Hazelcast and MongoDB at Cloud CMS

Distributed Caching with
Hazelcast + MongoDB
at Cloud CMS
Michael Uzquiano
uzi@cloudcms.com
@uzquiano

Agenda

• What is Cloud CMS?

• Why we chose MongoDB

• What is Hazelcast?

• Code Samples

• Implementation with MongoDB

http://www.cloudcms.com

• The fastest, easiest and most cost-effective way
to build cloud-connected web and mobile
applications.

• An application server in the cloud for cloud-
connected applications

• Built to leverage the strengths of MongoDB

• Hosted or On-Premise

Cloud CMS provides

• Content Management
• Users, Groups, Roles, Permissions
• Personalization (Behavioral Targeting)
• Analytics, Reporting
• Identity Management
• Integrated Services
• Email Campaigns, CRM, Integrated Billing

Mobile Ready
• iOS, Android, Windows Mobile

• JavaScript, Java, PHP, Ruby, Node.js

• jQuery, jQuery Mobile, Dojo, YUI,
Sencha Touch, Titanium,
PhoneGap

Hazelcast and MongoDB at Cloud CMS

The right tool for the job

• JSON

• Query and Indexing

• Doesn’t overstep into application domain
• No transactions
• No referential integrity
• No triggers, foreign keys, procedures

• Gets out of the way so we can tackle these

Community + Momentum

• Really great language drivers

• Community contributors

• Frequent release schedule

• Exciting roadmap

Performance

• Very fast
• Anywhere from 2 to 10x faster than MySQL
• About 50 times faster than CouchDB
• Lots of benchmarks but the point is, it’s fast!

• Sharding built-in, automatic, and *Just Works™
• *Just Works™ guarantee applies only if you have a cluster of shard replica sets
with config servers and routing servers and you define your own shard key(s) with
appropriate uniformity and granularity

• Asynchronous replication for
redundancy/failover

What is Hazelcast?
• In-Memory Data Grid (IMDG)
• Clustering and highly scalable data distribution
solution for Java
• Distributed Data Structures for Java
• Distributed Hashtable (DHT) and more

What does Hazelcast do?
• Scale your application
• Share data across cluster
• Partition your data
• Send/receive messages
• Balance load across cluster
• Process in parallel on many JVM

Advantages
• Open source (Apache License)
• Super light, simple, no-dependency
• Distributed/partitioned implementation of map,
queue, set, list, lock and executor service
• Transactional (JCA support)
• Topic for pub/sub messaging
• Cluster info and membership events
• Dynamic clustering, backup, fail-over

Data Partitioning in a Cluster
If you have 5 million objects in your 5-node cluster,
then each node will carry
1 million objects and 1 million backup objects.

Server1 Server2 Server3 Server4 Server5

SuperClient in a Cluster
• -Dhazelcast.super.client=true
• As fast as any member in the cluster
• Holds no-data

Server1 Server2 Server3 Server4 Server5

Code Samples – Cluster Interface

importcom.hazelcast.core.*;
importjava.util.Set;

Cluster cluster = Hazelcast.getCluster();
cluster.addMembershipListener(listener);

Member localMember = cluster.getLocalMember();
System.out.println (localMember.getInetAddress());

Set setMembers = cluster.getMembers();

Code Samples – Distributed Map

importcom.hazelcast.core.Hazelcast;
importjava.util.Map;

Map<String, User>map = Hazelcast.getMap(”users");

map.put ("1", user);

User user = map.get("1");

Code Samples – Distributed Queue

importjava.util.concurrent.BlockingQueue;
importjava.util.concurrent.TimeUnit;

BlockingQueue<Task>queue = Hazelcast.getQueue(“tasks");

queue.offer(task);

Task t = queue.poll();
Task t = queue.poll(5, TimeUnit.SECONDS);

Code Samples – Distributed Set

importjava.util.Set;

Set<Price>set= Hazelcast.getSet(“IBM-Quote-History");

set.add (new Price (10, time1));

for (Price price : set) {
// process price
}

Code Samples – Distributed Lock

importjava.util.concurrent.locks.Lock;

Lockmylock= Hazelcast.getLock(mylockobject);
mylock.lock();
try {
// do something
} finally {
mylock.unlock();
}

Code Samples – Distributed Topic

importcom.hazelcast.core.*;

public class Sample implements MessageListener {

public static void main(String[] args) {
Sample sample = new Sample();
Topic topic = Hazelcast.getTopic ("default");
topic.addMessageListener(sample);
topic.publish ("my-message-object");
}

public void onMessage(Object msg) {
System.out.println("Got msg :" + msg);
}
}

Code Samples – Distributed Events
importcom.hazelcast.core.IMap;
importcom.hazelcast.core.EntryListener;
importcom.hazelcast.core.EntryEvent;

publicclassSampleimplementsEntryListener{
publicstaticvoidmain(String[]args){
Sample sample =newSample();
IMap map =Hazelcast.getMap("default");
map.addEntryListener(sample,true);
map.addEntryListener(sample,"key");
}

publicvoidentryAdded(EntryEventevent){
System.out.println("Added "+event.getKey()+":"+event.getValue());
}

publicvoidentryRemoved(EntryEventevent){
System.out.println("Removed "+event.getKey()+":"+event.getValue());
}

publicvoidentryUpdated(EntryEventevent){
System.out.println("Updated "+event.getKey()+":"+event.getValue());
}
}

Code Samples – Executor Service

FutureTask<String>futureTask= new
DistributedTask<String>(new Echo(input), member);

ExecutorServicees=Hazelcast.getExecutorService();

es.execute(futureTask);

String result = futureTask.get();

Sample Configuration
<hazelcast>
<group>
<name>dev</name>
<password>dev-pass</password>
</group>
<network>
<portauto-increment="true">5701</port>
<join>
<multicastenabled="true">
<multicast-group>224.2.2.3</multicast-group>
<multicast-port>54327</multicast-port>
</multicast>
<tcp-ipenabled="false">
<interface>192.168.1.2-5</interface>
<hostname>istanbul.acme</hostname>
</tcp-ip>
</join>
<interfacesenabled="false">
<interface>10.3.17.*</interface>
</interfaces>
</network>
<executor-service>
<core-pool-size>16</core-pool-size>
<max-pool-size>64</max-pool-size>
<keep-alive-seconds>60</keep-alive-seconds>
</executor-service>
<queuename="tasks">
<max-size-per-jvm>10000</max-size-per-jvm>
</queue>
</hazelcast>

Distributed Job Queue
Elasticity with Cloud CMS


Upload of a 20 page PDF

Write PDF to GridFS
Add 2 jobs to Queue
(each to build 10 pngs)


Jobs may run asynchronously
(returns once transaction complete)

Picks job from Picks Job from
queue and works on it queue and works on it

Job Scheduler determines
which jobs get priority

Implementation with
MongoDB
com.hazelcast.core.MapStore

MapStore
public interface MapStore<K,V> extends MapLoader<K,V>
{
void store(Kk, V v);
void storeAll(Map<K,V>kvMap);
void delete(Kk);
void deleteAll(Collection<K>ks);
}

public interface MapLoader<K,V>
{
V load(Kk);
Map<K,V>loadAll(Collection<K>ks);
Set<K>loadAllKeys();
}

MapLoaderLifecycleSupport
public interface MapLoaderLifecycleSupport
{
void init(HazelcastInstancehazelcastInstance,
Properties properties,
String mapName);

void destroy();
Set<K>loadAllKeys();
}

public class MongoUsersMapStore
implements MapStore, MapLoaderLifecycleSupport
{

}

{
private Mongo mongo;
private DBCollectioncol;

public void init(HazelcastInstancehazelcastInstance,
Properties properties, String mapName) {

this.mongo = new Mongo(“localhost”, 27017);

String dbname = properties.get(“db”);
String cname = properties.get(“collection”);

DB db = this.mongo.getDB(dbname);
this.col = db.getCollection(cname);
}
}

{

...

public void destroy() {
this.mongo.close();
}

public Set<K>loadAllKeys() {
return null;
}
}

{

...

public Set loadAllKeys() {
Set keys = new HashSet();

BasicDBList fields = new BasicDBList();
fields.add(“_id”);

DBCursor cursor = this.col.find(null, fields);
while (cursor.hasNext()) {
keys.add(cursor.next().get(“_id”));
}

return keys;
}
}

{
...

public void store(Kk, V v) {
DBObjectdbObject = convert(v);
dbObject.put(“_id”, k);

this.col.save(dbObject);
}

public void delete(Kk) {
DBObjectdbObject = new BasicDBObject();
dbObject.put(“_id”, k);

this.col.remove(dbObject);
}
}

{
...

public void storeAll(Map map) {
for (Object key : map.keySet()) {
store(key, map.get(key));
}
}

public void deleteAll(Collection keys) {
for (Object key: keys) {
delete(key);
}
}
}

{
...

public void storeAll(Map map) {
for (Object key : map.keySet()) {
store(key, map.get(key));
}
}

public void deleteAll(Collection keys) {
BasicDBListdbo = new BasicDBList();
for (Object key : keys) {
dbo.add(newBasicDBObject("_id", key));
}
BasicDBObjectdbb = new BasicDBObject("$or", dbo);
this.col.remove(dbb);
}
}

Spring Config
<beans xmlns="http://www.springframework.org/schema/beans"
xmlns:hz="http://www.hazelcast.com/schema/spring">

<hz:hazelcast id="hzInstance”>
<hz:config>
<hz:map name=”users” backup-count="1"
max-size="2000” eviction-percentage="25"
eviction-policy="LRU"
merge-policy="hz.LATEST_UPDATE">
<hz:map-store enabled="true"
write-delay-seconds="0"
implementation="mymap" />
</hz:map>
</hz:config>
</hz:hazelcast>

<bean id=”mymap” class="org.sample.MongoUsersMapStore" />

Implementation with
MongoDB
com.hazelcast.core.EntryListener

EntryListener
public interface EntryListener<K,V>
{
void entryAdded(EntryEvent<K, V> event);
void entryUpdated(EntryEvent<K, V> event);
void entryRemoved(EntryEvent<K, V> event);
void entryEvicted(EntryEvent<K, V> event);
}

EntryListener
public class UserCacheEntryListener<String, User>
{
private Map<String, User> cache;

public User getUser(String key) {
return cache.get(key);
}

public void entryAdded(EntryEvent<String, User> event) {
cache.put(event.getKey(), event.getValue());
}

}

Spring Framework

• Spring Data for MongoDB
• http://www.springsource.org/spring-data/mongodb
• com.hazelcast.spring.mongodb.MongoMapStore

• Based on Spring API Template pattern
• com.hazelcast.spring.mongodb.MongoTemplate

• Easy to get started, base implementation

• You still might want to roll your own

Questions?

• Michael Uzquiano
• uzi@cloudcms.com
• @uzquiano

• Cloud CMS
• http://www.cloudcms.com
• @cloudcms

• Hazelcast
• http://www.hazelcast.com
• https://github.com/hazelcast/hazelcast

Hazelcast and MongoDB at Cloud CMS

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