in-memory no sql- gids2014

In-­‐Memory  NoSQL  

Miko  Matsumura,  VP  of  Marke9ng  and  Developer  Rela9ons,  Hazelcast  

Why  NoSQL-­‐Venkat  Said  It  Best!  

•  Impedance  Mismatch  or  “impotence”  mismatch?  

•  Using  OODB  

•  CAP  Theorem  and  eventual  consistency  

Company  Snapshot  

•  Founded  in  2008  •  Open  source  business  model    •  Headquarters  in  Palo  Alto,  CA  •  R&D  in  Istanbul,  Turkey  •  $2.5M  Series  A  funding  led  by  Bain  Capital  (June  2013)    

•  Key  investors/advisors  include  Rod  Johnson,  founder  of  Spring  Source,  and  Ali  Kutay,  CEO  of  WebLogic  and  Golden  Gate  So\ware  

Open source, Apache v2 License

2MB Jar with no Dependency

In-Memory

Embeddable

Dynamic Clustering, Backup and Failover

Partitioned

Highly Available

Scalable

Hazelcast  is  

In-Memory Data Grid

Distributed (Elastic) Cache

NoSQL Key-Value Store

Clustering, Scalability, Partitioning, Big Data

Cloud Computing

Keywords  

Who  Uses  Hazelcast?  

Projects  that  use  Hazelcast  

GETTING  RID  OF  IMPEDENCE  

All you need is Map

James Gosling Creator of Java

Map  

import java.util.Map; !import java.util.HashMap;!!Map map = new HashMap();!map.put(“1”, “value”); !map.get(“1”);!

Thread  Safe  Map  

import java.util.Map; !import java.util.concurrent.*;!!Map map = new ConcurrentHashMap();!map.put(“1”, “value”); !map.get(“1”);!

Distributed  Map  

import java.util.Map; !import com.hazelcast.core.Hazelcast;!!HazelcastInstance h;!h = Hazelcast.newHazelcastInstance();!Map map = h.getMap(“mymap”);!map.put(“1”, “value”); !map.get(“1”);!

Map

Queue

Set

List

MultiMap

AtomicNumber

CountdownLatch

Lock

Semaphore

Topic

IdGeneretor

Hazelcast  Data  Structures  

Database

Application

Hazelcast

We  scale  the  applica9on  9er  

Big Data Sharing/Caching

Session Replication

Hibernate 2nd Level Cache

Producer - Consumer

Spring Cache

Parallel Processing

Pub/Sub Messaging

Use  Cases  

All you need is JCache

Greg Luck Creator of Ehcache

Co Spec Lead Jsr107 JCache

Hazelcast  leads  standardiza9on  •  Hazelcast  CTO  Greg  Luck  is  the  newest  addi_on  to  the  

management  team    

•  Greg  Co-­‐led  the  standardiza9on  effort  with  Oracle  which  makes  Hazelcast  one  of  the  standard-­‐bearers  

•  Greg  has  credibility  in  this  market  having  founded  Ehcache,  the  world’s  most  popular  Java  cache  

•  Jcache  will  be  released  Mon  March  17th  by  JCP  and  widely  implemented  by  IMDG  vendors  and  embedded  in  Java  EE  8  and  Spring  

•  The  caching  standard  will  expand  IMDG  market  and  adop9on  of  Hazelcast  

19  

JCache is the standard way for Java to talk to caches

From Local Cache to

Distributed Cache

From  Local  to  Distributed  Caching  

Req Local  Caching  

Req

V =

read(K)

Local  Caching  

Req

K: V

Local  Caching  

Req

K: V

Local  Caching    

V =

read(K)

Req

Local  Caching  

K: V

K: V

Req

K: V

Local  Caching  

Local  Caching  

K: V K: V

Req Local  Caching  

K: V K: V

Req

V =

read(K)

Local  Caching  

K: V K: V

Req Local  Caching  

K: V K: V K: V

Req

Adding a new Node increases load on Backend!

Local  Caching  

K: V K: V K: V

Req

Hazelcast Cluster 2X capacity

Distributed  Caching  

Req

V =

read(K)

Hazelcast Cluster 2X capacity

Distributed  Caching  

Use Case - Distributed Caching

Req

K: V Hazelcast Cluster 2X capacity

K: V

Req

Hazelcast Cluster 2X capacity

Distributed  Caching  

K: V

Req

Hazelcast Cluster 3X capacity

Distributed  Caching  

Features  

•  Java  Concurrency  API  –  Lock,  Semaphore,  CountdownLatch,  ExecutorService  

•  Java  Collec_on  API:  –  Map,  Queue,  Set,  List  

•  Mul_Map    •  Topics  (PubSub)  •  EntryProcessor  •  Predicate  API  (Secondary  Indexes)  •  SQL  Query  •  Off-­‐Heap  Support  *    •  Na_ve  Client:  C#*,  C++*,  Java,  REST,  Memcache  •  Dynamic  Discovery,  Redundancy  and  Recovery    

Features…  

•  WAN  Replica_on  •  Backup  Groups  •  Indexing  Key,  values  and  value  proper_es  •  Distributed  SQL-­‐like  Query  and  Predicate  API  •  Write  Behind  /  Write  Through  Persistence  •  2PC  Transac_ons  (v3)  •  EntryListeners  •  EntryProcessors  (v3)  •  Automa_c  Evic_on  •  Data  Affinity  •  HTTP  Session  Clustering  •  Spring  &  Hibernate  2nd  Level  Cache  •  Control  Par__oning  (v  3)  •  EC2  Auto  Discovery  

Features…  

•  SPI  (v3)  •  Con_nuous  Query  (v3)  •  Custom  Pluggable  Serializa_on  •  Portable  Serializa_on  (v3)  •  Par_al  Deserializa_on  (v3)  •  Binary  and/or  Object  Storage  (v3)  •  Dynamic  Indexing  (v3)  •  2PC  Transac_ons  (v3)  •  Server  Side  Before  and  A\er  Interceptors  (v3)  •  JAAS  Authen_ca_on  and  Authoriza_on  (v3)  *  •  Socket  Encryp_on    

Management  Center  *  

Roadmap  2014  

Scale  Up    •  Second    Genera_on  off-­‐heap  

–  200GB+  versus  20GB  in  3.1  –  Exposed  via  API  –  In  addi_on  to  map:  

•  Queue  •  Set  

Roadmap  2014  

Opera_onal  Store    •  Persistence  

–  Faster  restart  and  data  load  –  Opera_onal  storage  rather  than  DB  

Roadmap  2014  

Distributed  Compute    •  Map/Reduce  •  Scheduled  Executor  Service  

Roadmap  2014  

Cache    •  JCache  

–  New  standard  December  2013  –  Being  implemented  by  most  vendors  (Hazelcast,  Coherence,  Infinispan,  Gemfire,  Terracona)  

–  February  2014  –  javax.cache  –  To  be  included  in  Java  EE  8,  Spring  

Hazelcast  V3