HBaseCon 2013: How (and Why) Phoenix Puts the SQL Back into NoSQL
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This document discusses Phoenix, an open source SQL skin for HBase. Phoenix provides a SQL interface and JDBC driver for HBase, allowing users to query HBase data using SQL and leveraging existing SQL tools and frameworks. It compiles SQL queries into efficient native HBase operations, improving performance. Examples demonstrate how Phoenix handles different types of queries, such as aggregating metrics data and finding the longest GC times, through optimizations like skip scans and server-side processing.
HBaseCon 2013: How (and Why) Phoenix Puts the SQL Back into NoSQL
- 1. Phoenix James Taylor @JamesPlusPlus http://phoenix-hbase.blogspot.com/ We put the SQL back in NoSQL https://github.com/forcedotcom/phoenix
- 2. In the dawn of time… Completed
- 3. Relational Databases were invented Completed
- 4. But we all know the problems folks ran into Completed
- 5. And then there was HBase Completed
- 6. And it was good Completed 1. Horizontally scalable
- 7. And it was good Completed 1. Horizontally scalable 2. Maintains data locality
- 8. And it was good Completed 1. Horizontally scalable 2. Maintains data locality 3. Runs on commodity hardware
- 9. But somewhere, something terrible went wrong Completed
- 10. But somewhere, something terrible went wrong Completed 1. It takes too much expertise to write an application
- 11. But somewhere, something terrible went wrong Completed 1. It takes too much expertise to write an application 2. It takes too much code to do anything
- 12. But somewhere, something terrible went wrong Completed 1. It takes too much expertise to write an application 2. It takes too much code to do anything 3. Your application is tied too closely with your data model
- 13. What is Phoenix? Completed SQL skin for HBase
- 14. What is Phoenix? Completed SQL skin for HBase An alternate client API
- 15. What is Phoenix? Completed SQL skin for HBase An alternate client API An embedded JDBC driver that allows you to run at HBase native speed
- 16. What is Phoenix? Completed SQL skin for HBase An alternate client API An embedded JDBC driver that allows you to run at HBase native speed Compiles your SQL into native HBase calls
- 17. What is Phoenix? Completed SQL skin for HBase An alternate client API An embedded JDBC driver that allows you to run at HBase native speed Compiles your SQL into native HBase calls so you don’t have to!
- 19. Why SQL for HBase? Completed Broaden HBase adoption Give folks an API they already know
- 20. Why SQL for HBase? Completed Broaden HBase adoption Give folks an API they already know Reduce the amount of code users need to write SELECT TRUNC(date,'DAY’), AVG(cpu_usage) FROM web_stat WHERE domain LIKE 'Salesforce%’ GROUP BY TRUNC(date,'DAY’)
- 21. Why SQL for HBase? Completed Broaden HBase adoption Give folks an API they already know Reduce the amount of code users need to write SELECT TRUNC(date,'DAY’), AVG(cpu_usage) FROM web_stat WHERE domain LIKE 'Salesforce%’ GROUP BY TRUNC(date,'DAY') Performance optimizations transparent to the user Aggregation Skip Scan Secondary indexing (soon!)
- 22. Why SQL for HBase? Completed Broaden HBase adoption Give folks an API they already know Reduce the amount of code users need to write SELECT TRUNC(date,'DAY’), AVG(cpu_usage) FROM web_stat WHERE domain LIKE 'Salesforce%’ GROUP BY TRUNC(date,'DAY') Performance optimizations transparent to the user Aggregation Skip Scan Secondary indexing (soon!) Leverage existing tooling SQL client/terminal OLAP engine
- 23. Example Row Key Server Metrics HOST VARCHAR DATE DATE RESPONSE_TIME INTEGER GC_TIME INTEGER CPU_TIME INTEGER IO_TIME INTEGER … Over metrics data for clusters of servers with a schema like this:
- 24. Example Server Metrics HOST VARCHAR DATE DATE RESPONSE_TIME INTEGER GC_TIME INTEGER CPU_TIME INTEGER IO_TIME INTEGER … Over metrics data for clusters of servers with a schema like this: Key Values
- 25. Example With 90 days of data that looks like this: SERVER METRICS HOST DATE RESPONSE_TIME GC_TIME sf1.s1 Jun5 10:10:10.234 1234 sf1.s1 Jun 5 11:18:28.456 8012 … sf3.s1 Jun5 10:10:10.234 2345 sf3.s1 Jun 6 12:46:19.123 2340 sf7.s9 Jun 4 08:23:23.456 5002 1234 …
- 26. Example Walk through query processing for three scenarios 1. Chart Response Time Per Cluster
- 27. Example Walk through query processing for three scenarios 1. Chart Response Time Per Cluster
- 28. Example Walk through query processing for three scenarios 1. Chart Response Time Per Cluster 2. Identify 5 Longest GC Times
- 29. Example Walk through query processing for three scenarios 1. Chart Response Time Per Cluster 2. Identify 5 Longest GC Times
- 30. Example Walk through query processing for three scenarios 1. Chart Response Time Per Cluster 2. Identify 5 Longest GC Times 3. Identify 5 Longest GC Times again and again
- 31. Scenario 1 Chart Response Time Per Cluster Completed SELECT host, trunc(date,’DAY’), min(response_time), max(response_time) FROM server_metrics WHERE date > CURRENT_DATE() – 7 AND substr(host, 1, 3) IN (‘sf1’, ‘sf3, ‘sf7’) GROUP BY substr(host, 1, 3), trunc(date,’DAY’)
- 32. Scenario 1 Chart Response Time Per Cluster Completed SELECT host, trunc(date,’DAY’), min(response_time), max(response_time) FROM server_metrics WHERE date > CURRENT_DATE() – 7 AND substr(host, 1, 3) IN (‘sf1’, ‘sf3, ‘sf7’) GROUP BY substr(host, 1, 3), trunc(date,’DAY’)
- 33. Scenario 1 Chart Response Time Per Cluster Completed SELECT host, trunc(date,’DAY’), min(response_time), max(response_time) FROM server_metrics WHERE date > CURRENT_DATE() – 7 AND substr(host, 1, 3) IN (‘sf1’, ‘sf3, ‘sf7’) GROUP BY substr(host, 1, 3), trunc(date,’DAY’)
- 34. Scenario 1 Chart Response Time Per Cluster Completed SELECT host, trunc(date,’DAY’), min(response_time), max(response_time) FROM server_metrics WHERE date > CURRENT_DATE() – 7 AND substr(host, 1, 3) IN (‘sf1’, ‘sf3, ‘sf7’) GROUP BY substr(host, 1, 3), trunc(date,’DAY’)
- 35. Scenario 1 Chart Response Time Per Cluster Completed SELECT host, trunc(date,’DAY’), min(response_time), max(response_time) FROM server_metrics WHERE date > CURRENT_DATE() – 7 AND substr(host, 1, 3) IN (‘sf1’, ‘sf3, ‘sf7’) GROUP BY substr(host, 1, 3), trunc(date,’DAY’)
- 36. Step 1: Client Identify Row Key Ranges from Query Completed SELECT host, trunc(date,’DAY’), min(response_time), max(response_time) FROM server_metrics WHERE date > CURRENT_DATE() – 7 AND substr(host, 1, 3) IN (‘sf1’, ‘sf3’, ‘sf7’) GROUP BY substr(host, 1, 3), trunc(date,’DAY’) Row Key Ranges HOST DATE
- 37. Step 1: Client Identify Row Key Ranges from Query Completed SELECT host, trunc(date,’DAY’), min(response_time), max(response_time) FROM server_metrics WHERE date > CURRENT_DATE() – 7 AND substr(host, 1, 3) IN (‘sf1’, ‘sf3’, ‘sf7’) GROUP BY substr(host, 1, 3), trunc(date,’DAY’) Row Key Ranges HOST DATE
- 38. Step 1: Client Identify Row Key Ranges from Query Completed SELECT host, trunc(date,’DAY’), min(response_time), max(response_time) FROM server_metrics WHERE date > CURRENT_DATE() – 7 AND substr(host, 1, 3) IN (‘sf1’, ‘sf3’, ‘sf7’) GROUP BY substr(host, 1, 3), trunc(date,’DAY’) Row Key Ranges HOST DATE
- 39. Step 1: Client Identify Row Key Ranges from Query Completed SELECT host, trunc(date,’DAY’), min(response_time), max(response_time) FROM server_metrics WHERE date > CURRENT_DATE() – 7 AND substr(host, 1, 3) IN (‘sf1’, ‘sf3’, ‘sf7’) GROUP BY substr(host, 1, 3), trunc(date,’DAY’) Row Key Ranges HOST DATE sf1
- 40. Step 1: Client Identify Row Key Ranges from Query Completed SELECT host, trunc(date,’DAY’), min(response_time), max(response_time) FROM server_metrics WHERE date > CURRENT_DATE() – 7 AND substr(host, 1, 3) IN (‘sf1’, ‘sf3’, ‘sf7’) GROUP BY substr(host, 1, 3), trunc(date,’DAY’) Row Key Ranges HOST DATE sf1 sf3
- 41. Step 1: Client Identify Row Key Ranges from Query Completed SELECT host, trunc(date,’DAY’), min(response_time), max(response_time) FROM server_metrics WHERE date > CURRENT_DATE() – 7 AND substr(host, 1, 3) IN (‘sf1’, ‘sf3’, ‘sf7’) GROUP BY substr(host, 1, 3), trunc(date,’DAY’) Row Key Ranges HOST DATE sf1 sf3 sf7
- 42. Step 1: Client Identify Row Key Ranges from Query Completed SELECT host, trunc(date,’DAY’), min(response_time), max(response_time) FROM server_metrics WHERE date >CURRENT_DATE() – 7 AND substr(host, 1, 3) IN (‘sf1’, ‘sf3’, ‘sf7’) GROUP BY substr(host, 1, 3), trunc(date,’DAY’) Row Key Ranges HOST DATE sf1 t1 – * sf3 sf7
- 43. Step 2: Client Overlay Row Key Ranges with Regions Completed R1 R2 R3 R4 sf1 sf4 sf6 sf1 sf3 sf7
- 44. Step 3: Client Execute Parallel Scans Completed R1 R2 R3 R4 sf1 sf4 sf6 sf1 sf3 sf7 scan1 scan3 scan2
- 45. Step 4: Server Filter using Skip Scan Completed sf1.s1 t0SKIP
- 46. Step 4: Server Filter using Skip Scan Completed sf1.s1 t1INCLUDE
- 47. Step 4: Server Filter using Skip Scan Completed sf1.s2 t0 SKIP
- 48. Step 4: Server Filter using Skip Scan Completed sf1.s2 t1INCLUDE
- 49. Step 4: Server Filter using Skip Scan sf1.s3 t0SKIP
- 50. Step 4: Server Filter using Skip Scan sf1.s3 t1INCLUDE
- 51. SERVER METRICS HOST DATE sf1.s1 Jun 2 10:10:10.234 sf1.s2 Jun 3 23:05:44.975 sf1.s2 Jun 9 08:10:32.147 sf1.s3 Jun 1 11:18:28.456 sf1.s3 Jun 3 22:03:22.142 sf1.s4 Jun 1 10:29:58.950 sf1.s4 Jun 2 14:55:34.104 sf1.s4 Jun 3 12:46:19.123 sf1.s5 Jun 8 08:23:23.456 sf1.s6 Jun 1 10:31:10.234 Step 5: Server Intercept Scan in Coprocessor SERVER METRICS HOST DATE sf1 Jun 1 sf1 Jun 2 sf1 Jun 3 sf1 Jun 8 sf1 Jun 9
- 52. Step 6: Client Perform Final Merge Sort Completed R1 R2 R3 R4 scan1 scan3 scan2 SERVER METRICS HOST DATE sf1 Jun5 sf1 Jun 9 sf3 Jun 1 sf3 Jun 2 sf7 Jun 1 sf7 Jun 8
- 53. Scenario 2 Find 5 Longest GC Times Completed SELECT host, date, gc_time FROMserver_metrics WHERE date > CURRENT_DATE() – 7 AND substr(host, 1, 3) IN (‘sf1’, ‘sf3, ‘sf7’) ORDER BY gc_time DESC LIMIT 5
- 54. Scenario 2 Find 5 Longest GC Times • Same client parallelization and server skip scan filtering
- 55. Scenario 2 Find 5 Longest GC Times Completed • Same client parallelization and server skip scan filtering • Server holds 5 longest GC_TIME value for each scan R2 SERVER METRICS HOST DATE GC_TIME sf1.s1 Jun 2 10:10:10.234 22123 sf1.s1 Jun 3 23:05:44.975 19876 sf1.s1 Jun 9 08:10:32.147 11345 sf1.s2 Jun 1 11:18:28.456 10234 sf1.s2 Jun 3 22:03:22.142 10111
- 56. Scenario 2 Find 5 Longest GC Times Completed • Same client parallelization and server skip scan filtering • Server holds 5 longest GC_TIME value for each scan • Client performs final merge sort among parallel scans Scan1 SERVER METRICS HOST DATE GC_TIME sf1.s1 Jun 2 10:10:10.234 25865 sf1.s1 Jun 3 23:05:44.975 22123 sf1.s1 Jun 9 08:10:32.147 20176 sf1.s2 Jun 1 11:18:28.456 19876 sf1.s2 Jun 3 22:03:22.142 17111 Scan2 Scan3
- 57. Scenario 3 Find 5 Longest GC Times Completed CREATE INDEX gc_time_index ON server_metrics(gc_time DESC, date DESC) INCLUDE (host, response_time)
- 58. Scenario 3 Find 5 Longest GC Times Completed CREATE INDEX gc_time_index ON server_metrics (gc_time DESC, date DESC) INCLUDE (host, response_time)
- 59. Scenario 3 Find 5 Longest GC Times Completed CREATE INDEX gc_time_index ON server_metrics (gc_time DESC, date DESC) INCLUDE (host, response_time)
- 60. Scenario 3 Find 5 Longest GC Times Completed CREATE INDEX gc_time_index ON server_metrics (gc_time DESC, date DESC) INCLUDE (host, response_time) Row Key Server Metrics GC Time Index GC_TIME INTEGER DATE DATE HOST VARCHAR RESPONSE_TIME INTEGER
- 61. Scenario 3 Find 5 Longest GC Times Completed SELECT host, date, gc_time FROMserver_metrics WHERE date > CURRENT_DATE() – 7 AND substr(host, 1, 3) IN (‘sf1’, ‘sf3, ‘sf7’) ORDER BY gc_time DESC LIMIT 5
- 62. Phoenix Roadmap Completed Secondary Indexing Hash Joins Apache Drill integration Count distinct and percentile Derived tables SELECT * FROM (SELECT * FROM t) Cost-based query optimizer OLAP extensions WINDOW, PARTITION OVER, RANK Monitoring and management Transactions