couchbase 102 - developing with couchbase

Friday, October 11, 13

Technical Evangelist

twi0er: @scalabl3email: jasdeep@couchbase.com

Jasdeep Jaitla

Couchbase 102: Development

Technical Evangelist

twi0er: @scalabl3email: jasdeep@couchbase.com

Jasdeep Jaitla

Couchbase 102: Development

SETUP SDK

Supported SDK'swww.couchbase.com/communi/es

• Each supported SDK page has instrucHons for setup

• PHP, Ruby, NodeJS and Python clients are wrappers around libcouchbase C library, so libcouchbase must be installed first

• For other community clients, click on "All Clients" on leR nav, scroll down the page and you can see clients for Go, Erlang, Clojure, TCL, other nodejs and Perl.

Installing Libcouchbase

• Mac Tips before Libcouchbase & SDK Install

• Make sure you have XCode & Command Line Tools Installed

• Install Homebrew if you don't have it already

• Do a $ brew update, $ brew upgrade and $ brew doctor to be sure you're up to date

www.couchbase.com/communi/es/c/geBng-‐started

Installing Libcouchbase

• Mac Via Homebrew

• $ brew install libcouchbase

• PC-‐Windows

• Download appropriate Zip from website

• Redhat/CentOS

• wget the yum repositories

• $ sudo yum install -‐y libcouchbase2-‐libevent libcouchbase-‐devel

• Ubuntu

• wget ubuntu repositories

• $ sudo apt-‐get install libcouchbase2-‐libevent libcouchbase-‐dev

www.couchbase.com/communi/es/c/geBng-‐started

Make a Connectionrequire 'rubygems'require 'couchbase' cb = Couchbase.connect(

:bucket => "default",:hostname => "localhost")

data = { jsonkey: "value", created_at: Time.now }

cb.add("mydata", data)puts cb.get("mydata")

#!/usr/bin/env pythonfrom couchbase import Couchbasefrom pprint import pprintfrom datetime import datetime

cb = Couchbase.connect(bucket='default')

data = { "jsonkey": "value", "created_at": datetime.now() }

cb.add('mydata', data)result = cb.get('mydata')pprint(result.value, indent=4)

PYTHON

Make a Connection

import com.couchbase.client.CouchbaseClient;import java.net.URI;import java.util.*;import com.google.gson.Gson;import com.google.gson.GsonBuilder;

public class HelloWorld {

public static void main(String[] args) throws Exception {

List<URI> hosts = Arrays.asList(new URI("http://127.0.0.1:8091/pools")

CouchbaseClient cb = new CouchbaseClient(hosts, "default", "");Gson json = new Gson();

Hashtable data = new Hashtable();data.put("jsonkey", "value");data.put("created_at", new Date());

cb.add("mydata", json.toJson(data))System.out.println(cb.get("mydata"));

cb.shutdown();

Make a Connection

var Couchbase = require('couchbase');var cb = new Couchbase.Connection({bucket: "default"}, function(err) { });

var data = { jsonkey: "value", created_at: new Date().toString() };cb.add("mydata", data);

console.log(cb.get("mydata"));

NODEJS

<?php// adjust these parameters to match your installation$cb = new Couchbase("127.0.0.1:8091", "", "", "default");

$data = array("jsonkey" => "value", "created_at" => date("Y-m-d H:i:s"));

$cb->add("mydata",json_encode($data));var_dump($cb->get("mydata"));

RDBMS VS COUCHBASE

RDBMS Organization

• RDBMS organizes data as tables- Tables represent data in rows; n columns of m rows- Table rows have a specific schema, each column as a static

type- Simple Datatypes: strings, numbers, datetimes, booleans,

can be represented by columns in a single table- Complex Datatypes: dictionaries/hashes, arrays/lists

cannot be represented in a single table [Impedence Mismatch]

• All rows have identical schema, schema changes require taking database offline, migrations, royal pains

• Reading/Writing/Transactions require mutex and locking

Couchbase Organization

• Couchbase operates like a Key-Value Document Store - Simple Datatypes: strings, numbers, datetime, boolean, and

binary data can be stored; they are stored as Base64 encoded strings

- Complex Datatypes: dictionaries/hashes, arrays/lists, can be stored in JSON format (simple lists can be string based with delimiter)

- JSON is a special class of string with a specific format for encoding simple and complex data structures

• Schema is unenforced and implicit, schema changes are programmatic, done online, and can vary from Document to Document

Complex Datatypes

• Simple Types are easy, make them columns

• Complex Types are more challenging, require separate tables and joins, slower to store and retrieve

• ORM's reduce complexity but trade off additional speed/scale, hard to optimize

public class User {

private String name;private String email;private Integer age;private Boolean gender_male;private DateTime created_at;private ArrayList items_viewed;private Hashtable preferences;private ArrayList<Books> authored;

public User(...) {...

Complex Datatypes

• Can represent both simple and complex data types in JSON data structures

• Can modify schema on the fly, and Documents of a specific "type" can vary in schema

• "Type" is arbitrary, it's a programming strategy, there are no actual "types", but it's typical to embed the class name as a "doctype" json key

Couchbase

{"doctype": "User","name": "Jasdeep Jaitla","email": "jasdeep@couchbase.com","age": 38,"gender_male": true,"created_at": "2013-09-20 23:59:59","items_viewed": [

"12345", "23456", 34567"],"preferences": {

"email_notifications": true,"sms_notifications": false

},"authored": [

{ "title": "Couchbase Models","price": 49.95 }

Benefits of JSON

• Can Represent Complex Objects and Data Structures• Very simple notation, lightweight, compact, readable• The most common API return type for Integrations - Facebook, Twitter, you name it, return JSON

• Native to Javascript (can be useful)• Serialization and Deserialization are very fast

OPERATIONS

• get (key)– Retrieve a document

• set (key, value)– Store a document, overwrites if exists

• add (key, value)– Store a document, error/excepHon if exists

• replace (key, value)– Store a document, error/excepHon if doesn’t exist

• cas (key, value, cas)– Compare and swap, mutate document only if it hasn’t changed while execuHng this operaHon

Store & Retrieve OperaHons

Atomic Counter OperationsThese operaHons are always executed in order atomically.

• incr (key)– Increase an atomic counter value, default by 1

• cb.incr(“my_counter”) # now it’s 2

• decr (key)– Decrease an atomic counter value, default by 1

• cb.decr(“my_counter”) # now it’s 1

Non-JSON OperationsYou can use these creaHvely!

• prepend (key, value)– prepend exisHng string in couchbase with value

• cb.prepend(“mykey”, "jumped over the lazy dog") • cb.prepend("mykey", "the brown fox ")

• append (key, value)– append exisHng string in couchbase with value

• cb.append(“mykey2”, "oranges") • cb.append("mykey2", " apples bananas")

CONCURRENCY

Optimistic Concurrency with CAS

• Every storage operation (including touch) creates a new "CAS" value, which is just a long int

• The CAS value simply represents the current state of the Document, it's like a version number

• You can use this CAS for "Optimistic Concurrency"- value, flags, cas = get("mykey", :extended => true)

- This will only succeed if the CAS matches• replace("mykey", newvalue, :cas => cas)

- If another process had changed the "mykey" document, a new CAS will have been generated, and that replace operation will fail

EXPIRATIONS

Storage Operations with Expirations

• CMS Framework Cache can be configured to use Couchbase- In most frameworks this is simple, as they typically already

have memcached support

• Create/Update the Value and Expiration- [Ruby] set/add/replace("mykey", value, :ttl => 30)

- [Java] set/add/replace("mykey", 30, value)

• Update the expiration only- [Ruby] touch("mykey", :ttl => 30)

- [Java] touch("mykey", 30)

DURABILITY

Using Storage with Observe

• Callback when it has been written to disk on active partition• Callback when it has been written to a replica(s)• Callback when it has been written to replica(s) disk• Observe Persisted to Disk and Replicated

• [Ruby] set/add/replace("mykey", value, :observe => {:persisted => 1, :replicated => 1})

• [Java] set/add/replace("mykey", 0, value, PersistTo.MASTER, ReplicateTo.ONE)

• Observe Replicated Only• [Ruby] set/add/replace("mykey", value,

:observe => {:replicated => 1})• [Java] set/add/replace("mykey", 0, value, ReplicateTo.ONE)

BASIC KEY PATTERNS

Objects Serialized to JSON and Back User Objectstring uid

string firstname

string lastname

int age

array favorite_colors

string email

u::jasdeep@couchbase.com{

“uid”: 123456,“firstname”: “jasdeep”,“lastname”: “Jaitla”,“age”: 22,“favorite_colors”: [“blue”, “black”],“email”: “jasdeep@couchbase.com”

User Objectstring uid

string firstname

string lastname

int age

array favorite_colors

string email

u::jasdeep@couchbase.com{

“uid”: 123456,“firstname”: “jasdeep”,“lastname”: “Jaitla”,“age”: 22,“favorite_colors”: [“blue”, “black”],“email”: “jasdeep@couchbase.com”

Basic Keying

• Use a Unique value for key (email, username, sku, isbn, etc.)- Users

• u::jasdeep@couchbase.com• u::scalabl3

- Products• p::978-0321573513 [isbn]

• Predictable Keys can follow Key-Value patterns (Users typically can be done this way and are the most numerous items)

• Unpredictable Keys (GUID, UUID, etc.) require Views (Map-Reduce Indexes) to find their documents

Counter-ID

ApplicaHon

id = incr("counter-‐key")

add("key" + id, data)

ApplicaHon

count = get("counter-‐key")

mulH-‐get(keys[])

Data Crea/on

Iterate Through Collec/on

Counter-ID

• Similar to IDENTITY column in RDBMS• Creating New Document is a pair of operations, INCR and ADD- Initialize one Key as an Atomic Counter (I do at App Start)- Increment Counter and save new value

✴ id = client.incr("blog::couchbase::comment_count")

- Use the id as component of key for new document✴ client.add(""blog::couchbase::c"::" + id, self.to_json)

Lookup Pattern

ApplicaHon

add("jasdeep@couchbase.com", "u::550e8400-‐e29b-‐41d4-‐a716")

add("u::550e8400-‐e29b-‐41d4-‐a716", data)

add("scalabl3", "u::550e8400-‐e29b-‐41d4-‐a716")

ApplicaHon

key = get("jasdeep@couchbase.com")

get(key)

Data Crea/on

Data Retrieval

Lookup Pattern

• Create simple document that has referential data (Key) to primary document- Primary Document u::a2bf2-23317-2302- Lookup Document: u::jasdeep@couchbase.com

{ u::a2bf2-23317-2302 }• Lookup Documents aren't JSON, they should just be the Key

as a string so you skip JSON parsing• Requires Two GET operations, first GET Lookup, then GET

primary Document- key = client.get("u::jasdeep@couchbase.com")- doc = client.get(key)

User Data Multiple Social Networks & Emails

u::count

u::1001

{ "name": "Jasdeep Jaitla",

"facebook_id": 16172910,

"email": "jj@scalabl3.com",

“password”: ab02d#Jf02K

"created_at": "5/1/2012 2:30am",

“facebook_access_token”: xox0v2dje20,

“twi0er_access_token”: 20jffieieaaixixj }

\::16172910

nflx::2939202

twtr::2920283830

em::jj@jasdeep.com

em::jj@scalabl3.com

uname::scalabl3

Combine Counter-ID and Lookup

ApplicaHon

add("jasdeep@couchbase.com", id)

add("u::" + id, data)

add("scalabl3", id)

ApplicaHon

key = get("jasdeep@couchbase.com")

get(key)

Data Crea/on

Data Retrieval

id = incr("user::count")

Combine Counter-ID and Lookup

Pro's• Binary Operations, overall faster than large volume of View

Queries• Essentially creates several ways to find a single document• Is always consistent, just like all other Binary operations

Con's• Increases Number of Documents, therefore Metadata usage in

RAM- But this is generally a non-issue for most people

User Data (Sample)

CREATE TABLE Users

id, int, identity(1000) PRIMARY KEY

name, nvarchar(100)

facebook_id, bigint

email, nvarchar(255)

created_at, datetime

u::count

u::1001

{ "name": "Jasdeep Jaitla",

"facebook_id": 16172910,

"email": "jj@scalabl3.com",

"created_at": "5/1/2012 2:30am" }

\::16172910

em::jj@scalabl3.com

INSERT INTO Users(name, facebook_id, email, created_at)VALUES ("Jasdeep Jaitla", 16172910, "jj@scalabl3.com", "5/1/2012 2:30am")

Get User By FBSELECT * FROM UsersWHERE facebook_id = 16172910

Get User by EmailSELECT * FROM UsersWHERE email = “jj@scalabl3.com”

user_data = { "name": "Jasdeep Jaitla", "facebook_id": 16172910, "email": "jj@scalabl3.com", "created_at": "5/1/2012 2:30am" }

uid = couch.incr("u::count") + 1000couch.add ("u::" + uid, user_data)couch.set ("em::" + user_email, uid)couch.set ("fb::" + user_fb, uid)

Get User By FB uid = couch.get("fb::16172910")user_data = couch.get ("u::" + uid)

Get User By Emailuid = couch.get("em::jj@scalabl3.com")user_data = couch.get ("u::" + uid)

Each Table Grows and it gets Slower for Each Request

RDBMS Couchbase

MULTIPLE DOCUMENTS

Aligning Documents to Behaviors

user::1{name: “Jasdeep”,points: 1000,shopping_carts: [ 1000, 1001, 1002 ],products_bought: [ 2000, 2001, 2002],games_won: [ 3000, 3001, 3002, 3004],notifications: [ “Lorem”, “Ipsum”, “docet”, ...]}

user::1{ name: “Jasdeep” }user::1::points{ points: 1000 }user::1::shopping_carts{ carts: [ 1000, 1001, 1002 ],products_bought: [ 2000, 2001, 2002] }user::1::games_won{ game_ids: [ 3000, 3001, 3002, 3004] }user::1::notification_count57user::1::notifications::57{ message: “Hi Bob” }user::1::notifications::56{ message: “Happy Hour?” }

Behavior Driven Design

• Reduce the number of User Actions that affect a single document

• Instead, separate that user document in a predictable key structure and make it accessible via getters and setters in your class

• Like TDD/BDD encourages smaller, simpler methods, that are easier to write and maintain

RDBMS TO COUCHBASE

The Most Common Mental Adjustments #1

• In SQL we tend to want to avoid hitting the database as much as possible

• We know, intuitively, that it’s costly when tying up connection pools, and overloading the db servers

• Even with caching and indexing tricks, and massive improvements over the years, SQL still gets bogged down by complex joins and huge indexes

• In Couchbase, get’s and set’s are so fast they are trivial, and not bottlenecks, this is hard for many people to accept (reptilian SQL mind)

Complex Joins vs Multiple Gets

select * from (((client left join brochure on client.cid = brochure.cid)

left join translation on brochure.bid = translation.bid )

left join version on translation.tid = version.tid)

left join language on language.lid = translation.lid

order by client.cid, brochure.bid, translation.tid, version.vid

shopping_cart_id =

couch.get(“u::1001::transaction::count”)

cart_items =

couch.get(“u::sc::” + shopping_cart_id”)

foreach item_id in cart_items.items

cart_details.push(

couch.get(“product::” + item_id) )

The Most Common Mental Adjustments #2

• The key to finding data is the Key!

• Key design can give you many different ways to access data, being able to predict key values, and use them creatively

• Many newcomers see Views as a replacement for key design, because it seems more “SQL”-like

• Use Views for what you cannot do with Key Design, and there are lots of things you can't do with Key Design

Resources

Main Resource Portalwww.couchbase.com/communiHes

Code Samples Going through Opera/onswww.github.com/couchbaselabs/DeveloperDay

Couchbase Q & Awww.couchbase.com/communiHes/q-‐and-‐a

My Email: jasdeep@couchbase.comMy Twiher: @scalabl3

Couchbase 103: ViewsMonday October 14th, 10:00am PDT

couchbase 102 - developing with couchbase

couchbase cb

value data

couchbase import couchbase

default data

var data

complex data types

new hashtable data

json data structures

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