I'm looking for a solution to share a cache between two tomcat web apps running on different hosts. The cache is being used for data synchronization, so the cache must be guaranteed to be up-to-date at all times between the two tomcat instances. (Sorry, I'm not 100% sure if the correct terminology for this requirement is "consistency" or something more specific like having ACID property). Another requirement is of course is that it should be fast to access the cache, with about equal numbers of writes as reads. I do have access to a shared filesystem so that is a consideration.
I've looked at something like ehcache but in order to get a shared cache between the webapps I would either need to implement on top of a Terracotta environment or using the new ehcache cache server. The former (Terracotta) seems like overkill for this, while the cache web server seems like it wouldn't provide the fast performance that I want.
Another solution I've looked at is building something simple on top of a fast key-value store like Redis or memcachedb. Redis is in-memory but can easily be configured to be a centralized cache, while memcachedb is a disk-based persistent cache which could work because I have a shared filesystem.
I'm looking for suggestions on how to best solve this problem. The solution needs to be a relatively mature technology as it will be used in a production environment.
Thanks in advance!
I'm quite sure that you don't require terracotta or ehcache server if you need a distributed cache. Ehcache with one of the four replication mechanisms would do.
However, based on what you've written I guess that you're looking for more than just a cache. Memcached/Ehcache are examples of what you might call a caching layer for your application - nothing more.
If you find yourself using words like 'guaranteed' 'up-to-date' 'ACID' you're better off using an in-memory DB like Oracle Times Ten/MySQL Cluster/Redis with a disk-based persistent storage.
You can use memcached (not memcachedb) for fast and efficient caching. Redis or memcachedb could be an overkill unless you want persistent caching. Memcached can be clustered very easily and you can use spymemcached java client to access it. Memcacached is very mature and is running in several hundred thousands, if not millions of production servers. It can be monitored through Nagios and Munin systems when in production.
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I have a java enterprise application that does a lot of fetching of cached data.
The data is stored in a 3 server redis cluster and is accessed by 5 backend api nodes.
I am seeing that we are putting alot of stress on the redis caches, which is why I am wondering if it is dumb to put a in-mem cache such as Ehcache in front of redis. With this solution I would set the TTL to be very short in the Ehcache.
Is this a common solution or is it more reasonable to look into expanding the redis cluster?
Thing you are talking about is called near cache. It's absolutely legit solution in some cases. It provides trade-off between performance and freshness of the values. However you can only consider this option if seeing a bit stale values is tolerable in your case. Just FYI, Apache Ignite supports this feature out of the box.
I need to keep some data in cache on server. The servers are in cluster and call can go to any of them. In such a scenario is it better to use a replicated/distributed cache like EhCache Or to use session stickiness of LB.
If the data size(in cache) is big, won't it have a performance impact of serialization and de-serialization across all servers?
Also in case of distributed cache, whats the optimal number of servers till which such cache is effective. Since data is replicated to all nodes, and say number of nodes is 20, its like master to master replication across all nodes. By that I mean, each node will get notifications from other 19 and will update modifications to other 19.Does such type os setup scale?
As always in distributed systems, the answer depands on different things:
A load balancer with sticky sessions is for sure the simpler way for the developer, since it doesn’t make any difference if the application runs on 1, 2 or 100 servers. If this is all you care about, stick with it and you can stop reading right here.
I’m not sure how session aware load balancers are implemented and what their general limit in terms of requests per second would be, but they have at least one big disadvantage over the distributed cache. - What to do if the machine handling the sessions is down? - If you distributed your cache, any machine can serve the request and it doesn’t matter if one of them fails. The serialisation/deserialisation part is not a big problem, rather the network could be the bottleneck if you don't run it in at least a 1 Gbit network environment, but it should be ok.
For distributed cache you could go either with Hazelcast, Infinispan or similar solutions, which would simplify the access from your own application. (Update: these implementations use DHT to distribute the cache)
Fully replicated cache you could use EhCached, which you mentioned, or Infinispan. Here the advantage over the distributed cache is the much faster access since you have all the data replicated on every machine and only need to access it localy. The disadvantage is slower writes (so rather use it for read very often, write very seldom scenarios) and the fact that your cache is limited by the amount which one machine is able to store. If you are running your applications on servers with 64GB of RAM this is ok. If you want to distribute them over small amazon instances, this is probably a bad idea. I think before you will hit any problems with updating too many nodes, you will run out of memory, and that one is at least very easy to calculate: AVG_CACHE_NEEDED_PER_CLIENT * NUMBER_OF_CLIENTS < MEMORY_FOR_CACHE_AVAILABLE (on one server). If you need more cache than you have available on any node in your EhCached cluster, full replication won't be possible any more.
Or you could use a Redis cluster or similar independent from your application and the servers your application is running on. This would allow you to scale the cache at a different speed than the rest of your application, however the access to the data wouldn’t be that trivial.
Of course the actual decision depends on your very specific use-case and the demands you are putting on your application.
Personally I was very happy when I found out today that Azure WebPages have a load balancer with sticky session support, and I don’t need to reconfigure my application to use Redis as a session object store, and can just keep everything as it is.
But for a huge workload with hundreds of servers a simple load balancer probably will be rather overwhelmed, and distributed cache, or centralized replicated cache (Redis) will be the way to go.
We are trying to develop a system for distributed caching. Right now, we have 12 applications and they all load same cache. So each jvm loads cache in its in-memory. Problem with this system is redundant data. All 12 applications are loading same cache.
We want to develop a system where you add one or two(for failover) JVM's which load cache and the other 12 applications call these new Cache JVM's.
Can someone suggest me if there are any technologies/frameworks that has solution for my needs?
Thanks
Have a look at Memcached. It may offer a solution to your distributed cache needs.
Also, as #Guy Bouallet mentioned, ehcache is also a viable solution.
Ehcache is a good alternative. It can be used to cache data loaded from database, Web pages or other key/value elements in a distributed environment.
I personally used it in several professional applications and it had shown to be an efficent solution.
I've been researching this for a week now, but I'd like some thoughts on my particular situation...
2 physical servers:
Server A - public WAR, admin WAR
Server B - public WAR
Requirements:
Both WARs need to view the same data.
admin WAR modifies / adds data to the cache.
public WARs modify other parts of the cache / add data to it.
entire cache needs to reside in memory on each physical server (if I add something on Server A admin WAR or public WAR, it needs to show up on Server B public WAR) so in the event of a failure, we aren't waiting for half the cache to be populated
1,500 active users/server, vast majority of traffic is read, very little write
Additional hardware is out of the question.
Is there a good third party caching solution for this scenario? It seems most distributed caching systems want to leave half the data on Server A and half on Server B, which wouldn't meet our failover performance needs.
Thanks for any ideas!
You should look at Redis
http://www.gigaspaces.com/ has a solution for that, it allows you to create "Space" that serves as cache in replicated mode, so each node will have exact copy of data.
They also have solution for fail-over or hot stand by.
Edit:
Gigaspace is far more than just a shared cache, but you can use just the caching solution. It's called In memory data grid. They have dramaticaly changed they web pages so I can't find exact page. But if you search through the documentation yo'll find it.
You can start here
http://www.gigaspaces.com/datagrid
But the technology is not free.
Take a look at the replicated options for EhCache.
Sounds like you've been searching for information on "distributed caches", which has a different defintion than "replicated cache". A distributed cache is a larger cache system spread out among many machines, so that the loss of anyone machine in the cluster does not bring down the entire cache, but just a portion. In this scenario the total size of your cache can reach (number of machines times memory of each machine).
In a replicated cache, the cached data is replicated across each machine, limiting you to a total cache size of max(memory of any one machine).
It seems most distributed caching systems want to leave half the data
on Server A and half on Server B, which wouldn't meet our failover
performance needs.
No, you can tweak it easy. Otherwise you need sticky seesion (you have to know exactly, which cache stores your data). You can choose any solution on the market EhCache, GigaSpace, GridGain etc. I would recommend to use JBoss Cache, imho the simplest and exactly what you need
There are many solutions in this space.
Memcached
EhCache
Infinispan
All of them can be configured as distributed caches. AFAIK Infinispan works best when left an an embedded cache in JBoss AS, last I checked it was difficult to integrate into other app servers. If you have money I would recommend BigMemory from Terracotta. Its the commercial derivative of EhCache and provides alot of additional nice-to-have features.
We use Apache Commons JCS and have been very pleased with it. It claims to be almost twice as fast as EHCache. For the situation you have described, you would probably configure a Lateral TCP Cache.
I am new to memcached and caching in general. I have a java web application running on Ubuntu + Tomcat + MySQL on a VPS Server with 1GB of memory.
Does it make sense to add a memcached layer with about 256MB for caching? Will this be too much load on the server? Which is more appropriate caching rendered html pages or database objects?
Please advise.
If you're going to cache pages, don't use memcached, use Varnish. However, there's a good chance that's not a great use of memory. Cacheing pages trades memory for computation and database work, but it does cost quite a lot of memory per page, so it's best for cases where the computation and database work needed to produce a single page amounts to a lot (or the pages are very small!). Also, consider that page cacheing won't be effective, or even possible, if you want to use per-user customisation on your pages (eg showing the number of items in a shopping cart). At least not without getting into some truly hairy shenanigans (edge-side includes, anyone?).
If you're not going to cache pages, and your app is on a single machine, then there's no point using memcached or similar. The point of cache servers like that is to make the memory on one machine work as a cache for another - like how a file server shares a disk, they're essentially memory servers. On a single machine, you might as well give all the memory to Java and cache objects on the heap.
Are you using an object-relational mapper? If so, see if it has any support for a second-level cache. The big three implementations (Hibernate, OpenJPA, and EclipseLink) all support in-memory caches. They're likely to do a much better job than you would if you did the cacheing yourself.
But, if you're not using a mapper, you have no choice but to do the cacheing yourself. There are extension points in LinkedHashMap for building LRU caches, and then of course there's the people's favourite, SoftReference, in combination with a HashMap. Plus, there are probably cache implementations out there you could download and use - i'd be shocked if there wasn't something in the Apache Commons libraries.
memcached won't add any noticeable load on your server, but it will be memory your app can't use. If you only plan to have a single app server for a while, you're better off using an in-JVM cache.
As far what to cache, the answer falls somewhere in the middle of the above. You don't want to cache exactly what's in your database and you certainly don't want to cache the final output. You have a data model representation in your application that isn't exactly what's in the DB (e.g. a User object might be made up of multiple queries from a few different tables). Cache that kind of thing as it's most reusable.
There's lots of info in the memcached site that should help you understand and get going with caching in general and memcached specifically.
It might make sense to do that, why don't try a smaller size like 64 MB and see how that goes. When you use more resources for the memcache, there is less for everything else. You should try it and see what will give you the best performance.