This document contains a presentation on Cassandra and how it can be used. It discusses Cassandra's architecture based on Dynamo and BigTable, as well as how it provides availability, scalability, and performance. It covers data modeling techniques in Cassandra like column families, static vs dynamic columns, and using timestamps for time series data. Examples are provided for modeling user login data and social network activity. Anti-patterns like super columns and read-before-write are also discussed. The document concludes with information on an Ebay use case involving social signals and recommendations.

1. Cassandra
Nick Bailey
@nickmbailey
nick@datastax.com
Thursday, May 30, 13

2. ©2012 DataStax
Introduction
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3. ©2012 DataStax
Why does Cassandra Exist?
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Analytics
+
Real Time
4
Big Data
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Architecture
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Dynamo
+
BigTable
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Why do people like Cassandra?
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Availability
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Scalability
9
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Performance
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Multi Datacenter Support
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Hadoop Support
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Hadoop Support
• InputFormat
• Run tasktrackers/datanodes locally
• Run namenode/jobtracker anywhere
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Data Locality
Workload Partitioning
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Data Modeling
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Keyspace,
Column Families
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Database,
Tables
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Column Family =
Row Key + Columns (name, value)
...
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Static Column Families
Dynamic Column Families
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Static - Users Column Family
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Row Key
g_m_bluth
password:
banana stand
name: George
Michael
tobias_f
password:
c_weathers
name:Tobias phone: 512-7777
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24. ©2012 DataStax
Dynamic - Friend Column Family
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Row Key
g_m_bluth <date>:ann_v <date>:maeby
tobias_f <date>:barry_z <date>:carl_w <date>:lindsay ...
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Time Series Data
• Event logs
• Metrics
• Sensor Data
• Etc
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Time Series - Login CF
26
Row Key
g_m_bluth
1369633061:
United States
1369625839:
Mexico
...
tobias_f
1369932413:
Canada
1369681738:
United States
...
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What Else?
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Counter Columns
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• Inc/Dec operations
• Not idempotent
• Possibility for over counting
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Expiring Columns
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• TTL - Time to live
• Set per column
• Possibly an anti-pattern (we’ll get to that later)
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Secondary Indexes
30
• Select * from Users where name=Nick;
• Only support ‘=’ clauses (for first condition)
• Often misused
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CQL
Cassandra Query Language
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CREATE COLUMNFAMILY songs (
id uuid PRIMARY KEY,
title text,
album text,
artist text,
data blob);
INSERT INTO songs (id, title, artist, album)
VALUES ('a3e64f8f...', 'La Grange', 'ZZ Top', 'Tres Hombres');
SELECT * FROM songs;
id | album | artist | title
-------------+--------------+----------------+----------------
2b09185b... | Roll Away | Back Door Slam | Outside Woman...
8a172618... | We Must Obey | Fu Manchu | Moving in Ste...
a3e64f8f... | Tres Hombres | ZZ Top | La Grange
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How do I start?
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Define your questions
34
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SELECT time, location FROM
logins WHERE user =
‘nickmbailey’ ORDER BY time
DESC LIMIT 10;
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WHERE user = ‘nickmbailey’
Row Key
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ORDER BY time DESC LIMIT
10;
Store columns in chronological
order
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CREATE COLUMN FAMILY logins (
! user,
time,
location,
PRIMARY KEY (user, time));
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What about?
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SELECT time FROM logins
WHERE user = ‘nickmbailey’
and location = ‘United States’;
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g_m_bluth
1369633061:
United States
1369625839:
Mexico
....
1369622839:
Canada
1369422839:
Canada
1368422839:
Canada
....
1368421839:
Canada
1367421839:
United States
1367411839:
Mexico
....
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42. ©2012 DataStax
CREATE COLUMN FAMILY
logins (user, time, location,
PRIMARY KEY (user, location));
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g_m_bluth
United States:
1369633061
Canada:
1369622839
....
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To Normalize or Not
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SELECT time, location FROM.....
+
SELECT city, state, zip.... FROM
locations.....
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g_m_bluth
1369633061:
<United States,
Austin,
Texas,
78701>
1369625839:
<Mexico,
Tiajuana,
88191>
1358633061:
<United
States,Austin,
Texas,
78701>
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Anti Patterns
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Batched Writes
• Failure case is suboptimal
• Increased chance of failure
• Tune to your workload
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BOP/OPP
• You don’t really need it
• Your Ops Team will hate you
• Really, you don’t need it.
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Super Columns
• Performance penalty
• Speed
• Memory
• Replaced by CQL3
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Read Before Write
• Race conditions
• Hurts performance
• Cache
• IO
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Queues
• More generally, many deletes within a row
• A delete in Cassandra is actually a tombstone
• Read 1000 tombstones in order to find 10
columns
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Use Cases
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Ebay
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http://www.youtube.com/
watch?v=F-fYqPu2ciQ
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Ebay
• dozens of nodes
• 200 TB+ of storage
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Ebay
• Social Signals
• Hunch Taste Graph
• Various Time Series
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Social Signals
• Like, Own, Want
• Need:
• scalable counters
• high performance writes
• want to find most popular items in a given
category
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Social Signals
59
Row Key
item_id_1 like: 300 own:104 want:105
item_id_2 ... ... ...
ItemCount
Row Key
user_id_1 like: 50 own:10 want:75
user_id_2 ... ... ...
UserCount
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60. ©2012 DataStax
Social Signals
60
Row Key
item_id_1 user_id_1:<time> user_id_2:<time> ...
item_id_2 ... ... ...
ItemLike
Row Key
user_id_1 <time>: <item_id> <time>: <item_id> ...
user_id_2 ... ... ...
UserLike
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61. ©2012 DataStax
Social Signals - Possibilities
• Store aggregated counts per category
• Column names are counts
• Get top N items in a category
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62. Questions?
Thursday, May 30, 13

63. Come to the Summit!
Ask me for a discount code
June 11-12, 2013
San Francisco, CA
http://www.datastax.com/company/news-and-events/events/
cassandrasummit2013
Thursday, May 30, 13