Initial read_gbq implementation (WIP)

.pre-commit-config.yaml

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+repos:
+-   repo: https://github.com/psf/black
+    rev: 20.8b1
+    hooks:
+    - id: black
+      language_version: python3
+      exclude: versioneer.py
+-   repo: https://gitlab.com/pycqa/flake8
+    rev: 3.8.3
+    hooks:
+    - id: flake8
+      language_version: python3
+-   repo: https://github.com/pycqa/isort
+    rev: 5.8.0
+    hooks:
+    - id: isort
+      language_version: python3

dask_bigquery/__init__.py

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+from .core import read_gbq

dask_bigquery/core.py

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+from __future__ import annotations
+
+import logging
+import warnings
+from collections.abc import Iterable
+from contextlib import contextmanager
+from functools import partial
+
+import dask
+import dask.dataframe as dd
+import pandas as pd
+import pyarrow
+from google.cloud import bigquery, bigquery_storage
+
+
+@contextmanager
+def bigquery_client(project_id="dask-bigquery", with_storage_api=False):
+    # Ignore google auth credentials warning
+    warnings.filterwarnings(
+        "ignore", "Your application has authenticated using end user credentials"
+    )
+
+    bq_storage_client = None
+    bq_client = bigquery.Client(project_id)
+    try:
+        if with_storage_api:
+            bq_storage_client = bigquery_storage.BigQueryReadClient(
+                credentials=bq_client._credentials
+            )
+            yield bq_client, bq_storage_client
+        else:
+            yield bq_client
+    finally:
+        bq_client.close()
+
+
+def _stream_to_dfs(bqs_client, stream_name, schema, timeout):
+    """Given a Storage API client and a stream name, yield all dataframes."""
+    return [
+        pyarrow.ipc.read_record_batch(
+            pyarrow.py_buffer(message.arrow_record_batch.serialized_record_batch),
+            schema,
+        ).to_pandas()
+        for message in bqs_client.read_rows(name=stream_name, offset=0, timeout=timeout)
+    ]
+
+
+@dask.delayed
+def _read_rows_arrow(
+    *,
+    make_create_read_session_request: callable,
+    partition_field: str = None,
+    project_id: str,
+    stream_name: str = None,
+    timeout: int,
+) -> pd.DataFrame:
+    """Read a single batch of rows via BQ Storage API, in Arrow binary format.
+    Args:
+        project_id: BigQuery project
+        create_read_session_request: kwargs to pass to `bqs_client.create_read_session`
+        as `request`
+        partition_field: BigQuery field for partitions, to be used as Dask index col for
+        divisions
+            NOTE: Please set if specifying `row_restriction` filters in TableReadOptions.
+        stream_name: BigQuery Storage API Stream "name".
+            NOTE: Please set if reading from Storage API without any `row_restriction`.
+            https://cloud.google.com/bigquery/docs/reference/storage/rpc/google.cloud.bigquery.storage.v1beta1#stream
+    NOTE: `partition_field` and `stream_name` kwargs are mutually exclusive.
+    Adapted from
+    https://github.com/googleapis/python-bigquery-storage/blob/a0fc0af5b4447ce8b50c365d4d081b9443b8490e/google/cloud/bigquery_storage_v1/reader.py.
+    """
+    with bigquery_client(project_id, with_storage_api=True) as (bq_client, bqs_client):
+        session = bqs_client.create_read_session(make_create_read_session_request())
+        schema = pyarrow.ipc.read_schema(
+            pyarrow.py_buffer(session.arrow_schema.serialized_schema)
+        )
+
+        if (partition_field is not None) and (stream_name is not None):
+            raise ValueError(
+                "The kwargs `partition_field` and `stream_name` are mutually exclusive."
+            )
+
+        elif partition_field is not None:
+            shards = [
+                df
+                for stream in session.streams
+                for df in _stream_to_dfs(
+                    bqs_client, stream.name, schema, timeout=timeout
+                )
+            ]
+            # NOTE: if no rows satisfying the row_restriction, then `shards` will be empty list
+            if len(shards) == 0:
+                shards = [schema.empty_table().to_pandas()]
+            shards = [shard.set_index(partition_field, drop=True) for shard in shards]
+
+        elif stream_name is not None:
+            shards = _stream_to_dfs(bqs_client, stream_name, schema, timeout=timeout)
+            # NOTE: BQ Storage API can return empty streams
+            if len(shards) == 0:
+                shards = [schema.empty_table().to_pandas()]
+
+        else:
+            raise NotImplementedError(
+                "Please specify either `partition_field` or `stream_name`."
+            )
+
+    return pd.concat(shards)
+
+
+def read_gbq(
+    project_id: str,
+    dataset_id: str,
+    table_id: str,
+    partition_field: str = None,
+    partitions: Iterable[str] = None,
+    row_filter="",
+    fields: list[str] = (),
+    read_timeout: int = 3600,
+):
+    """Read table as dask dataframe using BigQuery Storage API via Arrow format.
+    If `partition_field` and `partitions` are specified, then the resulting dask dataframe
+    will be partitioned along the same boundaries. Otherwise, partitions will be approximately
+    balanced according to BigQuery stream allocation logic.
+    If `partition_field` is specified but not included in `fields` (either implicitly by requesting
+    all fields, or explicitly by inclusion in the list `fields`), then it will still be included
+    in the query in order to have it available for dask dataframe indexing.
+    Args:
+        project_id: BigQuery project
+        dataset_id: BigQuery dataset within project
+        table_id: BigQuery table within dataset
+        partition_field: to specify filters of form "WHERE {partition_field} = ..."
+        partitions: all values to select of `partition_field`
+        fields: names of the fields (columns) to select (default None to "SELECT *")
+        read_timeout: # of seconds an individual read request has before timing out
+    Returns:
+        dask dataframe
+    See https://github.com/dask/dask/issues/3121 for additional context.
+    """
+    if (partition_field is None) and (partitions is not None):
+        raise ValueError("Specified `partitions` without `partition_field`.")
+
+    # If `partition_field` is not part of the `fields` filter, fetch it anyway to be able
+    # to set it as dask dataframe index. We want this to be able to have consistent:
+    # BQ partitioning + dask divisions + pandas index values
+    if (partition_field is not None) and fields and (partition_field not in fields):
+        fields = (partition_field, *fields)
+
+    # These read tasks seems to cause deadlocks (or at least long stuck workers out of touch with
+    # the scheduler), particularly when mixed with other tasks that execute C code. Anecdotally
+    # annotating the tasks with a higher priority seems to help (but not fully solve) the issue at
+    # the expense of higher cluster memory usage.
+    with bigquery_client(project_id, with_storage_api=True) as (
+        bq_client,
+        bqs_client,
+    ), dask.annotate(priority=1):
+        table_ref = bq_client.get_table(".".join((dataset_id, table_id)))
+        if table_ref.table_type == "VIEW":
+            # Materialize the view since the operations below don't work on views.
+            logging.warning(
+                "Materializing view in order to read into dask. This may be expensive."
+            )
+            query = f"SELECT * FROM `{full_id(table_ref)}`"
+            table_ref, _, _ = execute_query(query)
+
+        # The protobuf types can't be pickled (may be able to tweak w/ copyreg), so instead use a
+        # generator func.
+        def make_create_read_session_request(row_filter=""):
+            return bigquery_storage.types.CreateReadSessionRequest(
+                max_stream_count=100,  # 0 -> use as many streams as BQ Storage will provide
+                parent=f"projects/{project_id}",
+                read_session=bigquery_storage.types.ReadSession(
+                    data_format=bigquery_storage.types.DataFormat.ARROW,
+                    read_options=bigquery_storage.types.ReadSession.TableReadOptions(
+                        row_restriction=row_filter,
+                        selected_fields=fields,
+                    ),
+                    table=table_ref.to_bqstorage(),
+                ),
+            )
+
+        # Create a read session in order to detect the schema.
+        # Read sessions are light weight and will be auto-deleted after 24 hours.
+        session = bqs_client.create_read_session(
+            make_create_read_session_request(row_filter=row_filter)
+        )
+        schema = pyarrow.ipc.read_schema(
+            pyarrow.py_buffer(session.arrow_schema.serialized_schema)
+        )
+        meta = schema.empty_table().to_pandas()
+        delayed_kwargs = dict(prefix=f"{dataset_id}.{table_id}-")
+
+        if partition_field is not None:
+            if row_filter:
+                raise ValueError("Cannot pass both `partition_field` and `row_filter`")
+            delayed_kwargs["meta"] = meta.set_index(partition_field, drop=True)
+
+            if partitions is None:
+                logging.info(
+                    "Specified `partition_field` without `partitions`; reading full table."
+                )
+                partitions = pd.read_gbq(
+                    f"SELECT DISTINCT {partition_field} FROM {dataset_id}.{table_id}",
+                    project_id=project_id,
+                )[partition_field].tolist()
+                # TODO generalize to ranges (as opposed to discrete values)
+
+            partitions = sorted(partitions)
+            delayed_kwargs["divisions"] = (*partitions, partitions[-1])
+            row_filters = [
+                f'{partition_field} = "{partition_value}"'
+                for partition_value in partitions
+            ]
+            delayed_dfs = [
+                _read_rows_arrow(
+                    make_create_read_session_request=partial(
+                        make_create_read_session_request, row_filter=row_filter
+                    ),
+                    partition_field=partition_field,
+                    project_id=project_id,
+                    timeout=read_timeout,
+                )
+                for row_filter in row_filters
+            ]
+        else:
+            delayed_kwargs["meta"] = meta
+            delayed_dfs = [
+                _read_rows_arrow(
+                    make_create_read_session_request=make_create_read_session_request,
+                    project_id=project_id,
+                    stream_name=stream.name,
+                    timeout=read_timeout,
+                )
+                for stream in session.streams
+            ]
+
+        return dd.from_delayed(dfs=delayed_dfs, **delayed_kwargs)

requirements.txt

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+dask
+distributed
+google-cloud-bigquery
+google-cloud-bigquery-storage
+pandas
+pandas-gbq
+pyarrow

setup.cfg

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+[flake8]
+exclude = __init__.py
+max-line-length = 120
+ignore = F811

setup.py

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+#!/usr/bin/env python
+
+from setuptools import setup
+
+with open("README.md", "r", encoding="utf-8") as f:
+    long_description = f.read()
+
+setup(
+    name="dask-bigquery",
+    version="0.0.1",
+    description="Dask + BigQuery intergration",
+    license="BSD",
+    packages=["dask_bigquery"],
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    python_requires=">=3.7",
+    install_requires=open("requirements.txt").read().strip().split("\n"),
+    extras_require={"test": ["pytest"]},
+    include_package_data=True,
+    zip_safe=False,
+)