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README.md

Running Stress tests on Google Container Engine

Glossary:

Setup Instructions

On GCP:

  1. Login to GCP with your Google account (for example, your @gmail account) at https://cloud.google.com. If do not have a Google account, you will have to create an account first.
  2. Enable billing on Google cloud platform. Instructions here (see the 'Enable billing' section).
  3. Create a Project from the GCP console.i.e Click on the project dropdown box on the top right (to the right of the search box) and click 'Create a project' option.
  4. Enable the Container Engine API. Instructions here (See the 'Enable the Container Engine API’ section). Alternatively, you can do the following:
    • Click on the 'Products & Services' icon on the top left (i.e the icon with three small horizontal bars) and select 'API Manager'
    • Select the 'Container Engine API' under 'Google Cloud APIs' on the main page. Note that you might have to click on 'More' under 'Google Cloud APIs' to see the 'Container Engine API' link
    • Click on the 'Enable' button. If the API is already enabled, the button's label would be 'Disable' instead (do NOT click the button if its label is 'Disable')
  5. Create a Cluster from the GCP console.
    • Go to the Container Engine section from GCP console i.e: Click on the 'Products & Services' icon on the top left (i.e the icon with three small horizontal bars) and click on 'Container Engine'
    • Click 'Create Container Cluster' and follow the instructions.
    • The instructions for 'Name/Zone/MachineType' etc are here (NOTE: The page also has instructions to setting up default clusters and configuring kubectl. We will be doing that later)
    • For the cluster size, a smaller size of < 10 GCE instances is good enough for our use cases - assuming that we are planning to run a reasonably small number of stress client instances. For the machine type, something like '2 vCPUs 7.5 GB' (available in the drop down box) should be good enough.
    • IMPORTANT: Before hitting the 'Create' button, click on 'More' link just above the 'Create' button and Select 'Enabled' for BigQuery , 'Enabled' for Cloud Platform and 'Read/Write' for Cloud User Accounts.
    • Create the cluster by clicking 'Create' button.

On your machine (or the machine from which stress tests on GKE are launched):

  1. You need a working gRPC repository on your machine. If you do not have it, clone the grpc repository from github (https://github.com/grpc/grpc) and follow the instructions here
  2. Install Docker. Instructions here
  3. Install Google Cloud SDK. Instructions here. This installs the gcloud tool
  4. Install kubectl, Kubernetes command line tool using gcloud. i.e
    • $ gcloud components update kubectl
  5. Install Google python client apis:
    • ‘$ sudo pip install --upgrade google-api-python-client’
    • Note: Do $ sudo apt-get install python-pip (or $ easy_install -U pip) if you do not have pip
  6. Install the requests Python package if you don’t have it already by doing sudo pip install requests. More details regarding requests package are here
  7. Set the gcloud defaults: See the instructions here under "Set gcloud defaults" section)
    • Make sure you also fetch the cluster credentials for kubectl command to use. I.e $ gcloud container clusters get-credentials CLUSTER_NAME

Launching Stress tests

The stress tests are launched by the following script (path is relative to GRPC root directory) : tools/run_tests/stress_test/run_stress_tests_on_gke.py

The script has several parameters and you can find out more details by using the --help flag.

  • <grpc_root_dir>$ tools/run_tests/stress_test/run_stress_tests_on_gke.py --help

Example $ tools/run_tests/stress_test/run_stress_tests_on_gke.py --project_id=sree-gce --test_duration_secs=180 --num_clients=5

Launches the 5 instances of stress test clients, 1 instance of stress test server and runs the test for 180 seconds. The test would be run on the default container cluster (that you have set in gcloud) in the project sree-gce.

Note: we currently do not have the ability to launch multiple instances of the server. This can be added very easily in future