Docker-Locust

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 consist of 3 different roles:

• Master: Instance that will run Locust's web interface where you start and stop the load test and see live statistics.
• Slave: Instance that will simulate users and attack the target url based on user parameters.
• Controller: Instance that will be run for automatic mode and will download the HTML report at the end of load test.

This architecture support following type of deployment:

• single container (standalone mode): If user have only one single machine.
• multiple containers (normal mode): If user have more than one machine and want to create bigger load. This type of deployment might be used in docker-swarm or kubernetes case. An example for deployment in different containers can be seen in docker-compose.

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

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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