Near Real time Map-Reduce with Views: Couchbase Connect 2014

Near Real time Map/Reduce with

ViewsSarath Lakshman | Software Engineer, Couchbase

Overview of incremental Map/Reduce

Architecture of Couchbase views

Database Change Protocol (DCP) for Views

Index on-disk storage lower layer rewrites

Faster index updates and indexing latency

Read Your Own Writes (RYOW) for view queries

Overview

©2014 Couchbase, Inc. 2

Overview to Map/Reduce views

Performing indexing and aggregations

In Couchbase, Map-Reduce is specifically used to create Indexes

Map functions are applied to JSON documents and their output or "emit" data is stored in an index

What are Map/Reduce views ?


Sample view for players of tetris with level > 100

Map function


function (doc, meta) {if (doc.game == “tetris” && doc.level > 100) {

emit([doc.level, meta.id], doc.score);}

}

Finding maximum score

function (keys, values, rereduce) {

var maxscore = 0;

for (var i = 0; i < values.length; i++) {

if (values[i] > maxscore) {

maxscore = values[i];

}

}

return maxscore;

}

Reduce function

©2014 Couchbase, Inc.

Incremental reduction


20 60

60

[105,u1], 10 [110,u2], 20 [105,u3], 50 [101,u4], 60

reduce

re-reduce

emitted values

Incremental reduction


200 60

200

[105,a], 200 [110,b], 20 [105,c], 50 [101,d], 60

reduce

re-reduce

emitted values

Design documents


Couchbase

Bucket

Design Document 2

ViewViewView

Design Document 1

ViewView

Indexers Are Allocated

Per Design Doc

Can Only Access Data in

the Bucket NamespaceAll Updated at Same Time

Querying view indexes


http://localhost:8092/default/_design/players/_view/players?limit=10

View engine architecture

View engine architecture and internals

Couchbase 3.0 Architecture


Document update

Main/Replica

XDCR



Document update

Main/Replica

XDCR



Document update

Main/Replica

XDCR

Copy On Write (COW) – Append only b+ tree


KP1

KV1 KV2 KV3

KP2

Root

KV

1

KV

2

KV

3

KP

1

KP

2Root

Key-Value and Key-Pointer nodes

Append-Only file

Copy On Write (COW) – Append only b+ tree


KP1

KV1 KV2 KV3

KP2

Root

KV

1

KV

2

KV

3

KP

1

KP

2Root

Root#

KP2#

KV3#

KV3# KP2# Root#

Append-Only file

B-tree state after

an update on KV3

View engine index maintenance


Loader WriterMapper

Index update pipeline

JSON

Document

1

• View index update is performed every five seconds when there are at-least

5000 doc changes

• Update pipeline is invoked for each design document



Loader WriterMapper

Index update pipeline

KV1 KV2

emitted

values

JSON

Document

2

• View index update is performed every five seconds when there are at-least

5000 doc changes

• Update pipeline is invoked for each design document



Batcher SorterWALOn-Disk

Btree

build/update

Index Writer

Bottom-up

btree build

Bulk

update

Initial buildIncremental

Update

View queries


Node

1

Node

2

Node

3

Query

Request

Couchbase Cluster

View queries


Node

1

Node

2

Node

3

Query Coord

Query

Request

Scatter

Requests

Couchbase Cluster

View queries


Node

1

Node

2

Node

3

Query Coord

Query

Request

Query

ResultsK-way

stream

merger

queue

Scatter

Requests

Gather

results

Couchbase Cluster

Index fragmentation and compaction


View-1

btree

View-2

btree

View-1

btree

View-2

btree

Compactor

Old

index

file

Compacted

index file

Index-file.1 Index-file.2

Index fragmentation and compaction


View-1

btree

View-2

btree

View-1

btree

View-2

btree

Apply

deltas

UpdaterWAL

Old

index

file

Compacted

index file

Index-file.1 Index-file.2

Indexing latency in Couchbase 3.0

Improvements in view performance

25

Document updates are delivered to view engine in near real time

Disk reads are not required from document storage engine

Faster index creation

Lower indexing latencies

Ability for view engine to rollback during node failures without full index rebuild

Database Change Protocol and Views

Lock contention and bottlenecks in Erlang VM

View engine needs to be faster in order to consume and process database changes through DCP

Rewritten index builder, index updater and index compactor

Future work will improve rebalance duration with views

Rewrite of index engine in storage layer


Benchmark on indexing latency


Time taken (ms) for an updated document to get indexed in a view index

4 nodes, 1 bucket, 20M docs of size 2KB, 250 mutations/sec

0

10000

20000

30000

40000

Couchbase 2.5.1 Couchbase 3.0

34916

597

Query and consistency

How to achieve Read Your Own Writes (RYOW) ?

29

Stale = ok Least query latency

Returns the query results from index storage

Index is by default incrementally updated every 5 seconds

Stale = update_after Similar to stale=ok

Forces indexer to perform index update immediately as part of query ignoring index update interval

Stale = false Higher query latency

Triggers indexer update and wait for index to be updated at-least up to the current document store

Query results are returned only after index is updated

Query options for staleness / consistency


Performing RYOW in Couchbase 2.5 Stale=false only ensured that persisted documents are available in the view query results

User should ensure a document has persisted by using Observe command before querying

RYOW using Couchbase 3.0 Stale=false ensures that query results are at-least up-to-date till the point in time of request

How view engine ensures at-least point-in-time consistency Document dataset is partitioned to smaller set across the cluster

Each partition has a sequence number that incremented on every update operation on a document

View engine notes down the current sequence numbers for all the partitions during a stale=false query and waits until index is updated at-least up to those sequence numbers

Read Your Own Writes (RYOW)


Questions

[email protected]

32

Data & Analytics

Near Real time Map-Reduce with Views: Couchbase Connect 2014