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• And have the .md file be called the same as the url

For ease of updating

Hi, I just read your article and I found it super interesting. Thank you for sharing your work! At my job, we have pretty much the same requirements: large amount of sensor data, upserts, cost effectiveness. I am tempted to try your approach, but I have two questions.

Why did you decide to split the .avro files by fixed time intervals (2020-01-01T13:30:00--2020-01-01T13:40:00), rather than by sensor id and a (much longer) fixed time interval (e.g. sensor-1_2020-01-01T00:00:00--2020-01-31T00:00:00)? Wouldn't this make reading much easier, assuming that you mostly want to only read a single sensor time series? Or would this not work so well because the updates per file are rather small, which would make the upserts inefficient?

And another one: how did you implement the upserts efficiently? Were you, by any chance, able to implement this in Apache Spark? This is what we're looking at, we would need to feed the query store from a data lake, and then Spark would be our preferred way to go.

Thank you!

See #1

Hello, I was reading the How to Read Programming Error Messages article and noticed that the images used for the Python stack trace/traceback are duplicated. The second image should be the annotated image.