Maps an replicated maps: it allows to share data between your cluster’s nodes.Hazelcast has a lot of other structures that can be super useful in a clustered environment: Hopefully, the Hazelcast library allowed to use its lock mechanism to secure our counter. Whereas the JVM has internal mechanisms to handle concurrency, there is nothing in the JDK to handle it at the cluster level. Here it was just a counter, but it can be even more trickier with complex data. We’ve seen that concurrency can be a vicious problem: even without throwing comprehensive errors, your data may be corrupted. We can play at stopping and starting a node to see that the cluster reacts to such node failure. INFO. :5701 Initializing cluster partition table arrangement.Īnd specifically the time we the second member is recognized. One important thing to understand is that it’s a library: there is no middleware to install, therefore it’s super easy to use it in a Java application.įor our sample, we will expose a Hazelcast instance as an injectable bean in our application and use a Hazelcast Lock in our counter ( "/" ) public class Application extends ResourceConfig It provides the Consensus algorithm, but more importantly a list of object that super useful when you want to deal with a cluster. Hopefully there is an opensource library that can do it for us that is called Hazelcast. We will not code a Raft implementation for this simple exercise. It’s called a consensus algorithm, and you can look at the explanation of the Raft Protocol to understand it better: it’s the protocal that is used by etcd for instance. It’s a complex problem, which involves the election of a master in the cluster, have to take into account network partitions and deal with plenty of problems of that nature. We now have to find a way for our cluster members to be aware of their peers. We now have to find a mechanism that will help the two application to communicate. The problem here is that the two applications are totally independent and access the common file without knowing that the other may access it. It’s even worse than when we had not put any synchronization on our single webapp. We have more errors, and the counter is…. Now let’s add some concurrency and give 4 threads to ab. We do not notice any failed request, and the result is what we expect: our counter has been incremented up to 99. The -c parameter allows to specify the concurrency. Percentage of the requests served within a certain time (ms )ġ00% 217 (longest request ) # cat /tmp/counter Time per request: 109.568 (mean, across all concurrent requests ) Licensed to The Apache Software Foundation, īenchmarking localhost (be patient ).done In this example, there is no synchronization, so output is inconsistent.# ab -l -n 100 -c 1 This is ApacheBench, Version 2.3 Ĭopyright 1996 Adam Twiss, Zeus Technology Ltd, Understanding the problem without Synchronization By convention, a thread that needs consistent access to an object's fields has to acquire the object's lock before accessing them, and then release the lock when it's done with them.įrom Java 5 the package contains several lock implementations. Every object has a lock associated with it. Synchronization is built around an internal entity known as the lock or monitor. It can be achieved by using the following three ways: Mutual Exclusive helps keep threads from interfering with one another while sharing data.
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