From 45252b2645b06d3db93d55eae5dc68752b132115 Mon Sep 17 00:00:00 2001 From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com> Date: Mon, 27 Nov 2023 19:18:32 -0500 Subject: [PATCH 01/13] Adding language about name uniqueness --- website/docs/docs/build/dimensions.md | 3 ++- website/docs/docs/build/entities.md | 2 +- website/docs/docs/build/measures.md | 3 ++- 3 files changed, 5 insertions(+), 3 deletions(-) diff --git a/website/docs/docs/build/dimensions.md b/website/docs/docs/build/dimensions.md index b8679fe11b0..08373ac0180 100644 --- a/website/docs/docs/build/dimensions.md +++ b/website/docs/docs/build/dimensions.md @@ -15,7 +15,8 @@ In a data platform, dimensions is part of a larger structure called a semantic m Groups are defined within semantic models, alongside entities and measures, and correspond to non-aggregatable columns in your dbt model that provides categorical or time-based context. In SQL, dimensions is typically included in the GROUP BY clause.--> -All dimensions require a `name`, `type` and in some cases, an `expr` parameter. +All dimensions require a `name`, `type` and in some cases, an `expr` parameter. The `name` for your dimension must be unique to the semantic model and can not be the same as an existing `entity` or `measure` within that same project. + | Parameter | Description | Type | | --------- | ----------- | ---- | diff --git a/website/docs/docs/build/entities.md b/website/docs/docs/build/entities.md index 464fa2c3b8c..3e57e53c5f8 100644 --- a/website/docs/docs/build/entities.md +++ b/website/docs/docs/build/entities.md @@ -8,7 +8,7 @@ tags: [Metrics, Semantic Layer] Entities are real-world concepts in a business such as customers, transactions, and ad campaigns. We often focus our analyses around specific entities, such as customer churn or annual recurring revenue modeling. We represent entities in our semantic models using id columns that serve as join keys to other semantic models in your semantic graph. -Within a semantic graph, the required parameters for an entity are `name` and `type`. The `name` refers to either the key column name from the underlying data table, or it may serve as an alias with the column name referenced in the `expr` parameter. +Within a semantic graph, the required parameters for an entity are `name` and `type`. The `name` refers to either the key column name from the underlying data table, or it may serve as an alias with the column name referenced in the `expr` parameter. The `name` for your entity must be unique to the semantic model and can not be the same as an existing `measure` or `dimension` within that same project. Entities can be specified with a single column or multiple columns. Entities (join keys) in a semantic model are identified by their name. Each entity name must be unique within a semantic model, but it doesn't have to be unique across different semantic models. diff --git a/website/docs/docs/build/measures.md b/website/docs/docs/build/measures.md index 74d37b70e94..9a03bdcb6f7 100644 --- a/website/docs/docs/build/measures.md +++ b/website/docs/docs/build/measures.md @@ -34,7 +34,8 @@ measures: When you create a measure, you can either give it a custom name or use the `name` of the data platform column directly. If the `name` of the measure is different from the column name, you need to add an `expr` to specify the column name. The `name` of the measure is used when creating a metric. -Measure names must be **unique** across all semantic models in a project. +Measure names must be across all semantic models in a project and can not be the same as an existing `measure` or `dimension` within that same model. + ### Description From ba53693f6c443bd34e411098d7f9ad8561204b69 Mon Sep 17 00:00:00 2001 From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com> Date: Mon, 27 Nov 2023 19:22:26 -0500 Subject: [PATCH 02/13] updating language --- website/docs/docs/build/dimensions.md | 2 +- website/docs/docs/build/entities.md | 2 +- website/docs/docs/build/measures.md | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/website/docs/docs/build/dimensions.md b/website/docs/docs/build/dimensions.md index 08373ac0180..683ff730d3c 100644 --- a/website/docs/docs/build/dimensions.md +++ b/website/docs/docs/build/dimensions.md @@ -15,7 +15,7 @@ In a data platform, dimensions is part of a larger structure called a semantic m Groups are defined within semantic models, alongside entities and measures, and correspond to non-aggregatable columns in your dbt model that provides categorical or time-based context. In SQL, dimensions is typically included in the GROUP BY clause.--> -All dimensions require a `name`, `type` and in some cases, an `expr` parameter. The `name` for your dimension must be unique to the semantic model and can not be the same as an existing `entity` or `measure` within that same project. +All dimensions require a `name`, `type` and in some cases, an `expr` parameter. The `name` for your dimension must be unique to the semantic model and can not be the same as an existing `entity` or `measure` within that same model. | Parameter | Description | Type | diff --git a/website/docs/docs/build/entities.md b/website/docs/docs/build/entities.md index 3e57e53c5f8..e44f9e79af6 100644 --- a/website/docs/docs/build/entities.md +++ b/website/docs/docs/build/entities.md @@ -8,7 +8,7 @@ tags: [Metrics, Semantic Layer] Entities are real-world concepts in a business such as customers, transactions, and ad campaigns. We often focus our analyses around specific entities, such as customer churn or annual recurring revenue modeling. We represent entities in our semantic models using id columns that serve as join keys to other semantic models in your semantic graph. -Within a semantic graph, the required parameters for an entity are `name` and `type`. The `name` refers to either the key column name from the underlying data table, or it may serve as an alias with the column name referenced in the `expr` parameter. The `name` for your entity must be unique to the semantic model and can not be the same as an existing `measure` or `dimension` within that same project. +Within a semantic graph, the required parameters for an entity are `name` and `type`. The `name` refers to either the key column name from the underlying data table, or it may serve as an alias with the column name referenced in the `expr` parameter. The `name` for your entity must be unique to the semantic model and can not be the same as an existing `measure` or `dimension` within that same model. Entities can be specified with a single column or multiple columns. Entities (join keys) in a semantic model are identified by their name. Each entity name must be unique within a semantic model, but it doesn't have to be unique across different semantic models. diff --git a/website/docs/docs/build/measures.md b/website/docs/docs/build/measures.md index 9a03bdcb6f7..29feecd5d2c 100644 --- a/website/docs/docs/build/measures.md +++ b/website/docs/docs/build/measures.md @@ -34,7 +34,7 @@ measures: When you create a measure, you can either give it a custom name or use the `name` of the data platform column directly. If the `name` of the measure is different from the column name, you need to add an `expr` to specify the column name. The `name` of the measure is used when creating a metric. -Measure names must be across all semantic models in a project and can not be the same as an existing `measure` or `dimension` within that same model. +Measure names must be unique across all semantic models in a project and can not be the same as an existing `measure` or `dimension` within that same model. ### Description From a3a45d3a8bafda4c04a4bfcfd6b45dabfd2c2574 Mon Sep 17 00:00:00 2001 From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com> Date: Mon, 27 Nov 2023 19:32:17 -0500 Subject: [PATCH 03/13] Update website/docs/docs/build/measures.md Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com> --- website/docs/docs/build/measures.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/docs/build/measures.md b/website/docs/docs/build/measures.md index 29feecd5d2c..feea2b30ca4 100644 --- a/website/docs/docs/build/measures.md +++ b/website/docs/docs/build/measures.md @@ -34,7 +34,7 @@ measures: When you create a measure, you can either give it a custom name or use the `name` of the data platform column directly. If the `name` of the measure is different from the column name, you need to add an `expr` to specify the column name. The `name` of the measure is used when creating a metric. -Measure names must be unique across all semantic models in a project and can not be the same as an existing `measure` or `dimension` within that same model. +Measure names must be unique across all semantic models in a project and can not be the same as an existing `entity` or `dimension` within that same model. ### Description From dfe7b6ef61eac406e7f2dd79fc48a1ac8faaf5c1 Mon Sep 17 00:00:00 2001 From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com> Date: Mon, 27 Nov 2023 20:34:06 -0500 Subject: [PATCH 04/13] New best practice guide for clone --- .../clone-incremental-models.md | 89 +++++++++++++++++++ website/sidebars.js | 1 + 2 files changed, 90 insertions(+) create mode 100644 website/docs/best-practices/clone-incremental-models.md diff --git a/website/docs/best-practices/clone-incremental-models.md b/website/docs/best-practices/clone-incremental-models.md new file mode 100644 index 00000000000..ae5c38d69f4 --- /dev/null +++ b/website/docs/best-practices/clone-incremental-models.md @@ -0,0 +1,89 @@ +--- +title: "Clone incremental models as the first step of your CI job" +id: "clone-incremental-models" +description: Learn how to define clone incremental models as the first step of your CI job. +displayText: Clone incremental models as the first step of your CI job +hoverSnippet: Learn how to clone incremental models for CI jobs. +--- + +### Main content + +Imagine that you've created a [Slim CI job](https://docs.getdbt.com/docs/dbt-cloud/using-dbt-cloud/cloud-enabling-continuous-integration#slim-ci) in dbt Cloud. + +img width="1332" alt="Screenshot 2023-10-27 at 10 37 28 AM" src="https://github.com/dbt-labs/docs.getdbt.com/assets/53586774/3574bce0-cad2-44f6-8e22-8cbf33b61cf8" + +Your CI job: +- defers to your production environment +- runs the command `dbt build --select state:modified+` to run and test all of the models you've modified and their downstream dependencies +- is triggered whenever a developer on your team opens a PR against the main branch + +Now imagine your dbt project looks like this: + +img width="1074" alt="Screenshot 2023-10-27 at 10 39 43 AM" src="https://github.com/dbt-labs/docs.getdbt.com/assets/53586774/30d4dab9-1d6a-460e-b3d8-867f29d1c0db" + +When you open a PR that modifies `dim_wizards`, your CI job will kickoff and build _only the modified models and their downstream dependencies_ (in this case: `dim_wizards` and `fct_orders`) into a temporary schema that's unique to your PR. + +This build mimics the behavior of what _will_ happen once the PR is merged into the main branch (so you have confidence that you're not introducing breaking changes), without requiring a build of your _entire_ dbt project. + +**But what happens when one of the modified models (or one of their downstream dependencies) is an incremental model?** + +Because your CI job is building modified models into a PR-specific schema, on the first execution of `dbt build --select state:modified+` the modified incremental model will be built in its entirety **because it does not yet exist in the PR-specific schema** aka [is_incremental will be false](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/configuring-incremental-models#understanding-the-is_incremental-macro). You're running in `full-refresh` mode. + +This can be suboptimal because: +- typically incremental models are your largest datasets, so they take a long time to build in their entirety which can slow down development time and incur warehouse $$$ +- there are situations where a `full-refresh` of the incremental model passes successfully in your CI job but an _incremental_ build of that same table in prod would **fail** when the PR is merged into main (think schema drift where [on_schema_change](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/configuring-incremental-models#what-if-the-columns-of-my-incremental-model-change) config is set to `fail`) + +We can alleviate the above problems by zero copy cloning the relevant, pre-exisitng incremental models into our PR-specific schema as the first step of the CI job using the `dbt clone` command. This way, the incremental models already exist in the PR-specific schema when you first execute the command `dbt build --select state:modified+` so the `is_incremental` flag will be `true`. + +Now, we'll have 2 commands for our dbt Cloud CI check to execute: +1. Clone all of the pre-existing, incremental models that have been modified or are downstream of another model that has been modified -> `dbt clone --select state:modified+,config.materialized:incremental,state:old` +2. Build all of models that have been modified and their downstream dependencies `dbt build --select state:modified+` + +Because of our first clone step, the incremental models selected in our `dbt build` in the second step will run in incremental mode. + +img width="768" alt="Screenshot 2023-10-27 at 10 47 22 AM" src="https://github.com/dbt-labs/docs.getdbt.com/assets/53586774/7a5feed1-9665-4c30-9ee9-e1d63dc75fe5" + +Your CI jobs will run faster, and you're more accurately mimicking the behavior of "what will happen once the PR has been merged into main". + +Disclaimers: +- `dbt clone` is only available with dbt version 1.6+ +- this strategy only works for warehouse that support zero copy cloning (otherwise `dbt clone` will just create pointer views) +- some teams may want to test that their incremental models run in _both_ incremental mode _and_ full-refresh mode + + +### Additional information + +**Relevant slack thread:** https://dbt-labs.slack.com/archives/C05FWBP9X1U/p1692830261651829 + +**From my "Better CI for better data quality coalesce talk:** +If you use the incremental materialization in your dbt project, you should consider cloning your relevant, pre-existing incremental models into your PR-specific schema as the first step of your CI check. This will force your second step to run in incremental mode (where is_incremental is true) because now the models already exist in your PR-specific schema (via cloning). This is beneficial because it more accurately mimics what will happen when you merge your changes into production and it will save time and money by not rebuilding your incremental models (which are often large data sets) from scratch for every PR that modifies them. + +**Expansion on "think schema drift where [on_schema_change](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/configuring-incremental-models#what-if-the-columns-of-my-incremental-model-change) config is set to `fail`" from above:** +Let’s imagine you have an incremental model my_incremental_model with the following config: + +```sql + +{{ + config( + materialized='incremental', + unique_key='unique_id', + on_schema_change='fail' + ) +}} + +``` + +Now, let’s say I open up a PR that adds a new column to `my_incremental_model` - in this case: +- an incremental build will fail +- a `full-refresh` will succeed + +If you have a daily production job that just executes a `dbt build` (without a `--full-refresh` flag), once the PR is merged into main and the job kicks off, you will get a failure. So the question is - what do you want to happen in CI? +- Do you want to also get a failure in CI, so that you know that once this PR is merged into main you need to immediately execute a `dbt build --full-refresh --select my_incremental_model` in production in order to avoid a failure in prod? This will block your CI check from passing. +- Do you want your CI check to succeed, because once you do run a `full-refresh` for this model in prod you will be in a successful state? This may lead to you being surprised that your production job is suddenly failing when you merge this PR into main because you didn’t realize you would need to execute a `dbt build --full-refresh --select my_incremental_model` in production. + +Probably not a perfect solution here, it’s all just tradeoffs! Personally, I'd rather have the failing CI job and have to manually override the blocking branch protection rule so that I'm not surprised and can proactively run the appropriate command in production once I merge the PR in. + +**Expansion on "why `state:old`"** + +For brand new incremental models we actually want those to run in `full-refresh` mode in CI, because they will run in `full-refresh` mode in production when the PR is merged into `main` because they also don't exist yet in the production environment... they're brand new! +If you don't specify this, you won't get an error just a “No relation found in state manifest for…” - so it technically works with our without specifying `state:old`. But adding `state:old` is more explicit and means it won't even try to clone the brand new incremental models. \ No newline at end of file diff --git a/website/sidebars.js b/website/sidebars.js index 720b752ed41..e63ff5eae9a 100644 --- a/website/sidebars.js +++ b/website/sidebars.js @@ -1050,6 +1050,7 @@ const sidebarSettings = { "best-practices/materializations/materializations-guide-7-conclusion", ], }, + "best-practices/clone-incremental-models", "best-practices/writing-custom-generic-tests", "best-practices/best-practice-workflows", "best-practices/dbt-unity-catalog-best-practices", From bf26e2491053a19823c77575f8cde316fc21468f Mon Sep 17 00:00:00 2001 From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com> Date: Tue, 28 Nov 2023 21:31:44 -0500 Subject: [PATCH 05/13] Updates to the doc --- .../clone-incremental-models.md | 81 +++++++++--------- .../img/best-practices/clone-command.png | Bin 0 -> 44464 bytes .../static/img/best-practices/dag-example.png | Bin 0 -> 200045 bytes .../static/img/best-practices/slim-ci-job.png | Bin 0 -> 232318 bytes 4 files changed, 39 insertions(+), 42 deletions(-) create mode 100644 website/static/img/best-practices/clone-command.png create mode 100644 website/static/img/best-practices/dag-example.png create mode 100644 website/static/img/best-practices/slim-ci-job.png diff --git a/website/docs/best-practices/clone-incremental-models.md b/website/docs/best-practices/clone-incremental-models.md index ae5c38d69f4..8b45e3ef478 100644 --- a/website/docs/best-practices/clone-incremental-models.md +++ b/website/docs/best-practices/clone-incremental-models.md @@ -6,60 +6,57 @@ displayText: Clone incremental models as the first step of your CI job hoverSnippet: Learn how to clone incremental models for CI jobs. --- -### Main content +Before you begin, you must be aware of a few conditions: +- `dbt clone` is only available with dbt version 1.6 and newer. Refer to our [upgrade guide](https://docs.getdbt.com/docs/dbt-versions/upgrade-core-in-cloud) for help enabling newer versions in dbt Cloud +- This strategy only works for warehouse that support zero copy cloning (otherwise `dbt clone` will just create pointer views). +- Some teams may want to test that their incremental models run in both incremental mode and full-refresh mode. -Imagine that you've created a [Slim CI job](https://docs.getdbt.com/docs/dbt-cloud/using-dbt-cloud/cloud-enabling-continuous-integration#slim-ci) in dbt Cloud. +Imagine you've created a [Slim CI job](https://docs.getdbt.com/docs/dbt-cloud/using-dbt-cloud/cloud-enabling-continuous-integration#slim-ci) in dbt Cloud and it is configured to: -img width="1332" alt="Screenshot 2023-10-27 at 10 37 28 AM" src="https://github.com/dbt-labs/docs.getdbt.com/assets/53586774/3574bce0-cad2-44f6-8e22-8cbf33b61cf8" +- Defer to your production environment. +- Run the command `dbt build --select state:modified+` to run and test all of the models you've modified and their downstream dependencies. +- Trigger whenever a developer on your team opens a PR against the main branch. -Your CI job: -- defers to your production environment -- runs the command `dbt build --select state:modified+` to run and test all of the models you've modified and their downstream dependencies -- is triggered whenever a developer on your team opens a PR against the main branch + -Now imagine your dbt project looks like this: +Now imagine your dbt project looks something like this in the DAG: -img width="1074" alt="Screenshot 2023-10-27 at 10 39 43 AM" src="https://github.com/dbt-labs/docs.getdbt.com/assets/53586774/30d4dab9-1d6a-460e-b3d8-867f29d1c0db" + -When you open a PR that modifies `dim_wizards`, your CI job will kickoff and build _only the modified models and their downstream dependencies_ (in this case: `dim_wizards` and `fct_orders`) into a temporary schema that's unique to your PR. +When you open a pull request (PR) that modifies `dim_wizards`, your CI job will kickoff and build _only the modified models and their downstream dependencies_ (in this case, `dim_wizards` and `fct_orders`) into a temporary schema that's unique to your PR. -This build mimics the behavior of what _will_ happen once the PR is merged into the main branch (so you have confidence that you're not introducing breaking changes), without requiring a build of your _entire_ dbt project. +This build mimics the behavior of what will happen once the PR is merged into the main branch (so you have confidence that you're not introducing breaking changes) without requiring a build of your entire dbt project. -**But what happens when one of the modified models (or one of their downstream dependencies) is an incremental model?** +## What happens when one of the modified models (or one of their downstream dependencies) is an incremental model? -Because your CI job is building modified models into a PR-specific schema, on the first execution of `dbt build --select state:modified+` the modified incremental model will be built in its entirety **because it does not yet exist in the PR-specific schema** aka [is_incremental will be false](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/configuring-incremental-models#understanding-the-is_incremental-macro). You're running in `full-refresh` mode. +Because your CI job is building modified models into a PR-specific schema, on the first execution of `dbt build --select state:modified+`, the modified incremental model will be built in its entirety _because it does not yet exist in the PR-specific schema_ and [is_incremental will be false](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/configuring-incremental-models#understanding-the-is_incremental-macro). You're running in `full-refresh` mode. This can be suboptimal because: -- typically incremental models are your largest datasets, so they take a long time to build in their entirety which can slow down development time and incur warehouse $$$ -- there are situations where a `full-refresh` of the incremental model passes successfully in your CI job but an _incremental_ build of that same table in prod would **fail** when the PR is merged into main (think schema drift where [on_schema_change](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/configuring-incremental-models#what-if-the-columns-of-my-incremental-model-change) config is set to `fail`) +- Typically incremental models are your largest datasets, so they take a long time to build in their entirety which can slow down development time and incur high warehouse costs. +- There are situations where a `full-refresh` of the incremental model passes successfully in your CI job but an _incremental_ build of that same table in prod would fail when the PR is merged into main (think schema drift where [on_schema_change](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/configuring-incremental-models#what-if-the-columns-of-my-incremental-model-change) config is set to `fail`) -We can alleviate the above problems by zero copy cloning the relevant, pre-exisitng incremental models into our PR-specific schema as the first step of the CI job using the `dbt clone` command. This way, the incremental models already exist in the PR-specific schema when you first execute the command `dbt build --select state:modified+` so the `is_incremental` flag will be `true`. +You can alleviate these problems by zero copy cloning the relevant, pre-exisitng incremental models into your PR-specific schema as the first step of the CI job using the `dbt clone` command. This way, the incremental models already exist in the PR-specific schema when you first execute the command `dbt build --select state:modified+` so the `is_incremental` flag will be `true`. -Now, we'll have 2 commands for our dbt Cloud CI check to execute: -1. Clone all of the pre-existing, incremental models that have been modified or are downstream of another model that has been modified -> `dbt clone --select state:modified+,config.materialized:incremental,state:old` -2. Build all of models that have been modified and their downstream dependencies `dbt build --select state:modified+` +You'll have two commands for your dbt Cloud CI check to execute: +1. Clone all of the pre-existing incremental models that have been modified or are downstream of another model that has been modified: `dbt clone --select state:modified+,config.materialized:incremental,state:old` +2. Build all of the models that have been modified and their downstream dependencies: `dbt build --select state:modified+` -Because of our first clone step, the incremental models selected in our `dbt build` in the second step will run in incremental mode. +Because of your first clone step, the incremental models selected in your `dbt build` on the second step will run in incremental mode. -img width="768" alt="Screenshot 2023-10-27 at 10 47 22 AM" src="https://github.com/dbt-labs/docs.getdbt.com/assets/53586774/7a5feed1-9665-4c30-9ee9-e1d63dc75fe5" + -Your CI jobs will run faster, and you're more accurately mimicking the behavior of "what will happen once the PR has been merged into main". +Your CI jobs will run faster, and you're more accurately mimicking the behavior of what will happen once the PR has been merged into main. -Disclaimers: -- `dbt clone` is only available with dbt version 1.6+ -- this strategy only works for warehouse that support zero copy cloning (otherwise `dbt clone` will just create pointer views) -- some teams may want to test that their incremental models run in _both_ incremental mode _and_ full-refresh mode +## Additional help +**Relevant `dbt clone` Slack thread:** https://dbt-labs.slack.com/archives/C05FWBP9X1U/p1692830261651829 -### Additional information +### From the "Better CI for better data quality" coalesce talk +"If you use the incremental materialization in your dbt project, you should consider cloning your relevant, pre-existing incremental models into your PR-specific schema as the first step of your CI check. This will force your second step to run in incremental mode (where is_incremental is true) because now the models already exist in your PR-specific schema (via cloning). This is beneficial because it more accurately mimics what will happen when you merge your changes into production and it will save time and money by not rebuilding your incremental models (which are often large data sets) from scratch for every PR that modifies them." -Grace Goheen, dbt Labs Product Manager -**Relevant slack thread:** https://dbt-labs.slack.com/archives/C05FWBP9X1U/p1692830261651829 +### Expansion on "think schema drift" where [on_schema_change](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/configuring-incremental-models#what-if-the-columns-of-my-incremental-model-change) config is set to `fail`" from above -**From my "Better CI for better data quality coalesce talk:** -If you use the incremental materialization in your dbt project, you should consider cloning your relevant, pre-existing incremental models into your PR-specific schema as the first step of your CI check. This will force your second step to run in incremental mode (where is_incremental is true) because now the models already exist in your PR-specific schema (via cloning). This is beneficial because it more accurately mimics what will happen when you merge your changes into production and it will save time and money by not rebuilding your incremental models (which are often large data sets) from scratch for every PR that modifies them. - -**Expansion on "think schema drift where [on_schema_change](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/configuring-incremental-models#what-if-the-columns-of-my-incremental-model-change) config is set to `fail`" from above:** -Let’s imagine you have an incremental model my_incremental_model with the following config: +Imagine you have an incremental model `my_incremental_model` with the following config: ```sql @@ -73,17 +70,17 @@ Let’s imagine you have an incremental model my_incremental_model with the foll ``` -Now, let’s say I open up a PR that adds a new column to `my_incremental_model` - in this case: -- an incremental build will fail -- a `full-refresh` will succeed +Now, let’s say you open up a PR that adds a new column to `my_incremental_model`. In this case: +- An incremental build will fail. +- A `full-refresh` will succeed. -If you have a daily production job that just executes a `dbt build` (without a `--full-refresh` flag), once the PR is merged into main and the job kicks off, you will get a failure. So the question is - what do you want to happen in CI? +If you have a daily production job that just executes `dbt build` without a `--full-refresh` flag, once the PR is merged into main and the job kicks off, you will get a failure. So the question is - what do you want to happen in CI? - Do you want to also get a failure in CI, so that you know that once this PR is merged into main you need to immediately execute a `dbt build --full-refresh --select my_incremental_model` in production in order to avoid a failure in prod? This will block your CI check from passing. -- Do you want your CI check to succeed, because once you do run a `full-refresh` for this model in prod you will be in a successful state? This may lead to you being surprised that your production job is suddenly failing when you merge this PR into main because you didn’t realize you would need to execute a `dbt build --full-refresh --select my_incremental_model` in production. +- Do you want your CI check to succeed, because once you do run a `full-refresh` for this model in prod you will be in a successful state? This may lead unpleasant surprises if your production job is suddenly failing when you merge this PR into main if you don’t remember you need to execute a `dbt build --full-refresh --select my_incremental_model` in production. -Probably not a perfect solution here, it’s all just tradeoffs! Personally, I'd rather have the failing CI job and have to manually override the blocking branch protection rule so that I'm not surprised and can proactively run the appropriate command in production once I merge the PR in. +There’s probably no perfect solution here; it’s all just tradeoffs! Our preference would be to have the failing CI job and have to manually override the blocking branch protection rule so that there are no surprises and we can proactively run the appropriate command in production once the PR is merged. -**Expansion on "why `state:old`"** +### Expansion on "why `state:old`" -For brand new incremental models we actually want those to run in `full-refresh` mode in CI, because they will run in `full-refresh` mode in production when the PR is merged into `main` because they also don't exist yet in the production environment... they're brand new! -If you don't specify this, you won't get an error just a “No relation found in state manifest for…” - so it technically works with our without specifying `state:old`. But adding `state:old` is more explicit and means it won't even try to clone the brand new incremental models. \ No newline at end of file +For brand new incremental models, you want them to run in `full-refresh` mode in CI, because they will run in `full-refresh` mode in production when the PR is merged into `main`. They also don't exist yet in the production environment... they're brand new! +If you don't specify this, you won't get an error just a “No relation found in state manifest for…”. 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