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A continuous test runner for gathering SLA data

Project description

# SLA Monitor Worker

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.

TODO: Unit tests not working

## Installing

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.

## Terraform

Excute the following in the root folder to run terraform. Obviously, have Terraform installed. Set the bucket and table variables to existing backend resources for remote state.


# pip install iam-starter
cd terraform && \
export AWS_ENV="dev" && \
export AWS_BACKEND_REGION="us-east-1" && \
export AWS_DEFAULT_REGION="us-east-1" && \
export AWS_REMOTE_BUCKET="billtrust-tfstate-$AWS_ENV" && \
export AWS_REMOTE_TABLE="tfstate_$AWS_ENV" && \
iam-starter \
--profile $AWS_ENV \
--command \
"terraform init \
-backend-config=\"region=$AWS_BACKEND_REGION\" \
-backend-config=\"bucket=$AWS_REMOTE_BUCKET\" \
-backend-config=\"dynamodb_table=$AWS_REMOTE_TABLE\" && \
terraform apply \
--auto-approve \
-var \"aws_env=$AWS_ENV\" \
-var \"aws_region=$AWS_DEFAULT_REGION\""

## Using

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_SNSTOPICNAME="sla-monitor-result-published-$AWS_ENV" \
-e SLARUNNER_S3BUCKETNAME="sla-monitoring-logs-$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

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'"

## Variables

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

#### command


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

#### service


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.

#### groups


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.

#### delay


How long to wait between commands being run in seconds.

#### disabled


To disable sla-runner at startup.

#### sns-topic-arn


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": "1574515200",
"testExecutionSecs": "914"

#### s3-bucket-name


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"

#### dry-run


If there is any value at all in this variable, the test will run once, output the sns topic it would publish to, the result message, the log output of the command, and the name of the object that would be written to the bucket. It will NOT publish to sns or write the object to s3. Only for testing purposes.

## 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 \
--region us-east-1 \
--host-source-path . \
--container-source-path /src \

## 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 -t sla-runner . && \
docker run --rm -it --entrypoint make sla-runner publish

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

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