cassandra - deep dive

CassandraA Decentralized Structured Storage System

By Sameera Nelson

Outline …

Introduction

Data Model

System Architecture

Failure Detection & Recovery

Local Persistence

Performance

Statistics

What is Cassandra ?

Distributed Storage System

Manages Structured Data

Highly available , No SPoF

Not a Relational Data Model

Handle high write throughput

◦ No impact on read efficiency

Motivation

Operational Requirements in Facebook

◦ Performance

◦ Reliability/ Dealing with Failures

◦ Efficiency

◦ Continues Growth

Application◦ Inbox Search Problem, Facebook

Similar Work

Google File System◦ Distributed FS, Single master/Slave

Ficus/ Coda

◦ Distributed FS

Farsite

◦ Distributed FS, No centralized server

Bayou◦ Distributed Relational DB System

Dynamo

◦ Distributed Storage system

Data Model

Data Model

Figure from Eben Hewitt’s slides.

Supported Operations

insert(table; key; rowMutation)

get(table; key; columnName)

delete(table; key; columnName)

Query Language

CREATE TABLE users

( user_id int PRIMARY KEY,

fname text,

lname text );

INSERT INTO users

(user_id, fname, lname) VALUES (1745, 'john', 'smith');

SELECT * FROM users;

Data Structure

Log-Structured Merge Tree

System Architecture

Architecture

Fully Distributed …No Single Point of Failure

Cassandra Architecture

PartitioningData distribution across nodes

ReplicationData duplication across nodes

Cluster MembershipNode management in cluster

adding/ deleting

Partitioning

The Token Ring

Partitioning Partitions using Consistent hashing

Partitioning Assignment in to the relevant partition

Partitioning, Vnodes

Replication

Based on configured replication factor

Replication

Different Replication Policies

◦Rack Unaware

◦Rack Aware

◦Data center Aware

Cluster Membership

Based on scuttlebutt

Efficient Gossip based mechanism

Inspired for real life rumor

spreading.

Anti Entropy protocol

◦ Repair replicated data by comparing &

reconciling differences

Cluster Membership

Gossip Based

Failure Detection &

Recovery

Failure DetectionTrack state

◦ Directly, Indirectly

Accrual Detection mechanism

 Permanent Node change

◦ Admin should explicitly add or remove

Hints

◦ Data to be replayed in replication

◦ Saved in system.hints table

Accrual Failure Detector

• Node is faulty, suspicion level

monotonically increases.

• Φ(t) k• k - threshold variable

• Node is correct

• Φ(t) = 0

Local Persistence

Write Request

Write Operation

Write OperationLogging data in commit log/ memtable

Flushing data from the memtable

◦Flushing data on threshold

Storing data on disk in SSTables Mark with tombstone

Compaction Remove deletes, Sorts, Merges data,

consolidation

Write Operation

Compaction

Read RequestDirect/ Background (Read repair)

Read Operation

Delete Operation

Data not removed immediately

Only Tombstone is written

Deleted in Compacting Process

Additional Features

Adding compression Snappy Compression

Secondary index support

SSL support

◦ Client/ Node

◦ Node/ Node

Rolling commit logs

SSTable data file merging

Performance

Performance

High Throughput & Low Latency

◦ Eliminating on-disk data modification 

◦ Eliminate erase-block cycles

◦ No Locking for concurrency control

◦ Maintaining integrity not required

High Availability

Linear Scalability

Fault Tolerant

Statistics

Stats from Netflix

Liner scalability

Stats from Netflix

Some users

Thank you

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Structure