About the use case and user

The use case used for this Friction Log covers an actual requirement to build analytics dashboards and insight across a 40+GB MongoDB dataset. Rick was able to do his prototype, end-to-end in about a day, on top of Rockset. The user, Rick has some data and analytics experience but isn't a deep data engineering junkie.

Friction Logs

Easy to Grok

The SQL query language is easy to use, google if you run into problems, etc. There really was minimal learning overhead my data was in Rockset.

Import was easy

Importing a dataset from Mongo was very straightforward, from a first-time / try it out experience.

Fast feedback loop

The time to ingest data, write a query, integrate it into an API, and then a React app was very fast. I was able to try things out on the fly, change on the fly, etc.

Helpful Optimization Tips

Rockset analyzed my queries and provided helpful ways to optimize it, which most of the time really improved the query time.

Not all ingest options are clear

There are a lot of caveats to know if you want to really nail down a good ingest. For example, mapping a timestamp field is really important. The interface didn't always tell me that.

Error on ingest

At one point the ingest froze at 55% and there wasn't anything I could do to fix it. Support was timely and helpful and ultimately the solution was to re-ingest from scratch.

It's not clear if lambda queries are valuable

At first, I presumed lambda queries allowed the system to further optimize the results but in the end, it just seems to be a way to memorialize a query in an endpoint.

The query editor's linter doesn't always work

Many times the editor's linter would be wrong or show an error when there wasn't one.

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