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!

18. Phoenix Performance

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

63. Thank you!
Questions/comments?