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Couchbase Server 2.0 in Production

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Couchbase Server 2.0: Overview

• Membase + CouchDB• Managed memory caching layer = super high performance• Clustering and online data redistribution (Rebalancing)• Indexing and Querying via JSON Map-Reduce• New SDK’s and client libraries

• Developer Preview available at:• http://www.couchbase.com/downloads

=Simple. Fast. Elastic.

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www.facebook.com/farm_town_wars

Web Servers

Load Balancer

Couchbase Servers

Let’s build a social game…

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COUCHBASE SERVER 2.0 IN PRODUCTION: DEV/OPS LIFECYCLE

Initial Setup

View Development Sizing

Client setup

Monitor Grow

Backup/Restore

Upgrade

Deploy

MaintainFailures

Test

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Couchbase Server 2.0 in production: Initial Setup

Extremely easy to get up and running:

• RPM/deb/OSX/exe installation

• Simple Web UI and setup wizard

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Data and Indexes

• Data goes “in and out” via the Memcached protocol

• Queries/indexes are created and accessed via an HTTP protocol

• Both are available separately, Couchbase-provided SDK’s will expose a single API to the developer and abstract the actual traffic

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Data and Indexes

Example JSON Document:

{

"_id": "Keith4540",

"jsonType": "player",

"name": "Keith4540",

"level": 4,

…

…

}

Example view:

function (doc) {

if (doc.jsonType == "player"){

emit(["Level", doc.level], doc._id);}

}

+

=Secondary index of players, by level

(Demo)

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Data and Indexes

• Indexes/views are based on incremental map-reduce:• Indexes are updated with incremental changes (not

batch)

• View processing is done per-vbucket:• Parallel processing on subset of data• Couchbase provides scatter-gather aggregation

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Data and Indexes

• Views are “developed” off of a random (or specific) sampling of the overall dataset and then deployed• Faster• Less load on system

• Updated views can be applied without rescanning entire dataset

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Couchbase Client SDKs

Java Client SDK

.Net SDK

PHP SDK

Ruby SDK

Python SDK

Spy memcachedConnection

HTTP couchDBconnection

Java client API

User Code

Couchbase Server

CouchbaseClient cb = new CouchbaseClient(listURIs,"aBucket", "letmein");// this is all the same as beforecb.set("hello", 0, "world");cb.get("hello");Map<String, Object> manyThings =cb.getBulk(Collection<String> keys);/* accessing a view View view = cb.getView("design_document", "my_view");Query query = new Query();query.getRange("abegin", "theend");

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COUCHBASE SERVER 2.0 IN PRODUCTION: DEV/OPS LIFECYCLE

Sizing

Client setup

Deploy

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Couchbase Server 2.0 in production: Sizing

Sizing Question: How many nodes do I need?

Considerations:RAMDiskNetworkData Safety

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Sizing

Sizing Question: How many nodes do I need?

Considerations:RAM• Metadata• Working set• Buffer/overhead

Active+Replica

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Sizing

500,000 Documents to begin with:-20 bytes in length-average Document size of 2k-metadata of about 150 bytes per key

~1Gb to store active data, an extra 1Gb to store replica data

Adding in some headroom: Give 3GB RAM to Couchbase to start and grow with

=

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Sizing

Sizing Question: How many nodes do I need?

Considerations:Disk:• Sustained write rate• Index generation (space and IO)• Append-only B-Tree• Compaction• Rebalance capacity

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Sizing

Sizing Question: How many nodes do I need?

Considerations:Network:• Client traffic• Replication• Rebalancing

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Sizing

Sizing Question: How many nodes do I need?

Considerations:Data Safety

1 node = single point of failure (bad)2 nodes = 1 replica copy (better)3+ nodes = 1 replica copy AND data/load distribution

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Sizing

Sizing Question: How many nodes do I need?

Considerations:RAMDisk NetworkData Safety

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Farm Town Wars App Code

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Couchbase JavaClient library

Query API

Couchbase

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(“smart”) library

Farm Town Wars App Code

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Couchbase PHP Client Library

Moxi (Couchbase proxy)

Query API

Couchbase

11210

Client-side Moxi

OR80928092

Client-side Deployment

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COUCHBASE SERVER 2.0 IN PRODUCTION: DEV/OPS LIFECYCLE

Monitor Grow

Backup/Restore

UpgradeMaintainFailures

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Monitoring

IMMENSE amount of information available

-Real-time traffic graphs

-REST API accessible

-Per bucket, per node and aggregate statistics

-Application and inter-node traffic

-RAM <-> Disk

-Inter-system timing

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Growth

Going from 1 hundred users to 2 million users…

– RAM usage is growing:• Ejecting data to and fetching data from disk

• Resident item ratios decreasing – might impact failover

• Cache miss ratio increases

– Disk write queue grows higher than usual

Need to add a few more nodes...

Now we have more RAM and more disk throughput without any downtime

(Demo)

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General Maintenance

• Persistence is using CouchDB technology:– Append-only B-tree

– Reliability and data integrity

– Constantly growing on-disk files

• Compaction is the answer:– Automatic

– “Fragmentation” data

– Scheduled compaction

– Partial compaction via per-vbucket database (saves disk space and time)

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• Backup/Restore:– Backup is as simple as a file-level copy (thanks CouchDB!)

– Server will automatically “warmup” from disk files upon reboot

• Upgrade:– Add nodes of new version, rebalance…

– Remove nodes of old version, rebalance…

– Done!

– No disruption

– Upgrade from existing Membase 1.7 installations to

Couchbase 2.0

General Maintenance

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Failures

• Failures happen:

– Hardware

– Network– Bugs

• Failover to replica data for immediate access

• Remove and rebalance “malfunctioning” node

(Demo)

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COUCHBASE SERVER 2.0 IN PRODUCTION: DEV/OPS LIFECYCLE

Initial Setup

View Development Sizing

Client setup

Monitor Grow

Backup/Restore

Upgrade

Deploy

MaintainFailures

Test

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THANK YOU!

Q&A