Question is clear as you see in the title, it would be appreciated to hear your ideas about adv./disadv. differences between them.
UPDATE:
I have decided to use Hazelcast because of the advantages like distributed caching/locking mechanism as well as the extremely easy configuration while adapting it to your application.
We tried both of them for one of the largest online classifieds and e-commerce platform. We started with ehcache/terracotta(server array) cause it's well-known, backed by Terracotta and has bigger community support than hazelcast. When we get it on production environment(distributed,beyond one node cluster) things changed, our backend architecture became really expensive so we decided to give hazelcast a chance.
Hazelcast is dead simple, it does what it says and performs really well without any configuration overhead.
Our caching layer is on top of hazelcast for more than a year, we are quite pleased with it.
Even though Ehcache has been popular among Java systems, I find it less flexible than other caching solutions. I played around with Hazelcast and yes it did the job, it was easy to get running etc and it is newer than Ehcache. I can say that Ehcache has much more features than Hazelcast, is more mature, and has big support behind it.
There are several other good cache solutions as well, with all different properties and solutions such as good old Memcache, Membase (now CouchBase), Redis, AppFabric, even several NoSQL solutions which provides key value stores with or without persistence. They all have different characteristics in the sense they implement CAP theorem, or BASE theorem along with transactions.
You should care more about, which one have the functionality you want in your application, again, you should consider CAP theorem or BASE theorem for your application.
This test was done very recently with Cassandra on the cloud by Netflix. They reached to million writes per second with about 300 instances. Cassandra is not a memory cache but you data model is like a cache, which is consist of key value pairs. You can as well use Cassandra as a distributed memory cache.
Hazelcast has been a nightmare to scale and stability is still a major issue.
The dedicated client to grid component choices are
The messy version that cant survive node loss anywhere, negating the point of backups (superclient), or
An incredibly slow native client option that does not allow for any type of load balancing to processing nodes in the grid.
If any host could request records from this data grid it would be a sweet design, but you are stuck with those two lackluster option to get anything out of it.
Also multiple issues with database thread pools locking up on individual members and not writing anything to the databases, causing permanent records loss is a frequent issue and we often have to take the whole thing down for hours to refresh any of the JVM's. Split brain is also still an issue, although in 1.9.6 it seems to have calmed down a little.
Rallying to move to Ehcache and improving the database layer instead of using this as a band-aid.
Hazelcast serializes everything whenever there is a node (standard-one), so the data you will save to Hazelcast must implement serialization.
http://open.bekk.no/efficient-java-serialization/
Hazelcast has been a nightmare for me. I was able to get it "working" in a clustered Websphere environment. I use the term "working" loosely. First, all of Hazelcast's documentation is out of date and only shows examples using deprecated method calls. Trying to use the new code without comments in the Javadocs and no examples in the documentation is very hard. Also, the J2EE container code simply does not work at this point because it does not support XA transactions in Websphere. An error is thrown calling code that follows their only J2EE example explicitly(it does look like Milestone 3.0 is addressing this). I had to forget about joining Hazelcast to a J2EE transaction. It does seem Hazelcast is definitely geared to a non EJB/Non-J2EE container environment. Making calls to Hazelcast.getAllInstances() fails to retain any information about Hazelcast's state when switching from one enterprise java bean to another. That forces me to create a new Hazelcast instance just to run calls that give me access to my data. That causes many Hazelcast Instances to start up on the same JVM. Also,retrieving data from Hazelcast is not fast. I tried retrieving data using both the Native Client and directly as a member of the cluster. I stored 51 lists, each containing only 625 objects in Hazelcast. I could not perform a query directly on a list and did not want to store a map just to get access to that feature (SQL operations can be performed on a map). It took about a half second to retrieve each list of 625 objects because Hazelcast Serializes the entire list and sends it over the wire rather than just giving me the delta (what has changed). Another thing, I had to switch to a TCPIP configuration and explicitly list the ip addresses of the servers I wanted to be in the cluster. The default Multicast configuration did not work and from the group discussions in google, other people are experiencing that difficulty as well. To sum up; I did eventually get 8 machines communicating in a cluster through many hours of torturous programmatic configuration and trial and error (the documentation will be little help) but when I did, I still had no control over the number of instances and partitions being created on each JVM due to the half finished nature of Hazelcast for EJB/J2EE and it was VERY SLOW. I implemented a real use case in the unemployment insurance application I work on and the code was much faster making direct calls to the database. It would have been cool if Hazelcast worked as advertised because I really did not want to use a separate service to implement what I am trying to do. I have used MongoDB extensively so I may skip the whole in memory cache and just serialize my objects as documents in a separate repository.
One advantage of Ehcache is that it is backed by a company (Terracotta) that does extensive performance, failover, and platform testing in a large performance lab. Terracotta provides support, indemnity, etc. For many companies, that sort of thing is important.
I have not used Hazelcast but I've heard that it is easy to use and that it works. I haven't heard anything with respect to scalability or performance of Hazelcast vs Terracotta/Ehcache but given the amount of scalability and failover testing that Terracotta does, it's hard for me to imagine that Hazelcast would be competitive in a production deployment. But I presume it would work fine for smaller uses.
[Bias: I'm a former employee of Terracotta.]
Developers describe Ehcache as "Java's Most Widely-Used Cache". Ehcache is an open-source, standards-based cache for boosting performance, offloading your database, and simplifying scalability. It's the most widely-used Java-based cache because it's robust, proven, and full-featured. Ehcache scales from in-process, with one or more nodes, all the way to mixed in-process/out-of-process configurations with terabyte-sized caches. On the other hand, Hazelcast is detailed as "Clustering and highly scalable data distribution platform for Java". With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.
Ehcache and Hazelcast are primarily classified as "Cache" and "In-Memory Databases" tools respectively.
Related
Which is better suited for the following environment:
Persistence not a compulsion.
Multiple servers (with Ehcache some cache sync must be required).
Infrequent writes and frequent reads.
Relatively small database (very less memory requirement).
I will pour out what's in my head currently. I may be wrong about these.
I know Redis requires a separate server (?) and Ehcache provides local cache so it must be faster but will replicate cache across servers (?). Updating all caches after some update on one is possible with Ehcache.
My question is which will suit better for the environment I mentioned?
Whose performance will be better or what are scenarios when one may outperform another?
Thanks in advance.
You can think Redis as a shared data structure, while Ehcache is a memory block storing serialized data objects. This is the main difference.
Redis as a shared data structure means you can put some predefined data structure (such as String, List, Set etc) in one language and retrieve it in another language. This is useful if your project is multilingual, for example: Java the backend side , and PHP the front side. You can use Redis for a shared cache. But it can only store predefined data structure, you cannot insert any Java objects you want.
If your project is only Java, i.e. not multilingual, Ehcache is a convenient solution.
You will meet issues with EhCache scaling and need resources to manage it during failover and etc.
Redis benefits over EhCache:
It uses time proven gossip protocol for Node discovery and synchronization.
Availability of fully managed services like AWS ElastiCache, Azure Redis Cache. Such services offers full automation, support and management of Redis, so developers can focus on their applications and not maintaining their databases.
Correct large memory amount handling (we all know that Redis can manage with hundreds of gigabytes of RAM on single machine). It doesn't have problems with Garbage collection like Java.
And finally existence of Java Developer friendly Redis client - Redisson.
Redisson provides many Java friendly objects on top of Redis, like:
Set
ConcurrentMap
List
Queue
Deque
BlockingQueue
BlockingDeque
ReadWriteLock
Semaphore
Lock
AtomicLong
CountDownLatch
Publish / Subscribe
ExecutorService
and many more...
Redisson supports local cache for Map structure which cold give you 45x performance boost for read operations.
Here is the article describing detailed feature comparison of Ehcache and Redis.
I have recently encountered a question that since redis is not distributed and don't support parallelism(multi-core), isn't elastic search be a better choice to use instead of redis for caching purpose.
This is all in reference to a simple web, where we used redis to cache db query.
I have kind of got the idea here,
but still not sure whether it has any real benefits. Opening up this thread to discuss the advantages/disadvantages in doing so.
It's not really what you asked for but you might want to have a look at Aerospike.
Redis is an in-memory data structure store known for speed and often used as a cache. Both Redis and Aerospike are open source, however, when applications need persistence or when applications must scale but servers have maxed out of RAM, developers should consider Aerospike, a distributed key-value store that is just as fast or faster than Redis, but scales more simply and with the economics of flash/SSDs.
I noticed that Spring reference application (Sagan) uses the SimpleCacheManager implementation. See here for source code of Sagan.
I was surprised by this choice because I thought that all but small applications running on a single node would use something like a Redis cache manager and not the simple cache manager.
How can a large application like Sagan -which I assume runs on cloudfoundry- use this simple implementation?
Any comment welcome.
Well, the SimpleCacheManager choice has been made because it was the simplest solution that could possibly work. Note that Sagan is, at least for now, not storing a lot of data in that cache and merely using it to respect various APIs rate-limiting and get better performance on some parts of the application.
Yes, Sagan is running on CloudFoundry (see this presentation) and is using CF marketplace services.
Even if cache consistency between instances is not a constraint for now, we could definitely add another marketplace service, here a Redis Cloud instance, and use this as a central cache repository.
Now that we're considering using that cache for more features, it even makes sense to at least consider that use case, since it could lower our monthly bill (pay a small fee for a redis service and use less memory for our CF instances).
In any case, thanks a lot balteo for this insightful question, we've created a Github issue for that.
I'm researching distributing caching solutions for java.
I have to develop application that would be clustered on 5-10 nodes.
each node will have its own portion of cache.
I'm researching for distributed cache providers with fault tolorace: coherence, ehcache, infinispan, hazelcast...
I should process task on the node that contains the data.
thus I should be able to detect by API that object is in local node.
now hazelcast does this.
ehcache is bound to terracota server array and have not found way to do what i want(L2->L1 copying does not count)?
still researching infinispan.
have not come to coherence yet.
open minded for any alternative.
could anyone point products that is suited for my needs?
researching these details takes to read almost full documentation per product, so it goes really slow
Infinispan not only has the ability to store data in a distributed fashion, but it also comes with a distributed execution framework that should help you.
I was currently looking into memcached as way to coordinate a group of server, but came across Apache's ZooKeeper along the way. It looks interesting, and Yahoo uses it, so it shouldn't be bad, but I'd never heard of it before, so I'm kind of skeptical. Has anyone else given it a try? Any comments or ideas?
ZooKeeper and Memcached have different purposes. You can use memcached to do server coordination, but you'll have to do most of this work yourself. Memcached only allows coordination in that it caches common data lookups to be used by multiple clients. From reading ZooKeeper's documentation, it has a much broader focus than this. ZooKeeper seems to provide support for server clustering, which isn't the same as the cache clustering memcached provides.
Have a look at Brad Fitzpatrick's Linux Journal article on memcached to get a better idea what I mean.
To get an overview of what Zookeper is capable of, watch the following presentation by it's creators. It's capable of so much more (creating queue's, electing master processes amongst a group of peers, distributed high performance run time configurations, rendezvous points for dis-joined processes, determining if processes are still running, etc).
http://zookeeper.sourceforge.net/index.sf.shtml
To answer your question, if "coordination" is what you are looking for Zookeeper is much better targeted at that than memcached.
Zookeeper is great for coordinating data across servers. It does a good job of ordering every transaction and making guarantees that transactions happen in order. However when first breaking into it the documentation sucks; it's very 'high-level' without enough concrete examples or explanations as how to properly handle certain events. One of the included examples (as of version 3.3.3) had its own bugs in it.
Your code will also need to be cognizant of event driven interactions, and polling interactions. With massively distributed architecture, when acting upon 'events' you can inadvertently create a stampede that could not be desirable for your environment (herding effect).