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 website. Please refer to the Quickstart to start your Simulator journey.

• Quickstart
  • Install
  • Creating a benchmark
  • Provisioning the environment
  • SSH to nodes
  • Running a test.
  • What's next
• Key Concepts and Terminology
• Define test scenario
  • TestSuite configuration
    • Specify the test environment and non-class-specific parameters
    • Specify test class(es) and number of threads per worker
    • Setting up operations frequency
    • Configuring parameters
    • Latency Testing
  • Controlling the Cluster Layout
    • Set number of members and clients
    • Control distribution of workers over machines
      • Default distribution algorithm
      • Reserving machines for members only
        • Order of the IP addresses
    • Running tests against an already running cluster
    • Running tests against a cluster in Hazelcast Cloud
  • Controlling the Hazelcast Configuration
    • IP addresses and other configuration auto-filling
• Run the test
  • Configure test duration
  • Specify TestSuite file to be used
  • Installing Simulator on remote machines
• Report generation
  • Basics
  • Generate comparison reports
  • Extensive reports
  • Warmup and cooldown
• Simulator Properties reference
• Advanced topics
  • Writing a Simulator test
    • Adding properties
    • ThreadState
    • AfterRun and BeforeRun
    • Verification
    • TearDown
    • Complete Lifecycle of Calls on the Test
    • Stopping a Test
    • Code Generation
  • Profiling your Simulator Test
  • GC analysis
  • Reducing Fluctuations
  • Enabling Diagnostics
  • Logging
  • Running multiple tests in parallel
  • Various forms of testing
• Get Help

Quickstart

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.

Install

1. Checkout the Simulator git repository:

   git clone https://github.com/hazelcast/hazelcast-simulator.git

2. Install tools

   • JDK 17 or newer
   • Maven
   • Terraform
   • Python 3.8
   • Python3-pip
   • python3-gobject
   • rsync

3. Create a virtual environment, activate it and install dependencies

   $ cd hazelcast-simulator
   $ # python 3.11 is the version that is currently supported
   $ python3.11 -m venv venv 
   $ # activate the virtual environment appropriate to your shell, e.g. in bash:
   $ source ./venv/bin/activate
   $ pip install -r requirements.txt

signal-processing-algorithms is only needed when you are going to do performance regression testing. The library is used for change point detection:

4. Build Simulator:

   $ ./build

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

5. Add the Simulator to your path

   Open ~/.bash_profile and add the following line:

   PATH=<path-to-simulator>/bin/:$PATH
   

Creating a benchmark

The first step is to create a benchmark, which 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 perftest create command, you want to edit the inventory_plan.yaml. This is where you can configure the type of instances, the number etc. The specified cidr_block will need to be updated to prevent conflicts.

To provision the environment, you will first need to configure your AWS credentials and update team tag on inventory_plan according to the values on following repository:

In your ~/.aws/credentials file you need something like this:

[default]
aws_access_key_id=AKIAIOSFODNN7EXAMPLE
aws_secret_access_key=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY

Or when you make use of a token:

[default]
aws_access_key_id=AKIAIOSFODNN7EXAMPLE
aws_secret_access_key=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
aws_session_token=AQoEXAMPLEH4aoAH0gNCAPyJxz4BlCFFxWNE1OPTgk5TthT+FvwqnKwRcOIfrRh3c/LTo6UDdyJwOOvEVPvLXCrrrUtdnniCEXAMPLE/IvU1dYUg2RVAJBanLiHb4IgRmpRV3zrkuWJOgQs8IZZaIv2BXIa2R4OlgkBN9bkUDNCJiBeb/AXlzBBko7b15fjrBs2+cTQtpZ3CYWFXG8C5zqx37wnOE49mRl/+OtkIKGO7fAE

Or alternatively you can use aws sso login and autorize your terminal via SSO.

To apply the configuration on an existing environment, execute the following command from within the benchmark directory:

inventory apply

After the apply command has completed, a new file inventory.yaml file is created containing created machines. This is an Ansible specific file. Simulator uses Ansible to configure the remote machines.

To install Java on the remote machines call:

inventory install java

Note that starting from 5.5.0-SNAPSHOT onwards, the private snapshots repository is used, so access to this repository needs to be configured both locally (where simulator is being built and perftest is kicked off) and remotely (on load generators and so on...). See here for details.

You can pass a custom URL to configure the correct JVM. To get a listing of examples URL's call:

inventory install java --examples 

And run the following to install a specific Java version.

inventory install java --url https://corretto.aws/downloads/latest/amazon-corretto-17-x64-linux-jdk.tar.gz 

This command will update the JAVA_HOME/PATH on the remote machine to reflect the last installed Java version.

Install the Simulator:

inventory install simulator

To destroy the environment, call the following:

inventory destroy

SSH to nodes

To SSH into your remote nodes, the following command can be used from the test directory:

ssh -i key <username>@<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
       test:
         class: com.hazelcast.simulator.tests.map.IntByteMapTest
         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 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:

• Hazelcast mailing list
• Slack for chatting with the development team and other Hazelcast users.
• Stack Overflow

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 yaml file that contains the name of the Test classes and the properties you want to set on those 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 provider), 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.

• simulator.properties - 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, etc.).

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 YAML file which contains key-value pairs. The common name of the file is tests.yaml which is also the default (e.g. generated by perftest create as seen in Quickstart).

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

When you open up the default (generated by perftest create) tests.yaml file, you'll see (as well as a write_only variant):

- name: read_only
  repetitions: 1
  duration: 300s
  clients: 1
  members: 1
  loadgenerator_hosts: loadgenerators
  node_hosts: nodes
  driver: hazelcast5
  version: maven=5.1
  client_args: >
    -Xms3g
    -Xmx3g
  member_args: >
    -Xms3g
    -Xmx3g
  performance_monitor_interval_seconds: 1
  verify_enabled: True
  warmup_seconds: 0
  cooldown_seconds: 0
  license_key: <add_key_here_if_using_ee>
  parallel: False
  test:
    - class: com.hazelcast.simulator.tests.map.IntByteMapTest
      name: MyByteTest
      threadCount: 40
      getProb: 1
      putProb: 0
      keyCount: 1_000_000

Let's explain the lines one by one.

Specify the test environment and non-class-specific parameters

Each section within the tests.yaml file contains properties for the environment the defined tests for that section should be conducted in.

Property	Example value	Description
name	read_only	The name of the test suite (overriden by test-specific values)
repititions	1	The number of times this test suite should run (1 or more)
duration	300s	The amount of time this test suite should run for (45m, 1h, 2d, etc.)
clients	1	The number of Hazelcast Clients to use in this test suite (hosted on loadgenerator_hosts
members	1	The number of Hazelcast Members to use in this test suite (hosted on node_hosts)
loadgenerator_hosts	loadgenerators	Defines the host for Clients, based on either loadgenerators or nodes, allowing both separate and mixed client/member setups
node_hosts	nodes	Defines the host for Members - this should generally always be nodes, and only loadgenerator_hosts should be changed for mixed testing.
driver	hazelcast5	The Hazelcast Driver to use - for 5.0+ testing, this is either hazelcast5 or hazelcast-enterprise5 for OS or EE respectively
version	maven=5.1	The Hazelcast version to use - typically provided by maven, i.e. maven=5.3.0-SNAPSHOT
client_args	-Xms3g -Xmx3g	The command-line Java parameters passed to all clients in this test suite
member_args	-Xms3g -Xmx3g	The command-line Java parameters passed to all members in this test suite
performance_monitor_interval_seconds	1	The interval of the Simulator performance monitor
verify_enabled	True	Defines whether tests should be verified after completion or not (default true)
warmup_seconds	0	The number of seconds from the start of the test to exclude in reporting (only used for report generation)
cooldown_seconds	0	The number of seconds before the end of the test to exclude in reporting (only used for report generation)
license_key	your_ee_key	The Hazelcast Enterprise Edition license to use in your test, if using hazelcast-enterprise5 drivers
parallel	True	Defines whether tests should be run in parallel when multiple tests are defined within 1 suite (default false)
cp_priorities	- address: internalIp  priority: 1	Defines the leadership priority of the CP Subsystem members in the cluster. Use the internal IP address of the agent(s) you wish to configure.

Specify test class(es) and number of threads per worker

Beyond the environment parameters above, under the test section we define the actual tests to run. The first three properties shown in the above example are built-in "magic" properties of Simulator.

Property	Example value	Description
class	com.hazelcast.simulator.tests.map.IntByteMapTest	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.
name	MyByteTest	Defines a unique name for this Test. This property is only required when running multiple tests on the same test class, without it only 1 test will run per class type (as the class name is used as the name if not defined here). The tests are ordered alphabetically by name.
threadCount	40	Defines how many threads are running the Test methods in parallel. 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 the provided classes in the drivers directory or the Writing a Simulator test section.

Setting up operations frequency

Next up, there's some properties with special functionality, which all have their names ending with Prob (short for "probability"), such as getProb and putProb.

These properties conform to the format <methodName>Prob: <probability>, where:

• <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 com.hazelcast.simulator.tests.map.IntByteMapTest test contains the following methods:

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

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

As a complete example, a 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 method for controlling 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". An example:

putProb: 0.1
setProb: 0.2
getProb: -1

The above properties result in 10 % PUT operations, 20 % SET operations, and (1-0.1-0.2=0.7) 70 % 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 set of options, so you have to look at the source code of the test class for the available parameters and their meaning.

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 com.hazelcast.simulator.tests.map.IntByteMapTest as an example. It contains the 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 a value size of exactly 10 KB, we would edit the tests.yaml file as follows:

  test:
    - class: com.hazelcast.simulator.tests.map.IntByteMapTest
      # probabilites and thread count settings
      minSize: 10_000
      maxSize: 10_000
      keyCount: 1_000_000

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 this is very easy with the Simulator.

You can configure the fixed number of operations per second using following properties in the test section of tests.yaml:

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

Accepted time units for the 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 work 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.

All configuration about the cluster layout is managed through the inventory_plan.yaml file which handles the actual provisisiong, as well as the tests.yaml file which handles the allocation of workers between hosts.

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 inventory.yaml like this:

nodes:
  hosts:
    21.333.44.55:
      ansible_ssh_private_key_file: key
      ansible_user: ec2-user
      private_ip: 10.0.0.1
    22.333.44.55:
      ansible_ssh_private_key_file: key
      ansible_user: ec2-user
      private_ip: 10.0.0.2
    23.333.44.55:
      ansible_ssh_private_key_file: key
      ansible_user: ec2-user
      private_ip: 10.0.0.3

📚 The inventory.yaml file is generated after AWS machines have been provisioned using inventory apply.

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 a couple of examples.

Tests.yaml properties	Cluster layout
members: 1, clients: 1	10.0.0.1 - members: 1, clients: 0 10.0.0.2 - members: 0, clients: 1 10.0.0.3 - members: 0, clients: 0
members: 1, clients: 2	10.0.0.1 - members: 1, clients: 0 10.0.0.2 - members: 0, clients: 1 10.0.0.3 - members: 0, clients: 1
members: 1, clients: 3	10.0.0.1 - members: 1, clients: 1 10.0.0.2 - members: 0, clients: 1 10.0.0.3 - members: 0, clients: 1
members: 2, clients: 2	10.0.0.1 - members: 1, clients: 1 10.0.0.2 - members: 1, clients: 0 10.0.0.3 - members: 0, clients: 1
members: 4, clients: 2	10.0.0.1 - members: 2, clients: 0 10.0.0.2 - members: 1, clients: 1 10.0.0.3 - members: 1, clients: 1

Reserving machines for members only

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

perftest exec --dedicatedMemberMachines 2

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

Tests.yaml properties + perftest flag	Cluster layout
members: 2, clients: 4, --dedicatedMemberMachines 1	10.0.0.1 - members: 2, clients: 0 10.0.0.2 - members: 0, clients: 2 10.0.0.3 - members: 0, clients: 2
members: 3, clients: 4, --dedicatedMemberMachines 2	10.0.0.1 - members: 2, clients: 0 10.0.0.2 - members: 1, clients: 0 10.0.0.3 - 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).

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 way.

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.

⚠️ 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 the recommended setup.

Running tests against an 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 need to use the internal coordinator command, and 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 the 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 tests 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 tests against an already running cluster section with a minor difference:

• Specify the correct cluster name and enter the Cloud discovery token like this:

<hazelcast-client>
    <cluster-name>YOUR_CLUSTER_NAME</cluster-name>

    <network>
        <hazelcast-cloud enabled="true">
            <discovery-token>YOUR_CLUSTER_DISCOVERY_TOKEN</discovery-token>
        </hazelcast-cloud>
    </network>
</hazelcast-client>

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 perftest 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 ones generated by perftest create as shown in Quickstart. The reason for this is due to the auto-filling markers described below.

IP addresses and other configuration auto-filling

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

<hazelcast>
    ...
    <network>
        <join>
            <multicast enabled="false"/>
            <tcp-ip enabled="true">
                <!--MEMBERS-->   
            </tcp-ip>
        </join>
    </network>
</hazelcast>

This comment is actually a marker for 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 tests against an already running cluster for an example when editing this section is actually desired.

Run the test

The actual Simulator Test run is done with the perftest run command. The created tests.yaml script ( via perftest create in Quickstart) is a good start to customize your test setup.

Configure test duration

You can control the duration of the test execution by setting the duration property within the tests.yaml definition. 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.

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 the Running multiple tests in parallel section.

Specify TestSuite file to be used

By default perftest run will run the tests.yaml file in the current directory - you can specify a different file to use with perftest run another_file.yaml.

This is very convenient when you want to test multiple test scenarios on the 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.

inventory install simulator

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 only necessary if the JAR files have been changed. Configuration changes in your tests.yaml or simulator.properties don't require a new Simulator installation.

Report generation

Once a benchmark has been executed, if using perftest run, an HTML report will automatically be generated for you. You can disable this behaviour by passing the --skip_auto_gen_report flag to perftest. You can always generate an HTML report on demand by using the perftest report tool. Report generation requires Gnuplot 4+ and Python 3.x to be installed for generating the diagrams.

Basics

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:

perftest 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 perftest 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:

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

By default the perftest run creates a directory for each run inside runs/BenchmarkName with timestamp as the directory name. To automate comparisons of last runs of some benchmarks, you can simply run the perftest report with -l flag (or --last):

perftest report -l -o my-comparison-report runs/MyTestA runs/MyTestB

In order to have readable labels in comparison reports you can run the benchmark with --runLabel <your label> option (note that it will overwrite any previous benchmark results with given label):

perftest run --runLabel ver1
# do some changes
perftest run --runLabel ver2

perftest report -o my-comparison-report ver1 ver2

When comparing different benchmarks in the same report you can add --longLabel in generate report command to include test name in run labels in tables and graphs. This option cannot be used with --last.

--longLabel also works nicely with --runLabel for comparing combinations. For example, if the test suite contains test1 and test2 tests, and you issue the following commands:

perftest run --runLabel ver1
perftest run --runLabel ver2
perftest report --longLabel -o my-comparison-report runs/*/*

then the report will contain the following run labels: test1@ver1, test1@ver2, test2@ver1, test2@ver2.

Extensive reports

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

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

Warmup and 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 it works in Simulator is that the data is collected nevertheless. You just trim it out in the final report generation with the perftest report command. Example having a 1 minute (60 seconds) warmup and 30 second cooldown:

perftest 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 simulator.properties in your working directory. The default properties are always loaded from the ${SIMULATOR_HOME}/conf/simulator.properties file. Your local properties will override the defaults.

For the full reference of available settings and their descriptions, please refer to default simulator.properties.

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() {
        counter.incrementAndGet();
    }
}

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 tests file to start the test is as follows:

  test:
    - class: example.MyTest

The main property that needs to be in the tests 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 {
                counter.incrementAndGet();
                }

Any Throwable, apart from StopException, that is thrown will lead to a Failure being 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);
        counters[counterIndex].incrementAndGet();
    }
}

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

The countersLength value can be configured as shown below:

  test:
  - class: example.MyTest
    countersLength: 1000

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:

  test:
    - class: example.SomeTest
      config.nestedConfig.valu: 1000

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.

ThreadState

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 variable 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) {
        counter.incrementAndGet();
        state.increments++;
    }

    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) {
        counter.incrementAndGet();
        state.increments++;
    }

    @TimeStep public void get(GetThreadState list) {
        counter.get();
    }

    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) {
        counter.incrementAndGet();
        state.increments++;
    }

    @TimeStep public void get() {
        counter.get();
    }

    public class ThreadState extends BaseThreadState {
        long increments;
    }
}

The reason for 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, but 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 which 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) {
        counter.incrementAndGet();
        state.increments++;
    }

    @AfterRun public void afterRun(ThreadState state) {
        System.out.println(Thread.currentThread().getName()+
                        " completed with "+state.increments+" increments");
    }

    public class ThreadState extends BaseThreadState {
        long increments;
    }
}

Verification

Once a Simulator test is completed, you can run verifications using the @Verify annotation. In the case of IAtomicLong.inc 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) {
        state.increments++;
        counter.incrementAndGet();
    }

    @AfterRun public void afterRun(ThreadState state) {
        expected.addAndGet(state.increments);
    }

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

TearDown

To automatically remove created resources, a tearDown method 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 annotation is demonstrated:

public class MyTest extends AbstractTest {
    private IAtomicLong counter;

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

    @TimeStep public void inc() {
        counter.inc();
    }

    @TearDown public void tearDown() {
        counter.destroy();
    }
}

By default the tearDown method 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() {
        counter.inc();
    }

    @TearDown(global=true) public void tearDown() {
        counter.destroy();
    }
}

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

Complete 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 from the tests.yaml file. Please see the following example:

- name: my_test_suite
  duration: 300s

In this example, all tests in this suite will run for five minutes each. In some cases you need more control over when to stop. Currently there are the following options available:

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

    test:
      - class: example.MyTest
        iterations: 1000000

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, the Simulator 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.

📚 As the nuclear option, you can use the inventory destroy command to destroy your environment if you've got a rogue test that won't stop running!

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 elimination.

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 \
          -XX:+DebugNonSafepoints"

If these JFR_ARGS are added to the client_args and member_args properties of the tests.yaml, then both client and members will be 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:

Java 8:
-Xloggc:gc.log -XX:+PrintGC -XX:+PrintGCDetails -XX:+PrintGCTimeStamps  -XX:+PrintGCDateStamps

Java 9+:
-Xlog:gc:file=gc.log:utctime,pid,tags:filecount=32,filesize=64m

Reducing Fluctuations

For more stable performance numbers, set the minimum and maximum heap size to the same value, i.e. -Xms2G -Xmx2G

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

-Dhazelcast.initial.min.cluster.size=4

This prevents the 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.

members_args: "-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" \

client_args: "-Dhazelcast.diagnostics.enabled=true \
                               -Dhazelcast.diagnostics.metric.level=info" \

If these flags are added to the client_args and member_args respectively, both client and server will 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.

Logging

In some cases, especially when debugging, logging is required. One easy way to add logging is to add 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:

  test:
    - class: example.MyTest
      logFrequency: 10000

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

You can configure time rate based logging as shown below:

  test:
    - class: example.MyTest
      logRateMs: 100

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, the tests.yaml needs to be setup similarly to this:

- name: parallel_test
  repetitions: 1
  duration: 300s
  clients: 1
  members: 1
  loadgenerator_hosts: loadgenerators
  node_hosts: nodes
  driver: hazelcast5
  version: maven=5.1
  client_args: >
    -Xms3g
    -Xmx3g
  member_args: >
    -Xms3g
    -Xmx3g
  parallel: True
  test:
    - class: com.hazelcast.simulator.tests.map.MapCasTest
      threadCount: 3
      keyCount: 1000
    - class: com.hazelcast.simulator.tests.map.MapLockTest
      name: MapLock_1k_keys
      threadCount: 3
      keyCount: 1000
    - class: com.hazelcast.simulator.tests.map.MapLockTest
      name: MapLock_5k_keys
      threadCount: 3
      keyCount: 5000

The key aspects of this configuration that allows running multiple tests in parallel are:

• parallel: True needs to be set in the test suite properties - otherwise all tests are run serially.
• Multiple test entries need to be defined; you can't run 1 test in parallel!
• test entries that share the same test class should have unique name properties defined per test - otherwise only 1 of the tests will be run (as the class is used for the test name if not defined explicitly).

Controlling the load generation

Besides the cluster layout you can also control the number of Workers which 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.

perftest exec --targetCount 2

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

Various forms of testing

performance_monitor_interval_seconds: 1

Throughput testing

A throughput test is a test where the throughput is measured at some level of concurrency.

For example:

test:
  - class: com.hazelcast.simulator.tests.map.IntByteMapTest
    name: MyByteTest
    threadCount: 40
    getProb: 1
    putProb: 0
    keyCount: 1_000_000

Each load generator (e.g. client) will run with 40 threads and call the IMap.get method.

This is the most common form of throughput testing but isn't ideal. The ideal form of throughput testing would be to determine the maximum possible throughput of a system by increasing the load.

Latency testing

Simulator can also be used to run a latency test. It will determine the latency for a fixed throughput:

test:
  - class: com.hazelcast.simulator.tests.map.IntByteMapTest
    name: MyByteTest
    threadCount: 40
    getProb: 1
    putProb: 0
    keyCount: 1_000_000
    ratePerSecond: 100

With 1 client, there would be 100 requests per second. With 2 clients, there would be 200 requests per second. Simulator will handle coordinated omission correctly.

Stress testing

With a stress test the load is increased until the system collapses. This can be done using the rampupSeconds. This is demonstrated in the following example:

test:
  - class: com.hazelcast.simulator.tests.map.IntByteMapTest
    name: MyByteTest
    threadCount: 400
    getProb: 1
    putProb: 0
    keyCount: 1_000_000
    ratePerSecond: 100
    rampupSeconds: 400

The rampupSeconds is configured as 400; which means that every second 1 thread is going to start. This will happen at every load generator (e.g. client).

Soak testing

A soak test determines how the system behaves over a long period of time, for example to check if there any memory leaks. The only thing that needs to be done for this is to configure the test duration with a high value e.g. 12h. Example:

      - name: read_only
        repetitions: 1
        duration: 24h
        clients: 1
        members: 1
        loadgenerator_hosts: loadgenerators
        node_hosts: nodes
        driver: hazelcast5
        version: maven=5.1
        test:
          - class: com.hazelcast.simulator.tests.map.IntByteMapTest
            name: MyByteTest
            threadCount: 40
            getProb: 1
            putProb: 0
            keyCount: 1_000_000

Volume testing

A volume test determines if a system can handle large volumes of data. The simplest approach would be to set the keyCount to a large value (it depends on the test of this property is available):

      test:
        - class: com.hazelcast.simulator.tests.map.LongByteArrayMapTest
          name: LongByteArrayMapTest
          threadCount: 400
          getProb: 1
          putProb: 0
          keyDomain: 100_000_000
          ratePerSecond: 100
          minValueLength: 1_000
          maxValueLength: 1_000

This will load 100M x 1KB = 100 GB of data into the cluster.

Scalability testing

A scalability test determines how well a system scales when nodes are added. The simplest way to do a scalability test is to pick some performance test like a throughput test and run it. E.g.

      - name: read_only
        repetitions: 1
        duration: 300s
        clients: 1
        members: 1
        loadgenerator_hosts: loadgenerators
        node_hosts: nodes
        driver: hazelcast5
        version: maven=5.1
        test:
          - class: com.hazelcast.simulator.tests.map.IntByteMapTest
            name: MyByteTest
            threadCount: 40
            getProb: 1
            putProb: 0
            keyCount: 1_000_000

After completion, increase members and run it again. Make sure that sufficient node machines are available.

Network constraints

When testing the throughput, results are constrained by factors including CPU, memory, network, and/or a combination of those. Therefore, it's crucial to know these constraints and analyse the test results in their context.

Cloud providers specify the availability of CPU and memory for different instance types, howeveer they are much less verbose on network-related limits.

There are two main limitations in play related to network resources: bandwidth (bits/s) and packet count (packets/s). Hazelcast-simulator contains a tool that allows measuring the limits of bandwidth and packet count, based on iperf3.

In order to use the tool, you first need to install the simulator and iperf3 on the machines.

inventory install simulator
inventory install iperf3

Finally, you can run the pps benchmark:

iperf3test pps <server> <client>

where <server> and <client> are public IP addresses of the instances between which you want to measure max PPS.

The test generates high-PPS traffic from server to client and from client to server. The actual PPS stats are recorded on the server side and reported in the terminal output.

When running the tool on AWS instances, the output also includes the information about the number of pps_allowance_exceeded events recorded every second. High number of pps_allowance_exceeded events strongly suggests that the test has in fact run into the pps limit.

In case the two instances have a different PPS limit, the PPS of the connection between them is generally constrained by the smaller one. If you're running the test with the instance with bigger limit as the server, the actual PPS might be limited by the client-side limits. In such case, the test might run into the PPS limit of the connection, but on the server side pps_allowance_exceeded might show 0 events/s.

For any pair of instances A and B, it is advised to run the PPS test for both A and B as the server. This ensure a clear picture of all the PPS limits across instances.

---

Injecting latencies

It's often useful to simulate the effects of network latency between nodes and clients, especially in scenarios where you want to test performance across multiple data centers (DCs) or regions.

Simulator provides a convenience command to inject network latencies between groups of clients and nodes using the inventory inject_latencies command. This allows you to simulate different network conditions in a simple setup.

There are two methods to inject latencies:

1. Basic global assignment: Assign a single latency value that will be applied bi-directionally between groups.
2. Advanced group assignment: Assign different latency values between different groups.

For both methods you must have created your machines using the inventory apply command and edit the generated inventory.yamlin your project root to assign each host to a group.

loadgenerators:
  hosts:
    3.121.207.133:
      ansible_ssh_private_key_file: key
      ansible_user: ec2-user
      private_ip: 10.0.55.38
      group: group1

nodes:
  hosts:
    3.122.199.101:
      ansible_ssh_private_key_file: key
      ansible_user: ec2-user
      private_ip: 10.0.44.25
      group: group2

In this example, we have two hosts, each belonging to a different group (group1 and group2). Latencies will only be applied between different groups. If a host is not assigned a group, no direction of latency will be applied to that host.

Note that the latency is applied to the outbound traffic from a source group to the target group.

---

Method 1: Basic Global Assignment

Once your hosts are grouped, you can inject latencies by running the following command:

inventory inject_latencies --latency 10 --interface eth0 --rtt

This command will:

Apply a round-trip time (RTT) latency of 10ms between hosts in different groups (e.g., between group1 and group2). No latency will be applied between hosts in the same group (e.g., clients or members within group1 or group2 will communicate without added delay).

Explanation of Flags:

--latency: The desired latency in milliseconds (in this case, 10ms).

--interface: The network interface where the latency should be applied (default is eth0).

--rtt: When set, this flag indicates the latency is a round-trip time (RTT), meaning 10ms latency will be split into 5ms each way. Otherwise, 10ms will be applied in each direction.

---

Method 2: Advanced Group Assignment

If you want to assign different latencies between different groups, you can do so by supplying a latency profile in a YAML file.

Create a YAML file with the following format:

latency_profiles:
  relationships:
    - source: group1
      target: group2
      latency: 5
    - source: group2
      target: group1
      latency: 2

In this example, we have two groups (group1 and group2) with different latencies between them. Communication from group1 to group2 will have a 5ms latency, while communication from group2 to group1 will have a 2ms latency.

source: The group of nodes where the latency will be applied to outgoing traffic. target: The group of nodes to which the outgoing traffic from the source group will experience a delay.

To apply these latencies, run the following command:

inventory inject_latencies --interface eth0 --profiles latency_profile.yaml     

Explanation of Flags:

--interface: The network interface where the latency should be applied (default is eth0).

--profiles: The path to the YAML file containing the latency profiles.

Note that if the latency profile file is not provided, the command will default to the basic global assignment method.

Removing Latencies

To remove the injected latencies, run the following command:

inventory clear_latencies

---

CP subsystem leader priority

It is possible to assign leadership priority to a member or list of members in a CP group(s). This is useful when you want to attribute certain behaviours to an agent in the cluster. For example, you may wish to inject a latency on the leader of a CP group. Ensure the internal IP of the agent(s) are used.

Here is an example of usage in the tests.yaml file:

- name: my-test
  <<: *defaults
  cp_priorities:
    - address: 10.0.55.178
      priority: 1
    - address: 10.0.55.179
      priority: 2
  test:
    - class: com.hazelcast.simulator.tests.cp.IAtomicLongTest
      threadCount: 135
      getProb: .8

Consult Configuring Leadership Priority for more information about the CP subsystem priority.

Persistence

For Hot Restart persistence and CP Subsystem persistence, selecting on device storage for your machine will provide better performance. NAS solutions like AWS EBS can significantly degrade system performance due to their higher latency.

Select instances with local storage, preferably NVMe SSDs, to ensure low-latency access and optimal performance.

Formatting and mounting the storage device can be time-consuming. The simulator automates this process and also handles the clearing of persisted data. To enable automatic formatting and mounting, set the mount_volume and mount_path parameters in your tests.yaml file:

    mount_volume: /dev/nvme1n1
    mount_path: /dir/to/persistence

Parameters:

mount_volume: Specifies the device to mount (e.g., /dev/nvme1n1).

mount_path: Defines the directory where the device will be mounted (e.g., /dir/to/persistence).

Get Help

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

• Hazelcast mailing list
• Slack for chatting with the development team and other Hazelcast users.
• Stack Overflow