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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.

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

Preparation

Before you get started, make sure the wrappers extension is installed on your database:

create extension if not exists wrappers;

and then create the foreign data wrapper:

create foreign data wrapper bigquery_wrapper
  handler big_query_fdw_handler
  validator big_query_fdw_validator;

Secure your credentials (optional)

By default, Postgres stores FDW credentials inide 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 `key_id`
insert into vault.secrets (name, secret)
values (
  'bigquery',
  '
    {
      "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",
      ...
    }
  '
)
returning key_id;

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 Vault"

```sql
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'
  );
```

=== "Without Vault"

```sql
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'
  );
```

Creating Foreign Tables

The BigQuery Wrapper supports data reads and writes from BigQuery.

Integration Select Insert Update Delete Truncate
BigQuery ✅ ✅ ✅ ✅ ❌

For example:

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

Foreign table options

The full list of foreign table options are below:

  • table - Source table or view name in BigQuery, required.

    This can also be a subquery enclosed in parentheses, for example,

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

    Note: When using subquery in this option, full qualitified table name must be used.

  • location - Source table location, optional. Default is 'US'.

  • timeout - Query request timeout in milliseconds, optional. Default is '30000' (30 seconds).

  • rowid_column - Primary key column name, optional for data scan, required for data modify

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.

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
);

-- Add some test data
insert into your_project_id.your_dataset_id.people values
  (1, 'Luke Skywalker', current_timestamp()), 
  (2, 'Leia Organa', current_timestamp()), 
  (3, 'Han Solo', current_timestamp());

Basic example

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

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

select * from 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 people (
  id bigint,
  name text,
  ts timestamp
)
  server bigquery_server
  options (
    table 'people',
    location 'EU',
    rowid_column 'id'
  );

-- insert new data
insert into people(id, name, ts)
values (4, 'Yoda', '2023-01-01 12:34:56');

-- update existing data
update people
set name = 'Anakin Skywalker'
where id = 1;

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