Cassandra introduction 2016

Introduction to CassandraDuyHai DOANApache Cassandra Evangelist

@doanduyhai

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

2

@doanduyhai

Cassandra history•  created at Facebook

•  open-sourced since 2008

•  current version: 3.4

•  column-oriented ☞ distributed table

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5 Cassandra key points•  Linear scalability •  Continuous availability •  Multi Data-center native •  Operational simplicity •  Spark integration

@doanduyhai

1) Linear scalability

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C*

C*C*

NetcoSports 3 nodes, ≈3GB

1k+ nodes, PB+

YOU

@doanduyhai

2) Continuous availability

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•  thanks to the Dynamo architecture

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3) Multi Data-centers

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•  out-of-the-box (config only)

•  AWS config for multi-regions DCs

•  GCE support

•  Microsoft Azure support

•  CloudStack support

@doanduyhai

Multi DC usagesData locality, disaster recovery

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C*

C*

C*

C*

C* C*

C* C* C*

C*

C*

C*

C*

New York (DC1) London (DC2)

Async replication

@doanduyhai

Multi DC usagesVirtual DC for workload segregation

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C*

C*

C*

C*

C* C*

C* C* C*

C*

C*

C*

C*

Production (LIVE)

Analytics(Spark)

Async replication

Same room

@doanduyhai

Multi DC usagesProd data copy for back-up/benchmark

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C*

C*

C*

C*

C* C*

C* C* C*

C*

C*

C*

C*

Use LOCAL_XXX Consistency

Levels

My tiny test DCREAD-ONLY!!!

Async replication

@doanduyhai

4) Operational simplicity

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•  1 node = 1 process + 2 config files (cassandra.yaml + cassandra-rackdc.properties)

•  No role between nodes, perfect symmetry

•  deployment automation

•  OpsCenter* for•  monitoring•  provisioning•  services (repair, performance, …)

* only with Datastax Enterprise from Cassandra 3.x

@doanduyhai

4) Operational simplicity

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5) Eco System

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•  Apache Spark – Apache Cassandra integration•  analytics•  joins, aggregation•  SparkSQL/Dataframe integration with CQL (predicates push down)

•  Apache Zeppelin – Apache Cassandra integration•  web-based notebook•  tabular/graph display

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Q & A

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Main Cassandra use-cases

@doanduyhai

Cassandra use-cases

16 Messaging

Collections/ Playlists

Fraud detection

Recommendation/ Personalization

Internet of things/ Sensor data

@doanduyhai

Cassandra use-cases

17 Messaging

Collections/ Playlists

Fraud detection

Recommendation/ Personalization

Internet of things/ Sensor data

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Q & A

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Data Distribution

@doanduyhai

The tokens

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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*

@doanduyhai

Token ranges

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A : −x,−3x4

⎤

⎦

⎥⎥

⎤

⎦

⎥⎥

B : −3x4

,− 2x4

⎤

⎦

⎥⎥

⎤

⎦

⎥⎥

C : −2x4

,− x4

⎤

⎦

⎥⎥

⎤

⎦

⎥⎥

D : − x4

,0⎤

⎦

⎥⎥

⎤

⎦

⎥⎥

E : 0, x4

⎤

⎦

⎥⎥

⎤

⎦

⎥⎥

F : x4

,2x4

⎤

⎦

⎥⎥

⎤

⎦

⎥⎥

G : 2x4

,3x4

⎤

⎦

⎥⎥

⎤

⎦

⎥⎥

H : 3x4

,x⎤

⎦

⎥⎥

⎤

⎦

⎥⎥

C*

C*

C*

C*

C* C*

C* C*

@doanduyhai

Distributed tables

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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)),

@doanduyhai

Distributed tables

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H

A

E

D

B C

G F

user_id1

user_id2

user_id3

user_id4

user_id5

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Linear scalability

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H

A

E

D

B C

G F

Today = high load•  disk occupation 80%

•  CPU 70%

•  saturated memory

@doanduyhai

Scaling out

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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

@doanduyhai

Automatic data re-balancing with virtual nodes

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A: B: C: D: E: F: G:H:

A: B: C: D: E: F: G:H: I:J:

+2 nodes

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Q & A

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Replication Model & Consistency

@doanduyhai

Failure tolerance

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Replication factor (RF) = 3

H

A

E

D

B C

G F

1

2 3

{A, H, G}

{B, A, H} {C, B, A}

@doanduyhai

Coordinator node

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Responsible for handling requests (read/write)

Every node can be coordinator•  masterless •  no SPOF •  proxy role

H

A

E

D

B C

G F

coordinator

request

1

2 3

@doanduyhai

Consistency level

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Tunable at runtime •  ONE •  QUORUM (strict majority w.r.t RF)•  ALL

Applicable to any request (read/write)

@doanduyhai

Consistency in action

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B A A

B A A

Read ONE: A

data replication in progress …

Write ONE: B

ack

RF = 3, Write ONE, Read ONE

@doanduyhai


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B A A

B A A

Read QUORUM: A


Write ONE: B

ack

RF = 3, Write ONE, Read QUORUM

@doanduyhai


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B A A

B A A

Read ALL: B


Write ONE: B

ack

RF = 3, Write ONE, Read ALL

@doanduyhai

Last Write Win

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H

A

E

D

B C

G F

coordinator

Read the value back

1

2 3

B (t2) A (t1)

A (t1)

@doanduyhai


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B B A

B B A

Read ONE: A


Write QUORUM: B

ack

RF = 3, Write QUORUM, Read ONE

@doanduyhai


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B B A

B B A

Read QUORUM: A


Write QUORUM: B

ack

RF = 3, Write QUORUM, Read QUORUM

@doanduyhai

Consistency level = trade-off

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@doanduyhai

Consistency level

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ONE Fast, may not read latest written value

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Consistency level

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QUORUM Strict majority w.r.t. Replication Factor

Good balance

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Consistency level

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ALL Paranoid

Slow, lost of high availability

@doanduyhai

Consistency level common patterns

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ONERead + ONEWrite☞ available for read/write even (N-1) replicas down

QUORUMRead + QUORUMWrite☞ available for read/write even if (RF - 1) replica (s) down

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Q & A

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Last Write Win & Compaction

@doanduyhai

Last Write Win (LWW)

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jdoe age name

33 John DOE

INSERT INTO users(login, name, age) VALUES('jdoe', 'John DOE', 33);

#partition

@doanduyhai


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jdoe age (t1) name (t1)

33 John DOE

auto-generated timestamp (μs)

.

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UPDATE users SET age = 34 WHERE login = 'jdoe';


33 John DOE jdoe

age (t2)

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SSTable1 SSTable2

@doanduyhai


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DELETE age FROM users WHERE login = 'jdoe';


33 John DOE jdoe

age (t2)

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SSTable1 SSTable2

tombstone

SSTable3

jdoe age (t3)

ý

@doanduyhai


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SELECT age FROM users WHERE login = 'jdoe';


33 John DOE jdoe

age (t2)

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SSTable1 SSTable2 SSTable3

jdoe age (t3)

ý

? ? ?

@doanduyhai


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33 John DOE jdoe

age (t2)

34


jdoe age (t3)

ý

✓ ✕ ✕

@doanduyhai

Compaction

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jdoe age (t3)

ý jdoe

age (t1) name (t1)

33 John DOE jdoe

age (t2)

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New SSTable


ý John DOE

Basic Data Modeling

@doanduyhai

Table creation

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CREATE TABLE users (login text,name text,age int,…PRIMARY KEY(login));

partition key (#partition)

@doanduyhai

DML statements

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UPDATE users SET age = 34 WHERE login = 'jdoe';

DELETE age FROM users WHERE login = 'jdoe';


@doanduyhai

What’s about joins ?

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How can I join data between tables ?

How can I model 1 – N relationships ?

How to model a mailbox ?

Emails User 1 n

@doanduyhai

Compound primary key

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CREATE TABLE mailbox ( login text, message_id timeuuid,interlocutor text,message text, PRIMARY KEY((login), message_id));

partition key clustering column unicity

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Compound primary key

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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

@doanduyhai

Queries

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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’;

@doanduyhai

Queries

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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 WHEREand message_id <= ‘2014-11-25 23:59:59’ ??? and message_id >= ‘2014-11-20 00:00:00’;

@doanduyhai

Queries

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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 WHEREand message_id <= ‘2014-11-25 23:59:59’and message_id >= ‘2014-11-20 00:00:00’;

@doanduyhai

Without #partition

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No #partition ☞ no token☞ where are my data ?

C*

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C* C*

❓ ❓

❓ ❓

❓

❓

❓

❓

@doanduyhai

Queries

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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%‘;

@doanduyhai

WHERE clause restrictions

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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 (⚠)

@doanduyhai


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What if I want to perform "arbitrary" WHERE clause ?•  search form scenario, dynamic search fields

@doanduyhai


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NEW SASI secondary index (contribution from Apple engineers) •  ☞ https://github.com/apache/cassandra/blob/trunk/doc/SASI.md•  ☞ integrated Query Planner (for multi-criteria searches) •  ☞ Cassandra version ≥ 3.4•  ☞ still buggy (CASSANDRA-11383, CASSANDRA-11399, …)

SELECT * FROM users WHERE firstname LIKE '%John%'; SELECT * FROM users WHERE age >= 20 AND age <=30;

@doanduyhai


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Or Datastax Enterprise Search (battle field proven) •  ☞ 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';

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Q & A

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Advanced Data Modeling

@doanduyhai

Collection types

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CREATE TABLE users (login text,name text,age int,friends set<text>,hobbies list<text>,languages map<int, text>,…PRIMARY KEY(login));

@doanduyhai

User Defined Type (UDT)

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Instead of

CREATE TABLE users (login text,…street_number int,street_name text,postcode int,country text,…PRIMARY KEY(login));

@doanduyhai

User Defined Type (UDT)

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CREATE TYPE address (street_number int,street_name text,postcode int,country text);

CREATE TABLE users (login text,…location frozen <address>,…PRIMARY KEY(login));

@doanduyhai

UDT Insert

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INSERT INTO users(login,name, location) VALUES ('jdoe', 'John DOE',{

'street_number': 124, 'street_name': 'Congress Avenue', 'postcode': 95054, 'country': ‘USA’ });

@doanduyhai

JSON syntax for INSERT/UPDATE/DELETE

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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"');

@doanduyhai

JSON syntax for SELECT

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> 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"

@doanduyhai

Why Materialized Views ?Relieve the pain of manual denormalization

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CREATE TABLE user( id int PRIMARY KEY, country text, …);

CREATE TABLE user_by_country( country text, id int, …, PRIMARY KEY(country, id));

@doanduyhai

Materialzed View In Action

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CREATE MATERIALIZED VIEW user_by_country AS SELECT country, id, firstname, lastnameFROM user WHERE country IS NOT NULL AND id IS NOT NULLPRIMARY KEY(country, id)

CREATE TABLE user_by_country ( country text, id int, firstname text, lastname text, PRIMARY KEY(country, id));

@doanduyhai

User Defined Functions (UDF)

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CREATE [OR REPLACE] FUNCTION [IF NOT EXISTS]maxOf (col1 int, col2 int) CALL ON NULL INPUT | RETURNS NULL ON NULL INPUT RETURN intLANGUAGE javaAS $$ return Math.max(col1, col2); $$;

SELECT maxOf(col1, col2) FROM table WHERE id = xxx;

@doanduyhai

User Defined Aggregates (UDA)

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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 javaAS $$ return accu + colum; $$;

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Q & A

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@doanduyhai

[email protected]

https://academy.datastax.com/

Thank You

Cassandra introduction 2016

Technology

Transcript of Cassandra introduction 2016