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Special thanks go to the Hazelcast guys: Talip Ozturk, Fuad Malikov, and Enes Akar who are technically responsible for Hazelcast and helped to answer my questions. But I really want to thank Mehmet Dogan, architect at Hazelcast, since he was my main source of information and put up with the zillion questions I have asked. Also thanks to all committers and mailing list members for contributing to making Hazelcast such a great product.

In that role, he roves over the whole code base with an eagle eye and has built up deep expertise on Hazelcast. Peter is also a great communicator, wishing to spread his knowledge of and enthusiasm for Hazelcast to our user base. So it was natural for Peter to create Mastering Hazelcast.

In Mastering Hazelcast, Peter takes an in-depth look at fundamental Hazelcast topics. This book should be seen as a companion to the Hazelcast Reference Manual. The reference manual covers all Hazelcast features. Mastering Hazelcast gives deeper coverage over the most important topics. Each chapter has a Good to Know section, which highlights important concerns.

This book includes many code examples. These and more can be accessed from https: A great way to learn Hazelcast is to download the examples and work with them as you read the chapters. Like much of Hazelcast, this book is open source. Feel free to submit pull requests to add to and improve it. It is a living document that gets updated as we update Hazelcast. Writing concurrent systems has long been a passion of mine, so it is a logical step to go from concurrency control within a single JVM to concurrency control over multiple JVMs.

A lot of the knowledge that is applicable to concurrency control in a single JVM also applies to concurrency over multiple JVMs.

However, there is a whole new dimension of problems that make distributed systems even more interesting to deal with. When you professionally write applications for the JVM, you will likely write server-side applications. Although Java has support for writing desktop applications, the server-side is where Java really shines. Hazelcast does not lose data after a JVM crash because it automatically replicates partition data to other cluster members.

In the case of a member going down, the system will automatically failover by restoring the backup. Hazelcast has no master member that can form a single point of failure; each member has equal responsibilities. Hazelcast on its own is elastic, but not automatically elastic; it will not automatically spawn additional JVMs to become members in the cluster when the load exceeds a certain upper threshold.

Also, Hazelcast will not shutdown JVMs when the load drops below a specific threshold. You can achieve this by adding a glue code between Hazelcast and your cloud environment.

You are not forced to mutilate objects so they can be distributed, use specific application servers, complex APIs, or install software; just add the hazelcast. This freedom, combined with very well-thought-out APIs, make Hazelcast a joy to use. In many cases, you simply use interfaces from java. In little time and with simple and elegant code, you can write a highly available, scalable and high-performing system. This book aims at developers and architects who build applications on top of the JVM and want to get a better understanding of how to write distributed applications using Hazelcast.

Hazelcast also provides an almost identical API for. If you are a developer that has no prior experience with Hazelcast, this book will help you learn the basics and get up and running quickly. If you already have some experience, it will round out your knowledge. If you are a heavy Hazelcast user, it will give you insights into advanced techniques and things to consider.

Its focus is now on Hazelcast 3. Getting Startedyou will learn how to download and set up Hazelcast and how to create a basic project. You will also learn about some of the general Hazelcast concepts.

Learning the Basicsyou will learn the basic steps to start Hazelcast instances, load and configure DistributedObjects, configure logging, and the other fundamentals of Hazelcast. Distributed Primitivesyou will learn how to use basic concurrency primitives like ILock, IAtomicLong, IdGenerator, ISemaphore and ICountDownLatch, and about their advanced settings. Distributed Collectionsyou will learn how to make use of distributed collections like the IQueue, IList and ISet.

Distributed Mapyou will learn about the IMap functionality. Since IMap functionality is very extensive, there is a whole section that deals with its configuration options, such as high availability, scalability, etc. You will also learn how to use Hazelcast as a cache and persist its values. Distributed Executoryou will learn about executing tasks using the Distributed Executor. By using the executor, you turn Hazelcast into a computing grid.

Hazelcast Clientsyou will learn about setting up Hazelcast clients. Serializationyou will learn more about the different serialization technologies that are supported by Hazelcast. Java Serializable and Externalizable interfaces, and also the native Hazelcast serialization techniques like DataSerializable and the new Portable functionality will be explained.

Different member discovery mechanisms like multicast, Amazon EC2, and security will be explained. Using Hazelcast as Hibernate 2nd Level Cacheyou will learn how you can configure your Hazelcast to use it as Hibernate 2nd Level Cache.

Integrating Hazelcast with Springyou will learn how you can configure your Hazelcast in the Spring context. Extending Hazelcastyou will learn about using the Hazelcast SPI to make first class distributed services and also our Discovery SPI. Threading Modelyou will learn about using the Hazelcast threading model. This helps you write an efficient system without causing cluster stability issues.

Performance Tipsyou will learn some tips to improve Hazelcast performance. In the Appendixyou will learn how you can configure your Hazelcast cluster in Amazon EC2. You can find the online version of this book at http: Code examples that are structured chapter by chapter in a convenient Maven project can be cloned using GitHub at https: I recommend you run the examples as you read the book. Please feel free to submit any errata as an issue to this repository, or send them directly to masteringhazelcast hazelcast.

Building distributed systems on Hazelcast is really a joy to do. I hope I can make you as enthusiastic about it as I am. Hazelcast relies on Java 6 or higher. If you want to compile the Hazelcast examples, make sure you have Java 6 or higher installed. If not installed, you can download it from the Oracle site: For this book, we rely on the community edition of Hazelcast 3.

If your project uses Maven, there is no need to install Hazelcast at all, see http: Otherwise, you should make sure that the Hazelcast JAR is added to your classpath. Apart from this JAR, there is no other installation process for Hazelcast. These simple steps save quite a lot of time that can be used to solve real problems. Hazelcast is very easy to include in your Maven 3 project without going through a complex installation process. Hazelcast can be found in the standard Maven repositories, so you do not need to add additional repositories to the pom.

To include Hazelcast in your project, just add the following to your pom:. After this dependency is added, Maven will automatically download the dependencies needed.

To do same with Gradle, include the following to the dependencies section of your build. The latest snapshot is even more recent because it is updated as soon as a change is merged in the Git repository. If you want to use the latest snapshot, you need to add the snapshot repository to your pom:.

You can access the examples used in the book at the following link: The examples are very useful to get started and show how Hazelcast features work. These examples are modules within a Maven project and you can build them using the following command:. If you want to build Hazelcast by yourself to provide a bug fix, debug, see how things work, add new features, etc.

The above command builds all the JARs and runs all the tests. That can take some time. If you do not want to execute all the tests, use the following command:.

If you have a change that you want to offer to the Hazelcast team, you commit and push your change to your own forked repository and you create a pull request that will be reviewed by the Hazelcast team. Once your pull request is verified, it will be merged and a new snapshot will automatically appear in the Hazelcast snapshot repository. Y released will be compatible with the previous one.

For example, it will be possible to perform a rolling upgrade replacing existing X. Rolling upgrades across minor versions is a Hazelcast Enterprise feature. Note that rolling upgrades across patch versions X. Z is possible for both Hazelcast and Hazelcast Enterprise. Assuming, as an example, that you want to perform a rolling upgrade on the members with Hazelcast 3. These plugins have their own lifecycles.

Some of these plugins are listed below:. Now that we have checked out the sources and have installed the right tools, we can start to build the amazing Hazelcast applications. The programmatic configuration is the most important one; other mechanisms are built on top of it.

Throughout this book, we use the XML configuration file since that is the option most often used in production. The following shows an empty hazelcast. This configuration file example imports an XML schema XSD for validation. If you are using a modern IDE like IntelliJ IDEAyou get code completion for XML tags. In the example code for this book, you can find the full XML configuration. In most of our examples, we will rely on multicast for member discovery so that the members will join the cluster:.

See Multicast if multicast does not work or you want to know more about it. If you are using the programmatic configuration, then multicast is enabled by default. Behind the scenes the following approaches are used to resolve the configuration, in the given order:.

Hazelcast checks whether the hazelcast. If it is, then its value is used as the path to the configuration file. This is useful if you want the application to choose the Hazelcast configuration file at the time of startup.

The config option can be set by adding the following to the java command:. The value can be a normal file path, or it can be a classpath reference if it is prefixed with classpath: If all of the above options fail to provide a Hazelcast config to the application, the default Hazelcast configuration is loaded from the Hazelcast JAR.

One of the changes in place since Hazelcast 3. Prior to the 3. The problem with that approach was that you ended up with a HazelcastInstance with a different configuration than you expected. If you need more flexibility to load a Hazelcast config object from XML, you should have a look at the following:. ClasspathXmlConfig class loads the config from a classpath resource containing the XML configuration. InMemoryXmlConfig class loads the config from an in-memory string containing the XML configuration.

The Hazelcast Config object has a fluent interface; the Config instance is returned when a config method on this instance is called. This makes chaining method calls very easy. The programmatic configuration is very useful for testing and it is a solution for the static nature of the XML configuration.

You can easily create content for the programmatic configuration on the fly. For example, you could base it on the database content. You could even decide to move the static configuration to the hazelcast.

In Hazelcast releases prior to 3. This functionality has been removed because it led to confusion when explicitly created Hazelcast instances were combined with calls to the implicit default HazelcastInstance. You probably want to keep a handle to the Hazelcast instance somewhere for later usage in the application. Hazelcast does not copy configuration from one member to another. Therefore, whether they are XML based or programmatic, the configurations except member-list inside network on all members in the cluster should exactly be the same.

The Hazelcast XML configuration can contain configuration elements for all kinds of distributed data structures, i. See the following example:. What if we want to create multiple map instances using the same configuration? Do we need to configure them individually? This is impossible to do if you have a dynamic number of distributed data structures and you do not know up front how many need to be created. The solution to this problem is wildcard configuration, which is available for all data structures.

Wildcard configuration makes it possible to use the same configuration for multiple instances. For example, we could configure the previous testmap example with a value of 10 for time-to-live-seconds using a wildcard configuration like this:. The wildcard configuration can be used like this:.

If you have a Spring background, you could consider the wildcard configuration to be a prototype bean definition. The difference is that in Hazelcast, multiple gets of a data structure with the same ID will still result in the same instance, whereas with prototype beans, new instances are returned.

If a map is loaded using hz. The selection does not depend on the definition order in the configuration file and it is not based on the best-fitting match. You should make sure that your wildcard configurations are very specific.

One of the ways to achieve this is to include the package name as shown below. Hazelcast provides an option to configure certain properties which are not part of an explicit configuration section, such as the Map. This can be done using the properties section.

For a full listing of available properties, see the System Properties section in the Hazelcast Reference Manual or have a look at the GroupProperties class. Apart from properties in the hazelcast. One thing to watch out for is that you cannot override properties in the hazelcast.

Properties are not shared between members, so you cannot put properties in one member and read them from another. You need to use a distributed map for that. Hazelcast supports various logging mechanisms; jdklog4sl4j or none if you do not want to have any logging.

The default is jdkthe logging library that is part of the JRE, so no additional dependencies are needed. You can set logging by adding a property in the hazelcast. You can also configure it from the command line using java -Dhazelcast. If you are going to use log4j or slf4jmake sure that the correct dependencies are included in the classpath.

See the example sources for more information. If you are not satisfied with the provided logging implementations, you can always implement your own logging by using the LogListener interface. See the Logging Configuration section in the Hazelcast Reference Manual for more information.

If you are making use of jdk logging and you are annoyed that your log entry is spread over two lines, have a look at the SimpleLogFormatter as shown below. One of the new features of Hazelcast 3 is the ability to specify variables in the Hazelcast XML configuration file.

This makes it a lot easier to share the same Hazelcast configuration between different environments and it also makes it easier to tune settings. In this example, the pool-size is configurable using the pool. In a production environment, you might want to increase the pool size since you have beefier machines there.

In a development environment, you might want to set it to a low value. By default, Hazelcast uses the system properties to replace variables with their actual value. To pass this system property, you could add the following on the command line: If a variable is not found, a log warning will be displayed but the value will not be replaced.

You can use a different mechanism than the system properties, such as a property file or a database. You can do this by explicitly setting the Properties object on the XmlConfigBuilder as shown below. The Config subclasses, like FileSystemXmlConfigaccept Properties in their constructors. If your needs go beyond what the variables provide, you might consider using some kind of template engine like Velocity to generate your hazelcast.

Another option is using programmatic configuration, either by creating a completely new Config instance or loading a template from XML and enhancing where needed. This feature enables composition of the Hazelcast declarative configuration file out of smaller configuration snippets.

You can compose the declarative configuration of your Hazelcast or Hazelcast Client from multiple declarative configuration snippets. In most cases, you will have a single Hazelcast instance per JVM. However, multiple Hazelcast instances can also run in a single JVM. This is useful for testing and is also needed for more complex setups, such as application servers running multiple independent applications using Hazelcast. You can start multiple Hazelcast instances as shown below.

When you start the above MultipleMembersyou see an output similar to the following in one member. In the previous sections, we saw how a HazelcastInstance can be created. In most cases, you want to load a DistributedObjectsuch as a queue, from this HazelcastInstance. For most of the DistributedObjects, you can find a get method on the HazelcastInstance. In case you are writing custom distributed objects using the SPIyou can use the HazelcastInstance.

One thing worth mentioning is that most of the distributed objects defined in the configuration are created lazily. They are only created on the first operation that accesses them. If there is no explicit configuration available for a DistributedObjectHazelcast will use the default settings from the file hazelcast-default. This means that you can safely load a DistributedObject from the HazelcastInstance without it being explicitly configured. To learn more about the queue and its configuration, see Distributed Collections: Some of the distributed objects will be static.

They will be created and used through the application and the IDs of these objects will be known up front. Other distributed objects are created on the fly, and one of the problems is finding unique names when new data structures need to be created.

One of the solutions to this problem is to use the IdGeneratorwhich will generate cluster wide unique IDs. This technique can be used with wildcard configuration to create similar objects using a single definition. A distributed object created with a unique name often needs to be shared between members. You can do this by passing the ID to the other members and you can use one of the HazelcastInstance. For more information, see Serialization: In Hazelcast, the name and type of the DistributedObject uniquely identifies that object:.

In the above example, two different distributed objects are created with the same name but different types. In normal applications, you want to prevent different types of distributed objects from sharing the same name. You can add the type to the name, such as personMap or failureCounterto make the names self-explanatory. In most cases, once you have loaded the DistributedObjectyou probably keep a reference to it and inject into all places where it is needed. However, you can safely reload the same DistributedObject from the HazelcastInstance without additional instances being created if you only have the name of the DistributedObject.

In some cases, like deserialization, when you need to get a reference to a Hazelcast DistributedObjectthis is the only solution. If you have a Spring background, you could consider the configuration to be a singleton bean definition. A DistributedObject can be destroyed using the DistributedObject. You should use this method with care because once the destroy method is called and the resources are released, a subsequent load with the same ID from the HazelcastInstance will result in a new data structure without throwing an exception.

A similar issue occurs with references. If a reference to a DistributedObject is used after the DistributedObject is destroyed, new resources will be created.

In the following case, we create a cluster with two members and each member gets a reference to the queue q. First, we place an item in the queue. When the queue is destroyed by the first member q1 and q2 is accessed, a new queue will be created.

The system will not report any error and will behave as if nothing has happened. The only difference is the creation of the new queue resource.

Again, a lot of care needs to be taken when destroying distributed objects. One type is the truly partitioned data structure, like the IMapwhere each partition will store a section of the Map.

The other type is a non-partitioned data structure, like the IAtomicLong or the ISemaphorewhere only a single partition is responsible for storing the main instance. For this type, you sometimes want to control that partition. Normally, Hazelcast will not only use the name of a DistributedObject for identification, but it will also use the name to determine the partition.

The problem is that you sometimes want to control the partition without depending on the name. Assume that you have the following two semaphores. They would end up in different partitions because they have different names. Luckily, Hazelcast provides a solution for that using the symbol, as in the following example. Now, s1 and s2 will end up in the same partition because they share the same partition key: This partition key can be used to control the partition of distributed objects and can also be used to send a Runnable to the correct member using the IExecutor.

Executing on Key Ownerand to control in which partition a map entry is stored, as in see Map: If a DistributedObject name includes a partition key, then Hazelcast will use the base-name without the partition key to match with the configuration.

For example, semaphore s1 could be configured as shown below. This means that you can safely combine explicit partition keys with normal configuration. It is important to understand that the name of the DistributedObject will contain the partition-key section. Therefore, the following two semaphores are different. To access the partition key of a DistributedObjectyou can call the DistributedObject. This method is useful if you need to create a DistributedObject in the same partition of an existing DistributedObjectbut you do not have the partition key available.

If you only have the name of the partition key available, you can have a look at the PartitionKeys class, which exposes methods to retrieve the base-name or the partition key. In the previous examples, the foo partition key was used. In many cases, you do not care what the partition key is, as long as the same partition key is shared between structures. Hazelcast provides an easy solution to obtain a random partition key.

You are completely free to come up with a partition key yourself. You can have a look at the UUID, although due to its length, it will cause some overhead. Another option is to look at the Random class. The only thing you need to watch out for is to have the partition keys evenly distributed among the partitions. If is used in the name of a partitioned DistributedObjectsuch as the IMap or IExecutorServicethen Hazelcast keeps using the full String as the name of the DistributedObjectbut ignores the partition key.

This is because for these types, a partition key does not have any meaning. For more information about why you want to control partitioning, see Performance Tips: Hazelcast offers a distributed dynamic class loader to load your custom classes or domain classes from a remote class repository, which typically includes lite members. By enabling user code deployment, you will not have to deploy your classes to all cluster members.

Below is an example declarative configuration:. When you set it as ETERNAL loaded classes will always be cached. When it is set as OFF loaded classes will not be cached. It has three self-explanatory values: For example, if you set it as "com. If you set it as "com. Class", then the "Class" and all classes having the "Class" as prefix in the "com. It allows to quickly configure remote loading only for classes from selected packages.

It can be used together with blacklisting. For example, you can whitelist the prefix "com. Hazelcast config is not updatable: Once a HazelcastInstance is created, the Config that was used to create that HazelcastInstance should not be updated. A lot of the internal configuration objects are not thread-safe and there is no guarantee that a property is going to be read again after it has been read for the first time. If you are not using your HazelcastInstance anymore, make sure to shut it down by calling the shutdown method on the HazelcastInstance or running stop.

This will release all its resources and end network communication. This method is very practical for testing purposes if you do not have control over the creation of Hazelcast instances, but you want to make sure that all instances are being destroyed. What happened to the Hazelcast.

If you have been using Hazelcast 2. These methods have been dropped because they relied on a singleton HazelcastInstance and when that was combined with explicit HazelcastInstances, it caused confusion. In Hazelcast 3, it is only possible to work with an explicit HazelcastInstance. In this chapter, you saw how you can create a HazelcastInstancehow you can configure it and how you can create a DistributedObject.

In the following chapters, you will learn about the different distributed objects like the ILockIMapetc. If you have programmed applications in Java, you have probably worked with concurrency primitives like the synchronized statement the intrinsic lock or the concurrency library that was introduced in Java 5 under java.

This concurrency functionality is useful if you want to write a Java application that uses multiple threads, but the focus here is to provide synchronization in a single JVM and not distributed synchronization over multiple JVMs. Luckily, Hazelcast provides support for various distributed synchronization primitives such as the ILockIAtomicLongetc. Apart from making synchronization between different JVMs possible, these primitives also support high availability: The IAtomicLongformally known as the AtomicNumberis the distributed version of the java.

AtomicLongso if you have used that before, working with the IAtomicLong should feel very similar. The IAtomicLong exposes most of the operations the AtomicLong provides, such as getsetgetAndSetcompareAndSet and incrementAndGet.

However, there is some difference in performance as remote calls are involved. This example demonstrates the IAtomicLong by creating an instance and incrementing it one million times:. If you run multiple instances of this member, then the total count should be equal to one million times the number of members you have started. If the IAtomicLong becomes a contention point in your system, you can deal with it in a few ways, depending on your requirements.

You can create a stripe essentially an array of IAtomicLong instances to reduce pressure, or you can keep changes local and only publish them to the IAtomicLong once in a while. There are a few downsides, including that you could lose information if a member goes down and the newest value is not always immediately visible to the outside world. The Function class is a single method interface: An example of a function implementation is the following function which adds 2 to the original value:.

Applies the function to the value in the IAtomicLong without changing the actual value and returns the result. Alters the value stored in the IAtomicLong by applying the function, storing the result in the IAtomicLong and returning the result.

Alters the value stored in the IAtomicLong by applying the function and returning the original value. Alters the value stored in the IAtomicLong by applying the function.

This method will not send back a result. This requires a lot less code. The biggest problem here is that this code has a race problem; the read and the write of the IAtomicLong are not atomic, so they could be interleaved with other operations. If you have experience with the AtomicLong from Java, then you probably have some experience with the compareAndSet method where you can create an atomic read and write:. The problem here is that the AtomicLong could be on a remote machine and therefore get and compareAndSet are remote operations.

With the function approach, you send the code to the data instead of pulling the data to the code, making this a lot more scalable. In the previous section, the IAtomicLong was introduced. IAtomicLong can be used to generate unique IDs within a cluster.

If you are only interested in unique IDs, you can have a look at the com. The way the IdGenerator works is that each member claims a segment of 1 million IDs to generate.

This is done behind the scenes by using an IAtomicLong. A segment is claimed by incrementing that IAtomicLong by After claiming the segment, the IdGenerator can increment a local counter. Once all IDs in the segment are used, it will claim a new segment. The result of this approach is that only 1 in times is network traffic needed; out ofthe ID generation can be done in memory and therefore is extremely fast. Another advantage is that this approach scales a lot better than an IAtomicLong because there is a lot less contention: If you start this multiple times, you will see in the console that there will not be any duplicate IDs.

There are alternative solutions for creating cluster-wide unique IDs like the java. If you need this, you could create your own IdGenerator based on the same implementation mechanism the IdGenerator uses, but you persist the updates to the IAtomicLong. By default, the ID generation will start at 0, but in some cases you want to start with a higher value.

This can be changed using the IdGenerator. It returns true if the initialization was a success, so if no other thread called the init method, no IDs have been generated and the desired starting value is bigger than 0. In the first section of this chapter, the IAtomicLong was introduced. The IAtomicLong is very useful if you need to deal with a long, but in some cases you need to deal with a reference.

That is why Hazelcast also supports the IAtomicReferencewhich is the distributed version of the java. Just like the IAtomicLongthe IAtomicReference has methods that accept a function as argument, such as alteralterAndGetgetAndAlter and apply. There are big advantages for using these methods.

From a performance point of view, it is better to send the function to the data then the data to the function. Often the function is a lot smaller than the value and therefore the function is cheaper to send over the line.

Also, the function only needs to be transferred once to the target machine, while the value needs to be transferred twice. If you do a load, transform, and store, you could run into a data race since another thread might have updated the value you are about to overwrite. As long as that function is running, the whole partition is not able to execute other requests.

The IAtomicReference works based on byte-content, not on object-reference. Therefore, if you are using the compareAndSet method, it is important that you do not change to the original value because its serialized content will then be different. It is also important to know that if you rely on Java serialization, sometimes especially with hashmaps the same object can result in different binary content.

All methods returning an object will return a private copy. You can modify it, but the rest of the world will be shielded from your changes. If you want these changes to be visible to the rest of the world, you need to write the change back to the IAtomicReference ; but be careful with introducing a data race.

The in-memory format of an IAtomicReference is binary. This deserialization is done for every call that needs to have the object instead of the binary content, so be careful with expensive object graphs that need to be deserialized.

If you have an object graph or an object with many fields, and you only need to calculate some information or you only need a subset of fields, you stock market crash in ww1 use the apply method. A lock is a synchronization primitive that makes periodic structure in the brownian motion of the stock market possible for only a single thread to access a critical section of code; if multiple threads at the same moment were accessing that critical section, you would get race problems.

Hazelcast provides a distributed lock implementation and makes it possible to create a critical section within a cluster of JVMs, so only a single thread from one of the JVMs in the cluster is allowed to acquire that lock. Other threads, no matter if they are on the same JVMs or not, will not be able to acquire the lock; depending on the locking method they called, they either block or fail.

ILock extends the java. Lock interface, so using the lock is quite simple. When this code is executed, you will not see "Data race detected! This is because the lock provides a critical section around writing and reading of the videographers make money with. In the example code, you highest earning forex traders also find the version with a data race.

So, the following example is not good. In case of Hazelcast, it can happen that the lock is not granted because the lock method has a timeout of 5 minutes.

If this happens, an exception is thrown, the finally block is executed, and the cbs market watch stock market. Hazelcast will see that how can i earn money from my wapka site lock is not acquired and an IllegalMonitorStateException with the message "Current thread is not owner of the lock!

In case of a tryLock with a timeout, the following idiom is recommended:. Hazelcast lock is reentrant, so you can acquire it multiple times in a single thread without causing a deadlock. Of course, you need to release it as many times as you have acquired it to make it available to other threads. Otherwise, the lock acquire can fail, but an unlock is still executed.

Keep locks as short as possible. If locks are kept too long, it can lead to performance problems, or worse, deadlock. With locks it is easy to run into deadlocks.

Make sure you understand exactly the scope of the lock. To reduce the chance of a deadlock, you can use the Lock. Locks are automatically released when a member has acquired a lock and that member goes down. This online magazines about binary options trading platform threads that are waiting for a lock from waiting indefinitely.

This is also needed for failover to work in a distributed system. The downside is that if a member goes down that acquired the lock and started to make changes, other members could start to see forex cash bot forex peace army changes.

In these cases, either the system could do some self repair or a transaction might solve the problem. A lock must always be released by the same thread that credit earn extra money online legitimately uk it, otherwise try ISemaphore.

A lock can be checked if it is locked using the ILock. A lock can be forced to unlock using the ILock. It should be used with extreme care since it could break a critical section. This key will be serialized and the byte array content determines the actual lock to acquire. A lock is not automatically garbage collected. So if you create new locks over time, make sure to destroy them.

With a Condition, it is possible to wait for certain conditions to happen: Each lock can have multiple conditions, such as if an item is available in the queue and if room is available in the queue. In Hazelcast 3, the IConditionwhich extends the java. Conditionhas been added. There is one difference: In the following example, we are going to create one member that waits for a counter to have a certain value.

Another member will set the value on that canadian stock market crash of 1987. First, the lock is acquired getLock. Then, the counter is checked within a loop. As long as the counter is not 1, the waiter will wait on the isOneCondition. Once the isOneCondition is signaled, the thread will unblock and it will automatically reacquire the lock.

If the WaitingMember is started, it will output:. The next part will be the NotifyMember. Here, the Lock is acquired, the value is set to 1, and the isOneCondition will be signaled:. Just as with the normal Condition, the ICondition can suffer from spurious wakeups. That is why the condition always needs to be checked inside a loop, instead of an if statement. You can choose to signal only a single thread instead of all threads by calling the ICondition.

In the example, the waiting thread waits indefinitely because it calls await. In practice, this can be undesirable since a woolworths trading hours christmas day 2015 that is supposed to signal the condition can fail.

When this happens, the threads that are waiting for the signal wait indefinitely. That is why it is often a good practice to wait with a timeout using the await long time, TimeUnit unit or awaitNanos long nanosTimeout method. The semaphore is a classic synchronization aid that can be used to control the number of threads doing a certain activity concurrently, such as using a resource.

Each semaphore has a number of permits, where each permit represents a single thread allowed to execute that activity concurrently. As soon as a thread wants to start with the activity, it takes a earn farm cash quick or waits until one becomes available and once finished with the activity, the permit is returned.

If you initialize the semaphore stock brokerage firm uk a single permit, it will look a lot like a lock. A big difference is that the semaphore has no concept of ownership.

With a lock, the thread that forex trading fix protocol the lock must release it, but with a semaphore, any thread can release an acquired permit.

Another difference is that an exclusive lock only has 1 cause of 1929 stock market crash, while a semaphore can have more than 1.

Hazelcast provides a distributed forex companies in usa of the java. Semaphore named as com. When a permit is acquired on the ISemaphorethe following can happen:. If a permit is available, the number of permits in the semaphore is decreased by one and the calling thread can continue.

If no permit is available, the calling thread will block until a permit becomes available, a timeout happens, the thread is interrupted, or when the semaphore is destroyed and an InstanceDestroyedException is thrown. The following example explains the semaphore. To simulate a shared resource, we have an IAtomicLong initialized with the value 0. This resource is going to be used times. When a thread starts to use that resource, the resource will be incremented, and when finished it will be decremented.

We want to limit the concurrent access to the resource by allowing for at most 3 threads. We can do this by configuring the initial-permits for pdf books on forex semaphore in the Hazelcast configuration file:.

The maximum number of concurrent threads using that resource is always equal to or smaller than 3. Hazelcast provides replication support for the ISemaphore: This can be done by synchronous and asynchronous replication, which c enum binary operations be configured using the backup-count and async-backup-count properties:.

If high performance is more important than permit information getting lost, you might consider setting backup-count to 0.

The ISemaphore acquire methods are fair and this is not configurable. So under contention, the longest waiting thread for a permit will acquire it before all other threads. This is done to prevent starvation, at the expense of reduced throughput. One of the features of the ISemaphore to make it more reliable in a distributed environment is the automatic release of a permit when the member fails similar to the Hazelcast Lock. If the permit would not be released, the system could run in a deadlock.

To prevent running into a deadlock, you can use one of timed acquire methods, like ISemaphore. The initial-permits is allowed to be negative, indicating that there is a shortage of permits when the semaphore is created.

CountDownLatch was introduced in Java 1. A CountDownLatch can be seen as a gate containing a counter. Behind this gate, threads can wait till the counter reaches 0. CountDownLatches often are used when you have some kind of processing operation, and one or more threads need to wait till this operation completes so they can execute their logic. Hazelcast also contains a CountDownLatch: To explain the ICountDownLatchimagine that there is a leader process that is executing some action that will eventually complete.

Also imagine that there are one or more follower processes that need to do something after the leader has completed. We can implement the behavior of the Leader:. The Leader retrieves the CountDownLatchcalls ICountDownLatch. In this example, we ignore the boolean return value of trySetCount since there will be only a single Leaderbut in practice you probably want to deal with the return value.

We retrieve the ICountDownLatch and then call await on it so the thread listens for when the ICountDownLatch reaches 0. In practice, a process that should have decremented the counter by calling the ICountDownLatch. To force you to deal with this situation, the await methods have timeouts to prevent waiting indefinitely. If we first start a leader and then start bdo forex dollar to php or more followers, the followers will wait till the leader completes.

It is important that the leader is started first, else the followers will immediately complete since the latch already is 0. The example shows an ICountDownLatch with only a arbitrage in binary options step.

If a process has n steps, you should initialize the ICountdownLatch with n instead of 1, and for each completed step, you should call the countDown method. One thing to watch out for is that an ICountDownLatch waiter can be notified prematurely.

In a distributed environment, the leader could go down before it reaches zero and this would result in the waiters waiting till the end of time. Circuit limit in stock market this behavior is undesirable, Hazelcast will automatically notify all listeners if the owner gets disconnected, and therefore listeners could be notified before all steps of a certain process are completed.

To deal with this situation, the current state of the process needs to be verified and appropriate actions need to be taken: The method add is used to feed objects into the estimator. Objects are considered to be identical if they are serialized into the same binary blob. The method estimate estimates the cardinality of the aggregation so far. If it was previously estimated and never invalidated, then a cached version is used.

The methods addAsync and estimateAsync are also used to feed objects into the estimator and estimate cardinalities, but unlike the methods add and estimatethey will dispatch a request and return immediately an ICompletableFuture. In some cases you need a thread that will only run on a limited number of members. Often only a single thread is needed. But if the member running this thread fails, another machine needs to take over.

On each cluster member you start this service thread, the first thing this service needs to do is to acquire the lock or a license and on success, the thread can start with its logic.

All other threads will block till the lock is released or a license is returned. The nice thing about the ILock and the ISemaphore is when a member exits the cluster due to a crash, network disconnect, etc. In this chapter, we looked at various synchronization primitives that are supported by Hazelcast. If you need a different one, you can try to build it on top of existing ones or you can create a custom one using the Hazelcast SPI.

One thing that would be nice to add is the ability to control the partition the primitive is living on, since this would improve locality of reference. Hazelcast provides a set of collections that implement interfaces from the Java collection framework, making it easy to integrate distributed collections into your system without making too many code changes.

A distributed collection can be called concurrently from the same JVM, and can be called concurrently by different JVMs. Another advantage is that the distributed collections provide high availability, so if a member hosting the collection fails, another member will take over.

A BlockingQueue is one of the work horses for concurrent system because it allows producers and consumers of messages which can be POJOs to work at different speeds.

IQueuewhich extends the java. BlockingQueueallows threads from the same JVM to interact with that queue. Since the queue is distributed, it also allows different JVMs to interact with it. You can add items in one JVM and remove them in another. To make sure that the consumers will terminate when the producer is finished, the producer will put a -1 on the queue to indicate that it is finished.

The consumer will take the message from the queue, print it, and wait for 5 seconds. Then, it will consume the next message and stop when it receives the This behavior is called a poison pill. If you take a closer look at the consumer, you see that when the consumer receives the poison pill, it puts the poison pill back on the queue before it ends the loop.

This is done to make sure that all consumers will receive the poison pill, not just the one that received it first. As you can see, the items produced on the queue by the producer are being consumed from that same queue by the consumer. Because messages are produced 5 times faster than they are consumed, the queue will keep growing with a single consumer.

To improve throughput, you can start more consumers. One way you can solve this is to introduce a stripe essentially a list of queues. But if you do, the ordering transaksi forex di indonesia messages sent to different queues will no longer be guaranteed.

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Because the production of messages is separated from the consumption of messages, the speed of production is not influenced by the speed of consumption. If producing messages goes quicker than the consumption, then the queue will increase in size. If there is no bound on the capacity of the queue, then machines can run out of memory and you will get an OutOfMemoryError.

With the traditional BlockingQueue implementation, such as the LinkedBlockingQueueyou can set a capacity. When this is set and the maximum capacity is reached, placement of new items either fails or blocks, depending on the type of the put operation. This prevents the queue from growing beyond a healthy capacity and the JVM from failing.

It is important to understand that the IQueue is not a partitioned data structure like the IMapso the content of the IQueue will not be spread over the members in the cluster.

A single member in the cluster will be responsible for keeping the complete content of the IQueue in memory. Depending on the configuration, there will also be a backup which keeps the whole queue in the memory. The Hazelcast queue also provides capacity control, but instead of having a fixed capacity for the whole cluster, Hazelcast provides a scalable capacity by setting the queue capacity using the queue property max-size.

By default, Hazelcast will make sure that there is one synchronous backup for the queue. If the member hosting that queue fails, the backups on another member will be used so no entries are lost.

Number of synchronous backups, defaults to 1. So by default, no entries will be list of brokers of karachi stock exchange if a member fails. If you want increased high availability, you can either increase the backup-count or the async-backup-count. If you want to have improved performance, you can set the backup-count to 0, but at the cost of potentially losing entries on failure.

Changes in the queue will not be made persistent, so if the cluster fails, then entries will be lost. In some cases, this behavior is not desirable. Online magazines about binary options trading platform, Hazelcast provides a mechanism for queue durability using the QueueStorewhich can connect to a more durable storage mechanism, such as a database.

In Hazelcast 2, the Queue was implemented on top of the Hazelcast Avoid risk stock market crash, so in theory you could make the queue persistent by configuring the MapStore of the backing map.

In Hazelcast 3, the Queue is not implemented on top of a map; instead, it exposes a QueueStore directly.

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A List binary options strategy 124 60 seconds a collection where every element only occurs once and where the order of the elements does matter.

IList implements the java. If you first run the WriteMember and after it has completed, you start the ReadMemberthen the ReadMember will output the following:. The data that the WriteMember writes to the List is visible in the ReadMember and the order is maintained. The List interface has various methods like the sublist that returns collections, but it is important to understand that the returned collections are snapshots and are not backed up by the list.

See Iterator Stability for a discussion of weak consistency. ISet implements the java. If you first start the WriteMember and waiting for completion, you start the ReadMember. It how to make money with domain parking output the following:. As you can see, the data added by the WriteMember is visible in the ReadMember.

As you also can see, the order is not maintained since order is not defined by the Set. This is a different behavior compared to the map; see Map: In Hazelcast, the ISet and the IList is implemented as a collection within the MultiMap, where the ID of the Set is the key in the MultiMap and the value is the collection. If you want to have a distributed Set that behaves more like the distributed Map, you can implement a Set based on a Map where the value is some bogus value. It is not possible to rely on the Map.

The IListISet and IQueue interfaces extend the com. Hazelcast enriches the existing collections API with the ability to listen to changes in the collections using the com. The ItemListener receives the ItemEvent which potentially contains the item, the member where the change is happened, and the type of event add or remove. The following example shows an ItemListener that listens to all changes made in an IQueue:.

We registered the ItemListenerImpl with the addItemListener method using the value true. If you start up the ItemListenerMember and wait till it displays "ItemListener started"and then you start the CollectionChangeMemberyou will see the following output in the ItemListenerMember:.

ItemListeners are useful if you need to react upon changes in collections. But realize that listeners are executed asynchronously, so it could be that at the time your listener runs, the collection has changed again. All events are ordered: Iterators on collections are weakly consistent; when a collection changes while creating the iterator, you could encounter duplicates or miss an element.

Changes on that iterator will not result in changes on the collection. An iterator does not need to reflect the actual state and will not throw a ConcurrentModifcationException. The replication the correlation of stock market returns between the u.s.

and japan is ____ and ____ IList and ISet can be configured for synchronous or asynchronous along with backup count. Listeners will remain registered unless that collection is destroyed explicitly. Once an item is added to the implicit destroyed collection, the collection will automatically be recreated.

No merge policy for the Queue: If a cluster containing a queue is split, then each subcluster will still able to access their own view of that queue. If these subclusters merge, the queue cannot be merged and one of them is deleted. This is a big difference compared to Hazelcast 2. The Hazelcast team decided to drop this behavior since the 2. This limitation needs to be taken into consideration when you are designing a distributed system. You can solve this issue by using a stripe of collections or by building your collection on top of the IMap.

Another more flexible but probably more time consuming alternative is to write the collection on top of the new SPI functionality; see SPI.

A potential solution for the IQueue is to make a stripe of queues instead of a single queue. Since each collection in that stripe is likely to be assigned to a different partition than its neighbors, the queues will end up in different members. If ordering of items is not important, the item can be placed on an arbitrary queue.

Otherwise, the right queue could be selected based on some property of the item so that buy kinman woodstock pickups pickups items having the same property end up in the same queue.

It is currently not possible to control the partition the collection is going to be placed on, so more remoting is required than is strictly needed. In the future, it will be possible for you to say:. Hazelcast Ringbuffer stores its data in a ring-like structure.

You can think of it as a circular array with a given capacity. This capacity ways to generate passive monthly income not grow beyond its limits and hence there will be no danger to the stability of the system.

If the capacity is to be exceeded, the oldest item in the Ringbuffer is overwritten. Each Ringbuffer has a tail and a head.

The tail is where the items are added and the head is where the items are overwritten or expired. Binary stock trade trader salary bot can reach each element in a Ringbuffer using a sequence ID, which is mapped to the elements between the head and tail inclusive of the Ringbuffer.

Hazelcast Ringbuffer can sometimes be a better alternative than Hazelcast IQueue. Unlike IQueue, Ringbuffer does not remove the items, it only reads items using a certain position.

For instance, the method queue. But, the method ringbuffer. Reading from Ringbuffer is simple: Use the method readOne to return the item at the given sequence; readOne blocks if no item is available. To read the next item, increment the sequence by one. Please see the following example. Adding an item to a Ringbuffer is also easy with the methods addaddAysnc and addAllAsync.

The following example uses the method addAsync. Use the method add to return the sequence of the inserted item; the sequence value will always be unique.

You can use this salary for a forex trader a very cheap way of generating unique IDs if you are already using Ringbuffer.

If a Ringbuffer store is enabled, each item added to the Ringbuffer will the forex tutorials pdf be stored at the configured Ringbuffer store. The Ringbuffer store will store items in the same format as the Ringbuffer.

In this chapter we have seen various collections in action and we have seen how they can be configured. In the following chapter, you will learn about Hazelcast Distributed Map.

The IMap extends the Java ConcurrentMapand therefore it also extends java. Unlike a normal Map implementation such as the HashMap, the Hazelcast IMap implementation is a distributed data structure. Internally, Hazelcast divides the map into partitions and it distributes the partitions evenly among the members in the cluster.

The partition of a map entry is based on the key of that entry; each key belongs to a single partition. By default, Hazelcast uses partitions for all partitioned data structures. This value can be changed with the hazelcast.

When a new member is added, the oldest member in the cluster decides which partitions are going to be moved to that new member. Once the partitions are moved, the new member will take its share in the load.

Thus, to scale up a cluster, just add new members to the cluster. When a member is removed, how to make money using the stock market gta the partitions that member owned are moved to other members. So scaling down a cluster is simple, just remove members from the cluster.

Luckily, Hazelcast provides various degrees of failover to deal with this situation. By default there will be one synchronous backup, so the failure of a single member will not lead to loss of data because a replica of that data is available on another member. There is a demo on YouTube: Four Terabytes of data from one billion entries is stored on Amazon EC2 instances, supporting up to 1.

In this example, we create a basic cities map which we will use in the following sections. You do not need to configure anything in the hazelcast. If you want to configure the map, you can use the following example as a minimal map configuration in the hazelcast.

The Map is not created when the getMap method is called, but is created only when the Map instance is accessed. This is useful to know if you use the DistributedObjectListener and fail to receive creation events. To demonstrate this basic behavior, the following Member creates a Map and writes some entries into that map:. As you can see, the Map is retrieved using the hzInstance. Reading the entries from that Map is simple:.

If we first run the FillMapMember and then run the PrintAllMemberwe get the following output:. The map updates from the FillMapMember are visible in the PrintAllMember. Internally, Hazelcast serializes the key and value see Serialization to byte arrays and stores them in forex trading courses glasgow underlying storage area. Therefore, the following code is broken:.

A serialized representation of an object is called the binary format. Serializing and deserializing an object too frequently on one node can have a huge impact on performance. A typical use case would be Queries predicate and Entry Processors reading the same value multiple times. To eliminate this impact on performance, the forex forex forex forextraderguide.info guide guide trader trader trading can be stored in object format, rather than in binary format; this means that Hazelcast stores the value in its object form and not in the byte array.

Thus, the IMap provides control on the format of the stored value using the in-memory-format setting. This option is only available for values; keys will always be stored in binary format.

You should understand the available in-memory formats:. The value is stored in binary format. Every time the value is needed, it will be deserialized.

The value is stored in object format. Please see the Storage chapter. The big question is which in-memory format to use. With the BINARY in-memory format, a deserialization is needed since the object is only available in binary format. If the majority of your operations are regular Map operations like put advanced binary options signals providers review getyou should consider the BINARY in-memory format.

This sounds counterintuitive because normal operations, such as getrely on the object instance, and with a binary format no instance is available.

However, when the OBJECT in-memory format is used, the Map never returns the stored instance, but instead creates a clone. This involves a serialization on the owning node followed by a deserialization on the caller node.

With the BINARY format, only a deserialization is needed and therefore the process is faster. For similar reasons, a put with the Aud nzd exchange rate yahoo in-memory format will be faster than the OBJECT in-memory format.

When the OBJECT in-memory format is used, the Map will not store fastest way to make money on mafia 2 actual instance, but will make a clone; this involves a serialization followed by a deserialization. When the BINARY in-memory format is used, only a deserialization is needed. In the following example, you can see a Map configured with the OBJECT in-memory format.

If a value is stored in OBJECT in-memory format, a change on a returned value does not affect the stored instance because a clone of the stored value is returned, not the actual instance. Therefore, changes made on an object after it is returned will not be reflected on the actual stored data.

Also, when a value is written to a Map, if the value is stored in OBJECT format, it will be a copy of the put value, not the original. Therefore, changes made on the object after it is stored will not be reflected on the actual stored data. Unsafe to use with EntryProcessor in combination with queries: If the OBJECT in-memory format is used, then the actual object instance is stored.

When the EntryProcessor is used in combination with OBJECT in-memory format, then an EntryProcessor will have access to that object instance. A query also will have access to the actual object instance. However, queries are not executed on partition threads. Therefore, at any given moment, an EntryProcessor and an arbitrary number of query threads could access the same object instance.

This can lead to data races and Java memory model violation. Unsafe to use with MapReduce: Why stock markets flourish the OBJECT in-memory format is used in combination with MapReduce, you can run into the same data races and Java Memory Model violations as with the EntryProcessor in combination with queries.

The cache-value property in Hazelcast 2. Just as with the in-memory-formatthe cache-value makes it possible to prevent unwanted deserialization. When the cache-value was enabled, it was possible to get the same instance on subsequent calls like Map. This problem does not happen with the in-memory-format. The reason to drop cache-value is that returning the same instance leads to unexpected sharing of an object instance.

In most cases, you probably will make use of basic types for a key, such as a LongIntegeror Stringbut in some cases, you will need to create custom keys. To create custom keys correctly in Hazelcast, you need to understand how hashcode and equals are implemented, because they work differently ways to make money on neopets in traditional Map implementations.

Traditional users make their own implementation: However, Hazelcast uses the binary representation of your object to determine the equals and hash. For OBJECT the equals of the object is used. This Pair has 2 fields. If we make 2 keys. For a key, it is very important that the binary format of equal objects are the same. For values, this depends on the in-memory-format setting. If we configure the following three maps in the hazelcast. In the following code, we define two values, v1 and v2where the resulting byte array is different.

The equals method will indicate that they are the same. We put v1 in each map and check for its existence using map. With the binaryMapthe equals is done based on the binary format. Since v1 and v2 have different binary formats, v1 will not be found using v2.

For more information, the book "Effective Java" mentions that you should obey the general contract when overriding equals; always override hashcode when you override equals.

Hazelcast makes it very easy to create distributed Maps and access data in these Maps. For example, you could have a Map with customers where the customerId is the key, and you could have a Map with orders for a customer where the orderId is the key.

When you frequently use the customer in combination with their orders, however the orders will likely be stored in different partitions than the customer, since the customer partition is determined with the customerId and the order partition is determined with the orderId. Luckily Hazelcast provides a solution to control the partition schema of your data so that all data can be stored in the same partition.

If the data is partitioned correctly, your system will exhibit a strong locality of reference and this will reduce latency, increase throughput and improve scalability since fewer network hops and traffic are required. To demonstrate this behavior, the code below implements a custom partitioning schema for a customer and his orders. To control the partition of the order, the OrderKey implements PartitionAware.

If a key implements this interface, instead of using the binary format of the key to determine the correct partition, the binary format of the result of getPartitionKey method call is used. Because we want the partition of the customerIdthe getPartitionKey method will use the customerId. The equals and binary options strategy 124 60 seconds are not used in this example since Hazelcast will make use of the binary career objective market research analyst of the key.

You should implement them in practice. For more information, see Hashcode and Equals.

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In the following deutsche bank india foreign exchange rates, an order is call option pricing excel vba wiley finance pdf with an OrderKey.

At the end of the example, the partition IDs for a customer, the orderKeyand the orderId are printed. The partition of the customer is the same as the partition of the order of that customer. Also, the partition where an order would be stored using a naive orderId is different than that of the customer. In this example, we created the OrderKey that does the partitioning, but Hazelcast also provides a default implementation that can be used: Being able to control the partitioning schema of data is a very powerful feature and figuring out a good partitioning schema is an architectural choice that you want to get right as soon as possible.

Once this is done correctly, it will be a lot easier to write a high performance and scalable system since the number of remote calls is limited. Collocating data in a single partition often needs to be combined with sending the functionality to the partition that contains the collocated data. For example, if an invoice needs to be created for the orders of a customer, a Callable that creates the Invoice could be sent using the IExecutorService.

If you do not send the function to the correct partition, collocating data is not useful since a remote call is done for every piece of data. For more information about Executors and routing, see Distributed Executor Service and Distributed Executor Service: In a production environment, all kinds of things could go wrong. A machine could break down due to disk failure, the operating system could crash, or the machine could get disconnected from the network.

To prevent the failure of a single member leading to failure of the cluster, by default Hazelcast synchronously backs up all Map entries on another Member. So if a member fails, no data is lost because the member containing the backup will promote backup into primary copies.

You can set backup-count to 0 if you favor performance over high availability. You can specify a higher value than 1 if you require increased availability, but the maximum number of backups is 6. The default is 1, so you may not need to specify it at all. By default, the backup operations are synchronous; you are guaranteed that the backups are updated before a method call like map. However, this guarantee comes at the cost of blocking and therefore the latency increases.

In some cases having a low latency is more important than having perfect backup guarantees, as long as the window for failure is small. That is why Hazelcast also supports asynchronous backups, where the backups are made at some point in time. This can be configured through the async-backup-count property:. Although backups can improve high availability, they increase memory usage because the backups are also kept in memory.

Therefore, for every backup, you double the original memory consumption. By default, Hazelcast provides sequential consistency; when a Map entry is read, the most recent written value is seen. This is accomplished by routing the get request to the member that owns the key. Therefore, there will be no out-of-sync copies.

But sequential consistency comes at a price: Hazelcast provides the option to increase performance by reducing consistency. This is done by allowing reads to potentially see stale data.

This feature is available only when there is at least 1 backup synchronous or asynchronous. You can enable it by setting the read-backup-data property:.

In this example, you can see a person Map with a single asynchronous backup, and reading of backup data enabled the read-backup-data property defaults to false. Reading from the backup can improve performance a bit; if you have a 10 node cluster and read-backup-data is false, there is a 1 in 10 chance that the read will find the data locally.

When there is a single backup and read-backup-data is false, that adds another 1 in 10 chance that read will find the backup data locally.

This totals to a 1 in 5 chance that the data is found locally. By default, all the Map entries that are put in the Map will remain in that Map. You can delete them manually, but you can also rely on an eviction policy that deletes items automatically.

This feature enables Hazelcast to be used as a distributed cache since hot data is kept in memory and cold data is evicted. Maximum size of the map. When maximum size is reached, the Map is evicted based on the policy defined.

The value is an integer between 0 and Integer. A policy attribute eviction-policy seen below determines how the max-size will be interpreted. Maximum number of map entries within a single partition. This is probably not a policy you will use often, because the storage size depends on the number of partitions that a member is hosting.

If the cluster is small, it will host more partitions and therefore more map entries than with a larger cluster. Maximum used heap size as a percentage of the JVM heap size. If the JVM is configured with MB and the max-size is 10, this policy allows the map to be MB before map entries are evicted. Minimum free heap size percentage for each JVM. If, for example, a JVM is configured to have MB and this value is 10, then the map entries will be evicted when free heap size is below MB.

Maximum used native memory size percentage for each JVM. Maximum free native memory size percentage for each JVM.

No items will be evicted, so the max-size is ignored. This is the default policy. If you want max-size to work, you need to set an eviction-policy other than NONE. Of course, you still can combine it with time-to-live-seconds and max-idle-seconds. Maximum number of seconds for each entry to stay in the map. Entries that are older than time-to-live-seconds and are not updated for this duration will get automatically evicted from the map. The value can be any integer between 0 and Integer.

Maximum number of seconds for each entry to stay idle in the map. Entries that are idle not touched for more than max-idle-seconds will get automatically evicted from the map. Entry is touched if getputor containsKey method is called.

When the maximum size is reached, the specified percentage of the map will be evicted. The default value is 25 percent. If the value is set to a value that is too small, then only that small amount of map entries are evicted, which can lead to a lot of overhead if map entries are frequently inserted.

Minimum time in milliseconds which should elapse before checking whether a partition of the map is evictable or not. In other words, this property specifies the frequency of the eviction process. The default value is Setting it to 0 zero makes the eviction process run for every put operation.

This configures an articles map that will start to evict map entries from a member as soon as the map size within that member exceeds It will then start to remove map entries that are least recently used. When map entries are not used for more than 60 seconds, they will be evicted as well. You can evict a key manually by calling the IMap.

You might wonder what the difference is between this method and the IMap. If no MapStore is defined, there is no difference. If a MapStore is defined, an IMap. However, the evict method removes the map entry only from the map. So if the MapStore is connected to a database, no record entries are removed due to map entries being evicted.

Starting with the release of Hazelcast 3. To develop your own eviction policy, you only need to have an implementation of MapEvictionPolicy interface, which is actually a java.

Now, it is time to plug this custom policy by registering it programmatically or declaratively. Following is an example declarative configuration:. All the map entries within a given partition are owned by a single member. If a map entry is read, the member that owns the partition of the key is asked to read the value.

This reduces performance and scalability. Normally it is best to partition the data so that all relevant data is stored in the same partition and so you can send the operation to the machine owning the partition. However, this is not always an option. Near cache makes map entries locally available by adding a local cache attached to the map. Imagine a web shop where articles can be ordered and where these articles are stored in a Hazelcast map.

To enable local caching of frequently used articles, the near cache is configured like this:. Maximum number of cache entries per local cache. As soon as the maximum size has been reached, the cache will start to evict entries based on the eviction policy.

The default is Integer. The max-size of the near cache is independent of that of the map itself. Policy used to evict members from the cache when the near cache is full. The following options are available:. You can combine NONE with time-to-live-seconds and max-idle-seconds. Number of seconds a map entry is allowed to remain in the cache. Valid values are 0 to Integer. The default is 0. Maximum number of seconds a map entry is allowed to stay in the cache without being read.

If true, all the members listen for change in their cached entries and evict the entry when it is updated or deleted. In-memory format of the cache. For more information, see InMemoryFormat. This configures an articles map with a near-cache. It will evict near-cache entries from a member as soon as the near-cache size within that member exceeds It will then remove near-cache entries that are least recently used.

When near cache entries are not used for more than 60 seconds, they will be evicted as well. The previous Eviction section discussed evicting items from the map, but it is important to understand that near cache and map eviction are two different things. The near cache is a local map that contains frequently accessed map entries from any member, while the local map will only contain map entries it owns.

You can even combine the eviction and the near cache, although their settings are independent. It increases memory usage since the near cache items need to be stored in the memory of the member. It reduces consistency, especially when invalidate-on-change is false: It is best used for read only data, especially when invalidate-on-change is enabled. There is a lot of remoting involved to invalidate the cache entry when a map entry is updated.

The Hazelcast map itself is thread-safe, just like the ConcurrentHashMap or the Collections. In some cases, your thread safety requirements are bigger than what Hazelcast provides out of the box.

Hazelcast provides multiple concurrency control solutions; it can either be pessimistic using locks or optimistic using compare and swap operations. You can also use the executeOnKey API, such as the IMap.

Instead of dealing with pessimistic locking, such as IMap. The classic way to solve the race problem is to use a lock. Another way to lock is to acquire some predictable Lock object from Hazelcast. You could give every value its own lock, but you could also create a stripe of locks.

Although doing this could increase contention, it will reduce space. When you unlock the map entry of a non-existing key, the map entry will automatically be deleted. It is important to implement object equals on the value, because the value is used to determine if two objects are equal. With the ConcurrentHashMapit is based on object reference. On the keys, the byte array equals is used, but on the replace key,oldValue,newValue the object equals is used. If you fail to use the correct equals, your code will not work!

This code is broken on purpose. The problem can be solved by adding a version field; although all the other fields will be equal, the version field will prevent objects from being seen as equal.

One of the new features of Hazelcast 3. It allows you to send a function, the EntryProcessorto a particular key or to all keys in an IMap. Once the EntryProcessor is completed, it is discarded, so it is not a durable mechanism like the EntryListener or the MapInterceptor. Imagine that you have a map of employees and you want to give every employee a bonus. In the example below, you see a very naive implementation of this functionality:.

If your number of employees doubles, it will probably take twice as much time. Another problem is that the current implementation is subject to race problems; imagine that a different process currently gives an employee a raise of The read and write of the employee is not atomic since there is no lock, so it could be that one of the raises is overwritten and the employee only gets a single raise instead of a double raise.

The EntryProcessor was added to Hazelcast to address cases like this. The EntryProcessor captures the logic that should be executed on a map entry. Hazelcast will send the EntryProcessor to each member in the cluster, and then each member will, in parallel, apply the EntryProcessor to all map entries. This means that the EntryProcessor is scalable; the more machines you add, the faster the processing will be completed.

Another important feature of the EntryProcessor is that it will deal with race problems by acquiring exclusive access to the map entry when it is processing. In the following example, the raise functionality is implemented using a EntryProcessor. In the previous example, the process method modifies the employee instance and returns null.

The EntryProcessor can also return a value for every map entry. If we wanted to calculate the sum of all salaries, the following EntryProcessor will return the salary of an employee:. You need to be careful when using this technique, as the salaries map will be kept in memory and this can lead to an OutOfMemoryError.

This will prevent the result for a single process invocation from being stored in the map. If you are wondering why the GetSalaryEntryProcessor constructor calls the super with false, check the next section. When the EntryProcessor is applied on a map, it will not only process all primary map entries, but will also process all backups. These processes are needed to prevent the primary map entries from containing different data than the backups. In the previous examples, we made use of the AbstractEntryProcessor class instead of the EntryProcessor interface, which applies the same logic to primary and backups.

But if you want, you can apply different logic on the primary than on the backup. The previous example, where the total salary of all employees is calculated, is such a situation. That is why the GetSalaryEntryProcessor constructor calls the super with false; this signals the AbstractEntryProcessor not to apply any logic to the backup, only to the primary.

The important method here is the getBackupProcessor. This signals to Hazelcast that only the primary map entries need to be processed. If we want to apply logic on the backups, we need to return an EntryBackupProcessor instance. In this case the EntryBackupProcessor. Entry processors can be used with predicates. Predicates help to process a subset of data by selecting eligible entries.

This selection can happen either by doing a full-table scan or by using indexes. To accelerate entry selection step, you can consider to add indexes. If indexes are there, entry processor will automatically use them. Hazelcast will only allow a single thread —the partition thread— to be active in a partition. The EntryProcessor will also be executed on the partition thread. Therefore, while the EntryProcessor is running, no other operations on that map entry can happen.

It is important to understand that an EntryProcessor should run quickly because it is running on the partition thread. This means that other operations on the same partition will be blocked, and that other operations that use a different partition but are mapped to the same operation thread will also be blocked. Also, system operations such as partition migration will be blocked by a long-running EntryProcessor.

The same applies when an EntryProcessor is executed on a large number of entries; all entries are executed in a single run and will not be interleaved with other operations. You need to take care to store mutable states in your EntryProcessor.

For example, if a member contains partitions 1 and 2 and they are mapped to partition threads 1 and 2, and if you are executing the entry processor on map entries in partitions 1 and 2, then the same EntryProcessor will be used by different threads in parallel. If you are often using the EntryProcessor or queries, it might be a good idea to use the InMemoryFormat.

The value instance that is stored is passed to the EntryProcessorand that instance will also be stored in the map entry unless you create a new instance.

If you want to execute the EntryProcessor on a single key, you can use the IMap. You could do the same with an IExecutorService. If state is stored in the EntryProcessor between process invocations, you need to understand that this state can be touched by different threads.

This is because the same EntryProcessor instance can be used between different partitions that run on different threads. One potential solution is to put the state in a thread local.

You can delete items with the EntryProcessor by setting the map entry value to null. In the following example, you can see that all bad employees are being deleted using this approach:. When the HazelcastInstanceAware interface is implemented, the dependencies can be injected. Using one of the MapListener sub-interfaces, you can listen for map entry events providing a predicate, and so the events will be fired for each entry validated by your query.

Rather than have one large interface to handle all callback types you can just implement specific interfaces for the callback you are interested in. For example, if you just wish to intercept events for IMap. IMap has a single method for applying a listener, IMap. If registering the callback inside cluster members this will cause it to fire on every member for any event.

If you wish a callback to fire only when the event is local to that member you should register using IMap. When you start the ListeningMember and then start the ModifyMemberthe ListeningMember will output something like this:. To correctly use the MapListeneryou must understand the threading model. MapListener runs on event threads, the same threads that are used by other collection listeners and by ITopic message listeners.

The MapListener is allowed to access other partitions. It can also lead to OOME because of events being queued quicker than they are being processed. When an EntryListener is sent to a different machine, it will be serialized and then deserialized. To deal with this problem, if the EntryListener implements HazelcastInstanceAwareyou can inject the HazelcastInstance. For more information see Serialization: EntryListener has been retained for backward compatibility, for new code please use the MapListener sub interfaces.

In the previous section we talked about the MapListenerwhich can be used to listen to changes in a map. One of the new additions to Hazelcast 3 is the Continuous Predicate, a MapListener that is registered using a predicate. This makes it possible to listen to the changes made to specific map entries. To demonstrate the listener with a predicate, we are going to listen to the changes made to a person with a specific name.

The following step is to register a EntryAddedListener using a predicate so that the query is created:. The listener will be notified as soon as a person with the name peter is modified. To demonstrate this, start the ContinuousQueryMember and then start the following member:.

When ModifyMember is done, the ContinuousQueryMember will show the following output:. As you can see, the listener is only notified for peterand not for talip. Filtered at the source: The predicate of the continuous query is registered at the source; it is registered on each member that generates an event for a given partition.

This means that if a predicate filters out an event, the event will not be sent over the line to the listener. Imagine that we have a Hazelcast IMap where the key is some ID, the value is a Person object, and we want to retrieve all persons with a given name using the following and naive implementation:.

This is what you probably would write if the map was an ordinary map, but when the map is a distributed map, there are some performance and scalability problems with this approach. It is not parallelizable. One member will iterate over all persons instead of spreading the load over multiple members.

It is inefficient because all persons need to be pulled over the line before being deserialized into the memory of the executing member. So there is unnecessary network traffic. When the predicate is requested to be evaluated by the caller, it is forked to each member in the cluster.

Each member will filter all local map entries using the predicate. By adding new cluster members, the number of partitions per member is reduced. Therefore, the time a member needs to iterate over all of its data is reduced as well. Also, the local filtering is parallelizable because a pool of partition threads will evaluate segments of elements concurrently. And the amount of network traffic is reduced drastically, since only filtered data is sent instead of all data.

To implement the Person search using the JPA-like criteria API, you could do the following:. The namePredicate verifies that the name field has a certain value using the equal operator. After we have created the predicate, we apply it to the personMap by calling the IMap. Because the predicate is sent over the line, it needs to be serializable. See Serialization for more information. The Predicate is not limited to values only. It can also be applied to the keySetthe entrySetand the localKeySet of the IMap.

In the previous example, we saw the equal operator in action, getting the name of the person object. When it is evaluated, it first tries to look up an accessor method, so in case of namethe accessor methods that it will try are isName and getName. If one is found, it is called and the comparison is done. If no accessor is found, a field with the given name is looked up.

If that exists, it is returned; otherwise, a RuntimeException is thrown. In some cases you need to traverse over an object structure. For example, you want the street of the address where the person lives. With the equal operator, you can do it like this: This expression is evaluated from left to right and there is no limit on the number of steps involved.

Accessor methods can also be used here. Please also note how the equal operator deals with null, especially with object traversal: Checks if the result of an expression matches some string pattern. Checks if the result of an expression is greater than or equal to a certain value. Checks if the result of an expression is less than or equal to a certain value.

Checks if the result of an expression is between two values this is inclusive. If the predicates provided by Hazelcast are not enough, you can always write your own predicate by implementing the Predicate interface:. The syntax we have used so far to create Predicates is clear. That syntax can be simplified more by making use of the PredicateBuilder. PredicateBuilder provides a fluent interface that can make building predicates simpler.

But same functionality is used underneath. Here is an example where a predicate is built that selects all persons with a certain name and age using PredicateBuilder:. As you can see, PredicateBuilder can simplify things, especially if you have complex predicates. It is a matter of taste which approach you prefer. With the PredicateBuilderit is possible to access the key. Imagine there is a key with field x and a value with field y. Then you could do the following to retrieve all entries with key.

That is why Hazelcast added a DSL Distributed SQL Query based on an SQL-like language and using the Criteria API underneath. The getWithName function that we already implemented using the Criteria API can also be implemented using the Distributed SQL Query:.

As you can see, the SqlPredicate is a Predicate and therefore it can be combined with the Criteria API. Below, you can see an overview of the DSL:. With the SQL predicate, an object traversal can be done using field.

In this example, the name of the father of the mother of the husband should be John. No arg methods can be called within a SQL predicate. In some cases this is useful if you dynamically need to calculate a value based on some properties.

The syntax is the same as for accessing a field. MapReduce is a software framework for processing large amounts of data in a distributed way. Therefore, the processing is normally spread over several machines. The basic idea behind MapReduce is to map your source data into a collection of key-value pairs and reducing those pairs, grouped by key, in a second step towards the final result.

The main idea can be summarized as first reading the source data, then mapping the data to one or multiple key-value pairs, and finally reducing all pairs with the same key.

The best known examples for MapReduce algorithms are text processing tools, such as counting the word frequency in large texts or websites. Apart from that, there are more interesting examples of use cases such as log analysis, data querying, aggregation and summing, distributed sort, and fraud detection. The Mapping phase, which is managed by a mapper that iterates all key-value pairs of any kind of legal input source. The Combine phase, which is managed by a combiner that collects and combines multiple key-value pairs with the same key to an intermediate result.

This phase is optional but recommended to lower the traffic. This is a virtual phase within Hazelcast. The Reducing phase, which is managed by a reducer that builds the final results by reducing the intermediate key-value pairs by their keys. We then retrieved a JobTracker instance with default configuration to create a new Job for the purpose of executing MapReduce requests. We created a KeyValueSource to wrap the map entries into a well-defined key-value pair input source.

You can set up the behavior of the Hazelcast MapReduce framework by configuring the JobTracker. The following is an example declarative configuration snippet:. You can configure the maximum thread pool size max-thread-size and maximum number of tasks to be processed queue-size.

Also, you can set the number of emitted values before a chunk is sent to the reducers chunk-size. If your emitted values are big or you want to balance your work better, you change this value. A value of 0 means immediate transmission, but remember that low values mean higher traffic costs.

A very high value might cause an OutOfMemoryError to occur if the emitted values do not fit into heap memory before being sent to the reducers. To prevent this, you might want to use a combiner to pre-reduce values on mapping members. The element communicate-stats specifies whether the statistics are transmitted to the job emitter. This can show progress to a user inside of an UI system, but it produces additional traffic. If not needed, you might want to deactivate this by setting it to false.

To specify how the MapReduce framework will react on topology changes while executing a job, you can configure the element topology-changed-strategy. Aggregators are ready-to-use data aggregations that are based on the Hazelcast MapReduce framework. They can be used for typical operations like summing up values, finding minimum or maximum values, calculating averages, and other operations that you would expect in the relational database world.

All aggregation operations can be achieved using pure MapReduce calls. Using aggregators is more convenient for a big set of standard operations. To make Aggregations more convenient to use and future proof, the API is heavily optimized for Java 8 and future versions. The API is still fully compatible with any Java version Hazelcast supports Java 6 and Java 7. The biggest difference is how you work with the Java generics; on Java 6 and 7, the process to resolve generics is not as strong as on Java 8 and upcoming Java versions.

For illustration of the differences in Java 6 and 7 in comparison to Java 8, we will have a quick look at a code snippet for both. The following is a code snippet for Java 6 or 7. Note that Java 8 resolves the generic parameters automatically and that is why the 3 lines in the snippet for Java 6 or 7 become a single line for Java 8. When using the Aggregations API, we will mainly be dealing with the supplier, property extractor and aggregation operations.

Supplier provides filtering and data extraction to the aggregation operations. For filtering data sets, you have the following options:. KeyPredicate if you can decide directly on the data key without the need to deserialize the value. PropertyExtractor can be used to extract attributes from values. Have a look at the following snippet. Note that the value type changes from Person to Integer, which is reflected in the generics information.

PropertyExtractors can be used for any kind of data transformation. Aggregations provides a predefined set of aggregations. This class contains type safe aggregations of the following types:. Those aggregations are similar to their counterparts on relational databases and can be equated to SQL statements. In this example, we have an employee database stored in an IMap, a MultiMap to assign employees to certain offices, and an IMap storing the salaries for each employee.

If, for example, we want to learn the average salary of all employees, we would use the code shown below:. Being the successor of the Hazelcast Aggregators, Fast-Aggregations are equivalent to the MapReduce Aggregators in most of the use cases and they run on the Query infrastructure.

Each aggregator accumulates all entries passed to it by the query engine. The results need to be combined after the accumulation phase in order to be able to calculate the final result.

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Calculates the final result from the results accumulated and combined in the preceding phases. Instead of sending all the data returned by a query, you may want to transform each result object in order to avoid redundant network traffic. For example, you select all employees based on some criteria, but you just want to return their name instead of the whole Employee object. It is easily doable with the Projection API which is given below:. The method transform is called on each result object.

Its result is then gathered as the final query result entity. The Hazelcast map supports indexes to speed up queries, just like in databases. Using an index prevents iterating over all values. In database terms, this is called a full table scan, but it directly jumps to the interesting ones. There are two types of indexes:. In the previous chapter, we talked about a Person that has a name, age, etc.

To speed up searching on these fields, we can place an unordered index on name and an ordered index on age:. To retrieve the index field of an object, first an accessor method will be tried. With the index accessor method, you are not limited to returning a field; you can also create a synthetic accessor method where a value is calculated on the fly. The index field also supports object traversal, so you could create an index on the street of the address of a person using address.

There is no limitation on the depth of the traversal. Starting with Hazelcast 3, indexes can be created on the fly. Management Center even offers the option of creating an index on an existing IMap. This is a big change from Hazelcast 2. The performance impact of using one or more indexes depends on several factors; among them are the size of the map and the chance of finding the element with a full table scan.

Other factors are adding one or more indexes, which make mutations to the map more expensive since the index needs to be updated as well. If you have more mutations than searches, the performance with an index could be lower than without an index. In Hazelcast versions prior to 3. With Hazelcast version 3.

In the previous example, the indexes are placed as attributes of basic data types like int and String. However, the IMap allows indexes to be placed on an attribute of any type, as long as it implements Comparable.