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You might be paying too much for BigQuery
The document discusses the costs and functionalities associated with Google BigQuery, highlighting its features as a fully-managed data analytics service. It emphasizes the importance of optimizing queries to reduce costs, particularly by narrowing scans and utilizing techniques like table decorators and query caching. The document also outlines various operational aspects, such as data storage, query execution, and advanced tips for efficient usage of BigQuery.
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You might be paying too much for BigQuery
- 1. You might bepaying too much for BigQuery Ryuji Tamagawa @ Osaka, Japan
- 2.
- 3. Agenda about me about BigQuery Basics Advanced Tips& Tricks What you pay for when using BigQuery How BigQuery runs your queries
- 4. Agenda about me about BigQuery Basics Advanced Tips& Tricks Selecting columns Table decorators Dividing tables How query cache works
- 5. Agenda about me about BigQuery Basics Advanced Tips& Tricks CPUs & Network are FOR FREE Sub query optimesed Repeated Fields
- 6. About me Software engineerworking for ISV, from architecture design to troubleshooting in the field Translator working with O’Reilly Japan ‘Google BigQuery Analytics’ is the 25th book Active in GCPUG, especially in #bq_sushi A bed for 6 cats
- 8. About BigQuery (forthose who don’t know yet) Full-managed structured data store queryable with SQL Very easy to use Fast, in that almost no slowdown with Big Data Cost-effective Built on Google’s infrastructure components
- 9. About BigQuery (forthose who don’t know yet) Basic operations are available within Web UI You can ‘dryrun’ from Web UI to check the amount of data to be scanned You can use the command line interface (bq) to integrate BigQuery into your workflow API are provided for Python, Java
- 10. BigQuery is foranalytics Essentially, data model is same as relational databases with some extension BigQuery is for analytics, not for transaction processing You can insert rows(batch or streaming), but can not update or delete them. There’s no index - tables are always read by ‘fullscan’ You can insert rows from GCS or via HTTP in CSV or JSON format.
- 11. Basics You might bepaying too much for BigQuery
- 12. What you payfor Storage - $0.020 per GB / month Queries - $5 per TB processed (scanned) Streaming inserts - $0.01 per 100,000 rows until July 20, 2015. After July 20, 2015, $0.01 per 200 MB, with individual rows calculated using a 1 KB minimum size. What matters is the Storage
- 13. A simple example Load1TB data to a table everyday, keep each table for a month Query the daily data 5 times everyday to aggregation For storage : 1TB * 30 (tables) = $0.020 * 1000 * 30 = $600 For Queries: 1TB * 5 (Queries) * 30 (days) = $750
- 14. How your datais stored Your data is stored 1. in thousands of disks ( depending on the size) 2. in columnar format ( ColumnIO or something) 3. compressed (However, the cost is based on uncompressed size)
- 15. How BigQuery runsyour query Requested rows are read from DFS, sent to compute nodes Compute nodes (could be thousands) form processing tree on the fly Results are written back to DFS as a table (anonymous or named) distributed file storage layer (tables) compute node compute node compute node compute node compute node compute node compute node results
- 16. How BigQuery runsyour query When doing JOIN between large tables or GROUP BY on a large dataset, keys needs to be hashed and associated data will send to nodes depends on the hash value for in-memory join or grouping. distributed file storage layer (tables) compute node compute node compute node compute node compute node compute node compute node results compute node compute node compute node compute node compute node compute node ‘Shuffle’
- 17. Advanced You might bepaying too much for BigQuery
- 18. Narrowing your scanis the key BigQuery does not have indexes - always do fullscan BigQuery uses columnar storage. Selecting least columns you need makes the cost lower C1 C2 C3 C4 R1 R2 R3 R4 R5 R6 R7 R8 R8
- 19. Narrowing your scanis the key SELECT C1, C2, C3, C4 FROM t For scanned cell (red-filled), you’ll pay C1 C2 C3 C4 R1 Scaned Scaned Scaned Scaned R2 Scaned Scaned Scaned Scaned R3 Scaned Scaned Scaned Scaned R4 Scaned Scaned Scaned Scaned R5 Scaned Scaned Scaned Scaned R6 Scaned Scaned Scaned Scaned R7 Scaned Scaned Scaned Scaned R8 Scaned Scaned Scaned Scaned R8 Scaned Scaned Scaned Scaned
- 20. Narrowing your scanis the key SELECT C1, C3 FROM t You’ll pay only for C1 & C3 C1 C2 C3 C4 R1 Scaned Scaned R2 Scaned Scaned R3 Scaned Scaned R4 Scaned Scaned R5 Scaned Scaned R6 Scaned Scaned R7 Scaned Scaned R8 Scaned Scaned R8 Scaned Scaned You shouldn’t ‘SELECT *’ unintentionally
- 21. Narrowing your scanis the key BigQuery’s tables can have virtually any number of rows Watch out : All those rows will be scanned, no matter what ‘WHERE’ you use in your queries There are 2 ways to work around this: table decorators dividing tables C1 C2 C3 C4 R1 R2 R3 R4 R5 R6 R7 R8 R9 R99999999990 R99999999991 R99999999992 R99999999993 R99999999994
- 22. Narrowing your scanis the key Snapshot decorators: you can limit your scan within a snapshot of the table at a given time SELECT … FROM t@1430665200000 Time-range decorators: you can limit your scan between a given time range SELECT … FROM t@-1430751600000 You can pass the time within last 7 days C1 C2 C3 added R1 4/1 R2 4/1 R3 4/1 R4 4/1 R99999999990 5/8 R99999999991 5/8 R99999999992 5/8 R69999999990 5/3 R69999999991 5/3 R69999999992 5/3 R79999999990 5/5 R79999999991 5/5 R79999999992 5/5
- 23. Table arrangement estimated Batchinsert creates an inserted ‘block’ Recent inserted blocks (within last 7 day) are left separated from ‘main’ block of the table Blocks past 7day will be merged with ‘main’ block of the table streaming inserted rows are not stored in blocks but in BigTable C1 C2 C3 added R1 4/1 R2 4/1 R3 4/1 R4 4/1 R99999999990 5/8 R99999999991 5/8 R99999999992 5/8 R69999999990 5/3 R69999999991 5/3 R69999999992 5/3 R79999999990 5/5 R79999999991 5/5 R79999999992 5/5 This is my estimation : as far as I know, Google didn’t officially mentioned about things like this. MainBlockBlockof5/3Blockof5/5Blockof5/8 As of 2015/5/8
- 24. Table arrengement estimated Batchinsert creates an inserted ‘block’ Becent inserted blocks (within last 7 day) are left separated from ‘main’ block of the table Blocks past 7day will be merged with ‘main’ block of the table Streaming inserted rows are not stored in blocks but in BigTable C1 C2 C3 added R1 4/1 R2 4/1 R3 4/1 R4 4/1 R6999999999 0 5/3 R6999999999 1 5/3 R6999999999 2 5/3 R99999999990 5/8 R99999999991 5/8 R99999999992 5/8 R79999999990 5/5 R79999999991 5/5 R79999999992 5/5 MainBlockBlockof5/5Blockof5/8 As of 2015/5/11 If you focus on last 7 days, decorators are very useful for saving costs
- 25. Narrowing your scanis the key Tables are often split by date in BigQuery You can easily union them within FROM clause, separated with comma ( BQ- specific notation) TABLE_DATE_RANGE function is useful, ex : SELECT … FROM (TABLE_DATE_RANGE(sample.T, TIMESTAMP(‘2015-05-01’), TIMESTAMP(‘2015-05-10’))) T20150401 C1 C2 C3 added R1 12:00 R2 13:23 R3 14:10 R4 14:30 T20150501 C1 C2 C3 added R1 9:09 R2 10:12 R3 11:00 R4 13:56 T20150510 C1 C2 C3 added R1 9:09 R2 10:12 R3 11:00 R4 13:56
- 26. Narrowing your scanis the key With traditional RDB, usually you don’t split tables like this : Expensive ‘Enterprise’ editions support features like this, but it takes your time for design, operation, and maintenance In BigQuery, splitting tables like this sometimes even makes your query faster The difference comes from the architectural difference: BigQuery is designed from ground to reads and processes data from many disks with many compute nodes T20150401 C1 C2 C3 added R1 12:00 R2 13:23 R3 14:10 R4 14:30 T20150503 C1 C2 C3 added R1 9:09 R2 10:12 R3 11:00 R4 13:56 T20150503-1 C1 C2 C3 added R1 9:09 R2 10:12 R3 11:00 R4 13:56
- 27. Narrowing your scanis the key DFS Layer compute node compute node compute node compute node compute node compute node compute node results T2015 0501 T2015 0502 T2015 0503 T2015 0508 T2015 0509 T2015 0510 Actually, any single table is stored in many disks and the data from a table is read by many nodes.
- 28. Using query cache Resultof a query will sent to anonymous dataset, with a name generated from name of the tables and their last update timestamp and the query. When a query is executed, BigQuery checks if the cached result exists at first. If the query returns the cached result, it costs nothing.
- 29. Query cache isfree Applications like dashboards, which runs almost same queries again and again, can save costs by utilizing query cache You can write code that save query results somewhere for later use and avoid running same query, but sometimes you don’t have to worry about it - query cache does same for you
- 30. Query cache isenabled when: The query is deterministic (e.g. without NOW() ) The table does NOT have a streaming buffer The result of the query was not saved to a named table Actually a large result (>128MB) can not be cached because in such case you have to specify ‘allowLargeResult’ and thus the result must be saved to a named table.
- 31. Tips & Tricks Youmight be paying too much for BigQuery
- 32. Trade offs -time & cost Generally, normalizing your data model makes: the size of the data small, which means in BigQuery, you pay less may use more CPU time and network traffic, especially when you run complex queries between large tables
- 33. Trade offs -time & cost You think ‘cost’ in terms of CPU, network, storage in on- premise way When using BigQuery: You don’t pay money for CPU nor network It takes time to run queries that consume much CPU and/or network - queries using EACH keyword If you don’t have to run queries interactively, they could be run in batch mode with less cost, with an ‘appropriate’ schema.
- 34. distributed file storagelayer (tables) compute node compute node compute node compute node compute node compute node compute noderesults compute node compute node compute node compute node compute node compute node compute node compute node
- 35. 免費 distributed file storagelayer (tables) compute node compute node compute node compute node compute node compute node compute noderesults compute node compute node compute node compute node compute node compute node compute node compute node
- 36. Subqueries optimized For example,if you have several types of log in one table and you want to join them with different tables depending on the type, you don’t have to worry SELECT id, desc FROM (select l.id as id, s1.desc as desc from samples.log l join samples.subTable1 s1 on l.value=s1.subid where l.type=0) t1, (select l.id as id, s2.desc as desc from samples.log l join samples.subTable2 s2 on l.value=s2.subid where l.type=1) t2 This query scan log table only once DFS Layer compute node compute node compute node subTalbe1 Log subTable2 compute node
- 37. Repeated fields You canstore array-like data in a row This is not standardized feature of SQL It’s like ‘materialized view’ or pre-joined table - could be compact to store & fast to query You should have a good understanding of the logic, or you will get unexpected result Do not use too complex schema (e.g. deeply nested repeated field) The functions for repeated fields are useful, but watch out for combinational explosion (e.g. FLATTEN)
- 38. Thank you forlistening. Questions?