cassandra's sweet spot - an introduction to apache cassandra

Cassandra’s sweet spot

Dave Gardner@davegardnerisme

jobs.hailocab.com

Looking for an expert backend Java dev – speak to me!

meetup.com/Cassandra-London

Next event 21st November

Building applications with Cassandra

• Key features

• Creating an application

• Data modeling

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

“They have approximately nothing in common. And, no, Cassandra is definitely not dying off.”

28th July 2010http://comments.gmane.org/gmane.comp.db.cassandra.user/7773

Top Tip #1

To use a NoSQL solution effectively, we need to identify it's sweet spot.

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_phpuk2011.html

“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

Headline features

1. Elastic

Read and write throughput increases linearly as new machines are added

http://cassandra.apache.org/

Headline features

2. Decentralised

Fault tolerant with no single point of failure; no “master” node

The dynamo paper

• Consistent hashing• Vector clocks• Gossip protocol• Hinted handoff• Read repair

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

The dynamo paper

RF = 3#1

Client

Coordinator

Headline features

3. Rich data model

Column based, range slices, column slices, secondary indexes, counters, expiring columns

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

Row Key

The big table paper

Column

Column Family

Headline features

4. You're in control

Tunable consistency, per operation

Consistency levels

How many replicas must respond to declare success?

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

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

Headline features

5. Performant

Well known for high write performance

http://www.datastax.com/docs/1.0/introduction/index#core-strengths-of-cassandra

Benchmark*

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

* Add pinch of salt

Recap: headline features

1. Elastic

2. Decentralised

3. Rich data model

4. You’re in control (tunable consistency)

5. Performant

A simple ad-targeting application

Some ads

Our user knowledge

Choose which ad to show

Allow us to capture user behaviour/data via “pixels” - placing users into segments (different buckets)

http://pixel.wehaveyourkidneys.com/add.php?add=foo

Record clicks and impressions of each ad; storing data per-ad and per-segment

http://pixel.wehaveyourkidneys.com/adImpression.php?ad=1http://pixel.wehaveyourkidneys.com/adClick.php?ad=1

Real-time ad performance analytics, broken down by segment(which segments are performing well?)

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

Recommendations based on best-performing ads

(this is left as an exercise for the reader)

Additional requirements

• Large number of users

• High volume of impressions

• Highly available – downtime is money

A good fit for Cassandra?

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

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

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.

Live demo before we start

Data modeling

Start from your queries, work backwards

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

Data model basics: conflict resolution

Per-column timestamp-based conflict resolution

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

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

Data model basics: conflict resolution

Per-column timestamp-based conflict resolution

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

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

Data model basics: column ordering

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

{ 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

{ badger: foo, zebra: foo}

with AsciiType column schema

Data modeling: user segments

Add user to bucket X, with expiry time YWhich buckets is user X in?

["user"][<uuid>][<bucketId>] = 1

[CF] [rowKey] [columnName] = value

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

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

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

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/

Add user to bucket X, with expiry time Y

Which buckets is user X in?

["user"][<uuid>][<bucketId>] = 1

[CF] [rowKey] [columnName] = value

Expiring columns

An expiring column will be automatically deleted after n seconds

$pool = new ConnectionPool( 'whyk', array('localhost') );$users = new ColumnFamily($pool, 'users');$users->insert( $userUuid, array($segment => 1), NULL, // default TS $expires );

Using phpcassa client: https://github.com/thobbs/phpcassa

UPDATE users USING TTL = 3600SET 'foo' = 1WHERE 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

Top Tip #5

Try to exploit Cassandra’s columnar data model; avoid read-before write and locking by safely mutating individual columns

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

Top Tip #6

Friends don’t let friends use Super Columns.

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

Try again using regular columns:

["ads"][<adId>][<stamp>-"click"] = #["ads"][<adId>][<stamp>-"impression"] = #

[CF] [Row] [Col] = value

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/timesA: Column slice against single row specifying a start stamp and end stamp + 1

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/

Counters

• Distributed atomic counters

• Easy to use

• Not idempotent

http://www.datastax.com/dev/blog/whats-new-in-cassandra-0-8-part-2-counters

$stamp = date('YmdH');$ads->add( $adId, // row key "$stamp-impression", // column 1 // increment );

We’ll store performance metrics in hour buckets for graphing.

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

Data modeling: performance/segment

We can add in another dimension to our stats so we can breakdown by segment.

["ads"][<adId>] [<stamp>-<segment>-"click"] = #

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 segmentA: Column slice against single row specifying a start stamp and end stamp + 1

$stamp = date('YmdH');$ads->add( "$adId-segments", // row key "$stamp-$segment-impression", // column 1 // incr );

We’ll store performance metrics in hour buckets for graphing.

Data modeling: segment stats

Track overall clicks/impressions per bucket; which buckets are most clicky?

["segments"][<adId>-"segments"] [<stamp>-<segment>-"click"] = #

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

Recap: Common data modeling patterns

1. Using column names with no value

[cf][rowKey][columnName] = 1

Recap: Common data modeling patterns

2. Counters

[cf][rowKey][columnName]++

And also…

3. Serialising a whole object

[cf][rowKey][columnName] = { foo: 3, bar: 11 }

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

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;

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/

In conclusion

Cassandra is founded on sound design principles

In conclusion

The Cassandra data model, sometimes mentioned as a weakness, is incredibly powerful

In conclusion

The clients are getting better; CQL is a step forward

In conclusion

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

In conclusion

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

Thanks

Learn more about Cassandrameetup.com/Cassandra-London

Checkout the code https://github.com/davegardnerisme/we-have-your-kidneys

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

cassandra's sweet spot - an introduction to apache cassandra

segment http

repair http

operation http

ad storing data

simple ad

master node http

big data

expiring columns http

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