introduction to couchbase server 2.0
DESCRIPTION
Silicon Valley NoSQL Meetup - Nov 2012. View with animations: video version here: https://vimeo.com/54691785 http://www.meetup.com/Silicon-Valley-NoSQL/events/88257222/ For more information visit: www.couchbase.comTRANSCRIPT
1
Introduction to Couchbase Server 2.0
Dipti BorkarDirector, Product Management
2
2.0
NoSQL Document Database
Couchbase Server
3
Easy Scalabili
ty
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
JSONJSONJSON
JSONJSON
PERFORMANCE
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”}
JSONJSON
JSON JSON
5
New in 2.0
JSON support Indexing and Querying
Cross data center replicationIncremental Map Reduce
JSONJSONJSON
JSONJSON
6
Couchbase Server 2.0 Architecture
Hea
rtbe
at
Proc
ess
mon
itor
Glo
bal s
ingl
eton
sup
ervi
sor
Confi
gura
tion
man
ager
on each node
Reba
lanc
e or
ches
trat
or
Nod
e he
alth
mon
itor
one per cluster
vBuc
ket s
tate
and
repl
icati
on m
anag
er
httpREST managem
ent A
PI/W
eb UI
HTTP8091
Erlang port mapper4369
Distributed Erlang21100 - 21199
Erlang/OTP
storage interface
Couchbase EP Engine
11210Memcapable 2.0
Moxi
11211Memcapable 1.0
Memcached
New Persistence Layer
8092Query API
Query Engine
Data Manager Cluster Manager
7
Couchbase Server 2.0 Architecture
Hea
rtbe
at
Proc
ess
mon
itor
Glo
bal s
ingl
eton
sup
ervi
sor
Confi
gura
tion
man
ager
on each node
Reba
lanc
e or
ches
trat
or
Nod
e he
alth
mon
itor
one per cluster
vBuc
ket s
tate
and
repl
icati
on m
anag
er
httpREST managem
ent A
PI/W
eb UI
HTTP8091
Erlang port mapper4369
Distributed Erlang21100 - 21199
Erlang/OTP
storage interface
Couchbase EP Engine
11210Memcapable 2.0
Moxi
11211Memcapable 1.0
Object-level Cache
New Persistence Layer
8092Query API
Query Engine
8
Couchbase deployment
Data Flow
Cluster Management
Web Application
CouchbaseClient Library
9
COUCHBASE OPERATIONS
10
33 2
Single node - Couchbase Write Operation2
Managed Cache
Dis
k Q
ueue
Disk
Replication Queue
App Server
Couchbase Server Node
Doc 1Doc 1
Doc 1
To other node
11
33 2
Single node - Couchbase Update Operation2
Managed Cache
Dis
k Q
ueue
Replication Queue
App Server
Couchbase Server Node
Doc 1’
Doc 1
Doc 1’Doc 1
Doc 1’
Disk
To other node
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GET
Doc 1
33 2
Single node - Couchbase Read Operation2
Dis
k Q
ueue
Replication Queue
App Server
Couchbase Server Node
Doc 1
Doc 1Doc 1
Managed Cache
Disk
To other node
13
33 2
Single node - Couchbase Cache Eviction2
Dis
k Q
ueue
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
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33 2
Single node – Couchbase Cache Miss2
Dis
k Q
ueue
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
Doc 1
Doc 1
Doc 1
Managed Cache
Disk
To other node
15
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
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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
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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
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DEMO TIME
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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
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33 2
Eventually indexed Views – Data flow2
Managed Cache
Dis
k Q
ueueDisk
Replication Queue
App Server
Couchbase Server Node
Doc 1Doc 1
Doc 1
To other node
View engine
Doc 1
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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
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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
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33 2
Cross data center replication – Data flow2
Managed Cache
Dis
k Q
ueue
Disk
Replication Queue
App Server
Couchbase Server Node
Doc 1Doc 1
Doc 1
To other node
XDCR Queue
Doc 1
To other cluster
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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
DocRAM
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
DocRAM
DISK
Doc Doc Doc
DISK
Doc Doc Doc
DISK
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DEMO TIME
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Demo: The next big social game
3 Objects (documents) within game:• Players•Monsters• Items
Gameplay:• Players fight monsters•Monsters drop items• Players own items
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Player Document
{"jsonType": "player","uuid": "35767d02-a958-4b83-8179-616816692de1","name": "Keith4540","hitpoints": 75,"experience": 663,"level": 4,"loggedIn": false
}
Player ID
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Item Document
{"jsonType": "item","name": "Katana_e5890c94-11c6-65746ce6c560","uuid": "e5890c94-11c6-4856-a7a6-65746ce6c560","ownerId": "Dale9887"
}
Item ID
Player ID
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Monster Document
{"jsonType": "monster","name": "Bauchan9932","uuid": "d10dfc1b-0412-4140-b4ec-affdbf2aa5ec","hitpoints": 370,"experienceWhenKilled": 52,"itemProbability": 0.5050581341872865
}
Monster ID
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GAME ON!
31
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
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Full Text Search
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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)
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QUESTIONS?
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