feat: reads using global ctx

python/datafusion/__init__.py

@@ -48,6 +48,8 @@
 
 from .dataframe import DataFrame
 
+from .io import read_parquet, read_avro, read_csv, read_json
+
 from .expr import (
     Expr,
     WindowFrame,
@@ -89,6 +91,10 @@
     "functions",
     "object_store",
     "substrait",
+    "read_parquet",
+    "read_avro",
+    "read_csv",
+    "read_json",
 ]
 
 

python/datafusion/io.py

@@ -0,0 +1,181 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+
+"""IO read functions using global context."""
+
+import pathlib
+
+from datafusion.dataframe import DataFrame
+from datafusion.expr import Expr
+import pyarrow
+from ._internal import SessionContext as SessionContextInternal
+
+
+def read_parquet(
+    path: str | pathlib.Path,
+    table_partition_cols: list[tuple[str, str]] | None = None,
+    parquet_pruning: bool = True,
+    file_extension: str = ".parquet",
+    skip_metadata: bool = True,
+    schema: pyarrow.Schema | None = None,
+    file_sort_order: list[list[Expr]] | None = None,
+) -> DataFrame:
+    """Read a Parquet source into a :py:class:`~datafusion.dataframe.Dataframe`.
+
+    Args:
+        path: Path to the Parquet file.
+        table_partition_cols: Partition columns.
+        parquet_pruning: Whether the parquet reader should use the predicate
+            to prune row groups.
+        file_extension: File extension; only files with this extension are
+            selected for data input.
+        skip_metadata: Whether the parquet reader should skip any metadata
+            that may be in the file schema. This can help avoid schema
+            conflicts due to metadata.
+        schema: An optional schema representing the parquet files. If None,
+            the parquet reader will try to infer it based on data in the
+            file.
+        file_sort_order: Sort order for the file.
+
+    Returns:
+        DataFrame representation of the read Parquet files
+    """
+    if table_partition_cols is None:
+        table_partition_cols = []
+    return DataFrame(
+        SessionContextInternal._global_ctx().read_parquet(
+            str(path),
+            table_partition_cols,
+            parquet_pruning,
+            file_extension,
+            skip_metadata,
+            schema,
+            file_sort_order,
+        )
+    )
+
+
+def read_json(
+    path: str | pathlib.Path,
+    schema: pyarrow.Schema | None = None,
+    schema_infer_max_records: int = 1000,
+    file_extension: str = ".json",
+    table_partition_cols: list[tuple[str, str]] | None = None,
+    file_compression_type: str | None = None,
+) -> DataFrame:
+    """Read a line-delimited JSON data source.
+
+    Args:
+        path: Path to the JSON file.
+        schema: The data source schema.
+        schema_infer_max_records: Maximum number of rows to read from JSON
+            files for schema inference if needed.
+        file_extension: File extension; only files with this extension are
+            selected for data input.
+        table_partition_cols: Partition columns.
+        file_compression_type: File compression type.
+
+    Returns:
+        DataFrame representation of the read JSON files.
+    """
+    if table_partition_cols is None:
+        table_partition_cols = []
+    return DataFrame(
+        SessionContextInternal._global_ctx().read_json(
+            str(path),
+            schema,
+            schema_infer_max_records,
+            file_extension,
+            table_partition_cols,
+            file_compression_type,
+        )
+    )
+
+
+def read_csv(
+    path: str | pathlib.Path | list[str] | list[pathlib.Path],
+    schema: pyarrow.Schema | None = None,
+    has_header: bool = True,
+    delimiter: str = ",",
+    schema_infer_max_records: int = 1000,
+    file_extension: str = ".csv",
+    table_partition_cols: list[tuple[str, str]] | None = None,
+    file_compression_type: str | None = None,
+) -> DataFrame:
+    """Read a CSV data source.
+
+    Args:
+        path: Path to the CSV file
+        schema: An optional schema representing the CSV files. If None, the
+            CSV reader will try to infer it based on data in file.
+        has_header: Whether the CSV file have a header. If schema inference
+            is run on a file with no headers, default column names are
+            created.
+        delimiter: An optional column delimiter.
+        schema_infer_max_records: Maximum number of rows to read from CSV
+            files for schema inference if needed.
+        file_extension:  File extension; only files with this extension are
+            selected for data input.
+        table_partition_cols:  Partition columns.
+        file_compression_type:  File compression type.
+
+    Returns:
+        DataFrame representation of the read CSV files
+    """
+    if table_partition_cols is None:
+        table_partition_cols = []
+
+    path = [str(p) for p in path] if isinstance(path, list) else str(path)
+
+    return DataFrame(
+        SessionContextInternal._global_ctx().read_csv(
+            path,
+            schema,
+            has_header,
+            delimiter,
+            schema_infer_max_records,
+            file_extension,
+            table_partition_cols,
+            file_compression_type,
+        )
+    )
+
+
+def read_avro(
+    path: str | pathlib.Path,
+    schema: pyarrow.Schema | None = None,
+    file_partition_cols: list[tuple[str, str]] | None = None,
+    file_extension: str = ".avro",
+) -> DataFrame:
+    """Create a :py:class:`DataFrame` for reading Avro data source.
+
+    Args:
+        path: Path to the Avro file.
+        schema: The data source schema.
+        file_partition_cols: Partition columns.
+        file_extension: File extension to select.
+
+    Returns:
+        DataFrame representation of the read Avro file
+    """
+    if file_partition_cols is None:
+        file_partition_cols = []
+    return DataFrame(
+        SessionContextInternal._global_ctx().read_avro(
+            str(path), schema, file_partition_cols, file_extension
+        )
+    )

python/tests/test_context.py

@@ -23,6 +23,7 @@
 import pyarrow.dataset as ds
 import pytest
 
+
 from datafusion import (
     DataFrame,
     RuntimeConfig,

python/tests/test_io.py

@@ -0,0 +1,97 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+import os
+import pathlib
+
+from datafusion import column
+import pyarrow as pa
+
+
+from datafusion.io import read_avro, read_csv, read_json, read_parquet
+
+
+def test_read_json_global_ctx(ctx):
+    path = os.path.dirname(os.path.abspath(__file__))
+
+    # Default
+    test_data_path = os.path.join(path, "data_test_context", "data.json")
+    df = read_json(test_data_path)
+    result = df.collect()
+
+    assert result[0].column(0) == pa.array(["a", "b", "c"])
+    assert result[0].column(1) == pa.array([1, 2, 3])
+
+    # Schema
+    schema = pa.schema(
+        [
+            pa.field("A", pa.string(), nullable=True),
+        ]
+    )
+    df = read_json(test_data_path, schema=schema)
+    result = df.collect()
+
+    assert result[0].column(0) == pa.array(["a", "b", "c"])
+    assert result[0].schema == schema
+
+    # File extension
+    test_data_path = os.path.join(path, "data_test_context", "data.json")
+    df = read_json(test_data_path, file_extension=".json")
+    result = df.collect()
+
+    assert result[0].column(0) == pa.array(["a", "b", "c"])
+    assert result[0].column(1) == pa.array([1, 2, 3])
+
+
+def test_read_parquet_global():
+    parquet_df = read_parquet(path="parquet/data/alltypes_plain.parquet")
+    parquet_df.show()
+    assert parquet_df is not None
+
+    path = pathlib.Path.cwd() / "parquet/data/alltypes_plain.parquet"
+    parquet_df = read_parquet(path=path)
+    assert parquet_df is not None
+
+
+def test_read_csv():
+    csv_df = read_csv(path="testing/data/csv/aggregate_test_100.csv")
+    csv_df.select(column("c1")).show()
+
+
+def test_read_csv_list():
+    csv_df = read_csv(path=["testing/data/csv/aggregate_test_100.csv"])
+    expected = csv_df.count() * 2
+
+    double_csv_df = read_csv(
+        path=[
+            "testing/data/csv/aggregate_test_100.csv",
+            "testing/data/csv/aggregate_test_100.csv",
+        ]
+    )
+    actual = double_csv_df.count()
+
+    double_csv_df.select(column("c1")).show()
+    assert actual == expected
+
+
+def test_read_avro():
+    avro_df = read_avro(path="testing/data/avro/alltypes_plain.avro")
+    avro_df.show()
+    assert avro_df is not None
+
+    path = pathlib.Path.cwd() / "testing/data/avro/alltypes_plain.avro"
+    avro_df = read_avro(path=path)
+    assert avro_df is not None

python/tests/test_wrapper_coverage.py

@@ -34,6 +34,8 @@ def missing_exports(internal_obj, wrapped_obj) -> None:
         return
 
     for attr in dir(internal_obj):
+        if attr in ["_global_ctx"]:
+            continue
         assert attr in dir(wrapped_obj)
 
         internal_attr = getattr(internal_obj, attr)

src/context.rs

@@ -43,7 +43,7 @@ use crate::store::StorageContexts;
 use crate::udaf::PyAggregateUDF;
 use crate::udf::PyScalarUDF;
 use crate::udwf::PyWindowUDF;
-use crate::utils::{get_tokio_runtime, wait_for_future};
+use crate::utils::{get_global_ctx, get_tokio_runtime, wait_for_future};
 use datafusion::arrow::datatypes::{DataType, Schema, SchemaRef};
 use datafusion::arrow::pyarrow::PyArrowType;
 use datafusion::arrow::record_batch::RecordBatch;
@@ -68,7 +68,7 @@ use datafusion::prelude::{
     AvroReadOptions, CsvReadOptions, DataFrame, NdJsonReadOptions, ParquetReadOptions,
 };
 use datafusion_ffi::table_provider::{FFI_TableProvider, ForeignTableProvider};
-use pyo3::types::{PyCapsule, PyDict, PyList, PyTuple};
+use pyo3::types::{PyCapsule, PyDict, PyList, PyTuple, PyType};
 use tokio::task::JoinHandle;
 
 /// Configuration options for a SessionContext
@@ -299,6 +299,16 @@ impl PySessionContext {
         })
     }
 
+    #[classmethod]
+    #[pyo3(signature = ())]
+    fn _global_ctx(
+        _cls: &Bound<'_, PyType>,
+    ) -> PyResult<Self> {
+        Ok(Self {
+            ctx: get_global_ctx().clone(),
+        })
+    }
+
     /// Register an object store with the given name
     #[pyo3(signature = (scheme, store, host=None))]
     pub fn register_object_store(

src/utils.rs

@@ -22,6 +22,7 @@ use pyo3::prelude::*;
 use std::future::Future;
 use std::sync::OnceLock;
 use tokio::runtime::Runtime;
+use datafusion::execution::context::SessionContext;
 
 /// Utility to get the Tokio Runtime from Python
 #[inline]
@@ -35,6 +36,13 @@ pub(crate) fn get_tokio_runtime() -> &'static TokioRuntime {
     RUNTIME.get_or_init(|| TokioRuntime(tokio::runtime::Runtime::new().unwrap()))
 }
 
+/// Utility to get the Global Datafussion CTX
+#[inline]
+pub(crate) fn get_global_ctx() -> &'static SessionContext {
+    static RUNTIME: OnceLock<SessionContext> = OnceLock::new();
+    RUNTIME.get_or_init(|| SessionContext::new())
+}
+
 /// Utility to collect rust futures with GIL released
 pub fn wait_for_future<F>(py: Python, f: F) -> F::Output
 where