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Transform data with data manipulation language (DML) | BigQuery

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The BigQuery data manipulation language (DML) lets you to update, insert, and delete data from your BigQuery tables.

You can execute DML statements just as you would a SELECT statement, with the following conditions:

• You must use GoogleSQL. To enable GoogleSQL, see Switching SQL dialects.
• You cannot specify a destination table for the query.

For more information about how to compute the number of bytes processed by a DML statement, see On-demand query size calculation.

• Each DML statement initiates an implicit transaction, which means that changes made by the statement are automatically committed at the end of each successful DML statement.

• Rows that were recently written using the tabledata.insertall streaming method can't be modified with data manipulation language (DML), such as UPDATE, DELETE, MERGE, or TRUNCATE statements. The recent writes are those that occurred within the last 30 minutes. All other rows in the table remain modifiable by using UPDATE, DELETE, MERGE, or TRUNCATE statements. The streamed data can take up to 90 minutes to become available for copy operations.

  Alternatively, rows that were recently written using the Storage Write API can be modified using UPDATE, DELETE, or MERGE statements. For more information, see Use data manipulation language (DML) with recently streamed data.

• Correlated subqueries within a when_clause, search_condition, merge_update_clause or merge_insert_clause are not supported for MERGE statements.

• Queries that contain DML statements cannot use a wildcard table as the target of the query. For example, a wildcard table can be used in the FROM clause of an UPDATE query, but a wildcard table cannot be used as the target of the UPDATE operation.

The following sections describe the different types of DML statements and how you can use them.

Use the INSERT statement to add new rows to an existing table. The following example inserts new rows into the table dataset.Inventory with explicitly specified values.

INSERT dataset.Inventory (product, quantity)
VALUES('whole milk', 10),
      ('almond milk', 20),
      ('coffee beans', 30),
      ('sugar', 0),
      ('matcha', 20),
      ('oat milk', 30),
      ('chai', 5)

/+-------------------+----------+
 |      product      | quantity |
 +-------------------+----------+
 | almond milk       |       20 |
 | chai              |        5 |
 | coffee beans      |       30 |
 | matcha            |       20 |
 | oat milk          |       30 |
 | sugar             |        0 |
 | whole milk        |       10 |
 +-------------------+----------+/

For more information about INSERT statements, see INSERT statement.

Use the DELETE statement to delete rows in a table. The following example deletes all rows in the table dataset.Inventory that has the quantity value 0.

DELETE dataset.Inventory
WHERE quantity = 0

/+-------------------+----------+
 |      product      | quantity |
 +-------------------+----------+
 | almond milk       |       20 |
 | chai              |        5 |
 | coffee beans      |       30 |
 | matcha            |       20 |
 | oat milk          |       30 |
 | whole milk        |       10 |
 +-------------------+----------+/

To delete all rows in a table, use the TRUNCATE TABLE statement instead. For more information about DELETE statements, see DELETE statement.

Use the TRUNCATE statement to remove all rows from a table but leaving the table metadata intact, including table schema, description, and labels. The following example removes all rows from the table dataset.Inventory.

TRUNCATE dataset.Inventory

To delete specific rows in a table. Use the DELETE statement instead. For more information about the TRUNCATE statement, see TRUNCATE statement.

Use the UPDATE statement to update existing rows in a table. The UPDATE statement must also include the WHERE keyword to specify a condition. The following example reduces the quantity value of rows by 10 for products that contain the string milk.

UPDATE dataset.Inventory
SET quantity = quantity - 10,
WHERE product LIKE '%milk%'

/+-------------------+----------+
 |      product      | quantity |
 +-------------------+----------+
 | almond milk       |       10 |
 | chai              |        5 |
 | coffee beans      |       30 |
 | matcha            |       20 |
 | oat milk          |       20 |
 | whole milk        |        0 |
 +-------------------+----------+/

UPDATE statements can also include FROM clauses to include joined tables. For more information about UPDATE statements, see UPDATE statement.

The MERGE statement combines the INSERT, UPDATE, and DELETE operations into a single statement and performs the operations atomically to merge data from one table to another. For more information and examples about the MERGE statement, see MERGE statement.

BigQuery manages the concurrency of DML statements that add, modify, or delete rows in a table.

During any 24 hour period, the first 1500 INSERT statements run immediately after they are submitted. After this limit is reached, the concurrency of INSERT statements that write to a table is limited to 10. Additional INSERT statements are added to a PENDING queue. Up to 100 INSERT statements can be queued against a table at any given time. When an INSERT statement completes, the next INSERT statement is removed from the queue and run.

If you must run DML INSERT statements more frequently, consider streaming data to your table using the Storage Write API.

The UPDATE, DELETE, and MERGE DML statements are called mutating DML statements. If you submit one or more mutating DML statements on a table while other mutating DML jobs on it are still running (or pending), BigQuery runs up to 2 of them concurrently, after which up to 20 are queued as PENDING. When a previously running job finishes, the next pending job is dequeued and run. Queued mutating DML statements share a per-table queue with maximum length 20. Additional statements past the maximum queue length for each table fail with the error message: Resources exceeded during query execution: Too many DML statements outstanding against table PROJECT_ID:DATASET.TABLE, limit is 20.

Interactive priority DML jobs that are queued for more than 7 hours fail with the following error message:

DML statement has been queued for too long

Mutating DML statements that run concurrently on a table cause DML statement conflicts when the statements try to mutate the same partition. The statements succeed as long as they don't modify the same partition. BigQuery tries to rerun failed statements up to three times.

• An INSERT DML statement that inserts rows to a table doesn't conflict with any other concurrently running DML statement.

• A MERGE DML statement does not conflict with other concurrently running DML statements as long as the statement only inserts rows and does not delete or update any existing rows. This can include MERGE statements with UPDATE or DELETE clauses, as long as those clauses aren't invoked when the query runs.

Preview

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. You can process personal data for this feature as outlined in the Cloud Data Processing Addendum, subject to the obligations and restrictions described in the agreement under which you access Google Cloud. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

Fine-grained DML is a performance enhancement designed to optimize the execution of UPDATE, DELETE, and MERGE statements (also known as mutating DML statements). Without fine-grained DML enabled, mutations are performed at the file-group level, which can lead to inefficient data rewrites. Fine-grained DML introduces a more granular approach that aims to reduce the amount of data that needs to be rewritten, and to reduce overall slot consumption.

To enable fine-grained DML, set the enable_fine_grained_mutations table option to TRUE when you run a CREATE TABLE or ALTER TABLE DDL statement.

To create a new table with fine-grained DML, use the CREATE TABLE statement:

CREATE TABLE mydataset.mytable (
  product STRING,
  inventory INT64)
OPTIONS(enable_fine_grained_mutations = TRUE);

To alter an existing table with fine-grained DML, use the ALTER TABLE statement:

ALTER TABLE mydataset.mytable
SET OPTIONS(enable_fine_grained_mutations = TRUE);

To alter all existing tables in a dataset with fine-grained DML, use the ALTER TABLE statement:

FOR record IN
 (SELECT CONCAT(table_schema, '.', table_name) AS table_path
 FROM mydataset.INFORMATION_SCHEMA.TABLES)
DO
 EXECUTE IMMEDIATE
   "ALTER TABLE " || record.table_path || " SET OPTIONS(enable_fine_grained_mutations = TRUE)";
END FOR;

After the enable_fine_grained_mutations option is set to TRUE, mutating DML statements are run with fine-grained DML capabilities enabled and use existing DML statement syntax.

To disable fine-grained DML on a table, set enable_fine_grained_mutations to FALSE by using the ALTER TABLE DDL statement.

To determine if a table has been enabled with fine-grained DML, query the INFORMATION_SCHEMA.TABLES view. The following example checks which tables within a dataset have been enabled with this feature:

SELECT
  table_schema AS datasetId,
  table_name AS tableId,
  is_fine_grained_mutations_enabled
FROM
  DATASET_NAME.INFORMATION_SCHEMA.TABLES;

Replace DATASET_NAME with the name of the dataset in which to check if any tables have fine-grained DML enabled.

Enabling fine-grained DML for a table can incur additional BigQuery storage costs to store the extra mutation metadata that is associated with fine-grained DML operations. The actual cost depends on the amount of data that is modified, but for most situations it's expected to be negligible in comparison to the size of the table itself.

Fine-grained DML operations process deleted data in a hybrid approach that distributes rewrite costs across numerous table mutations. Each DML operation processes a portion of the deleted data, and the system also offloads the remaining processing to the background. These deleted data processing jobs incur additional BigQuery compute costs.

You can use BigQuery reservations to allocate dedicated BigQuery compute resources for these offline deleted data processing jobs. Reservations let you set a cap on the cost of performing these operations. This approach is particularly useful for very large tables with frequent fine-grained mutating DML operations, which otherwise would have high on-demand costs due to the large number of bytes processed when performing each offline fine-grained deleted data processing job.

Offline fine-grained deleted data processing jobs are considered background jobs and use the BACKGROUND assignment type, rather than the QUERY assignment type. Projects that perform fine-grained DML operations without a BACKGROUND assignment process deleted data using on-demand pricing.

For projects configured to use on-demand compute pricing, fine-grained DML statements won't reduce scanned bytes.

To find the offline fine-grained DML deleted data processing jobs:

SELECT
  *
FROM
  region-us.INFORMATION_SCHEMA.JOBS
WHERE
  job_id LIKE "%fine_grained_mutation_garbage_collection%"

If the table receives a large number of DML jobs, then the deleted data processing could be handled entirely by DMLs, eliminating the need for background processing.

Projects that perform fine-grained DML operations with a BACKGROUND assignment process deleted data using slots, and are subject to the configured reservation's resource availability. If there aren't enough resources available within the configured reservation, processing deleted data might take longer than anticipated.

Projects that perform fine-grained DML operations by using on-demand pricing, or without a BACKGROUND assignment, process deleted data by using the on-demand pricing and regularly have deleted data processed using internal BigQuery resources.

Tables enabled with fine-grained DML are subject to the following limitations:

• You can't use the tabledata.list method to read content from a table with fine-grained DML enabled. Instead, query the table with a SELECT statement to read table records.
• A table enabled with fine-grained DML cannot be previewed using the BigQuery console.
• You can't copy a table with fine-grained DML enabled after executing an UPDATE, DELETE, or MERGE statement.
• You can't create a table snapshot or table clone of a table with fine-grained DML enabled enabled after executing an UPDATE, DELETE, or MERGE statement.
• You can't enable fine-grained DML on a table in a replicated dataset, and you can't replicate a dataset that contains a table with fine-grained DML enabled.
• DML statements executed in a multi-statement transaction aren't optimized with fine-grained DML.
• You can't enable fine-grained DML on temporary tables created with the CREATE TEMP TABLE statement.

For best performance, Google recommends the following patterns:

• Avoid submitting large numbers of individual row updates or insertions. Instead, group DML operations together when possible. For more information, see DML statements that update or insert single rows.

• If updates or deletions generally happen on older data, or within a particular range of dates, consider partitioning your tables. Partitioning ensures that the changes are limited to specific partitions within the table.

• Avoid partitioning tables if the amount of data in each partition is small and each update modifies a large fraction of the partitions.

• If you often update rows where one or more columns fall within a narrow range of values, consider using clustered tables. Clustering ensures that changes are limited to specific sets of blocks, reducing the amount of data that needs to be read and written. The following is an example of an UPDATE statement that filters on a range of column values:

  UPDATE mydataset.mytable
  SET string_col = 'some string'
  WHERE id BETWEEN 54 AND 75;

  Here is a similar example that filters on a small list of column values:

  UPDATE mydataset.mytable
  SET string_col = 'some string'
  WHERE id IN (54, 57, 60);

  Consider clustering on the id column in these cases.

• If you need OLTP functionality, consider using Cloud SQL federated queries, which enable BigQuery to query data that resides in Cloud SQL.

For best practices to optimize query performance, see Introduction to optimizing query performance.

• For DML syntax information and samples, see DML syntax.
• For information about using DML statements in scheduled queries, see Scheduling queries.

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