Couchbase Server 2.0 and Incremental Map reduce for real-time analytics

Couchbase Server 2.0 -‐ Webinar Series

Couchbase Server 2.0 Use Cases Overview

Introducing Couchbase Server 2.0

Couchbase Server 2.0 and Indexing/Querying

Couchbase Server 2.0 and Incremental Map Reduce for Real-Time Analytics

Couchbase Server 2.0 and Cross Data Center Replication

Couchbase Server 2.0 and Full-Text Search Integration

1Wednesday, October 10, 12




h"p://www.couchbase.com/webinars









h"p://www.couchbase.com/webinars






2

Incremental Map Reduce for Real-‐Time Analy?cs

Jasdeep JaitlaTechnical Evangelist


New in Two

JSON support Indexing and Querying

Cross data center replication

Incremental Map Reduce


New in Two

JSON support Indexing and Querying

Cross data center replication

Incremental Map Reduce


4

What we’ll talk about

• Quick RelaAonal vs Document Databases•Why Views are Helpful• Anatomy of Views

•Map• Reduce

• Simple Example of Map Reduce• Use Case -‐ Analyzing Reddit in Real-‐Time

• Demo• Breakdown

• Final Words on Views


DOCUMENT DATABASE PRIMER


6

RelaAonal vs Document Data Model

Rela?onal data model Document data modelCollecAon of complex documents witharbitrary, nested data formats and

varying “record” format.

Highly-‐structured table organizaAon with rigidly-‐defined data formats and

record structure.

JSONJSON

JSON

C1 C2 C3 C4

{

}


7

SQL Normalized Data

Addresses

1 DEN 30303CO

2 MV 94040CA

3 CHI 60609IL

Users

KEY First ZIP_IDLast

4 NY 10010NY

1 Jasdeep 2Jaitla

2 Joe 2Smith

3 Ali 2Dodson

4 John 3Doe

ZIP_ID CITY ZIPSTATE

To get informa?on about specific user, you perform a join across two tables


7

SQL Normalized Data

Addresses

1 DEN 30303CO

2 MV 94040CA

3 CHI 60609IL

Users


4 NY 10010NY

1 Jasdeep 2Jaitla

2 Joe 2Smith

3 Ali 2Dodson

4 John 3Doe




7

SQL Normalized Data

Addresses

1 DEN 30303CO

2 MV 94040CA

3 CHI 60609IL

Users


4 NY 10010NY

1 Jasdeep 2Jaitla

2 Joe 2Smith

3 Ali 2Dodson

4 John 3Doe



foreign key


7

SQL Normalized Data

Addresses

1 DEN 30303CO

2 MV 94040CA

3 CHI 60609IL

Users


4 NY 10010NY

1 Jasdeep 2Jaitla

2 Joe 2Smith

3 Ali 2Dodson

4 John 3Doe



foreign key

SELECT * FROM Users u INNER JOIN Addresses a ON u.zip_id = a.zip_id WHERE key=1


Addresses

1 DEN 30303CO

2

3 CHI 60609IL

4 NY 10010NY


Users


2

2 Joe 2Smith

3 Ali 2Dodson

4 John 3Doe

8

All data in a single document

Documents are Aggregates

1 Jasdeep Jaitla

94103CASF


Addresses

1 DEN 30303CO

2

3 CHI 60609IL

4 NY 10010NY


Users


2

2 Joe 2Smith

3 Ali 2Dodson

4 John 3Doe

8



1 Jasdeep Jaitla

94103CASF

1 Jasdeep Jaitla


+

Addresses

1 DEN 30303CO

2

3 CHI 60609IL

4 NY 10010NY


Users


2

2 Joe 2Smith

3 Ali 2Dodson

4 John 3Doe

8



1 Jasdeep Jaitla

94103CASF

1 Jasdeep Jaitla


+

Addresses

1 DEN 30303CO

2

3 CHI 60609IL

4 NY 10010NY


Users


2

2 Joe 2Smith

3 Ali 2Dodson

4 John 3Doe

8



1 Jasdeep Jaitla

94103CASF

1 Jasdeep Jaitla

SF 94103CA


+

Addresses

1 DEN 30303CO

2

3 CHI 60609IL

4 NY 10010NY


Users


2

2 Joe 2Smith

3 Ali 2Dodson

4 John 3Doe

8



1 Jasdeep Jaitla

94103CASF

1 Jasdeep Jaitla

SF 94103CA


+

Addresses

1 DEN 30303CO

2

3 CHI 60609IL

4 NY 10010NY


Users


2

2 Joe 2Smith

3 Ali 2Dodson

4 John 3Doe

8



=1 Jasdeep Jaitla

94103CASF

1 Jasdeep Jaitla

SF 94103CA


+

Addresses

1 DEN 30303CO

2

3 CHI 60609IL

4 NY 10010NY


Users


2

2 Joe 2Smith

3 Ali 2Dodson

4 John 3Doe

8



{ “ID”: 1, “First”: “Jasdeep”, “Last”: “Jaitla”, “ZIP”: “94103”, “CITY”: “SF”, “STATE”: “CA” } JSON

=1 Jasdeep Jaitla

94103CASF

1 Jasdeep Jaitla

SF 94103CA


+

Addresses

1 DEN 30303CO

2

3 CHI 60609IL

4 NY 10010NY


Users


2

2 Joe 2Smith

3 Ali 2Dodson

4 John 3Doe

8




=

Document Data is an Aggregate

1 Jasdeep Jaitla

94103CASF

1 Jasdeep Jaitla

SF 94103CA


+

Addresses

1 DEN 30303CO

2

3 CHI 60609IL

4 NY 10010NY


Users


2

2 Joe 2Smith

3 Ali 2Dodson

4 John 3Doe

8




=

couchbase.get(“user::1”)

Document Data is an Aggregate

1 Jasdeep Jaitla

94103CASF

1 Jasdeep Jaitla

SF 94103CA


9

Document Database Schema is Flexible & Dynamic

{ “ID”: 1, “FIRST”: “Jasdeep”, “LAST”: “Jaitla”, “ZIP”: “94103”, “CITY”: “SF”, “STATE”: “CA”

JSON


9



}

Just add informa?on to a document

JSON


9



}

Just add informa?on to a document

JSON

,”STATUS”:

{ “TEXT”: “Wow!”, “GEO_LOC”: “27.4” “LIKES”: 45 }


MAP-‐REDUCE BASICS


Document Keys

11

JSONJSON

JSON

{

}

Document Keys Come In Many Flavors

• Human Readable• Incremental Counter Index• UUID• Timestamp Based• Social Media Account ID• Random Numbers

Q:11Wednesday, October 10, 12

Document Keys

11

JSONJSON

JSON

{

}



Q: Does Couchbase have a mechanism for creating unique keys?


Document Keys

11

JSONJSON

JSON

{

}



If I use unique usernames or emails for keys, will I need a map-query?Q:


Document Keys

11

JSONJSON

JSON

{

}



Q: If I use UUID’s for ID’s will I need a map-reduce to find Documents?


Document Keys

12

A:


Document Keys

12

If your keys are indeterminable, you will need Secondary Indexes -- Views (Map or Map/Reduce) or Elastic Search to find Documents.

A:


Document Keys

12

If your keys are indeterminable, you will need Secondary Indexes -- Views (Map or Map/Reduce) or Elastic Search to find Documents.

A:

There are many pa_erns for key creaAon, it’s a skill and an art to design your keys.


Document Keys

13

A:


Document Keys

13

If you want to find Documents based on more than one parameter, you may need Views as well.

A:


Document Keys

13

If you want to find Documents based on more than one parameter, you may need Views as well.

A:

In many cases Lookups can also be done without Views, using a Lookup Pa_ern, but that’s not always

the case especially for Ame based or geo based values.


ANATOMY OF A VIEW


Buckets >> Design Documents >> Views

15

Couchbase Bucket



15

Couchbase Bucket

Design Document 1



15

Couchbase Bucket

Design Document 1

View



15

Couchbase Bucket

Design Document 1

ViewView



15

Couchbase Bucket

Design Document 1

ViewViewView



15

Couchbase Bucket

Design Document 1 Design Document 2

ViewViewView



15

Couchbase Bucket


ViewViewViewView



15

Couchbase Bucket


View ViewViewViewView



15

Couchbase Bucket



Indexers Are Allocated Per Design Doc



15

Couchbase Bucket




All Updated at Same TimeAll Updated at Same TimeAll Updated at Same Time



15

Couchbase Bucket





Can Only Access Data in the Bucket Namespace




15

Couchbase Bucket








15

Couchbase Bucket






function(doc, meta) {emit(doc.username, doc.email)

}

Map() funcAon = index

16

Every Document passes through View Map() functions

Map



}


16

json doc


Map



}


16

json doc doc metadata


Map



}


16

create row



Map



}


16

indexed keycreate row



Map



}


16

indexed key output value(s)create row



Map


function(doc, meta) {emit(doc.email, meta.id)

}

Text or Numeric Based Keys

17

Map



}


17

text key

Map



}


17

text key

Map

doc.email meta.id

[email protected] u::1




function(doc, meta) {emit(dateToArray(doc.timestamp), 1)

}

Array Based Index Keys

18

Array Based Index Keys get sorted by each element starting with first element

Map



}


18

array key


Map



}


18

array key


Map

dateToArray(doc.?mestamp) value

[2012,10,9,18,45] 1

[2012,9,26,11,15] 1

[2012,8,13,2,12] 1


Querying Views

32 3219Wednesday, October 10, 12

Beer Database Example

20

{ "name": "Aventinus Weizenstarkbier / Doppel Weizen Bock", "abv": 8.2, "ibu": 0, "srm": 0, "upc": 0, "type": "beer", "brewery_id": "110f1f2012", "updated": "2010-07-22 20:00:20", "description": "Dark-ruby, almost black-colored and streaked with fine top-fermenting yeast, this beer has a compact and persistent head. This is a very intense wheat doppelbock with a complex spicy chocolate-like arome with a hint of banana and raisins. On the palate, you experience a soft touch and on the tongue it is very rich and complex, though fresh with a hint of caramel. It finishes in a rich soft and lightly bitter impression.", "style": "South German-Style Weizenbock", "category": "German Ale"}

{ "id": "110f37fa30", "rev": "1-000000000", "expiration": 0, "flags": 0, "type": "json"}

meta doc


Beer Database Example

20

{ "name": "Aventinus Weizenstarkbier / Doppel Weizen Bock", "abv": 8.2, "ibu": 0, "srm": 0, "upc": 0, "type": "beer", "brewery_id": "110f1f2012", "updated": "2010-07-22 20:00:20", "description": "Dark-ruby, almost black-colored and streaked with fine top-fermenting yeast, this beer has a compact and persistent head. This is a very intense wheat doppelbock with a complex spicy chocolate-like arome with a hint of banana and raisins. On the palate, you experience a soft touch and on the tongue it is very rich and complex, though fresh with a hint of caramel. It finishes in a rich soft and lightly bitter impression.", "style": "South German-Style Weizenbock", "category": "German Ale"}

{ "id": "110f37fa30", "rev": "1-000000000", "expiration": 0, "flags": 0, "type": "json"}

meta docalcohol by volume (abv)

brewery_id (key)document key


30

The index definiAon


30

The index definiAon

+row


30

The index definiAon

indexed key+row


30

The index definiAon

indexed key value(s)+row


31

The result set: beers keyed by brewery_id


31

The result set: beers keyed by brewery_id

brewery_id

document key (of the beer)

alcohol by volume (abv)


34 34

We are reducing doc.abv with _stats


34 34

We are reducing doc.abv with _stats

add _stats built-in reduction


33

Use a built-‐in reduce funcAon with a group query

Find average alcohol by volume per brewery.


33

Use a built-‐in reduce funcAon with a group query

Find average alcohol by volume per brewery.

set group=true & reduce=true

add _stats built-in reduction


35 35

Group reduce (reduce by unique key)


35 35

Group reduce (reduce by unique key)

group=true & reduce=true

number of beers by this brewery max abvmin abv


Using Incremental Map-‐ReduceUse Case Example

36 3626Wednesday, October 10, 12

reddalyzer.com

27

reddalyzer.comReal-Time Analysis of Redditusing Couchbase & Clojure


Quick Demo


Sample Reddit Post -‐ Document

29

{ "over_18": false, "banned_by": null, "is_self": false, "link_flair_text": null, "hidden": false, "edited": false, "kind": "link", "subreddit_id": "t5_2qh55", "downs": 5, "domain": "ibelieveicanfry.com", "selftext": "", "approved_by": null, "score": 5, "author": "ibelieveicanfry", "name": "t3_yph1p", "num_comments": 0, "selftext_html": null, "link_flair_css_class": null, "likes": null, "media_embed": { }, "media": null, "title": "I don't buy the bottled Thai Sweet Chili Sauce anymore...", "thumbnail": "", "permalink": "/r/food/comments/yph1p/i_dont_buy_the_bottled_thai_sweet_chili_sauce/", "url": "http://www.ibelieveicanfry.com/2012/08/thai-sweet-chili-sauce.html", "created": 1345745189, "num_reports": null, "saved": false, "subreddit": "food", "ups": 10, "created_utc": 1345745189, "author_flair_css_class": null, "id": "yph1p", "author_flair_text": null, "clicked": false}


Sample Reddit Post -‐ Document

29

{ "over_18": false, "banned_by": null, "is_self": false, "link_flair_text": null, "hidden": false, "edited": false, "kind": "link", "subreddit_id": "t5_2qh55", "downs": 5, "domain": "ibelieveicanfry.com", "selftext": "", "approved_by": null, "score": 5, "author": "ibelieveicanfry", "name": "t3_yph1p", "num_comments": 0, "selftext_html": null, "link_flair_css_class": null, "likes": null, "media_embed": { }, "media": null, "title": "I don't buy the bottled Thai Sweet Chili Sauce anymore...", "thumbnail": "", "permalink": "/r/food/comments/yph1p/i_dont_buy_the_bottled_thai_sweet_chili_sauce/", "url": "http://www.ibelieveicanfry.com/2012/08/thai-sweet-chili-sauce.html", "created": 1345745189, "num_reports": null, "saved": false, "subreddit": "food", "ups": 10, "created_utc": 1345745189, "author_flair_css_class": null, "id": "yph1p", "author_flair_text": null, "clicked": false}

“score”: 5

“subreddit”: “food”

“created_utc”: 1345745189

“kind”: “link”


Map Subreddits by Name & Day

30

function (doc, meta) { // Skip documents that aren't JSON if (meta.type == "json") { // Skip docs that aren't links if(doc.kind == "link") { var dt = new Date(doc.created_utc * 1000);

//Get day of week, but start week on Saturday, not Sunday, so that //we can pull out the weekend easily. var ssday = dt.getUTCDay() + 1; if (ssday == 7) ssday = 0;

emit([doc.subreddit, ssday], {hour: dt.getUTCHours(), score: doc.score}); } }}

emit([doc.subreddit, ssday], {hour: dt.getUTCHours(), score: doc.score});



30




ensure doc.kind == “link”

ensure meta.type == “json”




30







30




convert doc.created_utcto Date Object

calculate day of week




30







30




emit (create) row




30







30



emit([doc.subreddit, ssday], {hour: dt.getUTCHours(), score: doc.score}); } }} order by doc.subreddit then order by day of week




30







30



emit([doc.subreddit, ssday], {hour: dt.getUTCHours(), score: doc.score}); } }} output hour of day output karma score




30






Map Output

31



Map Output

31

{"id":"zx4sc","key":["funny",0],"value":{"hour":9,"score":0}},{"id":"zxak2","key":["funny",0],"value":{"hour":13,"score":1}},{"id":"ytw3t","key":["funny",1],"value":{"hour":0,"score":938}},{"id":"yv3uf","key":["funny",1],"value":{"hour":19,"score":2508}},......



indexed key output value(s)

Map Output

31




Map Output

31



output value(s)indexed key


Map Output

31




Reduce Map

32

function (keys, values, rereduce) { var out = {freqs: [], score: []} //Prefill the arrays with zeroes for(i = 0; i < 24; i++) { out.freqs[i] = 0; out.score[i] = 0; } for(v in values) {

if(!rereduce) { //Values are the output of map out.freqs[values[v].hour] += 1; out.score[values[v].hour] += values[v].score; }

else { //Values are the output of reduce // Combine the arrays for(h in values[v].freqs) { out.freqs[h] += values[v].freqs[h]; out.score[h] += values[v].score[h];

} } } return out;

}

{

"freqs": [ 178344, 174476, 171569, 161836, 146411, 120881, 94139, 75880, 62617, 56553, 57811, 70185, 88880, 114252,137301, 156750, 166376, 172562, 177094, 182093, 180485, 180434, 178706, 176525 ],

"score": [ 2856922, 2688783,2392233, 1954973, 1623642, 1355241, 1187087, 1061364, 1009152, 1165220, 1506009, 2207945, 3081796, 3868605, 4441859,4633668, 4200795, 4291777, 3986492, 3757385, 3420142, 3032258, 3029148, 2975291 ] }

h"p://localhost:8092/reddalyzr/_design/reddit/_view/posthours?stale=update_arer


http://10.1.3.248:8092/reddalyzr/_design/reddit/_view/posthours?stale=update_after&connection_timeout=60000&limit=10&skip=0


Reduce Map

32




} } } return out;

}

{

"freqs": [ 178344, 174476, 171569, 161836, 146411, 120881, 94139, 75880, 62617, 56553, 57811, 70185, 88880, 114252,137301, 156750, 166376, 172562, 177094, 182093, 180485, 180434, 178706, 176525 ],

"score": [ 2856922, 2688783,2392233, 1954973, 1623642, 1355241, 1187087, 1061364, 1009152, 1165220, 1506009, 2207945, 3081796, 3868605, 4441859,4633668, 4200795, 4291777, 3986492, 3757385, 3420142, 3032258, 3029148, 2975291 ] }

For every row increment post count and post score (karma)





Reduce Map

32




} } } return out;

}

{

"freqs": [ 178344, 174476, 171569, 161836, 146411, 120881, 94139, 75880, 62617, 56553, 57811, 70185, 88880, 114252,137301, 156750, 166376, 172562, 177094, 182093, 180485, 180434, 178706, 176525 ],

"score": [ 2856922, 2688783,2392233, 1954973, 1623642, 1355241, 1187087, 1061364, 1009152, 1165220, 1506009, 2207945, 3081796, 3868605, 4441859,4633668, 4200795, 4291777, 3986492, 3757385, 3420142, 3032258, 3029148, 2975291 ] }





Reduce Map

32




} } } return out;

}

{

"freqs": [ 178344, 174476, 171569, 161836, 146411, 120881, 94139, 75880, 62617, 56553, 57811, 70185, 88880, 114252,137301, 156750, 166376, 172562, 177094, 182093, 180485, 180434, 178706, 176525 ],

"score": [ 2856922, 2688783,2392233, 1954973, 1623642, 1355241, 1187087, 1061364, 1009152, 1165220, 1506009, 2207945, 3081796, 3868605, 4441859,4633668, 4200795, 4291777, 3986492, 3757385, 3420142, 3032258, 3029148, 2975291 ] }

Array of Results





Reduce Map

32




} } } return out;

}

{

"freqs": [ 178344, 174476, 171569, 161836, 146411, 120881, 94139, 75880, 62617, 56553, 57811, 70185, 88880, 114252,137301, 156750, 166376, 172562, 177094, 182093, 180485, 180434, 178706, 176525 ],

"score": [ 2856922, 2688783,2392233, 1954973, 1623642, 1355241, 1187087, 1061364, 1009152, 1165220, 1506009, 2207945, 3081796, 3868605, 4441859,4633668, 4200795, 4291777, 3986492, 3757385, 3420142, 3032258, 3029148, 2975291 ] }





View UpdaAng

33

Couchbase Bucket

Design Document 2

View View

Design Document 1

ViewViewView


View UpdaAng

33

Couchbase Bucket

Design Document 2

View View

Design Document 1

ViewViewView

Updates every 3 seconds or 5000 document operations


View UpdaAng

33

Couchbase Bucket

Design Document 2

View View

Design Document 1

ViewViewView

Updates every 3 seconds or 5000 document operations

This is a Configurable Setting


View UpdaAng

33

Couchbase Bucket

Design Document 2

View View

Design Document 1

ViewViewView


View UpdaAng

33

Couchbase Bucket

Design Document 2

View View

Design Document 1

ViewViewView

Can also be Triggered to Update by client queries by using

stale=false parameter


Why is it Incremental?

34

View Indexes are Append Only B+ Trees, so new data is just added to them, and they are compacted and opAmized automaAcally

Views are only Re-‐Indexed if you change their definiAon and republish them. The original index

stays available unAl new redefined index completes indexing.


Ques?ons?

[email protected]

Twi"er@scalabl3


Automotive

Couchbase Server 2.0 and Incremental Map reduce for real-time analytics