Using row-level security with other BigQuery features

This document describes how to use row-level access security with other BigQuery features.

Before you read this document, familiarize yourself with row-level security by reading Introduction to BigQuery row-level security and Working with row-level security.

The TRUE filter

Row-level access policies can filter the result data that you see when running queries. To run non-query operations, such as DML, you need full access to all rows in the table. Full access is granted by using a row access policy with the filter expression set to TRUE. This row-level access policy is called the TRUE filter.

Any user can be granted TRUE filter access, including a service account.

Examples of non-query operations are:

• Other BigQuery APIs, such as the BigQuery Storage Read API.
• Some bq command-line tool commands, such as the bq head command.
• Copying a table

TRUE filter example

CREATE ROW ACCESS POLICY all_access ON project.dataset.table1
GRANT TO ("group:all-rows-access@example.com")
FILTER USING (TRUE);

Features that work with the TRUE filter

When you use a DML operation on a table protected by row access policies, you must use a TRUE filter which implies access to the whole table. Any operations that don't alter the table schema maintain any row access policies on the table.

For example, the ALTER TABLE RENAME TO statement copies row access policies from the original table to the new table. As another example, the TRUNCATE TABLE statement removes all of the rows from a table but maintains the table schema as well as any row access policies.

Copy jobs

To copy a table with one or more row-level access policies on it, you must first be granted TRUE filter access on the source table. All row-level access policies on the source table are also copied to the new destination table. If you copy a source table without row-level access policies onto a destination table that does have row-level access policies, then the row-level access policies are removed from the destination table, unless the --append_table flag is used or "writeDisposition": "WRITE_APPEND" is set.

Cross-region copies are allowed and all policies are copied. Subsequent queries might be broken after the copy is complete if the queries contain invalid table references in subquery policies.

Row-level access policies on a table must have unique names. A collision in row-level access policy names during the copy results in an invalid input error.

Required permissions to copy a table with a row-level access policy

To copy a table with one or more row-level access policies, you must have the following permissions, in addition to the roles to copy tables and partitions.

Tabledata.list in BigQuery API

You need TRUE filter access in order to use the tabledata.list method in the BigQuery API on a table with row-level access policies.

DML

To execute a DML statement that updates a table that has row-level access policies, you need TRUE filter access for the table.

In particular, MERGE statements interact with row-level access policies as follows:

• If a target table contains row-level access policies, then you need TRUE filter access to the target table.
• If a source table contains row-level access policies, then the MERGE statement only acts on the rows that are visible to the user.

Table snapshots

Table snapshots support row-level security. The permissions that you need for the base table (source table) and the table snapshot (destination table) are described in Required permissions to copy a table with a row-level access policy.

BigQuery table with JSON columns

Row-level access policies cannot be applied on JSON columns. To learn more about the limitations for row-level security, see Limitations.

Execution graph

You can't use the query execution graph for jobs with row-level access policies.

Extract jobs

If a table has row-level access policies, then only the data that you can view is exported to Cloud Storage when you run an extract job.

Partitioned and clustered tables

Row-level security does not participate in query pruning, which is a feature of partitioned tables.

While row-level security is compatible with partitioned and clustered tables, the row-level access policies that filter row data aren't applied during partition pruning. You can still use partition pruning on a table that uses row-level security by specifying a WHERE clause that operates on the partition column. Similarly, row-level access policies themselves don't create any performance benefits for queries against clustered tables, but they don't interfere with other filtering that you apply.

Query pruning is performed during the execution of row-level access policies using the filters with the policies.

Rename a table

You don't need TRUE filter access to rename a table with one or more row access policies on it. You can rename a table with a DDL statement.

As an alternative, you can also copy a table and give the destination table a different name. If the source table has a row-level access policy on it, see table copy jobs on this page for more information.

Streaming updates

To perform streaming table UPDATE or DELETE operations with change data capture, you must have TRUE filter access.

Time travel

Only a table administrator can access historical data for a table that has, or has previously had, row-level access policies. Other users get an access denied error if they use a time travel decorator on a table that has had row-level access. For more information, see Time travel and row-level access.

Logical, materialized, and authorized views

This section describes different types of BigQuery views and how they interact with row-level security.

Logical or materialized views

Logical or materialized views are built from queries against tables. The query results are usually a subset of the table data.

The data displayed in either type of view is filtered according to the underlying source table's row-level access policies. However, you can't reference views or materialized views in row-level access policies.

Performance for materialized views

In addition, when a materialized view is derived from an underlying table that has row-level access policies, then the query performance is the same as when you query the source table directly. In other words, if the source table has row-level security, you don't see the typical performance benefits of querying a materialized view versus querying the source table.

Authorized views

You can also authorize a logical or materialized view, which means sharing the view with specific users or groups (principals). Principals can then query a view, but don't have access to the underlying table. For more information, see Authorized views.

Wildcard queries

Wildcard queries against tables with row-level access policies fail with an INVALID_INPUT error.

What's next

• For information about best practices for row-level access policies, see Best practices for row-level security in BigQuery.