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Cassandra introduction 2016
1.
Introduction to Cassandra DuyHai
DOAN Apache Cassandra Evangelist
2.
Datastax • Founded in
April 2010 • We contribute a lot to Apache Cassandra™ • 400+ customers (25 of the Fortune 100), 450+ employees • Headquarter in San Francisco Bay area • EU headquarter in London, offices in France and Germany • Datastax Enterprise = OSS Cassandra + extra features © 2016 DataStax, All Rights Reserved. 2
3.
Cassandra history • created
at Facebook • open-sourced since 2008 • current version: 3.2 • column-oriented ☞ distributed table © 2016 DataStax, All Rights Reserved. 3
4.
5 Cassandra key
points • Linear scalability • Continuous availability • Multi Data-center native • Operational simplicity • Spark integration © 2016 DataStax, All Rights Reserved. 4
5.
1) Linear scalability ©
2016 DataStax, All Rights Reserved. 5 C* C* C* NetcoSports 3 nodes, ≈3GB 1k+ nodes, PB+ YOU
6.
2) Continuous availability ©
2016 DataStax, All Rights Reserved. 6 • thanks to the Dynamo architecture
7.
3) Multi Data-centers ©
2016 DataStax, All Rights Reserved. 7 • out-of-the-box (config only) • AWS config for multi-regions DCs • GCE support • Microsoft Azure support • CloudStack support
8.
Multi DC usages Data
locality, disaster recovery © 2016 DataStax, All Rights Reserved. 8 C* C* C* C* C* C* C* C* C* C* C* C* C* New York (DC1) London (DC2) Async replication
9.
Multi DC usages Virtual
DC for workload segregation © 2016 DataStax, All Rights Reserved. 9 C* C* C* C* C* C* C* C* C* C* C* C* C* Production (LIVE) Analytics (Spark) Async replication Same room
10.
Multi DC usages Prod
data copy for back-up/benchmark © 2016 DataStax, All Rights Reserved. 10 C* C* C* C* C* C* C* C* C* C* C* C* C* Use LOCAL_XXX Consistency Levels My tiny test DC READ-ONLY!!! Async replication
11.
4) Operational simplicity ©
2016 DataStax, All Rights Reserved. 11 • 1 node = 1 process + 2 config files (cassandra.yaml + cassandra-rackdc.properties) • deployment automation • OpsCenter for • monitoring • provisioning* • services* (repair, performance, …) * only with Datastax Enterprise
12.
4) Operational simplicity ©
2016 DataStax, All Rights Reserved. 12
13.
5) Spark integration ©
2016 DataStax, All Rights Reserved. 13 • Cassandra + Spark = awesome ! • Spark/Cassandra connector = most advanced connector right now for NoSQL db • predicates push-down • early filtering • dataframe integration • Analytics, aggregation, streaming …
14.
Main Cassandra use-cases ©
2016 DataStax, All Rights Reserved. 14
15.
Cassandra use-cases © 2016
DataStax, All Rights Reserved. 15 Messaging Collections/ Playlists Fraud detection Recommendation/ Personalization Internet of things/ Sensor data
16.
Cassandra use-cases © 2016
DataStax, All Rights Reserved. 16 Messaging Collections/ Playlists Fraud detection Recommendation/ Personalization Internet of things/ Sensor data
17.
© 2016 DataStax,
All Rights Reserved. 17 Q & A ! "
18.
Layers © 2016 DataStax,
All Rights Reserved. 18 • Cluster • Amazon DynamoDB paper • masterless • Storage engine • Google Big Table • columns/columns family ☞ distributed tables
19.
Data Distribution © 2016
DataStax, All Rights Reserved. 19
20.
The tokens © 2016
DataStax, All Rights Reserved. 20 Random hash of #partition à token = hash(#p) Hash: ] –x, x ] hash range: 264 values x = 264/2 C* C* C* C* C* C* C* C*
21.
Token ranges © 2016
DataStax, All Rights Reserved. 21 A: −x,− 3x 4 ⎤ ⎦ ⎥ ⎥ ⎤ ⎦ ⎥ ⎥ B: − 3x 4 ,− 2x 4 ⎤ ⎦ ⎥ ⎥ ⎤ ⎦ ⎥ ⎥ C: − 2x 4 ,− x 4 ⎤ ⎦ ⎥ ⎥ ⎤ ⎦ ⎥ ⎥ D: − x 4 ,0 ⎤ ⎦ ⎥ ⎥ ⎤ ⎦ ⎥ ⎥ E: 0, x 4 ⎤ ⎦ ⎥ ⎥ ⎤ ⎦ ⎥ ⎥ F: x 4 , 2x 4 ⎤ ⎦ ⎥ ⎥ ⎤ ⎦ ⎥ ⎥ G: 2x 4 , 3x 4 ⎤ ⎦ ⎥ ⎥ ⎤ ⎦ ⎥ ⎥ H : 3x 4 ,x ⎤ ⎦ ⎥ ⎥ ⎤ ⎦ ⎥ ⎥ C* C* C* C* C* C* C* C*
22.
Distributed tables © 2016
DataStax, All Rights Reserved. 22 H A E D B C G F user_id1 user_id2 user_id3 user_id4 user_id5 CREATE TABLE users( user_id int, …, PRIMARY KEY(user_id) ),
23.
Distributed tables © 2016
DataStax, All Rights Reserved. 23 H A E D B C G F user_id1 user_id2 user_id3 user_id4 user_id5
24.
Linear scalability © 2016
DataStax, All Rights Reserved. 24 H A E D B C G F Today = high load • disk occupation 80% • CPU 70% • saturated memory
25.
Scaling out © 2016
DataStax, All Rights Reserved. 25 H A E D B C G F I J +2 nodes • disk occupation 50% • CPU 50% • memory ✌︎ Automatic data rebalancing • each node gives up some tokens • flag to throttle network bandwidth • streamingthroughput
26.
Automatic data re-balancing
with virtual nodes © 2016 DataStax, All Rights Reserved. 26 A: B: C: D: E: F: G: H: A: B: C: D: E: F: G: H: I: J: +2 nodes
27.
© 2016 DataStax,
All Rights Reserved. 27 Q & A ! "
28.
Replication Model &
Consistency © 2016 DataStax, All Rights Reserved. 28
29.
Failure tolerance © 2016
DataStax, All Rights Reserved. 29 Replication factor (RF) = 3 H A E D B C G F 1 2 3 {A, H, G} {B, A, H} {C, B, A}
30.
Coordinator node © 2016
DataStax, All Rights Reserved. 30 Responsible for handling requests (read/write) Every node can be coordinator • masterless • round robin master for each request • no SPOF • proxy role H A E D B C G F coordinator request 1 2 3
31.
Consistency level © 2016
DataStax, All Rights Reserved. 31 Tunable at runtime • ONE • QUORUM (strict majority w.r.t RF) • ALL Applicable to any request (read/write)
32.
Consistency in action ©
2016 DataStax, All Rights Reserved. 32 B A A B A A Read ONE: A data replication in progress … Write ONE: B ack RF = 3, Write ONE, Read ONE
33.
Consistency in action ©
2016 DataStax, All Rights Reserved. 33 B A A B A A Read QUORUM: A data replication in progress … Write ONE: B ack RF = 3, Write ONE, Read QUORUM
34.
Consistency in action ©
2016 DataStax, All Rights Reserved. 34 B A A B A A Read ALL: B data replication in progress … Write ONE: B ack RF = 3, Write ONE, Read ALL
35.
Consistency in action ©
2016 DataStax, All Rights Reserved. 35 B B A B B A Read ONE: A data replication in progress … Write QUORUM: B ack RF = 3, Write QUORUM, Read ONE
36.
Consistency in action ©
2016 DataStax, All Rights Reserved. 36 B B A B B A Read QUORUM: A data replication in progress … Write QUORUM: B ack RF = 3, Write QUORUM, Read QUORUM
37.
Consistency level =
trade-off © 2016 DataStax, All Rights Reserved. 37
38.
Consistency level © 2016
DataStax, All Rights Reserved. 38 ONE Fast, may not read latest written value
39.
Consistency level © 2016
DataStax, All Rights Reserved. 39 QUORUM Strict majority w.r.t. Replication Factor Good balance
40.
Consistency level © 2016
DataStax, All Rights Reserved. 40 ALL Paranoid Slow, lost of high availability
41.
Consistency level common
patterns © 2016 DataStax, All Rights Reserved. 41 ONERead + ONEWrite ☞ available for read/write even (N-1) replicas down QUORUMRead + QUORUMWrite ☞ available for read/write even if (RF - 1) replica (s) down
42.
© 2016 DataStax,
All Rights Reserved. 42 Q & A ! "
43.
Last Write Win
& Compaction © 2016 DataStax, All Rights Reserved. 43
44.
Last Write Win
(LWW) © 2016 DataStax, All Rights Reserved. 44 jdoe age name 33 John DOE INSERT INTO users(login, name, age) VALUES('jdoe', 'John DOE', 33); #partition
45.
Last Write Win
(LWW) © 2016 DataStax, All Rights Reserved. 45 INSERT INTO users(login, name, age) VALUES('jdoe', 'John DOE', 33); jdoe age (t1) name (t1) 33 John DOE auto-generated timestamp (μs) .
46.
Last Write Win
(LWW) © 2016 DataStax, All Rights Reserved. 46 UPDATE users SET age = 34 WHERE login = 'jdoe'; jdoe age (t1) name (t1) 33 John DOE jdoe age (t2) 34 SSTable1 SSTable2
47.
Last Write Win
(LWW) © 2016 DataStax, All Rights Reserved. 47 DELETE age FROM users WHERE login = 'jdoe'; jdoe age (t1) name (t1) 33 John DOE jdoe age (t2) 34 SSTable1 SSTable2 tombstone SSTable3 jdoe age (t3) ý
48.
Last Write Win
(LWW) © 2016 DataStax, All Rights Reserved. 48 SELECT age FROM users WHERE login = 'jdoe'; jdoe age (t1) name (t1) 33 John DOE jdoe age (t2) 34 SSTable1 SSTable2 SSTable3 jdoe age (t3) ý ???
49.
Last Write Win
(LWW) © 2016 DataStax, All Rights Reserved. 49 SELECT age FROM users WHERE login = 'jdoe'; jdoe age (t1) name (t1) 33 John DOE jdoe age (t2) 34 SSTable1 SSTable2 SSTable3 jdoe age (t3) ý ✓✕✕
50.
Compaction © 2016 DataStax,
All Rights Reserved. 50 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
51.
Basic Data Modeling ©
2016 DataStax, All Rights Reserved. 51
52.
Table creation © 2016
DataStax, All Rights Reserved. 52 CREATE TABLE users ( login text, name text, age int, … PRIMARY KEY(login)); partition key (#partition)
53.
DML statements © 2016
DataStax, All Rights Reserved. 53 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';
54.
What’s about joins
? © 2016 DataStax, All Rights Reserved. 54 How can I join data between tables ? How can I model 1 – N relationships ? How to model a mailbox ? EmailsUser 1 n
55.
Compound primary key ©
2016 DataStax, All Rights Reserved. 55 CREATE TABLE mailbox ( login text, message_id timeuuid, interlocutor text, message text, PRIMARY KEY((login), message_id)); partition key clustering column unicity
56.
Compound primary key ©
2016 DataStax, All Rights Reserved. 56 rsmith 2014-11-21 16:00:00 ‘bobm’, ‘It’s really…’ 2014-11-21 17:32:12 ‘bobm’, ‘It depends..’ 2014-11-21 21:21:09 ‘bobm’, ‘Don’t do…’ … hsue 2014-11-21 11:04:43 ‘jdoe’, ‘Hi, …’ 2014-11-21 11:22:43 ‘rsmith’, ‘Hello,…’ jdoe 2014-11-21 11:00:00 ‘hsue’, ‘Hi there!’ 2014-11-21 11:22:43 ‘rsmith’, ‘Hello,…’ 2014-11-21 13:06:19 ‘bobm’, ‘Do you…’ ordered by clustering column (date) Not ordered
57.
Queries © 2016 DataStax,
All Rights Reserved. 57 Get message by user and message_id (date) Get message by user and date interval SELECT * FROM mailbox WHERE login = 'jdoe' and message_id = ‘2014-11-21 16:00:00’; SELECT * FROM mailbox WHERE login = 'jdoe' and message_id <= ‘2014-11-25 23:59:59’ and message_id >= ‘2014-11-20 00:00:00’;
58.
Queries © 2016 DataStax,
All Rights Reserved. 58 Get message by message_id only Get message by date interval SELECT * FROM mailbox WHERE message_id = ‘2014-11-21 16:00:00’; ??? SELECT * FROM mailbox WHERE and message_id <= ‘2014-11-25 23:59:59’ ??? and message_id >= ‘2014-11-20 00:00:00’;
59.
Queries © 2016 DataStax,
All Rights Reserved. 59 Get message by message_id only (#partition not provided) Get message by date interval (#partition not provided) SELECT * FROM mailbox WHERE message_id = ‘2014-11-21 16:00:00’; SELECT * FROM mailbox WHERE and message_id <= ‘2014-11-25 23:59:59’ and message_id >= ‘2014-11-20 00:00:00’;
60.
Without #partition © 2016
DataStax, All Rights Reserved. 60 No #partition ☞ no token ☞ where are my data ? C* C* C* C* C* C* C* C* ❓ ❓ ❓ ❓ ❓ ❓ ❓ ❓
61.
Queries © 2016 DataStax,
All Rights Reserved. 61 Get message by user range (range query on #partition) Get message by user pattern (non exact match on #partition) SELECT * FROM mailbox WHERE login >= hsue and login <= jdoe; SELECT * FROM mailbox WHERE login like ‘%doe%‘;
62.
WHERE clause restrictions ©
2016 DataStax, All Rights Reserved. 62 All DML queries 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.
WHERE clause restrictions ©
2016 DataStax, All Rights Reserved. 63 What if I want to perform "arbitrary" WHERE clause ? • search form scenario, dynamic search fields
64.
WHERE clause restrictions ©
2016 DataStax, All Rights Reserved. 64 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 Search) • ☞ Same JVM, 1-cluster-2-products (Solr & Cassandra)
65.
WHERE clause restrictions ©
2016 DataStax, All Rights Reserved. 65 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 Search) • ☞ 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.
© 2016 DataStax,
All Rights Reserved. 66 Q & A ! "
67.
Advanced Data Modeling ©
2016 DataStax, All Rights Reserved. 67
68.
Collection types © 2016
DataStax, All Rights Reserved. 68 CREATE TABLE users ( login text, name text, age int, friends set<text>, hobbies list<text>, languages map<int, text>, … PRIMARY KEY(login));
69.
User Defined Type
(UDT) © 2016 DataStax, All Rights Reserved. 69 Instead of CREATE TABLE users ( login text, … street_number int, street_name text, postcode int, country text, … PRIMARY KEY(login));
70.
User Defined Type
(UDT) © 2016 DataStax, All Rights Reserved. 70 CREATE TYPE address ( street_number int, street_name text, postcode int, country text); CREATE TABLE users ( login text, … location frozen <address>, … PRIMARY KEY(login));
71.
UDT Insert © 2016
DataStax, All Rights Reserved. 71 INSERT INTO users(login,name, location) VALUES ( 'jdoe', 'John DOE', { 'street_number': 124, 'street_name': 'Congress Avenue', 'postcode': 95054, 'country': ‘USA’ });
72.
JSON syntax for
INSERT/UPDATE/DELETE © 2016 DataStax, All Rights Reserved. 72 CREATE TABLE users ( id text PRIMARY KEY, age int, state text ); INSERT INTO users JSON '{"id": "user123", "age": 42, "state": "TX"}’; INSERT INTO users(id, age, state) VALUES('me', fromJson('20'), 'CA'); UPDATE users SET age = fromJson('25’) WHERE id = fromJson('"me"'); DELETE FROM users WHERE id = fromJson('"me"');
73.
JSON syntax for
SELECT © 2016 DataStax, All Rights Reserved. 73 > SELECT JSON * FROM users WHERE id = 'me'; [json] ---------------------------------------- {"id": "me", "age": 25, "state": "CA”} > SELECT JSON age,state FROM users WHERE id = 'me'; [json] ---------------------------------------- {"age": 25, "state": "CA"} > SELECT age, toJson(state) FROM users WHERE id = 'me'; age | system.tojson(state) -----+---------------------- 25 | "CA"
74.
Why Materialized Views
? Relieve the pain of manual denormalization © 2015 DataStax, All Rights Reserved. 74 CREATE TABLE user( id int PRIMARY KEY, country text, …); CREATE TABLE user_by_country( country text, id int, …, PRIMARY KEY(country, id));
75.
Materialzed View In
Action © 2015 DataStax, All Rights Reserved. 75 CREATE MATERIALIZED VIEW user_by_country AS SELECT country, id, firstname, lastname FROM user WHERE country IS NOT NULL AND id IS NOT NULL PRIMARY KEY(country, id) CREATE TABLE user_by_country ( country text, id int, firstname text, lastname text, PRIMARY KEY(country, id));
76.
User Defined Functions
(UDF) © 2016 DataStax, All Rights Reserved. 76 CREATE [OR REPLACE] FUNCTION [IF NOT EXISTS] maxOf (col1 int, col2 int) CALL ON NULL INPUT | RETURNS NULL ON NULL INPUT RETURN int LANGUAGE java AS $$ return Math.max(col1, col2); $$; SELECT maxOf(col1, col2) FROM table WHERE id = xxx;
77.
User Defined Aggregates
(UDA) © 2016 DataStax, All Rights Reserved. 77 CREATE [OR REPLACE] AGGREGATE [IF NOT EXISTS] sum(bigint) SFUNC accumulatorFunction STYPE bigint [FINALFUNC finalFunction] INITCOND 0; CREATE FUNCTION accumulatorFunction(accu bigint, column bigint) RETURNS NULL ON NULL INPUT RETURN bigint LANGUAGE java AS $$ return accu + colum; $$;
78.
© 2016 DataStax,
All Rights Reserved. 78 Q & A ! "
79.
© 2015 DataStax,
All Rights Reserved. 79 @doanduyhai duy_hai.doan@datastax.com https://academy.datastax.com/ Thank You
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