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Developing with Views:See Inside the Data

J Chris AndersonArchitect

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What we’ll talk about

• Lifecycle of a view• Index definition, build, and query phase• Consistency options (async by default)• Emergent Schema - Views and Documents• Patterns:• Secondary index• Basic aggregations (avg ratings by brewery)• Time-based analytics with group_level• Leaderboard• Schema Evolution

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view Lifecycle:Define - Build - query

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View Definition (in JavaScript)

like:CREATE INDEX city ON brewery city;

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Distributed Index Build Phase

• Optimized for lookups, in-order access and aggregations• 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

• Automatically kept up to date

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Active Docs Active Docs Active Docs

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• 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

Dynamic Range Queries with Optional Aggregation

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?startkey=“J”&endkey=“K”{ “rows”:[{“key”:“Juneau”,“value”:null}]}

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Queries run against stale indexes by default

• stale=update_after (default if nothing is specified)–always get fastest response–can take two queries to read your own writes

• stale=ok–auto update will trigger eventually–might not see your own writes for a few minutes– least frequent updates -> least resource impact

• stale=false–Use with Persistence observe if data needs to be included in

view results–BUT aware of delay it adds, only use when really required

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Development vs. Production Views

• Development views index a subset of the data.• Publishing a view builds the

index across the entire cluster.• Queries on production

views are scattered to all cluster members and results are gathered and returned to the client.

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Emergent Schema

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Emergent Schema

JSON.org

Github API

Twitter API

"Capture the user's intent"

• Falls out of your key-value usage• Helps to know what's efficient• Mostly you can relax

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Query Pattern:Find by Attribute

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Find documents by a specific attribute

• Lets find beers by brewery_id!

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The index definition

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The result set: beers keyed by brewery_id

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Query Pattern:Basic Aggregations

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Use a built-in reduce function with a group query

• Lets find average abv for each brewery!

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We are reducing doc.abv with _stats

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Group reduce (reduce by unique key)

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Query Pattern:Time-based Rollups

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Find patterns in beer comments by time

{   "type": "comment",   "about_id": "beer_Enlightened_Black_Ale",   "user_id": 525,   "text": "tastes like college!",   "updated": "2010-07-22 20:00:20"}{   "id": "f1e62"}

timestamp

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Query with group_level=2 to get monthly rollups

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dateToArray() is your friend

• String or Integer based timestamps• Output optimized for group_level queries• array of JSON numbers:

[2012,9,21,11,30,44]

dateT

oArra

y()

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group_level=2 results

• Monthly rollup• Sorted by time—sort the query results in your

application if you want to rank by value—no chained map-reduce

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group_level=3 - daily results - great for graphing

• Daily, hourly, minute or second rollup all possible with the same index.

• http://crate.im/posts/couchbase-views-reddit-data/

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Query Pattern:Leaderboard

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Aggregate value stored in a document

• Lets find the top-rated beers!{   "brewery": "New Belgium Brewing",   "name": "1554 Enlightened Black Ale",   "abv": 5.5,   "description": "Born of a flood...",   "category": "Belgian and French Ale",   "style": "Other Belgian-Style Ales",   "updated": "2010-07-22 20:00:20", “ratings” : { “jchris” : 5, “scalabl3” : 4, “damienkatz” : 1 }, “comments” : [ “f1e62”, “6ad8c” ]}

ratings

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Sort each beer by its average rating

• Lets find the top-rated beers!

average

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WHat Not to Write

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Most common mistakes

• Reduces that don’t reduce• Trying to do too many things with one view• Emitting too much data into a view value• Expecting view query performance to be as fast as get/set• Recursive queries require application code.

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Geographic index

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Experimental Status

• Not yet using Superstar trees • (only fast on large clusters)

• Optimized for bulk loading

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Full Text index

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Elastic Search Adapter

ElasticSearch

• Elastic Search is good for ad-hoc queries and faceted browsing• Our adapter is aware of changing Couchbase topology• Indexed by Elastic Search after stored to disk in Couchbase

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Questions?

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Views Under The Hood

J Chris AndersonArchitect

THIS TALK IS NOT WRITTEN YETmaybe combine with Dustin’s internals talk about vbucket handoff

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What we’ll talk about

• Key areas/topics discussed

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Dynamic Time Range Queries

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The B-tree Index

• Helps to know what's efficient• Superstar

http://damienkatz.net/2012/05/stabilizing_couchbase_server_2.html

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• Incremental reduce values are stored in the tree

Logical View B-tree

REDUCES

REDUCES

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• Incremental reduce values are stored in the tree

Logical View B-tree

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2525 REDUCES

REDUCES

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• Incremental reduce values are stored in the tree

Reduce!

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_count

function(keys, values) { return keys ? values.length : sum(values);}

_count

function(keys, values) { return keys ? values.length : sum(values);}

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• You can query that tree dynamically• Lots of the patterns are about pulling value from this data structure

Dynamic Queries

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{ }{ }?startkey=“abba”&endkey=“robot”{ “value”:19}?startkey=“abba”&endkey=“robot”{ “value”:19}

_count

function(keys, values) { return keys ? values.length : sum(values);}

_count

function(keys, values) { return keys ? values.length : sum(values);}

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• Queries use cached values from B-tree inner nodes when possible• Take advantage of in-order tree traversal with group_level queries

Dynamic Queries

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{ }{ }?startkey=“abba”&endkey=“robot”{ “value”:19}?startkey=“abba”&endkey=“robot”{ “value”:19}

(7 5 5 2)(7 5 5 2)

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_count

function(keys, values) { return keys ? values.length : sum(values);}

_count

function(keys, values) { return keys ? values.length : sum(values);}

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• Incremental reduce values are stored in the tree

Respect Reduce! (anti-pattern)

function(keys, values) { return values;}

function(keys, values) { return values;}

DO NOT DO THIS!

IT DOESN’T reduce

DO NOT DO THIS!

IT DOESN’T reduce

[“ace”, “argh!”,“asphalt”]s[“ace”, “argh!”,“asphalt”]s[“front”, “garage”,“hibernate”]s[“front”, “garage”,“hibernate”]s[“pluto”, “nectar”,“mirage”]s[“pluto”, “nectar”,“mirage”]s

[“ace”, “argh!”,“asphalt”, “front”, “garage”,“hibernate”][“ace”, “argh!”,“asphalt”, “front”, “garage”,“hibernate”]

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Just use the Map

• If you think you need “the identity reduce”—just use the map.

[“ace”, “argh!”,“asphalt”, “front”, “garage”,“hibernate”][“ace”, “argh!”,“asphalt”, “front”, “garage”,“hibernate”]

USE THE MAP

USE THE MAP

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Lookup via key-range

• Find tables during yesterdays lunch shift• Find shifts owned by which manager

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?startkey=“abba”&endkey=“robot”{ “value”:19}?startkey=“abba”&endkey=“robot”{ “value”:19}

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Schema evolution

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Application and Views

• Interactive schema fully controlled by application• If your code can handle it, the database can• Learn to write views defensively

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Incremental schema evolution

• Use a view to decide which documents need work• Make your workers idempotent• Once all your data is cleaned up, and old clients are no

longer writing the old format• The cleanup view is obsolete, so is any app code for

dealing with the old case• You've evolved!