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BigQuery Pipeline

Utility class for building data pipelines in BigQuery.

Provides methods for query, copy table and delete table.

Supports Jinja2 templated SQL.

Usage

Create an instance of BQPipeline. By setting query_project, default_project and default_dataset, you can omit project and dataset from table references in your SQL statements.

query_project is the project that will be billed for queries.

default_project is the project used when a tablespec does not specify a project.

default_dataset is the dataset used when a tablespec does not specify project or dataset.

Place files containing a single BigQuery Standard SQL statement per file.

Note that if you reference a project with a '-' in the name, you must quote the tablespec in your SQL: my-project.dataset_id.table_id

#!/usr/bin/env python
# -*- coding: utf-8 -*-
from bqpipeline.bqpipeline import BQPipeline

bq = BQPipeline(job_name='myjob',
                query_project='myproject',
                default_project='myproject',
                default_dataset='mydataset',
                json_credentials_path='credentials.json')

replacements = {
    'project': 'myproject',
    'dataset': 'mydataset'
}

bq.copy_table('source_table', 'dest_table')

bq.run_queries(['q1.sql', 'q2.sql'], **replacements)

bq.export_csv_to_gcs('tmp_table_2', 'gs://my-bucket/path/to/tmp_table_2-*.csv')

bq.delete_tables(['tmp_table_1', 'tmp_table_2'])

Note, that the run_queries method provided this utility can alternatively take a list of tuples where the first entry is the sql path, and the second is a destination table. You can see an example of this in example.py.

For detailed documentation about the methods provided by this utility class see docs.md.

Creating Service Account JSON Credentials

  1. Visit the Service Account Console
  2. Select a service account
  3. Select "Create Key"
  4. Select "JSON"
  5. Click "Create" to download the file

File Layout

Installation

Optional: Install in virtualenv

python3 -m virtualenv venv
source venv/bin/activate

Install with pip

git clone https://github.com/GoogleCloudPlatform/professional-services.git
cd professional-services/tools/bqpipeline
python3 -m pip install .

Requirements

You'll need to download Python 3.4 or later

Google Cloud Python Client

pip

python3 -m pip install --user --upgrade pip

Optional: virtualenv

python3 -m pip install --user virtualenv

Disclaimer

This is not an official Google project.

References

Python Example Code google-cloud-bigquery Jinja2