Requirement : I have 4 servers : A,B,C,D. They all connect to data provider, get the data and persist it into mongodb for N mins. So that if, next time, same request arrives to another server, it takes data from mongodb only instead of making a call to data provider.
|A|
|B| |data provider|
|C|
|D|
But if, |data provider| response slow, there is a possibility that 2 different requests for same resource arrive to A, B. I want one request waiting until the response of first request is received. I am using queue for this which is fine for single server. But now I need need distributed cache due to multiple servers.
Implementation : After reading few articles over the net, I got to know that Distributed Cache in Java can be implemented using ehcache RMI replication. But I have few doubts before going ahead with ehchache. (Although there are more solutions like JCS etc, I decided to pick ehcache on the basis other answers on StackOverflow)
Doubts
What if one of the servers gets down? Does ehcache handles this automatically?
Interesting situation, but I fail too see how an extra cache (of any kind) would help solve the problem. Ultimately your problem boils down to one of coordination between servers and a cache has little to add there.
Instead I would either use a queue shared between the four servers where only one request for a resource is allowed on at a time. Another possibility is a shared Map where each server will lock a resource name while retrieving it. Other servers can then wait on this lock and once it is released try to retrieve the resource from MongoDB.
I haven't tried using it, but a combination of redis and redisson looks like a good fit for such a task.
Ehcache with RMI replication is NOT a distributed cache and will not help in your situation because there will not be any shared state on which to queue/isolate your accesses.
Distributed Ehcache - that is backed by Terracotta - can help as you could combine strong consistency with a CacheLoader to obtain that only one thread across servers does load a given resource. But unless you are ready to stick with Terracotta 3.7.x, this is no longer an open source option.
But as Martin said, this may not be the best answer to your use case, as I feel you already use MongoDB as your fast access storage, which makes the cache redundant.
Related
I'm using Terracotta Enterprise Ehcache along with a Java application, but at some points of the day the Terracotta starts to take too much time to answer put/get requests, sometimes locking client's threads and launching exceptions.
My infrastructure is composed by a cluster of 5 JBoss servers 6.2.0 and another cluster with 4 Terracotta Enterprise Ehcache 3.7.5 that stores a large amount of data.
The application does around 10 million accesses to the Terracotta Ehcache per day.
Originally I used criteria, but, when the problems started, I changed everything to use id searches only.
I tried to change the DGC interval, making it run more often or even only once a day, it didn't get any better.
I started with the persistence mode permanent-store and tried to change to temporary-swap-only, but the problem continues.
Tried to change the Terracotta cluster to work with 2 actives machines and 2 passives or 4 actives.
Tried to config my caches as eternal true or false.
All my caches are nonstop and I tried to use the timeoutBehavior as exception or noop.
Basically none of my efforts seems to produce any significant change and the Terracotta continues to enter in this state where it can't answer the requests anymore.
Right now the only thing that seems to "solve" the problem is to restart all the clients.
Does anybody have a similar scenario using Terracotta, with this kind of throughput? Any ideas for where to look now?
Yes i faced a similar issue of thread contention on terracota cluster setup. The slaves requests for get/put used to take time and a thread dump showed locking as the main reason. I dont remember the details as it was more than 4-6 months back. I had 2 options then:
Create an own cache server which would be a custom war and would run ehcache underneath and expose my own put, get, delete etc operations as REST endpoints.
Use cache replication as ehcache provides.
I first tried with replication suing RMI and then with JGroups. RMI based approach worked excellently and was much stable so I decided to move to RMI based replication which ehcache provides OOTB. My setup was to use ehcache as a cache provider for hibernate based JPA and RMI absed solution worked very well and effectively. It is intelligent enough to see when the other servers in cluster go down and when it comes up. Replication is async and transparent. Since the second approach worked well I didnt try out the first one.
I have recently started taking a look into Infinispan as our caching layer. After reading through the operation modes in Infinispan as mentioned below.
Embedded mode: This is when you start Infinispan within the same JVM as your applications.
Client-server mode: This is when you start a remote Infinispan instance and connect to it using a variety of different protocols.
Firstly, I am confuse now which will be best suited to my application from the above two modes.
I have a very simple use case, we have a client side code that will make a call to our REST Service using the main VIP of the service and then it will get load balanced to individual Service Server where we have deployed our service and then it will interact with the Cassandra database to retrieve the data basis on the user id. Below picture will make everything clear.
Suppose for example, if client is looking for some data for userId = 123 then it will call our REST Service using the main VIP and then it will get load balanced to any of our four service server, suppose it gets load balanced to Service1, and then service1 will call Cassandra database to get the record for userId = 123 and then return back to Client.
Now we are planning to cache the data using Infinispan as compaction is killing our performance so that our read performance can get some boost. So I started taking a look into Infinispan and stumble upon two modes as I mentioned below. I am not sure what will be the best way to use Infinispan in our case.
Secondly, As from the Infinispan cache what I will be expecting is suppose if I am going with Embedded Mode, then it should look like something like this.
If yes, then how Infinispan cache will interact with each other? It might be possible that at some time, we will be looking for data for those userId's that will be on another Service Instance Infinispan cache? Right? So what will happen in that scenario? Will infinispan take care of those things as well? if yes, then what configuration setup I need to have to make sure this thing is working fine.
Pardon my ignorance if I am missing anything. Any clear information will make things more clear to me to my above two questions.
With regards to your second image, yes, architecture will exactly look like this.
If yes, then how Infinispan cache will interact with each other?
Please, take a look here: https://docs.jboss.org/author/display/ISPN/Getting+Started+Guide#GettingStartedGuide-UsingInfinispanasanembeddeddatagridinJavaSE
Infinispan will manage it using JGroups protocol and sending messages between nodes. The cluster will be formed and nodes will be clustered. After that you can experience expected behaviour of entries replication across particular nodes.
And here we go to your next question:
It might be possible that at some time, we will be looking for data for those userId's that will be on another Service Instance Infinispan cache? Right? So what will happen in that scenario? Will infinispan take care of those things as well?
Infinispan was developed for this scenario so you don't need to worry about it at all. If you have for example 4 nodes and setting distribution mode with numberOfOwners=2, your cached data will live on exactly 2 nodes in every moment. When you issue GET command on NON owner node, entry will be fetched from the owner.
You can also set clustering mode to replication, where all nodes contain all entries. Please, read more about modes here: https://docs.jboss.org/author/display/ISPN/Clustering+modes and choose what is the best for your use case.
Additionally, when you add new node to the cluster there will StateTransfer take place and synchronize/rebalance entries across cluster. NonBlockingStateTransfer is implemented already so your cluster will be still capable of serving responses during that joining phase. See: https://community.jboss.org/wiki/Non-BlockingStateTransferV2
Similarly for removing/crashing nodes in your cluster. There will be automatic rebalancing process so for example some entries (numOwners=2) which after crash live only at one node will be replicated respectively to live on 2 nodes according to numberOfOwners property in distribution mode.
To sum it up, your cluster will be still up to date and this does not matter which node you are asking for particular entry. If it does not contain it, entry will be fetched from the owner.
if yes, then what configuration setup I need to have to make sure this thing is working fine.
Aforementioned getting started guide is full of examples plus you can find some configuration file examples in the Infinispan distribution: ispn/etc/config-samples/*
I would suggest you to take a look at this source too: http://refcardz.dzone.com/refcardz/getting-started-infinispan where you can find even more basic and very quick configuration examples.
This source also provides decision related information for your first question: "Should I use embedded mode or remote client-server mode?" From my point of view, using remote cluster is more enterprise-ready solution (see: http://howtojboss.com/2012/11/07/data-grid-why/). Your caching layer is very easily scalable, high-available and fault tolerant then and is independent of your database layer and application layer because it simply sits between them.
And you could be interested about this feature as well: https://docs.jboss.org/author/display/ISPN/Cache+Loaders+and+Stores
I think in newest Infinispan release supports to work in a special, compatibility mode for those users interested in accessing Infinispan in multiple ways .
follow below link to configure your cache environment to support either embedded or remotely.
Interoperability between Embedded and Remote Server Endpoints
My application sends http requests to a webservice, but because the Terms of Service limit it to one query per second it is very important for me not to send more queries than I need. I put the results of some queries into a database that I check before trying the query again but some queries results are not well suited to putting in database so I would like some sort of dumb cache that would intercept my webservice calls and if the call was a duplicate just send the results of the previous call. I would expect to be able to configure the size of the cache and have it automatically remove the oldest entry if it fills up, it would be great if the cache could be configured as a file rather than use heap memory because my application is already quite memory intensive
For a simple caching solution try Google Guava libraries. The CacheBuilder/CacheLoader could be configured to your requirements. Guava provides a simple caching solution that is more sofisticated than java's own HashMap but light weight when compared to Ehcache and others. This cache could be used in a web service request interceptor that helps to decide whether to initiate a web service call.
A good tutorial with an example for Guava cache could be found here
I've heard the term "clustering" used for application servers like GlassFish, as well as with Terracotta; and I'm trying to understand what the word clustering implies when used in conjunction with application servers, and when used in conjunction with Terracotta.
My understanding is:
If a GlassFish server is clustered, then it means we have multiple physical/virtual machines, each with their own JRE/JVM running separate instances of GlassFish. However, since they are clustered, they will all communicate through their admin server ("DAS"), and have the same apps deployed to all of them. They will effectively act (to the end user) as if they are a single app server - but now with load balancing, failover/redundancy and scalability added into the mix.
Terracotta is, essentially, a product that makes multiple JVMs, running on different physical/virtual machines, act as if they are a single JVM.
Thus, if my understanding is correct, the following are implied:
You cluster app servers when you want load balancing and failover tolerance
You use Terracotta when any particular JVM is too small to contain your application and you need more "horsepower"
Thus, technically, if you have a GlassFish cluster of, say, 5 server instances; each of those 5 instances could actually be an array/cluster of Terracotta instances; meaning each GlassFish server instance is actually a GlassFish instance living across the JVMs of multiple machines itself
If any of these assertions/assumptions are untrue, please correct me! If I have gone way off-base and clearly don't understand clustering and/or the very purpose of Terracotta, please point me in the right direction!
Terracotta enables you to have a shared state across all your nodes (its stateful). Basically it creates a shared memory space between different JVM's. This is useful when nodes in a cluster all need access to the same objects.
If your application is stateless and you just need load balancing and fail over you can use a solution like JGroups. In this scenario each node just handles requests and has little idea about other nodes. Objects in memory are not shared across nodes and each JVM just runs on its own and has no idea about other JVM's. This often works nicely for request / response type applications. A webserver serving content (without sessions) does this for example.
Dealing with a stateless cluster is often simpler then dealing with a stateful cluster. This is because in a stateless cluster nodes know almost nothing about each other which results in less things that can go wrong.
GlassFish sits a bit in the middle of the above concepts. Objects in memory within GlassFish are visible to all nodes. However the frontend (HTTP connectors) work stateless.
So to answer your questions:
1) Yes, those are the two most obvious reasons. However sometimes people only want failover or only want load balancing or sometimes both. Not all clustering solutions fix both of these problems.
2) Yes. Altough technically speaking Terracotta only solves the shared memory part, not the CPU part. However by solving the memory part it automatically solves the CPU part since you can now just add JVM's to the shared memory space.
3) I don't know if thats practically possible but as a thought experiment; Yes.
Clustering can mean one of the following:
Multiple instances can be managed as one. Deploy an application to the cluster, it is deployed to all instances in the cluster. Make a configuration change, and that change will be pushed to all nodes in the cluster. GlassFish supports this out of the box.
Service Availability. If any one instance fails, the application is available on another instance. Without high availability enabled, any instance failure also results in session loss for any session being managed by that instance. GlassFish supports this out of the box.
High availability. If any one instance fails, the application is available on another instance, and there is no session loss because a session replica is also maintained on another instance. GlassFish supports this. You will have to choose either #2 or #3 in any one cluster.
What you are asking about IMHO is really #3, because it is the only real case where Terracotta - in the context of high availability clustering - will offer value w/GlassFish. GlassFish already offers built-in high availability, so there had better be a very good reason to add Terracotta to the solution because it will complicate the deployment architecture.
The primary reason I can think of adding Terracotta is that you may want to offload session management to a data grid and free up GlassFish to run business logic. This may be due to more frequent garbage collection or wanting to manage more users per GlassFish instance. However, I'm not sure that Terracotta can do this seamlessly. With GlassFish built-in HA clustering, replicating sessions is seamless (no application logic modifications). You may have to write code to put/get data from a Terracotta cache I'll let you research :-) Oracle GlassFish Server also integrates (seamlessly) with Coherence to solve this problem. You can separate session management into a Coherence data grid without modifying your application code.
Unless you know for a fact up front that your application must scale to a very large number of concurrent users, start with built-in HA clustering, run tests, and go from there.
Hope this helps.
My DAL is implemented with Hibernate and I want to use EHCache as its second level cache with its distributed capabilities (for scalability and HA).
Seeing as EHCache provides distributed caching only with Terracotta my question is what is the role of the Terracotta server instance? Does it also hold data? Does it only coordinate the distribution between the partitioned cache parts?
My confusion derives mainly from this explanation regarding TSA which says the server holds the data but I think that maybe in my scenario the cache and the Terracotta server are sort of merged. Am I correct?
If the server does hold data then why shouldn't the bottleneck just move from the db to the Terracotta server?
Update:
Affe's answer answered the second part of my question which was the important part but just in case someone comes by looking for the first part I'll say that the TC server has to hold all the data that the EHCache in memory holds and so if you want a distributed cache (not replicated) then the L2 (TC server) must hold all the objects itself as well.
Thanks in advance,
Ittai
The idea is it's still significantly faster to contact the terracotta cluster via the terracotta driver and do what's basically a Map lookup, than to acquire a database connection and execute an SQL statement. Even if that does become the application's choke point, overall throughput would be expected to still be significantly higher than a JDBC Connection + SQL choke point. Open connections and open cursors are big resource hogs in the database, an open socket to the terracotta cluster is not!
You can get ehcache clustered without using terracotta. They have documentation for doing it via RMI, JGroups and JMS. We are using JMS since we have a significant JMS infrastructure to handle the communication already. I don't know how well it will scale in the long term, but our current concern is just HA.