Koverse, Inc. CTO Aaron Cordova's (@aaroncordova) introduction to Apache Accumulo, delivered at Strata Conference Santa Clara, February 2014.

1. APACHE ACCUMULO
From a design perspective

2. SCALABLE KEY-VALUE STORE
BASED ON GOOGLE'S
BIGTABLE

3. BIGTABLE FEATURES
• Distributes data across many commodity servers
• Sorts data by key for fast lookup of values by key
• Scan across multiple key value pairs
• Highly consistent writes to single row
• Support for MapReduce jobs

4. DATA MODEL
Key
Value
Row ID
Column
Timestamp
Family Qualifier

5. Row ID Col Fam Col Qual Timestamp Value
Bob Email id0023 20120301
Hey joe, can
you send ...
Bob Email id0024 20120302
Re: next
Thursday ...
Bob UserPrefs Background 20130101 Grey
Fred Email id0001 20080302
Welcome to
gmail ...
Sarah Email id0004 20130201 Hi again ...
Sara Videos ytid009 20100303
nsu736:)jdudjd
k$:)378;'$$)

6. Tablet servers HDFS DataNodes
Commit Layer Replication Layer

7. SINCE 2006
• Several BigTable implementations
• Apache Hbase
• Apache Cassandra
• Apache Accumulo
• others …

8. BIGTABLE IS BIGTABLE RIGHT?

9. HBASE

10. HBASE
• Open source Apache project started by developers at
Powerset, bought by Microsoft
• Now used at Facebook, StumbleUpon, other big web sites
• Fast reads
• Row-oriented API
• Each column family has it's own set of files

11. CASSANDRA

12. CASSANDRA
• Apache project started at Facebook
• Combines elements of BigTable and Amazon's Dynamo
into one system
• Used at Netflix, other web sites
• Fast writes
• Tunable consistency

13. Tablet servers
Commit and Replication Layer

14. CONSISTENCY
• Highly consistent means: writes in one place
• Eventually consistent: writes in > one place
• Writes in > one place: network partition tolerance
• Partition tolerance: geographically distributed servers
• *Google uses Spanner to synchronize multiple dbs

15. Tablet servers
Data Center A Data Center B

16. Data Center A Data Center B
Tablet servers

17. OVERVIEW
• Both highly scalable
• Used to build web applications that can serve millions of
users at once
• Serves as a low-latency persistence layer for real time
service of requests
• Available in single data center or cross data center options

18. USE CASE
• Most data comes from users
• Schema defined by the application
• Data builds up over time

19. Many UsersDb
Web
application

20. ACCUMULO

21. ACCUMULO
• Can support the web application use-case
• But what are those other extra features for?

22. ACCUMULO ‘EXTRAS’
• Dynamic Column Families
• ColumnVisibility
• Key-value oriented API
• Iterators
• Batch Scanners

23. BIG ORGANIZATIONS
• Missions other than internet services
• Various disparate operational systems that
generate data
• Desire to look across and analyze that data
• Desire to deliver results to their own population

24. USE CASE IS DISCOVERING
AND ANALYZING ALL DATA

25. ISSUES
• Scale
• Unknown / multiple schema
• Support for analysis without data movement
• Varying levels of sensitivity in the same system
• Support a high number of low-latency user requests

26. Many Users
Analyze
Db
Data sets

27. SCALE?

28. CHECK
(IT’S BIGTABLE)

29. NO CONTROL OVER OR
MANY DIFFERENT SCHEMA?

30. MAP EXISTING FIELDSTO
COLUMNS DYNAMICALLY

31. INCLUDING COLUMN
FAMILIES

32. VARYING LEVELS OF DATA
SENSITIVITY?

33. COLUMNVISIBILITY

34. DATA MODEL
Key
Value
Row ID
Column
Time
stamp
Family Qualifier Visibility

35. Row ID Col Fam Col Qual Col Vis Timestamp Value
Bob Email id0023
personal
comms
20120301
Hey joe, can
you send ...
Bob Email id0024
personal
comms
20120302
Re: next
Thursday ...
Bob UserPrefs Background prefs 20130101 Grey
Fred Email id0001
personal
comms
20080302
Welcome to
gmail ...
Sarah Email id0004
personal
comms
20130201 Hi again ...
Sara Videos ytid009 public post 20100303
nsu736:)jdu
djdk
$:)378;'$$)

36. DATA OFVARYING
SENSITIVITY LEVELS CAN BE
PHYSICALLY CO-LOCATED

37. FRAMEWORKS LIKE HADOOP
MAP REDUCE LOVE IT WHEN
DATA IS ALLTOGETHER

38. LOOK ACROSS DATASETS?

39. SECONDARY INDICES

40. SECONDARY INDICES
• Application-created data: known
• Pre-existing data? unknown

41. DATA DISCOVERY!

42. SECONDARY INDICES
RowID
Col Qual Value
RID00001 age 54
RID00001 name bob
RID00002 name fred
RID00003 age 43
RID00003 height 5’9”
RID00003 name harry
RID00004 name carl
RID00005 name evan
RowID
Col Fam Col Qual
43 age RID00003
54 age RID00001
5’9” height RID00003
bob name RID00001
carl name RID00004
evan name RID00005
fred name RID00002
harry name RID00003

43. PARTIAL ROW SCANS

44. BATCH SCANNERS

45. RowID
Col Qual Value
RID00001 age 54
RID00001 name bob
RID00002 name fred
RID00003 age 43
RID00003 height 5’9”
RID00003 name harry
RID00004 name carl
RID00005 name evan
Batch Scanner

46. COLUMNVISIBILITY APPLIES
TO INDEXESTOO

47. ANALYSIS?

48. MAPREDUCE: CHECK

49. SHUFFLE-SORTED?
• Between Map and Reduce phases is shuffle-sort
• Sorting by key is necessary so all the values for a
given key end up next to each other …
• BigTable also sorts keys …

50. ITERATORS

51. Value combine(Iterator<Value> values)

52. PRE-COMPUTATION

53. Many Users
Analyze
Db
Data sets

54. ACCUMULO