Installing OpenSearch Benchmark

You can install OpenSearch Benchmark directly on a host running Linux or macOS, or you can run OpenSearch Benchmark in a Docker container on any compatible host. This page provides general considerations for your OpenSearch Benchmark host as well as instructions for installing OpenSearch Benchmark.

Choosing appropriate hardware

OpenSearch Benchmark can be used to provision OpenSearch nodes for testing. If you intend to use OpenSearch Benchmark to provision nodes in your environment, then install OpenSearch Benchmark directly on each host in the cluster. Additionally, you must configure each host in the cluster for OpenSearch. See Installing OpenSearch for guidance on important host settings.

Remember that OpenSearch Benchmark cannot be used to provision OpenSearch nodes when you run OpenSearch Benchmark in a Docker container. If you want to use OpenSearch Benchmark to provision nodes, or if you want to distribute the benchmark workload with the OpenSearch Benchmark daemon, then you must install OpenSearch Benchmark directly on each host using Python and pip.

When you select a host, you should also think about which workloads you want to run. To see a list of default benchmark workloads, visit the opensearch-benchmark-workloads repository on GitHub. As a general rule, make sure that the OpenSearch Benchmark host has enough free storage space to store the compressed data and the fully decompressed data corpus once OpenSearch Benchmark is installed.

If you want to benchmark with a default workload, then use the following table to determine the approximate minimum amount of required free space needed by adding the compressed size with the uncompressed size.

Your OpenSearch Benchmark host should use solid-state drives (SSDs) for storage because they perform read and write operations significantly faster than traditional spinning-disk hard drives. Spinning-disk hard drives can introduce performance bottlenecks, which can make benchmark results unreliable and inconsistent.

Installing on Linux and macOS

If you want to run OpenSearch Benchmark in a Docker container, see Installing with Docker. The OpenSearch Benchmark Docker image includes all of the required software, so there are no additional steps required.

To install OpenSearch Benchmark directly on a UNIX host, such as Linux or macOS, make sure you have Python 3.8 or later installed.

If you need help installing Python, refer to the official Python Setup and Usage documentation.

Checking software dependencies

Before you begin installing OpenSearch Benchmark, check the following software dependencies.

Use pyenv to manage multiple versions of Python on your host. This is especially useful if your “system” version of Python is earlier than version 3.8.

• Check that Python 3.8 or later is installed:

  python3 --version
  

• Check that pip is installed and functional:

  pip --version
  

• Optional: Check that your installed version of git is Git 1.9 or later using the following command. git is not required for OpenSearch Benchmark installation, but it is required in order to fetch benchmark workload resources from a repository when you want to perform tests. See the official Git Documentation for help installing Git.

  git --version
  

Completing the installation

After the required software is installed, you can install OpenSearch Benchmark using the following command:

pip install opensearch-benchmark

After the installation completes, you can use the following command to display help information:

opensearch-benchmark -h

Now that OpenSearch Benchmark is installed on your host, you can learn about Configuring OpenSearch Benchmark.

Installing with Docker

You can find the official Docker images for OpenSearch Benchmark on Docker Hub or on the Amazon ECR Public Gallery.

Docker limitations

Some OpenSearch Benchmark functionality is unavailable when you run OpenSearch Benchmark in a Docker container. Specifically, the following restrictions apply:

• OpenSearch Benchmark cannot distribute load from multiple hosts, such as load worker coordinator hosts.
• OpenSearch Benchmark cannot provision OpenSearch nodes and can only run tests on previously existing clusters. You can only invoke OpenSearch Benchmark commands using the benchmark-only pipeline.

Pulling the Docker images

To pull the image from Docker Hub, run the following command:

docker pull opensearchproject/opensearch-benchmark:latest

To pull the image from Amazon Elastic Container Registry (Amazon ECR):

docker pull public.ecr.aws/opensearchproject/opensearch-benchmark:latest

Running Benchmark with Docker

To run OpenSearch Benchmark, use docker run to launch a container. OpenSearch Benchmark subcommands are passed as arguments when you start the container. OpenSearch Benchmark then processes the command and stops the container after the requested operation completes.

For example, the following command prints the help text for OpenSearch Benchmark to the command line and then stops the container:

docker run opensearchproject/opensearch-benchmark -h

Establishing volume persistence in a Docker container

To make sure your benchmark data and logs persist after your Docker container stops, specify a Docker volume to mount to the image when you work with OpenSearch Benchmark.

Use the -v option to specify a local directory to mount and a directory in the container where the volume is attached.

The following example command creates a volume in a user’s home directory, mounts the volume to the OpenSearch Benchmark container at /opensearch-benchmark/.benchmark, and then runs a test benchmark using the geonames workload. Some client options are also specified:

run -v $HOME/benchmarks:/opensearch-benchmark/.benchmark opensearchproject/opensearch-benchmark execute_test --target-hosts https://198.51.100.25:9200 --pipeline benchmark-only --workload geonames --client-options basic_auth_user:admin,basic_auth_password:admin,verify_certs:false --test-mode

See Configuring OpenSearch Benchmark to learn more about the files and subdirectories located in /opensearch-benchmark/.benchmark.

Next steps

• Configuring OpenSearch Benchmark
• Creating custom workloads