Learning Cassandra

Learning Cassandra

Dave Gardner@davegardnerisme

What I’m going to cover

• How to NoSQL• Cassandra basics (dynamo and

big table)• How to use the data model in

real life

How to NoSQL

1. Find data store that doesn’t use SQL2. Anything3. Cram all the things into it4. Triumphantly blog this success5. Complain a month later when it

bursts into flames

http://www.slideshare.net/rbranson/how-do-i-cassandra/4

Choosing NoSQL

“NoSQL DBs trade off traditional features to better support new and emerging use cases”

http://www.slideshare.net/argv0/riak-use-cases-dissecting-the-sol

utions-to-hard-

problems

Choosing Cassandra: Tradeoffs

More widely used, tested and documented softwareMySQL first OS release 1998

For a relatively immature productCassandra first open-sourced in 2008

Choosing Cassandra: Tradeoffs

Ad-hoc queryingSQL join, group by, having, order

For a rich data model with limited ad-hoc querying abilityCassandra makes you denormalise

Choosing NoSQL

“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

What do we get in return?

Proven horizontal scalability

Cassandra scales reads and writes linearly as new nodes are added

Netflix benchmark: linear scaling

http://techblog.netflix.com/2011/11/benchmarking-cassandra-scalability-on.html

What do we get in return?

High availability

Cassandra is fault-resistant with tunable consistency levels

What do we get in return?

Low latency, solid performance

Cassandra has very good write performance

http://blog.cubrid.org/dev-platform/nosql-benchmarking/

* Add pinch of salt

Performance benchmark *

What do we get in return?

Operational simplicity

Homogenous cluster, no “master” node, no SPOF

What do we get in return?

Rich data model

Cassandra is more than simple key-value – columns, composites, counters, secondary indexes

How to NoSQL version 2

Learn about each solution

• What tradeoffs are you making?• How is it designed?• What algorithms does it use?

http://www.alberton.info/nosql_databases_what_when_why_phpuk2011.html

Amazon Dynamo + Google Big Table

Consistent hashingVector clocks *Gossip protocolHinted handoffRead repair

http://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf

ColumnarSSTable storage

Append-onlyMemtable

Compaction

http://labs.google.com/papers/bigtable-osdi06.pdf

* not in Cassandra

The dynamo paper

#1

#4

#6

#2

#3

Client

#5

tokens are integers from0 to 2127

The dynamo paper

#1

#4

#6

#2

#3

Client

#5

Coordinator

consistent hashing

Consistency levels

How many replicas must respond to declare success?

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

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

The dynamo paper

RF = 3CL = One

#1

#4

#6

#2

#3

Client

#5

Coordinator

The dynamo paper

RF = 3CL = Quorum

#1

#4

#6

#2

#3

Client

#5

Coordinator

The dynamo paper

RF = 3CL = One

#1

#4

#6

#2

#3

Client

#5

Coordinator

+ hint

The dynamo paper

RF = 3CL = One

#1

#4

#6

#2

#3

Client

#5

Coordinator

Read repair

The big table paper

• Sparse "columnar" data model• SSTable disk storage• Append-only commit log• Memtable (buffer and sort)• Immutable SSTable files• Compactionhttp://labs.google.com/papers/bigtable-osdi06.pdfhttp://www.slideshare.net/geminimobile/bigtable-4820829

The big table paper

Name

Value

Column

+ timestamp

The big table paper

Name

Value

Column

Name

Value

Column

Name

Value

Column

we can have millions of columns

*

* theoretically up to 2 billion

The big table paper

Name

Value

Column

Name

Value

Column

Name

Value

Column

Row Key

Row

The big table paper

Column Family

ColumnRow Key Column Column

ColumnRow Key Column Column

ColumnRow Key Column Column

we can have billions of rows

The big table paper

Write Memtable

SSTable

SSTable

SSTable

SSTable

Commit Log

Memory

Disk

Flushed on time/size trigger

Immutable

Data model basics: conflict resolution

Per-column timestamp-based conflict resolution

http://cassandra.apache.org/

{ column: foo, value: bar, timestamp: 1000}

{ column: foo, value: zing, timestamp: 1001}

Data model basics: conflict resolution

Per-column timestamp-based conflict resolution

http://cassandra.apache.org/

{ column: foo, value: bar, timestamp: 1000}

{ column: foo, value: zing, timestamp: 1001}

bigger timestamp

Data model basics: column ordering

Columns ordered at time of writing, according to Column Family schema

http://cassandra.apache.org/

{ column: zebra, value: foo, timestamp: 1000}

{ column: badger, value: foo, timestamp: 1001}

Data model basics: column ordering

Columns ordered at time of writing, according to Column Family schema

http://cassandra.apache.org/

{ badger: foo, zebra: foo}

with AsciiType column schema

Key point

Each “query” can be answered from a single slice of disk

(once compaction has finished)

Data modeling – 1000ft introduction

• Start from your queries and work backwards

• Denormalise in the application(store data more than once)

http://www.slideshare.net/mattdennis/cassandra-data-modelinghttp://blip.tv/datastax/data-modeling-workshop-5496906

Pattern 1: not using the value

Storing that user X is in bucket Y

Row key: f97be9cc-5255-457…Column name: fooValue: 1

https://github.com/davegardnerisme/we-have-your-kidneys/blob/master/www/add.php#L53-58

we don’t really care about this

Pattern 1: not using the value

Q: is user X in bucket foo?f97be9cc-5255-4578-8813-76701c0945bd

bar: 1foo: 1

06a6f1b0-fcf2-41d9-8949-fe2d416bde8ebaz: 1zoo: 1

503778bc-246f-4041-ac5a-fd944176b26daaa: 1

A: single column fetch

Pattern 1: not using the value

Q: which buckets is user X in?f97be9cc-5255-4578-8813-76701c0945bd

bar: 1foo: 1

06a6f1b0-fcf2-41d9-8949-fe2d416bde8ebaz: 1zoo: 1

503778bc-246f-4041-ac5a-fd944176b26daaa: 1

A: column slice fetch

Pattern 1: not using the value

We could also use expiring columns to automatically delete columns N seconds after insertion

UPDATE users USING TTL = 3600SET 'foo' = 1WHERE KEY = 'f97be9cc-5255-4578-8813-76701c0945bd'

Pattern 2: counters

Real-time analytics to count clicks/impressions of ads in hourly buckets

Row key: 1Column name: 2011103015-clickValue: 34

https://github.com/davegardnerisme/we-have-your-kidneys/blob/master/www/adClick.php

Pattern 2: counters

Increment by 1 using CQL

UPDATE adsSET '2011103015-impression' = '2011103015-impression' + 1WHERE KEY = '1’

Pattern 2: counters

Q: how many clicks/impressions for ad 1 over time range?1

2011103015-click: 12011103015-impression: 34342011103016-click: 122011103016-impression: 54112011103017-click: 22011103017-impression: 345

A: column slice fetch, between column X and Y

Pattern 3: time series

Store canonical reference of impressions and clicks

Row key: 20111030Column name: <time UUID>Value: {json}

http://rubyscale.com/2011/basic-time-series-with-cassandra/

Cassandra can order columns by time

Pattern 4: object properties as columns

Store user properties such as name, email, etc.

Row key: f97be9cc-5255-457…Column name: nameValue: Bob Foo-Bar

http://www.wehaveyourkidneys.com/adPerformance.php?ad=1

Anti-pattern 1: read-before-write

Instead store as independent columns and mutate individually

(see pattern 4)

Anti-pattern 2: super columns

Friends don’t let friends use super columns.

http://rubyscale.com/2010/beware-the-supercolumn-its-a-trap-for-the-unwary/

Anti-pattern 3: OPP

The Order Preserving Partitioner unbalances your load and makes your life harder

http://ria101.wordpress.com/2010/02/22/cassandra-randompartitioner-vs-orderpreservingpartitioner/

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

There’s more: Brisk

Integrated Hadoop distribution (without HDFS installed). Run Hive and Pig queries directly against Cassandra

DataStax offer this functionality in their “Enterprise” product

http://www.datastax.com/products/enterprise

Hive: SQL-like interface to Hadoop

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 totalFROM tempUsersGROUP BY segmentIdORDER BY total DESC;

In conclusion

Cassandra is founded on sound design principles

In conclusion

The data model is incredibly powerful

In conclusion

CQL and a new breed of clients are making it easier to use

In conclusion

Hadoop integration means we can analyse data directly from a Cassandra cluster

In conclusion

There is a strong community and multiple companies offering professional support

Thanks

Learn more about Cassandrameetup.com/Cassandra-London

Sample ad-targeting project on Github https://github.com/davegardnerisme/we-have-your-kidneys

Watch videos from Cassandra SF 2011http://www.datastax.com/events/cassandrasf2011/presentations

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