SQLAlchemy dialect and API client for BigQuery.
Usage
SQLAchemy
from sqlalchemy import *
from sqlalchemy.engine import create_engine
from sqlalchemy.schema import *
engine = create_engine('bigquery://project')
table = Table('dataset.table', MetaData(bind=engine), autoload=True)
print(select([func.count('*')], from_obj=table).scalar())API Client
from pybigquery.api import ApiClient
api_client = ApiClient()
print(api_client.dry_run_query(query=sqlstr).total_bytes_processed)Project
project in bigquery://project is used to instantiate BigQuery client with the specific project ID. To infer project from the environment, use bigquery:// – without project
Authentication
Follow the Google Cloud library guide for authentication. Alternatively, you can provide the path to a service account JSON file in create_engine():
engine = create_engine('bigquery://', credentials_path='/path/to/keyfile.json')Location
To specify location of your datasets pass location to create_engine():
engine = create_engine('bigquery://project', location="asia-northeast1")Table names
To query tables from non-default projects, use the following format for the table name: project.dataset.table, e.g.:
sample_table = Table('bigquery-public-data.samples.natality')Batch size
By default, arraysize is set to 5000. arraysize is used to set the batch size for fetching results. To change it, pass arraysize to create_engine():
engine = create_engine('bigquery://project', arraysize=1000)Requirements
Install using
pip install pybigquery
Testing
Load sample tables:
./scripts/load_test_data.sh
This will create a dataset test_pybigquery with tables named sample_one_row and sample.
Set up an environment and run tests:
pyvenv .env source .env/bin/activate pip install -r dev_requirements.txt pytest
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Metadata
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Release files for pybigquery 0.4.5
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