This document introduces Hazelcast, an open source in-memory data grid for clustering data and distributing applications. Some key points: - Hazelcast provides a distributed ConcurrentMap that allows putting and getting data in a clustered environment across multiple nodes. - It offers features like asynchronous operations, data expiration, and filtering data "in grid". - The system partitions data and replicates it automatically across nodes for elastic scaling and high availability. It can also redistribute data as nodes are added or removed. - Other features include a distributed event bus for pub/sub messaging, persistent distributed counters and locks for coordination, and an executor service for distributed computing across the cluster.

1. HAZELCAST
OU
LE CLUSTERING FACILE !
BBL – février 2014

2. A propos…
!
Sylvain Wallez
Architecte et dev expert freelance
Web/Java/Scala
! 2014 - Fondateur de Actoboard
! 2011 - Backend architect de Sigfox
! 2008 - CTO de Goojet/Scoop.it
! 2006 - Backend architect Joost
! 2003 - Premier VP Apache français
! 2000 - Cofondateur & CTO Anyware Technologies
sylvain@bluxte.net
http://bluxte.net
Twitter: @bluxte
!

3. Mise en bouche
Une appli distribuée en 5 lignes !

4. Mise en bouche
!
La Map classique
public static void main(String[] args) {
Map<String, String> map = new HashMap<>();
map.put("hello", "world");
String value = map.get("hello");
}

5. Mise en bouche
!
La ConcurrentMap
public static void main(String[] args) {
ConcurrentMap<String, String> map = new ConcurrentHashMap<>();
map.put("hello", "world");
String value = map.get("hello");
String previous = map.putIfAbsent("goodbye", "marylou");
}

6. Mise en bouche
!
La ConcurrentMap distribuée
public static void main(String[] args) {
HazelcastInstance hz = Hazelcast.newHazelcastInstance();
ConcurrentMap<String, String> map = hz.getMap("mymap");
map.put("hello", "world");
String value = map.get("hello");
String previous = map.putIfAbsent("goodbye", "marylou");
}

7. Mise en bouche
Démo

8. Hazelcast features
Mais que vient-on de voir ?

9. Buzzword bingo !
In-memory data grid
! Open source, Apache Licence
! P2P elastic cache
! Distributed event bus
! NoSQL datastore
! Cluster coordination
! Distributed computing
!

10. La concurrence
Oracle Coherence
! IBM Extreme Scale
! VMware Gemfire
! Gigaspaces
! JBoss Infinispan
! Gridgain
! Terracotta
!

11. In-memory data grid
!
La Map classique, plus des bonus
Asynchrone
HazelcastInstance hz = Hazelcast.newHazelcastInstance();
IMap<String, String> map = hz.getMap("mymap");
map.put("hello", "world");
String value = map.get("hello");
Future<String> future = map.getAsync("hello");
Expiration
map.put("goodbye", "marylou", 10, TimeUnit.MINUTES);
Set<Entry<String, String>> set = map.entrySet(new Predicate<String, String>() {
public boolean apply(Entry<String, String> entry) {
return entry.getKey().contains("z");
}
});
Filtrage « in grid »

12. P2P elastic cache
!
Partitionnement et réplication automatiques

13. P2P elastic cache
!
Ajout dynamique de nouveaux noeuds
New!
Multicast

14. P2P elastic cache
!
Redistribution des données

15. P2P elastic cache
!
Redistribution des données

16. P2P elastic cache
!
Redistribution des données

17. P2P event bus
!
Les topics : broadcast à tous les listeners
ITopic<String> topic = hz.getTopic("alerts");
topic.addMessageListener(new MessageListener<String>() {
public void onMessage(Message<String> message) {
}
println("Received " + message.getMessageObject() +
" from " + message.getPublishingMember());
});
Nœud d’origine
du message

18. P2P event bus
!
Les queues
BlockingQueue<String> queue = hz.getQueue("jobs");
queue.offer("Make me a sandwich");
BlockingQueue<String> queue = hz.getQueue("jobs");
while(true) {
String job = queue.take();
println("Now working on " + job);
}
(Aussi en asynchrone avec poll() et les listeners)

19. NoSQL datastore
!
Stockage persistant

20. NoSQL datastore
!
La Map « interrogeable »
Indexation des
propriétés
IMap<Long, Employee> map = hz.getMap("employee");
map.addIndex("active", true);
map.addIndex("age", true);
Collection<Employee> employees =
map.values(new SqlPredicate("active AND age < 30"));
Sous ensemble de SQL sur
les propriétés JavaBean

21. Cluster coordination
!
Compteurs distribués
IAtomicLong reqCount = hz.getAtomicLong("users");
// Start request
long count = reqCount.incrementAndGet();
try {
// Do some stuff
println("There are " + count + " request in progress");
} finally {
// End request
reqCount.decrementAndGet();
}

22. Cluster coordination
!
Locks distribués (à utiliser avec parcimonie)
Lock lock = hz.getLock("maintenance_mode");
lock.lock();
try {
// There can be only one in the cluster
} finally {
lock.unlock();
}
ILock lock = hz.getLock("maintenance_mode");
lock.lock(10, TimeUnit.SECONDS);
try {
// There can be only one in the cluster
} finally {
lock.unlock();
}
Protection :
durée limitée

23. Distributed computing
!
Un ExecutorService distribué
ExecutorService executor = hz.getExecutorService("compute");
Runnable job = null;
Callable<String> jobWithResult = null;
executor.execute(job);
Future<String> future = executor.submit(jobWithResult);

24. Distributed computing
!
Un ExecutorService distribué
! Fonctions
Data
locality
avancées de IExecutorService
IExecutorService executor = hz.getExecutorService("compute");
Runnable job = null;
Callable<String> jobWithResult = null;
Choix explicite
du noeud
executor.executeOnKeyOwner(job, 42);
Member member = hz.getPartitionService().getPartition(42).getOwner();
println("Submitting to IP address " + member.getInetSocketAddress());
executor.submitToMember(jobWithResult, member, new ExecutionCallback<String>() {
public void onResponse(String response) {
println("Result is " + response);
}
Callback
public void onFailure(Throwable t) {
println("Job failed");
asynchrone
}
});
}

25. Administration

26. Administration
JMX
! API de statistiques sur Cluster et Member
! Webapp Management Center
!

27. Management Center

28. Management Center

29. Management Center

30. Merci !
Questions ?
Réponses !