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Locust Load Testing

This repository is for setting up load testing environment on GKE with terraform.

diagram

Pre Request

  • gcloud >= Google Cloud SDK 349.0.0
  • kubernetes-cli >= 1.22.1
  • terraform >= 1.0.5
  • python >= 3.9 (To generate diagram)

How to Set Up on GKE

Configure Makefile

Copy Makefile.example and fill out attributes below:

value description
PROJECT_ID GCP Project ID
CLUSTER_NAME Cluster base name. Due to the cluster deletion takes time, this tool add a random texts at the end of the base cluster name
REGION GCP Region name
ZONE GCP Zone name
MACHINE_TYPE Machine type of loading machines. Please see machine types for more details
CREDENTIALS The full path to the Service Account JSON file.
SERVICE_ACCOUNT_EMAIL Service Account Email. Eg. [User name]@[Project name].iam.gserviceaccount.com
TARGET_HOST Target host URL

Set Up Google Kubernetes Cluster (GKE)

  1. Navigate to deploy folder.

    make init_all

    to set up terraform

  2. Run

    make build

    to set up a GKE cluster and initialize and gcloud command pointing to the created GKE cluster.

  3. Run

    make a_locust

    to set up locust and required config maps (storing load test scripts) for performance testing.

  4. Run

    make locust

    This will do port forwarding to the local. Then you can access to Locust Master with localhost:8089.

  5. Stop make locust and Run

    make refresh

    This will refresh the Locust Cluster with updated main.py script file and values.yaml content. Once the Locust Cluster up and running, connect the master with make locust

Tear Down GKE Cluster

Run

make d_all

Update Code for Load Testing

At each load testing scripts update, workers need to be redeployed to read the latest config maps where testing scripts are stored according to the Kubernetes specification. This way allows you to update with one command.

  1. If you are already connecting the load cluster with make locust, Ctrl+C to stop it.
  2. All code is stored under locust directory. main.py is the main logic, and libraries are under the lib directory.
  3. Once code is updated, run 
    make refresh
    to reload ConfigMap and Locust clusters to read the updated config map.
  4. Run make locust again to connect the load cluster.

How to Adjust Balance of Workers and Users

To generate the load at a lower cost, you may want to use as few workers as possible. This is a sample step on how to adjust the number of users and workers appropriately.

In the case of generating 10000 RPS, here are the steps that I tried.

  1. Enable HPA, start from 10 workers with 2000 users, and see how much load the Locust cluster can generate. In this case, Locust generated 3000 RPS and saturated there. No CPU errors are observed in Cloud Logging, which implies CPU is still not pushed to the limit.
  2. Assuming 3 times more users would generate 10000 RPS. Change users to 6000 and run make refresh to restore ConfigMap and Locust pods.
  3. You observed workers automatically scaled to 15 and the load reached higher than 10000 RPS.
  4. Adjust the initial worker to 15 in the values.yaml and make refresh to update the Locust pods.

Reference Settings

In the case where you use spike_load.py to generate 10000RPS with the Locust Cluster on GKE, here is the reference configuration.

spike_load.py hatches users at once and hold requests until all users are spawned in each worker (not across all workers).

parameters description
Machine type of locust worker (MACHINE_TYPE in Makefile) e2-standard-2
Replicas for worker (line 66 of values.yaml) 15
User amount (line 15 of spike_load.ph, user_amount) 10000

With this settings,

  • The first second RPS is around 600
  • It'll reach 10000RPS in 15 to 20 seconds, and go higher. You may want to pace the access with constant_pacing function if you exactly target 10000RPS and dwell (stay) for a while.

In spike_load.py, the below line configure the dwell load time. This code means dwell 120 seconds with amount of user_amount users. Adust dwell time accordingly.

 targets_with_times = Step(user_amount, 120)

How to Run Locally

You may want to iterate try and error quickly while building a testing script. Loading the testing script every time on GKE is quite troublesome. For the development phase, you can leverage Docker to run a small cluster locally.

Spin up the small locust cluster, run

docker-compose up --build --scale worker=1

and you can access to the master from localhost:8089

Tips

Test Script Locally first and move on the production.

Locust stops with exceptions when syntax errors are included in the loading script. For a faster turnaround, you may want to make sure the script works correctly at the local first and move on to the production.

Help of Commands

Run make help

How to Access Locust Master Manually

  1. Go to GCP console > Services & Ingress
  2. Open locust-cluster, scroll down to Ports
  3. Click PORT FORWARDING button of master-p3, with port 8089 row
  4. A dialog will be popped up and displays the port forwarding code in there. Copy & Paste onto the terminal, and run.
  5. You can access the locust-cluster master pod with localhost:8080 from your browser.

How to Configure gcloud for The GKE Cluster by Default

This can be done just run make build, but also separately as below:

  1. Build cluster with

    make build_cluster

  2. Run

    make gcloud_init

    This command will configure your gcloud environment pointing to the newly created GKE cluster.

How to Generate Diagram

  1. Install Diagrams following this step.
  2. Go to docs directory and run python diagram.py

How to Enable Autoscaling

Autoscaling is depending on Kubernetes's Horizontal Pod Autoscaler(HPA). To enable HPA, Kubernetes manifest needs to include resource to sepecify the pod's resource allocation so that Kubernetes can manage the pods based on the CPU usage.