pyarrow-bigquery 0.5.0

A simple library to write to and read from BigQuery tables as PyArrow tables.

pyarrow-bigquery

A simple library to write to and download from BigQuery tables as PyArrow tables.

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Installation

pip install pyarrow-bigquery

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Quick Start

This guide will help you quickly get started with pyarrow-bigquery, a library that allows you to read from and write to Google BigQuery using PyArrow.

Reading

pyarrow-bigquery offers four methods to read BigQuery tables as PyArrow tables. Depending on your use case and/or the table size, you can choose the most suitable method.

Read from a Table Location

When the table is small enough to fit in memory, you can read it directly using read_table.

import pyarrow.bigquery as bq

table = bq.read_table("gcp_project.dataset.small_table")

print(table.num_rows)

Read from a Query

Alternatively, if the query results are small enough to fit in memory, you can read them directly using read_query.

import pyarrow.bigquery as bq

table = bq.read_query(
    project="gcp_project",
    query="SELECT * FROM `gcp_project.dataset.small_table`"
)

print(table.num_rows)

Read in Batches

If the target table is larger than memory or you prefer not to fetch the entire table at once, you can use the bq.reader iterator method with the batch_size parameter to limit how much data is fetched per iteration.

import pyarrow.bigquery as bq

for table in bq.reader("gcp_project.dataset.big_table", batch_size=100):
    print(table.num_rows)

Read Query in Batches

Similarly, you can read data in batches from a query using reader_query.

import pyarrow.bigquery as bq

for table in bq.reader_query(
    project="gcp_project",
    query="SELECT * FROM `gcp_project.dataset.small_table`"
):
    print(table.num_rows)

Writing

The package provides two methods to write to BigQuery. Depending on your use case or the table size, you can choose the appropriate method.

Write the Entire Table

To write a complete table at once, use the bq.write_table method.

import pyarrow as pa
import pyarrow.bigquery as bq

table = pa.Table.from_arrays([[1, 2, 3, 4]], names=['integers'])

bq.write_table(table, 'gcp_project.dataset.table')

Write in Batches

If you need to write data in smaller chunks, use the bq.writer method with the schema parameter to define the table structure.

import pyarrow as pa
import pyarrow.bigquery as bq

schema = pa.schema([
    ("integers", pa.int64())
])

with bq.writer("gcp_project.dataset.table", schema=schema) as writer:
    writer.write_batch(record_batch)
    writer.write_table(table)

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API Reference

Writing

pyarrow.bigquery.write_table

Writes a PyArrow Table to a BigQuery Table. No return value.

Parameters:

• table: pa.Table
  The PyArrow table.

• where: str
  The destination location in the BigQuery catalog.

• project: str, default None
  The BigQuery execution project, also the billing project. If not provided, it will be extracted from where.

• table_create: bool, default True
  Specifies if the BigQuery table should be created.

• table_expire: None | int, default None
  The number of seconds after which the created table will expire. Used only if table_create is True. Set to None to disable expiration.

• table_overwrite: bool, default False
  If the table already exists, it will be destroyed and a new one will be created.

• worker_type: threading.Thread | multiprocessing.Process, default threading.Thread
  The worker backend for fetching data.

• worker_count: int, default os.cpu_count()
  The number of threads or processes to use for fetching data from BigQuery.

• batch_size: int, default 100
  The batch size for fetched rows.

bq.write_table(table, 'gcp_project.dataset.table')

pyarrow.bigquery.writer (Context Manager)

Context manager version of the write method. Useful when the PyArrow table is larger than memory size or the table is available in chunks.

Parameters:

• schema: pa.Schema
  The PyArrow schema.

• where: str
  The destination location in the BigQuery catalog.

• project: str, default None
  The BigQuery execution project, also the billing project. If not provided, it will be extracted from where.

• table_create: bool, default True
  Specifies if the BigQuery table should be created.

• table_expire: None | int, default None
  The number of seconds after which the created table will expire. Used only if table_create is True. Set to None to disable expiration.

• table_overwrite: bool, default False
  If the table already exists, it will be destroyed and a new one will be created.

• worker_type: threading.Thread | multiprocessing.Process, default threading.Thread
  The worker backend for writing data.

• worker_count: int, default os.cpu_count()
  The number of threads or processes to use for writing data to BigQuery.

• batch_size: int, default 100
  The batch size used for writes. The table will be automatically split to this value.

Depending on your use case, you might want to use one of the methods below to write your data to a BigQuery table, using either pa.Table or pa.RecordBatch.

pyarrow.bigquery.writer.write_table (Context Manager Method)

Context manager method to write a table.

Parameters:

• table: pa.Table
  The PyArrow table.

import pyarrow as pa
import pyarrow.bigquery as bq

schema = pa.schema([("value", pa.list_(pa.int64()))])

with bq.writer("gcp_project.dataset.table", schema=schema) as writer:
    for a in range(1000):
        writer.write_table(pa.Table.from_pylist([{'value': [a] * 10}]))

pyarrow.bigquery.writer.write_batch (Context Manager Method)

Context manager method to write a record batch.

Parameters:

• batch: pa.RecordBatch
  The PyArrow record batch.

import pyarrow as pa
import pyarrow.bigquery as bq

schema = pa.schema([("value", pa.list_(pa.int64()))])

with bq.writer("gcp_project.dataset.table", schema=schema) as writer:
    for a in range 1000:
        writer.write_batch(pa.RecordBatch.from_pylist([{'value': [1] * 10}]))

Reading

pyarrow.bigquery.read_table

Parameters:

• source: str
  The BigQuery table location.

• project: str, default None
  The BigQuery execution project, also the billing project. If not provided, it will be extracted from source.

• columns: str, default None
  The columns to download. When not provided, all available columns will be downloaded.

• row_restrictions: str, default None
  Row-level filtering executed on the BigQuery side. More information is available in the BigQuery documentation.

• worker_type: threading.Thread | multiprocessing.Process, default threading.Thread
  The worker backend for fetching data.

• worker_count: int, default os.cpu_count()
  The number of threads or processes to use for fetching data from BigQuery.

• batch_size: int, default 100
  The batch size used for fetching. The table will be automatically split into this value.

pyarrow.bigquery.read_query

Parameters:

• project: str
  The BigQuery query execution (and billing) project.

• query: str
  The query to be executed.

• worker_type: threading.Thread | multiprocessing.Process, default threading.Thread
  The worker backend for fetching data.

• worker_count: int, default os.cpu_count()
  The number of threads or processes to use for fetching data from BigQuery.

• batch_size: int, default 100
  The batch size used for fetching. The table will be automatically split into this value.

table = bq.read_query("gcp_project", "SELECT * FROM `gcp_project.dataset.table`")

pyarrow.bigquery.reader

Parameters:

• `

source: str`
The BigQuery table location.

• project: str, default None
  The BigQuery execution project, also the billing project. If not provided, it will be extracted from source.

• columns: str, default None
  The columns to download. When not provided, all available columns will be downloaded.

• row_restrictions: str, default None
  Row-level filtering executed on the BigQuery side. More information is available in the BigQuery documentation.

• worker_type: threading.Thread | multiprocessing.Process, default threading.Thread
  The worker backend for fetching data.

• worker_count: int, default os.cpu_count()
  The number of threads or processes to use for fetching data from BigQuery.

• batch_size: int, default 100
  The batch size used for fetching. The table will be automatically split into this value.

import pyarrow as pa
import pyarrow.bigquery as bq

parts = []
for part in bq.reader("gcp_project.dataset.table"):
    parts.append(part)

table = pa.concat_tables(parts)

pyarrow.bigquery.reader_query

Parameters:

• project: str
  The BigQuery query execution (and billing) project.

• query: str
  The query to be executed.

• worker_type: threading.Thread | multiprocessing.Process, default threading.Thread
  The worker backend for fetching data.

• worker_count: int, default os.cpu_count()
  The number of threads or processes to use for fetching data from BigQuery.

• batch_size: int, default 100
  The batch size used for fetching. The table will be automatically split into this value.

for batch in bq.reader_query("gcp_project", "SELECT * FROM `gcp_project.dataset.table`"):
    print(batch.num_rows)