chdb VS Meerschaum

Hey HN, creator of DB Pilot here.

I first announced DB Pilot on HN back in April: https://news.ycombinator.com/item?id=35761979.

Since then a lot has improved: More databases are supported, most of the product can now be used for free, and most importantly:

The app now comes with an analytics workspace powered by an embedded ClickHouse instance, running locally on your machine. This allows you to query local files, files on S3, PostgreSQL, SQLite & more - and all of those at once.

Embedding ClickHouse was possible thanks to chDB (https://github.com/chdb-io/chdb). A recent discussion on HN about it: https://news.ycombinator.com/item?id=37985005

I recommend using ClickHouse instead of DuckDB.

It has been around since 2016, and it covers and extends the feature set of DuckDB with a huge margin. Worth noting that it never has breaking changes in its table format MergeTree.

I'm tracking the progress of DuckDB and see that it is modeled after ClickHouse, but does not approach it in terms of feature completeness, stability, or performance.

The closest to DuckDB option is to use its self-contained version, clickhouse-local: https://clickhouse.com/blog/extracting-converting-querying-l... or an embedded version, chdb: https://github.com/chdb-io/chdb

Different beasts, but if by any chance you love ClickHouse already and just want to run OLAP queries in-process, there's chdb: https://github.com/chdb-io/chdb

> Can you embed it in Python as a library?

https://github.com/chdb-io/chdb

    pip install chdb

New kid on the block that I prefer over DuckDB is CHDB (https://github.com/chdb-io/chdb). Embedded ClickHouse, so once you out grow your laptop you can simply move to an actual OLAP that's Open-source.

This sounds like a great use case for Meerschaum. You can organize your scripts into plugins and build out incremental transformations in SQL. We use Meerschaum Compose for client integrations and ETL in a similar workflow to yours.

My company has been using a lot of PySpark, but we're working with not-large data (<1TB/source/day) so Spark can be a bit of overkill sometimes and I've been looking for a light-weight replacement. I think I found a replacement that fits all our needs called Meerschaum but I don't see a lot of other DEs talking about it.

Do the tables have something like a datetime or integer index column? At my job, we use the ETL Python package Meerschaum to sync our tables, and for large ones, we split the sync into chunks with --begin (inclusive) and --end (exclusive).

I worked as the first data engineer for a student internship for two years, during which I rewrote the system several times until I had a time-series ETL system that fit their needs perfectly. After leaving, I took what I learned and started the ETL package Meerschaum, and after a few consulting contracts to deploy Meerschaum, I landed a DE job to manage Meerschaum deployments internally. A bit unconventional but worked out as I had hoped.

There's a whole lot more that you can do with the framework, but this post is getting kinda long. Please check out the project homepage for more details, and I'd really love know what y'all think! Can you see a use case for the framework in your stack?

I'm the author of the ETL framework Meerschaum which is meant for this exact purpose. You can build an ETL pipeline in a few lines of Python, e.g. here's a quick video. Check out the Getting Started guide and the docs on writing your first plugin to get your data flowing!

You can prototype some scripts with a tool called Meerschaum that I built for this kind of purpose. Once you're ready to deploy your prototype, you could refactor it for something more suited for enterprise like Airflow.