Simple Comparisons on some In-Memory Data Grids

 

Fetures	Feature Description	GemFire7	Coherence3.7	Infinispan5	Gigaspaces7	Notes
1. topologies	peer-peer; client-server	High	High	High	High
2. cross-site /   WAN replication	datacenter-datacenter; region-region	High	Low	Med	High	Coherence support by incubator "Push replication"; Infinispan basic support in V5.2
3. read-most scalability	via replication	High	High	High	High
4. write-most scalability	via partitioning and replication between primary and backups	High	High	Med	High
5. high availability / HA	via replication and persistence  into disk (DB)	High	High	High	High
6. asychronous     replication	useful for slow changing data and WAN replication	High	Med	High	High	Coherence support by incubator "Push replication";
7. partition rehashing	consistent hashing to reduce data relocation	High	High	High	High	all seem to use consistent hashing-like algorithms
8. dynamic clustering	adds or loses nodes	High	High	High	Med
9. updating among     partition backups	master-backup: less deadlock prone and IO master-master/update anywhere: more dealock prone and IO	High	High	Low	High	Infinispan only has master-master that incurs deadlock for slow network and large caches
10. cache loader and       writer	2 application interfaces: loades data from DB and saves data into Cache. With cache loader and writer, applications only need to interace with Cache!	High	High	Med	High	Infinispan writers only support JPA, not JDBC or Hibernate!
11. read-through and      read-ahead	populates cache with DB in batch mode	Med	High	Med	High	no prefetch/batch support from Gemfire and infinispan
12. write-through and      write-behind	persists cache into wherever they were loaded.write-behind persists data asynchronously	High	High	Med	High
13. event notification	Cache also works as a messaging and parallel processing bus like JMS/MDB	Med	Med	Med	High
14. continous querying	register contents-based interests with Cache and receive updates continously.	High	High	Low	High
15. cache querying	SQL like query: not only searches  based on key matching	High	High	Low	High
16. locking and tx	JTA (global tx) and ACID properties	High	High	Med	High
17. off-heap	puts cache data off Java heap so that GC pause time is reduce!	Low	High	Low	Low
18. key affinity /      colocation	colocates related cache objects on the same partition to reduce IO	High	High	High	High
19. customized      partitioning	puts cache into specific cluster node to bypass cache hashing algorithm	High	Low	Med	Low
20. synchronous      requests like RFQ	synchronou requests should go through the same partition routing as asynchrous messages.	Low	Low	Low	High
21. API	Map,Restful and appropriate interfaces	Med	Med	Med	High
22. language bindings	Java, C++ etc	Med	Med	Med	High
23. map-reduce /      scatter-gather	submits aggregation tasks to run across multiple or all cluster nodes	High	High	High	High
24. monitoring	monitor the health of clusters	High	Med	Med	High
25. J2EE (JMS,Web,      Remoting)	how much J2EE? to support?	Low	Low	Low	High
26. migration effort	how to migrate to a different cache production in a J2EE app. server	High	High	High	Low	GigaSpaces is an app. server