BigQuery

BigQuery is a completely serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data, with BI, machine learning and AI built in.

The BigQuery Wrapper allows you to read and write data from BigQuery within your Postgres database.

Preparation

Before you can query BigQuery, you need to enable the Wrappers extension and store your credentials in Postgres.

Enable Wrappers

Make sure the wrappers extension is installed on your database:

create extension if not exists wrappers with schema extensions;

Enable the BigQuery Wrapper

Enable the bigquery_wrapper FDW:

create foreign data wrapper bigquery_wrapper
  handler big_query_fdw_handler
  validator big_query_fdw_validator;

Store your credentials (optional)

By default, Postgres stores FDW credentials inside pg_catalog.pg_foreign_server in plain text. Anyone with access to this table will be able to view these credentials. Wrappers is designed to work with Vault, which provides an additional level of security for storing credentials. We recommend using Vault to store your credentials.

-- Save your BigQuery service account json in Vault and retrieve the created `key_id`
select vault.create_secret(
  '
    {
      "type": "service_account",
      "project_id": "your_gcp_project_id",
      "private_key_id": "your_private_key_id",
      "private_key": "-----BEGIN PRIVATE KEY-----\n...\n-----END PRIVATE KEY-----\n",
      ...
    }
  ',
  'bigquery',
  'BigQuery service account json for Wrappers'
);

Connecting to BigQuery

We need to provide Postgres with the credentials to connect to BigQuery, and any additional options. We can do this using the create server command:

With VaultWithout Vault

create server bigquery_server
  foreign data wrapper bigquery_wrapper
  options (
    sa_key_id '<key_ID>', -- The Key ID from above.
    project_id 'your_gcp_project_id',
    dataset_id 'your_gcp_dataset_id'
  );

create server bigquery_server
  foreign data wrapper bigquery_wrapper
  options (
    sa_key '
    {
       "type": "service_account",
       "project_id": "your_gcp_project_id",
       ...
    }
   ',
    project_id 'your_gcp_project_id',
    dataset_id 'your_gcp_dataset_id'
  );

Create a schema

We recommend creating a schema to hold all the foreign tables:

create schema if not exists bigquery;

Options

The following options are available when creating BigQuery foreign tables:

• table - Source table or view name in BigQuery, required
• location - Source table location (default: 'US')
• timeout - Query request timeout in milliseconds (default: 30000)
• rowid_column - Primary key column name (required for data modification)

You can also use a subquery as the table option:

table '(select * except(props), to_json_string(props) as props from `my_project.my_dataset.my_table`)'

Note: When using subquery, full qualified table name must be used.

Entites

Tables

The BigQuery Wrapper supports data reads and writes from BigQuery tables and views.

Operations

Object	Select	Insert	Update	Delete	Truncate
Tables	✅	✅	✅	✅	❌

Usage

create foreign table bigquery.my_bigquery_table (
  id bigint,
  name text,
  ts timestamp
)
  server bigquery_server
  options (
    table 'people',
    location 'EU'
  );

Notes

• Supports where, order by and limit clause pushdown
• When using rowid_column, it must be specified for data modification operations
• Data in the streaming buffer cannot be updated or deleted until the buffer is flushed (up to 90 minutes)

Query Pushdown Support

This FDW supports where, order by and limit clause pushdown.

Inserting Rows & the Streaming Buffer

This foreign data wrapper uses BigQuery’s insertAll API method to create a streamingBuffer with an associated partition time. Within that partition time, the data cannot be updated, deleted, or fully exported. Only after the time has elapsed (up to 90 minutes according to BigQuery’s documentation), can you perform operations.

If you attempt an UPDATE or DELETE statement on rows while in the streamingBuffer, you will get an error of UPDATE or DELETE statement over table datasetName - note that tableName would affect rows in the streaming buffer, which is not supported.

Supported Data Types

Postgres Type	BigQuery Type
boolean	BOOL
bigint	INT64
double precision	FLOAT64
numeric	NUMERIC
text	STRING
varchar	STRING
date	DATE
timestamp	DATETIME
timestamp	TIMESTAMP
timestamptz	TIMESTAMP
jsonb	JSON

Limitations

This section describes important limitations and considerations when using this FDW:

• Large result sets may experience network latency during data transfer
• Data in streaming buffer cannot be modified for up to 90 minutes
• Only supports specific data type mappings between Postgres and BigQuery
• Materialized views using foreign tables may fail during logical backups

Examples

Some examples on how to use BigQuery foreign tables.

Let's prepare the source table in BigQuery first:

-- Run below SQLs on BigQuery to create source table
create table your_project_id.your_dataset_id.people (
  id int64,
  name string,
  ts timestamp,
  props jsonb
);

-- Add some test data
insert into your_project_id.your_dataset_id.people values
  (1, 'Luke Skywalker', current_timestamp(), parse_json('{"coordinates":[10,20],"id":1}')),
  (2, 'Leia Organa', current_timestamp(), null),
  (3, 'Han Solo', current_timestamp(), null);

Basic example

This example will create a "foreign table" inside your Postgres database called people and query its data:

create foreign table bigquery.people (
  id bigint,
  name text,
  ts timestamp,
  props jsonb
)
  server bigquery_server
  options (
    table 'people',
    location 'EU'
  );

select * from bigquery.people;

Data modify example

This example will modify data in a "foreign table" inside your Postgres database called people, note that rowid_column option is mandatory:

create foreign table bigquery.people (
  id bigint,
  name text,
  ts timestamp,
  props jsonb
)
  server bigquery_server
  options (
    table 'people',
    location 'EU',
    rowid_column 'id'
  );

-- insert new data
insert into bigquery.people(id, name, ts, props)
values (4, 'Yoda', '2023-01-01 12:34:56', '{"coordinates":[10,20],"id":1}'::jsonb);

-- update existing data
update bigquery.people
set name = 'Anakin Skywalker', props = '{"coordinates":[30,40],"id":42}'::jsonb
where id = 1;

-- delete data
delete from bigquery.people
where id = 2;