1.
Introduction to Apache Accumulo
Boulder/Denver BigData Meetup - March 21,2012
Jared Winick
@jaredwinick

2.
Accumulo /əˈkjuˈmj ʊ/
ʊˈlo
1. Sorted, distributed key/value store with
cell-based access control and
customizable server-side processing

3.
http://yourmotivational.com/uploads/8604.jpg

4.
Annotation Added
Jeff Dean: Designs, Lessons and Advice from Building Large Distributed Systems
http://www.cs.cornell.edu/projects/ladis2009/talks/dean-keynote-ladis2009.pdf

5.
Enables interactive access to…
Trillions of records
petabytes of indexed data
across 100s-1000s of servers

6.
Short Accumulo History Lesson
http://www.flickr.com/photos/mr_t_in_dc/4249886990/sizes/l/in/photostream/

7.
2006

8.
2008
http://upload.wikimedia.org/wikipedia/commons/8/84/National_Security_Agency_headquarters%2C_Fort_Meade%2C_Maryland.jpg

9.
2011

10.
2012

11.
Uses of BigTable and Kin
(BigTable) (HBase)
•Google Analytics1
•Messages3,4,6
•Crawl1
•Insights5,6
•AppEngine Datastore2
•Many more1
(Cassandra) (Accumulo)
•Rainbird (realtime analytics)7
•???
1.) http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/bigtable-osdi06.pdf
2.) http://code.google.com/appengine/articles/storage_breakdown.html
3.) http://www.facebook.com/note.php?note_id=454991608919
4.) http://mvdirona.com/jrh/TalksAndPapers/KannanMuthukkaruppan_StorageInfraBehindMessages.pdf
5.) http://www.facebook.com/note.php?note_id=10150103900258920
6.) http://borthakur.com/ftp/SIGMODRealtimeHadoopPresentation.pdf
7.) http://www.slideshare.net/kevinweil/rainbird-realtime-analytics-at-twitter-strata-2011

12.
Accumulo /əˈkjuˈmj ʊ/
ʊˈlo
1. Sorted, distributed key/value store with
cell-based access control and
customizable server-side processing

13.
Multi-dimension Key
Key
Column Value
Row ID Timestamp
Family Qualifier Visibility
http://incubator.apache.org/accumulo/user_manual_1.4-incubating/Accumulo_Design.html

14.
Keys Sorted Lexicographically
Row ID, Column Family, Column Qualifier, Column Visibility, Timestamp
Everything is a byte[] except the Timestamp which is a long

15.
Physical Layout
Key Value
Row ID Col Fam Col Qual Col Vis Time Value
Alice properties age public March 2011 31
Alice properties phone private Feb 2011 555-1234
Alice purchases Xbox public Feb 2011 $299
Bob properties phone private March 2011 555-4321
Bob purchases iPhone Public Feb 2011 $399

16.
Queries
•By exact Key or range of Keys
•Data is always returned in sorted order
Query Requirements Drive
Data Model Design

17.
http://incubator.apache.org/accumulo/user_manual_1.4-incubating/Accumulo_Design.html

18.
Hadoop
Clients
MapReduce
Read/
Analytics
Write
Accumulo
Configuration/
Storage
State
Hadoop HDFS Zookeeper

19.
Table
Tablets
Accumulo
…
Tablet
Server
… …
Tablet
Server
…
... …
Tablet
Server
…
Master
Data
Node
Data
Node
... Data
Node
Name
Node
Hadoop HDFS

20.
Table Tablet Server Failure
Tablets
1.) Detect Failure
Accumulo
Tablet
Server
Tablet
Server ... Tablet
Server
Master
2.) Reassign
Data
Node
Data
Node
... Data
Node
Name
Node
Hadoop HDFS

21.
Writes
Write-
Ahead Accumulo
Log (WAL) Tablet Server
1 Tablet
2
MemTable
Client
Data
Node
... Data
Node
Data
Node
Hadoop HDFS

22.
Writes
Write-
Ahead Accumulo
Log (WAL) Tablet Server
1 Tablet
2
MemTable
Client
3
File 1
Data
Node
... Data
Node
Data
Node
Hadoop HDFS

23.
Compactions
Minor Major
The process of flushing The process of
a MemTable of a Tablet combining multiple files
to a single file in HDFS into a single file

24.
Tablet Splits
• Tablets are split when they reach a max size
• Always split on row boundary
• Master assigns a split Tablet to another Tablet
server (no data is moved!)

25.
Reads
Accumulo
Tablet Server
Tablet
MemTable
Client
File 1 File 1

26.
Accumulo /əˈkjuˈmj ʊ/
ʊˈlo
1. Sorted, distributed key/value store with
cell-based access control and
customizable server-side processing

27.
Iterators: Server-side programming
http://wiki.eeng.dcu.ie/ee557/287-EE/version/default/part/ImageData/data/server-side_intro.gif

28.
Iterators
Can be run at: Can do things like:
•Scan Time •Aggregation (Combiners)
•Minor Compaction •Age-Off
•Major Compaction •Filtering (access control)
•Transformation
Push Processing to the Data

29.
Accumulo /əˈkjuˈmj ʊ/
ʊˈlo
1. Sorted, distributed key/value store with
cell-based access control and
customizable server-side processing

30.
Access Control
• Every key-value has a visibility label
• Label is defined with boolean operators
• Label is arbitrary and ad-hoc
Public Private | Admin Finance | (HR & Manager)
• Authorizations presented at scan time
• Data is filtered out automatically by system-
level Iterator

31.
Access Control – Typical Architecture
Trusted Zone
6.) Return Data 5.) Return Visible Data
Web Server Accumulo
1.) Pass Credentials 4.) Proxy Authorization
3.) Return
Authorizations
2.) Lookup
User Enterprise
Identity
Management

32.
Access Control – Typical Architecture
Trusted Zone
Accumulo
6.) Return [6,8] 5.) Return [6,8] SECRET&PROJECT X, 6
Web Server SECRET&PROJECT Y, 8
1.) PKI Cert 4.) Proxy Bob’s Auths SECRET&PROJECT Z, 3
Bob
3.) Auths:[SECRET, UNCLASSIFIED,
2.) Lookup PROJECT X, PROJECT Y]
Bob Enterprise
Identity
Management

33.
Demo

34.
Application Requirements
Build an application to analyze trends in Twitter
messages.
•Query for word/phrase and view real-time activity
in a time series graph
•View at different time ranges (1 day, 7 days, 30
days, etc)
•Allow multiple query terms to compare activity (ex.
Breakfast,Lunch)
•Automatically extract daily trends for the user

35.
Demo Setup/Data
• Twitter Streaming API
• US country codes only messages
• 1,2,3-grams built
• Data since Dec 24 – Live
• Running on average workstation, 1 SATA disk,
6 GB memory.
• 72GB, 2.6 billion entries and counting

36.
Data Model
• Tweets table
– Row ID: n-gram
– Column Family: Date Granularity (DAY, HOUR)
– Column Qual: Date Value
– Value: Count
– SummingCombiner (Iterator) used to update Count
Row ID Col Fam Col Qual Value
breakfast DAY 20120318 31
breakfast DAY 20120319 56
… … … …
lunch HOUR 2012031801 3
lunch HOUR 2012031802 4

37.
Data Model
• Trends table
– Row ID: (Date Granularity + Date Value)
– Column Family: (Integer.MAX_VALUE –
trendScore)
– Column Qual: n-gram
– Value: []
Row ID Col Fam Col Qual Value
DAY:20120318 2147483145 church
DAY:20120318 2147483316 hangover
… … … …
DAY:20120319 2147476521 the broncos
DAY:20120319 2147477704 tim tebow

38.
MapReduce Analytics
• Utilize MapReduce for building trends
• AccumuloInputFormat reads from tweets
table
• AccumuloOutputFormat writes to trends
table
• AccumuloStorage LoadFunc for Pig
available on github

39.
Summary
•Accumulo exploits locality to enable
interactive access to huge data sets while
adding cell-level access control and server-
side programming
•Nothing in life is free. Accumulo comes with
the complexity and responsibility of
managing a distributed system and designing
indexes on your data

40.
References
• Documentation, Mailing Lists, Links
http://incubator.apache.org/accumulo/
• HBase Shootout
http://www.slideshare.net/cloudera/h-base-and-accumulo-todd-lipcom-jan-25-2012
• Trendulo
https://github.com/jaredwinick/trendulo