Best Practices - Couchbase Indexing in Production: Couchbase Connect 2014

Best Practices

Couchbase Indexing in ProductionDavid Maier | Senior Solutions Engineer, Couchbase

• Introduction

• Document Modeling Basics

• Ways to query with Couchbase Server

• How Indexing works in Couchbase 3.x compared to 2.x

• Database Design Considerations for Views

• Configuration Settings and their Effects

• Resource Requirements

Agenda

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• Views are a powerful feature for real time applications

• Indexing can be a pretty heavy weighted operation

Introduction

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Management

Many others..

90%Views/Queries Key Access10%

Document Modeling Basics

• JavaScript Object Notation

• Meta data

• Document value

JSON Document Structure

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Management

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Normalized vs. Denormalized Data

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• Normalized

• Uses references for 1-many relationships

• Reduces data duplicates

• Smaller document size

• Denormalized

• Uses nested data

• Aggregate view of data

• Allows atomic operations

• No client side joins

Normalized vs. Denormalized Data

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Management

Many others..

Atomic Counters

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Management

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• Similar to sequences / Auto-Incrementing Columns from the rel. world

• Initialize and then increment a counter value

• Use the counter value as part of a key

Reference Documents for Lookups

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Management

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• Second document which references the primary one

Ways to query with

Couchbase Server

Retrieval via Key Patterns and Lookup Documents

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Management

Many others..

• Via key pattern

• ‘person::$firstname.$lastname’

• With lookup document

• Just 2 steps to retrieve an user by email address

• Most efficient way

• B-Tree traversal vs. direct access

Retrieval via Key Patterns and Lookup Documents

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Management

Many others..

• Access multiple documents by using a counter value

Indexing and Querying via Views

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Management

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• Organized in Design Documents

• Incremental Map-Reduce

• Spread load across nodes

• Each node indexes it’s data

Map Reduce

Process,

filter, map

and emit a

row

Aggregate

mapped data

Built in:

_count,

_sum, _stats

Indexing and Querying via Views

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Many others..

• Multiple roles

• A primary index provides access to all document id-s of a bucket

• A secondary index is an alternative access path regarding a (compound) key attribute

• A View provides you an alternative view on your data

Indexing and Querying via Views

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Management

Indexing and Querying via Views

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Management

Indexing and Querying via Views

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Management

• Simple View Access

• Exact Match

• Range

• With Reduction

• With Grouping

Best Practices for Selection, Projection and Aggregation

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Management

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• Try to avoid computing too many things in a View

• Check for attribute existence

• Select (filter) data to avoid unnecessary entries in the View

• Use document types to make Views more selective

• Project (map) only necessary data and emit it as value

• Do not emit the full document

• If possible then emit a null value and do an additional Get to retrieve the whole document

• Use the built in reduce functions if possible

Best Practices for Selection, Projection and Aggregation

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Patch

Management

Many others..

How Indexing works in Couchbase

3.x compared to 2.x

2.x Architecture

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Many others..

3.x Architecture

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Management

Many others..

The Semantic of ‘stale = false’

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Management

• 'stale = false’

• Default is ‘update_after’

• Used to enforce an index update at query time

• Adds latency if used with every query

• 2.x

• Data was eventually indexed and result was eventual consistent

• The data which did previously hit the disk was indexed

• 3.x

• Data is indexed from memory and so 'stale = false' works as semantically expected

Database Design Considerations

for Views

Number of Design Documents by Bucket

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• Indexers are allocated per Design Document

• Effects number of in parallel used CPU-s

• Bad cases

• One Design Document contains all Views

All Views are updated the same time

A lot to do for the Indexer

• One View per Design Document

Resource intensive because one Indexer per View

• Use a good balance regarding the number of Views per Design Document !

Separated buckets for Indexing / Querying

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Management

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• Creating a View for the entire bucket is heavy weighted

• View function is executed for every Set operation

• Separate the data which should be queried by Views by storing it in a separated bucket

• But don't create too much buckets !

• Overhead regarding the cluster management

XDCR: A separated cluster for Indexing / Querying

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• Use a separated Cluster for Indexing and Querying to avoid the load on the main one

• Reporting cluster vs. operational one

• Active-Passive XDCR

Configuration Settings

and their Effects

Indexing Settings

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• Index path

To use separated disks for the data and the indexes in order to improve I/O performance

Indexing Settings

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• Indexing interval

Controls how up to date the index is by default

• ‘stale = false’ as explained before

Indexing Settings

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• Maximum number of in parallel working Indexers

To increase the number of threads per node means higher level of concurrency, but also higher disk and CPU load

Rebalance Settings

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• Index aware rebalance

• By default indexing happens as part of the rebalance operation

• Ensures that you get query results from a new node during rebalance that are consistent with the query results you would have received from the node before rebalance started

Performance impact if enabled, so rebalance takes significantly more time

Rebalance Settings

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• Rebalance before compaction

• Default is 16, which means that 16 vBuckets are moved before rebalance is paused

Higher value may increase rebalance performance because it implicitly increases the rebalance priority

Rebalance Settings

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• Rebalance moves per node

• The default is 1

The number of vBuckets moved at a time during the rebalance operation

Compaction Settings

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• (Auto) Compaction

• Necessary because append only structures are used

• In-place updates are expensive

• Removes thumb stone objects and fragmentation

• Process Database and View compaction in parallel

Implies a heavier processing and disk I/O load during the compaction process

Compaction Settings

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Resource Requirements

Resource Requirements

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More CPU cores are recommended

Configure your OS File System Buffer !

Use SSD-s for Views !

CPU Disk (size, I/O)

Number of Views per Design Document

Number of the emitted items

Compaction

Complexity of

Map/Reduce

functions

Size of the emitted

value

0 200

ms

0 5000

q / s

Questions?david.maier@couchbase.com