meltanolabs/tap-bigquery

tap-bigquery

BigQuery tap class.

Built with the Meltano Singer SDK.

Capabilities

• catalog
• state
• discover
• about
• stream-maps
• schema-flattening
• batch

Settings

Setting	Required	Default	Description
project_id	True	None	GCP Project
credentials_path	False	None	The path to the service account credentials file.
filter_schemas	False	None	If an array of schema names is provided, the tap will only process the specified BigQuery schemas and ignore others. If left blank, the tap automatically determines ALL available BigQuery schemas.
stream_maps	False	None	Config object for stream maps capability. For more information check out Stream Maps.
stream_map_config	False	None	User-defined config values to be used within map expressions.
flattening_enabled	False	None	'True' to enable schema flattening and automatically expand nested properties.
flattening_max_depth	False	None	The max depth to flatten schemas.
batch_config	False	None

A full list of supported settings and capabilities is available by running: tap-bigquery --about

(meltanolabs-tap-bigquery-py3.9) Patricks-MBP:tap-bigquery pnadolny$ poetry run tap-bigquery --about --format=markdown

tap-bigquery

Google BigQuery tap.

Built with the Meltano Singer SDK.

Capabilities

• catalog
• state
• discover
• about
• stream-maps
• schema-flattening
• batch

Settings

Setting	Required	Default	Description
project_id	True	None	GCP Project
credentials_path	False	None	The path to the service account credentials file.
filter_schemas	False	None	If an array of schema names is provided, the tap will only process the specified BigQuery schemas and ignore others. If left blank, the tap automatically determines ALL available BigQuery schemas.
stream_maps	False	None	Config object for stream maps capability. For more information check out Stream Maps.
stream_map_config	False	None	User-defined config values to be used within map expressions.
flattening_enabled	False	None	'True' to enable schema flattening and automatically expand nested properties.
flattening_max_depth	False	None	The max depth to flatten schemas.
batch_config	False	None

A full list of supported settings and capabilities is available by running: tap-bigquery --about

Configure using environment variables

This Singer tap will automatically import any environment variables within the working directory's .env if the --config=ENV is provided, such that config values will be considered if a matching environment variable is set either in the terminal context or in the .env file.

Source Authentication and Authorization

Usage

You can easily run tap-bigquery by itself or in a pipeline using Meltano.

Executing the Tap Directly

tap-bigquery --version
tap-bigquery --help
tap-bigquery --config CONFIG --discover > ./catalog.json

Developer Resources

Follow these instructions to contribute to this project.

Initialize your Development Environment

pipx install poetry
poetry install

Create and Run Tests

Create tests within the tests subfolder and then run:

poetry run pytest

You can also test the tap-bigquery CLI interface directly using poetry run:

poetry run tap-bigquery --help

Testing with Meltano

Note: This tap will work in any Singer environment and does not require Meltano. Examples here are for convenience and to streamline end-to-end orchestration scenarios.

Next, install Meltano (if you haven't already) and any needed plugins:

# Install meltano
pipx install meltano
# Initialize meltano within this directory
cd tap-bigquery
meltano install

Now you can test and orchestrate using Meltano:

# Test invocation:
meltano invoke tap-bigquery --version
# OR run a test `elt` pipeline:
meltano elt tap-bigquery target-jsonl

SDK Dev Guide

See the dev guide for more instructions on how to use the SDK to develop your own taps and targets.

tap-bigquery's Issues

Prohibitively long discovery times

We're seeing extremely long --discover times (anywhere from 5-25min) running this tap on a BigQuery setup with a large amount of tables. We're running this via meltano invoke <tap name> --discover.

We're using this tap in an embedded ETL context where we want to provide our customers a more structured approach to providing their credential details, then they're able to see their datasets, then they can see the tables within the dataset they select, and so on, and discovery is how we do that with a variety of other taps. However, the timing as it works out here is way too long for that process to be smooth.

Any clue how we might optimize this?

Support Python >=3.10

Support configuring a destination for large result sets

BigQuery queries have a limited response size¹ so syncs may fail when a large response is generated.

The python-bigquery-sqlalchemy² library supports passing a destination query parameter so the fix for this probably involves adding a new setting (e.g. destination_table) and passing that to the SQLAlchemy URL construction in

tap-bigquery/tap_bigquery/connector.py

Lines 45 to 47 in 0e37a0f

	def get_sqlalchemy_url(self, config: dict) -> str:
	"""Concatenate a SQLAlchemy URL for use in connecting to the source."""
	return f"bigquery://{config['project_id']}"

The string in question is a fully qualified table, e.g. different-project.different-dataset.table.

Footnotes

1. https://cloud.google.com/bigquery/docs/writing-results ↩

2. https://github.com/googleapis/python-bigquery-sqlalchemy?tab=readme-ov-file#connection-string-parameters ↩