Datastax day 2016 introduction to apache cassandra

Getting started with Apache Apache
Cassandra™
DuyHai DOAN
Apache Cassandra™ Evangelist

1 Apache Cassandra™ use-cases
2 Why do I need Apache Cassandra™ ?
3 Distribution, replication & consistency model
4 Features Summary
© DataStax, All Rights Reserved. 2

Before (< 2016)
© 2016 DataStax, All Rights Reserved. 4
Messaging
Collections/
Playlists
Fraud
detection
Recommendation/
Personalization
Internet of things/
Sensor data

Before (< 2016)
Messaging
Collections/
Playlists
Fraud
detection
Recommendation/
Personalization
Internet of things/
Sensor data

Today (≥ 2016)

Why do I need Apache Cassandra™ ?

Linear Scalability
C*
C* C*
NetcoSports
3 nodes, ≈3GB
1k+ nodes, PB+
YOU

Continuous availability
•  thanks to the Dynamo architecture

Multi data-centers/cloud native
•  out-of-the-box (conﬁg only)
•  AWS/GCE/Azure/CloudStack support
•  Cloud/Bare-metal

Multi-DC usages
Data locality, disaster recovery
C*
C*
C*
C*
C* C*
C* C* C*
C*
C*
C*
C*
New York (DC1) London (DC2)
Async
replication

Multi-DC usages
Virtual DC for workload segregation
C*
C*
C*
C*
C* C*
C* C* C*
C*
C*
C*
C*
Production
(LIVE)
Analytics
(Spark)
Async
replication
Same room

Multi-DC usages
Prod data copy for back-up/benchmark
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

Operational simplicity
•  1 node = 1 process + 2 conﬁg ﬁles (cassandra.yaml +
cassandra-rackdc.properties)
•  deployment automation (Ansible …)
•  No role between nodes, perfect symmetry

Eco System
•  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

Q & A
! "

Apache Cassandra™ Architecture

The Tokens
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*

Token Ranges
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
⎤
⎦
⎥
⎥
⎤
⎦
⎥
⎥
H
A
E
D
B C
G F

Distributed Tables
user_id1
user_id2
user_id3
user_id4
user_id5
CREATE TABLE users(
user_id int,
…,
PRIMARY KEY(user_id)
);
H
A
E
D
B C
G F

Distributed Tables
user_id1user_id2
user_id3
user_id4
user_id5
H
A
E
D
B C
G F

Linear Scalability
Today = high load, production
In danger
H
A
E
D
B C
G F

Scaling Out
+2 nodes to lower the pressure
H
A
E
D
B
C
G
F
I
J

Q & A
! "

Failure Tolerance
Replication factor (RF) = 3
H
A
E
D
B C
G F
1
2 3
{A, H, G}
{B, A, H} {C, B, A}

Coordinator Node
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

Consistency Level
Tunable at runtime
•  ONE
•  QUORUM (strict majority w.r.t RF)
•  ALL
Applicable to any request (read/write)

Consistency In Action
B A A
B A A
Read ONE: A
data replication in progress …
Write ONE: B
ack
RF = 3, Write ONE, Read ONE

B A A
B A A
Read QUORUM: A
Write ONE: B
ack
RF = 3, Write ONE, Read QUORUM

B A A
B A A
Read ALL: B
Write ONE: B
ack
RF = 3, Write ONE, Read ALL

Last Write Win
H
A
E
D
B C
G F
coordinator
Read the
value back
1
2 3
B (t2) A (t1)
A (t1)

B B A
B B A
Read ONE: A
Write QUORUM: B
ack
RF = 3, Write QUORUM, Read ONE

B B A
B B A
Read QUORUM: A
Write QUORUM: B
ack
RF = 3, Write QUORUM, Read QUORUM

Consistency Level = Trade-off

Consistency Level
ONE
Fast, may not read latest written value

Consistency Level
QUORUM
Strict majority w.r.t. Replication Factor
Good balance

Consistency Level
ALL
Paranoid
Slow, lost of high availability

Consistency Level Common Patterns
ONERead + ONEWrite
☞ available for read/write even (N-1) replicas down
QUORUMRead + QUORUMWrite
☞ available for read/write even if (RF - 1) replica (s) down

Q & A
! "

What is a keyspace/schema ?
Simple table container
Deﬁnes how data are replicated in the cluster (RF)
Keyspace/
Schema
Cluster
1 n
Table
1 n

Keyspace/Schema Creation
CREATE KEYSPACE single_dc WITH REPLICATION =
{ 'class': 'SimpleStrategy', 'replication_factor': 3}
CREATE KEYSPACE multi_dc WITH REPLICATION =
{ 'class': 'NetworkTopologyStrategy', 'DC1': 3, 'DC2': 3, 'DC3': 1, …}
Single Data-center
Multi Data-center

DDL Syntax
CREATE TABLE users (
login text,
name text,
age int,
…
PRIMARY KEY(login));
ALTER TABLE users ADD address text;
ALTER TABLE users DROP address;
…
DROP TABLE

DML Syntax
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';

Built-In Security Features
Role
•  CREATE ROLE x WITH PASSWORD y (NOSUPERUSER | SUPERUSER)
•  ALTER ROLE x WITH PASSWORD y (NOSUPERUSER | SUPERUSER)
•  DROP ROLE x
Permissions
•  GRANT <xxx> PERMISSION ON <resource> TO <role_name>
•  REVOKE <xxx> PERMISSION ON <resource> FROM <role_name>

Collections
CREATE TABLE xxx(
…,
li list<text>,
se set<text>,
ma map<int, text>,
…
);
UPDATE xxx SET li = li + [append] …
UPDATE xxx SET se = se + {append}
UPDATE xxx SET ma[key] = value …

User Defined Types
CREATE TYPE address (
number int,
street text,
zipcode text,
city text,
country text
);

LightWeight Transactions
INSERT INTO users(…) VALUES(...) IF NOT EXISTS;
DELETE users WHERE ... IF EXISTS;
UPDATE users SET age = xxx WHERE ... IF age = 30;
Linearizable writes on a single partition

Time To Live
INSERT INTO users(…) VALUES(...) USING TTL = 3600;
UPDATE users USING TTL = 3600 SET age = xxx
WHERE ...;

User Defined Functions/Aggregates
CREATE FUNCTION toUpperCase(input text)
RETURNS NULL ON NULL INPUT
RETURNS int
LANGUAGE java
AS $$ return input.toUpperCase(); $$;
SELECT toUpperCase(firstname) FROM users WHERE …
SELECT max(salary) FROM users WHERE ...

Materialized Views
CREATE MATERIALIZED VIEW user_by_country
AS SELECT * FROM users
WHERE user_id IS NOT NULL AND country IS NOT NULL
PRIMARY KEY ((country), user_id);

JSON Syntax for INSERT/UPDATE/DELETE
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"');

JSON Syntax for SELECT
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"

Q & A
! "

Datastax day 2016 introduction to apache cassandra

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