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 thebq head
command. - Copying a table
Example
Create the TRUE
filter
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
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 permissions required to copy a table without a row-level access policy.
Permission | Resource |
---|---|
bigquery.rowAccessPolicies.list
|
The source table. |
bigquery.rowAccessPolicies.getIamPolicy
|
The source table. |
The TRUE filter
|
The source table. |
bigquery.rowAccessPolicies.create
|
The destination table. |
bigquery.rowAccessPolicies.setIamPolicy
|
The destination table. |
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.
BigQuery BI Engine and Looker Studio
BigQuery BI Engine does not accelerate queries that are run on tables with one or more row-level access policies; those queries are run as standard queries in BigQuery.
The data in a Looker Studio dashboard is filtered according to the underlying source table's row-level access policies.
Column-level security
Row-level security and column-level security, which includes both column-level access control and dynamic data masking, are fully compatible.
Key points are:
- You can apply a row-level access policy to filter data in any column, even
if you don't have access to the data in that column.
- Attempts to access these columns with the subquery row-level access policy results in an error indicating that access is denied. These columns aren't considered system-referenced columns.
- Attempts to access these columns with the non-subquery row-level access policy bypass column-level security.
- If the column is restricted due to column-level security, and the column is
named in the query's
SELECT
statement or subquery row-level access policies, you receive an error. - Column-level security also applies with a
SELECT *
query statement. TheSELECT *
is treated the same as a query which explicitly names a restricted column.
Example of row-level security and column-level security interacting
This example walks you through the steps for securing a table and then querying it.
The data
Suppose that you have the DataOwner role for a dataset named
my_dataset
which includes a table with three columns, named my_table
.
The table contains the data shown in the following table.
In this example, one user is Alice, whose email address is
alice@example.com
. A second user is Bob, Alice's colleague.
rank | fruit | color |
---|---|---|
1 | apple | red |
2 | orange | orange |
3 | lemon | yellow |
4 | lime | green |
The security
You want Alice to be able to see all the rows that have odd numbers in the
rank
column, but not even-numbered rows. You don't want Bob to see any rows,
even or odd. You don't want anyone to see any data in the fruit
column.
To restrict Alice from seeing the even-numbered rows, you create a row-level access policy which has a filter expression based on the data that appears in the
rank
column. To prevent Bob from seeing even or odd rows, you don't include him in the grantee list.CREATE ROW ACCESS POLICY only_odd ON my_dataset.my_table GRANT TO ('user:alice@example.com') FILTER USING (MOD(rank, 2) = 1);
To restrict all users from seeing data in the column named
fruit
, you create a column-level security policy tag that prohibits all users from accessing any of its data.
Finally, you also restrict access to the column named color
in two ways:
the column is governed both by a column-level security policy tag prohibiting
all access by anyone, and is affected by a row-level access policy, which
filters some of the row data in the color
column.
This second row-level access policy only displays rows with the value
green
in thecolor
column.CREATE ROW ACCESS POLICY only_green ON my_dataset.my_table GRANT TO ('user:alice@example.com') FILTER USING (color="green");
Bob's query
If Alice's coworker Bob tries to query data from my_dataset.my_table
, he
doesn't see any rows, because Bob isn't in the grantee list for any row-level
access policy on the table.
Query | my_dataset.my_table
|
Comments | ||
---|---|---|---|---|
rank (Some data is affected by the row access policy only_odd ) |
fruit (All data is secured by a CLS policy tag) |
color (All data is secured by a CLS policy tag, and some data is affected by the row access policy only_green ) |
||
SELECT rank FROM my_dataset.my_table
|
(0) rows returned. |
Bob is not on the row-level access policy's grantee list;
therefore this query succeeds, but no row data is returned. A message is displayed to Bob that says his results may be filtered by the row access policy. |
Alice's queries
When Alice runs queries to access data from my_dataset.my_table
, her
results depend on the query she runs and the security, as shown
in the following table.
Query | my_dataset.my_table
|
Comments | ||
---|---|---|---|---|
rank (Some data is affected by the row access policy only_odd ) |
fruit (All data is secured by a CLS policy tag) |
color (All data is secured by a CLS policy tag, and some data is affected by the row access policy only_green ) |
||
|
(2) odd-numbered rows are returned. |
Alice is on the grantee list for the only_odd
row-level access policy on data in the rank column. Therefore, Alice sees
only the odd-numbered row data. Even-numbered rows are hidden by the
row-level access policy named only_odd . A message is displayed to Alice that says her results may be filtered by the row access policy. |
||
|
|
The fruit column was explicitly named in the query. The column-level security applies. Access is denied. |
||
|
|
The color column was explicitly named in the query. The column-level security applies, before the row-level access policy on data in the color column is engaged.Access is denied. |
||
|
|
The `fruit ` column was explicitly named in the query. The column-level security applies, before the row-level access policy on data in the rank column is engaged.Access is denied. |
||
|
|
The color column was explicitly named in the queryThe column-level security on the color column applies,
before row-level access policies on data in the
rank and the color columns are engaged. Access is denied. |
||
|
|
|
The fruit and color columns were explicitly
named in the query. The column-level security on the fruit and
color columns applies, before the row-level access policy on
data in the color column is engaged.Access is denied. |
|
|
|
|
The fruit and color columns were implicitly
named by using "SELECT * " in the query. The column-level security on the fruit and the
color columns applies, before the row-level access policies
on data in the rank or the color columns are engaged.
Access is denied. |
TRUE
filter access
Finally, as explained in
the section about TRUE
filter access,
if Alice or Bob has TRUE
filter access, then they can
see all of the rows in the table, and use it in non-query jobs. However, if the
table has column-level security, then it still applies and can affect the
results.
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.
Legacy SQL
Row-level access policies are not compatible with Legacy SQL. Queries over tables with row-level access policies must use GoogleSQL. Legacy SQL queries are rejected.
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 do not 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.
Views and materialized views
The data displayed in a view or a materialized view is filtered according to the underlying source table's row-level access policies.
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.
You can't reference views or materialized views in row-level access policies.
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.