I have all sorts of metrics I would like to count and later query. For example I have a lambda that processes stuff from a queue, and for each batch I would like to save a count like this:

{
  "processes_count": 6,
  "timestamp": 1695422215,
  "count_by_type": {
    "type_a": 4,
    "type_b": 2
  }
}

I would like to dump these pieces somewhere and later have the ability to query how many were processed within a time range.

So these are the options I considered:

1. write the json to the logs, and later have a component (beats?) that processed these logs and send to a timeseries db.
2. in the end of each execution send it directly to a timeseries db (like elasticearch).

What is better in terms of cost / scalability? Are there more options I should consider?

CodePudding user response：

I think Cloud Watch Embedded Metric Format (EMF) would be good here. There are client libraries for Node.js, Python, Java, and C#.

CW EMF allows you to push metrics out of Lambda into CloudWatch in a managed async way. So it's a cost-effective and low-effort way of producing metrics.

The client library produces a particular JSON format to stdout, when CW sees a message of this type it automatically creates the metrics for you from it.

You can also include key-value pairs in the EMF format which allows you to go back and query the data with these keys in the future.

High-level clients are available with Lambda Powertools in Python and Java.