NoSQL Matters 2013 - Introduction to Map Reduce with Couchbase 2.0

Friday, April 26, 13

Introduc)on to Map Reduce with Couchbase Tugdual Grall / @tgrall
NoSQL Ma)ers ‘13 -‐ Cologne -‐ April 25th 2013 Friday, April 26, 13

About Me • Tugdual “Tug” Grall Couchbase •
Technical Evangelist eXo • CTO Oracle • Developer/Product Manager • Mainly Java/SOA Developer in consul@ng ﬁrms • Web • @tgrall • hEp://blog.grallandco.com • tgrall • NantesJUG co-‐founder • Pet Project : • hEp://www.resultri.com Friday, April 26, 13

What’s the Problem ? Lots of Data Big Data SaaS/Cloud
CompuDng Big Users Friday, April 26, 13

Solu)on Distribute: • the data • the processing of the
data Friday, April 26, 13

Map Reduce MapReduce is a programming model for processing
large data sets, and the name of an implementa@on of the model by Google. MapReduce is typically used to do distributed compu@ng on clusters of computers. hEp://research.google.com/archive/mapreduce.html Friday, April 26, 13

In details • Developer speciﬁes 2 methods: map (in_key,
in_value) -> list(out_key, intermediate_value) • Processes input data • Produces key, values pairs reduce (out_key, list(intermediate_value)) -> list(out_value) • Combines all intermediate values for a par@cular key • Produce a set of merged output values Friday, April 26, 13

Execu)on Friday, April 26, 13

Most common use case © Yahoo inc. Friday, April 26,
13

What about Couchbase? Friday, April 26, 13

Couchbase Open Source Project • Leading NoSQL database project
focused on distributed database technology and surrounding ecosystem • Supports both key-‐value and document-‐oriented use cases • All components are available under the Apache 2.0 Public License • Obtained as packaged soXware in both enterprise and community edi@ons. Couchbase Open Source Project Friday, April 26, 13

Couchbase Server Core Principles Easy Scalability Consistent High
Performance Always On 24x365 Grow cluster without applica@on changes, without down@me with a single click Consistent sub-‐millisecond read and write response @mes with consistent high throughput No down@me for soXware upgrades, hardware maintenance, etc. Flexible Data Model JSON document model with no ﬁxed schema. JSON JSON JSON JSON JSON PERFORMANCE Friday, April 26, 13

Addi)onal Couchbase Server Features Built-‐in clustering – All nodes equal
Data replica@on with auto-‐failover Zero-‐down@me maintenance Built-‐in managed cached Append-‐only storage layer Online compac@on Monitoring and admin API & UI SDK for a variety of languages Friday, April 26, 13

Heartbeat Process monitor Global singleton supervisor Conﬁgura@on manager on each
node Rebalance orchestrator Node health monitor one per cluster vBucket state and replica@on manager hVp REST management API/Web UI HTTP 8091 Erlang port mapper 4369 Distributed Erlang 21100 -‐ 21199 Erlang/OTP storage interface Couchbase EP Engine 11210 Memcapable 2.0 Moxi 11211 Memcapable 1.0 Memcached New Persistence Layer 8092 Query API Query Engine Data Manager Cluster Manager Couchbase Server 2.0 Architecture Friday, April 26, 13

New Persistence Layer storage interface Couchbase EP Engine 11210 Memcapable
2.0 Moxi 11211 Memcapable 1.0 Object-‐level Cache Disk Persistence 8092 Query API Query Engine HTTP 8091 Erlang port mapper 4369 Distributed Erlang 21100 -‐ 21199 Heartbeat Process monitor Global singleton supervisor Conﬁgura@on manager on each node Rebalance orchestrator Node health monitor one per cluster vBucket state and replica@on manager hVp REST management API/Web UI Erlang/OTP Server/Cluster Management & CommunicaDon (Erlang) RAM Cache, Indexing & Persistence Management (C & V8) The Unreasonable Effectiveness of C by Damien Katz Couchbase Server 2.0 Architecture Friday, April 26, 13

COUCHBASE SERVER CLUSTER Basic Opera)on • Docs distributed evenly across
servers • Each server stores both ac)ve and replica docs Only one server ac@ve at a @me • 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 • Mul)ple app servers can access same document at same )me 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 Friday, April 26, 13

How to access the data? Friday, April 26, 13

Couchbase.get(“my-key”); Friday, April 26, 13

Key { “string” : “string”,
“string” : value, “string” : { “string” : “string”, “string” : value }, “string” : [ array ] } JSON OBJECT (“DOCUMENT”) • How to ﬁnd document based on its aVributes? get employee by email get products by type ... • You need to look “into” the document/value Look at a document Friday, April 26, 13

Create an index ! How to? Friday, April 26, 13

{ "name": "Aventinus", "abv": 8.2, "ibu": 0, "srm": 0, "upc":
0, "type": "beer", "brewery_id": "110f1f2012", "updated": "2010-07-22 20:00:20", "description": "Dark-ruby, ... Weizenbock", "category": "German Ale" } { "id": "110f37fa30", "rev": "1-000000000", "expiration": 0, "flags": 0, "type": "json" } Key Value Aven@nus 8.2 Avenue Ale 4.1 ... ... { "name": "Aventinus", "abv": 8.2, "ibu": 0, "srm": 0, "upc": 0, "type": "beer", "brewery_id": "110f1f2012", "updated": "2010-07-22 20:00:20", "description": "Dark-ruby, ... Weizenbock", "category": "German Ale" } { "id": "110f37fa30", "rev": "1-000000000", "expiration": 0, "flags": 0, "type": "json" } { "name": "Aventinus", "abv": 8.2, "ibu": 0, "srm": 0, "upc": 0, "type": "beer", "brewery_id": "110f1f2012", "updated": "2010-07-22 20:00:20", "description": "Dark-ruby, ... Weizenbock", "category": "German Ale" } { "id": "110f37fa30", "rev": "1-000000000", "expiration": 0, "flags": 0, "type": "json" } { "name": "Aventinus", "abv": 8.2, "ibu": 0, "srm": 0, "upc": 0, "type": "beer", "brewery_id": "110f1f2012", "updated": "2010-07-22 20:00:20", "description": "Dark-ruby, ... Weizenbock", "category": "German Ale" } { "id": "110f37fa30", "rev": "1-000000000", "expiration": 0, "flags": 0, "type": "json" } { "name": "Aventinus", "abv": 8.2, "ibu": 0, "srm": 0, "upc": 0, "type": "beer", "brewery_id": "110f1f2012", "updated": "2010-07-22 20:00:20", "description": "Dark-ruby, ... Weizenbock", "category": "German Ale" } { "id": "110f37fa30", "rev": "1-000000000", "expiration": 0, "flags": 0, "type": "json" } { "name": "Aventinus", "abv": 8.2, "ibu": 0, "srm": 0, "upc": 0, "type": "beer", "brewery_id": "110f1f2012", "updated": "2010-07-22 20:00:20", "description": "Dark-ruby, ... Weizenbock", "category": "German Ale" } { "id": "110f37fa30", "rev": "1-000000000", "expiration": 0, "flags": 0, "type": "json" } { "name": "Aventinus", "abv": 8.2, "ibu": 0, "srm": 0, "upc": 0, "type": "beer", "brewery_id": "110f1f2012", "updated": "2010-07-22 20:00:20", "description": "Dark-ruby, ... Weizenbock", "category": "German Ale" } { "id": "110f37fa30", "rev": "1-000000000", "expiration": 0, "flags": 0, "type": "json" } { "name": "Aventinus", "abv": 8.2, "ibu": 0, "srm": 0, "upc": 0, "type": "beer", "brewery_id": "110f1f2012", "updated": "2010-07-22 20:00:20", "description": "Dark-ruby, ... Weizenbock", "category": "German Ale" } { "id": "110f37fa30", "rev": "1-000000000", "expiration": 0, "flags": 0, "type": "json" } { "name": "Aventinus", "abv": 8.2, "ibu": 0, "srm": 0, "upc": 0, "type": "beer", "brewery_id": "110f1f2012", "updated": "2010-07-22 20:00:20", "description": "Dark-ruby, ... Weizenbock", "category": "German Ale" } { "id": "110f37fa30", "rev": "1-000000000", "expiration": 0, "flags": 0, "type": "json" } Create the index Friday, April 26, 13

Concrete Example • This map func)on: receives the document
and metadata as developer you just have to emit the K,V Friday, April 26, 13

Map Func)on Text Friday, April 26, 13

doc.email meta.id [email protected] u::1 [email protected] u::7 [email protected] u::2 [email protected] u::5
[email protected] u::6 ye@@couchbase.com u::4 [email protected] u::3 ?startkey=”b1” & endkey=”zz” Pulls the Index-‐Keys between UTF-‐8 Range speciﬁed by the startkey and endkey. ?startkey=”bz” & endkey=”zn” Pulls the Index-‐Keys between UTF-‐8 Range speciﬁed by the startkey and endkey. Friday, April 26, 13

[email protected] u::6 ye@@couchbase.com u::4 [email protected] u::3 ?key=”[email protected]” Match a Single Index-‐Key Friday, April 26, 13

[email protected] u::6 ye@@couchbase.com u::4 [email protected] u::3 ?keys=[“[email protected]”, “[email protected]”] Query Mul@ple in the Set (Array Nota@on) Friday, April 26, 13

How it works ? Friday, April 26, 13

COUCHBASE SERVER CLUSTER 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 Friday, April 26, 13

Couchbase Server 2.0: Views • Views can cover a few
diﬀerent use cases Primary Index Simple secondary indexes (the most common) Complex secondary, ter@ary and composite indexes Aggrega@on func@ons (reduc@on) • Example: count the number of “North American Ales” Organizing related data • Built using Map/Reduce Map func@on creates a matrix from document ﬁelds Reduce func@on summarizes (reduces) informa@on Friday, April 26, 13

Distributed Index Build Phase • Op)mized for lookups, in-‐order access
and aggrega)ons • All view reads from disk (diﬀerent performance proﬁle) • View builds against every document on every node This is why you should group them in a design document • Automa)cally kept up to date “Incremental” Map Reduce Friday, April 26, 13

Dynamic Range Queries with Op5onal Aggrega5on •Eﬃciently fetch an row
or group of related rows. •Queries use cached values from B-‐tree inner nodes when possible •Take advantage of in-‐order tree traversal with group_level queries 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 Ac@ve Docs Ac@ve Docs Ac@ve Docs ?startkey=“J”&endkey=“K” {“rows”:[{“key”:“Juneau”,“value”:null}]} Friday, April 26, 13

Append Only Index • Disk acDvity is slow • UpdaDng
disk blocks is very slow • Appending new data to the end of the current ﬁle is fast • Overhead of reverse reading is small • Because exisDng blocks are not re-‐used, can lead to fragmentaDon Couchbase will compact the index automa@cally Doc View Processor Disk Doc View Processor Changed Documents Appended Original Friday, April 26, 13

Adding a new Document A-R 15 I-R 8 M-R 5
A B C D F G H I K L N O Q R A-C 3 D-F 2 G-H 2 I-L 3 N-R 4 A-H 7 I-R 7 A-R 14 M new root new key new reductions Friday, April 26, 13

What about Reduce ? • Out of the box func)ons
: _count() _sum() _stats() • Create your own if needed function(key, values, rereduce) { if (rereduce) { var result = 0; for (var i = 0; i < values.length; i++) { result += values[i]; } return result; } else { return values.length; } } Friday, April 26, 13

Reduce Func)on • Key and Arrays of values as parameters
• WriVen Javascript • Called aner the map func)on • Used to reduce the result of a map of single values • Used with grouping • Could be ignored when querying reuse the index Friday, April 26, 13

• Map() Result • Reduce() • Result Reduce in Ac)on
Key Value Belgian-‐Style Dubbel 1 Belgian-‐Style Dubbel 1 Belgian-‐Style Dubbel 1 Belgian-‐Style Pale Ale 1 Belgian-‐Style White 1 Belgian-‐Style White 1 ... ... _count() Key Value Belgian-‐Style Dubbel 3 Belgian-‐Style Pale Ale 1 Belgian-‐Style White 2 Friday, April 26, 13

How to use it? • Use client SDK to call
the view: View view = client.getView("beer", "by_name"); Query query = new Query(); query.setIncludeDocs(true) .setLimit(20) .setRangeStart(ComplexKey.of(startKey)) .setRangeEnd(ComplexKey.of(startKey + "\uefff")); ViewResponse result = client.query(view, query); for(ViewRow row : result) { .... } Friday, April 26, 13

Demonstra)on Friday, April 26, 13

≠ Hadoop & Couchbase • Deal with “Big Data” •
“More” is be)er than “Faster” • Batch Oriented • Usually used to “extract/transform” data • Fully distributed Map, Shuﬄe, Reduce • Distributed • Executed where the document is • Deal with “indexing” data • As fast as possible • Use to query the data in the Database Friday, April 26, 13

Map Reduce in Couchbase • Like many other NoSQL Database
: Used for queries ! • Index are distributed on each node of the cluster • Index are updated Incrementally • Write you Map Reduce in Javascript Friday, April 26, 13

Thank you! [email protected] @tgrall Get Couchbase Server at hEp://www.couchbase.com/download

NoSQL Matters 2013 - Introduction to Map Reduce with Couchbase 2.0

NoSQL Matters 2013 - Introduction to Map Reduce with Couchbase 2.0

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