NoSql with Cassandra

Marek Koniew

and a little bit of MongoDB

Cassandra basics

SEDA architecture

Key ranges

Replication Factor

Gossip

Bootstrap & Repair

CQL: DDL & DML

54:00

Cassandra Ring - Coordinator role

51:00

Data Center aware & Rack aware

Data Center aware

Rack Aware

Different topology strategies

48:00

MongoDB deployment schema

45:00

Read / Write consistency

ANY (hinted handoff)

ONE

TWO

QUORUM

LOCAL QUORUM

EACH QUORUM

ALL

SERIAL (lighweight transactions)

39:00

Cassandra blazing fast writes

No read before write

Commit Log

Memtable

SSTable (immutable)

Flush

42:00

Write inconsistency

Not required odd number of nodes

Cassandra counters – not working

Schema updates

30:00

Expensive Read

Caches (Row, Key)

Merging SSTables

Bloom Filter

Compaction

Read repair

36:00

Creating filter logical OR

Checking filter logical AND

Possible false positives

NOT possible false negatives

Bloom FIlter

33:00

Atomic Operations

Cassandra „Lightweight Transactions” in 2.0 using Paxos

UPDATE usersSET reset_token = null AND password = ‘newpassword’IF reset_token = ‘some-generated-reset-token’

MongoDB atomic operations

db.runCommand( {

findAndModify: "people",

query: { name: "Tom", state: "active", rating: { $gt: 10 } },

sort: { rating: 1 },

update: { $inc: { score: 1 } }

} )

27:00

Tunable Consistency

Cassandra AP

Cassandra consistency level ALL = AC

MongoDB CP

24:00

Cassandra – One additional

server adds performance to whole ring.

Mongo – one more server in replica set increases read performance

Adding shard requires adding whole replica set

Scalability

21:00

Indexing

Cassandra secondary indexes

MongoDB secondary, geospatial, unique

Every node has its own part of index

Does not scale !!!

18:00

Schema denormalization

Create additional table and duplicate data

Use instead of indexes and joins

select * from audiofile where id = 1

select * from audiofile where artist = Sting

15:00

CQL: Cassandra query language (v3.1.1)

http://cassandra.apache.org/doc/cql3/CQL.html

DDL: Data definition language

DML: Data modification language

CREATE TABLE monkeySpecies (

species text PRIMARY KEY,

common_name text,

population varint,

average_size int

)

CREATE KEYSPACE Excelsior

WITH replication = {

'class':'SimpleStrategy',

'replication_factor' : 3

}

SELECT time, value

FROM events

WHERE event_type = 'myEvent'

AND time > '2011-02-03'

AND time <= '2012-01-01'

INSERT INTO NerdMovies

(movie, director, main_actor, year)

VALUES ('Serenity', 'Joss Whedon', 'Nathan Fillion',

2005)

USING TTL 86400;

12:00

Time series example

CREATE TABLE timeseries (

pkey date,

skey time,

temperature 19,

PRIMARY KEY (pkey, skey)

)

select * from timeseries

9:00

Map-Reduce

Hadoop map reduce is used

Advanced Task Tracker balancing

Use Pig & Hive. Almost not possible Java code

6:00

MongoDB map-reduce

3:00

Solr Integration

Hadoop – strong analitics

Solr – sharded index

Automatic replication

1:00

Full text search

Solr sharding the same problem like with secondary indexes

MongoDB full text search

db.articles.runCommand( "text", { search: "bake coffee -cake" } )

db.articles.ensureIndex( { subject: "text" } )

Questions