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Hazelcast Simulator

Hazelcast Simulator is a production simulator used to test Hazelcast and Hazelcast-based applications in clustered environments. It also allows you to create your own tests and perform them on your Hazelcast clusters and applications that are deployed to cloud computing environments. In your tests, you can provide any property that can be specified on these environments (Amazon EC2 or your own environment): properties such as hardware specifications, operating system, Java version, etc.

Hazelcast Simulator allows you to add potential production problems, such as real-life failures, network problems, overloaded CPU, and failing nodes to your tests. It also provides a benchmarking and performance testing platform by supporting performance tracking and also supporting various out-of-the-box profilers.

You can use Hazelcast Simulator for the following use cases:

  • In your pre-production phase to simulate the expected throughput/latency of Hazelcast with your specific requirements.
  • To test if Hazelcast behaves as expected when you implement a new functionality in your project.
  • As part of your test suite in your deployment process.
  • When you upgrade your Hazelcast version.

Hazelcast Simulator is available as a downloadable package on the Hazelcast web site. Please refer to the Quickstart to start your Simulator journey.

Table of Content


This is a 5 minute tutorial where that shows you how to get Simulator running on your local machine. Also contains pointers where to go next.


  1. Checkout the Simulator git repository

    git clone
  2. Build Simulator:

    cd hazelcast-simulator

    This will automatically build the Java code, download the artifacts and prepare the simulator for usage.

  3. Add the Simulator to your path

    Open ~/.bash_profile and add the following line:

  4. Congratulations! You've successfully installed Hazelcast Simulator.

Creating a benchmark

The first step is to create a benchmark, This can be done using the perftest tool.

perftest create myproject

This will create a fully configured benchmark that will run in EC2.

There are various benchmark templates. These can be accessed using:

perftest create --list 

And a benchmark using a specific benchmark can be created using

perftest create --template <templatename> myproject

In the future more templates will be added.

Provisioning the environment

Simulator makes use of Terraform for provisioning. After you have created a benchmark using the benchmark create command, you want to edit the inventory_plan.yaml. This is where you can configure the type of instances, the number etc.

To apply the configuration on the existing environment, execute the following command: inventory apply After apply command has completes, a new file 'inventory.yaml' file is created containing created machines. This is an Ansible specific file. Simulator uses Ansible to configure to remote machines.

To install Java on the remote machines call: inventory install java

You can pass a custom URL to cofigure the correct JVM. To get a listing of examples URL's call: inventory install java --list

And run the following to install a specific Java version. inventory install java --url This command will update the JAVA_HOME/PATH on the remote machine to reflect the last installed Java version.

To destroy the environment, call the following:
inventory destroy

SSH to nodes

To SSH to your remote nodes, the following command can be used:
ssh -i key <password>@<ip>

Running a test.

In the generated benchmark directory, a tests.yaml file is created and it will contain something like this:

 - name: write_only
   duration: 300s
   repetitions: 1
   clients: 1
   members: 1
   driver: hazelcast5
   version: maven=5.0
   client_args: -Xms3g -Xmx3g
   member_args: -Xms3g -Xmx3g
   loadgenerator_hosts: loadgenerators
   node_hosts: nodes
   verify_enabled: False
   performance_monitor_interval_seconds: 1
   warmup_seconds: 0
   cooldown_seconds: 0
      threadCount: 40
      getProb: 0
      putProb: 1
      keyCount: 1_000_000

To run the benchmark

perftest run

What's next

The quickstart was to just get you up and running. In order to do some real performance testing, you'll probably need to:

  • Define test scenario - specify how many puts/gets to use, how many entries to preload, how big the values should be, latency vs. throughput test etc.
  • Configure cluster - Hazelcast version, configuration of the Hazelcast itself, number of members and clients, number of threads per client, GC options etc.
  • Run the test - set test duration, select which test scenario to be run etc.
  • Setup the testing environment - run it on on-premise machines, in AWS, configuring for running clusters in OpenShift, Kubernetes etc.
  • Create better charts - create charts with multiple runs being compared, adjust warmup and cooldown periods, adjust legents etc.

You can use the following channels for getting help from Hazelcast:

Key Concepts and Terminology

The following are the key concepts mentioned with Hazelcast Simulator.

  • Test - A test class for the functionality you want to test, e.g. a Hazelcast map. This test class looks similar to a JUnit test, but it uses custom annotations to define methods for different test phases (e.g. @Setup, @Warmup, @Run, @Verify).

  • TestSuite - A property file that contains the name of the Test class and the properties you want to set on that Test class instance. A TestSuite contains one or multiple tests. It can also contain the same Test class with different names and configurations.

  • Worker - This term Worker is used twice in Simulator.

    • Simulator Worker - A Java Virtual Machine (JVM) responsible for running the configured Tests. It can be configured to spawn a Hazelcast client or member instance, which is used in the tests. We refer to this Worker in the context of a Simulator component like Agent and Coordinator.

    • Test Worker - A Runnable implementation to increase the test workload by spawning several threads in each Test instance. We refer to this Worker in the context of a Test, e.g. how many worker threads a Test should create.

  • Agent - A JVM responsible for managing client and member Workers. There is always one Agent per physical machine, no matter how many Workers are spawned on that machine. It serves as communication relay for the Coordinator and monitoring instance for the Workers.

  • Coordinator - A JVM that can run anywhere, such as on your local machine. The Coordinator is actually responsible for running the TestSuite using the Agents and Workers. You configure it with a list of Agent IP addresses, and you run it by executing a command like "run this testsuite with 10 member worker and 100 client worker JVMs for 2 hours".

  • Coordinator Machine - a machine on which you execute the Coordinator (see above). This is the place typically where the user interacts with Simulator commands. Typically your local computer but can be installed anywhere.

  • Coordinator Remote - A JVM that can run anywhere, such as on your local machine. The CoordinatorRemote is responsible for sending instructions to the Coordinator. For basic simulator usages the remote is not needed, but for complex scenarios such as rolling upgrade or high availability testing, a much more interactive approach is required. The coordinator remote talks to the coordinator using TCP/IP.

  • Provisioner - Spawns and terminates cloud instances, and installs Hazelcast Simulator on the remote machines. It can be used in combination with EC2 (or any other cloud), but it can also be used in a static setup, such as a local machine or a cluster of machines in your data center.

  • Failure - An indication that something has gone wrong. Failures are picked up by the Agent and sent back to the Coordinator.

  • - The configuration file you use to adapt the Hazelcast Simulator to your business needs (e.g. cloud provider, SSH username, Hazelcast version, Java profiler settings).

Define test scenario

This section describes how you can control what the test should do - should it do only PUTs or also GETs and if so, in which ratio? Or should it execute SQL queries etc.?

TestSuite configuration

The TestSuite defines the Simulator Tests which are executed during the Simulator run. The TestSuite configuration is a simple properties file which contains key = value pairs. The common name of the file is which is also the default (e.g. generated by simulator-wizard as seen in Quickstart).

We will use file name through the rest of the documentation for the TestSuite configuration. However, the file can be named arbitrarily. See Specify TestSuite file to be used section on details how to specify different properties file.

When you open up the default (generated by simulator-wizard) file, you'll see:

class =
threadCount = 10

getProb = 0.9
putProb = 0.1

keyCount = 1000

Let's explain the lines one by one.

Specify test class and number of threads per worker

The first two properties are built-in "magic" properties of Simulator.

Property Example value Description
class Defines the fully qualified class name for the Simulator Test. Used to create the test class instance on the Simulator Worker. This is the only mandatory property which has to be defined.
threadCount 5 Defines how many threads are running in parallel the Test methods. In other words, defines the number of worker threads for Simulator Tests which use the @RunWithWorker annotation.

📚 For details about available values for class, refer to Selecting a test class section.

Setting up operations frequency

Secondly, there's a group of properties with special meaning which name of ends with Prob (short for "probability"), such as getProb and putProb.

The property conforms to a format <methodName>Prob = <probability>, where:

  • <probability> is a float number from 0 to 1 that sets a probability of execution of the method (see below). For example, probability of 0.1 means 10 % probability.

  • <methodName> corresponds to the name of a timestep method (a method annotated with @TimeStep annotation) in the test class configured with class property. For example, the test contains following methods:

    public void put(ThreadState state) {
      map.put(state.randomKey(), state.randomValue());
    public void get(ThreadState state) {

As a complete example, the putProb = 0.1 property sets the probability of execution of the put method to 10 %. In other words, out of all the things being done by the test, 10 % will be PUTs. This is the basic way how to control the ratio of operations. For example, if you want to execute 80 % GETs and 20 % PUTs with IntByteMapTest you would set getProb = 0.8 and putProb = 0.2.

A special case of probability value is -1 which means "calculate the remaining probability to 1". Example:

putProb = 0.1
setProb = 0.2
getProb = -1

Above properties result in 10 % PUT operations, 20 % SET operation and (1-0.1-0.2=0.7) 70 % of GET operations.

Configuring parameters

All the other properties are values passed directly to the test class and are usually used for adjusting parameters of the test such as number of entries being preloaded in the Map, size of the value etc. Each test class has its own of such options, so you have to look at the source code of the test class for the available parameters and their meeting.

The property must match a public field in the test class. If a defined property cannot be found in the Simulator Test class or the value cannot be converted to the according field type, a BindException is thrown. If there is no property defined for a public field, its default value will be used.

Let's continue using as an example. It contains following public fields:

public class IntByteMapTest extends HazelcastTest {
    public int keyCount = 1000;    
    public int minSize = 16;
    public int maxSize = 2000;
    ... and more

Hopefully the names of the properties are self-explanatory. Therefore, if we wanted to change the test scenario and preload 1 million entries with value size exactly 10 KB, we would edit file in a following way:

class =

# probabilites and thread count settings

keyCount = 1000000
minSize = 10000
maxSize = 10000

Latency Testing

In general, when doing performance testing, you should always distinguish between throughput and latency testing.

  • Throughput test - stress out the system as much as possible and get as many operations per second as possible.
  • Latency test - measure operation latencies while doing a fixed number of operations per second.

By default the timestep-threads operate in throughput testing mode - they will loop over the timestep methods as fast as they can. As a bonus you get an impression of the latency for that throughput. However, for a proper latency test, you want to control the rate and measure the latency for that rate. Luckily using the Simulator this is very easy.

You can configure the fixed number of operations per second using following properties in

  • ratePerSecond=<X> - where <X> is a desired number of operations per second load generating client/member (not worker thread!). Example: if in your test, you configure 5 clients and you want to stress the cluster by 500 000 operations per second, you set ratePerSecond=100000, because 5 clients times 100 000 ops = desired 500 K ops.

  • interval=<Y> - where <Y> is the time interval between subsequent calls load generating client/member (not worker thread!). Example: if in your test, you configure 5 clients and you want to stress the cluster by 500 000 operations per second, you set interval=100us, because 5 clients times 100 000 ops = desired 500 K ops.

Possible time units in interval property:

  • ns - nanoseconds
  • us - microseconds
  • ms - milliseconds
  • s - seconds
  • m - minutes
  • h - hours
  • d - days

📚 From the descriptions above, you can see that if you set the number of operations per second, different values of the threadCount property don't affect it. The formulas are:

  • number of clients * ratePerSecond = total number of operations per second
  • number of clients * (1000 / interval_in_ms) = total number of operations

Both ways do exactly the same and it's just a matter of preference which one you use.

Controlling the Cluster Layout

Hazelcast has two basic instance types: member and client. The member instances form the cluster and client instances connect to an existing cluster. Hazelcast Simulator can spawn Workers for both instance types. You can configure the number of member and client Workers and also their distribution on the available remote machines.

📚 To see how Simulator can help with setting up remote machines, refer to the [Set up cluster environment](#Set up cluster environment) section.

All configuration about the cluster layout is done through the coordinator command which is usually called from the run script (as for example created in Quickstart).

Set number of members and clients

Use the options --members and --clients to control how many member and client Workers you want to have. The following command creates a cluster with four member Workers and eight client Workers (which connect to that cluster).

coordinator --members 4 --clients 8

A setup without client Workers is fine, but out of the box it won't work without member Workers.

Control distribution of workers over machines

Through this section, we'll assume that we have 3 remote machines that we're going to use. In other words, there are 3 IP addresses specified in the agents.txt like this:

📚 To find out what is the agents.txt file and how to get remote machines setup, refer to Set up cluster environment section.

Default distribution algorithm

The Workers will be distributed among the available remote machines with a round robin selection. First the members are distributed in the round robin fassion (going through the IP addresses from the top to the bottom). Once there are no more members to be distributed, Simulator continues (= not starting from the first IP address but continuing with the next one) with distribution of the clients. By default, the machines will be mixed with member and client Workers. Let's see couple of examples.

Coordinator arguments Cluster layout
--members 1 --clients 1 - members:  1, clients:  0 - members: 0, clients: 1 - members: 0, clients: 0
--members 1 --clients 2 - members:  1, clients:  0 - members: 0, clients: 1 - members: 0, clients: 1
--members 1 --clients 3 - members:  1, clients:  1 - members: 0, clients: 1 - members: 0, clients: 1
--members 2 --clients 2 - members:  1, clients:  1 - members: 1, clients: 0 - members: 0, clients: 1
--members 4 --clients 2 - members:  2, clients:  0 - members: 1, clients: 1 - members: 1, clients: 1

Reserving machines for members only

You can reserve machines for members only (which is a Hazelcast recommended setup) using:

coordinator --dedicatedMemberMachines 2

The algorithm that takes the first 2 IP addresses and distributes the members only across them in a round robin fassion. Then takes the rest of the IP addresses and distributes the clients across them, again in the round robin fassion. Continuing our example:

Coordinator arguments Cluster layout
--members 2 --clients 4 --dedicatedMemberMachines 1 - members:  2, clients:  0 - members: 0, clients: 2 - members: 0, clients: 2
--members 3 --clients 4 --dedicatedMemberMachines 2 - members:  2, clients:  0 - members: 1, clients: 0 - members: 0, clients: 4

You cannot specify more dedicated member machines than you have available. If you define client Workers, there must be at least a single remote machine left (e.g. with three remote machines you can specify a maximum of two dedicated member machines).

If you need more control over the cluster layout, you can make use of the coordinator-remote which allows full control on layout, versions of clients, servers, etc., refer to the Fine-grained control with Coordinator Remote section.

Order of the IP addresses

The order of the IP addresses matters. Simulator goes from the top to the bottom and applies the algorithm described above deterministically and always the same.

That allows you to fine tune the configuration of the environment. Imagine a typical usecase where you want to run the members on more powerful machines (e.g. more CPUs, more memory) and use lighter and cheaper (e.g. in the cloud) machines for the clients.

Example: Suppose you have available 3 "big" machines that you want use for 6 members, two members per machine and you have 4 "light" machines that you want to use for 8 clients. Following command and agents.txt file achieves this setup:

coordinator --members 6 --clients 8 --dedicatedMemberMachines 2 ...
$ cat agents.txt

⚠️ Running multiple members on a single machines is a Hazelcast performance anti-pattern and should be avoided. We used it only for a demonstration of the cluster layout distribution. Consult Hazelcast documentation for more information about recommended setup.

Running test against already running cluster

There are cases where you already have a running cluster and you want to execute performance test against it. In other words, you don't want the Simulator to manage your members but only orchestrate the clients. In order to do this, you have to:

  • Specify --members to 0 - Simulator will not care about members at all, won't control their lifecycle etc.
  • Put member IP addresses in the client-hazelcast.xml - since Simulator doesn't control the member lifecycle, it can't possibly know the IP addresses of the members. Therefore, you have to manually provide it through editing the client configuration. For more information about this, refer to Controlling the Hazelcast configuration.
  • Specify correct <cluster-name> in the client-hazelcast.xml - for the same reason as with IP addresses, you have to adjust the <cluster-name> configuration to match the one in the running cluster.

Running test against a cluster in Hazelcast Cloud

If you want to test the performance of the Hazelcast Cloud managed cluster, you follow the same setup as described in Running test against already running cluster section with minor difference:

  • Specify correct cluster name and enter the Cloud discovery token through like this:
    <hazelcast-cloud enabled="true">

in client-hazelcast.xml.

Controlling the Hazelcast Configuration

You can specify Hazelcast configuration by placing a hazelcast.xml (member configuration) or client-hazelcast.xml (client configuration) in your working directory (the one from which you're executing the run script or coordinator command). Simulator will handle the upload of them and makes sure that the workers are started with them transparently.

If there's no hazelcast.xml or client-hazelcast.xml in the working directory, Coordinator uses the default files ${SIMULATOR_HOME}/conf/hazelcast.xml and ${SIMULATOR_HOME}/conf/client-hazelcast.xml.

The recommended approach is to either copy the default XML configurations (listed above) into your working directory and then modify them, or use the generated ones by simulator-wizard as shown in Quickstart. The reason are the auto-filling markers described below.

IP addresses and other configuration auto-filling

When you look at the default hazelast.xml or client-hazelcast.xml configurations (described above), you'll probably notice the following comment:

            <multicast enabled="false"/>
            <tcp-ip enabled="true">
                <!--MEMBERS-->   <------ THIS

This comment is actually a marker of Simulator where it then automatically places the IP addresses of the members. Therefore, you don't have to care about it which greatly simplifies the testing.

In general, do not remove this comment or put member IP address manually if you let Simulator handle the member lifecycle as well (= most of the time, everytime the --members is greater than zero).

See Running test against already running cluster for an example when editing this section is actually desired.

Passing JVM options to client or member processes

Often you need to pass additional JVM arguments to the client or member processes such as enabling GC logging, enabling JFR or passing other useful arguments like -Dhazelcast.partition.count for Hazelcast partition count. You can achieve this simply via coordinator --memberArgs and coordinator --clientArgs. Complete example:

gcArgs="-verbose:gc -Xloggc:verbosegc.log"
gcArgs="${gcArgs} -XX:+PrintGCTimeStamps -XX:+PrintGCDetails -XX:+PrintTenuringDistribution -XX:+PrintGCApplicationStoppedTime -XX:+PrintGCApplicationConcurrentTime"

memberJvmArgs="${memberJvmArgs} -Xmx${memberHeapSZ} -XX:+HeapDumpOnOutOfMemoryError"
memberJvmArgs="${memberJvmArgs} ${gcArgs}"

clientJvmArgs="-Xmx${clientHeapSZ} -XX:+HeapDumpOnOutOfMemoryError"
clientJvmArgs="${clientJvmArgs} ${gcArgs}"

coordinator --members ${members} \
            --clients ${clients} \
            --duration ${duration} \
            --memberArgs "${memberJvmArgs}" \
            --clientArgs "${clientJvmArgs}" \
            --driver hazelcast4 \
            --version maven=4.2 \

Run the test

The actual Simulator Test run is done by the coordinator command. The created run script (via simulator-wizard in Quickstart) is a good start to customize your test setup.

It takes four optional parameters to define the number of member and client Workers, the run duration and the name of the TestSuite file. So the following command will spawn 4 member Workers, twenty 20 Workers and will run for five minutes (with the default file).

./run 4 20 5m

Configure test duration

You can control the duration of the test execution by using the --duration argument of coordinator command. You can specify the time unit for this argument by using

  • s for seconds
  • m for minutes
  • h for hours
  • d for days

If you omit the time unit the value will be parsed as seconds. The default duration is 60 seconds.

You can see the usage of the --duration argument in the following example commands.

coordinator --duration 90s
coordinator --duration 3m
coordinator --duration 12h
coordinator --duration 2d

The duration is used as the run phase of a Simulator Test (that's the actual test execution). If you have long running warmup or verify phases, the total runtime of the TestSuite will be longer.

📚 There is another option for the use case where you want to run a Simulator Test until some event occurs (which is not time bound), e.g. stop after five million operations have been done. In this case, the test code must stop the TestContext itself. See Stopping a test section.

If you want to run multiple tests in parallel, please refer to Running multiple tests in parallel section.

Specify testSuite file to be used

You can specify the used TestSuite file by passing it directly to the coordinator command.


This is very convenient when you want to test multiple test scenarios on a same cluster setup.

Installing Simulator on remote machines

Simulator needs to be installed on the remote machines before you run the tests. If you already have your cloud instances provisioned (see Controlling provisioned machines) or run a static setup (see Using static setup), you can just install Hazelcast Simulator with the following command.

provisioner --install

This is also useful whenever you update or change your local Simulator installation (e.g. when developing a test TestSuite) and want to re-install Hazelcast Simulator on the remote machines.

This is just necessary if the JAR files have been changed. Configuration changes in your or don't require a new Simulator installation.

Report generation

Once a benchmark has been executed, an HTML report can be generated using the benchmark_report tool. This tool requires Gnuplot 4+ and Python 3.x to be installed for generating the diagrams.


Assume that a benchmark has been executed and the directory 2021-05-31__23_19_13 has been created. To create a report for that benchmark, you can use the following command:

benchmark_report -o my-benchmark-report 2021-05-31__23_19_13

The name my-benchmark-report is output directory's name. The generated report contains detailed throughput and latency information. If dstats information is available, it shows detailed information about resource utilization such as network, CPU, and memory.

Generate comparison reports

The benchmark_report tool is also able to make comparisons between two or more benchmarks. Suppose that you executed a test with some configuration, the resulting directory is 2021-05-31__23_19_13. Then you changed the configuration, e.g. changed the Hazelcast version and executed again with resulting directory 2021-05-31__23_35_40.

You can create a single report plotting those two benchmarks in the same chart allowing easy comparison with:

benchmark_report -o my-comparison-report 2021-05-31__23_19_13 2021-05-31__23_35_40

Extensive reports

You can create a very detailed report with more charts with -f switch:

benchmark_report -f -o my-full-report 2021-05-31__23_19_13 

Warmup / cooldown

It's often desired to strip the beginning or the end of the test out of the resulting charts e.g. because of JIT compiler warmup etc.

The way how it works in Simulator is that the data is collect nevertheless. You just trim it out in the final report generation with the benchmark_report command. Example having 1 minute (60 seconds) warmup and 30 second cooldown:

benchmark_report -w 60 -c 30 -o my-trimmed-benchmark-report 2021-05-31__23_19_13

Simulator Properties reference

You can configure Simulator itself using the file in your working directory. The default properties are always loaded from the ${SIMULATOR_HOME}/conf/ file. Your local properties are overriding the defaults.

For the full reference of available settings and their explanation, please refer to default

Advanced topics

Writing a Simulator test

The main part of a Simulator test is writing the actual test. The Simulator test is heavily inspired by the JUnit testing and Java Microbenchmark Harness (JMH) frameworks. To demonstrate writing a test, we will start with a very basic case and progressively add additional features.

For the initial test case we are going to use the IAtomicLong. Please see the following snippet:

package example;


public class MyTest extends AbstractTest{
  private IAtomicLong counter;

  @Setup public void setup(){
    counter = targetInstance.getAtomicLong("c");

  @TimeStep public void inc(){

The above code example shows one of the most basic tests. AbstractTest is used to remove duplicate code from tests; so it provides access to a logger, testContext, targetInstance HazelcastInstance, etc.

A Simulator test class needs to be a public, non-abstract class with a public no-arg constructor.

Assume the property file to start the test is as follows:


The main property that needs to be in the property file is the class property which needs to point to the full class name.

Just like the other annotated methods, Timestep methods need to be public due to the code generator and they are allowed to throw Throwable like checked exceptions:

  @TimeStep public void inc() throws Exception{

Any Throwable, apart from the StopException, that is being thrown will lead to a Failure to be reported.

Adding properties

Properties can be added to a test to make it easy to modify them from the outside. Properties must be public fields and can be primitives, wrappers around primitives like java.lang.Long, enums, strings and classes. Properties are case sensitive.

In the below example the countersLength property has been added and it defaults to 20.

public class MyTest extends AbstractTest{
  public int countersLength = 20;

  private IAtomicLong[] counters;

  @Setup public void setup(){
    this.counters = new IAtomicLong[countersLength];
    for(int k=0;k<countersLength;k++)
      counters[k] = targetInstance.getAtomicLong(""+k);

  @TimeStep public void inc(BaseThreadState state){
      int counterIndex = state.randomInt(countersLength);

In most cases it is best to provide defaults for properties to make customization of a test less verbose.

The countersLength can be configured as shown below:


The order of the properties in the file is irrelevant.

Properties do not need to be simple fields. The property binding supports complex object graphs to be created and configured. Properties can be nested and no-arg constructor must be used to build up the graph of objects. Please see the following example:

public class SomeTest{
	pubic Config config;

	public static class Config{
		NestedConfig nestedConfig;

	public static class NestedConfig{
		public int value;	

The config object can be configured as shown below:


If a property is not used in a test, the test fails during its startup. The reason is that if you would make a typing error and, in reality, something different is tested different from what you think is being tested, it is best to know this as soon as possible.


A Simulator test instance is shared between all timestep-threads for that test and only on the test instance level where there was a state. But in some cases you want to track the state for each timestep-thread. Of course a thread-local can be used for this, but the Simulator has a more practical and faster mechanism, ThreadState.

In the following code example, a ThreadState is defined that tracks the number of increments per thread:

import com.hazelcast.Simulator.test.BaseThreadState

public class MyTest extends AbstractTest{
  public int countersLength; 

  private AtomicLong counter;

  @Setup public void setup(){
    this.counter = targetInstance.getAtomicLong("counter");

  @TimeStep public void inc(ThreadState state){

  public class ThreadState extends BaseThreadState{
    long increments;

In this example, tracking the number of increments is not that interesting since nothing is done with it. But it can be used to verify that the data structure under the test (IAtomicLong in this case) is working correctly. Please see the Verification section for more information.

The class of the ThreadState is determined by timestep code-generator and it will automatically create an instance of this class per timestep-thread. This instance will then be passed to each invocation of the timestep method in that timestep-thread. This means that you do not need to deal with more expensive thread-locals.

Extending the BaseThreadState class is the recommended way to define your own ThreadState because it provides various random utility methods that are needed frequently.

However, ThreadState does not need to extend BaseThreadState. ThreadState can be any class as long as it has a no-arg constructor, or it has a constructor with the type of the enclosing class as argument (a non-static inner class). ThreadState class unfortunately needs to be a public class due to the code generator. But the internals of the class do not require any special treatment.

Another restriction is that all timestep, beforeRun and afterRun methods (of the same execution group) need to have the same type for the ThreadState argument. So the following is not valid:

public class MyTest extends AbstractTest{

  @TimeStep public void inc(IncThreadState state){

  @TimeStep public void get(GetThreadState list){
  public class IncThreadState{long increments;}
  public class GetThreadState{}

It is optional for any timestep, beforeRun, and afterRun methods to declare this ThreadState argument. So the following is valid:

public class MyTest extends AbstractTest{

  @TimeStep public void inc(ThreadState state){

  @TimeStep public void get(){

  public class ThreadState extends BaseThreadState{
    long increments;

The reason of having a single test instance shared between all threads, instead of having a test instance per thread (and dropping the need for the ThreadState) is that it will be a lot more cache friendly. It is not the test instance which needs to be put into the cache, everything referred from the test instance.

Another advantage is that if there is a shared state, it is easier to share it; for example, keys to select from for a map.get test between threads, instead of each test instance generating its own keys (and therefore increasing memory usage). In the future a @Scope option will probably be added so that you can choose if each thread gets its own test instance or that the test instance is going to be shared.

AfterRun and BeforeRun

The timestep methods are called by a timestep-thread and each thread will do a loop over its timestep methods. In some cases before this loop begins or after this loop ends, some additional logic is required. For example initialization of the ThreadState object is needed when the loop starts, or updating some shared state when the loop completes. This can be done using beforeRun and afterRun methods. Multiple beforeRun and afterRun methods can be defined, but the order of their execution is unfortunately not defined, so be careful with that.

The beforeRun and afterRun methods accept the ThreadState as an argument, but this argument is allowed to be omitted.

In the following example, beforeRun and afterRun methods are defined that log when the timestep thread starts, and log when it completes. It also writes the number of increments the timestep thread executed:

public class MyTest extends AbstractTest{
  public int countersLength; 

  private AtomicLong counter;

  @Setup public void setup(){
    this.counter = targetInstance.getAtomicLong("counter");

  @BeforeRun public void beforeRun(ThreadState state){
    System.out.println(Thread.currentThread().getName()+" starting");

  @TimeStep public void inc(ThreadState state){

  @AfterRun public void afterRun(ThreadState state){
      " completed with "+state.increments+" increments");

  public class ThreadState extends BaseThreadState{
    long increments;


Once a Simulator test is completed, you can do the verifications using the @Verify annotation. In the case of test, you could count the number of increments per thread. After the test completes, you can verify the total count of expected increments and the actual number of increments.

public class MyTest extends AbstractTest{
  private IAtomicLong counter;
  private IAtomicLong expected;

  @Setup public void setup(){
    this.counter = targetInstance.get("counter");
    this.expected = targetInstance.get("expected");  

  @TimeStep public void inc(ThreadState state){
  @AfterRun public void afterRun(ThreadState state){
  @Verify public void verify(){
    assertEquals(expected.get(), counter.get())
  public class ThreadState extends BaseThreadState {
    long increments;

In the above example once the timestep-loop completes, each timestep-thread will call the afterRun method and add the actual number of increments to the expected IAtomicLong object. In the verify method the expected number of increments is compared with the actual number of increments.

The example also shows we make use of the JUnit's assertEquals method. So you can use JUnit or any other framework that can verify behaviors. It is even fine to throw an exception.

It is allowed to define zero, one or more verify methods.

By default the verify will run on all workers, but it can be configured to run on a single worker using the global property on the @Verify annotation.


To automatically remove created resources, a tearDown can be added. It depends on the situation if this is needed at all for your test because in most cases the workers will be terminated anyway after the Simulator test completes. But just in case you need to tear down the resources, it is possible.

In the following example the tearDown is demonstrated:

public class MyTest extends AbstractTest{
  private IAtomicLong counter;

  @Setup public void setup(){
    counter = targetInstance.getAtomicLong("c");

  @TimeStep public void inc(){;

  @TearDown public void tearDown(){

By default the tearDown is executed on all participating workers, but can be influenced using the global property as shown below:

public class MyTest extends AbstractTest{
  private IAtomicLong counter;

  @Setup public void setup(){
    counter = targetInstance.getAtomicLong("c");

  @TimeStep public void inc(){;

  @TearDown(global=true) public void tearDown(){

When global is set to true, only one worker is going to trigger the destroy. It is allowed to define multiple tearDown methods.

Total Lifecycle of Calls on the Test

  • setup
  • prepare local
  • prepare global
    • timestep-thread:before run
    • timestep-thread:timestep ...
    • timestep-thread:after run
  • local verify
  • global verify
  • local teardown
  • global teardown

Stopping a Test

By default a Simulator test will run for a given amount of time using the duration property. Please see the following example:

coordinator --duration 5m

In this example, the test will run for five minutes. In some cases you need more control on when to stop. Currently there are following options available:

  • Configuring the number of iterations: The number of iterations can be specified using the test properties:


    In this case the test will run for 1000k iterations.

  • StopException to stop a single thread: When a timestep thread wants to stop, it can throw a StopException. This exception does not lead to a failure of the test. It also has no influence on any other timestep thread.

  • TestContext.stop to stop all timestep threads: All timestep threads for a given period on a single worker can be stopped using the TestContext.stop method.

In all cases, Coordinator will wait for all timestep threads of all workers to complete. If a duration has been specified, the test will not run longer than this duration.

📚 Use the coordinator --waitForTestCaseCompletion command to let Coordinator wait indefinitely.

Code Generation

The timestep methods rely on code generation, that is why a JDK is required to run a timestep based test. The code is generated on the fly based on the test and its test parameters. The philosophy is that you should not pay the price for something that is not used. For example, if there is a single timestep method, no randomization/switch-case is needed to execute the right method. If no logging is configured, no logs are generated.

This way many features can be added to the timestep test without impacting the performance if the actual feature is not used.

The generator timestep worker code can be found in the worker directory. Feel free to have a look at it and send any suggestions how it can be improved.

Currently there is no support for dead code eliminatio

Profiling your Simulator Test

To determine, for example, where the time is spent or other resources are being used, you want to profile your application. The recommended way to profile is using the Java Flight Recorder (JFR) which is only available in the Oracle JVMs. The JFR, unlike the other commercial profilers like JProbe and Yourkit, does not make use of sampling or instrumentation. It hooks into some internal APIs and is quite reliable and causes very little overhead. The problem with most other profilers is that they distort the numbers and frequently point you in the wrong direction; especially when I/O or concurrency is involved. Most of the recent performance improvements in Hazelcast are based on using JFR.

To enable the JFR, the JVM settings for the member or client need to be modified depending on what needs to be profiled. Please see the following example:

JFR_ARGS="-XX:+UnlockCommercialFeatures  \
          -XX:+FlightRecorder \
          -XX:StartFlightRecording=duration=120m,filename=recording.jfr  \
          -XX:+UnlockDiagnosticVMOptions \

coordinator --members 1 \
            --workerVmOptions "$JFR_ARGS" \
            --clients 1 \
            --clientVmOptions "$JFR_ARGS" \

In the above example, both client and members are configured with JFR. Once the Simulator test has completed, all artifacts including the JFR files are downloaded. The JFR files can be opened using the Java Mission Control command jmc.

GC analysis

By adding the following options to member/client args, the benchmark generator will do a gc comparison:

-Xloggc:gc.log -XX:+PrintGC -XX:+PrintGCDetails -XX:+PrintGCTimeStamps  -XX:+PrintGCDateStamps

Reducing Fluctuations

Fore more stable performance numbers, set the minimum and maximum heap size to the same value. Please see the following example:

coordinator --members 1 \
            --workerVmOptions "-Xmx4g -Xms4g" \
            --clients 1 \
            --clientVmOptions "-Xmx1g -Xms1g" \

Also set the minimum cluster size to the expected number of members using the following property:


This prevents Hazelcast cluster from starting before the minimum number of members has been reached. Otherwise, the benchmark numbers of the tests can be distorted due to partition migrations during the test. Especially with a large number of partitions and short tests, this can lead to a very big impact on the benchmark numbers.

Enabling Diagnostics

Hazelcast has a diagnostics system which provides detailed insights on what is happening inside the client or server HazelcastInstance. It is designed to run in production and has very little performance overhead. It has so little overhead that we always enable it when doing benchmarks.

coordinator --members 1 \
            --workerVmOptions "-Dhazelcast.diagnostics.enabled=true \
                               -Dhazelcast.diagnostics.metric.level=info \
                               -Dhazelcast.diagnostics.invocation.sample.period.seconds=30 \
                               -Dhazelcast.diagnostics.pending.invocations.period.seconds=30 \
                               -Dhazelcast.diagnostics.slowoperations.period.seconds=30" \
            --clients 1 \
            --clientVmOptions "-Dhazelcast.diagnostics.enabled=true \
                               -Dhazelcast.diagnostics.metric.level=info" \

Using the above example, both client and server have diagnostics enabled. Both will write a diagnostics file. Once the Simulator run is completed and the artifacts are downloaded, the diagnostics files can be analyzed.


In some cases, especially when debugging, logging is required. One easy way to add logging is to add the logging into the timestep method. But this can be inefficient and it is frequently noisy. Using some magic properties logging can be enabled on any timestep based Simulator test. There are two types of logging:

  • frequency based; for example every 1000th iteration, each timestep thread will log where it is.
  • time rate based; for example every 100ms each timestep thread will log where it is. Time rate based is quite practical because you do not get swamped or a shortage of log entries, like the frequency based one.

You can configure frequency based logging as shown below:


In this example, every 10000 iteration, a log entry is made per timestep thread.

You can configure time rate based logging as shown below:


In this example, at most every 100ms, a log entry is made per timestep thread.

Running multiple tests in parallel

It's possible to run multiple tests simultaneously. In order to do that:

  • You use the extended notation in file which is:

    TestId@key = value

    TestId is a arbitrary name that serves as identifier, so that Coordinator knows which properties belong to which tests. An example which runs two tests to probably be self explanatory
  • You pass --parallel to the coordinator command, e.g.

    coordinator --members ${members} \

Coordinated Omission

By default the Simulator prevents the coordinated omission problems by using the expected start time of a request instead of the actual time. So instead of trying to do some kind of a repair after it happened, the Simulator actually prevents the problem happening in the first place. Similar technique is used in JLBH.

If you are interested in the impact of coordinated omission, the protection against it can be disabled using the accountForCoordinatedOmission property:


Be extremely careful when setting this property to false and publishing the results. Because the number will be a lot more positive than they actually are.

The rate of doing requests is controlled using the Metronome abstraction and a few flavors are available. One very interesting metronome is the ConstantCombinedRateMetronome. By default each timestep-thread will wait for a given amount of time for the next request and if there is some kind of an obstruction, e.g., a map.get is obstructed by a fat entry processor, a bubble of requests is built up that is processed as soon as the entry processor has completed.

Instead of building up this bubble, the ConstantCombinedRateMetronome can be used. If one thread is obstructing while it wants to do a get, other timestep-threads from the same execution group will continue with the requests this timestep thread was supposed to do. This way the bubble is prevented; unless all timestep threads from the same execution group are obstructed.

The ConstantCombinedRateMetronome can be configured as shown below:


Measuring Jitter

To measure jitter caused by the OS/JVM it is possible to active a Jitter thread using:


This thread will do nothing else than measuring time and recording it in a probe. The content of this probe results in hdr files and can be visualized using the benchmark report generator.

By default jitter greater or equal 1000ns is recorded, but can be configured using the recordJitterThresholdNs property:


To disable the threshold, set recordJitterThresholdNs to 0. Warning: if the recordJitterThresholdNs is set to a value higher than zero, the latency distribution looks distorted because only the outliers are recorded and not the samples below the threshold.

Measuring jitter is only recommended when doing a latency test because you will loose 1 core. Each test instance will create its own jitter thread (if the test is configured to use a jitter thread). So it is extremely unlikely that you want to run tests in parallel with this feature enabled.

Controlling the load generation

Beside the cluster layout you can also control which Workers will execute their RUN phase (= the actual test). The default is that client Workers are preferred over member Workers. That means if client Workers are used, they will create the load in the cluster, otherwise the member Workers will be used. In addition you can limit the number of Workers which will generate the load.

coordinator --targetType member --targetCount 2

This will limit the load generation to two member Workers, regardless of the client Workers' availability. Please have a look at command line help via coordinator --help to see all allowed values for these arguments.

Get Help

You can use the following channels for getting help with Hazelcast: