Apache Cassandra @Geneva JUG 2013.02.26

2,790 views

Published on

Apache Cassandra Overview, presented @GenevaJUG 2013.02.26

Published in: Technology
0 Comments
5 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
2,790
On SlideShare
0
From Embeds
0
Number of Embeds
32
Actions
Shares
0
Downloads
27
Comments
0
Likes
5
Embeds 0
No embeds

No notes for slide

Apache Cassandra @Geneva JUG 2013.02.26

  1. Apache Cassandrahttp://cassandra.apache.org Benoit Perroud Software Engineer @Verisign & Apache Committer Geneva JUG, 26.02.2013
  2. Agenda• NoSQL Quick Overview• Apache Cassandra Fundamentals – Design principles – Data & Query Model• Real Life Uses Cases – Illustrated in CQL3• What‟s new in 1.2• Conclusion• Q&A 2
  3. NoSQL• [Wikipedia] NoSQL is a term used to designate database management systems that differ from classic relational database management systems (RDBMS) in some way. These data stores may not require fixed table schemas, usually avoid join operations, do not attempt to provide ACID properties and typically scale horizontally.• Pioneers : Google BigTable, Amazon Dynamo, etc. 3
  4. Scalability• [Wikipedia] Scalability is a desirable property of a system, a network, or a process, which indicates its ability to either handle growing amounts of work in a graceful manner or to be readily enlarged.• Scalability in two dimensions : – Scale up → scale vertically (increase RAM in an existing node) – Scale out → scale horizontally (add a node to the cluster)• In summary : handle load and peaks. 4
  5. Availability• [Wikipedia] Availability refers to the ability of the users to access and use the system. If a user cannot access the system, it is said to be unavailable. Generally, the term downtime is used to refer to periods when a system is unavailable.• In summary : minimize downtime. 5
  6. CAP Theorem• Consistency : all nodes see the same data at the same time• Availability : node failures do not prevent survivors from continuing to operate• Partition Tolerance : the system continues to operate despite arbitrary message loss• According to the theorem, a distributed system can satisfy any two of these guarantees at the same time, but not all three. 6
  7. NoSQL Promises• Scale horizontally – Double computational power or storage by doubling size of the cluster. Cluster shrinking should also be true (tight provisioning) – Adding nodes to the cluster in constant time• High availability – No / few / under control SPoF• On commodity hardware – 32 cores, 64GB RAM, 12x2TB HDD IS commodity hardware • Let see how Cassandra achieves all of these 7
  8. Apache Cassandra• Apache Cassandra is could be simplified as a scalable, distributed, sparse and eventually consistent hash map. But its actually way more.• Originally developed by Facebook, hit AFS incubator early 2008, version 1.0 in 2010, version 1.2 early 2013• Inspired from Amazon Dynamo and Google BigTable• Version at time of speaking 1.2.2• Under high development by several startups : Datastax, Acunu, Netflix, Twitter, Rackspace, … 8
  9. Apache Cassandra is a scalable distributed, sparse, eventually consistent hash map• Gossip protocol (spreading states like a rumor)• Consistent hashing – Node responsible for key range and replica sets• Replication factor (RF) to achieve persistence• No single point of failure 100% keyspace 0• Key space is 2^128 bits 87 12 ? ? More on this later 75 Take half of key range 25 with VNodes ? of most loaded node ? 62 37 ? ? 50 Take half of key range 9 of most loaded node
  10. Apache Cassandra is a scalable distributed, sparse, eventually consistent hash map• Schemaless – A schema (metadata) may be determined for convenience – Column names are stored for every rows• [Wikipedia] Bloom filter is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set. 10
  11. Apache Cassandra is a scalable distributed, sparse, eventually consistent hash map• [Wikipedia] A quorum is the minimum number of votes that a distributed transaction has to obtain in order to be allowed to perform an operation in a distributed system. A quorum-based technique is implemented to enforce consistent operation in a distributed system.• Quorum : W + R > N – N : number of replica, R : number of node read, W : number of node written. – Condition met when: • R = 1, W = N • R = N, W = 1 • R = N/2, W = N/2 (+1 if N is even) 11
  12. Apache Cassandra is a scalable distributed, sparse, eventually consistent hash map• Key space [0,99], previously put(22, 1, t1)• Replication factor 2• Consistency : ONE coordinator 0 Put (22, 2, t2) 80 20 Async put(22,2, t2) 60 40 owner replica 12
  13. Apache Cassandra is a scalable distributed, sparse, eventually consistent hash map• Key space [0,99], previously put(13, 1, t1)• Replication factor 3• Consistency : QUORUM (R = 2, W = 2) 0 Read(13) = 2, t2 Put (13, 2, t2) Put (13, 2, t2) 80 20 Read(13) = 1, t1 Read repair 60 40 13
  14. Apache Cassandra is a scalable distributed, sparse, eventually consistent hash map• Can be seen as a multilevel map : Map of SortedMap of Objects• Keyspace > ColumnFamily > row > column name = value – # use keyspace1; – # set ColumnFamily1[key1][columName1] = value1; – # get ColumnFamily1[key1][columName1]; 14
  15. Data Model : KeyspaceKeyspace > ColumnFamily > row > column name = value• Equivalent to database name in SQL world• Define replication factor and network topology – Network topology include multi datacenters topology – Replication factor can be defined per datacenters 15
  16. Data Model : Column FamilyKeyspace > ColumnFamily > row > column name = value• Equivalent to table name in SQL world – Term may change in upcoming releases to stop confusing users• Define – Type of the keys – Column name comparator – Additional metadata (types of certain known columns) 16
  17. Data Model : RowKeyspace > ColumnFamily > row > column name = value• Defined by the key. – Eventually stored to a node and its replicas• Keys are typed• 2 strategies of key partitioner on the key space – Random partitioner • md5(key), murmur3(key), evenly distribute keys on nodes – Byte Ordered partitioner • Keep order while iterating through the keys, may lead to hot spots 17
  18. Data Model : Column Name Keyspace > ColumnFamily > row > column name = value • Could be seen as column in SQL world • Not mandatory to be declared – If declared, their corresponding values have types – Or secondary index • Ordered • Column Names are often used as values Column names Event1ColumnFamily 24.04.2012 07:00 08:00 239 255 18 Row key Values
  19. Data Model : ValueKeyspace > ColumnFamily > row > column name = value• Can be typed, seen as array of bytes otherwise• Existing types include – Bytes – Strings (ASCII or UTF-8 strings) – Integer, Long, Float, Double, Decimal – UUID, dates – Counters (of long)• Can expire• No foreign keys (!) 19
  20. Write path1. Write to commit log Memory2. Update MemTable CF1 MemTable CF2 MemTable CFn MemTable …3. Acknowledge the client4. When MemTable reaches a Disks CF1 CFn Commit log threshold, flush to disk as Bloom filter … SSTable SSTable Index Data … SSTable SSTable 20
  21. Read path• Versions of the same column Memory can be spread at the same time CF1 CF2 CFn MemTable MemTable MemTable … – In the MemTable – In the MemTable being flushed Disks – In one or multiple SSTable Commit log CF1 CFn …• All versions read, and resolved / Bloom filter SSTable Index merged using timestamp Data … – Keys and Rows cache SSTable SSTable – Bloom filters allow to skip reading unnecessary SSTables – SSTables are indexed – Compaction keep things reasonable 21
  22. Compaction• Runs regularly as a background operation• Merge SSTables together• Remove expired and deleted values• Has impact on general I/O availability (and thus performance) – This is where most of tuning happens – Can be throttled• Two type of compaction – Size-tiered • Fewer I/O consumption  write-heavy workload – Leveled • Guarantee to read from fewer SSTables  read-heavy workload• See http://www.datastax.com/dev/blog/leveled-compaction-in-apache-cassandra for complete details. 22
  23. Query Model• Thrift API – CLI – Higher level third-party libraries • Hector • Pycassa • Phpyandra • Astyanax • Helenus• CQL (Cassandra Query Language) – And newly CQL3 released with C*1.2 23
  24. Query Model• Cassandra is more than a key – value store. – Get – Put – Delete – Update – But also various range queries • Key range • Column range (slice) – Secondary indexes 24
  25. Query Model : Get• Get single key – Give me key „a‟• Get multiple keys – Give me rows for keys „a‟, „c‟, „d‟ and „f‟ Ordered regarding column name comparator ‘1’ ‘2’ ‘3’ ‘4’ ‘5’ „c‟ 8 9 10 11 „e‟ 12 13 14RandomPartitionner „f‟ 15 16 17 „a‟ 18 „b‟ 19 20 20 „d‟ 22 23 24 25 26 25
  26. Query Model : Get Range• Range – Query for a range of key • Give me all rows with keys between „c‟ and „f‟. • Mind the partitioner. ‘1’ ‘2’ ‘3’ ‘4’ ‘5’ „c‟ 8 9 10 11 „e‟ 12 13 14 „f‟ 15 16 17 „a‟ 18 „b‟ 19 20 20 „d‟ 22 23 24 25 26 26
  27. Query Model : Get Slice• Slice – Query for a slice of columns • For key „c‟, give me all columns between „3‟ and „5‟ • For key „d‟, give me all columns between „3‟ and „5‟ ‘1’ ‘2’ ‘3’ ‘4’ ‘5’ „c‟ 8 9 10 11 „e‟ 12 13 14 „f‟ 15 16 17 „a‟ 18 „b‟ 19 20 20 „d‟ 22 23 24 25 26 27
  28. Query Model : Get Range Slice• Range and Slice can be combined : rangeSliceQuery – For keys between „b‟ and „d‟, give me columns between „2‟ and „4‟ ‘1’ ‘2’ ‘3’ ‘4’ ‘5’ „a‟ 8 9 10 11 „b‟ 12 13 14 „c‟ 15 16 17 „d‟ 18 „e‟ 19 20 20 „f‟ 22 23 24 25 26 28
  29. Query Model : Secondary Index• Secondary Index – Give me all rows where value for column „2‟ is „12‟ ‘1’ ‘2’ ‘3’ ‘4’ ‘5’ „a‟ 8 9 10 11 „b‟ 12 13 14 „c‟ 15 16 17 „d‟ 18 „e‟ 19 20 20 „f‟ 22 23 24 25 26 29
  30. Real Life Use Case : Doodle Clone• Living demo http://doodle.noisette.ch Data model Polls { id, label, [choices], email, limit, [ subscribers ] }• Id generation – TimeUUID is your friend• Avoid super column families – Use composite, or CQL3 • Subscriber‟s name uniqueness per poll ? – Cassandra anti-pattern (read after write)• Limit to n subscribers per option ? – Cassandra anti-pattern (read after write) 31
  31. Real Life Use Case : Doodle CloneCREATE KEYSPACE Doodle WITH replication = {class: SimpleStrategy, replication_factor : 1};USE doodle;CREATE TABLE Polls ( id uuid, label text, choices list<text>, email text, maxChoices int, subscribers list<text>, PRIMARY KEY (id)) WITH compaction = { class : LeveledCompactionStrategy } AND read_repair_chance = 0.0;INSERT INTO Polls (id, label, email, choices) VALUES (eba080a0-8011-11e2-9e96-0800200c9a66, Test poll1, benoit@noisette.ch, [Monday, Tuesday, Wednesday, Thursday, Friday]);UPDATE Polls SET subscribers = subscribers + [ Benoit ] WHERE id = eba080a0-8011-11e2-9e96- 0800200c9a66;UPDATE Polls SET subscribers = subscribers + [ Maxime, Nicolas ] WHERE id = eba080a0-8011- 11e2-9e96-0800200c9a66;DELETE subscribers[0] FROM Polls WHERE id = eba080a0-8011-11e2-9e96-0800200c9a66; 32
  32. Real Life Use Case : Heavy Writes• Cassandra is a really good fit when the ratio read / write is close to 0 – Event logging / redo logs – Time series• Best practice to write data in its raw format AND in aggregated forms at the same time• But need compation tuning – {min,max}_compaction_threshold – memtable_flush_writers – … no magic solution here, only pragmatic approach • change configuration in one node, and mesure the difference (load, latency, …) 33
  33. Real Life Use Case : Counters• Cassandra >= 0.8 (CASSANDRA-1072) CREATE TABLE Events (id uuid, count counter, PRIMARY KEY (id)); UPDATE Events SET count = count + 1 WHERE id = 95b64d72-8014-11e2-9e96-0800200c9a66;• Example counterCF[entity1][2012-06-14 18:30:00] counterCF[entity1][2012-06-14 18:30:05] Query per entity counterCF[entity1][2012-06-14 18:30:10] number of hits for „entity1‟ … between 18:30:00 and 19:00:00 counterCF[entity2][2012-06-14 18:30:05] counterCF[2012-06-14 18:30:00][entity1] counterCF[2012-06-14 18:30:00][entity2] Query per date range counterCF[2012-06-14 18:30:00][entity3] all entities being hit between … 18:30:00 and 19:00:00 counterCF[2012-06-14 18:30:05][entity1] ! need complete date enumeration 34
  34. Real Life Use Case : Bulk Loading• Data is transformed (e.g. using MapReduce)• Data is bulk loaded – ColumFamilyOutputFormat (< v1.1) • Not real bulk loading – BulkOutputFormat (>= v1.1) • SSTable generated during the tranformation, and streamed• Prefer Leveled Compaction Strategy – Reduce read latency – Size sstable_size_in_mb to your data 35
  35. Real Life Use Case : Bulk Loading• Data is transformed (e.g. using MapReduce)• Data is bulk loaded – ColumFamilyOutputFormat (< v1.1) • Not real bulk loading – BulkOutputFormat (>= v1.1) • SSTable generated during the tranformation, and streamed• Prefer Leveled Compaction Strategy – Reduce read latency – Size sstable_size_in_mb to your data 36
  36. Real Life Use Case : λ Architecture• Enabling real-time queries to end-users – “Hybrid Approach to Enable Real-Time Queries to End-Users”, Software Developer Journal February 2013 37
  37. What‟s New in 1.2 • CQL3 – http://cassandra.apache.org/doc/cql3/CQL.html • Virtual Nodes (vnodes) • Atomic batches • Murmur3Partitioner • Off-heap SSTable metadata • Query tracing • … a lot more … 38Illustrations credits to Datastax, http://www.datastax.com/dev/blog/upgrading-an-existing-cluster-to-vnodes
  38. Conclusion• Cassandra is not a general purpose solution• But Cassandra is doing a really good job if used accordingly – Really good scalability • Netflix‟s 1M w/s on AWS http://techblog.netflix.com/2011/11/benchmarking-cassandra- scalability-on.html – Low operational cost • Admin friendly, no SPoF, Vnodes, snapshot, … – Advanced data and query model 39
  39. Thanks for your attention• Questions? benoit@noisette.ch @killerwhile• No? Cool … Apéro  40

×