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A continuous test runner for gathering SLA data

Project description

This is the test runner portion of the SLA monitor/reporter. It performs tests (or any command you want) repeatedly, and publishes success/failure to an SNS topic for external processing (for example, using lambda to write to a custom cloudwatch metric), as well as optionally uploading logs to an S3 bucket.


To install simply install via pip

pip install --user sla-runner

Highly recommended is iam-docker run:

pip install --user iam-docker-run

This project assumes you are using role based authentication, as would be used in a production environment in AWS. This mimics that environment by running with an actual role.



# pip install iam-starter
cd terraform && \
export AWS_ENV="dev" && \
export AWS_DEFAULT_REGION="us-east-1" && \
iam-starter \
    --profile $AWS_ENV \
    --command "terraform init && terraform apply \
        --auto-approve \
        -var \"aws_env=$AWS_ENV\" \
        -var \"aws_region=$AWS_DEFAULT_REGION\""


Use iam-docker-run outside of AWS to run tests. In real life scenarios on ECS, instead install sla-runner via pypi in your service container, and set --image to the image of the service container which contains your test.

docker build -t sla-monitor/sla-runner:latest .

export AWS_ENV="dev"
iam-docker-run \
    -e "SLARUNNER_COMMAND='/bin/bash /src/test-scripts/'" \
    -e "SLARUNNER_SERVICE=example-service" \
    -e "SLARUNNER_GROUPS='dev-team,critical'" \
    -e "SLARUNNER_DELAY=30" \
    -e "SLARUNNER_SNSTOPICARN='arn:aws:sns:us-east-1::sla-monitoring-worker-$AWS_ENV'" \
    -e "SLARUNNER_S3BUCKETNAME='sla-monitoring-logs-$AWS_ENV'" \
    --full-entrypoint "sla-runner" \
    --region us-east-1 \
    --profile $AWS_ENV \
    --role sla-monitor-runner-role-$AWS_ENV \
    --image sla-monitor/sla-runner:latest

iam-docker-run loads a custom env file (iam-docker-run.env) to prevent long commands like that. In ECS, add these as environment variables in the task definition or load from ssm via ssm-starter:

--full-entrypoint "ssm-starter --ssm-name slarunner --command 'sla-runner'"


The runner takes the following values which are provided by environment variable.



Command to be run repeatedly. Pretty straightforward. If there is an interrupt, the runner will attempt to finish the command gracefully before exit.



Name of the component service you’re testing. This will be used as the prefix for s3 uploads, and will be passed in the JSON event as “service” to SNS.



Name of the grouping of components you’re testing, in csv format. This will be passed in the JSON event as “groups” to SNS as a list, and is meant to provide secondary statistics if multiple services are part of the same component.



How long to wait between commands being run in seconds.



SNS topic arn to publish results to. It will be published as a JSON object. For example, the command above would produce the following:

    "service": "example-service",
    "group": ["dev-team", "critical"],
    "succeeded": true,
    "timestamp": "1574514000",
    "testExecutionSecs": "914" #seconds



Bucket to write logs to. This is an optional parameter. The object will be named as the timestamp followed by the result for easily searching by result, and will be prefixed by the service name. For example “example-service/1574514000_SUCCESS”

Development and Testing

docker build -t sla-runner:latest -f Dockerfile .
iam-docker-run \
    --image sla-runner:latest \
    --role sla-monitor-runner-role \
    --profile dev \
    --host-source-path . \
    --container-source-path /src
    --full-entrypoint "/bin/bash"

You will need to run pip install -e . again once you’re in to live link the code.

Publishing Updates to PyPi

For the maintainer - to publish an updated version of ssm-search, increment the version number in and run the following:

docker build -f ./Dockerfile.buildenv -t sla-runner:build . docker run –rm -it –entrypoint make sla-runner:build publish

At the prompts, enter the username and password to the repo.

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sla-runner-0.0.1.tar.gz (5.5 kB view hashes)

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