all your base

Apache Cassandra and why BASE is great for real-time analytics

Tim Moreton

• Cassandra -- What makes it different?

• Who’s using it, and for what?

• DIY Real Time Analytics on Cassandra

• The Easy Option -- Acunu Analytics

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BigTable Data model Dynamo distribution

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BigTable Data model Dynamo distribution

Incubator, 2009Top-Level, 2010

Open sourced, 2008

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• Multi-master architecture: no SPOF

• Tunable consistency, multi-DC aware

• High performance, optimised for writes

• Atomic counters

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user345: {chess: { lives: 2, score: 33 ...} ...

}

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Data model


}

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user345 [chess, lives]: 2

[chess, score]:44

user292 [go, lives]:4

[monop, avatar]: top_hat

[monop, score]: 33

user188 [monop, score]: 13

Row keyRows arranged randomly around cluster. Load balanced, but no ordering.Put stuff to access sequentially within a row.

Data model


}

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[chess, score]:44



[monop, score]: 33


Column keyCompound columns allow you to create multiple ordered ‘dictionaries’ in a row.

Data model


}

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[chess, score]:44



[monop, score]: 33


Flexible schemas“Columns” are really just cell identifiers. Rows can be VERY wide.

Data model

ONE QUORUM ALLWrite:

ONE QUORUM ALLRead:

Tunable consistency — per operation

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#Replicas

#Replicas


ONE QUORUM ALLRead:

Risk of replica failing,Multiple values


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#Replicas

#Replicas


ONE QUORUM ALLRead:

More likely to return out-of-date data


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#Replicas

#Replicas


ONE QUORUM ALLRead:

Never going to say “ok” if a replica is down


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#Replicas

#Replicas


ONE QUORUM ALLRead:


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#Replicas

#Replicas

DC 1 DC 2

r1 r2 r1 r2

Multi data center aware

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DC 1 DC 2

r1 r2 r1 r2

Multi data center aware

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user345

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Session Stores

• Read dominated• Updates to existing items• Probably fits in RAM• Distribute for availability• Want: Atomicity

Real Time Analytics

• Write dominated• Updates very rare• Read “results” mostly• Distribute for availability,

performance, capacity• Want: Rich queries

Source: Twitter

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An analytics app on Cassandra

eg: “show me the number of mentions of ‘Acunu’ per day, between May and November 2011, on Twitter”Batch (Hadoop) approach would require processing ~30 billion tweets, or ~4.2 TB of data

http://blog.twitter.com/2011/03/numbers.html

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eg: “show me the number of mentions of ‘Acunu’ per day, between May and November 2011, on Twitter”Batch (Hadoop) approach would require processing ~30 billion tweets, or ~4.2 TB of data


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Cassandra approach:For each tweet, increment a bunch of counters, such that answering a queryis as easy as reading some counters



Analytics13

[1234, man] +1[1234, acunu] +1[1234, rock] +1

12:32:15 I like #trafficlights12:33:43 Nobody expects...

12:33:49 I ate a #bee; woe is...12:34:04 Man, @acunu rocks!

Analytics

Key 00:01 00:02 ...

[01/05/11, acunu] 3 5 ...

[02/05/11, acunu] 12 4 ...

... ... ...

Row key is ‘big’ time bucket

Column key is ‘small’ time bucket

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[1234, man] +1[1234, acunu] +1[1234, rock] +1

12:32:15 I like #trafficlights12:33:43 Nobody expects...

12:33:49 I ate a #bee; woe is...12:34:04 Man, @acunu rocks!

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Solution Con

Scalability$$$

Not real time

Spartan query semantics: complex, DIY solutions

High Velocity Event Streams

HTTP JSON, MQ, flume

As events are ingested: ■Update real time views■Refresh dashboards ■Preserve original event data

Dashboards and API deliver pre-computed results:■Roll-ups■Drilldowns■Trends

Provide definitions and real time views:

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01101001010101010

010110

101010101001011010101011001011010101010010110101010101101

0010

01101001010

101010

0101101010101010010110101010110010110101010100110

100101001011010

101010100101101010101100101101010101

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create table foo ( x long, y string, t time(hour, min), z path('/'));create view select sum(x) from foo where y group by z;create view select count from foo where x, t group by t;

Via the RESTful HTTP API, command line tools, or the UI query builder

Acunu Analytics

Analytics16

Analytics

countgrouped by ...

day

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Analytics

countgrouped by ...

daycount

distinct (session)

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Analytics

countgrouped by ...

daycount

distinct (session)

count

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Analytics

countgrouped by ...

daycount

distinct (session)

count

avg(duration)

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Analytics

countgrouped by ...

daycount

distinct (session)

count ... geography

avg(duration)

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Analytics

countgrouped by ...

daycount

distinct (session)

count ... geography

... browseravg(duration)

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Analytics

21:00 all→1345 :00→45 :01→62 :02→87 ...

22:00 all→3221 :00→22 :00→19 :02→104 ...

... ...

UK all→228 user01→1 user14→12 user99→7 ...

US all→354 user01→4 user04→8 user56→17 ...

...

UK, 22:00 all→1904 ...

∅ all→87314 UK→238 US→354 ...

{cust_id: user01,session_id: 102,geography: UK,browser: IE,time: 22:02,

}

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Analytics

21:00 all→1345 :00→45 :01→62 :02→87 ...

22:00 all→3222 :00→22 :00→19 :02→105 ...

... ...



...

UK, 22:00 all→1905 ...

∅ all→87315 UK→239 US→354 ...

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{cust_id: user01,session_id: 102,geography: UK,browser: IE,time: 22:02,

}

Analytics

21:00 all→1345 :00→45 :01→62 :02→87 ...

22:00 all→3221 :00→22 :00→19 :02→104 ...

... ...



...

UK, 22:00 all→1904 ...

∅ all→87314 UK→238 US→354 ...

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Analytics

21:00 all→1345 :00→45 :01→62 :02→87 ...

22:00 all→3222 :00→22 :01→19 :02→105 ...

... ...



...

UK, 22:00 all→1905 ...

∅ all→87315 UK→239 US→354 ...

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where time 21:00-22:00count(*)

Analytics

21:00 all→1345 :00→45 :01→62 :02→87 ...

22:00 all→3222 :00→22 :01→19 :02→105 ...

... ...



...

UK, 22:00 all→1905 ...

∅ all→87315 UK→239 US→354 ...

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where time 22:00-23:00, group by minute

Analytics

21:00 all→1345 :00→45 :01→62 :02→87 ...

22:00 all→3222 :00→22 :01→19 :02→105 ...

... ...



...

UK, 22:00 all→1905 ...

∅ all→87315 UK→239 US→354 ...

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where geography=UK group all by user,

Analytics

21:00 all→1345 :00→45 :01→62 :02→87 ...

22:00 all→3222 :00→22 :01→19 :02→105 ...

... ...



...

UK, 22:00 all→1905 ...

∅ all→87315 UK→239 US→354 ...

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count all

Analytics

21:00 all→1345 :00→45 :01→62 :02→87 ...

22:00 all→3222 :00→22 :01→19 :02→105 ...

... ...



...

UK, 22:00 all→1905 ...

∅ all→87315 UK→239 US→354 ...

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count all

group all by geo

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Accuracy Performance

k

Identify the root causes of aggregate results

Proactively identify deviation from baseline and breaks from trends

APPROXIMATE AGGREGATESDRILLDOWN TO ORIGINAL EVENTS

TRENDING AND CORRELATION

Automatic handling of paths, timestamps and geospatial queries

HIERARCHICALAGGREGATES

Fast probabilistic data structures for COUNT UNIQUE, TOP n to trade accuracy for performance - predictably

■ Enhanced Cassandra for higher density, better scalability, simpler management

■ Roll-up and transform cubes in real time■ Leverage NoSQL for write-optimization,

schema freedom, and horizontal scalability

■ ‘Single pane of glass‘ management UI

DASHBOARDS UI, JSON APIs

ACUNU ANALYTICS

ENHANCED CASSANDRA

CASTLE: STORAGE ENGINE

OPSUI

COMMODITY HW OR CLOUD

REAL-TIME BIG DATA ANALYTICS, POWERED BY NOSQL

CASSANDRA ENHANCED FORHIGHER DENSITY, LOWER TCO

■ In-kernel storage engine designed and optimised for NoSQL databases

STORAGE CRAFTED FOR BIG DATA

DASHBOARDS UI, JSON APIs

ACUNU ANALYTICS

ENHANCED CASSANDRA

CASTLE: STORAGE ENGINE

OPSUI

COMMODITY HW OR CLOUD

Shameless plug

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Analytics

http://bit.ly/UBsdej



Apache, Apache Cassandra, Cassandra, Hadoop, and the eye and elephant logos are trademarks of the Apache Software Foundation.

THANK YOU@timmoreton @acunu

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http://www.apache.org/foundation/marks/

http://www.apache.org/foundation/marks/

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