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Cassandra introduction @ NantesJUG

The document provides an introduction to Cassandra presented by Duy Hai Doan. It discusses Cassandra's history, key features including linear scalability, availability, support for multiple data centers, operational simplicity, and analytics capabilities. It also covers Cassandra architecture including the cluster layer based on Dynamo and data-store layer based on BigTable, data distribution, replication, consistency levels, and the write path. The data model of using the last write to resolve conflicts is explained along with CQL basics and modeling one-to-many relationships with clustered tables.

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@doanduyhai Introduction to Cassandra DuyHai DOAN, Technical Advocate
@doanduyhai Who Am I ?! Duy Hai DOAN Cassandra technical advocate •  talks, meetups, confs •  open-source devs (Achilles, …) •  OSS Cassandra point of contact ☞ duy_hai.doan@datastax.com ☞ @doanduyhai 2
@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 3
@doanduyhai Agenda! Architecture •  Cluster, Replication, Consistency Data model •  Last Write Win (LWW), CQL basics, From SQL to CQL, Lightweight Transaction DSE Use Cases 4
@doanduyhai Cassandra history! NoSQL database •  created at Facebook •  open-sourced since 2008 •  current version = 2.1 •  column-oriented ☞ distributed table 5
@doanduyhai Cassandra 5 key facts! Key fact 1: linear scalability C* C*C* NetcoSports 3 nodes, ≈3GB 1k+ nodes, PB+ YOU 6
@doanduyhai Cassandra 5 key facts! Key fact 2: continuous availability (≈100% up-time) •  resilient architecture (Dynamo) 7
@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 8
@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 9
@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 10
@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 !!! 11
@doanduyhai Cassandra 5 key facts! Key fact 4: operational simplicity •  1 node = 1 process + 2 config file (main + IP) •  deployment automation •  OpsCenter for monitoring 12
@doanduyhai Cassandra 5 key facts! 13
@doanduyhai Cassandra 5 key facts! Key fact 5: analytics combo •  Cassandra + Spark = awesome ! •  realtime streaming/analytics/aggregation … 14
Cassandra architecture! Cluster Replication Consistency
@doanduyhai Cassandra architecture! Cluster layer •  Amazon DynamoDB paper •  masterless architecture Data-store layer •  Google Big Table paper •  Columns/columns family 16
@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 17
@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 18
@doanduyhai Distributed Table! n1 n2 n3 n4 n5 n6 n7 n8 A B C D E F G H user_id1 user_id2 user_id3 user_id4 user_id5 19
@doanduyhai Distributed Table! n1 n2 n3 n4 n5 n6 n7 n8 A B C D E F G H user_id1 user_id2 user_id3 user_id4 user_id5 20
@doanduyhai Linear scalability! n1 n2 n3 n4 n5 n6 n7 n8 n1 n2 n3 n4 n5 n6 n7 n8n9 n10 8 nodes 10 nodes 21
@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 22
@doanduyhai Coordinator node! Incoming requests (read/write) Coordinator node handles the request Every node can be coordinator àmasterless n1 n2 n3 n4 n5 n6 n7 n8 1 2 3 coordinator request 23
@doanduyhai Consistency! Tunable at runtime •  ONE •  QUORUM (strict majority w.r.t. RF) •  ALL Apply both to read & write 24
@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 25
@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 … 26
@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 27
@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 … 28
@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 29
@doanduyhai Consistency trade-off! 30
@doanduyhai Consistency level! ONE Fast, may not read latest written value 31
@doanduyhai Consistency level! QUORUM Strict majority w.r.t. Replication Factor Good balance 32
@doanduyhai Consistency level! ALL Paranoid Slow, no high availability 33
@doanduyhai Consistency summary! ONERead + ONEWrite ☞ available for read/write even (N-1) replicas down QUORUMRead + QUORUMWrite ☞ available for read/write even 1+ replica down 34
Q & R ! "!
Data model! Last Write Win! CQL basics! From SQL to CQL! Lightweight Transaction!
@doanduyhai Cassandra Write Path! Commit log1 . . . 1 Commit log2 Commit logn Memory 37
@doanduyhai Cassandra Write Path! Memory Commit log1 . . . 1 Commit log2 Commit logn MemTable Table1 MemTable Table2 MemTable TableN 2 . . . 38
@doanduyhai Cassandra Write Path! Commit log1 Commit log2 Commit logn Table1 SSTable1 Table2 Table3 SSTable2 SSTable3 3 Memory . . . 39
@doanduyhai Cassandra Write Path! Commit log1 Commit log2 Commit logn Table1 SSTable1 Table2 Table3 SSTable2 SSTable3 Memory. . . MemTable Table1 MemTable Table2 MemTable TableN . . . 40
@doanduyhai Cassandra Write Path! Commit log1 Commit log2 Commit logn Table1 SSTable1 Table2 Table3 SSTable2 SSTable3 Memory SSTable1 SSTable2 SSTable3 . . . 41
@doanduyhai Last Write Win (LWW)! jdoe age name 33 John DOE INSERT INTO users(login, name, age) VALUES(‘jdoe’, ‘John DOE’, 33); #partition 42
@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 . 43
@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 44
@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 45
@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 46
@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
@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 48
@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) … … … … … … 49
@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’; 50
@doanduyhai Simple Table! CREATE TABLE users ( login text, name text, age int, … PRIMARY KEY(login)); partition key (#partition) 51
@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 52
@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 53
@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 … … … … 54
@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’; 55
@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’; ❓ ❓ 56
@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’; 57
@doanduyhai Without #partition ? ? ? ? ? ? ? ? ❓ ❓ ❓ ❓ ❓ ❓ ❓ ❓ No #partition ☞ no token ☞ where are my data ? 58
@doanduyhai The importance of #partition In RDBMS, no primary key ☞ full table scan 😭 59
@doanduyhai The importance of #partition With Cassandra, no partition key ☞ full CLUSTER scan 😱 60
@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) 61
@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 ( ) 62
@doanduyhai WHERE clause restrictions! What if I want to perform « arbitrary » WHERE clause ? •  search form scenario, dynamic search fields 63
@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) 64
@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’; 65
@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)); 66 Keep the cardinality low ≈ 1000
@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 67
@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)); 68
@doanduyhai UDT insert! INSERT INTO users(login,name, location) VALUES ( ‘jdoe’, ’John DOE’, { ‘street_number’: 124, ‘street_name’: ‘Congress Avenue’, ‘postcode’: 95054, ‘country’: ‘USA’ }); 69
@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>) 70
@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)); 71
@doanduyhai From SQL to CQL 1 SELECT -  10 last comments -  10 author_login What to do with 10 author_login ??? Comment User 1 n 72
@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 73
@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)); 74
@doanduyhai Data modeling best practices! Start by queries •  identify core functional read paths •  1 read scenario ≈ 1 SELECT 75
@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 76
@doanduyhai Data modeling best practices! Person UDT - firstname/lastname - date of birth - gender - mood - location 77
@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 78
@doanduyhai Data modeling trade-off What if ... •  not possible to de-normalize with immutable data ? •  have to duplicate mutable data ? 79
@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 80
@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% 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 But always keep those scenarios rare (5%-10% max), focus on the 90% Example: Twitter tweet deletion 82
Q & R ! "!
@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 84
@doanduyhai Lightweight Transaction (LWT)! INSERT INTO account (id, email) VALUES (‘jdoe’, ‘john_doe@fiction.com’); SELECT * FROM account WHERE id= ‘jdoe’; (0 rows) SELECT * FROM account WHERE id= ‘jdoe’; (0 rows) INSERT INTO account (id, email) VALUES (‘jdoe’, ‘jdoe@fiction.com’); winner 85
@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 = ‘john_doe@fiction.com’ WHERE id=‘jdoe’; 86
@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 87
@doanduyhai Lightweight Transaction (LWT)! Recommendations •  LWT expensive (4 round-trips), do not abuse •  only for 1% – 5% use cases 88
@doanduyhai Lightweight Transaction (LWT)! 1 2 3 4Compare Swap / Learn Queue-in Consensus 89
Q & R ! "!
@doanduyhai DSE (Datastax Enterprise)! Security Analytics (Spark & Hadoop) Search (Solr) 91
@doanduyhai Use Cases! Messaging Collections/ Playlists Fraud detection Recommendation/ Personalization Internet of things/ Sensor data 92
@doanduyhai Use Cases! Messaging Collections/ Playlists Fraud detection Recommendation/ Personalization Internet of things/ Sensor data 93
Thank You @doanduyhai duy_hai.doan@datastax.com https://academy.datastax.com/

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