pybigquery 0.4.4

SQLAlchemy dialect for BigQuery

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