This service pre-generates reports based on the CloudWatch SLA Monitor custom metrics, and stores those reports on S3, so that they can be viewed in the webapp very quickly, as the rendering time is done in advance. This is in place of a REST API, since the data can all be generated in advance.
Each time SLA results are published from the SLA Runner, there are two Lambda function which suscribe to that SNS event, sla-monitor-store-results-lambda, and this report worker lambda, which reads the values previously written to CloudWatch by the store results lambda combined with the current value from the SNS event.
Because the S3 bucket which these results are stored in is private, it is not accessible over the Internet. You can use an S3 Proxy like the following to make it accessible via HTTP within a private subnet within your network.
docker build -f Dockerfile.s3proxy -t sla-monitor/sla-monitor-reports-s3proxy:latest .
iam-docker-run \
--image sla-monitor/sla-monitor-reports-s3proxy:latest \
-p 8080:80 \
-e AWS_S3_BUCKET=sla-monitor-reports-dev-us-east-1 \
-e CORS_ALLOW_ORIGIN="*" \
-e CORS_ALLOW_METHODS="GET" \
-e CORS_ALLOW_HEADERS="*" \
--role sla-monitor-reports-s3proxy-us-east-1 \
--profile devOnce the S3 reverse proxy web server is running, you can retrieve reports via:
http://localhost:8080/slamon/dev/services.json
http://localhost:8080/slamon/dev/myservicename/summary.json
http://localhost:8080/slamon/dev/myservicename/history/1d.json
Of course you will need to run the report lambda to generate and store this output on S3 to begin with, see the section below on invoking the report generator for local testing.
This service produces the following output.
s3://<bucketname>/slamon/<env>/<servicename>/summary.json
s3://sla-monitor-reports-dev-us-east-1/slamon/dev/myservice/summary.json
{
"serviceName": "myservice",
"env": "dev",
"currentStatus": "SUCCESS|FAILURE",
"currentStatusDurationMins": 1380,
"rangeSummaries": [
{
"timeRange": "1d",
"summary": {
"numAttempts": 100,
"numSuccesses": 99,
"successPercent": 99,
"failureMins": 13,
"numFailures": 2,
"numOutages": 1
}
},
],
"statusChanges": [
{
"status": "SUCCESS|FAILURE|UNKNOWN",
"fromTimestamp": "epoch",
"toTimestamp": "epoch",
"durationMins": 15
}
]
}NOTE: currentStatusDurationMins may have the value of -1 which indicates max visibility, that as far back as we have data, the duration has not changed.
s3://<bucketname>/slamon/<env>/<servicename>/history/<timerange>.json
s3://sla-monitor-reports-dev-us-east-1/slamon/dev/myservice/history/1d.json
{
"history": [
{
"timestamp": "1574533200",
"succeeded": true,
"testExecutionSecs": 34,
"resultS3Location": "s3://bucket/key"
},
{
"timestamp": "1574531101",
"succeeded": true,
"testExecutionSecs": 33,
"resultS3Location": "s3://bucket/key"
}
]
}s3://<bucketname>/slamon/<env>/services.json
s3://sla-monitor-reports-dev-us-east-1/slamon/dev/services.json
[
{
"service": "myservice"
},
{
"service": "anotherservice"
}
]Install dependencies for local building and deploying.
pip install iam-docker-runThere must be data written to CloudWatch via the SLA Runner project before this report service will have any data to test: https://github.com/billtrust/sla-monitor-runner
When invoking the test below, you'll need to specify the name of the service which contains the data you are testing by passing the TEST_SERVICE environment variable. This must match up to the service name you published the data under with the SLA Monitor Runner.
docker build -t sla-monitor/sla-monitor-report-sqsworker-lambda .
export AWS_ENV="dev"
iam-docker-run \
--image sla-monitor/sla-monitor-report-sqsworker-lambda \
--full-entrypoint '/bin/bash ./invokeLocal.sh' \
--container-source-path /app \
--host-source-path . \
--interactive \
-e AWS_ENV=$AWS_ENV \
-e TEST_SERVICE=myservicename \
--profile $AWS_ENVNote: This is dependent on the SNS Topic created in: https://github.com/billtrust/sla-monitor-store-results-lambda
docker build -t sla-monitor/sla-monitor-report-sqsworker-lambda .
export DEPLOY_BUCKET="company-deploy-bucket"
export AWS_ENV="dev"
export AWS_REGION="us-east-1"
iam-docker-run \
--image sla-monitor/sla-monitor-report-sqsworker-lambda \
--full-entrypoint "sls deploy --deployBucket $DEPLOY_BUCKET" \
--container-source-path /app \
--host-source-path . \
-e AWS_ENV=$AWS_ENV \
-e AWS_REGION=$AWS_REGION \
--profile $AWS_ENVThis will deploy the Docker container for the S3 Proxy web server to AWS ECS via Terraform. It assumes you have an existing ECS cluster and a VPC setup with private subnets, and that you already have a S3 bucket and DynamoDB table used for Terraform remote state. Alternatively, if you okay with having the data in these reports accessible on the Internet, you could have made the S3 bucket public.
Terraform will prompt you to enter several values for existing resources in your infrastructure. Rather than type these in, you will want to create a dev.tfvars file in your ./terraform/ directory with the values to each of the variables Terraform will prompt you for.
# pip install iam-starter
pushd terraform
# example: create a <env>.tfvars with your environment's input properties:
cat <<EOF > dev.tfvars
hostname = "api.slamon-dev.xxxxxxxxxx.com"
dnsname = "slamon-dev.xxxxxxxxxx.com"
vpc_id = "vpc-xxxxxxx"
aws_env = "dev"
alb_security_group_id = "sg-xxxxxxxxxxxx"
alb_subnet1 = "subnet-xxxxxxxx"
alb_subnet2 = "subnet-xxxxxxxx"
ecs_cluster = "xxxxxxxx"
alb_port = 80
EOF
# fill these exports in with your own values per environment
export PROFILE="dev"
export AWS_ENV="dev"
export AWS_DEFAULT_REGION="us-east-1"
export AWS_TFSTATE_BUCKET="mycompany-tfstate-$AWS_ENV"
export AWS_TFSTATE_REGION="us-east-1"
export AWS_TFSTATE_TABLE="tfstate_$AWS_ENV"
export TF_DATA_DIR="./.$AWS_ENV-terraform/"
TF_DATA_DIR="./.$AWS_ENV-terraform/" \
iam-starter \
--profile $PROFILE \
--command \
"terraform init \
-backend-config=\"region=$AWS_TFSTATE_REGION\" \
-backend-config=\"bucket=$AWS_TFSTATE_BUCKET\" \
-backend-config=\"dynamodb_table=$AWS_TFSTATE_TABLE\" && \
terraform apply \
-var-file=\"$AWS_ENV.tfvars\" \
-var \"aws_env=$AWS_ENV\" \
-var \"aws_region=$AWS_DEFAULT_REGION\""
popd
docker build -f Dockerfile.s3proxy -t sla-monitor/sla-monitor-reports-s3proxy:latest .
export ACCOUNT_ID=$( \
aws sts get-caller-identity \
--query 'Account' \
--output text \
--profile $PROFILE)
# authenticate local docker client with remote ecr repository
CMD=$(aws ecr get-login \
--no-include-email \
--region $AWS_DEFAULT_REGION \
--profile $PROFILE)
eval $CMD
# tag the image so it can be pushed to the repository
docker tag \
sla-monitor/sla-monitor-reports-s3proxy:latest \
$ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com/sla-monitor/sla-monitor-reports-s3proxy:latest
# push to the remote repository
docker push \
$ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com/sla-monitor/sla-monitor-reports-s3proxy:latestMIT License
Copyright (c) 2018 Factor Systems Inc.
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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