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James Taylor @ JamesPlusPlus http://phoenix- hbase.blogspot.com /

James Taylor @ JamesPlusPlus http://phoenix- hbase.blogspot.com /. Phoenix. We put the SQL back in NoSQL https:// github.com/forcedotcom/phoenix. Agenda. What/why HBase ? What/why Phoenix? How does Phoenix work? Demo Roadmap Q&A. Completed. What is HBase ?.

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James Taylor @ JamesPlusPlus http://phoenix- hbase.blogspot.com /

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  1. James Taylor @JamesPlusPlus http://phoenix-hbase.blogspot.com/ Phoenix We put the SQL back in NoSQL https://github.com/forcedotcom/phoenix

  2. Agenda • What/why HBase? • What/why Phoenix? • How does Phoenix work? • Demo • Roadmap • Q&A Completed

  3. What is HBase? • Developed as part of Apache Hadoop • Runs on top of HDFS • Key/value store • Sparse • Consistent • Distributed • Multidimensional • Sorted Completed

  4. Why Use HBase? • If you have lots of data • Scales linearly • Shards automatically • If you can live without transactions • If your data changes • If you need strict consistency Completed

  5. What is Phoenix? Completed

  6. What is Phoenix? • SQL skin for HBase Completed

  7. What is Phoenix? • SQL skin for HBase • Alternate client API Completed

  8. What is Phoenix? • SQL skin for HBase • Alternate client API • Embedded JDBC driver Completed

  9. What is Phoenix? • SQL skin for HBase • Alternate client API • Embedded JDBC driver • Runs at HBase native speed Completed

  10. What is Phoenix? • SQL skin for HBase • Alternate client API • Embedded JDBC driver • Runs at HBase native speed • Compiles your SQL into native HBase calls Completed

  11. What is Phoenix? • SQL skin for HBase • Alternate client API • Embedded JDBC driver • Runs at HBase native speed • Compiles your SQL into native HBase calls • So you don’t have to! Completed

  12. Phoenix

  13. Phoenix Phoenix

  14. Phoenix Performance

  15. Why Use Phoenix? • Give folks an API they already know Completed

  16. Why Use Phoenix? • Give folks an API they already know • Reduce the amount of code needed Completed

  17. Why Use Phoenix? • Give folks an API they already know • Reduce the amount of code needed • SELECT TRUNC(date,'DAY’), AVG(cpu) • FROM web_stat • WHERE domain LIKE 'Salesforce%’ • GROUP BY TRUNC(date,'DAY’) Completed

  18. Why Use Phoenix? • Give folks an API they already know • Reduce the amount of code needed • Perform optimizations transparently Completed

  19. Why Use Phoenix? • Give folks an API they already know • Reduce the amount of code needed • Perform optimizations transparently • Aggregation • Skip Scan • Secondary indexing (soon!) Completed

  20. Why Use Phoenix? • Give folks an API they already know • Reduce the amount of code needed • Perform optimizations transparently • Leverage existing tooling Completed

  21. Why Use Phoenix? • Give folks an API they already know • Reduce the amount of code needed • Perform optimizations transparently • Leverage existing tooling • SQL client/terminal • OLAP engine Completed

  22. How Does Phoenix Work? • Overlays HBase Data Model • Versioned Schema Respository • Query Processor Completed

  23. Phoenix Data Model • Phoenix maps HBase data model to the relational world

  24. Phoenix Data Model • Phoenix maps HBase data model to the relational world

  25. Phoenix Data Model • Phoenix maps HBase data model to the relational world

  26. Phoenix Data Model • Phoenix maps HBase data model to the relational world

  27. Phoenix Data Model • Phoenix maps HBase data model to the relational world

  28. Phoenix Data Model • Phoenix maps HBase data model to the relational world

  29. Phoenix Data Model • Phoenix maps HBase data model to the relational world

  30. Phoenix Data Model • Phoenix maps HBase data model to the relational world

  31. Phoenix Data Model • Phoenix maps HBase data model to the relational world Multiple Versions

  32. Phoenix Data Model • Phoenix maps HBase data model to the relational world Phoenix Table

  33. Phoenix Data Model • Phoenix maps HBase data model to the relational world Phoenix Table Key Value Columns

  34. Phoenix Data Model • Phoenix maps HBase data model to the relational world Phoenix Table Row Key Columns Key Value Columns

  35. Phoenix Metadata • Stored in a Phoenix HBase table • SYSTEM.TABLE Completed

  36. Phoenix Metadata • Stored in a Phoenix HBase table • SYSTEM.TABLE • Updated through DDL commands • CREATE TABLE • ALTER TABLE • DROP TABLE • CREATE INDEX • DROP INDEX Completed

  37. Phoenix Metadata • Stored in a Phoenix HBase table • SYSTEM.TABLE • Updated through DDL commands • CREATE TABLE • ALTER TABLE • DROP TABLE • CREATE INDEX • DROP INDEX • Keeps older versions as schema evolves Completed

  38. Phoenix Metadata • Stored in a Phoenix HBase table • SYSTEM.TABLE • Updated through DDL commands • CREATE TABLE • ALTER TABLE • DROP TABLE • CREATE INDEX • DROP INDEX • Keeps older versions as schema evolves • Correlates timestamps between schema and data • Flashback queries use schema was in-place then Completed

  39. Phoenix Metadata • Stored in a Phoenix HBase table • SYSTEM.TABLE • Updated through DDL commands • CREATE TABLE • ALTER TABLE • DROP TABLE • CREATE INDEX • DROP INDEX • Keeps older versions as schema evolves • Correlates timestamps between schema and data • Flashback queries use schema was in-place then • Accessible via JDBC metadata APIs • java.sql.DatabaseMetaData • Through Phoenix queries! Completed

  40. Example • Over metrics data for clusters of servers with a schema like this: Row Key

  41. Example • Over metrics data for clusters of servers with a schema like this: Key Values

  42. Example • With 90 days of data that looks like this:

  43. Example • Walk through query processing for three scenarios • Chart Response Time Per Cluster

  44. Example • Walk through query processing for three scenarios • Chart Response Time Per Cluster

  45. Example • Walk through query processing for three scenarios • Chart Response Time Per Cluster • Identify 5 Longest GC Times

  46. Example • Walk through query processing for three scenarios • Chart Response Time Per Cluster • Identify 5 Longest GC Times

  47. Example • Walk through query processing for three scenarios • Chart Response Time Per Cluster • Identify 5 Longest GC Times • Identify 5 Longest GC Times again and again

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