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1
Introduction to
Couchbase Server 2.0
Dipti Borkar
Director, Product Management
2
2.0
NoSQL Document
Database
Couchbase Server
3
Easy Scalability
Consistent High Performance
Always On
24x365
Grow cluster without application changes, without downtime with a single click
Consistent sub-millisecond read and write response times
with consistent high throughput
No downtime for software upgrades, hardware maintenance, etc.
Couchbase Server
JSONJSON
JSON
JSONJSON
Flexible Data Model
JSON document model with no fixed schema.
4
Flexible Data Model
• No need to worry about the database when changing your application
• Records can have different structures, there is no fixed schema
• Allows painless data model changes for rapid application development
{“ID”: 1,“FIRST”: “Dipti”,“LAST”: “Borkar”,“ZIP”: “94040”,“CITY”: “MV”,“STATE”: “CA”
}JSON
JSONJSON JSON
5
Couchbase Server Features
Built-in clustering – All nodes equal
Data replication with auto-failover
Zero-downtime maintenance
Clone to grow and scale horizontally
Built-in managed cached
Monitoring and administration APIs and GUI
SDK for a variety of languages
6
New in 2.0
JSON support Indexing and Querying
Cross data center replicationIncremental Map Reduce
JSONJSON
JSON
JSONJSON
7
Couchbase Server 2.0 Architecture
Hea
rtb
eat
Pro
cess
mo
nit
or
Glo
bal
sin
glet
on
su
per
viso
r
Co
nfi
gura
tio
n m
anag
er
on each node
Reb
alan
ce o
rch
estr
ato
r
No
de
hea
lth
mo
nit
or
one per cluster
vBu
cket
sta
te a
nd
rep
licat
ion
man
ager
httpR
EST
man
age
me
nt
AP
I/W
eb
UI
HTTP
8091Erlang port mapper
4369Distributed Erlang
21100 - 21199
Erlang/OTP
storage interface
Couchbase EP Engine
11210Memcapable 2.0
Moxi
11211Memcapable 1.0
Memcached
New Persistence Layer
8092Query API
Qu
ery
En
gin
e
Data Manager Cluster Manager
8
Couchbase Server 2.0 Architecture
Hea
rtb
eat
Pro
cess
mo
nit
or
Glo
bal
sin
glet
on
su
per
viso
r
Co
nfi
gura
tio
n m
anag
er
on each node
Reb
alan
ce o
rch
estr
ato
r
No
de
hea
lth
mo
nit
or
one per cluster
vBu
cket
sta
te a
nd
rep
licat
ion
man
ager
httpR
EST
man
age
me
nt
AP
I/W
eb
UI
HTTP
8091Erlang port mapper
4369Distributed Erlang
21100 - 21199
Erlang/OTP
storage interface
Couchbase EP Engine
11210Memcapable 2.0
Moxi
11211Memcapable 1.0
Object-level Cache
New Persistence Layer
8092Query API
Qu
ery
En
gin
e
9
Couchbase deployment
Data Flow
Cluster Management
Web Application
CouchbaseClient Library
10
COUCHBASE OPERATIONS
11
33 2
Single node - Couchbase Write Operation
2
Managed Cache
Dis
k Q
ueu
e
Disk
Replication Queue
App Server
Couchbase Server Node
Doc 1Doc 1
Doc 1
To other node
12
33 2
Single node - Couchbase Update Operation
2
Managed Cache
Dis
k Q
ueu
e
Replication Queue
App Server
Couchbase Server Node
Doc 1’
Doc 1
Doc 1’Doc 1
Doc 1’
Disk
To other node
13
GET
Do
c 1
33 2
Single node - Couchbase Read Operation
2
Dis
k Q
ueu
e
Replication Queue
App Server
Couchbase Server Node
Doc 1
Doc 1Doc 1
Managed Cache
Disk
To other node
14
33 2
Single node - Couchbase Cache Eviction
2
Dis
k Q
ueu
e
Replication Queue
App Server
Couchbase Server Node
Doc 1
Doc 6Doc 5Doc 4Doc 3Doc 2
Doc 1
Doc 6 Doc 5 Doc 4 Doc 3 Doc 2
Managed Cache
Disk
To other node
15
33 2
Single node – Couchbase Cache Miss
2
Dis
k Q
ueu
e
Replication Queue
App Server
Couchbase Server Node
Doc 1
Doc 3Doc 5 Doc 2Doc 4
Doc 6 Doc 5 Doc 4 Doc 3 Doc 2
Doc 4
GET
Do
c 1
Doc 1
Doc 1
Managed Cache
Disk
To other node
16
COUCHBASE SERVER CLUSTER
Cluster wide - Basic Operation
• Docs distributed evenly across servers
• Each server stores both active and replica docsOnly one server active at a time
• Client library provides app with simple interface to database
• Cluster map provides map to which server doc is onApp never needs to know
• App reads, writes, updates docs
• Multiple app servers can access same document at same time
User Configured Replica Count = 1
READ/WRITE/UPDATE
ACTIVE
Doc 5
Doc 2
Doc
Doc
Doc
SERVER 1
ACTIVE
Doc 4
Doc 7
Doc
Doc
Doc
SERVER 2
Doc 8
ACTIVE
Doc 1
Doc 2
Doc
Doc
Doc
REPLICA
Doc 4
Doc 1
Doc 8
Doc
Doc
Doc
REPLICA
Doc 6
Doc 3
Doc 2
Doc
Doc
Doc
REPLICA
Doc 7
Doc 9
Doc 5
Doc
Doc
Doc
SERVER 3
Doc 6
APP SERVER 1
COUCHBASE Client Library
CLUSTER MAP
COUCHBASE Client Library
CLUSTER MAP
APP SERVER 2
Doc 9
17
Cluster wide - Add Nodes to Cluster
• Two servers addedOne-click operation
• Docs automatically rebalanced across clusterEven distribution of docsMinimum doc movement
• Cluster map updated
• App database calls now distributed over larger number of servers
REPLICA
ACTIVE
Doc 5
Doc 2
Doc
Doc
Doc 4
Doc 1
Doc
Doc
SERVER 1
REPLICA
ACTIVE
Doc 4
Doc 7
Doc
Doc
Doc 6
Doc 3
Doc
Doc
SERVER 2
REPLICA
ACTIVE
Doc 1
Doc 2
Doc
Doc
Doc 7
Doc 9
Doc
Doc
SERVER 3 SERVER 4 SERVER 5
REPLICA
ACTIVE
REPLICA
ACTIVE
Doc
Doc 8 Doc
Doc 9 Doc
Doc 2 Doc
Doc 8 Doc
Doc 5 Doc
Doc 6
READ/WRITE/UPDATE READ/WRITE/UPDATE
APP SERVER 1
COUCHBASE Client Library
CLUSTER MAP
COUCHBASE Client Library
CLUSTER MAP
APP SERVER 2
COUCHBASE SERVER CLUSTER
User Configured Replica Count = 1
18
Cluster wide - Fail Over Node
REPLICA
ACTIVE
Doc 5
Doc 2
Doc
Doc
Doc 4
Doc 1
Doc
Doc
SERVER 1
REPLICA
ACTIVE
Doc 4
Doc 7
Doc
Doc
Doc 6
Doc 3
Doc
Doc
SERVER 2
REPLICA
ACTIVE
Doc 1
Doc 2
Doc
Doc
Doc 7
Doc 9
Doc
Doc
SERVER 3 SERVER 4 SERVER 5
REPLICA
ACTIVE
REPLICA
ACTIVE
Doc 9
Doc 8
Doc Doc 6 Doc
Doc
Doc 5 Doc
Doc 2
Doc 8 Doc
Doc
• App servers accessing docs
• Requests to Server 3 fail
• Cluster detects server failedPromotes replicas of docs to activeUpdates cluster map
• Requests for docs now go to appropriate server
• Typically rebalance would follow
Doc
Doc 1 Doc 3
APP SERVER 1
COUCHBASE Client Library
CLUSTER MAP
COUCHBASE Client Library
CLUSTER MAP
APP SERVER 2
User Configured Replica Count = 1
COUCHBASE SERVER CLUSTER
19
DEMO TIME
20
Indexing and Querying – The basics
• Define materialized views on JSON documents and then query across the data set
• Using views you can define• Primary indexes
• Simple secondary indexes (most common use case)
• Complex secondary, tertiary and composite indexes
• Aggregations (reduction)
• Indexes are eventually indexed
• Queries are eventually consistent with respect to documents
• Built using Map/Reduce technology • Map and Reduce functions are written in Javascript
21
33 2
Eventually indexed Views – Data flow
2
Managed Cache
Dis
k Q
ueu
e
Disk
Replication Queue
App Server
Couchbase Server Node
Doc 1Doc 1
Doc 1
To other node
View engine
Doc 1
22
COUCHBASE SERVER CLUSTER
Cluster wide - Indexing and Querying
User Configured Replica Count = 1
ACTIVE
Doc 5
Doc 2
Doc
Doc
Doc
SERVER 1
REPLICA
Doc 4
Doc 1
Doc 8
Doc
Doc
Doc
APP SERVER 1
COUCHBASE Client Library
CLUSTER MAP
COUCHBASE Client Library
CLUSTER MAP
APP SERVER 2
Doc 9
• Indexing work is distributed amongst nodes
• Large data set possible
• Parallelize the effort
• Each node has index for data stored on it
• Queries combine the results from required nodes
ACTIVE
Doc 5
Doc 2
Doc
Doc
Doc
SERVER 2
REPLICA
Doc 4
Doc 1
Doc 8
Doc
Doc
Doc
Doc 9
ACTIVE
Doc 5
Doc 2
Doc
Doc
Doc
SERVER 3
REPLICA
Doc 4
Doc 1
Doc 8
Doc
Doc
Doc
Doc 9
Query
23
Cross Data Center Replication – The basics
• Replicate your Couchbase data across clusters
• Clusters may be spread across geos
• Configured on a per-bucket basis
• Supports unidirectional and bidirectional operation
• Application can read and write from both clusters (active – active replication)
• Replication throughput scales out linearly
• Different from intra-cluster replication
24
33 2
Cross data center replication – Data flow
2
Managed Cache
Dis
k Q
ueu
e
Disk
Replication Queue
App Server
Couchbase Server Node
Doc 1Doc 1
Doc 1
To other node
XDCR Queue
Doc 1
To other cluster
25
Cluster wide - XDCR
COUCHBASE SERVER CLUSTERNY DATA CENTER
ACTIVE
Doc
Doc 2
SERVER 1
Doc 9
SERVER 2 SERVER 3
RAM
Doc Doc Doc
ACTIVE
Doc
Doc
Doc
RAM
ACTIVE
Doc
Doc
Doc
RAM
DISK
Doc Doc Doc
DISK
Doc Doc Doc
DISK
COUCHBASE SERVER CLUSTERSF DATA CENTER
ACTIVE
Doc
Doc 2
SERVER 1
Doc 9
SERVER 2 SERVER 3
RAM
Doc Doc Doc
ACTIVE
Doc
Doc
Doc
RAM
ACTIVE
Doc
Doc
Doc
RAM
DISK
Doc Doc Doc
DISK
Doc Doc Doc
DISK
26
DEMO TIME
27
Demo: The next big social game
3 Objects (documents) within game:• Players• Monsters• Items
Gameplay:
• Players fight monsters
• Monsters drop items
• Players own items
28
Player Document
{
"jsonType": "player",
"uuid": "35767d02-a958-4b83-8179-616816692de1",
"name": "Keith4540",
"hitpoints": 75,
"experience": 663,
"level": 4,
"loggedIn": false
}
Player ID
29
Item Document
{
"jsonType": "item",
"name": "Katana_e5890c94-11c6-65746ce6c560",
"uuid": "e5890c94-11c6-4856-a7a6-65746ce6c560",
"ownerId": "Dale9887"
}
Item ID
Player ID
30
Monster Document
{
"jsonType": "monster",
"name": "Bauchan9932",
"uuid": "d10dfc1b-0412-4140-b4ec-affdbf2aa5ec",
"hitpoints": 370,
"experienceWhenKilled": 52,
"itemProbability": 0.5050581341872865
}
Monster ID
31
GAME ON!
32
Full Text Search Integration
• Elastic Search is good for ad-hoc queries and faceted browsing• Couchbase adapter uses XDCR to push mutations to ESDocs
are indexed by Elastic Search• Couchbase ES Adapter is cluster-aware
ElasticSearch
Unidirectional Cross Data Center Replication
33
Full Text Search
Data
MR Query
Application Server
ES Queries over HTTPCouchbase SDK
MR
Views
MR
Views
MR
Views
MR
Views
Couchbase Server Cluster ElasticSearch Server Cluster
TS Query
Doc Refs
Cross Data Center ReplicationXDCR-based
CB-ES Transport
34
Couchbase SDKs
Java SDK
.Net SDK
PHP SDK
Ruby SDK
…and many more
Java client API
User Code
Couchbase Server
CouchbaseClient cb = new CouchbaseClient(listURIs,"aBucket", "letmein");
cb.set("hello", 0, "world");cb.get("hello");
http://www.couchbase.com/develop
Couchbase Java Library (spymemcached)
35
QUESTIONS?
35