Show HN: QuestDB with Python, Pandas and SQL in a Jupyter notebook – no install (opens in new tab)

We actually do have a web based demo which is https://demo.questdb.io/ preloaded with millions of rows of data.

That one focuses on SQL queries though.

The notebook in https://play.questdb.io/ offers a more rounded experience to try out any and all of our features.

You can use the notebook to try out data ingestion, dropping partitions and more that is simply not possible in a more sandboxed environment.

The other part is that we value our Python users and wanted to provide an example of how to use our database in conjunction with other tools commonly used in the data science space to slice and dice time series data.

Most databases store the latest state of something. We don't. We ingest events. After all, life is a function of time :-) The whole world ticks and we take those ticks and store them. If part of your application tracks anything happening over time (trades, ocean pollution levels, ships moving, rocket simulation metrics.. or whatever else then it makes sense to store those events in a time series database. What we provide, primarily, is two basic pieces of functionality: (1) Taking in lots of events FAST. Our ingestion rate is high (and we also integrate with things like Kafka, Pandas -- see the notebook, etc). Each of our time series tables (we support regular ones too) comes with a special timestamp column. (2) Specialized SQL to make sense of data that's changed over time, such as grouping and resampling by time and more. Take a look at our docs for things like SAMPLE BY, LATEST ON, ASOF JOIN, LT JOIN and more. On disk, we also guarantee that all records are sorted by time and this gives us great query performance for these time-based types of queries.

PS. We're also wire-compatible with PostgreSQL.

I take it you did not visit the link?