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Couchbase Q&A

Perry KrugSr. Solutions Architect

Simple. Fast. Elastic.

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Paid Production Deployments (partial list)

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Couchbase automatically distributes data across commodity servers. Built-in caching enables apps to read and write data with sub-millisecond latency. And with no schema to manage,

Couchbase effortlessly accommodates changing data management requirements.

Couchbase Server (a.k.a. Membase)

Simple. Fast. Elastic. NoSQL.

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Modern Interactive Software Architecture

Application Scales OutJust add more commodity web servers

Database Scales UpGet a bigger, more complex server

Expensive & disruptive sharding, doesn’t perform at web scale

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Data Layer Matches Application Logic Tier Architecture

Application Scales OutJust add more commodity web servers

Database Scales OutJust add more commodity data servers

Scaling out flattens the cost and performance curves

• Horizontally scalable with auto-sharding• High performance at web scale• Schema-less for flexibility

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COUCHBASE SOLUTION“THE BASICS”

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COUCHBASE CLIENT LIBRARY

Basic Operation

Docs distributed evenly across servers in the cluster

Each server stores both active & replica docs Only one server active at a time

Client library provides app with simple interface to database

Cluster map provides map to which server doc is on App never needs to know

App reads, writes, updates docs

Multiple App Servers can access same document at same time

Doc 4

Doc 2

Doc 5

SERVER 1

Doc 6

Doc 4

SERVER 2

Doc 7

Doc 1

SERVER 3

Doc 3

User Configured Replica Count = 1

Read/Write/Update

COUCHBASE CLIENT LIBRARY

Read/Write/Update

Doc 9

Doc 7

Doc 8 Doc 6

Doc 3

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

Doc 9

Doc 5

DOC

DOC

DOC

Doc 1

Doc 8 Doc 2

Replica Docs Replica Docs Replica Docs

Active Docs Active Docs Active Docs

CLUSTER MAP CLUSTER MAP

APP SERVER 1 APP SERVER 2

COUCHBASE SERVER CLUSTER

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Add Nodes

Two servers added to cluster One-click operation

Docs automatically rebalanced across cluster Even distribution of

docs Minimum doc

movement Cluster map updated

App database calls now distributed over larger # of servers

User Configured Replica Count = 1

Read/Write/Update Read/Write/Update

Doc 7

Doc 9

Doc 3

Active Docs

Replica Docs

Doc 6

COUCHBASE CLIENT LIBRARY

CLUSTER MAP

APP SERVER 1

COUCHBASE CLIENT LIBRARY

CLUSTER MAP

APP SERVER 2

Doc 4

Doc 2

Doc 5

SERVER 1

Doc 6

Doc 4

SERVER 2

Doc 7

Doc 1

SERVER 3

Doc 3

Doc 9

Doc 7

Doc 8 Doc 6

Doc 3

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

Doc 9

Doc 5

DOC

DOC

DOC

Doc 1

Doc 8 Doc 2

Replica Docs Replica Docs Replica Docs

Active Docs Active Docs Active Docs

SERVER 4 SERVER 5

Active Docs Active Docs

Replica Docs Replica Docs

COUCHBASE SERVER CLUSTER

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Fail Over Node

App servers happily accessing docs on Server 3

Server fails App server requests to server 3 fail Cluster detects server has failed

Promotes replicas of docs to active Updates cluster map

App server requests for docs now go to appropriate server

Typically rebalance would follow

User Configured Replica Count = 1

Doc 7

Doc 9

Doc 3

Active Docs

Replica Docs

Doc 6

COUCHBASE CLIENT LIBRARY

CLUSTER MAP

APP SERVER 1

COUCHBASE CLIENT LIBRARY

CLUSTER MAP

APP SERVER 2

Doc 4

Doc 2

Doc 5

SERVER 1

Doc 6

Doc 4

SERVER 2

Doc 7

Doc 1

SERVER 3

Doc 3

Doc 9

Doc 7 Doc 8

Doc 6

Doc 3

DOC

DOC

DOCDOC

DOC

DOC

DOC DOC

DOC

DOC

DOC DOC

DOC

DOC

DOC

Doc 9

Doc 5DOC

DOC

DOC

Doc 1

Doc 8

Doc 2

Replica Docs Replica Docs Replica Docs

Active Docs Active Docs Active Docs

SERVER 4 SERVER 5

Active Docs Active Docs

Replica Docs Replica Docs

COUCHBASE SERVER CLUSTER

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PERFORMANCE

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Your secret weapon: Sub-millisecond AND consistent latency

Object size (Bytes)

Late

ncy (

mic

ro s

econ

ds)

Consistently low latencies in microseconds for varying documents sizes with a mixed workload

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Your secret weapon: Sub-millisecond AND consistent latency

Number of servers in cluster

Op

era

tion

s p

er

secon

d

High throughput with 1.4 GB/sec data transfer rate using 4 servers

Linear throughput scalability

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SCALE

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Draw Something by OMGPOP

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As usage grew, game data went non-linear.

191715131197533/12826242220181614121082/6

Draw Something by OMGPOPDaily Active Users (millions)

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2

4

6

8

10

12

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By March 19, there were over 30,000,000 downloads of the app,

over 5,000 drawings being stored per second,over 2,200,000,000 drawings stored,

over 105,000 database transactions per second,and over 3.3 terabytes of data stored. Instagram (7.5M in 5 wks)

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The game exploded. But Couchbase did not.

Without a second of downtime, and while sustaining front-end performance, the cluster was continuously expanded to support growth, absorbing frequent server hardware failures.

Drawings/second

Total drawings

R/W latency (usec)

Servers

February

6February

13February

20February

27March

5March

12March

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0 5 12 50 500 1 2.2million million million million billion billion

6 6 6 18 54 72 90

30 40 32 31 38 29 34

0 3 50 333 1660 3000 5400

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Couchbase Demonstration

• Couchbase ServerTemplate Demo– Starting with one database node

under load– Dynamically scaling database– Easy management and monitoring– Not possible any other database

technology Couchbase Servers

In the EC2 or Datacenter

Web application server

Application user

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THANK YOU

COUCHBASE SIMPLE, FAST, ELASTIC NOSQL

HTTP://WWW.COUCHBASE.COM