 SELECT * FROM ... (select all columns)
G(S(T)) SELECT ... FROM ... GROUP BY
S(LJ(T, T)) SELECT ... FROM .. LEFT JOIN ...
WS[A(a,S(T))] WITH a AS SELECT .. (define aliases to queries)
O(S(T)) SELECT ... ORDER BY
CT(S(T)) CREATE TABLE AS SELECT ...
I(S(T)) INSERT INTO ... SELECT ...
E(S(T)) SELECT distinct ... FROM ... (duplicate elimination)
U(S(T),S(T)) SELECT ... UNION ALL SELECT ...](https://image.slidesharecdn.com/journeyofmigratingmillionsofqueriesonthecloud-220623231202-230de15f/85/Journey-of-Migrating-Millions-of-Queries-on-The-Cloud-7-320.jpg)
Ad
More Related Content
Similar to Journey of Migrating Millions of Queries on The Cloud (20)
More from takezoe (20)
Ad
Recently uploaded (20)
![Download Wondershare Filmora Crack [2025] With Latest](https://cdn.slidesharecdn.com/ss_thumbnails/neo4j-howkgsareshapingthefutureofgenerativeaiatawssummitlondonapril2024-240426125209-2d9db05d-250419-250428115407-a04afffa-thumbnail.jpg?width=560&fit=bounds)
Ad
Journey of Migrating Millions of Queries on The Cloud
- 1. Taro L. Saito, Naoki Takezoe, Yukihiro Okada, Takako Shimamoto, Dongmin Yu, Suprith Chandrashekharachar, Kai Sasaki, Shohei Okumiya, Yan Wang, Takashi Kurihara, Ryu Kobayashi, Keisuke Suzuki, Zhenghong Yang, Makoto Onizuka DBTest ‘22 on June 17th Journey of Migrating Millions of Queries on The Cloud
- 2. © 2022 Treasure Data Treasure Data Logs Device Data Batch Data PlazmaDB Table Schema Data Collection Cloud Storage Distributed Data Processing Jobs Job Management SQL Editor Scheduler Workflows Machine Learning Treasure Data OSS Third Party OSS is an enterprise customer data platform (CDP) on the cloud Data
- 3. © 2022 Treasure Data Upgrading query engine is always tough • Various customer use cases • There are many edge cases in queries, data, and combination of both • General benchmark and test cases are not enough • Need to minimize customer frustration caused by upgrading • Keep backward compatibility as much as possible • Notify customers of incompatible queries and how to fix them in advance if we will break compatibility • Activeness of OSS development • In particular, Trino development is super active • Monthly or more frequent release with hundreds of commits • No stable versions • But staying at the same version so long is also painful • Cannot use new features and optimizations unless backporting • Backporting will get harder over time
- 4. © 2022 Treasure Data Query simulator Test using production data and queries with security and safety Control Cluster Test Cluster Query Log Checksum / Query Metrics Report Query Set Real Database Test Database read write • Security: Don’t show customer data and query results • Safety: Don’t cause any side-effect on customer data replay queries
- 5. © 2022 Treasure Data Challenges in query simulation • Query simulation takes very long time • Very large number of queries need to be tested • Not only time, but also cost of test clusters • We need to make query simulation faster • Result verification is not straightforward • Many false positives and duplications • Result analysis tends to rely on personal knowledge • We need to make result verification easier
- 6. © 2022 Treasure Data How we can make query simulation faster? • Reduce the number of queries by clustering by query signature • Reduce the amount of data by narrowing table scan ranges • Test only specific queries (by period, running time, query type, etc)
- 7. © 2022 Treasure Data Clustering queries by query signature Reduce 90% of queries a day need to be tested Query signature Corresponding SQL statements S(T) SELECT ... FROM ... S[*](T) SELECT * FROM ... (select all columns) G(S(T)) SELECT ... FROM ... GROUP BY S(LJ(T, T)) SELECT ... FROM .. LEFT JOIN ... WS[A(a,S(T))] WITH a AS SELECT .. (define aliases to queries) O(S(T)) SELECT ... ORDER BY CT(S(T)) CREATE TABLE AS SELECT ... I(S(T)) INSERT INTO ... SELECT ... E(S(T)) SELECT distinct ... FROM ... (duplicate elimination) U(S(T),S(T)) SELECT ... UNION ALL SELECT ...
- 8. © 2022 Treasure Data Narrowing scan ranges Time distribution of records in a table Use only x% of total records by adding a time range predicate SELECT time, parh, user_agent FROM access SELECT time, path, user_agent FROM ( SELECT time, path, user_agent FROM access ) WHERE time >= from AND time < to Original scan range Only use this range
- 9. © 2022 Treasure Data Choose options depending on the purpose • For checking query compatibility • Group by query signature and narrow scan ranges • For checking performance differences • Test only long-running queries without scan range narrowing • For checking detailed behavior of particular queries • Test only specified queries without grouping by query signature and scan range narrowing
- 10. © 2022 Treasure Data Challenges in query simulation • Query simulation takes very long time • Very large number of queries need to be tested • Not only time, but also cost of test clusters • We need to make query simulation faster • Result verification is not straightforward • Many false positives and duplications • Result analysis tends to rely on personal knowledge • We need to make result verification easier
- 11. © 2022 Treasure Data How we can make result verification easier? • Exclude uncheckable queries as much as possible • Generate a human-readable report • Assistance tools for investigation
- 12. © 2022 Treasure Data Reporting for easier result verification • List problematic queries • Differences in query results, errors, performance, resource usage, scan ranges, worker distribution, etc • Exclude uncheckable queries • Non-deterministic queries • Failed queries with the same (or similar) error on both versions • Finally, check remaining queries by human • Reduced more than 90% of queries need to be checked • In addition, suggest potential cause of different results
- 13. © 2022 Treasure Data Assistance tools for investigation trino-compatibility-checker https://github.com/takezoe/trino-compatibility-checker Run the same query on multiple versions of Trino using docker and compare query results to identify the version that introduced the incompatibility ✅ 317: Right(37a6259cc0c1dae299a7866489dff0bd) ❌ 350: Left(java.sql.SQLException: Query failed (#20210526_154140_00004_yzz4q): Multiple entries with same key: @38f546e: null=expr and @38f546e: null=expr) ✅ 334: Right(37a6259cc0c1dae299a7866489dff0bd) ❌ 342: Left(java.sql.SQLException: Query failed (#20210526_154251_00003_2km75): Multiple entries with same key: @63f11e0f: null=expr and @63f11e0f: null=expr) ✅ 338: Right(37a6259cc0c1dae299a7866489dff0bd) ❌ 340: Left(java.sql.SQLException: Query failed (#20210526_154338_00002_2xtvz): Multiple entries with same key: @76615946: null=expr and @76615946: null=expr) ❌ 339: Left(java.sql.SQLException: Query failed (#20210526_154358_00002_jdnni): Multiple entries with same key: @4aca599d: null=expr and @4aca599d: null=expr)
- 14. © 2022 Treasure Data Trino bugs found by query simulation • #8027 'Multiple entries with same key' error on duplicated grouping of literal values • #19764 Missing shallowEquals() implementation for SampledRelation • #10861 Query fails if IS NOT NULL is used for information_schema • #10937 Predicate push down doesn’t work outside the scope of sub query • #11259 TRY should handle invalid value error in cast VACHAR as TIMESTAMP • #12199 Fix query planning failure on multiple subqueries due to IllegalStateException at ScopeAware.scopeAwareComparison() Also, we found many bugs in our target version that had been already fixed in the latest version so we could backported them
- 15. © 2022 Treasure Data Future work • More support for investigation • After finding bugs, compatibility/performance issues, the root cause investigation is still tough • Some tools or automations for drill-down investigation will be helpful • More efficient query simulation • Better workload compression (e.g. exclude more queries that won’t improve the test coverage) • Isolate simulation traffic from other production components (e.g. pseudo reproduction of real data by synthetic data)