Use history-based optimizations

This guide describes how to enable, disable, and analyze history-based optimizations for queries.

About history-based optimizations

History-based optimizations use information from already completed executions of similar queries to apply additional optimizations and further improve query performance such as slot time consumed and query latency. For example, when you apply history-based optimization, the first query execution might take 60 seconds, but the second query execution might take only 30 seconds if a history-based optimization was identified. This process continues until there are no additional optimizations to add.

The following is an example of how history-based optimizations work with BigQuery:

Execution count	Query slot time consumed	Notes
1	60	Original execution.
2	30	First history based-optimization applied.
3	20	Second history based-optimization applied.
4	21	No additional history based-optimizations to apply.
5	19	No additional history based-optimizations to apply.
6	20	No additional history based-optimizations to apply.

History-based optimizations are only applied when there is high confidence that there will be a beneficial impact to the query performance. In addition, when an optimization does not significantly improve query performance, that optimization is revoked and not used in future executions of that query.

Roles and permissions

To enable or disable history-based optimizations, you must have the required permissions to create BigQuery default configurations, and then you must use the ALTER PROJECT statement to enable history-based optimizations. Once you've enabled history-based optimizations, all jobs in that project use history-based optimizations, regardless of which user created the job. To learn more about required permissions for default configurations, see Required permissions for default configurations. To enable history-based optimizations, see Enable history-based optimizations.
To review the history-based optimizations for a job using the INFORMATION_SCHEMA.JOBS view, you must have the required role. For more information, see Required role for INFORMATION_SCHEMA.JOBS view.

Enable history-based optimizations

History-based optimizations are enabled by default. If history-based optimizations have been disabled for a project or organization, you can manually re-enable history-based optimizations by including the default_query_optimizer_options = 'adaptive=on' parameter in your ALTER PROJECT or ALTER ORGANIZATION statement. For example:

ALTERPROJECTPROJECT_NAME
SETOPTIONS(
`region-LOCATION.default_query_optimizer_options`='adaptive=on'
);

Replace the following:

PROJECT_NAME: the name of the project
LOCATION: the location of the project

Disable history-based optimizations

To disable history-based optimizations in a project, include the default_query_optimizer_options = 'adaptive=off' parameter in the ALTER PROJECT or ALTER ORGANIZATION statement. For example:

ALTERPROJECTPROJECT_NAME
SETOPTIONS(
`region-LOCATION.default_query_optimizer_options`='adaptive=off'
);

Replace the following:

PROJECT_NAME: the name of the project
LOCATION: the location of the project

Review history-based optimizations for a job

To review the history-based optimizations for a job, you can use a SQL query or a REST API method call.

SQL

You can use a query to get the history-based optimizations for a job. The query must include INFORMATION_SCHEMA.JOBS_BY_PROJECT and the query_info.optimization_details column name.

In the following example, the optimization details are returned for a job called sample_job. If no history-based optimizations were applied, NULL is produced for optimization_details:

SELECT
job_id,
query_info.optimization_details
FROM`PROJECT_NAME.region-LOCATION`.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHEREjob_id='sample_job'
LIMIT1;

The results look similar to the following:

-- The JSON in optimization_details has been formatted for readability.
/*------------+-----------------------------------------------------------------*
 | job_id | optimization_details |
 +------------+-----------------------------------------------------------------+
 | sample_job | { |
 | | "optimizations": [ |
 | | { |
 | | "semi_join_reduction": "web_sales.web_date,RIGHT" |
 | | }, |
 | | { |
 | | "semi_join_reduction": "catalog_sales.catalog_date,RIGHT" |
 | | }, |
 | | { |
 | | "semi_join_reduction": "store_sales.store_date,RIGHT" |
 | | }, |
 | | { |
 | | "join_commutation": "web_returns.web_item" |
 | | }, |
 | | { |
 | | "parallelism_adjustment": "applied" |
 | | }, |
 | | ] |
 | | } |
 *------------+-----------------------------------------------------------------*/

API

To get the optimization details for a job, you can call the jobs.get method.

In the following example, the jobs.get method returns the optimization details (optimizationDetails) in the full response:

{
"jobReference":{
"projectId":"myProject",
"jobId":"sample_job"
}
}

The results look similar to the following:

-- The unrelated parts in the full response have been removed.
{
"jobReference":{
"projectId":"myProject",
"jobId":"sample_job",
"location":"US"
},
"statistics":{
"query":{
"queryInfo":{
"optimizationDetails":{
"optimizations":[
{
"semi_join_reduction":"web_sales.web_date,RIGHT"
},
{
"semi_join_reduction":"catalog_sales.catalog_date,RIGHT"
},
{
"semi_join_reduction":"store_sales.store_date,RIGHT"
},
{
"join_commutation":"web_returns.web_item"
},
{
"parallelism_adjustment":"applied"
}
]
}
}
}
}
}

Estimate impact of history-based optimizations

To estimate the impact of history-based optimizations, you can use the following sample SQL query to identify project queries with the greatest estimated improvement to execution time.

WITH
jobsAS(
SELECT
*,
query_info.query_hashes.normalized_literalsASquery_hash,
TIMESTAMP_DIFF(end_time,start_time,MILLISECOND)ASelapsed_ms,
IFNULL(
ARRAY_LENGTH(JSON_QUERY_ARRAY(query_info.optimization_details.optimizations)) > 0,
FALSE)
AShas_history_based_optimization,
FROMregion-LOCATION.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHEREEXTRACT(DATEFROMcreation_time) > DATE_SUB(CURRENT_DATE(),INTERVAL30DAY)
),
most_recent_jobs_without_history_based_optimizationsAS(
SELECT*
FROMjobs
WHERENOThas_history_based_optimization
QUALIFYROW_NUMBER()OVER(PARTITIONBYquery_hashORDERBYend_timeDESC)=1
)
SELECT
job.job_id,
100*SAFE_DIVIDE(
original_job.elapsed_ms-job.elapsed_ms,
original_job.elapsed_ms)ASpercent_execution_time_saved,
job.elapsed_msASnew_elapsed_ms,
original_job.elapsed_msASoriginal_elapsed_ms,
FROMjobsASjob
INNERJOINmost_recent_jobs_without_history_based_optimizationsASoriginal_job
USING(query_hash)
WHERE
job.has_history_based_optimization
ANDoriginal_job.end_time < job.start_time
ORDERBYpercent_execution_time_savedDESC
LIMIT10;

The result of the preceding query is similar to the following if history-based optimizations were applied:

/*--------------+------------------------------+------------------+-----------------------*
 | job_id | percent_execution_time_saved | new_execution_ms | original_execution_ms |
 +--------------+------------------------------+------------------+-----------------------+
 | sample_job1 | 67.806850186245114 | 7087 | 22014 |
 | sample_job2 | 66.485800412501987 | 10562 | 31515 |
 | sample_job3 | 63.285605271764254 | 97668 | 266021 |
 | sample_job4 | 61.134141726887904 | 923384 | 2375823 |
 | sample_job5 | 55.381272089713754 | 1060062 | 2375823 |
 | sample_job6 | 45.396943168036479 | 2324071 | 4256302 |
 | sample_job7 | 38.227031526376024 | 17811 | 28833 |
 | sample_job8 | 33.826608962725111 | 66360 | 100282 |
 | sample_job9 | 32.087813758311604 | 44020 | 64819 |
 | sample_job10 | 28.356416319483539 | 19088 | 26643 |
 *--------------+------------------------------+------------------+-----------------------*/

The results of this query is only an estimation of history-based optimization impact. Many factors can influence query performance, including but not limited to slot availability, change in data over time, view or UDF definitions, and differences in query parameter values.

If the result of this sample query is empty, then either no jobs have used history-based optimizations, or all queries were optimized more than 30 days ago.

This query can be applied to other query performance metrics such as total_slot_ms and total_bytes_billed. For more information, see the schema for INFORMATION_SCHEMA.JOBS.