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Docker image for the Locust.io open source load testing tool

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zalando-incubator/docker-locust

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Docker-Locust

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The purpose of this project is to provide a ready and easy-to-use version of locust.io which also contains additional/useful features that are required.

Architecture

Docker-Locust container can be started in 4 different roles:

  • master: Runs Locust's web interface where you start and stop the load test and see live statistics.
  • slave: Simulates users and attacks the target url based on user parameters.
  • controller: Orchestrates Master in automatic mode and downloads reports when the test is over.
  • standalone: Automatically starts the above components locally.

There are 2 supported run types:

  • Manual: when a user manually starts and stops a test via a Locust Master UI.
  • Automatic: when a test is started by the Controller and runs for a specified time interval.

And there are 2 ways to deploy it:

  • Local deployment (using standalone mode or docker-compose): when a singe machine can generate enough traffic.
  • Distributed deployment: when multiple machines are required to generate a bigger load. This type of deployment might be used in AWS or Kubernetes. An example deployment with different container roles can be found in docker-compose. Using Automatic mode together with Distributed deployment requires the TOTAL_SLAVES variable to be set on the controller side.

Key advantages

  1. It allows locust to read load test scenario/script from different resources (any HTTP/HTTPS URL, S3 bucket, and local machine).
  2. It has the ability to be run in any CI tool e.g. Jenkins (It can start/stop load test automatically) and provides an HTML report at the end of a load test.
  3. It is also possible to be deployed in AWS to create bigger load.

Requirements

  1. docker engine version 1.9.1+
  2. docker-compose version 1.6.0+ (optional)

Getting Started

Single machine / Standalone mode


docker-locust will be run as standalone version by default. Standalone version is for users who has only 1 single machine.

bash <(curl -s https://raw.githubusercontent.com/zalando-incubator/docker-locust/master/local.sh) deploy

You will be prompted for certains inputs required (You can use our example in github as load test script).

Target url: https://targeturl.com
Where load test script is stored: https://raw.githubusercontent.com/zalando-incubator/docker-locust/master/example/simple.py
Number of slave(s): 4
Run type [automatic/manual]: manual

All of it can be simplify in one line:

bash <(curl -s https://raw.githubusercontent.com/zalando-incubator/docker-locust/master/local.sh) deploy --target=https://targeturl.com --locust-file=https://raw.githubusercontent.com/zalando-incubator/docker-locust/master/example/simple.py --slaves=4 --mode=manual

It is also possible to run with normal docker command:

docker run -i --rm -v $PWD/reports:/opt/reports -v ~/.aws:/root/.aws -v $PWD/:/opt/script -v $PWD/credentials:/meta/credentials -p 8089:8089 -e ROLE=standalone -e TARGET_HOST=https://targeturl.com -e LOCUST_FILE=https://raw.githubusercontent.com/zalando-incubator/docker-locust/master/example/simple.py -e SLAVE_MUL=4 -e AUTOMATIC=False registry.opensource.zalan.do/tip/docker-locust

Multiple machines


docker-locust can be run in multiple docker-containers. It is useful for users who has more than one machine to create bigger load. In this point we are using docker-compose, but it is also possible to run it in different ways, e.g. Cloudformation in AWS.

Run the application with the command:

DOCKER_COMPOSE=true bash <(curl -s https://raw.githubusercontent.com/zalando-incubator/docker-locust/master/local.sh) deploy

Read multiple files

docker-locust has the ability to read multiple files from s3 or any http/https, e.g. 1 file is the load test file / python file and 1 other file is json file where payloads are stored. Sample command:

bash <(curl -s https://raw.githubusercontent.com/zalando-incubator/docker-locust/master/local.sh) deploy --target=https://targeturl.com --locust-file=https://raw.githubusercontent.com/zalando-incubator/docker-locust/master/example/simple_post.py,https://raw.githubusercontent.com/zalando-incubator/docker-locust/master/example/payloads.json --slaves=4 --mode=manual

Report Generation

Please add following lines to your load test script, like this example.

from locust.web import app
from src import report
app.add_url_rule('/htmlreport', 'htmlreport', report.download_report)

Simply after load test run, append "/htmlreport" to the URL which will download the report of the recent run. Example:

Setup in jenkins

docker-locust can be run automatically by using CI tool like jenkins.

Sample case:

  • Number of users [total users that will be simulated]: 100
  • Hatch rate [number of user will be added per second]: 5
  • Duration [in seconds]: 30
  • Target url: https://targeturl.com
  • Load test script: simple.py
  • Number of slaves: 4

Steps:

  1. Put following command in "Execute shell" field:

    curl -O https://raw.githubusercontent.com/zalando-incubator/docker-locust/master/local.sh && DOCKER_COMPOSE=true bash local.sh deploy --target=https://targeturl.com --locust-file=https://raw.githubusercontent.com/zalando-incubator/docker-locust/master/example/simple.py --slaves=4 --mode=automatic --users=100 --hatch-rate=5 --duration=30
  2. Install html-publisher-plugin in jenkins to display load test result. Example configuration in jenkins job:

Troubleshooting

All output from containers can be see by running:

docker-compose logs -f

About the project versioning

A version number is combination between the locust version being supported and patch level, e.g. when a release is 0.7.3-p0, it support locust 0.7.3.

Capacity of docker-locust in AWS

All the data based on load testing against simple hello-world application with the default max_wait and min_wait values (1000ms).

No. Group Type EC2 vCPU RAM (GiB) Clock Speed (GHz) Enhanced Networking Max total RPS that can be created per 1 slave machine (rough number) Price per hour (EU - Frankfurt) RPS per cent
1 General Purpose t2.nano 1 0.5 up to 3.3 - 200 $0.0068 294.12
2 General Purpose t2.micro 1 1 up to 3.3 - 500 $0.014 357.14
3 General Purpose t2.small 1 2 up to 3.3 - 500 $0.027 185.19
4 General Purpose t2.medium 2 4 up to 3.3 - 1100 $0.054 203.7
5 General Purpose t2.large 2 8 up to 3.0 - 1100 $0.108 101.85
6 General Purpose t2.xlarge 4 16 up to 3.0 - 2200 $0.216 101.85
7 General Purpose t2.2xlarge 8 32 up to 3.0 - 4700 $0.432 108.8
8 General Purpose m4.large 2 8 2.4 Yes 700 $0.12 58.33
9 General Purpose m4.xlarge 4 16 2.4 Yes 1500 $0.24 62.5
10 General Purpose m4.2xlarge 8 32 2.4 Yes 2500 $0.48 52.08
11 General Purpose m4.4xlarge 16 64 2.4 Yes 6500 $0.96 67.71
12 General Purpose m4.10xlarge 40 160 2.4 Yes 10000 $2.4 41.67
13 General Purpose m4.16xlarge 64 256 2.3 Yes 17000 $3.84 44.27
14 General Purpose m3.medium 1 3.75 2.5 - 250 $0.079 31.65
15 General Purpose m3.large 2 7.5 2.5 - 600 $0.158 37.97
16 General Purpose m3.xlarge 4 15 2.5 - 1300 $0.315 41.27
17 General Purpose m3.2xlarge 8 30 2.5 - 2600 $0.632 41.14
18 Compute Optimized c4.large 2 3.75 2.9 Yes 800 $0.114 70.18
19 Compute Optimized c4.xlarge 4 7.5 2.9 Yes 1600 $0.227 70.48
20 Compute Optimized c4.2xlarge 8 15 2.9 Yes 2500 $0.454 55.07
21 Compute Optimized c4.4xlarge 16 30 2.9 Yes 6500 $0.909 71.51
22 Compute Optimized c4.8xlarge 36 60 2.9 Yes 12500 $1.817 68.79
23 Compute Optimized c3.large 2 3.75 2.8 Yes 650 $0.129 50.39
24 Compute Optimized c3.xlarge 4 7.5 2.8 Yes 1300 $0.258 50.39
25 Compute Optimized c3.2xlarge 8 15 2.8 Yes 2500 $0.516 48.45
26 Compute Optimized c3.4xlarge 16 30 2.8 Yes 5500 $1.032 53.29
27 Compute Optimized c3.8xlarge 32 60 2.8 Yes 9000 $2.064 43.6

NOTE:

  1. Please check AWS-EC2-Type and AWS-EC2-pricing before use data above, because some of them could be changed time to time.
  2. Number of total RPS could be different because of n reasons.

Contributions

Any feedback or contributions are welcome! Please check our guidelines.

Unit tests

Run the unit tests with this command:

local.sh test

License

See License

Security

See Security