Refactor queries_grouped_by_hash and add 4 additional Optimization Scripts #471
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| Original file line number | Diff line number | Diff line change |
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| /* | ||
| * Copyright 2025 Google LLC | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
|
|
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| DECLARE num_days_to_scan INT64 DEFAULT 30; | ||
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| CREATE SCHEMA IF NOT EXISTS optimization_workshop; | ||
| CREATE OR REPLACE TABLE optimization_workshop.bi_engine_disabled_reasons AS | ||
| SELECT reasons.code, count(*) | ||
| FROM `region-us`.INFORMATION_SCHEMA.JOBS as jbo, UNNEST(bi_engine_statistics.bi_engine_reasons) AS reasons | ||
| WHERE DATE(jbo.creation_time) >= CURRENT_DATE - num_days_to_scan | ||
| AND bi_engine_statistics.bi_engine_mode = 'DISABLED' | ||
| GROUP BY reasons.code; |
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|---|---|---|
| @@ -0,0 +1,64 @@ | ||
| /* | ||
| * Copyright 2025 Google LLC | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
|
|
||
| DECLARE num_days_to_scan INT64 DEFAULT 30; | ||
|
|
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| CREATE SCHEMA IF NOT EXISTS optimization_workshop; | ||
| CREATE OR REPLACE TABLE optimization_workshop.bi_engine_mode_duration AS | ||
| SELECT | ||
| day, | ||
| bi_engine_mode, | ||
| COUNT(*) as job_count, | ||
| AVG(time_ms) avg_time_ms, | ||
| MAX(median_time_ms) median_time_ms, | ||
| MAX(p75_time_ms) p75_time_ms, | ||
| MAX(p80_time_ms) p80_time_ms, | ||
| MAX(p90_time_ms) p90_time_ms, | ||
| MAX(p95_time_ms) p95_time_ms, | ||
| MAX(p99_time_ms) p99_time_ms, | ||
| FROM | ||
| ( | ||
| SELECT | ||
| day, | ||
| bi_engine_mode, | ||
| time_ms, | ||
| PERCENTILE_CONT(time_ms, 0.5) OVER (PARTITION BY day, bi_engine_mode) as median_time_ms, | ||
| PERCENTILE_CONT(time_ms, 0.75) OVER (PARTITION BY day, bi_engine_mode) as p75_time_ms, | ||
| PERCENTILE_CONT(time_ms, 0.8) OVER (PARTITION BY day, bi_engine_mode) as p80_time_ms, | ||
| PERCENTILE_CONT(time_ms, 0.90) OVER (PARTITION BY day, bi_engine_mode) as p90_time_ms, | ||
| PERCENTILE_CONT(time_ms, 0.95) OVER (PARTITION BY day, bi_engine_mode) as p95_time_ms, | ||
| PERCENTILE_CONT(time_ms, 0.99) OVER (PARTITION BY day, bi_engine_mode) as p99_time_ms, | ||
| FROM | ||
| ( | ||
| SELECT | ||
| DATE(jbo.creation_time) AS day, | ||
| bi_engine_statistics.bi_engine_mode as bi_engine_mode, | ||
| job_id, | ||
| TIMESTAMP_DIFF(jbo.end_time, jbo.creation_time, MILLISECOND) time_ms | ||
| FROM | ||
| FROM `region-us`.INFORMATION_SCHEMA.JOBS jbo | ||
| WHERE | ||
| DATE(creation_time) >= CURRENT_DATE - num_days_to_scan | ||
| AND jbo.end_time > jbo.start_time | ||
| AND jbo.error_result IS NULL | ||
| AND jbo.statement_type != 'SCRIPT' | ||
| ) | ||
| ) | ||
| GROUP BY | ||
| 1, | ||
| 2 | ||
| ORDER BY day, bi_engine_mode ASC; | ||
|
|
||
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Collaborator
There was a problem hiding this comment. remove this file and simply add duration columns to the existing queries_grouped_by_hash_org.sql script |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,88 @@ | ||
| /* | ||
| * Copyright 2025 Google LLC | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
|
|
||
| /* | ||
| * This script creates a table named queries_grouped_by_hash_project_duration, | ||
| * which contains the top 200 most expensive queries by total slot hours | ||
| * within the past 30 days focusing on the duration of each query hash. | ||
| * 30 days is the default timeframe, but you can change this by setting the | ||
| * num_days_to_scan variable to a different value. | ||
| * Queries are grouped by their normalized query pattern, which ignores | ||
| * comments, parameter values, UDFs, and literals in the query text. | ||
| * This allows us to group queries that are logically the same, but | ||
| * have different literals. | ||
| * | ||
| * For example, the following queries would be grouped together: | ||
| * SELECT * FROM `my-project.my_dataset.my_table` WHERE date = '2020-01-01' | ||
| * SELECT * FROM `my-project.my_dataset.my_table` WHERE date = '2020-01-02' | ||
| * SELECT * FROM `my-project.my_dataset.my_table` WHERE date = '2020-01-03' | ||
| */ | ||
| | ||
| DECLARE num_days_to_scan INT64 DEFAULT 30; | ||
| CREATE SCHEMA IF NOT EXISTS optimization_workshop; | ||
| CREATE OR REPLACE TABLE optimization_workshop.queries_grouped_by_hash_project_duration AS | ||
| SELECT | ||
| query_hash, | ||
| SUM(total_slot_ms) / 1000 / 60 / 60 total_slot_hours, | ||
| COUNT(*) AS job_count, | ||
| AVG(time_ms) avg_time_ms, | ||
| MAX(median_time_ms) median_time_ms, | ||
| MAX(p75_time_ms) p75_time_ms, | ||
| MAX(p80_time_ms) p80_time_ms, | ||
| MAX(p90_time_ms) p90_time_ms, | ||
| MAX(p95_time_ms) p95_time_ms, | ||
| MAX(p99_time_ms) p99_time_ms, | ||
| ARRAY_AGG( | ||
| STRUCT( | ||
| bqutil.fn.job_url(project_id || ':us.' || parent_job_id) AS parent_job_url, | ||
| bqutil.fn.job_url(project_id || ':us.' || job_id) AS job_url, | ||
| query as query_text | ||
| ) | ||
| ORDER BY total_slot_ms | ||
| DESC LIMIT 10) AS top_10_jobs,, | ||
| -- query hashes will all have the same referenced tables so we can use ANY_VALUE below | ||
| ANY_VALUE(ARRAY( | ||
| SELECT | ||
| ref_table.project_id || '.' || | ||
| IF(STARTS_WITH(ref_table.dataset_id, '_'), 'TEMP', ref_table.dataset_id) | ||
| || '.' || ref_table.table_id | ||
| FROM UNNEST(referenced_tables) ref_table | ||
| )) AS referenced_tables | ||
| FROM ( | ||
| SELECT | ||
| query_info.query_hashes.normalized_literals AS query_hash, | ||
| job_id, | ||
| total_slot_ms, | ||
| TIMESTAMP_DIFF(jbo.end_time, jbo.creation_time, MILLISECOND) time_ms, | ||
| query, | ||
| referenced_tables, | ||
| PERCENTILE_CONT(TIMESTAMP_DIFF(jbo.end_time, jbo.creation_time, MILLISECOND), 0.5) OVER (PARTITION BY query_info.query_hashes.normalized_literals) AS median_time_ms, | ||
| PERCENTILE_CONT(TIMESTAMP_DIFF(jbo.end_time, jbo.creation_time, MILLISECOND), 0.75) OVER (PARTITION BY query_info.query_hashes.normalized_literals) AS p75_time_ms, | ||
| PERCENTILE_CONT(TIMESTAMP_DIFF(jbo.end_time, jbo.creation_time, MILLISECOND), 0.8) OVER (PARTITION BY query_info.query_hashes.normalized_literals) AS p80_time_ms, | ||
| PERCENTILE_CONT(TIMESTAMP_DIFF(jbo.end_time, jbo.creation_time, MILLISECOND), 0.90) OVER (PARTITION BY query_info.query_hashes.normalized_literals) AS p90_time_ms, | ||
| PERCENTILE_CONT(TIMESTAMP_DIFF(jbo.end_time, jbo.creation_time, MILLISECOND), 0.95) OVER (PARTITION BY query_info.query_hashes.normalized_literals) AS p95_time_ms, | ||
| PERCENTILE_CONT(TIMESTAMP_DIFF(jbo.end_time, jbo.creation_time, MILLISECOND), 0.99) OVER (PARTITION BY query_info.query_hashes.normalized_literals) AS p99_time_ms | ||
| FROM | ||
| FROM `region-us`.INFORMATION_SCHEMA.JOBS jbo | ||
| WHERE | ||
| DATE(creation_time) >= CURRENT_DATE - num_days_to_scan | ||
| AND jbo.end_time > jbo.start_time | ||
| AND jbo.error_result IS NULL | ||
| AND jbo.statement_type != 'SCRIPT' | ||
| ) AS jobs_with_hash | ||
| GROUP BY query_hash | ||
| ORDER BY total_slot_hours DESC | ||
| LIMIT 200; |
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Collaborator
There was a problem hiding this comment. remove this file and simply add duration columns to the existing queries_grouped_by_hash_project.sql script |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,88 @@ | ||
| /* | ||
| * Copyright 2025 Google LLC | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
|
|
||
| /* | ||
| * This script creates a table named queries_grouped_by_hash_project_duration, | ||
| * which contains the top 200 most expensive queries by total slot hours | ||
| * within the past 30 days focusing on the duration of each query hash. | ||
| * 30 days is the default timeframe, but you can change this by setting the | ||
| * num_days_to_scan variable to a different value. | ||
| * Queries are grouped by their normalized query pattern, which ignores | ||
| * comments, parameter values, UDFs, and literals in the query text. | ||
| * This allows us to group queries that are logically the same, but | ||
| * have different literals. | ||
| * | ||
| * For example, the following queries would be grouped together: | ||
| * SELECT * FROM `my-project.my_dataset.my_table` WHERE date = '2020-01-01' | ||
| * SELECT * FROM `my-project.my_dataset.my_table` WHERE date = '2020-01-02' | ||
| * SELECT * FROM `my-project.my_dataset.my_table` WHERE date = '2020-01-03' | ||
| */ | ||
| | ||
| DECLARE num_days_to_scan INT64 DEFAULT 30; | ||
| CREATE SCHEMA IF NOT EXISTS optimization_workshop; | ||
| CREATE OR REPLACE TABLE optimization_workshop.queries_grouped_by_hash_project_duration AS | ||
| SELECT | ||
| query_hash, | ||
| SUM(total_slot_ms) / 1000 / 60 / 60 total_slot_hours, | ||
| COUNT(*) AS job_count, | ||
| AVG(time_ms) avg_time_ms, | ||
| MAX(median_time_ms) median_time_ms, | ||
| MAX(p75_time_ms) p75_time_ms, | ||
| MAX(p80_time_ms) p80_time_ms, | ||
| MAX(p90_time_ms) p90_time_ms, | ||
| MAX(p95_time_ms) p95_time_ms, | ||
| MAX(p99_time_ms) p99_time_ms, | ||
| ARRAY_AGG( | ||
| STRUCT( | ||
| bqutil.fn.job_url(project_id || ':us.' || parent_job_id) AS parent_job_url, | ||
| bqutil.fn.job_url(project_id || ':us.' || job_id) AS job_url, | ||
| query as query_text | ||
| ) | ||
| ORDER BY total_slot_ms | ||
| DESC LIMIT 10) AS top_10_jobs,, | ||
| -- query hashes will all have the same referenced tables so we can use ANY_VALUE below | ||
| ANY_VALUE(ARRAY( | ||
| SELECT | ||
| ref_table.project_id || '.' || | ||
| IF(STARTS_WITH(ref_table.dataset_id, '_'), 'TEMP', ref_table.dataset_id) | ||
| || '.' || ref_table.table_id | ||
| FROM UNNEST(referenced_tables) ref_table | ||
| )) AS referenced_tables | ||
| FROM ( | ||
| SELECT | ||
| query_info.query_hashes.normalized_literals AS query_hash, | ||
| job_id, | ||
| total_slot_ms, | ||
| TIMESTAMP_DIFF(jbo.end_time, jbo.creation_time, MILLISECOND) time_ms, | ||
| query, | ||
| referenced_tables, | ||
| PERCENTILE_CONT(TIMESTAMP_DIFF(jbo.end_time, jbo.creation_time, MILLISECOND), 0.5) OVER (PARTITION BY query_info.query_hashes.normalized_literals) AS median_time_ms, | ||
| PERCENTILE_CONT(TIMESTAMP_DIFF(jbo.end_time, jbo.creation_time, MILLISECOND), 0.75) OVER (PARTITION BY query_info.query_hashes.normalized_literals) AS p75_time_ms, | ||
| PERCENTILE_CONT(TIMESTAMP_DIFF(jbo.end_time, jbo.creation_time, MILLISECOND), 0.8) OVER (PARTITION BY query_info.query_hashes.normalized_literals) AS p80_time_ms, | ||
| PERCENTILE_CONT(TIMESTAMP_DIFF(jbo.end_time, jbo.creation_time, MILLISECOND), 0.90) OVER (PARTITION BY query_info.query_hashes.normalized_literals) AS p90_time_ms, | ||
| PERCENTILE_CONT(TIMESTAMP_DIFF(jbo.end_time, jbo.creation_time, MILLISECOND), 0.95) OVER (PARTITION BY query_info.query_hashes.normalized_literals) AS p95_time_ms, | ||
| PERCENTILE_CONT(TIMESTAMP_DIFF(jbo.end_time, jbo.creation_time, MILLISECOND), 0.99) OVER (PARTITION BY query_info.query_hashes.normalized_literals) AS p99_time_ms | ||
| FROM | ||
| FROM `region-us`.INFORMATION_SCHEMA.JOBS jbo | ||
| WHERE | ||
| DATE(creation_time) >= CURRENT_DATE - num_days_to_scan | ||
| AND jbo.end_time > jbo.start_time | ||
| AND jbo.error_result IS NULL | ||
| AND jbo.statement_type != 'SCRIPT' | ||
| ) AS jobs_with_hash | ||
| GROUP BY query_hash | ||
| ORDER BY total_slot_hours DESC | ||
| LIMIT 200; |
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