Want to synchronize the cache data between two servers. Both database is sharing the same database, but for better execution data i have cached the data into Hash Map at startup. Thus want to synchronize the cached data without restarting servers. (Both servers starts at same time).
Please suggest me the best and efficient way to do.
Instead of trying to synchronize the cached data between two server instances, why not centralize the caching instead using something like memcached/couchbase or redis? Using distributed caching with something like ehcache is far more complicated and error prone IMO vs centralizing the cached data using a caching server like those mentioned.
As an addendum to my original answer, when deciding what caching approach to use (in memory, centralized), one thing to take into account is the volatility of the data that is being cached.
If the data is stored in the DB, but does not change after the servers load it, then you don't even need synchronization between the servers. Just let them each load this static data into memory from the source and then go about their merry ways doing whatever it is they do. The data won't be changing, so no need to introduce a complicated pattern for keeping the data in sync between the servers.
If there is indeed a level of volatility in the data (like say you are caching looked up entity data from the DB in order to save hits to the DB), then I still think centralized caching is a better approach than in-memory distributed and synchronized caching. You just need to make sure that you use an appropriate expiration on the cached data to allow natural refresh of the data from time to time. Also, you might want to just drop the cached data from the centralized store when in the update path for a particular entity and then just let it be reloaded from the cache on the next request for that data. This is IMO better than trying to do a true write-through cache where you write to the underlying store as well as the cache. The DB itself might make tweaks to the data (via defaulting unsupplied values for example), and your cached data in that case might not match what's in the DB.
EDIT:
A question was asked in the comments about the advantages of a centralized cache (I'm guessing against something like an in memory distributed cache). I'll provide my opinion on that, but first a standard disclaimer. Centralized caching is not a cure-all. It aims to solve specific issues related to in-jvm-memory caching. Before evaluating whether or not to switch to it, you should understand what your problems are first and see if they fit with the benefits of centralized caching. Centralized caching is an architectural change and it can come with issues/caveats of its own. Don't switch to it simple because someone says it's better than what you are doing. Make sure the reason fits the problem.
Okay, now onto my opinion for what kinds of problems centralized caching can solve vs in-jvm-memory (and possibly distributed) caching. I'm going to list two things although I'm sure there are a few more. My two big ones are: Overall Memory Footprint and Data Synchronization Issues.
Let's start with Overall Memory Footprint. Say you are doing standard entity caching to protect your relational DB from undue stress. Let's also say that you have a lot of data to cache in order to really protect your DB; say in the range of many GBs. If you are doing in-jvm-memory caching, and you say had 10 app server boxes, you would need to get that additional memory ($$$) times 10 for each of the boxes that would need to be doing the caching in jvm memory. In addition, you would then have to allocate a larger heap to your JVM in order to accommodate the cached data. I'm from the opinion that the JVM heap should be small and streamlined in order to ease garbage collection burden. If you have a large chunks of Old Gen that can't be collected then your going to stress your garbage collector when it goes into a full GC and tries to reap something back from that bloated Old Gen space. You want to avoid long GC2 pause times and bloating your Old Gen is not going to help with that. Plus, if you memory requirement is above a certain threshold, and you happened to be running 32 bit machines for your app layer, you'll have to upgrade to 64 bit machines and that can be another prohibitive cost.
Now if you decided to centralize the cached data instead (using something like Redis or Memcached), you could significantly reduce the overall memory footprint of the cached data because you could have it on a couple of boxes instead of all of the app server boxes in the app layer. You probably want to use a clustered approach (both technologies support it) and at least two servers to give you high availability and avoid a single point of failure in your caching layer (more on that in a sec). By one having a couple of machines to support the needed memory requirement for caching, you can save some considerable $$. Also, you can tune the app boxes and the cache boxes differently now as they are serving distinct purposes. The app boxes can be tuned for high throughput and low heap and the cache boxes can be tuned for large memory. And having smaller heaps will definitely help out with overall throughput of the app layer boxes.
Now one quick point for centralized caching in general. You should set up your application in such a way that it can survive without the cache in case it goes completely down for a period of time. In traditional entity caching, this means that when the cache goes completely unavailable, you just are hitting your DB directly for every request. Not awesome, but also not the end of the world.
Okay, now for Data Synchronization Issues. With distributed in-jvm-memory caching, you need to keep the cache in sync. A change to cached data in one node needs to replicate to the other nodes and by sync'd into their cached data. This approach is a little scary in that if for some reason (network failure for example) one of the nodes falls out of sync, then when a request goes to that node, the data the user sees will not be accurate against what's currently in the DB. Even worse, if they make another request and that hits a different node, they will see different data and that will be confusing to the user. By centralizing the data, you eliminate this issue. Now, one could then argue that the centralized cache needs concurrency control around updates to the same cached data key. If two concurrent updates come in for the same key, how do you make sure the two updates don't stomp on each other? My thought here is to not even worry bout this; when an update happens, drop the item from the cache (and write though directly to the DB) and let it be reloaded on the next read. It's safer and easier this way. If you don't want to do that, then you can use CAS (Check-And-Set) functionality instead for optimistic concurrency control if you really want to update both the cache and db on updates.
So to summarize, you can save money and better tune your app layer machines if you centralize the data they cache. You also can get better accuracy of that data as you have less data synchronization issues to deal with. I hope this helps.
First, do try to forget about the premature optimization. Do you really need the cache? 99% that you do not need it. In this case you solution is in removing the redundant code.
If however you need it try to stop re-inventing wheels. There are perfect ready-to use libraries. For example ehCache that has distributed mode.
Use HazelCast. It allows data synchronization between servers using multicast protocol. It's easy to use. It supports locking and other features.
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