Cassandra introduction @ ParisJUG
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This document provides an introduction to Cassandra including: 1) An overview of Cassandra's key architecture including its linear scalability, continuous availability across data centers, and operational simplicity. 2) A discussion of Cassandra's data model including its use of Last Write Wins for conflict resolution and examples of modeling one-to-many relationships using clustered tables. 3) Details on Cassandra's consistency levels and how they impact availability and durability of writes and reads.
![@doanduyhai
Data distribution!
Random: hash of #partition → token = hash(#p)
Hash: ]-X, X]
X = huge number (264/2)
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![@doanduyhai
Token Ranges!
A: ]0, X/8]
B: ] X/8, 2X/8]
C: ] 2X/8, 3X/8]
D: ] 3X/8, 4X/8]
E: ] 4X/8, 5X/8]
F: ] 5X/8, 6X/8]
G: ] 6X/8, 7X/8]
H: ] 7X/8, X]
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![@doanduyhai
WHERE clause restrictions!
What if I want to perform « arbitrary » WHERE clause ?
• search form scenario, dynamic search fields
DO NOT RE-INVENT THE WHEEL !
☞ Apache Solr (Lucene) integration (Datastax Enterprise)
☞ Same JVM, 1-cluster-2-products (Solr & Cassandra)
SELECT * FROM users WHERE solr_query = ‘age:[33 TO *] AND gender:male’;
SELECT * FROM users WHERE solr_query = ‘lastname:*schwei?er’;
66](https://image.slidesharecdn.com/cassandraintroductionparisjug-150211081109-conversion-gate01/85/Cassandra-introduction-ParisJUG-66-320.jpg)
Cassandra introduction @ ParisJUG
- 1. @doanduyhai Introduction to Cassandra DuyHai DOAN, Technical Advocate
- 2. Copyright @ 2014 ParisJug. Licence CC - Creative Commons 2.0 France – Paternité - Pas d'Utilisation Commerciale - Partage des Conditions Initiales à l'Identique
- 3. @doanduyhai Who Am I ?! Duy Hai DOAN Cassandra technical advocate • talks, meetups, confs • open-source devs (Achilles, …) • OSS Cassandra point of contact ☞ [email protected] ☞ @doanduyhai 3
- 4. @doanduyhai Datastax! • Founded in April 2010 • We contribute a lot to Apache Cassandra™ • 400+ customers (25 of the Fortune 100), 200+ employees • Headquarter in San Francisco Bay area • EU headquarter in London, offices in France and Germany • Datastax Enterprise = OSS Cassandra + extra features 4
- 5. @doanduyhai Agenda! Architecture • Cluster, Replication, Consistency Data model • Last Write Win (LWW), CQL basics, From SQL to CQL, Lightweight Transaction DSE Use Cases 5
- 6. @doanduyhai Cassandra history! NoSQL database • created at Facebook • open-sourced since 2008 • current version = 2.1 • column-oriented ☞ distributed table 6
- 7. @doanduyhai Cassandra 5 key facts! Key fact 1: linear scalability C* C*C* NetcoSports 3 nodes, ≈3GB 1k+ nodes, PB+ YOU 7
- 8. @doanduyhai Cassandra 5 key facts! Key fact 2: continuous availability (≈100% up-time) • resilient architecture (Dynamo) 8
- 9. @doanduyhai Cassandra 5 key facts! Key fact 3: multi-data centers • out-of-the-box (config only) • AWS conf for multi-region DCs • GCE/CloudStack support • Microsoft Azure 9
- 10. @doanduyhai Multi-DC usages! New York (DC1) London (DC2) Data-locality, disaster recovery n2 n3 n4 n5 n6 n7 n8 n1 n2 n3 n4n5 n1 Async replication 10
- 11. @doanduyhai Multi-DC usages! Workload segregation/virtual DC n2 n3 n4 n5 n6 n7 n8 n1 n2 n3 n4n5 n1 Production (Live) Analytics (Spark/Hadoop) Same room Async replication 11
- 12. @doanduyhai Multi-DC usages! Prod data copy for testing/benchmarking n2 n3 n4 n5 n6 n7 n8 n1 n2 n3n1 Use LOCAL consistency My tiny test cluster Data copy NEVER WRITE HERE !!! 12
- 13. @doanduyhai Cassandra 5 key facts! Key fact 4: operational simplicity • 1 node = 1 process + 2 config file (main + IP) • deployment automation • OpsCenter for monitoring 13
- 14. @doanduyhai Cassandra 5 key facts! 14
- 15. @doanduyhai Cassandra 5 key facts! Key fact 5: analytics combo • Cassandra + Spark = awesome ! • realtime streaming/analytics/aggregation … 15
- 17. @doanduyhai Cassandra architecture! Cluster layer • Amazon DynamoDB paper • masterless architecture Data-store layer • Google Big Table paper • Columns/columns family 17
- 18. @doanduyhai Data distribution! Random: hash of #partition → token = hash(#p) Hash: ]-X, X] X = huge number (264/2) n1 n2 n3 n4 n5 n6 n7 n8 18
- 19. @doanduyhai Token Ranges! A: ]0, X/8] B: ] X/8, 2X/8] C: ] 2X/8, 3X/8] D: ] 3X/8, 4X/8] E: ] 4X/8, 5X/8] F: ] 5X/8, 6X/8] G: ] 6X/8, 7X/8] H: ] 7X/8, X] n1 n2 n3 n4 n5 n6 n7 n8 A B C D E F G H 19
- 22. @doanduyhai Linear scalability! n1 n2 n3 n4 n5 n6 n7 n8 n1 n2 n3 n4 n5 n6 n7 n8n9 n10 8 nodes 10 nodes 22
- 23. @doanduyhai Failure tolerance! Replication Factor (RF) = 3 n1 n2 n3 n4 n5 n6 n7 n8 1 2 3 {B, A, H} {C, B, A} {D, C, B} A B C D E F G H 23
- 24. @doanduyhai Coordinator node! Incoming requests (read/write) Coordinator node handles the request Every node can be coordinator àmasterless n1 n2 n3 n4 n5 n6 n7 n8 1 2 3 coordinator request 24
- 25. @doanduyhai Consistency! Tunable at runtime • ONE • QUORUM (strict majority w.r.t. RF) • ALL Apply both to read & write 25
- 26. @doanduyhai Consistency in action! RF = 3, Write ONE, Read ONE B A A B A A Read ONE: A data replication in progress … Write ONE: B 26
- 27. @doanduyhai Consistency in action! RF = 3, Write ONE, Read QUORUM B A A Write ONE: B Read QUORUM: A B A A data replication in progress … 27
- 28. @doanduyhai Consistency in action! RF = 3, Write ONE, Read ALL B A A Read ALL: B B A A data replication in progress … Write ONE: B 28
- 29. @doanduyhai Consistency in action! RF = 3, Write QUORUM, Read ONE B B A Write QUORUM: B Read ONE: A B B A data replication in progress … 29
- 30. @doanduyhai Consistency in action! RF = 3, Write QUORUM, Read QUORUM B B A Read QUORUM: B B B A data replication in progress … Write QUORUM: B 30
- 32. @doanduyhai Consistency level! ONE Fast, may not read latest written value 32
- 33. @doanduyhai Consistency level! QUORUM Strict majority w.r.t. Replication Factor Good balance 33
- 34. @doanduyhai Consistency level! ALL Paranoid Slow, no high availability 34
- 35. @doanduyhai Consistency summary! ONERead + ONEWrite ☞ available for read/write even (N-1) replicas down QUORUMRead + QUORUMWrite ☞ available for read/write even 1+ replica down 35
- 36. Q & R ! "!
- 37. Data model! Last Write Win! CQL basics! From SQL to CQL! Lightweight Transaction!
- 38. @doanduyhai Cassandra Write Path! Commit log1 . . . 1 Commit log2 Commit logn Memory 38
- 39. @doanduyhai Cassandra Write Path! Memory Commit log1 . . . 1 Commit log2 Commit logn MemTable Table1 MemTable Table2 MemTable TableN 2 . . . 39
- 40. @doanduyhai Cassandra Write Path! Commit log1 Commit log2 Commit logn Table1 SSTable1 Table2 Table3 SSTable2 SSTable3 3 Memory . . . 40
- 41. @doanduyhai Cassandra Write Path! Commit log1 Commit log2 Commit logn Table1 SSTable1 Table2 Table3 SSTable2 SSTable3 Memory. . . MemTable Table1 MemTable Table2 MemTable TableN . . . 41
- 42. @doanduyhai Cassandra Write Path! Commit log1 Commit log2 Commit logn Table1 SSTable1 Table2 Table3 SSTable2 SSTable3 Memory SSTable1 SSTable2 SSTable3 . . . 42
- 43. @doanduyhai Last Write Win (LWW)! jdoe age name 33 John DOE INSERT INTO users(login, name, age) VALUES(‘jdoe’, ‘John DOE’, 33); #partition 43
- 44. @doanduyhai Last Write Win (LWW)! jdoe age (t1) name (t1) 33 John DOE INSERT INTO users(login, name, age) VALUES(‘jdoe’, ‘John DOE’, 33); auto-generated timestamp . 44
- 45. @doanduyhai Last Write Win (LWW)! UPDATE users SET age = 34 WHERE login = ‘jdoe’; jdoe age (t1) name (t1) 33 John DOE jdoe age (t2) 34 SSTable1 SSTable2 45
- 46. @doanduyhai Last Write Win (LWW)! DELETE age FROM users WHERE login = ‘jdoe’; jdoe age (t3) ý tombstone jdoe age (t1) name (t1) 33 John DOE jdoe age (t2) 34 SSTable1 SSTable2 SSTable3 46
- 47. @doanduyhai Last Write Win (LWW)! SELECT age FROM users WHERE login = ‘jdoe’; ??? SSTable1 SSTable2 SSTable3 jdoe age (t3) ý jdoe age (t1) name (t1) 33 John DOE jdoe age (t2) 34 47
- 48. @doanduyhai Last Write Win (LWW)! SELECT age FROM users WHERE login = ‘jdoe’; ✓✕✕ SSTable1 SSTable2 SSTable3 jdoe age (t3) ý jdoe age (t1) name (t1) 33 John DOE jdoe age (t2) 34 48
- 49. @doanduyhai Compaction! SSTable1 SSTable2 SSTable3 jdoe age (t3) ý jdoe age (t1) name (t1) 33 John DOE jdoe age (t2) 34 New SSTable jdoe age (t3) name (t1) ý John DOE 49
- 50. @doanduyhai Historical data! history id date1(t1) date2(t2) … date9(t9) … … … … SSTable1 SSTable2 You want to keep data history ? • do not use internal generated timestamp !!! • ☞ time-series data modeling id date10(t10)date11(t11) … … … … … … 50
- 51. @doanduyhai CRUD operations! INSERT INTO users(login, name, age) VALUES(‘jdoe’, ‘John DOE’, 33); UPDATE users SET age = 34 WHERE login = ‘jdoe’; DELETE age FROM users WHERE login = ‘jdoe’; SELECT age FROM users WHERE login = ‘jdoe’; 51
- 52. @doanduyhai Simple Table! CREATE TABLE users ( login text, name text, age int, … PRIMARY KEY(login)); partition key (#partition) 52
- 53. @doanduyhai What about joins ?! How can I join data between tables ? How can I model 1 – N relationships ? How to model a mailbox ? EmailsUser 1 n 53
- 54. @doanduyhai Clustered table (1 – N)! CREATE TABLE mailbox ( login text, message_id timeuuid, interlocutor text, message text, PRIMARY KEY((login), message_id)); partition key clustering column (sorted) unicity 54
- 55. @doanduyhai SSTable2 SSTable1 On disk layout jdoe message_id1 message_id2 … message_id104 … … … … hsue message_id1 message_id2 … message_id78 … … … … jdoe message_id105 message_id106 … message_id169 … … … … 55
- 56. @doanduyhai Queries! Get message by user and message_id (date) SELECT * FROM mailbox WHERE login = jdoe and message_id = ‘2014-09-25 16:00:00’; Get message by user and date interval SELECT * FROM mailbox WHERE login = jdoe and message_id <= ‘2014-09-25 16:00:00’ and message_id >= ‘2014-09-20 16:00:00’; 56
- 57. @doanduyhai Queries! Get message by message_id only ? SELECT * FROM mailbox WHERE message_id = ‘2014-09-25 16:00:00’; Get message by date interval only ? SELECT * FROM mailbox WHERE and message_id <= ‘2014-09-25 16:00:00’ and message_id >= ‘2014-09-20 16:00:00’; ❓ ❓ 57
- 58. @doanduyhai Queries! Get message by message_id only (#partition not provided) SELECT * FROM mailbox WHERE message_id = ‘2014-09-25 16:00:00’; Get message by date interval only (#partition not provided) SELECT * FROM mailbox WHERE and message_id <= ‘2014-09-25 16:00:00’ and message_id >= ‘2014-09-20 16:00:00’; 58
- 59. @doanduyhai Without #partition ? ? ? ? ? ? ? ? ❓ ❓ ❓ ❓ ❓ ❓ ❓ ❓ No #partition ☞ no token ☞ where are my data ? 59
- 60. @doanduyhai The importance of #partition In RDBMS, no primary key ☞ full table scan 😭 60
- 61. @doanduyhai The importance of #partition With Cassandra, no partition key ☞ full CLUSTER scan 😱 61
- 62. @doanduyhai Queries! SELECT * FROM mailbox WHERE login >= ‘hsue’ and login <= ‘jdoe’; Get message by user range (range query on #partition) SELECT * FROM mailbox WHERE login like ‘%doe%‘; Get message by user pattern (non exact match on #partition) 62
- 63. @doanduyhai WHERE clause restrictions! All queries (INSERT/UPDATE/DELETE/SELECT) must provide #partition Only exact match (=) on #partition, range queries (<, ≤, >, ≥) not allowed • ☞ full cluster scan On clustering columns, only range queries (<, ≤, >, ≥) and exact match WHERE clause only possible • on columns defined in PRIMARY KEY • on indexed columns ( ) 63
- 64. @doanduyhai WHERE clause restrictions! What if I want to perform « arbitrary » WHERE clause ? • search form scenario, dynamic search fields 64
- 65. @doanduyhai WHERE clause restrictions! What if I want to perform « arbitrary » WHERE clause ? • search form scenario, dynamic search fields DO NOT RE-INVENT THE WHEEL ! ☞ Apache Solr (Lucene) integration (Datastax Enterprise) ☞ Same JVM, 1-cluster-2-products (Solr & Cassandra) 65
- 66. @doanduyhai WHERE clause restrictions! What if I want to perform « arbitrary » WHERE clause ? • search form scenario, dynamic search fields DO NOT RE-INVENT THE WHEEL ! ☞ Apache Solr (Lucene) integration (Datastax Enterprise) ☞ Same JVM, 1-cluster-2-products (Solr & Cassandra) SELECT * FROM users WHERE solr_query = ‘age:[33 TO *] AND gender:male’; SELECT * FROM users WHERE solr_query = ‘lastname:*schwei?er’; 66
- 67. @doanduyhai Collections & maps! CREATE TABLE users ( login text, name text, age int, friends set<text>, hobbies list<text>, languages map<int, text>, … PRIMARY KEY(login)); 67 Keep the cardinality low ≈ 1000
- 68. @doanduyhai User Defined Type (UDT)! CREATE TABLE users ( login text, … street_number int, street_name text, postcode int, country text, … PRIMARY KEY(login)); Instead of 68
- 69. @doanduyhai User Defined Type (UDT)! CREATE TYPE address ( street_number int, street_name text, postcode int, country text); CREATE TABLE users ( login text, … location frozen <address>, … PRIMARY KEY(login)); 69
- 70. @doanduyhai UDT insert! INSERT INTO users(login,name, location) VALUES ( ‘jdoe’, ’John DOE’, { ‘street_number’: 124, ‘street_name’: ‘Congress Avenue’, ‘postcode’: 95054, ‘country’: ‘USA’ }); 70
- 71. @doanduyhai UDT update! UPDATE users set location = { ‘street_number’: 125, ‘street_name’: ‘Congress Avenue’, ‘postcode’: 95054, ‘country’: ‘USA’ } WHERE login = jdoe; Can be nested ☞ store documents • but no dynamic fields (or use map<text, blob>) 71
- 72. @doanduyhai From SQL to CQL! Normalized Comment User 1 n CREATE TABLE comments ( article_id uuid, comment_id timeuuid, author_login text, // typical join id content text, PRIMARY KEY((article_id), comment_id)); 72
- 73. @doanduyhai From SQL to CQL 1 SELECT - 10 last comments - 10 author_login What to do with 10 author_login ??? Comment User 1 n 73
- 74. @doanduyhai From SQL to CQL 1 SELECT - 10 last comments - 10 author_login What to do with 10 author_login ??? 10 extra SELECT → N+1 SELECT problem ! Comment User 1 n 74
- 75. @doanduyhai From SQL to CQL! De-normalized Comment User 1 n CREATE TABLE comments ( article_id uuid, comment_id timeuuid, author frozen<person>, // person is UDT content text, PRIMARY KEY((article_id), comment_id)); 75
- 76. @doanduyhai Data modeling best practices! Start by queries • identify core functional read paths • 1 read scenario ≈ 1 SELECT 76
- 77. @doanduyhai Data modeling best practices! Start by queries • identify core functional read paths • 1 read scenario ≈ 1 SELECT Denormalize • wisely, only duplicate necessary & immutable data • functional/technical trade-off 77
- 78. @doanduyhai Data modeling best practices! Person UDT - firstname/lastname - date of birth - gender - mood - location 78
- 79. @doanduyhai Data modeling best practices! John DOE, male birthdate: 21/02/1981 subscribed since 03/06/2011 ☉ San Mateo, CA ’’Impossible is not John DOE’’ Full detail read from User table on click 79
- 80. @doanduyhai Data modeling trade-off What if ... • not possible to de-normalize with immutable data ? • have to duplicate mutable data ? 80
- 81. @doanduyhai Data modeling trade-off 2 strategies • either accept to normalize some data (extra SELECT required) • or de-normalize and update everywhere upon data mutation 81
- 82. @doanduyhai Data modeling trade-off 2 strategies • either accept to normalize some data (extra SELECT required) • or de-normalize and update everywhere upon data mutation But always keep those scenarios rare (5%-10% max), focus on the 90% 82
- 83. @doanduyhai Data modeling trade-off 2 strategies • either accept to normalize some data (extra SELECT required) • or de-normalize and update everywhere upon data mutation But always keep those scenarios rare (5%-10% max), focus on the 90% Example: Twitter tweet deletion 83
- 84. Q & R ! "!
- 85. @doanduyhai Lightweight Transaction (LWT)! What ? ☞ make operations linearizable Why ? ☞ solve a class of race conditions in Cassandra that would require installing an external lock manager 85
- 86. @doanduyhai Lightweight Transaction (LWT)! INSERT INTO account (id, email) VALUES (‘jdoe’, ‘[email protected]’); SELECT * FROM account WHERE id= ‘jdoe’; (0 rows) SELECT * FROM account WHERE id= ‘jdoe’; (0 rows) INSERT INTO account (id, email) VALUES (‘jdoe’, ‘[email protected]’); winner 86
- 87. @doanduyhai Lightweight Transaction (LWT)! How ? ☞ implementing Paxos protocol on Cassandra Syntax ? INSERT INTO account (id, email) VALUES (‘jdoe’, ‘john_doe@fiction.com’) IF NOT EXISTS; UPDATE account SET email = ‘jdoe@fiction.com’ IF email = ‘[email protected]’ WHERE id=‘jdoe’; 87
- 88. @doanduyhai Lightweight Transaction (LWT)! Recommendations • insert with LWT ☞ delete must use LWT INSERT INTO my_table … IF NOT EXISTS ☞ DELETE FROM my_table … IF EXISTS 88
- 89. @doanduyhai Lightweight Transaction (LWT)! Recommendations • LWT expensive (4 round-trips), do not abuse • only for 1% – 5% use cases 89
- 90. @doanduyhai Lightweight Transaction (LWT)! 1 2 3 4Compare Swap / Learn Queue-in Consensus 90
- 91. Q & R ! "!
- 92. @doanduyhai DSE (Datastax Enterprise)! Security Analytics (Spark & Hadoop) Search (Solr) 92