title, description, targets, legendCols=["mean"]): gridPos = self.layout.next_half_width_graph() return TimeSeries( title=title, @@ -508,8 +470,9 @@ def timeseries_id(self, title, description, targets): def table_info(self, title, description, targets, excluded_columns): gridPos = self.layout.next_half_width_graph() excludedByName = dict.fromkeys(excluded_columns, True) - transformations = [{"id": "organize", "options": { - "excludeByName": excludedByName}}] + transformations = [ + {"id": "organize", "options": {"excludeByName": excludedByName}} + ] return Table( title=title, dataSource=self.datasource, @@ -518,7 +481,7 @@ def table_info(self, title, description, targets, excluded_columns): gridPos=gridPos, showHeader=True, filterable=True, - transformations=transformations + transformations=transformations, ) def sub_panel(self): @@ -528,15 +491,15 @@ def sub_panel(self): def metric(name, filter=None, node_filter_enabled=True, table_id_filter_enabled=False): filters = [filter] if filter else [] if namespace_filter_enabled: - filters.append("namespace=~\"$namespace\"") + filters.append('namespace=~"$namespace"') if risingwave_name_filter_enabled: - filters.append("risingwave_name=~\"$instance\"") + filters.append('risingwave_name=~"$instance"') if table_id_filter_enabled: # We use "%table|" instead of "%table" here to match empty string for the table_id filter - filters.append("table_id=~\"$table|\"") + filters.append('table_id=~"$table|"') if node_filter_enabled: - filters.append("job=~\"$job\"") - filters.append("instance=~\"$node\"") + filters.append(f'{COMPONENT_LABEL}=~"${COMPONENT_VARIABLE}"') + filters.append(f'{NODE_LABEL}=~"${NODE_VARIABLE}"') if filters: return f"{name}{{{','.join(filters)}}}" else: @@ -557,5 +520,5 @@ def quantile(f, percentiles): "max": ["1.0", "max"], } return list( - map(lambda p: f(quantile_map[str(p)][0], quantile_map[str(p)][1]), - percentiles)) + map(lambda p: f(quantile_map[str(p)][0], quantile_map[str(p)][1]), percentiles) + ) diff --git a/grafana/risingwave-dev-dashboard.dashboard.py b/grafana/risingwave-dev-dashboard.dashboard.py index eab1ab3cbef0a..052dc0b591ab2 100644 --- a/grafana/risingwave-dev-dashboard.dashboard.py +++ b/grafana/risingwave-dev-dashboard.dashboard.py @@ -1,12 +1,14 @@ -import os import logging +import os import sys p = os.path.dirname(__file__) sys.path.append(p) -from common import * + from jsonmerge import merge +from common import * + source_uid = os.environ.get(SOURCE_UID, "risedev-prometheus") dashboard_uid = os.environ.get(DASHBOARD_UID, "Ecy3uV1nz") dashboard_version = int(os.environ.get(DASHBOARD_VERSION, "0")) @@ -22,18 +24,25 @@ def section_actor_info(outer_panels): panels = outer_panels.sub_panel() - excluded_cols = ['Time', 'Value', '__name__', 'job', 'instance'] + excluded_cols = ["Time", "Value", "__name__", f"{COMPONENT_LABEL}", f"{NODE_LABEL}"] return [ outer_panels.row_collapsed( "Actor/Table Id Info", [ - panels.table_info("Actor Id Info", - "Mapping from actor id to fragment id", - [panels.table_target(f"{metric('actor_info')}")], excluded_cols), - panels.table_info("Materialized View Info", - "Mapping from materialized view table id to it's internal table ids", - [panels.table_target(f"{metric('table_info')}")], excluded_cols), - ]) + panels.table_info( + "Actor Id Info", + "Mapping from actor id to fragment id", + [panels.table_target(f"{metric('actor_info')}")], + excluded_cols, + ), + panels.table_info( + "Materialized View Info", + "Mapping from materialized view table id to it's internal table ids", + [panels.table_target(f"{metric('table_info')}")], + excluded_cols, + ), + ], + ) ] @@ -47,8 +56,10 @@ def section_cluster_node(outer_panels): "Node Count", "The number of each type of RisingWave components alive.", [ - panels.target(f"sum({metric('worker_num')}) by (worker_type)", - "{{worker_type}}") + panels.target( + f"sum({metric('worker_num')}) by (worker_type)", + "{{worker_type}}", + ) ], ["last"], ), @@ -57,8 +68,8 @@ def section_cluster_node(outer_panels): "The memory usage of each RisingWave component.", [ panels.target( - f"avg({metric('process_resident_memory_bytes')}) by (job,instance)", - "{{job}} @ {{instance}}", + f"avg({metric('process_resident_memory_bytes')}) by ({COMPONENT_LABEL}, {NODE_LABEL})", + "{{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ) ], ), @@ -67,28 +78,31 @@ def section_cluster_node(outer_panels): "The CPU usage of each RisingWave component.", [ panels.target( - f"sum(rate({metric('process_cpu_seconds_total')}[$__rate_interval])) by (job,instance)", - "cpu usage (total) - {{job}} @ {{instance}}", + f"sum(rate({metric('process_cpu_seconds_total')}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL})", + "cpu usage (total) - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), - panels.target( - f"sum(rate({metric('process_cpu_seconds_total')}[$__rate_interval])) by (job,instance) / avg({metric('process_cpu_core_num')}) by (job,instance)", - "cpu usage (avg per core) - {{job}} @ {{instance}}", + f"sum(rate({metric('process_cpu_seconds_total')}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}) / avg({metric('process_cpu_core_num')}) by ({COMPONENT_LABEL}, {NODE_LABEL})", + "cpu usage (avg per core) - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), - panels.timeseries_count( "Meta Cluster", "RW cluster can configure multiple meta nodes to achieve high availability. One is the leader and the " "rest are the followers.", [ - panels.target(f"sum({metric('meta_num')}) by (worker_addr,role)", - "{{worker_addr}} @ {{role}}") + panels.target( + f"sum({metric('meta_num')}) by (worker_addr,role)", + "{{worker_addr}} @ {{role}}", + ) ], ["last"], ), - ]) + ], + ) ] @@ -102,8 +116,10 @@ def section_recovery_node(outer_panels): "Recovery Successful Rate", "The rate of successful recovery attempts", [ - panels.target(f"sum(rate({metric('recovery_latency_count')}[$__rate_interval])) by (instance)", - "{{instance}}") + panels.target( + f"sum(rate({metric('recovery_latency_count')}[$__rate_interval])) by ({NODE_LABEL})", + "{{%s}}" % NODE_LABEL, + ) ], ["last"], ), @@ -111,8 +127,10 @@ def section_recovery_node(outer_panels): "Failed recovery attempts", "Total number of failed reocovery attempts", [ - panels.target(f"sum({metric('recovery_failure_cnt')}) by (instance)", - "{{instance}}") + panels.target( + f"sum({metric('recovery_failure_cnt')}) by ({NODE_LABEL})", + "{{%s}}" % NODE_LABEL, + ) ], ["last"], ), @@ -122,9 +140,9 @@ def section_recovery_node(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('recovery_latency_bucket')}[$__rate_interval])) by (le, instance))", - f"recovery latency p{legend}" + - " - {{instance}}", + f"histogram_quantile({quantile}, sum(rate({metric('recovery_latency_bucket')}[$__rate_interval])) by (le, {NODE_LABEL}))", + f"recovery latency p{legend}" + + " - {{%s}}" % NODE_LABEL, ), [50, 99, "max"], ), @@ -134,8 +152,9 @@ def section_recovery_node(outer_panels): ), ], ["last"], - ) - ]) + ), + ], + ) ] @@ -152,7 +171,7 @@ def section_compaction(outer_panels): "The number of SSTables at each level", [ panels.target( - f"sum({metric('storage_level_sst_num')}) by (instance, level_index)", + f"sum({metric('storage_level_sst_num')}) by ({NODE_LABEL}, level_index)", "L{{level_index}}", ), ], @@ -162,7 +181,7 @@ def section_compaction(outer_panels): "The size(KB) of SSTables at each level", [ panels.target( - f"sum({metric('storage_level_total_file_size')}) by (instance, level_index)", + f"sum({metric('storage_level_total_file_size')}) by ({NODE_LABEL}, level_index)", "L{{level_index}}", ), ], @@ -207,7 +226,6 @@ def section_compaction(outer_panels): ), ], ), - panels.timeseries_count( "Compaction Task L0 Select Level Count", "Avg l0 select_level_count of the compact task, and categorize it according to different cg, levels and task types", @@ -218,7 +236,6 @@ def section_compaction(outer_panels): ), ], ), - panels.timeseries_count( "Compaction Task File Count", "Avg file count of the compact task, and categorize it according to different cg, levels and task types", @@ -229,7 +246,6 @@ def section_compaction(outer_panels): ), ], ), - panels.timeseries_bytes( "Compaction Task Size Distribution", "The distribution of the compact task size triggered, including p90 and max. and categorize it according to different cg, levels and task types.", @@ -237,8 +253,7 @@ def section_compaction(outer_panels): *quantile( lambda quantile, legend: panels.target( f"histogram_quantile({quantile}, sum(rate({metric('storage_compact_task_size_bucket')}[$__rate_interval])) by (le, group, type))", - f"p{legend}" + - " - cg{{group}}@{{type}}", + f"p{legend}" + " - cg{{group}}@{{type}}", ), [90, "max"], ), @@ -249,8 +264,9 @@ def section_compaction(outer_panels): "The number of compactions from one level to another level that are running.", [ panels.target( - f"avg({metric('storage_compact_task_pending_num')}) by(job, instance)", - "compactor_task_split_count - {{job}} @ {{instance}}", + f"avg({metric('storage_compact_task_pending_num')}) by({COMPONENT_LABEL}, {NODE_LABEL})", + "compactor_task_split_count - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -260,33 +276,32 @@ def section_compaction(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(irate({metric('compactor_compact_task_duration_bucket')}[$__rate_interval])) by (le, job))", - f"compact-task p{legend}" + - " - {{job}}", + f"histogram_quantile({quantile}, sum(irate({metric('compactor_compact_task_duration_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}))", + f"compact-task p{legend}" + + " - {{%s}}" % COMPONENT_LABEL, ), [50, 90, "max"], ), *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(irate({metric('compactor_compact_sst_duration_bucket')}[$__rate_interval])) by (le, job))", - f"compact-key-range p{legend}" + - " - {{job}}", + f"histogram_quantile({quantile}, sum(irate({metric('compactor_compact_sst_duration_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}))", + f"compact-key-range p{legend}" + + " - {{%s}}" % COMPONENT_LABEL, ), [90, "max"], ), *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('compactor_get_table_id_total_time_duration_bucket')}[$__rate_interval])) by (le, job))", - f"get-table-id p{legend}" + - " - {{job}}", + f"histogram_quantile({quantile}, sum(rate({metric('compactor_get_table_id_total_time_duration_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}))", + f"get-table-id p{legend}" + + " - {{%s}}" % COMPONENT_LABEL, ), [90, "max"], ), *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('compactor_remote_read_time_bucket')}[$__rate_interval])) by (le, job))", - f"remote-io p{legend}" + - " - {{job}}", + f"histogram_quantile({quantile}, sum(rate({metric('compactor_remote_read_time_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}))", + f"remote-io p{legend}" + " - {{%s}}" % COMPONENT_LABEL, ), [90, "max"], ), @@ -309,21 +324,21 @@ def section_compaction(outer_panels): "KBs read from next level during history compactions to next level", [ panels.target( - f"sum(rate({metric('storage_level_compact_read_next')}[$__rate_interval])) by(job) + sum(rate(" - f"{metric('storage_level_compact_read_curr')}[$__rate_interval])) by(job)", - "read - {{job}}", + f"sum(rate({metric('storage_level_compact_read_next')}[$__rate_interval])) by({COMPONENT_LABEL}) + sum(rate(" + f"{metric('storage_level_compact_read_curr')}[$__rate_interval])) by({COMPONENT_LABEL})", + "read - {{%s}}" % COMPONENT_LABEL, ), panels.target( - f"sum(rate({metric('storage_level_compact_write')}[$__rate_interval])) by(job)", - "write - {{job}}", + f"sum(rate({metric('storage_level_compact_write')}[$__rate_interval])) by({COMPONENT_LABEL})", + "write - {{%s}}" % COMPONENT_LABEL, ), panels.target( - f"sum(rate({metric('compactor_write_build_l0_bytes')}[$__rate_interval]))by (job)", - "flush - {{job}}", + f"sum(rate({metric('compactor_write_build_l0_bytes')}[$__rate_interval]))by ({COMPONENT_LABEL})", + "flush - {{%s}}" % COMPONENT_LABEL, ), panels.target( - f"sum(rate({metric('compactor_fast_compact_bytes')}[$__rate_interval]))by (job)", - "fast compact - {{job}}", + f"sum(rate({metric('compactor_fast_compact_bytes')}[$__rate_interval]))by ({COMPONENT_LABEL})", + "fast compact - {{%s}}" % COMPONENT_LABEL, ), ], ), @@ -334,12 +349,12 @@ def section_compaction(outer_panels): "Write refers to the process of compacting SSTables at one level to another level.", [ panels.target( - f"sum({metric('storage_level_compact_write')}) by (job)", - "write - {{job}}", + f"sum({metric('storage_level_compact_write')}) by ({COMPONENT_LABEL})", + "write - {{%s}}" % COMPONENT_LABEL, ), panels.target( - f"sum({metric('compactor_write_build_l0_bytes')}) by (job)", - "flush - {{job}}", + f"sum({metric('compactor_write_build_l0_bytes')}) by ({COMPONENT_LABEL})", + "flush - {{%s}}" % COMPONENT_LABEL, ), ], ), @@ -360,16 +375,18 @@ def section_compaction(outer_panels): "Compacting SSTable Count", "The number of SSTables that is being compacted at each level", [ - panels.target(f"{metric('storage_level_compact_cnt')}", - "L{{level_index}}"), + panels.target( + f"{metric('storage_level_compact_cnt')}", "L{{level_index}}" + ), ], ), panels.timeseries_count( "Compacting Task Count", "num of compact_task", [ - panels.target(f"{metric('storage_level_compact_task_cnt')}", - "{{task}}"), + panels.target( + f"{metric('storage_level_compact_task_cnt')}", "{{task}}" + ), ], ), panels.timeseries_bytes_per_sec( @@ -402,7 +419,6 @@ def section_compaction(outer_panels): f"sum(irate({metric('storage_level_compact_read_sstn_next')}[$__rate_interval])) by (le, group, level_index)", "cg{{group}}-L{{level_index}} read from next level", ), - panels.target( f"sum(irate({metric('storage_level_compact_read_sstn_curr')}[$__rate_interval])) by (le, group, level_index)", "cg{{group}}-L{{level_index}} read from current level", @@ -414,12 +430,14 @@ def section_compaction(outer_panels): "Total bytes gotten from sstable_bloom_filter, for observing bloom_filter size", [ panels.target( - f"sum by(le, job, instance)(rate({metric('compactor_sstable_bloom_filter_size_sum')}[$__rate_interval])) / sum by(le, job, instance)(rate({metric('compactor_sstable_bloom_filter_size_count')}[$__rate_interval]))", - "avg_meta - {{job}} @ {{instance}}", + f"sum by(le, {COMPONENT_LABEL}, {NODE_LABEL})(rate({metric('compactor_sstable_bloom_filter_size_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}, {NODE_LABEL})(rate({metric('compactor_sstable_bloom_filter_size_count')}[$__rate_interval]))", + "avg_meta - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum by(le, job, instance)(rate({metric('compactor_sstable_file_size_sum')}[$__rate_interval])) / sum by(le, job, instance)(rate({metric('compactor_sstable_file_size_count')}[$__rate_interval]))", - "avg_file - {{job}} @ {{instance}}", + f"sum by(le, {COMPONENT_LABEL}, {NODE_LABEL})(rate({metric('compactor_sstable_file_size_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}, {NODE_LABEL})(rate({metric('compactor_sstable_file_size_count')}[$__rate_interval]))", + "avg_file - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -428,71 +446,71 @@ def section_compaction(outer_panels): "Total bytes gotten from sstable_avg_key_size, for observing sstable_avg_key_size", [ panels.target( - f"sum by(le, job, instance)(rate({metric('compactor_sstable_avg_key_size_sum')}[$__rate_interval])) / sum by(le, job, instance)(rate({metric('compactor_sstable_avg_key_size_count')}[$__rate_interval]))", - "avg_key_size - {{job}} @ {{instance}}", + f"sum by(le, {COMPONENT_LABEL}, {NODE_LABEL})(rate({metric('compactor_sstable_avg_key_size_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}, {NODE_LABEL})(rate({metric('compactor_sstable_avg_key_size_count')}[$__rate_interval]))", + "avg_key_size - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum by(le, job, instance)(rate({metric('compactor_sstable_avg_value_size_sum')}[$__rate_interval])) / sum by(le, job, instance)(rate({metric('compactor_sstable_avg_value_size_count')}[$__rate_interval]))", - "avg_value_size - {{job}} @ {{instance}}", + f"sum by(le, {COMPONENT_LABEL}, {NODE_LABEL})(rate({metric('compactor_sstable_avg_value_size_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}, {NODE_LABEL})(rate({metric('compactor_sstable_avg_value_size_count')}[$__rate_interval]))", + "avg_value_size - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), - panels.timeseries_count( "Hummock Sstable Stat", "Avg count gotten from sstable_distinct_epoch_count, for observing sstable_distinct_epoch_count", [ panels.target( - f"sum by(le, job, instance)(rate({metric('compactor_sstable_distinct_epoch_count_sum')}[$__rate_interval])) / sum by(le, job, instance)(rate({metric('compactor_sstable_distinct_epoch_count_count')}[$__rate_interval]))", - "avg_epoch_count - {{job}} @ {{instance}}", + f"sum by(le, {COMPONENT_LABEL}, {NODE_LABEL})(rate({metric('compactor_sstable_distinct_epoch_count_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}, {NODE_LABEL})(rate({metric('compactor_sstable_distinct_epoch_count_count')}[$__rate_interval]))", + "avg_epoch_count - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), - panels.timeseries_latency( "Hummock Remote Read Duration", "Total time of operations which read from remote storage when enable prefetch", [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_remote_read_time_per_task_bucket')}[$__rate_interval])) by (le, job, instance, table_id))", - f"remote-io p{legend}" + - " - {{table_id}} @ {{job}} @ {{instance}}", + f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_remote_read_time_per_task_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id))", + f"remote-io p{legend}" + + " - {{table_id}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), [90, "max"], ), ], ), - panels.timeseries_ops( "Compactor Iter keys", "", [ panels.target( - f"sum(rate({metric('compactor_iter_scan_key_counts')}[$__rate_interval])) by (instance, type)", - "iter keys flow - {{type}} @ {{instance}} ", + f"sum(rate({metric('compactor_iter_scan_key_counts')}[$__rate_interval])) by ({NODE_LABEL}, type)", + "iter keys flow - {{type}} @ {{%s}} " % NODE_LABEL, ), ], ), - panels.timeseries_bytes( "Lsm Compact Pending Bytes", "bytes of Lsm tree needed to reach balance", [ panels.target( - f"sum({metric('storage_compact_pending_bytes')}) by (instance, group)", - "compact pending bytes - {{group}} @ {{instance}} ", + f"sum({metric('storage_compact_pending_bytes')}) by ({NODE_LABEL}, group)", + "compact pending bytes - {{group}} @ {{%s}} " % NODE_LABEL, ), ], ), - panels.timeseries_percentage( "Lsm Level Compression Ratio", "compression ratio of each level of the lsm tree", [ panels.target( - f"sum({metric('storage_compact_level_compression_ratio')}) by (instance, group, level, algorithm)", - "lsm compression ratio - cg{{group}} @ L{{level}} - {{algorithm}} {{instance}} ", + f"sum({metric('storage_compact_level_compression_ratio')}) by ({NODE_LABEL}, group, level, algorithm)", + "lsm compression ratio - cg{{group}} @ L{{level}} - {{algorithm}} {{%s}}" + % NODE_LABEL, ), ], ), @@ -503,7 +521,9 @@ def section_compaction(outer_panels): def section_object_storage(outer_panels): panels = outer_panels.sub_panel() - operation_rate_blacklist = "type!~'streaming_upload_write_bytes|streaming_read_read_bytes|streaming_read'" + operation_rate_blacklist = ( + "type!~'streaming_upload_write_bytes|streaming_read_read_bytes|streaming_read'" + ) operation_duration_blacklist = "type!~'streaming_upload_write_bytes|streaming_read'" write_op_filter = "type=~'upload|delete'" read_op_filter = "type=~'read|readv|list|metadata'" @@ -518,12 +538,12 @@ def section_object_storage(outer_panels): "", [ panels.target( - f"sum(rate({metric('object_store_read_bytes')}[$__rate_interval]))by(job,instance)", - "read - {{job}} @ {{instance}}", + f"sum(rate({metric('object_store_read_bytes')}[$__rate_interval]))by({COMPONENT_LABEL}, {NODE_LABEL})", + "read - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({metric('object_store_write_bytes')}[$__rate_interval]))by(job,instance)", - "write - {{job}} @ {{instance}}", + f"sum(rate({metric('object_store_write_bytes')}[$__rate_interval]))by({COMPONENT_LABEL}, {NODE_LABEL})", + "write - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -533,15 +553,17 @@ def section_object_storage(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('object_store_operation_latency_bucket', operation_duration_blacklist)}[$__rate_interval])) by (le, type, job, instance))", - "{{type}}" + f" p{legend}" + - " - {{job}} @ {{instance}}", + f"histogram_quantile({quantile}, sum(rate({metric('object_store_operation_latency_bucket', operation_duration_blacklist)}[$__rate_interval])) by (le, type, {COMPONENT_LABEL}, {NODE_LABEL}))", + "{{type}}" + + f" p{legend}" + + " - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), [50, 99, "max"], ), panels.target( - f"sum by(le, type, job, instance)(rate({metric('object_store_operation_latency_sum', operation_duration_blacklist)}[$__rate_interval])) / sum by(le, type, job, instance) (rate({metric('object_store_operation_latency_count')}[$__rate_interval]))", - "{{type}} avg - {{job}} @ {{instance}}", + f"sum by(le, type, {COMPONENT_LABEL}, {NODE_LABEL})(rate({metric('object_store_operation_latency_sum', operation_duration_blacklist)}[$__rate_interval])) / sum by(le, type, {COMPONENT_LABEL}, {NODE_LABEL}) (rate({metric('object_store_operation_latency_count')}[$__rate_interval]))", + "{{type}} avg - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -550,16 +572,19 @@ def section_object_storage(outer_panels): "", [ panels.target( - f"sum(rate({metric('object_store_operation_latency_count', operation_rate_blacklist)}[$__rate_interval])) by (le, type, job, instance)", - "{{type}} - {{job}} @ {{instance}}", + f"sum(rate({metric('object_store_operation_latency_count', operation_rate_blacklist)}[$__rate_interval])) by (le, type, {COMPONENT_LABEL}, {NODE_LABEL})", + "{{type}} - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({metric('object_store_operation_latency_count', write_op_filter)}[$__rate_interval])) by (le, media_type, job, instance)", - "{{media_type}}-write - {{job}} @ {{instance}}", + f"sum(rate({metric('object_store_operation_latency_count', write_op_filter)}[$__rate_interval])) by (le, media_type, {COMPONENT_LABEL}, {NODE_LABEL})", + "{{media_type}}-write - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({metric('object_store_operation_latency_count', read_op_filter)}[$__rate_interval])) by (le, media_type, job, instance)", - "{{media_type}}-read - {{job}} @ {{instance}}", + f"sum(rate({metric('object_store_operation_latency_count', read_op_filter)}[$__rate_interval])) by (le, media_type, {COMPONENT_LABEL}, {NODE_LABEL})", + "{{media_type}}-read - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -568,9 +593,10 @@ def section_object_storage(outer_panels): "", quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('object_store_operation_bytes_bucket')}[$__rate_interval])) by (le, type, job, instance))", - "{{type}}" + f" p{legend}" + - " - {{job}} @ {{instance}}", + f"histogram_quantile({quantile}, sum(rate({metric('object_store_operation_bytes_bucket')}[$__rate_interval])) by (le, type, {COMPONENT_LABEL}, {NODE_LABEL}))", + "{{type}}" + + f" p{legend}" + + " - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), [50, 99, "max"], ), @@ -580,8 +606,9 @@ def section_object_storage(outer_panels): "", [ panels.target( - f"sum(rate({metric('object_store_failure_count')}[$__rate_interval])) by (instance, job, type)", - "{{type}} - {{job}} @ {{instance}}", + f"sum(rate({metric('object_store_failure_count')}[$__rate_interval])) by ({NODE_LABEL}, {COMPONENT_LABEL}, type)", + "{{type}} - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ) ], ), @@ -590,14 +617,15 @@ def section_object_storage(outer_panels): "", [ panels.target( - f"sum(irate({metric('aws_sdk_retry_counts')}[$__rate_interval])) by (instance, job, type)", - "{{type}} - {{job}} @ {{instance}}", + f"sum(irate({metric('aws_sdk_retry_counts')}[$__rate_interval])) by ({NODE_LABEL}, {COMPONENT_LABEL}, type)", + "{{type}} - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), - panels.target( - f"sum(irate({metric('s3_read_request_retry_count')}[$__rate_interval])) by (instance, job, type)", - "{{type}} - {{job}} @ {{instance}}", - ) + f"sum(irate({metric('s3_read_request_retry_count')}[$__rate_interval])) by ({NODE_LABEL}, {COMPONENT_LABEL}, type)", + "{{type}} - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), + ), ], ), panels.timeseries_dollar( @@ -629,7 +657,7 @@ def section_object_storage(outer_panels): "accurate calculation.", [ panels.target( - f"sum({metric('storage_level_total_file_size')}) by (instance) * 0.023 / 1000 / 1000", + f"sum({metric('storage_level_total_file_size')}) by ({NODE_LABEL}) * 0.023 / 1000 / 1000", "Monthly Storage Cost", ), ], @@ -651,7 +679,8 @@ def section_streaming(outer_panels): [ panels.target( f"rate({metric('stream_source_output_rows_counts')}[$__rate_interval])", - "source={{source_name}} actor={{actor_id}} @ {{instance}}", + "source={{source_name}} actor={{actor_id}} @ {{%s}}" + % NODE_LABEL, ), ], ), @@ -695,9 +724,10 @@ def section_streaming(outer_panels): [ panels.target( f"rate({metric('stream_source_rows_per_barrier_counts')}[$__rate_interval])", - "actor={{actor_id}} source={{source_id}} @ {{instance}}" + "actor={{actor_id}} source={{source_id}} @ {{%s}}" + % NODE_LABEL, ) - ] + ], ), panels.timeseries_count( "Source Upstream Status", @@ -705,9 +735,10 @@ def section_streaming(outer_panels): [ panels.target( f"{metric('source_status_is_up')}", - "source_id={{source_id}}, source_name={{source_name}} @ {{instance}}" + "source_id={{source_id}}, source_name={{source_name}} @ {{%s}}" + % NODE_LABEL, ) - ] + ], ), panels.timeseries_ops( "Source Split Change Events frequency(events/s)", @@ -715,9 +746,10 @@ def section_streaming(outer_panels): [ panels.target( f"rate({metric('stream_source_split_change_event_count')}[$__rate_interval])", - "source={{source_name}} actor={{actor_id}} @ {{instance}}" + "source={{source_name}} actor={{actor_id}} @ {{%s}}" + % NODE_LABEL, ) - ] + ], ), panels.timeseries_count( "Kafka Consumer Lag Size", @@ -725,13 +757,13 @@ def section_streaming(outer_panels): [ panels.target( f"{metric('high_watermark')}", - "source={{source_id}} partition={{partition}}" + "source={{source_id}} partition={{partition}}", ), panels.target( f"{metric('latest_message_id')}", - "source={{source_id}} partition={{partition}} actor_id={{actor_id}}" - ) - ] + "source={{source_id}} partition={{partition}} actor_id={{actor_id}}", + ), + ], ), panels.timeseries_rowsps( "Sink Throughput(rows/s)", @@ -779,7 +811,8 @@ def section_streaming(outer_panels): [ panels.target( f"rate({table_metric('stream_backfill_snapshot_read_row_count')}[$__rate_interval])", - "table_id={{table_id}} actor={{actor_id}} @ {{instance}}" + "table_id={{table_id}} actor={{actor_id}} @ {{%s}}" + % NODE_LABEL, ), ], ), @@ -789,7 +822,8 @@ def section_streaming(outer_panels): [ panels.target( f"rate({table_metric('stream_backfill_upstream_output_row_count')}[$__rate_interval])", - "table_id={{table_id}} actor={{actor_id}} @ {{instance}}" + "table_id={{table_id}} actor={{actor_id}} @ {{%s}}" + % NODE_LABEL, ), ], ), @@ -800,7 +834,8 @@ def section_streaming(outer_panels): [ panels.target(f"{metric('all_barrier_nums')}", "all_barrier"), panels.target( - f"{metric('in_flight_barrier_nums')}", "in_flight_barrier"), + f"{metric('in_flight_barrier_nums')}", "in_flight_barrier" + ), ], ), panels.timeseries_latency( @@ -814,7 +849,8 @@ def section_streaming(outer_panels): f"barrier_send_latency_p{legend}", ), [50, 90, 99, 999, "max"], - ) + [ + ) + + [ panels.target( f"rate({metric('meta_barrier_send_duration_seconds_sum')}[$__rate_interval]) / rate({metric('meta_barrier_send_duration_seconds_count')}[$__rate_interval])", "barrier_send_latency_avg", @@ -832,7 +868,8 @@ def section_streaming(outer_panels): f"barrier_latency_p{legend}", ), [50, 90, 99, 999, "max"], - ) + [ + ) + + [ panels.target( f"rate({metric('meta_barrier_duration_seconds_sum')}[$__rate_interval]) / rate({metric('meta_barrier_duration_seconds_count')}[$__rate_interval])", "barrier_latency_avg", @@ -848,9 +885,10 @@ def section_streaming(outer_panels): f"barrier_inflight_latency_p{legend}", ), [50, 90, 99, 999, "max"], - ) + [ + ) + + [ panels.target( - f"max(sum by(le, instance)(rate({metric('stream_barrier_inflight_duration_seconds_sum')}[$__rate_interval])) / sum by(le, instance)(rate({metric('stream_barrier_inflight_duration_seconds_count')}[$__rate_interval])))", + f"max(sum by(le, {NODE_LABEL})(rate({metric('stream_barrier_inflight_duration_seconds_sum')}[$__rate_interval])) / sum by(le, {NODE_LABEL})(rate({metric('stream_barrier_inflight_duration_seconds_count')}[$__rate_interval])))", "barrier_inflight_latency_avg", ), ], @@ -860,14 +898,16 @@ def section_streaming(outer_panels): "", quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('stream_barrier_sync_storage_duration_seconds_bucket')}[$__rate_interval])) by (le,instance))", - f"barrier_sync_latency_p{legend}" + " - {{instance}}", + f"histogram_quantile({quantile}, sum(rate({metric('stream_barrier_sync_storage_duration_seconds_bucket')}[$__rate_interval])) by (le, {NODE_LABEL}))", + f"barrier_sync_latency_p{legend}" + + " - {{%s}}" % NODE_LABEL, ), [50, 90, 99, 999, "max"], - ) + [ + ) + + [ panels.target( - f"sum by(le, instance)(rate({metric('stream_barrier_sync_storage_duration_seconds_sum')}[$__rate_interval])) / sum by(le, instance)(rate({metric('stream_barrier_sync_storage_duration_seconds_count')}[$__rate_interval]))", - "barrier_sync_latency_avg - {{instance}}", + f"sum by(le, {NODE_LABEL})(rate({metric('stream_barrier_sync_storage_duration_seconds_sum')}[$__rate_interval])) / sum by(le, {NODE_LABEL})(rate({metric('stream_barrier_sync_storage_duration_seconds_count')}[$__rate_interval]))", + "barrier_sync_latency_avg - {{%s}}" % NODE_LABEL, ), ], ), @@ -880,7 +920,8 @@ def section_streaming(outer_panels): f"barrier_wait_commit_latency_p{legend}", ), [50, 90, 99, 999, "max"], - ) + [ + ) + + [ panels.target( f"rate({metric('meta_barrier_wait_commit_duration_seconds_sum')}[$__rate_interval]) / rate({metric('meta_barrier_wait_commit_duration_seconds_count')}[$__rate_interval])", "barrier_wait_commit_avg", @@ -894,11 +935,12 @@ def section_streaming(outer_panels): [ panels.target( f"rate({metric('stream_barrier_manager_progress')}[$__rate_interval])", - "{{instance}}", + "{{%s}}" % NODE_LABEL, ), ], ), - ]) + ], + ) ] @@ -1038,7 +1080,7 @@ def section_streaming_actors(outer_panels): f"rate({table_metric('stream_over_window_cache_miss_count')}[$__rate_interval])", "cache miss count - table {{table_id}} actor {{actor_id}}", ), - ] + ], ), panels.timeseries_percentage( "Executor Cache Miss Ratio", @@ -1074,7 +1116,8 @@ def section_streaming_actors(outer_panels): ), panels.target( f"1 - (sum(rate({metric('stream_materialize_cache_hit_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_materialize_cache_total_count')}[$__rate_interval])) by (table_id, fragment_id))", - "materialize executor cache miss ratio - table {{table_id}} fragment {{fragment_id}} {{instance}}", + "materialize executor cache miss ratio - table {{table_id}} fragment {{fragment_id}} {{%s}}" + % NODE_LABEL, ), panels.target( f"(sum(rate({metric('stream_over_window_cache_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_over_window_cache_lookup_count')}[$__rate_interval])) by (table_id, fragment_id))", @@ -1088,14 +1131,15 @@ def section_streaming_actors(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('stream_join_barrier_align_duration_bucket')}[$__rate_interval])) by (le, fragment_id, wait_side, job))", - f"p{legend} - fragment {{{{fragment_id}}}} {{{{wait_side}}}} - {{{{job}}}}", + f"histogram_quantile({quantile}, sum(rate({metric('stream_join_barrier_align_duration_bucket')}[$__rate_interval])) by (le, fragment_id, wait_side, {COMPONENT_LABEL}))", + f"p{legend} - fragment {{{{fragment_id}}}} {{{{wait_side}}}} - {{{{{COMPONENT_LABEL}}}}}", ), [90, 99, 999, "max"], ), panels.target( - f"sum by(le, fragment_id, wait_side, job)(rate({metric('stream_join_barrier_align_duration_sum')}[$__rate_interval])) / sum by(le,fragment_id,wait_side,job) (rate({metric('stream_join_barrier_align_duration_count')}[$__rate_interval]))", - "avg - fragment {{fragment_id}} {{wait_side}} - {{job}}", + f"sum by(le, fragment_id, wait_side, job)(rate({metric('stream_join_barrier_align_duration_sum')}[$__rate_interval])) / sum by(le,fragment_id,wait_side,{COMPONENT_LABEL}) (rate({metric('stream_join_barrier_align_duration_count')}[$__rate_interval]))", + "avg - fragment {{fragment_id}} {{wait_side}} - {{%s}}" + % COMPONENT_LABEL, ), ], ), @@ -1132,10 +1176,14 @@ def section_streaming_actors(outer_panels): "Multiple rows with distinct primary keys may have the same join key. This metric counts the " "number of join keys in the executor cache.", [ - panels.target(f"sum({metric('stream_join_cached_entry_count')}) by (fragment_id, side)", - "fragment {{fragment_id}} {{side}}"), - panels.target_hidden(f"{metric('stream_join_cached_entry_count')}", - "actor {{actor_id}} {{side}}"), + panels.target( + f"sum({metric('stream_join_cached_entry_count')}) by (fragment_id, side)", + "fragment {{fragment_id}} {{side}}", + ), + panels.target_hidden( + f"{metric('stream_join_cached_entry_count')}", + "actor {{actor_id}} {{side}}", + ), ], ), panels.timeseries_count( @@ -1144,14 +1192,15 @@ def section_streaming_actors(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('stream_join_matched_join_keys_bucket')}[$__rate_interval])) by (le, fragment_id, table_id, job))", - f"p{legend} - fragment {{{{fragment_id}}}} table_id {{{{table_id}}}} - {{{{job}}}}", + f"histogram_quantile({quantile}, sum(rate({metric('stream_join_matched_join_keys_bucket')}[$__rate_interval])) by (le, fragment_id, table_id, {COMPONENT_LABEL}))", + f"p{legend} - fragment {{{{fragment_id}}}} table_id {{{{table_id}}}} - {{{{{COMPONENT_LABEL}}}}}", ), [90, 99, "max"], ), panels.target( - f"sum by(le, job, actor_id, table_id) (rate({metric('stream_join_matched_join_keys_sum')}[$__rate_interval])) / sum by(le, job, fragment_id, table_id) (rate({table_metric('stream_join_matched_join_keys_count')}[$__rate_interval]))", - "avg - fragment {{fragment_id}} table_id {{table_id}} - {{job}}", + f"sum by(le, job, actor_id, table_id) (rate({metric('stream_join_matched_join_keys_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}, fragment_id, table_id) (rate({table_metric('stream_join_matched_join_keys_count')}[$__rate_interval]))", + "avg - fragment {{fragment_id}} table_id {{table_id}} - {{%s}}" + % COMPONENT_LABEL, ), ], ), @@ -1181,78 +1230,114 @@ def section_streaming_actors(outer_panels): "Aggregation Cached Keys", "The number of keys cached in each hash aggregation executor's executor cache.", [ - panels.target(f"sum({metric('stream_agg_cached_entry_count')}) by (table_id, fragment_id)", - "stream agg cached keys count | table {{table_id}} fragment {{fragment_id}}"), - panels.target(f"sum({metric('stream_agg_distinct_cached_entry_count')}) by (table_id, fragment_id)", - "stream agg distinct cached keys count | table {{table_id}} fragment {{fragment_id}}"), - panels.target_hidden(f"{metric('stream_agg_cached_entry_count')}", - "stream agg cached keys count | table {{table_id}} actor {{actor_id}}"), - panels.target_hidden(f"{metric('stream_agg_distinct_cached_entry_count')}", - "stream agg distinct cached keys count | table {{table_id}} actor {{actor_id}}"), + panels.target( + f"sum({metric('stream_agg_cached_entry_count')}) by (table_id, fragment_id)", + "stream agg cached keys count | table {{table_id}} fragment {{fragment_id}}", + ), + panels.target( + f"sum({metric('stream_agg_distinct_cached_entry_count')}) by (table_id, fragment_id)", + "stream agg distinct cached keys count | table {{table_id}} fragment {{fragment_id}}", + ), + panels.target_hidden( + f"{metric('stream_agg_cached_entry_count')}", + "stream agg cached keys count | table {{table_id}} actor {{actor_id}}", + ), + panels.target_hidden( + f"{metric('stream_agg_distinct_cached_entry_count')}", + "stream agg distinct cached keys count | table {{table_id}} actor {{actor_id}}", + ), ], ), panels.timeseries_count( "Aggregation Dirty Groups Count", "The number of dirty (unflushed) groups in each hash aggregation executor's executor cache.", [ - panels.target(f"sum({metric('stream_agg_dirty_groups_count')}) by (table_id, fragment_id)", - "stream agg dirty groups count | table {{table_id}} fragment {{fragment_id}}"), - panels.target_hidden(f"{metric('stream_agg_dirty_groups_count')}", - "stream agg dirty groups count | table {{table_id}} actor {{actor_id}}"), + panels.target( + f"sum({metric('stream_agg_dirty_groups_count')}) by (table_id, fragment_id)", + "stream agg dirty groups count | table {{table_id}} fragment {{fragment_id}}", + ), + panels.target_hidden( + f"{metric('stream_agg_dirty_groups_count')}", + "stream agg dirty groups count | table {{table_id}} actor {{actor_id}}", + ), ], ), panels.timeseries_bytes( "Aggregation Dirty Groups Heap Size", "The total heap size of dirty (unflushed) groups in each hash aggregation executor's executor cache.", [ - panels.target(f"sum({metric('stream_agg_dirty_groups_heap_size')}) by (table_id, fragment_id)", - "stream agg dirty groups heap size | table {{table_id}} fragment {{fragment_id}}"), - panels.target_hidden(f"{metric('stream_agg_dirty_groups_heap_size')}", - "stream agg dirty groups heap size | table {{table_id}} actor {{actor_id}}"), + panels.target( + f"sum({metric('stream_agg_dirty_groups_heap_size')}) by (table_id, fragment_id)", + "stream agg dirty groups heap size | table {{table_id}} fragment {{fragment_id}}", + ), + panels.target_hidden( + f"{metric('stream_agg_dirty_groups_heap_size')}", + "stream agg dirty groups heap size | table {{table_id}} actor {{actor_id}}", + ), ], ), panels.timeseries_count( "TopN Cached Keys", "The number of keys cached in each top_n executor's executor cache.", [ - panels.target(f"sum({metric('stream_group_top_n_cached_entry_count')}) by (table_id, fragment_id)", - "group top_n cached count | table {{table_id}} fragment {{fragment_id}}"), - panels.target(f"sum({metric('stream_group_top_n_appendonly_cached_entry_count')}) by (table_id, fragment_id)", - "group top_n appendonly cached count | table {{table_id}} fragment {{fragment_id}}"), - panels.target_hidden(f"{metric('stream_group_top_n_cached_entry_count')}", - "group top_n cached count | table {{table_id}} actor {{actor_id}}"), - panels.target_hidden(f"{metric('stream_group_top_n_appendonly_cached_entry_count')}", - "group top_n appendonly cached count | table {{table_id}} actor {{actor_id}}"), + panels.target( + f"sum({metric('stream_group_top_n_cached_entry_count')}) by (table_id, fragment_id)", + "group top_n cached count | table {{table_id}} fragment {{fragment_id}}", + ), + panels.target( + f"sum({metric('stream_group_top_n_appendonly_cached_entry_count')}) by (table_id, fragment_id)", + "group top_n appendonly cached count | table {{table_id}} fragment {{fragment_id}}", + ), + panels.target_hidden( + f"{metric('stream_group_top_n_cached_entry_count')}", + "group top_n cached count | table {{table_id}} actor {{actor_id}}", + ), + panels.target_hidden( + f"{metric('stream_group_top_n_appendonly_cached_entry_count')}", + "group top_n appendonly cached count | table {{table_id}} actor {{actor_id}}", + ), ], ), panels.timeseries_count( "Temporal Join Cache Keys", "The number of keys cached in temporal join executor's executor cache.", [ - panels.target(f"sum({metric('stream_temporal_join_cached_entry_count')}) by (table_id, fragment_id)", - "Temporal Join cached count | table {{table_id}} fragment {{fragment_id}}"), - panels.target_hidden(f"{metric('stream_temporal_join_cached_entry_count')}", - "Temporal Join cached count | table {{table_id}} actor {{actor_id}}"), + panels.target( + f"sum({metric('stream_temporal_join_cached_entry_count')}) by (table_id, fragment_id)", + "Temporal Join cached count | table {{table_id}} fragment {{fragment_id}}", + ), + panels.target_hidden( + f"{metric('stream_temporal_join_cached_entry_count')}", + "Temporal Join cached count | table {{table_id}} actor {{actor_id}}", + ), ], ), panels.timeseries_count( "Lookup Cached Keys", "The number of keys cached in lookup executor's executor cache.", [ - panels.target(f"sum({metric('stream_lookup_cached_entry_count')}) by (table_id, fragment_id)", - "lookup cached count | table {{table_id}} fragment {{fragment_id}}"), - panels.target_hidden(f"{metric('stream_lookup_cached_entry_count')}", - "lookup cached count | table {{table_id}} actor {{actor_id}}"), + panels.target( + f"sum({metric('stream_lookup_cached_entry_count')}) by (table_id, fragment_id)", + "lookup cached count | table {{table_id}} fragment {{fragment_id}}", + ), + panels.target_hidden( + f"{metric('stream_lookup_cached_entry_count')}", + "lookup cached count | table {{table_id}} actor {{actor_id}}", + ), ], ), panels.timeseries_count( "Over Window Cached Keys", "The number of keys cached in over window executor's executor cache.", [ - panels.target(f"sum({metric('stream_over_window_cached_entry_count')}) by (table_id, fragment_id)", - "over window cached count | table {{table_id}} fragment {{fragment_id}}"), - panels.target_hidden(f"{metric('stream_over_window_cached_entry_count')}", - "over window cached count | table {{table_id}} actor {{actor_id}}"), + panels.target( + f"sum({metric('stream_over_window_cached_entry_count')}) by (table_id, fragment_id)", + "over window cached count | table {{table_id}} fragment {{fragment_id}}", + ), + panels.target_hidden( + f"{metric('stream_over_window_cached_entry_count')}", + "over window cached count | table {{table_id}} actor {{actor_id}}", + ), ], ), panels.timeseries_actor_rowsps( @@ -1454,7 +1539,7 @@ def section_streaming_actors_tokio(outer_panels): ), ], ), - ] + ], ) ] @@ -1599,53 +1684,54 @@ def section_batch(outer_panels): ), ], ), - panels.timeseries_bytes( - "Mem Table Size", - "This metric shows the memory usage of mem_table.", - [ - panels.target( - f"sum({metric('state_store_mem_table_memory_size')}) by (job,instance)", - "mem_table size total - {{job}} @ {{instance}}", - ), - - panels.target( - f"{metric('state_store_mem_table_memory_size')}", - "mem_table size - table id {{table_id}} instance id {{instance_id}} {{job}} @ {{instance}}", - ), - ], - ), - - panels.timeseries_count( - "Mem Table Count", - "This metric shows the item counts in mem_table.", - [ - panels.target( - f"sum({metric('state_store_mem_table_item_count')}) by (job,instance)", - "mem_table counts total - {{job}} @ {{instance}}", + "Mem Table Size", + "This metric shows the memory usage of mem_table.", + [ + panels.target( + f"sum({metric('state_store_mem_table_memory_size')}) by ({COMPONENT_LABEL}, {NODE_LABEL})", + "mem_table size total - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), + ), + panels.target( + f"{metric('state_store_mem_table_memory_size')}", + "mem_table size - table id {{table_id}} instance id {{instance_id}} {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), + ), + ], ), - - panels.target( - f"{metric('state_store_mem_table_item_count')}", - "mem_table count - table id {{table_id}} instance id {{instance_id}} {{job}} @ {{instance}}", + panels.timeseries_count( + "Mem Table Count", + "This metric shows the item counts in mem_table.", + [ + panels.target( + f"sum({metric('state_store_mem_table_item_count')}) by ({COMPONENT_LABEL}, {NODE_LABEL})", + "mem_table counts total - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), + ), + panels.target( + f"{metric('state_store_mem_table_item_count')}", + "mem_table count - table id {{table_id}} instance id {{instance_id}} {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), + ), + ], ), - ], - ), panels.timeseries_latency( "Row SeqScan Next Duration", "", [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('batch_row_seq_scan_next_duration_bucket')}[$__rate_interval])) by (le, job, instance))", - f"row_seq_scan next p{legend}" + - " - {{job}} @ {{instance}}", + f"histogram_quantile({quantile}, sum(rate({metric('batch_row_seq_scan_next_duration_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + f"row_seq_scan next p{legend}" + + " - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), [50, 99, "max"], ), panels.target( - f"sum by(le, job, instance) (rate({metric('batch_row_seq_scan_next_duration_sum')}[$__rate_interval])) / sum by(le, job, instance) (rate({metric('batch_row_seq_scan_next_duration_count')}[$__rate_interval]))", - "row_seq_scan next avg - {{job}} @ {{instance}}", + f"sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}) (rate({metric('batch_row_seq_scan_next_duration_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}) (rate({metric('batch_row_seq_scan_next_duration_count')}[$__rate_interval]))", + "row_seq_scan next avg - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -1668,7 +1754,7 @@ def section_frontend(outer_panels): f"{metric('frontend_active_sessions')}", "", ), - ] + ], ), panels.timeseries_query_per_sec( "Query Per Second (Local Query Mode)", @@ -1694,8 +1780,10 @@ def section_frontend(outer_panels): "The Number of Running Queries (Distributed Query Mode)", "", [ - panels.target(f"{metric('distributed_running_query_num')}", - "The number of running query in distributed execution mode"), + panels.target( + f"{metric('distributed_running_query_num')}", + "The number of running query in distributed execution mode", + ), ], ["last"], ), @@ -1703,8 +1791,10 @@ def section_frontend(outer_panels): "The Number of Rejected queries (Distributed Query Mode)", "", [ - panels.target(f"{metric('distributed_rejected_query_counter')}", - "The number of rejected query in distributed execution mode"), + panels.target( + f"{metric('distributed_rejected_query_counter')}", + "The number of rejected query in distributed execution mode", + ), ], ["last"], ), @@ -1712,8 +1802,10 @@ def section_frontend(outer_panels): "The Number of Completed Queries (Distributed Query Mode)", "", [ - panels.target(f"{metric('distributed_completed_query_counter')}", - "The number of completed query in distributed execution mode"), + panels.target( + f"{metric('distributed_completed_query_counter')}", + "The number of completed query in distributed execution mode", + ), ], ["last"], ), @@ -1722,16 +1814,16 @@ def section_frontend(outer_panels): "", [ panels.target( - f"histogram_quantile(0.5, sum(rate({metric('distributed_query_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "p50 - {{job}} @ {{instance}}", + f"histogram_quantile(0.5, sum(rate({metric('distributed_query_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "p50 - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"histogram_quantile(0.9, sum(rate({metric('distributed_query_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "p90 - {{job}} @ {{instance}}", + f"histogram_quantile(0.9, sum(rate({metric('distributed_query_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "p90 - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"histogram_quantile(0.95, sum(rate({metric('distributed_query_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "p99 - {{job}} @ {{instance}}", + f"histogram_quantile(0.95, sum(rate({metric('distributed_query_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "p99 - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -1740,16 +1832,16 @@ def section_frontend(outer_panels): "", [ panels.target( - f"histogram_quantile(0.5, sum(rate({metric('frontend_latency_local_execution_bucket')}[$__rate_interval])) by (le, job, instance))", - "p50 - {{job}} @ {{instance}}", + f"histogram_quantile(0.5, sum(rate({metric('frontend_latency_local_execution_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "p50 - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"histogram_quantile(0.9, sum(rate({metric('frontend_latency_local_execution_bucket')}[$__rate_interval])) by (le, job, instance))", - "p90 - {{job}} @ {{instance}}", + f"histogram_quantile(0.9, sum(rate({metric('frontend_latency_local_execution_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "p90 - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"histogram_quantile(0.95, sum(rate({metric('frontend_latency_local_execution_bucket')}[$__rate_interval])) by (le, job, instance))", - "p99 - {{job}} @ {{instance}}", + f"histogram_quantile(0.95, sum(rate({metric('frontend_latency_local_execution_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "p99 - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -1775,54 +1867,58 @@ def section_hummock_read(outer_panels): "", [ panels.target( - f"sum(rate({table_metric('state_store_sst_store_block_request_counts')}[$__rate_interval])) by (job, instance, table_id, type)", - "{{table_id}} @ {{type}} - {{job}} @ {{instance}}", + f"sum(rate({table_metric('state_store_sst_store_block_request_counts')}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}, table_id, type)", + "{{table_id}} @ {{type}} - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({metric('state_store_sst_store_block_request_counts', meta_miss_filter)}[$__rate_interval])) by (job, instance, type)", - "total_meta_miss_count - {{job}} @ {{instance}}", + f"sum(rate({metric('state_store_sst_store_block_request_counts', meta_miss_filter)}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}, type)", + "total_meta_miss_count - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), - panels.timeseries_bytes( "Cache Size", "Hummock has three parts of memory usage: 1. Meta Cache 2. Block Cache" "This metric shows the real memory usage of each of these three caches.", [ panels.target( - f"avg({metric('state_store_meta_cache_size')}) by (job,instance)", - "meta cache - {{job}} @ {{instance}}", + f"avg({metric('state_store_meta_cache_size')}) by ({COMPONENT_LABEL}, {NODE_LABEL})", + "meta cache - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"avg({metric('state_store_block_cache_size')}) by (job,instance)", - "data cache - {{job}} @ {{instance}}", + f"avg({metric('state_store_block_cache_size')}) by ({COMPONENT_LABEL}, {NODE_LABEL})", + "data cache - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), - panels.timeseries_percentage( "Cache Miss Rate", "", [ panels.target( - f"(sum(rate({table_metric('state_store_sst_store_block_request_counts', meta_miss_filter)}[$__rate_interval])) by (job,instance,table_id)) / (sum(rate({table_metric('state_store_sst_store_block_request_counts', meta_total_filter)}[$__rate_interval])) by (job,instance,table_id))", - "meta cache miss rate - {{table_id}} @ {{job}} @ {{instance}}", + f"(sum(rate({table_metric('state_store_sst_store_block_request_counts', meta_miss_filter)}[$__rate_interval])) by ({COMPONENT_LABEL},{NODE_LABEL},table_id)) / (sum(rate({table_metric('state_store_sst_store_block_request_counts', meta_total_filter)}[$__rate_interval])) by ({COMPONENT_LABEL},{NODE_LABEL},table_id))", + "meta cache miss rate - {{table_id}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"(sum(rate({table_metric('state_store_sst_store_block_request_counts', data_miss_filter)}[$__rate_interval])) by (job,instance,table_id)) / (sum(rate({table_metric('state_store_sst_store_block_request_counts', data_total_filter)}[$__rate_interval])) by (job,instance,table_id))", - "block cache miss rate - {{table_id}} @ {{job}} @ {{instance}}", + f"(sum(rate({table_metric('state_store_sst_store_block_request_counts', data_miss_filter)}[$__rate_interval])) by ({COMPONENT_LABEL},{NODE_LABEL},table_id)) / (sum(rate({table_metric('state_store_sst_store_block_request_counts', data_total_filter)}[$__rate_interval])) by ({COMPONENT_LABEL},{NODE_LABEL},table_id))", + "block cache miss rate - {{table_id}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), - panels.timeseries_ops( "Iter keys flow", "", [ panels.target( - f"sum(rate({table_metric('state_store_iter_scan_key_counts')}[$__rate_interval])) by (instance, type, table_id)", - "iter keys flow - {{table_id}} @ {{type}} @ {{instance}} ", + f"sum(rate({table_metric('state_store_iter_scan_key_counts')}[$__rate_interval])) by ({NODE_LABEL}, type, table_id)", + "iter keys flow - {{table_id}} @ {{type}} @ {{%s}}" + % NODE_LABEL, ), ], ), @@ -1832,33 +1928,37 @@ def section_hummock_read(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_iter_merge_sstable_counts_bucket')}[$__rate_interval])) by (le, job, table_id, type))", - f"# merged ssts p{legend}" + - " - {{table_id}} @ {{job}} @ {{type}}", + f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_iter_merge_sstable_counts_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, table_id, type))", + f"# merged ssts p{legend}" + + " - {{table_id}} @ {{%s}} @ {{type}}" + % COMPONENT_LABEL, ), [50, 99, "max"], ), panels.target( - f"sum by(le, job, instance, table_id)(rate({table_metric('state_store_iter_merge_sstable_counts_sum')}[$__rate_interval])) / sum by(le, job, instance, table_id)(rate({table_metric('state_store_iter_merge_sstable_counts_count')}[$__rate_interval]))", - "# merged ssts avg - {{table_id}} @ {{job}} @ {{instance}}", + f"sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id)(rate({table_metric('state_store_iter_merge_sstable_counts_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id)(rate({table_metric('state_store_iter_merge_sstable_counts_count')}[$__rate_interval]))", + "# merged ssts avg - {{table_id}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), - panels.timeseries_latency( "Read Duration - Get", "Histogram of the latency of Get operations that have been issued to the state store.", [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_get_duration_bucket')}[$__rate_interval])) by (le, job, instance, table_id))", - f"p{legend}" + " - {{table_id}} @ {{job}} @ {{instance}}", + f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_get_duration_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id))", + f"p{legend}" + + " - {{table_id}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), [50, 99, "max"], ), panels.target( - f"sum by(le, job, instance, table_id)(rate({table_metric('state_store_get_duration_sum')}[$__rate_interval])) / sum by(le, job, instance, table_id) (rate({table_metric('state_store_get_duration_count')}[$__rate_interval]))", - "avg - {{table_id}} {{job}} @ {{instance}}", + f"sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id)(rate({table_metric('state_store_get_duration_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id) (rate({table_metric('state_store_get_duration_count')}[$__rate_interval]))", + "avg - {{table_id}} {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -1869,29 +1969,30 @@ def section_hummock_read(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_iter_init_duration_bucket')}[$__rate_interval])) by (le, job, instance, table_id))", - f"create_iter_time p{legend} - {{{{table_id}}}} @ {{{{job}}}} @ {{{{instance}}}}", + f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_iter_init_duration_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id))", + f"create_iter_time p{legend} - {{{{table_id}}}} @ {{{{{COMPONENT_LABEL}}}}} @ {{{{{NODE_LABEL}}}}}", ), [50, 99, "max"], ), panels.target( - f"sum by(le, job, instance)(rate({metric('state_store_iter_init_duration_sum')}[$__rate_interval])) / sum by(le, job,instance) (rate({metric('state_store_iter_init_duration_count')}[$__rate_interval]))", - "create_iter_time avg - {{job}} @ {{instance}}", + f"sum by(le, {COMPONENT_LABEL}, {NODE_LABEL})(rate({metric('state_store_iter_init_duration_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}) (rate({metric('state_store_iter_init_duration_count')}[$__rate_interval]))", + "create_iter_time avg - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_iter_scan_duration_bucket')}[$__rate_interval])) by (le, job, instance, table_id))", - f"pure_scan_time p{legend} - {{{{table_id}}}} @ {{{{job}}}} @ {{{{instance}}}}", + f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_iter_scan_duration_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id))", + f"pure_scan_time p{legend} - {{{{table_id}}}} @ {{{{{COMPONENT_LABEL}}}}} @ {{{{{NODE_LABEL}}}}}", ), [50, 99, "max"], ), panels.target( - f"sum by(le, job, instance)(rate({metric('state_store_iter_scan_duration_sum')}[$__rate_interval])) / sum by(le, job,instance) (rate({metric('state_store_iter_scan_duration_count')}[$__rate_interval]))", - "pure_scan_time avg - {{job}} @ {{instance}}", + f"sum by(le, {COMPONENT_LABEL}, {NODE_LABEL})(rate({metric('state_store_iter_scan_duration_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}) (rate({metric('state_store_iter_scan_duration_count')}[$__rate_interval]))", + "pure_scan_time avg - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), - panels.timeseries_ops( "Bloom Filter Ops", "", @@ -1910,7 +2011,6 @@ def section_hummock_read(outer_panels): ), ], ), - panels.timeseries_percentage( "Bloom Filter Positive Rate", "Positive / Total", @@ -1931,7 +2031,6 @@ def section_hummock_read(outer_panels): ), ], ), - panels.timeseries_count( "Slow Fetch Meta Unhits", "", @@ -1942,22 +2041,24 @@ def section_hummock_read(outer_panels): ), ], ), - panels.timeseries_ops( "Read Ops", "", [ panels.target( - f"sum(rate({table_metric('state_store_get_duration_count')}[$__rate_interval])) by (job,instanc,table_id)", - "get - {{table_id}} @ {{job}} @ {{instance}}", + f"sum(rate({table_metric('state_store_get_duration_count')}[$__rate_interval])) by ({COMPONENT_LABEL},{NODE_LABEL},table_id)", + "get - {{table_id}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({table_metric('state_store_get_shared_buffer_hit_counts')}[$__rate_interval])) by (job,instance,table_id)", - "shared_buffer hit - {{table_id}} @ {{job}} @ {{instance}}", + f"sum(rate({table_metric('state_store_get_shared_buffer_hit_counts')}[$__rate_interval])) by ({COMPONENT_LABEL},{NODE_LABEL},table_id)", + "shared_buffer hit - {{table_id}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({table_metric('state_store_iter_in_process_counts')}[$__rate_interval])) by(job,instance,table_id)", - "iter - {{table_id}} @ {{job}} @ {{instance}}", + f"sum(rate({table_metric('state_store_iter_in_process_counts')}[$__rate_interval])) by ({COMPONENT_LABEL},{NODE_LABEL},table_id)", + "iter - {{table_id}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -1967,8 +2068,8 @@ def section_hummock_read(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_get_key_size_bucket')}[$__rate_interval])) by (le, job, instance, table_id)) + histogram_quantile({quantile}, sum(rate({table_metric('state_store_get_value_size_bucket')}[$__rate_interval])) by (le, job, instance, table_id))", - f"p{legend} - {{{{table_id}}}} {{{{job}}}} @ {{{{instance}}}}", + f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_get_key_size_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id)) + histogram_quantile({quantile}, sum(rate({table_metric('state_store_get_value_size_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id))", + f"p{legend} - {{{{table_id}}}} {{{{{COMPONENT_LABEL}}}}} @ {{{{{NODE_LABEL}}}}}", ), [50, 99, "max"], ), @@ -1980,36 +2081,34 @@ def section_hummock_read(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_iter_size_bucket')}[$__rate_interval])) by (le, job, instance, table_id))", - f"p{legend} - {{{{table_id}}}} @ {{{{job}}}} @ {{{{instance}}}}", + f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_iter_size_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id))", + f"p{legend} - {{{{table_id}}}} @ {{{{{COMPONENT_LABEL}}}}} @ {{{{{NODE_LABEL}}}}}", ), [50, 99, "max"], ), ], ), - panels.timeseries_bytes( "Materialized View Read Size", "", [ *quantile( lambda quantile, legend: panels.target( - f'sum(histogram_quantile({quantile}, sum(rate({metric("state_store_iter_size_bucket")}[$__rate_interval])) by (le, job, instance, table_id)) * on(table_id) group_left(materialized_view_id) (group({metric("table_info")}) by (materialized_view_id, table_id))) by (materialized_view_id) + sum((histogram_quantile({quantile}, sum(rate({metric("state_store_get_key_size_bucket")}[$__rate_interval])) by (le, job, instance, table_id)) + histogram_quantile({quantile}, sum(rate({metric("state_store_get_value_size_bucket")}[$__rate_interval])) by (le, job, instance, table_id))) * on(table_id) group_left(materialized_view_id) (group({metric("table_info")}) by (materialized_view_id, table_id))) by (materialized_view_id)', - f"read p{legend} - materialized view {{{{materialized_view_id}}}}" + f'sum(histogram_quantile({quantile}, sum(rate({metric("state_store_iter_size_bucket")}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id)) * on(table_id) group_left(materialized_view_id) (group({metric("table_info")}) by (materialized_view_id, table_id))) by (materialized_view_id) + sum((histogram_quantile({quantile}, sum(rate({metric("state_store_get_key_size_bucket")}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id)) + histogram_quantile({quantile}, sum(rate({metric("state_store_get_value_size_bucket")}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id))) * on(table_id) group_left(materialized_view_id) (group({metric("table_info")}) by (materialized_view_id, table_id))) by (materialized_view_id)', + f"read p{legend} - materialized view {{{{materialized_view_id}}}}", ), [50, 99, "max"], ), ], ), - panels.timeseries_count( "Read Item Count - Iter", "", [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_iter_item_bucket')}[$__rate_interval])) by (le, job, instance, table_id))", - f"p{legend} - {{{{table_id}}}} @ {{{{job}}}} @ {{{{instance}}}}", + f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_iter_item_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id))", + f"p{legend} - {{{{table_id}}}} @ {{{{{COMPONENT_LABEL}}}}} @ {{{{{NODE_LABEL}}}}}", ), [50, 99, "max"], ), @@ -2022,8 +2121,8 @@ def section_hummock_read(outer_panels): "exist in the storage, the size of key is counted into this metric and the size of value is 0.", [ panels.target( - f"sum(rate({metric('state_store_get_key_size_sum')}[$__rate_interval])) by(job, instance) + sum(rate({metric('state_store_get_value_size_sum')}[$__rate_interval])) by(job, instance)", - "{{job}} @ {{instance}}", + f"sum(rate({metric('state_store_get_key_size_sum')}[$__rate_interval])) by({COMPONENT_LABEL}, {NODE_LABEL}) + sum(rate({metric('state_store_get_value_size_sum')}[$__rate_interval])) by({COMPONENT_LABEL}, {NODE_LABEL})", + "{{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -2033,31 +2132,31 @@ def section_hummock_read(outer_panels): "Operation Iter scans a range of key-value pairs.", [ panels.target( - f"sum(rate({metric('state_store_iter_size_sum')}[$__rate_interval])) by(job, instance)", - "{{job}} @ {{instance}}", + f"sum(rate({metric('state_store_iter_size_sum')}[$__rate_interval])) by({COMPONENT_LABEL}, {NODE_LABEL})", + "{{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), ], ), - panels.timeseries_latency( "Fetch Meta Duration", "", [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_iter_fetch_meta_duration_bucket')}[$__rate_interval])) by (le, job, instance, table_id))", - f"fetch_meta_duration p{legend}" + - " - {{table_id}} @ {{job}} @ {{instance}}", + f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_iter_fetch_meta_duration_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id))", + f"fetch_meta_duration p{legend}" + + " - {{table_id}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), [50, 99, "max"], ), panels.target( - f"sum by(le, job, instance, table_id) (rate({table_metric('state_store_iter_fetch_meta_duration_sum')}[$__rate_interval])) / sum by(le, job, instance, table_id) (rate({table_metric('state_store_iter_fetch_meta_duration_count')}[$__rate_interval]))", - "fetch_meta_duration avg - {{table_id}} @ {{job}} @ {{instance}}", + f"sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id) (rate({table_metric('state_store_iter_fetch_meta_duration_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id) (rate({table_metric('state_store_iter_fetch_meta_duration_count')}[$__rate_interval]))", + "fetch_meta_duration avg - {{table_id}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), - panels.timeseries_count( "Fetch Meta Unhits", "", @@ -2068,7 +2167,8 @@ def section_hummock_read(outer_panels): ), ], ), - ]) + ], + ) ] @@ -2083,12 +2183,14 @@ def section_hummock_write(outer_panels): "This metric shows the real memory usage of uploader.", [ panels.target( - f"sum({metric('uploading_memory_size')}) by (job,instance)", - "uploading memory - {{job}} @ {{instance}}", + f"sum({metric('uploading_memory_size')}) by ({COMPONENT_LABEL}, {NODE_LABEL})", + "uploading memory - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum({metric('state_store_uploader_uploading_task_size')}) by (job,instance)", - "uploading task size - {{job}} @ {{instance}}", + f"sum({metric('state_store_uploader_uploading_task_size')}) by ({COMPONENT_LABEL}, {NODE_LABEL})", + "uploading task size - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -2098,14 +2200,15 @@ def section_hummock_write(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('state_store_sync_duration_bucket')}[$__rate_interval])) by (le, job, instance))", - f"p{legend}" + " - {{job}} @ {{instance}}", + f"histogram_quantile({quantile}, sum(rate({metric('state_store_sync_duration_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + f"p{legend}" + + " - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), [50, 99, "max"], ), panels.target( - f"sum by(le, job, instance) (rate({metric('state_store_sync_duration_sum')}[$__rate_interval])) / sum by(le, job, instance) (rate({metric('state_store_sync_duration_count')}[$__rate_interval]))", - "avg - {{job}} @ {{instance}}", + f"sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}) (rate({metric('state_store_sync_duration_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}) (rate({metric('state_store_sync_duration_count')}[$__rate_interval]))", + "avg - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -2115,8 +2218,8 @@ def section_hummock_write(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f'sum(histogram_quantile({quantile}, sum(rate({metric("state_store_write_batch_size_bucket")}[$__rate_interval])) by (le, job, instance, table_id)) * on(table_id) group_left(materialized_view_id) (group({metric("table_info")}) by (materialized_view_id, table_id))) by (materialized_view_id, table_name)', - f"write p{legend} - materialized view {{{{materialized_view_id}}}}" + f'sum(histogram_quantile({quantile}, sum(rate({metric("state_store_write_batch_size_bucket")}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id)) * on(table_id) group_left(materialized_view_id) (group({metric("table_info")}) by (materialized_view_id, table_id))) by (materialized_view_id, table_name)', + f"write p{legend} - materialized view {{{{materialized_view_id}}}}", ), [50, 99, "max"], ), @@ -2127,12 +2230,13 @@ def section_hummock_write(outer_panels): "", [ panels.target( - f"sum(irate({table_metric('state_store_merge_imm_task_counts')}[$__rate_interval])) by (job,instance,table_id)", - "merge imm tasks - {{table_id}} @ {{instance}} ", + f"sum(irate({table_metric('state_store_merge_imm_task_counts')}[$__rate_interval])) by ({COMPONENT_LABEL},{NODE_LABEL},table_id)", + "merge imm tasks - {{table_id}} @ {{%s}}" % NODE_LABEL, ), panels.target( - f"sum(irate({metric('state_store_spill_task_counts')}[$__rate_interval])) by (job,instance,uploader_stage)", - "Uploader spill tasks - {{uploader_stage}} @ {{instance}} ", + f"sum(irate({metric('state_store_spill_task_counts')}[$__rate_interval])) by ({COMPONENT_LABEL},{NODE_LABEL},uploader_stage)", + "Uploader spill tasks - {{uploader_stage}} @ {{%s}}" + % NODE_LABEL, ), ], ), @@ -2141,27 +2245,29 @@ def section_hummock_write(outer_panels): "", [ panels.target( - f"sum(rate({table_metric('state_store_merge_imm_memory_sz')}[$__rate_interval])) by (job,instance,table_id)", - "Merging tasks memory size - {{table_id}} @ {{instance}} ", + f"sum(rate({table_metric('state_store_merge_imm_memory_sz')}[$__rate_interval])) by ({COMPONENT_LABEL},{NODE_LABEL},table_id)", + "Merging tasks memory size - {{table_id}} @ {{%s}}" + % NODE_LABEL, ), panels.target( - f"sum(rate({metric('state_store_spill_task_size')}[$__rate_interval])) by (job,instance,uploader_stage)", - "Uploading tasks size - {{uploader_stage}} @ {{instance}} ", + f"sum(rate({metric('state_store_spill_task_size')}[$__rate_interval])) by ({COMPONENT_LABEL},{NODE_LABEL},uploader_stage)", + "Uploading tasks size - {{uploader_stage}} @ {{%s}}" + % NODE_LABEL, ), ], ), - panels.timeseries_ops( "Write Ops", "", [ panels.target( - f"sum(rate({table_metric('state_store_write_batch_duration_count')}[$__rate_interval])) by (job,instance,table_id)", - "write batch - {{table_id}} @ {{job}} @ {{instance}} ", + f"sum(rate({table_metric('state_store_write_batch_duration_count')}[$__rate_interval])) by ({COMPONENT_LABEL},{NODE_LABEL},table_id)", + "write batch - {{table_id}} @ {{%s}} @ {{%s}} " + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({metric('state_store_sync_duration_count')}[$__rate_interval])) by (job,instance)", - "l0 - {{job}} @ {{instance}} ", + f"sum(rate({metric('state_store_sync_duration_count')}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL})", + "l0 - {{%s}} @ {{%s}} " % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -2171,27 +2277,30 @@ def section_hummock_write(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_write_batch_duration_bucket')}[$__rate_interval])) by (le, job, instance, table_id))", - f"write to shared_buffer p{legend}" + - " - {{table_id}} @ {{job}} @ {{instance}}", + f"histogram_quantile({quantile}, sum(rate({table_metric('state_store_write_batch_duration_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id))", + f"write to shared_buffer p{legend}" + + " - {{table_id}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), [50, 99, "max"], ), panels.target( - f"sum by(le, job, instance, table_id)(rate({table_metric('state_store_write_batch_duration_sum')}[$__rate_interval])) / sum by(le, job, instance, table_id)(rate({table_metric('state_store_write_batch_duration_count')}[$__rate_interval]))", - "write to shared_buffer avg - {{table_id}} @ {{job}} @ {{instance}}", + f"sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id)(rate({table_metric('state_store_write_batch_duration_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}, table_id)(rate({table_metric('state_store_write_batch_duration_count')}[$__rate_interval]))", + "write to shared_buffer avg - {{table_id}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('state_store_write_shared_buffer_sync_time_bucket')}[$__rate_interval])) by (le, job, instance))", - f"write to object_store p{legend}" + - " - {{job}} @ {{instance}}", + f"histogram_quantile({quantile}, sum(rate({metric('state_store_write_shared_buffer_sync_time_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + f"write to object_store p{legend}" + + " - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), [50, 99, "max"], ), panels.target( - f"sum by(le, job, instance)(rate({metric('state_store_write_shared_buffer_sync_time_sum')}[$__rate_interval])) / sum by(le, job, instance)(rate({metric('state_store_write_shared_buffer_sync_time_count')}[$__rate_interval]))", - "write to object_store - {{job}} @ {{instance}}", + f"sum by(le, {COMPONENT_LABEL}, {NODE_LABEL})(rate({metric('state_store_write_shared_buffer_sync_time_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}, {NODE_LABEL})(rate({metric('state_store_write_shared_buffer_sync_time_count')}[$__rate_interval]))", + "write to object_store - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -2200,8 +2309,9 @@ def section_hummock_write(outer_panels): "", [ panels.target( - f"sum(irate({table_metric('state_store_write_batch_tuple_counts')}[$__rate_interval])) by (job,instance,table_id)", - "write_batch_kv_pair_count - {{table_id}} @ {{instance}} ", + f"sum(irate({table_metric('state_store_write_batch_tuple_counts')}[$__rate_interval])) by ({COMPONENT_LABEL},{NODE_LABEL},table_id)", + "write_batch_kv_pair_count - {{table_id}} @ {{%s}}" + % NODE_LABEL, ), ], ), @@ -2210,12 +2320,13 @@ def section_hummock_write(outer_panels): "", [ panels.target( - f"sum(rate({table_metric('state_store_write_batch_size_sum')}[$__rate_interval]))by(job,instance,table_id) / sum(rate({table_metric('state_store_write_batch_size_count')}[$__rate_interval]))by(job,instance,table_id)", - "shared_buffer - {{table_id}} @ {{job}} @ {{instance}}", + f"sum(rate({table_metric('state_store_write_batch_size_sum')}[$__rate_interval]))by({COMPONENT_LABEL},{NODE_LABEL},table_id) / sum(rate({table_metric('state_store_write_batch_size_count')}[$__rate_interval]))by({COMPONENT_LABEL},{NODE_LABEL},table_id)", + "shared_buffer - {{table_id}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({metric('compactor_shared_buffer_to_sstable_size_sum')}[$__rate_interval]))by(job,instance) / sum(rate({metric('compactor_shared_buffer_to_sstable_size_count')}[$__rate_interval]))by(job,instance)", - "sync - {{job}} @ {{instance}}", + f"sum(rate({metric('compactor_shared_buffer_to_sstable_size_sum')}[$__rate_interval]))by({COMPONENT_LABEL}, {NODE_LABEL}) / sum(rate({metric('compactor_shared_buffer_to_sstable_size_count')}[$__rate_interval]))by({COMPONENT_LABEL}, {NODE_LABEL})", + "sync - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -2224,13 +2335,14 @@ def section_hummock_write(outer_panels): "This metric shows the statistics of mem_table size on flush. By default only max (p100) is shown.", [ panels.target( - f"histogram_quantile(1.0, sum(rate({metric('state_store_write_batch_size_bucket')}[$__rate_interval])) by (le, table_id, job, instance))", - "pmax - {{table_id}} @ {{job}} @ {{instance}}", + f"histogram_quantile(1.0, sum(rate({metric('state_store_write_batch_size_bucket')}[$__rate_interval])) by (le, table_id, {COMPONENT_LABEL}, {NODE_LABEL}))", + "pmax - {{table_id}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), - panels.target( - f"sum by(le, job, instance) (rate({metric('state_store_write_batch_size_sum')}[$__rate_interval])) / sum by(le, table_id, job, instance) (rate({metric('state_store_write_batch_size_count')}[$__rate_interval]))", - "avg - {{table_id}} {{job}} @ {{instance}}", + f"sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}) (rate({metric('state_store_write_batch_size_sum')}[$__rate_interval])) / sum by(le, table_id, {COMPONENT_LABEL}, {NODE_LABEL}) (rate({metric('state_store_write_batch_size_count')}[$__rate_interval]))", + "avg - {{table_id}} {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -2240,18 +2352,20 @@ def section_hummock_write(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('state_store_sync_size_bucket')}[$__rate_interval])) by (le, job, instance))", - f"p{legend}" + " - {{job}} @ {{instance}}", + f"histogram_quantile({quantile}, sum(rate({metric('state_store_sync_size_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + f"p{legend}" + + " - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), [50, 99, "max"], ), panels.target( - f"sum by(le, job, instance) (rate({metric('state_store_sync_size_sum')}[$__rate_interval])) / sum by(le, job, instance) (rate({metric('state_store_sync_size_count')}[$__rate_interval]))", - "avg - {{job}} @ {{instance}}", + f"sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}) (rate({metric('state_store_sync_size_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}, {NODE_LABEL}) (rate({metric('state_store_sync_size_count')}[$__rate_interval]))", + "avg - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), ], ), - ]) + ], + ) ] @@ -2268,8 +2382,9 @@ def section_hummock_tiered_cache(outer_panels): "", [ panels.target( - f"sum(rate({metric('foyer_storage_op_duration_count')}[$__rate_interval])) by (foyer, op, extra, instance)", - "{{foyer}} file cache {{op}} {{extra}} @ {{instance}}", + f"sum(rate({metric('foyer_storage_op_duration_count')}[$__rate_interval])) by (foyer, op, extra, {NODE_LABEL})", + "{{foyer}} file cache {{op}} {{extra}} @ {{%s}}" + % NODE_LABEL, ), ], ), @@ -2279,9 +2394,10 @@ def section_hummock_tiered_cache(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('foyer_storage_op_duration_bucket')}[$__rate_interval])) by (le, foyer, op, extra, instance))", - f"p{legend}" + - " - {{foyer}} file cache - {{op}} {{extra}} @ {{instance}}", + f"histogram_quantile({quantile}, sum(rate({metric('foyer_storage_op_duration_bucket')}[$__rate_interval])) by (le, foyer, op, extra, {NODE_LABEL}))", + f"p{legend}" + + " - {{foyer}} file cache - {{op}} {{extra}} @ {{%s}}" + % NODE_LABEL, ), [50, 99, "max"], ), @@ -2292,8 +2408,9 @@ def section_hummock_tiered_cache(outer_panels): "", [ panels.target( - f"sum(rate({metric('foyer_storage_op_bytes')}[$__rate_interval])) by (foyer, op, extra, instance)", - "{{foyer}} file cache - {{op}} {{extra}} @ {{instance}}", + f"sum(rate({metric('foyer_storage_op_bytes')}[$__rate_interval])) by (foyer, op, extra, {NODE_LABEL})", + "{{foyer}} file cache - {{op}} {{extra}} @ {{%s}}" + % NODE_LABEL, ), ], ), @@ -2302,8 +2419,8 @@ def section_hummock_tiered_cache(outer_panels): "", [ panels.target( - f"sum({metric('foyer_storage_total_bytes')}) by (foyer, instance)", - "{{foyer}} size @ {{instance}}" + f"sum({metric('foyer_storage_total_bytes')}) by (foyer, {NODE_LABEL})", + "{{foyer}} size @ {{%s}}" % NODE_LABEL, ), ], ), @@ -2312,8 +2429,8 @@ def section_hummock_tiered_cache(outer_panels): "", [ panels.target( - f"sum(rate({metric('foyer_storage_op_duration_count', file_cache_hit_filter)}[$__rate_interval])) by (foyer, instance) / (sum(rate({metric('foyer_storage_op_duration_count', file_cache_hit_filter)}[$__rate_interval])) by (foyer, instance) + sum(rate({metric('foyer_storage_op_duration_count', file_cache_miss_filter)}[$__rate_interval])) by (foyer, instance))", - "{{foyer}} file cache hit ratio @ {{instance}}", + f"sum(rate({metric('foyer_storage_op_duration_count', file_cache_hit_filter)}[$__rate_interval])) by (foyer, {NODE_LABEL}) / (sum(rate({metric('foyer_storage_op_duration_count', file_cache_hit_filter)}[$__rate_interval])) by (foyer, {NODE_LABEL}) + sum(rate({metric('foyer_storage_op_duration_count', file_cache_miss_filter)}[$__rate_interval])) by (foyer, {NODE_LABEL}))", + "{{foyer}} file cache hit ratio @ {{%s}}" % NODE_LABEL, ), ], ), @@ -2322,8 +2439,8 @@ def section_hummock_tiered_cache(outer_panels): "", [ panels.target( - f"sum(refill_queue_total) by (instance)", - "refill queue length @ {{instance}}", + f"sum(refill_queue_total) by ({NODE_LABEL})", + "refill queue length @ {{%s}}" % NODE_LABEL, ), ], ), @@ -2332,12 +2449,12 @@ def section_hummock_tiered_cache(outer_panels): "", [ panels.target( - f"sum(rate({metric('refill_duration_count')}[$__rate_interval])) by (type, op, instance)", - "{{type}} file cache refill - {{op}} @ {{instance}}", + f"sum(rate({metric('refill_duration_count')}[$__rate_interval])) by (type, op, {NODE_LABEL})", + "{{type}} file cache refill - {{op}} @ {{%s}}" % NODE_LABEL, ), panels.target( - f"sum(rate({metric('refill_total')}[$__rate_interval])) by (type, op, instance)", - "{{type}} file cache refill - {{op}} @ {{instance}}", + f"sum(rate({metric('refill_total')}[$__rate_interval])) by (type, op, {NODE_LABEL})", + "{{type}} file cache refill - {{op}} @ {{%s}}" % NODE_LABEL, ), ], ), @@ -2347,9 +2464,10 @@ def section_hummock_tiered_cache(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('refill_duration_bucket')}[$__rate_interval])) by (le, type, op, instance))", - f"p{legend} - " + - "{{type}} file cache refill - {{op}} @ {{instance}}", + f"histogram_quantile({quantile}, sum(rate({metric('refill_duration_bucket')}[$__rate_interval])) by (le, type, op, {NODE_LABEL}))", + f"p{legend} - " + + "{{type}} file cache refill - {{op}} @ {{%s}}" + % NODE_LABEL, ), [50, 99, "max"], ), @@ -2377,8 +2495,8 @@ def section_hummock_manager(outer_panels): *quantile( lambda quantile, legend: panels.target( f"histogram_quantile({quantile}, sum(rate({metric('hummock_manager_lock_time_bucket')}[$__rate_interval])) by (le, lock_name, lock_type))", - f"Lock Time p{legend}" + - " - {{lock_type}} @ {{lock_name}}", + f"Lock Time p{legend}" + + " - {{lock_type}} @ {{lock_name}}", ), [50, 99, "max"], ), @@ -2391,8 +2509,7 @@ def section_hummock_manager(outer_panels): *quantile( lambda quantile, legend: panels.target( f"histogram_quantile({quantile}, sum(rate({metric('meta_hummock_manager_real_process_time_bucket')}[$__rate_interval])) by (le, method))", - f"Real Process Time p{legend}" + - " - {{method}}", + f"Real Process Time p{legend}" + " - {{method}}", ), [50, 99, "max"], ), @@ -2403,61 +2520,78 @@ def section_hummock_manager(outer_panels): "", [ panels.target( - f"{metric('storage_version_size')}", "version size"), + f"{metric('storage_version_size')}", "version size" + ), ], ), panels.timeseries_id( "Version Id", "", [ - panels.target(f"{metric('storage_current_version_id')}", - "current version id"), - panels.target(f"{metric('storage_checkpoint_version_id')}", - "checkpoint version id"), - panels.target(f"{metric('storage_min_pinned_version_id')}", - "min pinned version id"), - panels.target(f"{metric('storage_min_safepoint_version_id')}", - "min safepoint version id"), + panels.target( + f"{metric('storage_current_version_id')}", + "current version id", + ), + panels.target( + f"{metric('storage_checkpoint_version_id')}", + "checkpoint version id", + ), + panels.target( + f"{metric('storage_min_pinned_version_id')}", + "min pinned version id", + ), + panels.target( + f"{metric('storage_min_safepoint_version_id')}", + "min safepoint version id", + ), ], ), panels.timeseries_id( "Epoch", "", [ - panels.target(f"{metric('storage_max_committed_epoch')}", - "max committed epoch"), panels.target( - f"{metric('storage_safe_epoch')}", "safe epoch"), - panels.target(f"{metric('storage_min_pinned_epoch')}", - "min pinned epoch"), + f"{metric('storage_max_committed_epoch')}", + "max committed epoch", + ), + panels.target(f"{metric('storage_safe_epoch')}", "safe epoch"), + panels.target( + f"{metric('storage_min_pinned_epoch')}", "min pinned epoch" + ), ], ), panels.timeseries_kilobytes( "Table Size", "", [ - panels.target(f"{table_metric('storage_version_stats', total_key_size_filter)}/1024", - "table{{table_id}} {{metric}}"), - panels.target(f"{table_metric('storage_version_stats', total_value_size_filter)}/1024", - "table{{table_id}} {{metric}}"), + panels.target( + f"{table_metric('storage_version_stats', total_key_size_filter)}/1024", + "table{{table_id}} {{metric}}", + ), + panels.target( + f"{table_metric('storage_version_stats', total_value_size_filter)}/1024", + "table{{table_id}} {{metric}}", + ), ], ), - panels.timeseries_kilobytes( "Materialized View Size", "", [ - panels.target(f"{table_metric('storage_materialized_view_stats', mv_total_size_filter)}/1024", - "{{metric}}, mv id - {{table_id}} "), + panels.target( + f"{table_metric('storage_materialized_view_stats', mv_total_size_filter)}/1024", + "{{metric}}, mv id - {{table_id}} ", + ), ], ), - panels.timeseries_count( "Table KV Count", "", [ - panels.target(f"{table_metric('storage_version_stats', total_key_count_filter)}", - "table{{table_id}} {{metric}}"), + panels.target( + f"{table_metric('storage_version_stats', total_key_count_filter)}", + "table{{table_id}} {{metric}}", + ), ], ), panels.timeseries_count( @@ -2471,36 +2605,54 @@ def section_hummock_manager(outer_panels): Additionally, a metric on all objects (including dangling ones) is updated with low-frequency. The metric is updated right before full GC. So subsequent full GC may reduce the actual value significantly, without updating the metric. """, [ - panels.target(f"{metric('storage_stale_object_count')}", - "not referenced by versions"), - panels.target(f"{metric('storage_old_version_object_count')}", - "referenced by non-current versions"), - panels.target(f"{metric('storage_current_version_object_count')}", - "referenced by current version"), - panels.target(f"{metric('storage_total_object_count')}", - "all objects (including dangling ones)"), + panels.target( + f"{metric('storage_stale_object_count')}", + "not referenced by versions", + ), + panels.target( + f"{metric('storage_old_version_object_count')}", + "referenced by non-current versions", + ), + panels.target( + f"{metric('storage_current_version_object_count')}", + "referenced by current version", + ), + panels.target( + f"{metric('storage_total_object_count')}", + "all objects (including dangling ones)", + ), ], ), panels.timeseries_bytes( "Object Total Size", "Refer to `Object Total Number` panel for classification of objects.", [ - panels.target(f"{metric('storage_stale_object_size')}", - "not referenced by versions"), - panels.target(f"{metric('storage_old_version_object_size')}", - "referenced by non-current versions"), - panels.target(f"{metric('storage_current_version_object_size')}", - "referenced by current version"), - panels.target(f"{metric('storage_total_object_size')}", - "all objects, including dangling ones"), + panels.target( + f"{metric('storage_stale_object_size')}", + "not referenced by versions", + ), + panels.target( + f"{metric('storage_old_version_object_size')}", + "referenced by non-current versions", + ), + panels.target( + f"{metric('storage_current_version_object_size')}", + "referenced by current version", + ), + panels.target( + f"{metric('storage_total_object_size')}", + "all objects, including dangling ones", + ), ], ), panels.timeseries_count( "Delta Log Total Number", "total number of hummock version delta log", [ - panels.target(f"{metric('storage_delta_log_count')}", - "delta log total number"), + panels.target( + f"{metric('storage_delta_log_count')}", + "delta log total number", + ), ], ), panels.timeseries_latency( @@ -2512,7 +2664,8 @@ def section_hummock_manager(outer_panels): f"version_checkpoint_latency_p{legend}", ), [50, 90, 99, 999, "max"], - ) + [ + ) + + [ panels.target( f"rate({metric('storage_version_checkpoint_latency_sum')}[$__rate_interval]) / rate({metric('storage_version_checkpoint_latency_count')}[$__rate_interval])", "version_checkpoint_latency_avg", @@ -2523,63 +2676,65 @@ def section_hummock_manager(outer_panels): "Write Stop Compaction Groups", "When certain per compaction group threshold is exceeded (e.g. number of level 0 sub-level in LSMtree), write op to that compaction group is stopped temporarily. Check log for detail reason of write stop.", [ - panels.target(f"{metric('storage_write_stop_compaction_groups')}", - "compaction_group_{{compaction_group_id}}"), + panels.target( + f"{metric('storage_write_stop_compaction_groups')}", + "compaction_group_{{compaction_group_id}}", + ), ], ), panels.timeseries_count( "Full GC Trigger Count", "total number of attempts to trigger full GC", [ - panels.target(f"{metric('storage_full_gc_trigger_count')}", - "full_gc_trigger_count"), + panels.target( + f"{metric('storage_full_gc_trigger_count')}", + "full_gc_trigger_count", + ), ], ), panels.timeseries_count( "Full GC Last Watermark", "the object id watermark used in last full GC", [ - panels.target(f"{metric('storage_full_gc_last_object_id_watermark')}", - "full_gc_last_object_id_watermark"), + panels.target( + f"{metric('storage_full_gc_last_object_id_watermark')}", + "full_gc_last_object_id_watermark", + ), ], ), - panels.timeseries_latency_ms( "Compaction Event Loop Time", "", [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(irate({metric('storage_compaction_event_consumed_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - f"meta consumed latency p{legend}" + - " - {{job}} @ {{instance}}", + f"histogram_quantile({quantile}, sum(irate({metric('storage_compaction_event_consumed_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + f"meta consumed latency p{legend}" + + " - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), [50, 99, "max"], ), - *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(irate({metric('storage_compaction_event_loop_iteration_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - f"meta iteration latency p{legend}" + - " - {{job}} @ {{instance}}", + f"histogram_quantile({quantile}, sum(irate({metric('storage_compaction_event_loop_iteration_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + f"meta iteration latency p{legend}" + + " - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), [50, 99, "max"], ), - *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(irate({metric('compactor_compaction_event_consumed_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - f"compactor consumed latency p{legend}" + - " - {{job}} @ {{instance}}", + f"histogram_quantile({quantile}, sum(irate({metric('compactor_compaction_event_consumed_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + f"compactor consumed latency p{legend}" + + " - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), [50, 99, "max"], ), - *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(irate({metric('compactor_compaction_event_loop_iteration_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - f"compactor iteration latency p{legend}" + - " - {{job}} @ {{instance}}", + f"histogram_quantile({quantile}, sum(irate({metric('compactor_compaction_event_loop_iteration_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + f"compactor iteration latency p{legend}" + + " - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), [50, 99, "max"], ), @@ -2595,7 +2750,6 @@ def section_hummock_manager(outer_panels): ), ], ), - panels.timeseries_count( "State Table Count", "The number of state_tables in each CG", @@ -2606,7 +2760,6 @@ def section_hummock_manager(outer_panels): ), ], ), - panels.timeseries_count( "Branched SST Count", "The number of branched_sst in each CG", @@ -2645,8 +2798,7 @@ def section_backup_manager(outer_panels): *quantile( lambda quantile, legend: panels.target( f"histogram_quantile({quantile}, sum(rate({metric('backup_job_latency_bucket')}[$__rate_interval])) by (le, state))", - f"Job Process Time p{legend}" + - " - {{state}}", + f"Job Process Time p{legend}" + " - {{state}}", ), [50, 99, "max"], ), @@ -2689,11 +2841,12 @@ def section_grpc_meta_catalog_service(outer_panels): "gRPC Meta: Catalog Service", [ grpc_metrics_target( - panels, "Create", "path='/meta.CatalogService/Create'"), + panels, "Create", "path='/meta.CatalogService/Create'" + ), + grpc_metrics_target(panels, "Drop", "path='/meta.CatalogService/Drop'"), grpc_metrics_target( - panels, "Drop", "path='/meta.CatalogService/Drop'"), - grpc_metrics_target(panels, "GetCatalog", - "path='/meta.CatalogService/GetCatalog'"), + panels, "GetCatalog", "path='/meta.CatalogService/GetCatalog'" + ), ], ) ] @@ -2705,10 +2858,12 @@ def section_grpc_meta_cluster_service(outer_panels): outer_panels.row_collapsed( "gRPC Meta: Cluster Service", [ - grpc_metrics_target(panels, "AddWorkerNode", - "path='/meta.ClusterService/AddWorkerNode'"), - grpc_metrics_target(panels, "ListAllNodes", - "path='/meta.ClusterService/ListAllNodes'"), + grpc_metrics_target( + panels, "AddWorkerNode", "path='/meta.ClusterService/AddWorkerNode'" + ), + grpc_metrics_target( + panels, "ListAllNodes", "path='/meta.ClusterService/ListAllNodes'" + ), ], ), ] @@ -2720,12 +2875,19 @@ def section_grpc_meta_stream_manager(outer_panels): outer_panels.row_collapsed( "gRPC Meta: Stream Manager", [ - grpc_metrics_target(panels, "CreateMaterializedView", - "path='/meta.StreamManagerService/CreateMaterializedView'"), - grpc_metrics_target(panels, "DropMaterializedView", - "path='/meta.StreamManagerService/DropMaterializedView'"), grpc_metrics_target( - panels, "Flush", "path='/meta.StreamManagerService/Flush'"), + panels, + "CreateMaterializedView", + "path='/meta.StreamManagerService/CreateMaterializedView'", + ), + grpc_metrics_target( + panels, + "DropMaterializedView", + "path='/meta.StreamManagerService/DropMaterializedView'", + ), + grpc_metrics_target( + panels, "Flush", "path='/meta.StreamManagerService/Flush'" + ), ], ), ] @@ -2738,13 +2900,25 @@ def section_grpc_meta_hummock_manager(outer_panels): "gRPC Meta: Hummock Manager", [ grpc_metrics_target( - panels, "UnpinVersionBefore", "path='/meta.HummockManagerService/UnpinVersionBefore'"), - grpc_metrics_target(panels, "UnpinSnapshotBefore", - "path='/meta.HummockManagerService/UnpinSnapshotBefore'"), - grpc_metrics_target(panels, "ReportCompactionTasks", - "path='/meta.HummockManagerService/ReportCompactionTasks'"), + panels, + "UnpinVersionBefore", + "path='/meta.HummockManagerService/UnpinVersionBefore'", + ), + grpc_metrics_target( + panels, + "UnpinSnapshotBefore", + "path='/meta.HummockManagerService/UnpinSnapshotBefore'", + ), + grpc_metrics_target( + panels, + "ReportCompactionTasks", + "path='/meta.HummockManagerService/ReportCompactionTasks'", + ), grpc_metrics_target( - panels, "GetNewSstIds", "path='/meta.HummockManagerService/GetNewSstIds'"), + panels, + "GetNewSstIds", + "path='/meta.HummockManagerService/GetNewSstIds'", + ), ], ), ] @@ -2761,8 +2935,8 @@ def section_grpc_hummock_meta_client(outer_panels): "", [ panels.target( - f"sum(irate({metric('state_store_report_compaction_task_counts')}[$__rate_interval])) by(job,instance)", - "report_compaction_task_counts - {{instance}} ", + f"sum(irate({metric('state_store_report_compaction_task_counts')}[$__rate_interval])) by({COMPONENT_LABEL}, {NODE_LABEL})", + "report_compaction_task_counts - {{%s}}" % NODE_LABEL, ), ], ), @@ -2771,20 +2945,20 @@ def section_grpc_hummock_meta_client(outer_panels): "", [ panels.target( - f"histogram_quantile(0.5, sum(irate({metric('state_store_unpin_version_before_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "unpin_version_before_latency_p50 - {{instance}} ", + f"histogram_quantile(0.5, sum(irate({metric('state_store_unpin_version_before_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "unpin_version_before_latency_p50 - {{%s}}" % NODE_LABEL, ), panels.target( - f"histogram_quantile(0.99, sum(irate({metric('state_store_unpin_version_before_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "unpin_version_before_latency_p99 - {{instance}} ", + f"histogram_quantile(0.99, sum(irate({metric('state_store_unpin_version_before_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "unpin_version_before_latency_p99 - {{%s}}" % NODE_LABEL, ), panels.target( f"sum(irate({metric('state_store_unpin_version_before_latency_sum')}[$__rate_interval])) / sum(irate({metric('state_store_unpin_version_before_latency_count')}[$__rate_interval]))", "unpin_version_before_latency_avg", ), panels.target( - f"histogram_quantile(0.90, sum(irate({metric('state_store_unpin_version_before_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "unpin_version_before_latency_p90 - {{instance}} ", + f"histogram_quantile(0.90, sum(irate({metric('state_store_unpin_version_before_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "unpin_version_before_latency_p90 - {{%s}}" % NODE_LABEL, ), ], ), @@ -2793,36 +2967,36 @@ def section_grpc_hummock_meta_client(outer_panels): "", [ panels.target( - f"histogram_quantile(0.5, sum(irate({metric('state_store_pin_snapshot_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "pin_snapshot_latency_p50 - {{instance}} ", + f"histogram_quantile(0.5, sum(irate({metric('state_store_pin_snapshot_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "pin_snapshot_latency_p50 - {{%s}}" % NODE_LABEL, ), panels.target( - f"histogram_quantile(0.99, sum(irate({metric('state_store_pin_snapshot_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "pin_snapshot_latency_p99 - {{instance}} ", + f"histogram_quantile(0.99, sum(irate({metric('state_store_pin_snapshot_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "pin_snapshot_latency_p99 - {{%s}}" % NODE_LABEL, ), panels.target( - f"histogram_quantile(0.9, sum(irate({metric('state_store_pin_snapshot_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "pin_snapshot_latencyp90 - {{instance}} ", + f"histogram_quantile(0.9, sum(irate({metric('state_store_pin_snapshot_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "pin_snapshot_latencyp90 - {{%s}}" % NODE_LABEL, ), panels.target( f"sum(irate({metric('state_store_pin_snapshot_latency_sum')}[$__rate_interval])) / sum(irate(state_store_pin_snapshot_latency_count[$__rate_interval]))", "pin_snapshot_latency_avg", ), panels.target( - f"histogram_quantile(0.5, sum(irate({metric('state_store_unpin_version_snapshot_bucket')}[$__rate_interval])) by (le, job, instance))", - "unpin_snapshot_latency_p50 - {{instance}} ", + f"histogram_quantile(0.5, sum(irate({metric('state_store_unpin_version_snapshot_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "unpin_snapshot_latency_p50 - {{%s}}" % NODE_LABEL, ), panels.target( - f"histogram_quantile(0.99, sum(irate({metric('state_store_unpin_version_snapshot_bucket')}[$__rate_interval])) by (le, job, instance))", - "unpin_snapshot_latency_p99 - {{instance}} ", + f"histogram_quantile(0.99, sum(irate({metric('state_store_unpin_version_snapshot_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "unpin_snapshot_latency_p99 - {{%s}}" % NODE_LABEL, ), panels.target( f"sum(irate({metric('state_store_unpin_snapshot_latency_sum')}[$__rate_interval])) / sum(irate(state_store_unpin_snapshot_latency_count[$__rate_interval]))", "unpin_snapshot_latency_avg", ), panels.target( - f"histogram_quantile(0.90, sum(irate({metric('state_store_unpin_snapshot_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "unpin_snapshot_latency_p90 - {{instance}} ", + f"histogram_quantile(0.90, sum(irate({metric('state_store_unpin_snapshot_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "unpin_snapshot_latency_p90 - {{%s}}" % NODE_LABEL, ), ], ), @@ -2831,12 +3005,12 @@ def section_grpc_hummock_meta_client(outer_panels): "", [ panels.target( - f"sum(irate({metric('state_store_pin_snapshot_counts')}[$__rate_interval])) by(job,instance)", - "pin_snapshot_counts - {{instance}} ", + f"sum(irate({metric('state_store_pin_snapshot_counts')}[$__rate_interval])) by({COMPONENT_LABEL}, {NODE_LABEL})", + "pin_snapshot_counts - {{%s}}" % NODE_LABEL, ), panels.target( - f"sum(irate({metric('state_store_unpin_snapshot_counts')}[$__rate_interval])) by(job,instance)", - "unpin_snapshot_counts - {{instance}} ", + f"sum(irate({metric('state_store_unpin_snapshot_counts')}[$__rate_interval])) by({COMPONENT_LABEL}, {NODE_LABEL})", + "unpin_snapshot_counts - {{%s}}" % NODE_LABEL, ), ], ), @@ -2845,20 +3019,20 @@ def section_grpc_hummock_meta_client(outer_panels): "", [ panels.target( - f"histogram_quantile(0.5, sum(irate({metric('state_store_get_new_sst_ids_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "get_new_sst_ids_latency_latency_p50 - {{instance}} ", + f"histogram_quantile(0.5, sum(irate({metric('state_store_get_new_sst_ids_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "get_new_sst_ids_latency_latency_p50 - {{%s}}" % NODE_LABEL, ), panels.target( - f"histogram_quantile(0.99, sum(irate({metric('state_store_get_new_sst_ids_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "get_new_sst_ids_latency_latency_p99 - {{instance}} ", + f"histogram_quantile(0.99, sum(irate({metric('state_store_get_new_sst_ids_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "get_new_sst_ids_latency_latency_p99 - {{%s}}" % NODE_LABEL, ), panels.target( f"sum(irate({metric('state_store_get_new_sst_ids_latency_sum')}[$__rate_interval])) / sum(irate({metric('state_store_get_new_sst_ids_latency_count')}[$__rate_interval]))", "get_new_sst_ids_latency_latency_avg", ), panels.target( - f"histogram_quantile(0.90, sum(irate({metric('state_store_get_new_sst_ids_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "get_new_sst_ids_latency_latency_p90 - {{instance}} ", + f"histogram_quantile(0.90, sum(irate({metric('state_store_get_new_sst_ids_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "get_new_sst_ids_latency_latency_p90 - {{%s}}" % NODE_LABEL, ), ], ), @@ -2867,8 +3041,8 @@ def section_grpc_hummock_meta_client(outer_panels): "", [ panels.target( - f"sum(irate({metric('state_store_get_new_sst_ids_latency_counts')}[$__rate_interval]))by(job,instance)", - "get_new_sst_ids_latency_counts - {{instance}} ", + f"sum(irate({metric('state_store_get_new_sst_ids_latency_counts')}[$__rate_interval]))by({COMPONENT_LABEL}, {NODE_LABEL})", + "get_new_sst_ids_latency_counts - {{%s}}" % NODE_LABEL, ), ], ), @@ -2877,20 +3051,20 @@ def section_grpc_hummock_meta_client(outer_panels): "", [ panels.target( - f"histogram_quantile(0.5, sum(irate({metric('state_store_report_compaction_task_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "report_compaction_task_latency_p50 - {{instance}}", + f"histogram_quantile(0.5, sum(irate({metric('state_store_report_compaction_task_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "report_compaction_task_latency_p50 - {{%s}}" % NODE_LABEL, ), panels.target( - f"histogram_quantile(0.99, sum(irate({metric('state_store_report_compaction_task_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "report_compaction_task_latency_p99 - {{instance}}", + f"histogram_quantile(0.99, sum(irate({metric('state_store_report_compaction_task_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "report_compaction_task_latency_p99 - {{%s}}" % NODE_LABEL, ), panels.target( f"sum(irate({metric('state_store_report_compaction_task_latency_sum')}[$__rate_interval])) / sum(irate(state_store_report_compaction_task_latency_count[$__rate_interval]))", "report_compaction_task_latency_avg", ), panels.target( - f"histogram_quantile(0.90, sum(irate({metric('state_store_report_compaction_task_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "report_compaction_task_latency_p90 - {{instance}}", + f"histogram_quantile(0.90, sum(irate({metric('state_store_report_compaction_task_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "report_compaction_task_latency_p90 - {{%s}}" % NODE_LABEL, ), ], ), @@ -2911,9 +3085,9 @@ def section_kafka_native_metrics(outer_panels): [ panels.target( f"{metric('rdkafka_top_msg_cnt')}", - "id {{ id }}, client_id {{ client_id }}" + "id {{ id }}, client_id {{ client_id }}", ), - ] + ], ), panels.timeseries_bytes( "Message Size in Producer Queue", @@ -2921,9 +3095,9 @@ def section_kafka_native_metrics(outer_panels): [ panels.target( f"{metric('rdkafka_top_msg_size')}", - "id {{ id }}, client_id {{ client_id }}" + "id {{ id }}, client_id {{ client_id }}", ), - ] + ], ), panels.timeseries_count( "Message Produced Count", @@ -2931,9 +3105,9 @@ def section_kafka_native_metrics(outer_panels): [ panels.target( f"{metric('rdkafka_top_tx_msgs')}", - "id {{ id }}, client_id {{ client_id }}" + "id {{ id }}, client_id {{ client_id }}", ) - ] + ], ), panels.timeseries_count( "Message Received Count", @@ -2941,20 +3115,19 @@ def section_kafka_native_metrics(outer_panels): [ panels.target( f"{metric('rdkafka_top_rx_msgs')}", - "id {{ id }}, client_id {{ client_id }}" + "id {{ id }}, client_id {{ client_id }}", ) - ] + ], ), - panels.timeseries_count( "Message Count Pending to Transmit (per broker)", "Number of messages awaiting transmission to broker", [ panels.target( f"{metric('rdkafka_broker_outbuf_msg_cnt')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, state {{ state }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, state {{ state }}", ), - ] + ], ), panels.timeseries_count( "Inflight Message Count (per broker)", @@ -2962,9 +3135,9 @@ def section_kafka_native_metrics(outer_panels): [ panels.target( f"{metric('rdkafka_broker_waitresp_msg_cnt')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, state {{ state }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, state {{ state }}", ) - ] + ], ), panels.timeseries_count( "Error Count When Transmitting (per broker)", @@ -2972,9 +3145,9 @@ def section_kafka_native_metrics(outer_panels): [ panels.target( f"{metric('rdkafka_broker_tx_errs')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, state {{ state }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, state {{ state }}", ) - ] + ], ), panels.timeseries_count( "Error Count When Receiving (per broker)", @@ -2982,9 +3155,9 @@ def section_kafka_native_metrics(outer_panels): [ panels.target( f"{metric('rdkafka_broker_rx_errs')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, state {{ state }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, state {{ state }}", ) - ] + ], ), panels.timeseries_count( "Timeout Request Count (per broker)", @@ -2992,9 +3165,9 @@ def section_kafka_native_metrics(outer_panels): [ panels.target( f"{metric('rdkafka_broker_req_timeouts')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, state {{ state }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, state {{ state }}", ) - ] + ], ), panels.timeseries_latency_ms( "RTT (per broker)", @@ -3024,7 +3197,7 @@ def section_kafka_native_metrics(outer_panels): f"{metric('rdkafka_broker_rtt_out_of_range')}/1000", "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}", ), - ] + ], ), panels.timeseries_latency_ms( "Throttle Time (per broker)", @@ -3054,18 +3227,17 @@ def section_kafka_native_metrics(outer_panels): f"{metric('rdkafka_broker_throttle_out_of_range')}/1000", "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}", ), - ] + ], ), - panels.timeseries_latency_ms( "Topic Metadata_age Age", "Age of metadata from broker for this topic (milliseconds)", [ panels.target( f"{metric('rdkafka_topic_metadata_age')}", - "id {{ id }}, client_id {{ client_id}}, topic {{ topic }}" + "id {{ id }}, client_id {{ client_id}}, topic {{ topic }}", ) - ] + ], ), panels.timeseries_bytes( "Topic Batch Size", @@ -3073,27 +3245,27 @@ def section_kafka_native_metrics(outer_panels): [ panels.target( f"{metric('rdkafka_topic_batchsize_avg')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}", ), panels.target( f"{metric('rdkafka_topic_batchsize_p75')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}", ), panels.target( f"{metric('rdkafka_topic_batchsize_p90')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}", ), panels.target( f"{metric('rdkafka_topic_batchsize_p99')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}", ), panels.target( f"{metric('rdkafka_topic_batchsize_p99_99')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}", ), panels.target( f"{metric('rdkafka_topic_batchsize_out_of_range')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}", ), panels.timeseries_count( "Topic Batch Messages", @@ -3101,31 +3273,31 @@ def section_kafka_native_metrics(outer_panels): [ panels.target( f"{metric('rdkafka_topic_batchcnt_avg')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}", ), panels.target( f"{metric('rdkafka_topic_batchcnt_p75')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}", ), panels.target( f"{metric('rdkafka_topic_batchcnt_p90')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}", ), panels.target( f"{metric('rdkafka_topic_batchcnt_p99')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}", ), panels.target( f"{metric('rdkafka_topic_batchcnt_p99_99')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}", ), panels.target( f"{metric('rdkafka_topic_batchcnt_out_of_range')}", - "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}" + "id {{ id }}, client_id {{ client_id}}, broker {{ broker }}, topic {{ topic }}", ), - ] - ) - ] + ], + ), + ], ), panels.timeseries_count( "Message to be Transmitted", @@ -3133,9 +3305,9 @@ def section_kafka_native_metrics(outer_panels): [ panels.target( f"{metric('rdkafka_topic_partition_xmit_msgq_cnt')}", - "id {{ id }}, client_id {{ client_id}}, topic {{ topic }}, partition {{ partition }}" + "id {{ id }}, client_id {{ client_id}}, topic {{ topic }}, partition {{ partition }}", ), - ] + ], ), panels.timeseries_count( "Message in pre fetch queue", @@ -3143,9 +3315,9 @@ def section_kafka_native_metrics(outer_panels): [ panels.target( f"{metric('rdkafka_topic_partition_fetchq_cnt')}", - "id {{ id }}, client_id {{ client_id}}, topic {{ topic }}, partition {{ partition }}" + "id {{ id }}, client_id {{ client_id}}, topic {{ topic }}, partition {{ partition }}", ), - ] + ], ), panels.timeseries_count( "Next offset to fetch", @@ -3153,9 +3325,9 @@ def section_kafka_native_metrics(outer_panels): [ panels.target( f"{metric('rdkafka_topic_partition_next_offset')}", - "id {{ id }}, client_id {{ client_id}}, topic {{ topic }}, partition {{ partition }}" + "id {{ id }}, client_id {{ client_id}}, topic {{ topic }}, partition {{ partition }}", ) - ] + ], ), panels.timeseries_count( "Committed Offset", @@ -3163,11 +3335,11 @@ def section_kafka_native_metrics(outer_panels): [ panels.target( f"{metric('rdkafka_topic_partition_committed_offset')}", - "id {{ id }}, client_id {{ client_id}}, topic {{ topic }}, partition {{ partition }}" + "id {{ id }}, client_id {{ client_id}}, topic {{ topic }}, partition {{ partition }}", ) - ] + ], ), - ] + ], ) ] @@ -3293,6 +3465,7 @@ def section_connector_node(outer_panels): ) ] + def section_sink_metrics(outer_panels): panels = outer_panels.sub_panel() return [ @@ -3311,7 +3484,7 @@ def section_sink_metrics(outer_panels): [50, 99, "max"], ), panels.target( - f"sum by(le, connector, sink_id)(rate({metric('sink_commit_duration_sum')}[$__rate_interval])) / sum by(le, type, job, instance) (rate({metric('sink_commit_duration_count')}[$__rate_interval]))", + f"sum by(le, connector, sink_id)(rate({metric('sink_commit_duration_sum')}[$__rate_interval])) / sum by(le, type, {COMPONENT_LABEL}, {NODE_LABEL}) (rate({metric('sink_commit_duration_count')}[$__rate_interval]))", "avg - {{connector}} @ {{sink_id}}", ), ], @@ -3320,19 +3493,24 @@ def section_sink_metrics(outer_panels): "Log Store Read/Write Epoch", "", [ - panels.target(f"{metric('log_store_latest_write_epoch')}", - "latest write epoch @ {{connector}} {{sink_id}} {{executor_id}}"), - panels.target(f"{metric('log_store_latest_read_epoch')}", - "latest read epoch @ {{connector}} {{sink_id}} {{executor_id}}"), + panels.target( + f"{metric('log_store_latest_write_epoch')}", + "latest write epoch @ {{connector}} {{sink_id}} {{executor_id}}", + ), + panels.target( + f"{metric('log_store_latest_read_epoch')}", + "latest read epoch @ {{connector}} {{sink_id}} {{executor_id}}", + ), ], ), panels.timeseries_latency( "Log Store Lag", "", [ - panels.target(f"(max({metric('log_store_latest_write_epoch')}) by (connector, sink_id, executor_id)" - + f"- max({metric('log_store_latest_read_epoch')}) by (connector, sink_id, executor_id)) / (2^16) / 1000", - "Consume lag @ {{connector}} {{sink_id}} {{executor_id}}" + panels.target( + f"(max({metric('log_store_latest_write_epoch')}) by (connector, sink_id, executor_id)" + + f"- max({metric('log_store_latest_read_epoch')}) by (connector, sink_id, executor_id)) / (2^16) / 1000", + "Consume lag @ {{connector}} {{sink_id}} {{executor_id}}", ), ], ), @@ -3340,10 +3518,11 @@ def section_sink_metrics(outer_panels): "Log Store Consume Persistent Log Lag", "", [ - panels.target(f"clamp_min((max({metric('log_store_first_write_epoch')}) by (connector, sink_id, executor_id)" - + f"- max({metric('log_store_latest_read_epoch')}) by (connector, sink_id, executor_id)) / (2^16) / 1000, 0)", - "Consume persistent log lag @ {{connector}} {{sink_id}} {{executor_id}}" - ), + panels.target( + f"clamp_min((max({metric('log_store_first_write_epoch')}) by (connector, sink_id, executor_id)" + + f"- max({metric('log_store_latest_read_epoch')}) by (connector, sink_id, executor_id)) / (2^16) / 1000, 0)", + "Consume persistent log lag @ {{connector}} {{sink_id}} {{executor_id}}", + ), ], ), panels.timeseries_rowsps( @@ -3361,8 +3540,9 @@ def section_sink_metrics(outer_panels): "", [ panels.target( - f"sum(rate({metric('log_store_read_rows')}[$__rate_interval])) by (instance, connector, sink_id, executor_id)", - "sink={{connector}} {{sink_id}} @ {{executor_id}} {{instance}}", + f"sum(rate({metric('log_store_read_rows')}[$__rate_interval])) by ({NODE_LABEL}, connector, sink_id, executor_id)", + "sink={{connector}} {{sink_id}} @ {{executor_id}} {{%s}}" + % NODE_LABEL, ), ], ), @@ -3381,8 +3561,9 @@ def section_sink_metrics(outer_panels): "", [ panels.target( - f"sum(rate({metric('log_store_write_rows')}[$__rate_interval])) by (instance, connector, sink_id, executor_id)", - "sink={{connector}} {{sink_id}} @ {{executor_id}} {{instance}}", + f"sum(rate({metric('log_store_write_rows')}[$__rate_interval])) by ({NODE_LABEL}, connector, sink_id, executor_id)", + "sink={{connector}} {{sink_id}} @ {{executor_id}} {{%s}}" + % NODE_LABEL, ), ], ), @@ -3404,7 +3585,7 @@ def section_sink_metrics(outer_panels): f"sum(rate({metric('kv_log_store_storage_read_size')}[$__rate_interval])) by (executor_id, connector, sink_id)", "{{executor_id}} - {{connector}} @ {{sink_id}}", ), - ] + ], ), panels.timeseries_ops( "Kv Log Store Write Storage Row Ops", @@ -3424,12 +3605,13 @@ def section_sink_metrics(outer_panels): f"sum(rate({metric('kv_log_store_storage_write_size')}[$__rate_interval])) by (executor_id, connector, sink_id)", "{{executor_id}} - {{connector}} @ {{sink_id}}", ), - ] + ], ), ], ) ] + def section_network_connection(outer_panels): panels = outer_panels.sub_panel() s3_filter = 'connection_type="S3"' @@ -3443,12 +3625,12 @@ def section_network_connection(outer_panels): "", [ panels.target( - f"sum(rate({metric('connection_read_rate')}[$__rate_interval])) by (job, instance) / (1024*1024)", - "{{job}} read @ {{instance}}", + f"sum(rate({metric('connection_read_rate')}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}) / (1024*1024)", + "{{%s}} read @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({metric('connection_write_rate')}[$__rate_interval])) by (job, instance) / (1024*1024)", - "{{job}} write @ {{instance}}", + f"sum(rate({metric('connection_write_rate')}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}) / (1024*1024)", + "{{%s}} write @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -3457,12 +3639,12 @@ def section_network_connection(outer_panels): "", [ panels.target( - f"sum(rate({metric('connection_read_rate', filter=s3_filter)}[$__rate_interval])) by (job, instance) / (1024*1024)", - "{{job}} read @ {{instance}}", + f"sum(rate({metric('connection_read_rate', filter=s3_filter)}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}) / (1024*1024)", + "{{%s}} read @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({metric('connection_write_rate', filter=s3_filter)}[$__rate_interval])) by (job, instance) / (1024*1024)", - "{{job}} write @ {{instance}}", + f"sum(rate({metric('connection_write_rate', filter=s3_filter)}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}) / (1024*1024)", + "{{%s}} write @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -3471,20 +3653,24 @@ def section_network_connection(outer_panels): "", [ panels.target( - f"sum(rate({metric('connection_read_rate', filter=grpc_filter)}[$__rate_interval])) by (job, instance, connection_type) / (1024*1024)", - "{{job}} {{connection_type}} read @ {{instance}}", + f"sum(rate({metric('connection_read_rate', filter=grpc_filter)}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}, connection_type) / (1024*1024)", + "{{%s}} {{connection_type}} read @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({metric('connection_write_rate', filter=grpc_filter)}[$__rate_interval])) by (job, instance, connection_type) / (1024*1024)", - "{{job}} {{connection_type}} write @ {{instance}}", + f"sum(rate({metric('connection_write_rate', filter=grpc_filter)}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}, connection_type) / (1024*1024)", + "{{%s}} {{connection_type}} write @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({metric('connection_read_rate', filter=grpc_filter)}[$__rate_interval])) by (job, instance) / (1024*1024)", - "{{job}} total read @ {{instance}}", + f"sum(rate({metric('connection_read_rate', filter=grpc_filter)}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}) / (1024*1024)", + "{{%s}} total read @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({metric('connection_write_rate', filter=grpc_filter)}[$__rate_interval])) by (job, instance) / (1024*1024)", - "{{job}} total write @ {{instance}}", + f"sum(rate({metric('connection_write_rate', filter=grpc_filter)}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}) / (1024*1024)", + "{{%s}} total write @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -3493,16 +3679,19 @@ def section_network_connection(outer_panels): "", [ panels.target( - f"sum(irate({metric('connection_io_err_rate', filter=s3_filter)}[$__rate_interval])) by (job, instance, op_type, error_kind)", - "{{job}} S3 {{op_type}} err[{{error_kind}}] @ {{instance}}", + f"sum(irate({metric('connection_io_err_rate', filter=s3_filter)}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}, op_type, error_kind)", + "{{%s}} S3 {{op_type}} err[{{error_kind}}] @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({metric('connection_io_err_rate', filter=grpc_filter)}[$__rate_interval])) by (job, instance, op_type, error_kind)", - "{{job}} grpc {{op_type}} err[{{error_kind}}] @ {{instance}}", + f"sum(rate({metric('connection_io_err_rate', filter=grpc_filter)}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}, op_type, error_kind)", + "{{%s}} grpc {{op_type}} err[{{error_kind}}] @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({metric('connection_io_err_rate')}[$__rate_interval])) by (job, instance, op_type, error_kind)", - "{{job}} total {{op_type}} err[{{error_kind}}] @ {{instance}}", + f"sum(rate({metric('connection_io_err_rate')}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}, op_type, error_kind)", + "{{%s}} total {{op_type}} err[{{error_kind}}] @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -3511,12 +3700,13 @@ def section_network_connection(outer_panels): "", [ panels.target( - f"sum({metric('connection_count', filter=s3_filter)}) by (job, instance)", - "{{job}} S3 @ {{instance}}", + f"sum({metric('connection_count', filter=s3_filter)}) by ({COMPONENT_LABEL}, {NODE_LABEL})", + "{{%s}} S3 @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum({metric('connection_count', filter=grpc_filter)}) by (job, instance, connection_type)", - "{{job}} {{connection_type}} @ {{instance}}", + f"sum({metric('connection_count', filter=grpc_filter)}) by ({COMPONENT_LABEL}, {NODE_LABEL}, connection_type)", + "{{%s}} {{connection_type}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -3525,12 +3715,13 @@ def section_network_connection(outer_panels): "", [ panels.target( - f"sum(irate({metric('connection_create_rate', filter=s3_filter)}[$__rate_interval])) by (job, instance)", - "{{job}} S3 @ {{instance}}", + f"sum(irate({metric('connection_create_rate', filter=s3_filter)}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL})", + "{{%s}} S3 @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(irate({metric('connection_create_rate', filter=grpc_filter)}[$__rate_interval])) by (job, instance, connection_type)", - "{{job}} {{connection_type}} @ {{instance}}", + f"sum(irate({metric('connection_create_rate', filter=grpc_filter)}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}, connection_type)", + "{{%s}} {{connection_type}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -3539,12 +3730,13 @@ def section_network_connection(outer_panels): "", [ panels.target( - f"sum(irate({metric('connection_err_rate', filter=s3_filter)}[$__rate_interval])) by (job, instance)", - "{{job}} S3 @ {{instance}}", + f"sum(irate({metric('connection_err_rate', filter=s3_filter)}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL})", + "{{%s}} S3 @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(irate({metric('connection_err_rate', filter=grpc_filter)}[$__rate_interval])) by (job, instance, connection_type)", - "{{job}} {{connection_type}} @ {{instance}}", + f"sum(irate({metric('connection_err_rate', filter=grpc_filter)}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}, connection_type)", + "{{%s}} {{connection_type}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -3566,13 +3758,13 @@ def section_network_connection(outer_panels): "queryValue": "", "refresh": 2, "skipUrlSync": False, - "type": "datasource" + "type": "datasource", } ) if namespace_filter_enabled: namespace_json = { - "definition": "label_values(up{risingwave_name=~\".+\"}, namespace)", + "definition": 'label_values(up{risingwave_name=~".+"}, namespace)', "description": "Kubernetes namespace.", "hide": 0, "includeAll": False, @@ -3581,8 +3773,8 @@ def section_network_connection(outer_panels): "name": "namespace", "options": [], "query": { - "query": "label_values(up{risingwave_name=~\".+\"}, namespace)", - "refId": "StandardVariableQuery" + "query": 'label_values(up{risingwave_name=~".+"}, namespace)', + "refId": "StandardVariableQuery", }, "refresh": 2, "regex": "", @@ -3592,12 +3784,8 @@ def section_network_connection(outer_panels): } name_json = { - "current": { - "selected": False, - "text": "risingwave", - "value": "risingwave" - }, - "definition": "label_values(up{namespace=\"$namespace\", risingwave_name=~\".+\"}, risingwave_name)", + "current": {"selected": False, "text": "risingwave", "value": "risingwave"}, + "definition": 'label_values(up{namespace="$namespace", risingwave_name=~".+"}, risingwave_name)', "hide": 0, "includeAll": False, "label": "RisingWave", @@ -3605,8 +3793,8 @@ def section_network_connection(outer_panels): "name": "instance", "options": [], "query": { - "query": "label_values(up{namespace=\"$namespace\", risingwave_name=~\".+\"}, risingwave_name)", - "refId": "StandardVariableQuery" + "query": 'label_values(up{namespace="$namespace", risingwave_name=~".+"}, risingwave_name)', + "refId": "StandardVariableQuery", }, "refresh": 2, "regex": "", @@ -3622,22 +3810,18 @@ def section_network_connection(outer_panels): templating_list.append(name_json) node_json = { - "current": { - "selected": False, - "text": "All", - "value": "__all" - }, - "definition": f"label_values({metric('process_cpu_seconds_total', node_filter_enabled=False)}, instance)", + "current": {"selected": False, "text": "All", "value": "__all"}, + "definition": f"label_values({metric('process_cpu_seconds_total', node_filter_enabled=False)}, {NODE_LABEL})", "description": "Reporting instance of the metric", "hide": 0, "includeAll": True, - "label": "Node", + "label": f"{NODE_VARIABLE_LABEL}", "multi": True, - "name": "node", + "name": f"{NODE_VARIABLE}", "options": [], "query": { - "query": f"label_values({metric('process_cpu_seconds_total', node_filter_enabled=False)}, instance)", - "refId": "StandardVariableQuery" + "query": f"label_values({metric('process_cpu_seconds_total', node_filter_enabled=False)}, {NODE_LABEL})", + "refId": "StandardVariableQuery", }, "refresh": 2, "regex": "", @@ -3647,22 +3831,18 @@ def section_network_connection(outer_panels): } job_json = { - "current": { - "selected": False, - "text": "All", - "value": "__all" - }, - "definition": f"label_values({metric('process_cpu_seconds_total', node_filter_enabled=False)}, job)", + "current": {"selected": False, "text": "All", "value": "__all"}, + "definition": f"label_values({metric('process_cpu_seconds_total', node_filter_enabled=False)}, {COMPONENT_LABEL})", "description": "Reporting job of the metric", "hide": 0, "includeAll": True, - "label": "Job", + "label": f"{COMPONENT_VARIABLE_LABEL}", "multi": True, - "name": "job", + "name": f"{COMPONENT_VARIABLE}", "options": [], "query": { - "query": f"label_values({metric('process_cpu_seconds_total', node_filter_enabled=False)}, job)", - "refId": "StandardVariableQuery" + "query": f"label_values({metric('process_cpu_seconds_total', node_filter_enabled=False)}, {COMPONENT_LABEL})", + "refId": "StandardVariableQuery", }, "refresh": 2, "regex": "", @@ -3672,11 +3852,7 @@ def section_network_connection(outer_panels): } table_id_json = { - "current": { - "selected": False, - "text": "All", - "value": "__all" - }, + "current": {"selected": False, "text": "All", "value": "__all"}, "definition": f"label_values({metric('table_info', node_filter_enabled=False)}, table_id)", "description": "Reporting table id of the metric", "hide": 0, @@ -3687,7 +3863,7 @@ def section_network_connection(outer_panels): "options": [], "query": { "query": f"label_values({metric('table_info', node_filter_enabled=False)}, table_id)", - "refId": "StandardVariableQuery" + "refId": "StandardVariableQuery", }, "refresh": 2, 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a/grafana/risingwave-user-dashboard.dashboard.py b/grafana/risingwave-user-dashboard.dashboard.py index c2b192c56f085..8d2cd4268030b 100644 --- a/grafana/risingwave-user-dashboard.dashboard.py +++ b/grafana/risingwave-user-dashboard.dashboard.py @@ -1,21 +1,24 @@ +import logging +import os +import sys + from grafanalib.core import ( Dashboard, - TimeSeries, - Target, GridPos, RowPanel, - Time, + Target, Templating, + Time, + TimeSeries, ) -import logging -import os -import sys p = os.path.dirname(__file__) sys.path.append(p) -from common import * + from jsonmerge import merge +from common import * + source_uid = os.environ.get(SOURCE_UID, "risedev-prometheus") dashboard_uid = os.environ.get(DASHBOARD_UID, "Fcy3uV1nz") dashboard_version = int(os.environ.get(DASHBOARD_VERSION, "0")) @@ -29,7 +32,7 @@ def section_actor_info(panels): - excluded_cols = ["Time", "Value", "__name__", "job", "instance"] + excluded_cols = ["Time", "Value", "__name__", f"{COMPONENT_LABEL}", f"{NODE_LABEL}"] return [ panels.row("Actor/Table Id Info"), panels.table_info( @@ -41,15 +44,19 @@ def section_actor_info(panels): panels.table_info( "Materialized View Info", "Mapping from materialized view table id to it's internal table ids", - [panels.table_target(f"group({metric('table_info')}) by (materialized_view_id, table_id, table_name, table_type)")], - excluded_cols + [ + panels.table_target( + f"group({metric('table_info')}) by (materialized_view_id, table_id, table_name, table_type)" + ) + ], + excluded_cols, ), ] def section_overview(panels): - mv_filter = "executor_identity=~\".*MaterializeExecutor.*\"" - sink_filter = "executor_identity=~\".*SinkExecutor.*\"" + mv_filter = 'executor_identity=~".*MaterializeExecutor.*"' + sink_filter = 'executor_identity=~".*SinkExecutor.*"' return [ panels.row("Overview"), panels.timeseries_rowsps( @@ -181,11 +188,13 @@ def section_overview(panels): ), panels.target( f"{metric('source_status_is_up')} == 0", - "source error: source_id={{source_id}}, source_name={{source_name}} @ {{instance}}", + "source error: source_id={{source_id}}, source_name={{source_name}} @ {{%s}}" + % NODE_LABEL, ), panels.target( - f"sum(rate({metric('object_store_failure_count')}[$__rate_interval])) by (instance, job, type)", - "remote storage error {{type}}: {{job}} @ {{instance}}", + f"sum(rate({metric('object_store_failure_count')}[$__rate_interval])) by ({NODE_LABEL}, {COMPONENT_LABEL}, type)", + "remote storage error {{type}}: {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -221,7 +230,7 @@ def section_overview(panels): f"{metric('frontend_active_sessions')}", "", ), - ] + ], ), ] @@ -237,8 +246,8 @@ def section_cpu(outer_panels): "The CPU usage of each RisingWave component.", [ panels.target( - f"sum(rate({metric('process_cpu_seconds_total')}[$__rate_interval])) by (instance)", - "{{instance}}", + f"sum(rate({metric('process_cpu_seconds_total')}[$__rate_interval])) by ({NODE_LABEL})", + "{{%s}}" % NODE_LABEL, ), ], ), @@ -247,8 +256,8 @@ def section_cpu(outer_panels): "Number of CPU cores per RisingWave component.", [ panels.target( - f"avg({metric('process_cpu_core_num')}) by (instance)", - "{{instance}}", + f"avg({metric('process_cpu_core_num')}) by ({NODE_LABEL})", + "{{%s}}" % NODE_LABEL, ), ], ), @@ -269,8 +278,8 @@ def section_memory(outer_panels): "The memory usage of each RisingWave component.", [ panels.target( - f"avg({metric('process_resident_memory_bytes')}) by (job,instance)", - "{{job}} @ {{instance}}", + f"avg({metric('process_resident_memory_bytes')}) by ({COMPONENT_LABEL},{NODE_LABEL})", + "{{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ) ], ), @@ -279,10 +288,10 @@ def section_memory(outer_panels): "", [ panels.target( - f"sum({metric('state_store_meta_cache_size')}) by (instance) + " - + f"sum({metric('state_store_block_cache_size')}) by (instance) + " - + f"sum({metric('uploading_memory_size')}) by (instance)", - "storage @ {{instance}}", + f"sum({metric('state_store_meta_cache_size')}) by ({NODE_LABEL}) + " + + f"sum({metric('state_store_block_cache_size')}) by ({NODE_LABEL}) + " + + f"sum({metric('uploading_memory_size')}) by ({NODE_LABEL})", + "storage @ {{%s}}" % NODE_LABEL, ), ], ), @@ -295,16 +304,19 @@ def section_memory(outer_panels): "streaming actor - {{actor_id}}", ), panels.target( - f"sum({metric('state_store_meta_cache_size')}) by (job,instance)", - "storage meta cache - {{job}} @ {{instance}}", + f"sum({metric('state_store_meta_cache_size')}) by ({COMPONENT_LABEL},{NODE_LABEL})", + "storage meta cache - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum({metric('state_store_block_cache_size')}) by (job,instance)", - "storage block cache - {{job}} @ {{instance}}", + f"sum({metric('state_store_block_cache_size')}) by ({COMPONENT_LABEL},{NODE_LABEL})", + "storage block cache - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum({metric('uploading_memory_size')}) by (job,instance)", - "storage write buffer - {{job}} @ {{instance}}", + f"sum({metric('uploading_memory_size')}) by ({COMPONENT_LABEL},{NODE_LABEL})", + "storage write buffer - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( f"sum({metric('stream_memory_usage')} * on(table_id, actor_id) group_left(materialized_view_id) table_info) by (materialized_view_id)", @@ -374,11 +386,13 @@ def section_memory(outer_panels): ), panels.target( f"rate({metric('stream_materialize_cache_hit_count')}[$__rate_interval])", - "Materialize - cache hit count - table {{table_id}} - actor {{actor_id}} {{instance}}", + "Materialize - cache hit count - table {{table_id}} - actor {{actor_id}} {{%s}}" + % NODE_LABEL, ), panels.target( f"rate({metric('stream_materialize_cache_total_count')}[$__rate_interval])", - "Materialize - total cache count - table {{table_id}} - actor {{actor_id}} {{instance}}", + "Materialize - total cache count - table {{table_id}} - actor {{actor_id}} {{%s}}" + % NODE_LABEL, ), ], ), @@ -416,7 +430,8 @@ def section_memory(outer_panels): ), panels.target( f"1 - (sum(rate({metric('stream_materialize_cache_hit_count')}[$__rate_interval])) by (table_id, actor_id) ) / (sum(rate({metric('stream_materialize_cache_total_count')}[$__rate_interval])) by (table_id, actor_id))", - "materialize executor cache miss ratio - table {{table_id}} - actor {{actor_id}} {{instance}}", + "materialize executor cache miss ratio - table {{table_id}} - actor {{actor_id}} {{%s}}" + % NODE_LABEL, ), ], ), @@ -425,12 +440,14 @@ def section_memory(outer_panels): "Storage cache statistics", [ panels.target( - f"sum(rate({metric('state_store_sst_store_block_request_counts')}[$__rate_interval])) by (job, instance, table_id, type)", - "memory cache - {{table_id}} @ {{type}} @ {{job}} @ {{instance}}", + f"sum(rate({metric('state_store_sst_store_block_request_counts')}[$__rate_interval])) by ({COMPONENT_LABEL}, {NODE_LABEL}, table_id, type)", + "memory cache - {{table_id}} @ {{type}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({metric('state_store_sst_store_block_request_counts', meta_miss_filter)}[$__rate_interval])) by (job, type)", - "total_meta_miss_count - {{job}} @ {{instance}}", + f"sum(rate({metric('state_store_sst_store_block_request_counts', meta_miss_filter)}[$__rate_interval])) by ({COMPONENT_LABEL}, type)", + "total_meta_miss_count - {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -439,12 +456,14 @@ def section_memory(outer_panels): "Storage bloom filter statistics", [ panels.target( - f"sum(rate({metric('state_store_read_req_check_bloom_filter_counts')}[$__rate_interval])) by (job,instance,table_id)", - "bloom filter total - {{table_id}} @ {{job}} @ {{instance}}", + f"sum(rate({metric('state_store_read_req_check_bloom_filter_counts')}[$__rate_interval])) by ({COMPONENT_LABEL},{NODE_LABEL},table_id)", + "bloom filter total - {{table_id}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"sum(rate({metric('state_store_read_req_positive_but_non_exist_counts')}[$__rate_interval])) by (job,instance,table_id)", - "bloom filter false positive - {{table_id}} @ {{job}} @ {{instance}}", + f"sum(rate({metric('state_store_read_req_positive_but_non_exist_counts')}[$__rate_interval])) by ({COMPONENT_LABEL},{NODE_LABEL},table_id)", + "bloom filter false positive - {{table_id}} @ {{%s}} @ {{%s}}" + % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -453,12 +472,12 @@ def section_memory(outer_panels): "Storage file cache statistics", [ panels.target( - f"sum(rate({metric('file_cache_latency_count')}[$__rate_interval])) by (op, instance)", - "file cache {{op}} @ {{instance}}", + f"sum(rate({metric('file_cache_latency_count')}[$__rate_interval])) by (op, {NODE_LABEL})", + "file cache {{op}} @ {{%s}}" % NODE_LABEL, ), panels.target( - f"sum(rate({metric('file_cache_miss')}[$__rate_interval])) by (instance)", - "file cache miss @ {{instance}}", + f"sum(rate({metric('file_cache_miss')}[$__rate_interval])) by ({NODE_LABEL})", + "file cache miss @ {{%s}}" % NODE_LABEL, ), ], ), @@ -478,12 +497,12 @@ def section_network(outer_panels): "Send/Recv throughput per node for streaming exchange", [ panels.target( - f"sum(rate({metric('stream_exchange_frag_send_size')}[$__rate_interval])) by (instance)", - "Send @ {{instance}}", + f"sum(rate({metric('stream_exchange_frag_send_size')}[$__rate_interval])) by ({NODE_LABEL})", + "Send @ {{%s}}" % NODE_LABEL, ), panels.target( - f"sum(rate({metric('stream_exchange_frag_recv_size')}[$__rate_interval])) by (instance)", - "Recv @ {{instance}}", + f"sum(rate({metric('stream_exchange_frag_recv_size')}[$__rate_interval])) by ({NODE_LABEL})", + "Recv @ {{%s}}" % NODE_LABEL, ), ], ), @@ -492,12 +511,12 @@ def section_network(outer_panels): "The remote storage read/write throughput per node", [ panels.target( - f"sum(rate({metric('object_store_read_bytes')}[$__rate_interval])) by (instance)", - "read - {{instance}}", + f"sum(rate({metric('object_store_read_bytes')}[$__rate_interval])) by ({NODE_LABEL})", + "read - {{%s}}" % NODE_LABEL, ), panels.target( - f"sum(rate({metric('object_store_write_bytes')}[$__rate_interval])) by (instance)", - "write - {{instance}}", + f"sum(rate({metric('object_store_write_bytes')}[$__rate_interval])) by ({NODE_LABEL})", + "write - {{%s}}" % NODE_LABEL, ), ], ), @@ -550,8 +569,10 @@ def section_storage(outer_panels): "Materialized View Size", "The storage size of each materialized view", [ - panels.target(f"{metric('storage_materialized_view_stats', mv_total_size_filter)}/1024", - "{{metric}}, mv id - {{table_id}} "), + panels.target( + f"{metric('storage_materialized_view_stats', mv_total_size_filter)}/1024", + "{{metric}}, mv id - {{table_id}} ", + ), ], ), panels.timeseries_count( @@ -584,12 +605,12 @@ def section_storage(outer_panels): "Compaction refers to the process of compacting SSTables at one level to another level.", [ panels.target( - f"sum({metric('storage_level_compact_write')}) by (job) > 0", - "Compaction - {{job}}", + f"sum({metric('storage_level_compact_write')}) by ({COMPONENT_LABEL}) > 0", + "Compaction - {{%s}}" % COMPONENT_LABEL, ), panels.target( - f"sum({metric('compactor_write_build_l0_bytes')}) by (job) > 0", - "Flush - {{job}}", + f"sum({metric('compactor_write_build_l0_bytes')}) by ({COMPONENT_LABEL}) > 0", + "Flush - {{%s}}" % COMPONENT_LABEL, ), ], ), @@ -598,12 +619,12 @@ def section_storage(outer_panels): "The remote storage read/write throughput", [ panels.target( - f"sum(rate({metric('object_store_read_bytes')}[$__rate_interval])) by (job)", - "read - {{job}}", + f"sum(rate({metric('object_store_read_bytes')}[$__rate_interval])) by ({COMPONENT_LABEL})", + "read - {{%s}}" % COMPONENT_LABEL, ), panels.target( - f"sum(rate({metric('object_store_write_bytes')}[$__rate_interval])) by (job)", - "write - {{job}}", + f"sum(rate({metric('object_store_write_bytes')}[$__rate_interval])) by ({COMPONENT_LABEL})", + "write - {{%s}}" % COMPONENT_LABEL, ), ], ), @@ -613,14 +634,14 @@ def section_storage(outer_panels): [ *quantile( lambda quantile, legend: panels.target( - f"histogram_quantile({quantile}, sum(rate({metric('state_store_sync_size_bucket')}[$__rate_interval])) by (le, job))", - f"p{legend}" + " - {{job}}", + f"histogram_quantile({quantile}, sum(rate({metric('state_store_sync_size_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}))", + f"p{legend}" + " - {{%s}}" % COMPONENT_LABEL, ), [50, 99], ), panels.target( - f"sum by(le, job) (rate({metric('state_store_sync_size_sum')}[$__rate_interval])) / sum by(le, job) (rate({metric('state_store_sync_size_count')}[$__rate_interval]))", - "avg - {{job}}", + f"sum by(le, {COMPONENT_LABEL}) (rate({metric('state_store_sync_size_sum')}[$__rate_interval])) / sum by(le, {COMPONENT_LABEL}) (rate({metric('state_store_sync_size_count')}[$__rate_interval]))", + "avg - {{%s}}" % COMPONENT_LABEL, ), ], ), @@ -631,8 +652,8 @@ def section_storage(outer_panels): def section_streaming(outer_panels): panels = outer_panels.sub_panel() - mv_filter = "executor_identity=~\".*MaterializeExecutor.*\"" - table_type_filter = "table_type=~\"MATERIALIZED_VIEW\"" + mv_filter = 'executor_identity=~".*MaterializeExecutor.*"' + table_type_filter = 'table_type=~"MATERIALIZED_VIEW"' mv_throughput_query = f'sum(rate({metric("stream_executor_row_count", filter=mv_filter)}[$__rate_interval]) * on(actor_id) group_left(materialized_view_id, table_name) (group({metric("table_info", filter=table_type_filter)}) by (actor_id, materialized_view_id, table_name))) by (materialized_view_id, table_name)' return [ @@ -645,7 +666,8 @@ def section_streaming(outer_panels): [ panels.target( f"rate({metric('stream_source_output_rows_counts')}[$__rate_interval])", - "source={{source_name}} actor={{actor_id}} @ {{instance}}", + "source={{source_name}} actor={{actor_id}} @ {{%s}}" + % NODE_LABEL, ), ], ), @@ -675,11 +697,13 @@ def section_streaming(outer_panels): [ panels.target( f"rate({metric('stream_backfill_snapshot_read_row_count')}[$__rate_interval])", - "Read Snapshot - table_id={{table_id}} actor={{actor_id}} @ {{instance}}", + "Read Snapshot - table_id={{table_id}} actor={{actor_id}} @ {{%s}}" + % NODE_LABEL, ), panels.target( f"rate({metric('stream_backfill_upstream_output_row_count')}[$__rate_interval])", - "Upstream - table_id={{table_id}} actor={{actor_id}} @ {{instance}}", + "Upstream - table_id={{table_id}} actor={{actor_id}} @ {{%s}}" + % NODE_LABEL, ), ], ), @@ -744,16 +768,16 @@ def section_batch(outer_panels): "", [ panels.target( - f"histogram_quantile(0.5, sum(rate({metric('distributed_query_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "p50 - {{job}} @ {{instance}}", + f"histogram_quantile(0.5, sum(rate({metric('distributed_query_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "p50 - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"histogram_quantile(0.9, sum(rate({metric('distributed_query_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "p90 - {{job}} @ {{instance}}", + f"histogram_quantile(0.9, sum(rate({metric('distributed_query_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "p90 - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"histogram_quantile(0.95, sum(rate({metric('distributed_query_latency_bucket')}[$__rate_interval])) by (le, job, instance))", - "p99 - {{job}} @ {{instance}}", + f"histogram_quantile(0.95, sum(rate({metric('distributed_query_latency_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "p99 - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -762,16 +786,16 @@ def section_batch(outer_panels): "", [ panels.target( - f"histogram_quantile(0.5, sum(rate({metric('frontend_latency_local_execution_bucket')}[$__rate_interval])) by (le, job, instance))", - "p50 - {{job}} @ {{instance}}", + f"histogram_quantile(0.5, sum(rate({metric('frontend_latency_local_execution_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "p50 - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"histogram_quantile(0.9, sum(rate({metric('frontend_latency_local_execution_bucket')}[$__rate_interval])) by (le, job, instance))", - "p90 - {{job}} @ {{instance}}", + f"histogram_quantile(0.9, sum(rate({metric('frontend_latency_local_execution_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "p90 - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), panels.target( - f"histogram_quantile(0.95, sum(rate({metric('frontend_latency_local_execution_bucket')}[$__rate_interval])) by (le, job, instance))", - "p99 - {{job}} @ {{instance}}", + f"histogram_quantile(0.95, sum(rate({metric('frontend_latency_local_execution_bucket')}[$__rate_interval])) by (le, {COMPONENT_LABEL}, {NODE_LABEL}))", + "p99 - {{%s}} @ {{%s}}" % (COMPONENT_LABEL, NODE_LABEL), ), ], ), @@ -779,6 +803,7 @@ def section_batch(outer_panels): ) ] + def section_connector_node(outer_panels): panels = outer_panels.sub_panel() return [ @@ -809,6 +834,7 @@ def section_connector_node(outer_panels): ) ] + templating_list = [] if dynamic_source_enabled: templating_list.append( @@ -822,13 +848,13 @@ def section_connector_node(outer_panels): "queryValue": "", "refresh": 2, "skipUrlSync": False, - "type": "datasource" + "type": "datasource", } ) if namespace_filter_enabled: namespace_json = { - "definition": "label_values(up{risingwave_name=~\".+\"}, namespace)", + "definition": 'label_values(up{risingwave_name=~".+"}, namespace)', "description": "Kubernetes namespace.", "hide": 0, "includeAll": False, @@ -837,8 +863,8 @@ def section_connector_node(outer_panels): "name": "namespace", "options": [], "query": { - "query": "label_values(up{risingwave_name=~\".+\"}, namespace)", - "refId": "StandardVariableQuery" + "query": 'label_values(up{risingwave_name=~".+"}, namespace)', + "refId": "StandardVariableQuery", }, "refresh": 2, "regex": "", @@ -848,12 +874,8 @@ def section_connector_node(outer_panels): } name_json = { - "current": { - "selected": False, - "text": "risingwave", - "value": "risingwave" - }, - "definition": "label_values(up{namespace=\"$namespace\", risingwave_name=~\".+\"}, risingwave_name)", + "current": {"selected": False, "text": "risingwave", "value": "risingwave"}, + "definition": 'label_values(up{namespace="$namespace", risingwave_name=~".+"}, risingwave_name)', "hide": 0, "includeAll": False, "label": "RisingWave", @@ -861,8 +883,8 @@ def section_connector_node(outer_panels): "name": "instance", "options": [], "query": { - "query": "label_values(up{namespace=\"$namespace\", risingwave_name=~\".+\"}, risingwave_name)", - "refId": "StandardVariableQuery" + "query": 'label_values(up{namespace="$namespace", risingwave_name=~".+"}, risingwave_name)', + "refId": "StandardVariableQuery", }, "refresh": 2, "regex": "", @@ -879,22 +901,18 @@ def section_connector_node(outer_panels): node_json = { - "current": { - "selected": False, - "text": "All", - "value": "__all" - }, - "definition": f"label_values({metric('process_cpu_seconds_total', node_filter_enabled=False)}, instance)", + "current": {"selected": False, "text": "All", "value": "__all"}, + "definition": f"label_values({metric('process_cpu_seconds_total', node_filter_enabled=False)}, {NODE_LABEL})", "description": "Reporting instance of the metric", "hide": 0, "includeAll": True, - "label": "Node", + "label": f"{NODE_VARIABLE_LABEL}", "multi": True, - "name": "node", + "name": f"{NODE_VARIABLE}", "options": [], "query": { - "query": f"label_values({metric('process_cpu_seconds_total', node_filter_enabled=False)}, instance)", - "refId": "StandardVariableQuery" + "query": f"label_values({metric('process_cpu_seconds_total', node_filter_enabled=False)}, {NODE_LABEL})", + "refId": "StandardVariableQuery", }, "refresh": 2, "regex": "", @@ -904,22 +922,18 @@ def section_connector_node(outer_panels): } job_json = { - "current": { - "selected": False, - "text": "All", - "value": "__all" - }, - "definition": f"label_values({metric('process_cpu_seconds_total', node_filter_enabled=False)}, job)", + "current": {"selected": False, "text": "All", "value": "__all"}, + "definition": f"label_values({metric('process_cpu_seconds_total', node_filter_enabled=False)}, {COMPONENT_LABEL})", "description": "Reporting job of the metric", "hide": 0, "includeAll": True, - "label": "Job", + "label": f"{COMPONENT_VARIABLE_LABEL}", "multi": True, - "name": "job", + "name": f"{COMPONENT_VARIABLE}", "options": [], "query": { - "query": f"label_values({metric('process_cpu_seconds_total', node_filter_enabled=False)}, job)", - "refId": "StandardVariableQuery" + "query": f"label_values({metric('process_cpu_seconds_total', node_filter_enabled=False)}, {COMPONENT_LABEL})", + "refId": "StandardVariableQuery", }, "refresh": 2, "regex": "",