BigQuery connector#

The BigQuery connector allows querying the data stored in BigQuery. This can be used to join data between different systems like BigQuery and Hive. The connector uses the BigQuery Storage API to read the data from the tables.

Beta disclaimer#

This connector is in Beta and is subject to change.

Changes may include, but are not limited to:

  • Type conversion

  • Partitioning

  • Parameters

BigQuery Storage API#

The Storage API streams data in parallel directly from BigQuery via gRPC without using Google Cloud Storage as an intermediary. It has a number of advantages over using the previous export-based read flow that should generally lead to better read performance:

Direct Streaming

It does not leave any temporary files in Google Cloud Storage. Rows are read directly from BigQuery servers using an Avro wire format.

Column Filtering

The new API allows column filtering to only read the data you are interested in. Backed by a columnar datastore, it can efficiently stream data without reading all columns.

Dynamic Sharding

The API rebalances records between readers until they all complete. This means that all Map phases will finish nearly concurrently. See this blog article on how dynamic sharding is similarly used in Google Cloud Dataflow.


To connect to BigQuery, you need:

  • To enable the BigQuery Storage Read API.

  • Network access from your Trino coordinator and workers to the Google Cloud API service endpoint. This endpoint uses HTTPS, or port 443.

  • To configure BigQuery so that the Trino coordinator and workers have permissions in BigQuery.

  • To set up authentication. Your authentiation options differ depending on whether you are using Dataproc/Google Compute Engine (GCE) or not.

    On Dataproc/GCE the authentication is done from the machine’s role.

    Outside Dataproc/GCE you have 3 options:

    • Use a service account JSON key and GOOGLE_APPLICATION_CREDENTIALS as described in the Google Cloud authentication getting started guide.

    • Set bigquery.credentials-key in the catalog properties file. It should contain the contents of the JSON file, encoded using base64.

    • Set bigquery.credentials-file in the catalog properties file. It should point to the location of the JSON file.


To configure the BigQuery connector, create a catalog properties file in etc/catalog named, for example,, to mount the BigQuery connector as the bigquery catalog. Create the file with the following contents, replacing the connection properties as appropriate for your setup:
bigquery.project-id=<your Google Cloud Platform project id>

Multiple GCP projects#

The BigQuery connector can only access a single GCP project.Thus, if you have data in multiple GCP projects, You need to create several catalogs, each pointing to a different GCP project. For example, if you have two GCP projects, one for the sales and one for analytics, you can create two properties files in etc/catalog named and, both having but with different project-id. This will create the two catalogs, sales and analytics respectively.

Configuring partitioning#

By default the connector creates one partition per 400MB in the table being read (before filtering). This should roughly correspond to the maximum number of readers supported by the BigQuery Storage API. This can be configured explicitly with the bigquery.parallelism property. BigQuery may limit the number of partitions based on server constraints.

Reading from views#

The connector has a preliminary support for reading from BigQuery views. Please note there are a few caveats:

  • BigQuery views are not materialized by default, which means that the connector needs to materialize them before it can read them. This process affects the read performance.

  • The materialization process can also incur additional costs to your BigQuery bill.

  • By default, the materialized views are created in the same project and dataset. Those can be configured by the optional bigquery.view-materialization-project and bigquery.view-materialization-dataset properties, respectively. The service account must have write permission to the project and the dataset in order to materialize the view.

  • Reading from views is disabled by default. In order to enable it, set the bigquery.views-enabled configuration property to true.

Configuration properties#





The Google Cloud Project ID where the data reside

Taken from the service account


The project ID Google Cloud Project to bill for the export

Taken from the service account


The number of partitions to split the data into

The number of executors


Enables the connector to read from views and not only tables. Please read this section before enabling this feature.



The project where the materialized view is going to be created

The view’s project


The dataset where the materialized view is going to be created

The view’s dataset


The number of retries in case of retryable server issues



The base64 encoded credentials key

None. See the requirements section.


The path to the JSON credentials file

None. See the requirements section.

Match dataset and table names case-insensitively


Duration for which remote dataset and table names will be cached. Higher values reduce the number of API calls to BigQuery but can cause newly created dataset or tables to not be visible until the configured duration. Set to 0ms to disable the cache.


Data types#

With a few exceptions, all BigQuery types are mapped directly to their Trino counterparts. Here are all the mappings:
















In Well-known text (WKT) format











Time zone is UTC



Time zone is UTC

System tables#

For each Trino table which maps to BigQuery view there exists a system table which exposes BigQuery view definition. Given a BigQuery view customer_view you can send query SELECT * customer_view$view_definition to see the SQL which defines view in BigQuery.


What is the Pricing for the Storage API?#

See the BigQuery pricing documentation.