Progressive NOSQL: Cassandra

A tunably consistent, highly-available, Distributed Database Tom Wilkie @tom_wilkie Founder & VP Engineering, Acunu 1

• Overview• Distribution• Storage• Datamodel• Usecases 2

• A distributed database for Big Data • Scale out on commodity servers • Best of bread performance • Multi-master architecture, no SPOF • Powerful multi data centre support4 4

BigTable, 2006 Dynamo, 2007 Open sourced, 2008 TLP, 2010 Incubator, 2009 v1.0 2011 6

BigTable: ... • Simple but powerful datamodel • Write-optimised storage system • Consistent, available but not partition tolerant • Master-slave distribution system, SPOF http://goo.gl/7T1Ej7 7

Dynamo: ... • Sophisticated distribution system with tradable consistency and availability • Over-simple datamodel http://goo.gl/Q80b48 8

Distribution: Consistent Hashing → r1, c1 v1 → r2, c2 v2 → r3, c3 v310 10

Distribution: Scaling11 11

Distribution: Scaling • .13 13

Distribution: Replication → r1, c1 v116 16

Distribution: Replication17 17

Distribution: Consistency Tuneable, per-operation consistency Timestamped values, N > R + W W R18 18

Distribution: Read Repair19 19

Writing to Cassandra Row Key Column Column Column Column24 24

Writing to Cassandra In the JVM Row Colu Colu Colu Colu Memtable On disk Commit log25 25

Writing to Cassandra In the JVM Full Memtable On disk Commit log26 26

Writing to Cassandra In the JVM New Memtable On disk Commit SSTable log27 27

Writing to Cassandra On disk Commit SSTable log SSTable SSTable SSTable SSTable SSTable28 28

Writing to Cassandra On disk Commit log SSTable29 29

Reading from Cassandra 30

2 Off-heap Row cache (no GC) 1 In the JVM Memtable 3 4 5 SSTable Bloom ﬁlter Key cache index 6 On disk Commit log SSTable31 31

SQL Cassandra Database row/key col_1 col_2 Keyspace row/key col_1 col_1 row/ col_1 col_1 Table Column Family33 33

col1 col2 col3 col4 col5 col6 col7 row1 x x x row2 x x x x x row3 x x x x x row4 x x x x row5 x x x x row6 x row7 x x x34 34

alice: { m2: { Sender: bob, Subject: ‘paper!’, ... } } bob: { m1: { Sender: alice, Subject: ‘rock?’, ... } } charlie: { m1: { Sender: alice, Subject: ‘rock?’, ... }, m2: { Sender: bob, Subject: ‘paper!’, ... } }35 35

• Overview• Distribution• Storage• Datamodelling• Usecases 36

Perfect for high velocity data Web, SCM, Retail Location Services Cloud Monitoring Social Gaming Social Media Ad Marketplaces Fraud Detection Smart Metering Oil/Gas Sensors 37 Conﬁdential 6Wednesday, 25 April 12 37

Not Covered... • Distribution: Hinted Handoff, Anti-entropy repair, Counter distribution • Storage: Counter storage, different compaction strategies, partitioning etc • Datamodel: de-normalisation, TTLs, secondary indexes, CQL, super-columns, schema optional • Operations: backup, nodetool, performance tuning • Integration: Hadoop, Client Libraries etc38 38

• Distributed, scalable database• Opensource, widely used• Tunably consistent• Highly-available• Partition tolerant• Write-optimised• Schema-optional 39

Data Platform 40

Data PlatformData driven applications Web UI Acunu Analytics Control Apache Cassandra CenterAcunu Storage Engine Conﬁgured and tuned OS Commodity Hardware 41

Control Center“Ive had the EC2 instance running for a little while and Ihave to say, Im impressed. You guys have done well with this product.” - Lloyd, JustDevelopIt 42

Control Center“The new UI has been critical in helping us work out what is wrong in our code” - Matt, TellyBug 43

Castle: Built for Big Data • Storage engine optimized for large slow disks, many cores, Big Data workloads • Enterprise density on commodity hardware • Lightning disk rebuilds:10x faster than RAID Shared memory interface Castle keys Userspace Acunu Kernel userspace interface values In-kernel async, shared memory ring workloads shared buffers kernelspace Streaming interface interface range key buffered key buffered queries insert value insert get value get Doubling Arrays • Opensource (GPLv2, MIT doubling array mapping layer for user libraries) insert Bloom ﬁlters queues key get arrays x range arrays queries management http://goo.gl/gzihe key • insert merges http://bitbucket.org/acunu Arrays mapping layer • modlist btree key Version tree Loadable Kernel Module, insert btree key get btree targeting CentOS’s 2.6.18 range queries value arrays44 • Cache block mapping & http://www.acunu.com/ cacheing layer "Extent" layer prefetcher extent block extent cache blogs/andy-twigg/why- freespace allocator manager 44 ﬂusher

Rebuild time 5 4 Rebuild Time (Hours) 3 2 1 0 RAID10, 8 Disks RAID5, 8 Disks RDA, 8 Disks RDA, 15 Disks46 46

Analytics counter updatesClick stream events AcunuSensor data Analytics etc • Simple, real-time, incremental analytics • Push processing into ingest phase 47

Questions? tom@acunu.com @tom_wilkie www.acunu.com 48

Introduction Live & historical aggregates...49 49

Realtime trends...50 50

Drill downs and roll ups51 51

Solution Con Scalability $$$ Not realtime Inefﬁcient Recomputation Spartan query semantics => complex, DIY solutions52 52

Progressive NOSQL: Cassandra

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