Custom metric properties and CloudWatch Insights

Custom metric properties and CloudWatch Insights

Make debugging easier with CloudWatch custom metric properties


7 min read

In the first post of the series we saw how we can use the aws-embedded-metrics npm package to easily output custom metrics from our Lambda functions. In the second post, we saw how we could use custom metrics to trigger alarms when our application went wrong. In this final post of the series, we will see how we can add properties to our custom metrics to help us diagnose what has gone wrong.

The code for this post can be found in the accompanying GitHub repo ready to be cloned and run.


  • Custom metrics can have arbitrary additional properties
  • These properties can be queried using CloudWatch Insights
  • You can't delete metrics, they are retained for 15 months

The application

Our application is a step function that consists of two Lambda functions that make calls to external APIs, followed by a Lambda function that persists the results. If calls to either of the external APIs fails, then the error is caught and handled by a Lambda function that records a custom metrics indicating that an error occurred.

Loan Processor step function diagram

We already have an alarm set up to alert us when either of the API calls fail, but what if we could easily find out exactly which request failed? Well, we can do just that by adding properties to our metrics.

Correlation IDs

To quote the first article below:

A Correlation ID is a unique identifier that is added to the very first interaction (incoming request) to identify the context and is passed to all components that are involved in the transaction flow. Correlation ID becomes the glue that binds the transaction together and helps to draw an overall picture of events.

In our application, each request has the following structure, where correlationId contains a unique identifier:

  "correlationId": "gMQdTVVTA6jM6GOgheblB",
  "firstName": "Trevor",
  "lastName": "Potato",
  "postcode": "MK3 9SE"

So by logging the correlation ID with each of our metrics, we will be able to tie a sequence of the log entries together.

Adding properties

Adding properties to our metrics is a straightforward process, we just use the setProperty method and specify a name and value.

First up is our error handler Lambda function:

export const handler = metricScope(
  (metrics) =>
    async (event: any): Promise<void> => {
        .setDimensions({ ProcessName: 'LoanProcessor' })
        .putMetric('ErrorCount', 1, Unit.Count)
        // Set our properties
        .setProperty('CorrelationId', event.correlationId)
        .setProperty('StateMachineName', event.stateMachineName)
        .setProperty('FailedStateName', event.failedStateName)
        .setProperty('Cause', event.cause);

The next step is to update the corresponding state in the step function definition. Here we use the Context Object to access the name of the state machine and the correlation ID from the input. We also use the States.StringToJson utility function to convert the error cause into an object, which will make reading the resulting log entry much easier.

"HandleIdentityCheckFailure": {
  "Next": "IdentityCheckFail",
  "Type": "Task",
  "Resource": "arn:aws:lambda:eu-west-2:XXXXXXXX:function:LoanProcessorTestStack-LoanProcessorErrorHandlerFu-XXXXXXXX",
  "Parameters": {
    "failedStateName": "IdentityCheckGateway",
    "stateMachineName.$": "$$.StateMachine.Name",
    "correlationId.$": "$$.Execution.Input.correlationId",
    "cause.$": "States.StringToJson($.Cause)"

Next we look at the proxy Lambda functions. In both cases, we want to record the URL being called and the status code returned. More importantly, we also want to record the API request id and the correlation id that we were passed in the original request.

  .setDimensions({ GatewayName: gatewayName })
  .putMetric('ResponseTime', responseTime, Unit.Milliseconds)
  // Set our properties
  .setProperty('StatusCode', response.status)
  .setProperty('GatewayUrl', url)
  .setProperty('CorrelationId', request.correlationId)
  .setProperty('RequestId', request.requestId);

Again, we need to update the corresponding state in the step function definition to ensure we have the appropriate values:

"IdentityCheckGateway": {
  "Next": "CreditReferenceGateway",
  // <snip>
  "Type": "Task",
  "ResultPath": "$.identityCheck",
  "Resource": "arn:aws:lambda:eu-west-2:XXXXXXXX:function:LoanProcessorTestStack-LoanProcessorIdentityCheckP-XXXXXXX",
  "Parameters": {
    "correlationId.$": "$$.Execution.Input.correlationId",
    "firstName.$": "$$.Execution.Input.firstName",
    "lastName.$": "$$.Execution.Input.lastName",
    "postcode.$": "$$.Execution.Input.postcode"

Generating some errors

The next thing to do is to run our application and wait for errors. This is straightforward, as we can configure the APIs being called to error a certain percentage of the time. With these set to 10%, we can run a unit test to call the step function every few seconds and wait.

Sure enough, before long we see that our alarm has triggered:

AWS Console showing the triggered alarm

And in the logs we see the following entry for the error handler Lambda function:

Lambda error in CloudWatch

Investigating the errors

Thankfully, AWS provides us with an alternative to just manually searching through the logs. By using the aws-embedded-metrics package, we have used structured logging. This means that we can query the logs using CloudWatch Insights and the properties that we specified.

As mentioned in an earlier post, AWS logs many metrics automatically and for free. These can be queried via CloudWatch Insights, see the following post for many useful examples:

CloudWatch Insights lets you query up to 20 log groups at the same time. Depending on your needs, this may not be very convenient. If this is the case, then there are plenty of plenty of third party log aggregation tools that might be a better fit for you.

Our query is only going to span on log group, so this limit will not affect us. What we need to do is select the log group for the error handling Lambda function and write our query.

CloudWatch Insights query for Loan Processor errors

The query above is filtering the log entries using the ErrorCount metric and the property ProcessName to identify entries where an error has occurred. It is then selecting several fields, the most important to use being the custom metric property CorrelationId.

For a full guide to the CloudWatch Logs Insights query syntax, please see the AWS documentation.

When we run the query, we get results similar to the following:

CloudWatch Insights results for Loan Processor query

Here we can see that we can easily retrieve the correlation ids for those instances that have failed. The next thing for us to do is to take one of those correlation ids and run a query to get more details about the failure.

CloudWatch Insights query by correlation id

Here we have selected two log groups, one for the identity check API and one for the credit reference API. We then filter the records by the correlation id and select the properties that we logged.

When we run the query, we get results similar to the following:

CloudWatch Insights results for correlation id query

Here we can see that the credit reference API failed and we can also see the request id that failed. If this was a third-party API, then we could contact the third party and provide them with this id for further investigation. We can also see the URL that was called, which could also be useful in diagnosing the underlying problem.

Hopefully, you can see that by adding properties to your custom metrics, you can help yourself when faced with working out why something has gone wrong.

Do as I say, not as do

Please note that the example code is very naive in its treatment of personally identifiable information (PII). The step function request contains a name and a postal code, these are passed around between states and are logged out at various points. This is a very bad idea.

A much better approach would be to store the request in something like a DynamoDB table, with a short Time to Live (TTL) to ensure it is deleted when no longer needed. A reference to this can then be passed around and logged, and the actual information only accessed when needed.

Can I delete metrics?

Throughout this series, you may have been wondering if you can delete metrics. Well, once created, a metric cannot be explicitly deleted. As explained by the Amazon CloudWatch FAQs:

Q: Can I delete any metrics? CloudWatch does not support metric deletion.

Metrics are retained for 15 months, so I wondered about whether I would be charged for them for 15 months. However, the following StackOverflow question answered my query: AWS CloudWatch unused custom metrics retention and pricing

The pricing page states that "All custom metrics charges are prorated by the hour and metered only when you send metrics to CloudWatch". By this, they mean that they charge 1/720 of the monthly costs for the custom metrics for every hour in which you push at least one data point to the custom metric (counting 30 * 24 = 720 hours in a month).


In this post, we saw how we can add extra information to our custom metrics using properties. These properties can then be queried using CloudWatch Insights, allowing us to such things as help us investigate system errors.