Effortlessly validate and test your Google BigQuery queries with the power of pandas DataFrames in Python.

Warning

This library is a work in progress!

Breaking changes should be expected until a 1.0 release, so version pinning is recommended.

• Use BQuest in combination with your favorite testing framework (e.g. pytest).
• Create temporary test tables from [JSON](https://cloud.google.com/bigquery/docs/loading-data) or [Pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html).
• Run BQ configurations and plain SQL queries on your test tables and check the result.

Via PyPi (standard):

pip install bquest

Via Github (most recent):

pip install git+https://github.com/ottogroup/bquest

BQuest also requires a dedicated BigQuery dataset for storing test tables, e.g.

resource "google_bigquery_dataset" "bquest" {
  dataset_id    = "bquest"
  friendly_name = "bquest"
  description   = "Source tables for bquest tests"
  location      = "EU"
  default_table_expiration_ms = 3600000
}

We recommend setting an expiration time for tables in the bquest dataset to assure removal of those test tables upon test execution.

TBD

For the actual testing bquest relies on an accessible BigQuery project which can be configured with the gcloud client. The corresponding GOOGLE_PROJECT_ID is extracted from this project and used with pandas-gbq to write temporary tables to the bquest dataset that has to be pre- configured before testing on that project.

For Github CI we have configured an identity provider in our testing project which allows only core members of this repository to access the testing projects' resources.