Python

I manage several apps that write various pieces of data to the local file system and rely on Fluentd to ship them to s3. There is solid monitoring around the fluentd aggregator process, but I wanted better visibility and alerting when things aren’t written to s3 as expected. The solution I came up with was a custom Datadog check. The files I am monitoring are written into a bucket named something like example-logs/data/event-files/year/month/day. A new path is set up in the s3 bucket for the current day’s date, e.g. logs/data/example-log/2018/08/15 each day. The Datadog check sends the count of objects in the current date’s directory as a gauge. You can then monitor that objects are created each day as expected and at a normal rate. ...

Pagerduty provides a built-in way to export your incident data but only a limited number of data fields are available on the basic plan. Rather than upgrade you can use the API to export the data to a CSV. I found this gist listed here. The python script works great but some of my incident messages contained JSON data that threw off Excel when opening the CSV. I slightly modified the script with character escaping to work around this (lines 98- 107). ...