Slides from my NoSQL Exchange 2011 talk introducing Apache Cassandra. This talk explained the fundamental concepts of Cassandra and then demonstrated how to build a simple ad-targeting application using PHP, with a focus on data modeling. Video of talk: http://skillsmatter.com/podcast/home/cassandra/js-2880

1. Cassandra’s sweet spot
Dave Gardner
@davegardnerisme

2. jobs.hailocab.com
Looking for an expert backend
Java dev – speak to me!
meetup.com/Cassandra-
London
Next event 21st November

3. Building applications with Cassandra
• Key features
• Creating an application
• Data modeling

4. Comparing Cassandra with X
“Can someone quickly explain the
differences between the two? Other than
the fact that MongoDB supports ad-hoc
querying I don't know whats different. It also
appears (using google trends) that MongoDB
seems to be growing while Cassandra is
dying off. Is this the case?”
27th July
2010http://comments.gmane.org/gmane.comp.db.cassandra.user/
7773

5. Comparing Cassandra with X
“They have approximately nothing in
common. And, no, Cassandra is
definitely not dying off.”
28th July 2010
http://comments.gmane.org/gmane.comp.db.cassandra.user/7773

6. Top Tip #1
To use a NoSQL solution effectively, we
need to identify it's sweet spot.

7. Top Tip #1
To use a NoSQL solution effectively, we
need to identify it's sweet spot.
This means learning about each solution;
how is it designed? what algorithms
does it use?
http://www.alberton.info/nosql_databases_what_when_why_phpuk2
011.html

8. Comparing Cassandra with X
“they say … I can’t decide between this project
and this project even though they look nothing
like each other. And the fact that you can’t
decide indicates that you don’t actually have a
problem that requires them.”
Benjamin Black – NoSQL Tapes (at 30:15)
http://nosqltapes.com/video/benjamin-black-on-nosql-cloud-computing-and-
fast_ip

9. Headline features
1. Elastic
Read and write throughput increases
linearly as new machines are added
http://cassandra.apache.org/

10. Headline features
2. Decentralised
Fault tolerant with no single point of
failure; no “master” node
http://cassandra.apache.org/

11. The dynamo paper
• Consistent hashing
• Vector clocks
• Gossip protocol
• Hinted handoff
• Read repair
http://www.allthingsdistributed.com/files/amazon-dynamo-
sosp2007.pdf

12. The dynamo paper
#
1 RF = 3
# #
6 2
Coordinator
# #
5 3
Client
#
4

13. Headline features
3. Rich data model
Column based, range slices, column
slices, secondary
indexes, counters, expiring columns
http://cassandra.apache.org/

14. The big table paper
• Sparse "columnar" data model
• SSTable disk storage
• Append-only commit log
• Memtable (buffer and sort)
• Immutable SSTable files
• Compaction
http://labs.google.com/papers/bigtable-osdi06.pdf
http://www.slideshare.net/geminimobile/bigtable-4820829

15. The big table paper
Column Family
Name Name Name
Row Key
Value Value Value
Column Column Column

16. Headline features
4. You're in control
Tunable consistency, per operation
http://cassandra.apache.org/

17. Consistency levels
How many replicas must respond to
declare success?

18. Consistency levels: write operations
Level Description
ANY One node, including hinted handoff
ONE One node
QUORUM N/2 + 1 replicas
LOCAL_QUORUM N/2 + 1 replicas in local data centre
EACH_QUORUM N/2 + 1 replicas in each data centre
ALL All replicas
http://wiki.apache.org/cassandra/API#Write

19. Consistency levels: read operations
Level Description
ONE 1st Response
QUORUM N/2 + 1 replicas
LOCAL_QUORUM N/2 + 1 replicas in local data centre
EACH_QUORUM N/2 + 1 replicas in each data centre
ALL All replicas
http://wiki.apache.org/cassandra/API#Read

20. Headline features
5. Performant
Well known for high write performance
http://www.datastax.com/docs/1.0/introduction/index#core-
strengths-of-cassandra

21. Benchmark*
http://blog.cubrid.org/dev-
platform/nosql-benchmarking/
* Add pinch of salt

22. Recap: headline features
1. Elastic
2. Decentralised
3. Rich data model
4. You’re in control (tunable consistency)
5. Performant

23. A simple ad-targeting application
Some ads
Choose which
ad to show
Our user knowledge

24. A simple ad-targeting application
Allow us to capture user behaviour/data
via “pixels” - placing users into segments
(different buckets)
http://pixel.wehaveyourkidneys.com/add.php?add=foo

25. A simple ad-targeting application
Record clicks and impressions of each
ad; storing data per-ad and per-segment
http://pixel.wehaveyourkidneys.com/adImpression.php?ad=1
http://pixel.wehaveyourkidneys.com/adClick.php?ad=1

26. A simple ad-targeting application
Real-time ad performance
analytics, broken down by segment
(which segments are performing well?)
http://www.wehaveyourkidneys.com/adPerformance.php?ad=1

27. A simple ad-targeting application
Recommendations based on best-
performing ads
(this is left as an exercise for the reader)

28. Additional requirements
• Large number of users
• High volume of impressions
• Highly available – downtime is money

29. A good fit for Cassandra?
Yes!
Big data, high availability and lots of
writes are all good signs that Cassandra
will fit well.
http://www.nosqldatabases.com/main/2010/10/19/what-is-
cassandra-good-for.html

30. A good fit for Cassandra?
Although there are many things that
people are using Cassandra for.
Highly available HTTP request routing
(tiny data!)
http://blip.tv/datastax/highly-available-http-request-routing-dns-
using-cassandra-5501901

31. Top Tip #2
Cassandra is an excellent fit where
availability matters, where there is a lot
of data or where you have a large
number of write operations.

32. Demo
Live demo before we start

33. Data modeling
Start from your queries, work backwards
http://www.slideshare.net/mattdennis/cassandra-data-modeling
http://blip.tv/datastax/data-modeling-workshop-5496906

34. Data model basics: conflict resolution
Per-column timestamp-based conflict
resolution
{ {
column: foo, column: foo,
value: bar, value: zing,
timestamp: 1000 timestamp: 1001
} }
http://cassandra.apache.org/

35. Data model basics: conflict resolution
Per-column timestamp-based conflict
resolution
{ {
column: foo, column: foo,
value: bar, value: zing,
timestamp: 1000 timestamp: 1001
} }
http://cassandra.apache.org/

36. Data model basics: column ordering
Columns ordered at time of
writing, according to Column Family
schema
{ {
column: zebra, column: badger,
value: foo, value: foo,
timestamp: 1000 timestamp: 1001
} }
http://cassandra.apache.org/

37. Data model basics: column ordering
Columns ordered at time of
writing, according to Column Family
schema
{
badger: foo, with AsciiType column
zebra: foo schema
}
http://cassandra.apache.org/

38. Data modeling: user segments
Add user to bucket X, with expiry time Y
Which buckets is user X in?
["user"][<uuid>][<bucketId>] = 1
[CF] [rowKey] [columnName] = value

39. Data modeling: user segments
user Column Family:
[f97be9cc-5255-4578-8813-76701c0945bd][bar] = 1
[f97be9cc-5255-4578-8813-76701c0945bd][foo] = 1
[06a6f1b0-fcf2-41d9-8949-fe2d416bde8e][baz] = 1
[06a6f1b0-fcf2-41d9-8949-fe2d416bde8e][zoo] = 1
[503778bc-246f-4041-ac5a-fd944176b26d][aaa] = 1
Q: Is user in segment X?
A: Single column fetch

40. Data modeling: user segments
user Column Family:
[f97be9cc-5255-4578-8813-76701c0945bd][bar] = 1
[f97be9cc-5255-4578-8813-76701c0945bd][foo] = 1
[06a6f1b0-fcf2-41d9-8949-fe2d416bde8e][baz] = 1
[06a6f1b0-fcf2-41d9-8949-fe2d416bde8e][zoo] = 1
[503778bc-246f-4041-ac5a-fd944176b26d][aaa] = 1
Q: Which segments is user X in?
A: Column slice fetch

41. Top Tip #3
With column slices, we get the columns
back ordered, according to our schema
We cannot do the same for rows
however, unless we use the Order
Preserving Partitioner

42. Top Tip #4
Don’t use the Order Preserving
Partitioner unless you absolutely have to
http://ria101.wordpress.com/2010/02/22/cassandra-
randompartitioner-vs-orderpreservingpartitioner/

43. Data modeling: user segments
Add user to bucket X, with expiry time Y
Which buckets is user X in?
["user"][<uuid>][<bucketId>] = 1
[CF] [rowKey] [columnName] = value

44. Expiring columns
An expiring column will be automatically
deleted after n seconds
http://cassandra.apache.org/

45. Data modeling: user segments
$pool = new ConnectionPool(
'whyk', array('localhost')
);
$users = new ColumnFamily($pool, 'users');
$users->insert(
$userUuid,
array($segment => 1),
NULL, // default TS
$expires
);
Using phpcassa client: https://github.com/thobbs/phpcassa

46. Data modeling: user segments
UPDATE users
USING TTL = 3600
SET 'foo' = 1
WHERE KEY =
'f97be9cc-5255-4578-8813-76701c0945bd'
Using CQL
http://www.datastax.com/dev/blog/what%E2%80%99s-new-in-
cassandra-0-8-part-1-cql-the-cassandra-query-language
http://www.datastax.com/docs/1.0/references/cql

47. Top Tip #5
Try to exploit Cassandra’s columnar data
model; avoid read-before write and
locking by safely mutating individual
columns

48. Data modeling: ad performance
Track overall ad performance; how many
clicks/impressions per ad?
["ads"][<adId>][<stamp>]["click"] = #
["ads"][<adId>][<stamp>]["impression"] = #
[CF] [Row] [S.Col] [Col] = value
Using super columns

49. Top Tip #6
Friends don’t let friends use Super
Columns.
http://rubyscale.com/2010/beware-the-supercolumn-its-a-trap-for-
the-unwary/

50. Data modeling: ad performance
Try again using regular columns:
["ads"][<adId>][<stamp>-"click"] = #
["ads"][<adId>][<stamp>-"impression"] = #
[CF] [Row] [Col] = value

51. Data modeling: ad performance
ads Column Family:
[1][2011103015-click] = 1
[1][2011103015-impression] = 3434
[1][2011103016-click] = 12
[1][2011103016-impression] = 5411
[1][2011103017-click] = 2
[1][2011103017-impression] = 345
Q: Get performance of ad X between two date/times
A: Column slice against single row specifying a start
stamp and end stamp + 1

52. Think carefully about your data
This scheme works because I’m
assuming each ad has a relatively short
lifespan. This means that there are lots
of rows and hence the load is spread.
Other options:
http://rubyscale.com/2011/basic-time-series-with-cassandra/

53. Counters
• Distributed atomic counters
• Easy to use
• Not idempotent
http://www.datastax.com/dev/blog/whats-new-in-cassandra-0-8-part-
2-counters

54. Data modeling: ad performance
$stamp = date('YmdH');
$ads->add(
$adId, // row key
"$stamp-impression", // column
1 // increment
);
We’ll store performance metrics in hour buckets for graphing.

55. Data modeling: ad performance
UPDATE ads
SET '2011103015-impression'
= '2011103015-impression' + 1
WHERE KEY = '1’

56. Data modeling: performance/segment
We can add in another dimension to our
stats so we can breakdown by segment.
["ads"][<adId>]
[<stamp>-<segment>-"click"] = #
[CF] [Row]
[Col] = value

57. Data modeling: performance/segment
ads Column Family:
[1][2011103015-bar-click] = 1
[1][2011103015-bar-impression] = 3434
[1][2011103015-foo-click] = 12
[1][2011103015-foo-impression] = 5411
[1][2011103016-bar-click] = 2
Q: Get performance of ad X between two date/times,
split by segment
A: Column slice against single row specifying a start
stamp and end stamp + 1

58. Data modeling: performance/segment
$stamp = date('YmdH');
$ads->add(
"$adId-segments", // row key
"$stamp-$segment-impression", // column
1 // incr
);
We’ll store performance metrics in hour buckets for graphing.

59. Data modeling: segment stats
Track overall clicks/impressions per
bucket; which buckets are most clicky?
["segments"][<adId>-"segments"]
[<stamp>-<segment>-"click"] = #
[CF] [Row]
[Col] = value

60. Recap: Data modeling
• Think about the queries, work
backwards
• Don’t overuse single rows; try to
spread the load
• Don’t use super columns
• Ask on IRC! #cassandra

61. Recap: Common data modeling patterns
1. Using column names with no value
[cf][rowKey][columnName] = 1

62. Recap: Common data modeling patterns
2. Counters
[cf][rowKey][columnName]++

63. And also…
3. Serialising a whole object
[cf][rowKey][columnName] = {
foo: 3,
bar: 11
}

64. There’s more: Brisk
Integrated Hadoop distribution (without
HDFS installed). Run Hive and Pig queries
directly against Cassandra
DataStax now offer this functionality in
their “Enterprise” product
http://www.datastax.com/products/enterprise

65. Hive
CREATE EXTERNAL TABLE tempUsers
(userUuid string, segmentId string, value string)
STORED BY
'org.apache.hadoop.hive.cassandra.CassandraStorageHandler'
WITH SERDEPROPERTIES (
"cassandra.columns.mapping" = ":key,:column,:value",
"cassandra.cf.name" = "users"
);
SELECT segmentId, count(1) AS total
FROM tempUsers
GROUP BY segmentId
ORDER BY total DESC;

66. There’s more: Supercharged Cassandra
Acunu have reengineered the entire Unix
storage stack, optimised specifically for
Big Data workloads
Includes instant snapshot of CFs
http://www.acunu.com/products/choosing-cassandra/

67. In conclusion
Cassandra is founded on sound design
principles

68. In conclusion
The Cassandra data model, sometimes
mentioned as a weakness, is incredibly
powerful

69. In conclusion
The clients are getting better; CQL is a
step forward

70. In conclusion
Hadoop integration means we can
analyse data directly from a Cassandra
cluster

71. In conclusion
Cassandra’s sweet spot is highly
available “big data” (especially time-
series) with large numbers of writes

72. Thanks
Learn more about Cassandra
meetup.com/Cassandra-London
Checkout the code https://github.com/davegardnerisme/we-have-
your-kidneys
Watch videos from Cassandra SF 2011
http://www.datastax.com/events/cassandrasf2011/presentations