Duckdb CLI is a powerhouse, it can load files as diverse as gzipped json lines, so you can stuff compressed logs straight into a directory yet still easily query them with SQL when you need to.
I’m a big fan of tmducken. We use it heavily in our prod systems. That said, we’ve recently started exploring ducktape [1] in our new projects and have been really impressed with the performance. It also support more complex types on insertions and queries which has been helpful for us. Not affiliated with the project, but just wanted to show it some love since it’s a bit newer. It was created by an active contributor to tmducken.
It really is - we use duckdb as our main workhorse in our entire stack.
As a side note, thank you for all the work you have done in the Clojure ecosystem! Techascent’s data science packages was a major tipping point in my company’s decision to build our data science ecosystem in Clojure and has been wonderful to work with.
Impressive, you can really do a lot on a single node when it comes to big-data queries nowadays, I agree too many jump straight to a Spark cluster or something similar when you can just write a small script on a single node.
> Developing such a high quality power tool in such an open manner is honorable.
Credit where credit is due, I would say their efforts are more than just "honorable", I could easily prefix that with an "extremely" and maybe add in a ", most excellent" afterwards.
At Cronitor we use ClickHouse, but we're leaving it behind for our next product and building directly on Parquet and DuckDB.
We think the future of observability in the AI age is self-hosted directly on NVMe backed by cheap and limitless object storage. I don't want to send customer conversations and agent thoughts to a giant multi-tenant borg SaaS database like Sentry or BetterStack.
You understand this is more than that right? This is saying, next time you need to analyze a big dataset, tell your AI to load it in DuckDB, and if it needs to run a query that SQL cannot do, tell it to do what they did in this blog, and it'll have the query run using Clojure super fast.
It's not really about querying a DB from Clojure, well not in the boring sense of how do I run some SQL over data in an existing DB.
What this is supposed to replace is say a big Spark Cluster for processing/querying large datasets.
Duckdb CLI is a powerhouse, it can load files as diverse as gzipped json lines, so you can stuff compressed logs straight into a directory yet still easily query them with SQL when you need to.
I’m a big fan of tmducken. We use it heavily in our prod systems. That said, we’ve recently started exploring ducktape [1] in our new projects and have been really impressed with the performance. It also support more complex types on insertions and queries which has been helpful for us. Not affiliated with the project, but just wanted to show it some love since it’s a bit newer. It was created by an active contributor to tmducken.
[1] https://github.com/dynamic-alpha/ducktape
tmducken creator here, this is so rad to see - duckdb is a monster for sql on csv.
It really is - we use duckdb as our main workhorse in our entire stack.
As a side note, thank you for all the work you have done in the Clojure ecosystem! Techascent’s data science packages was a major tipping point in my company’s decision to build our data science ecosystem in Clojure and has been wonderful to work with.
Why would you need this over just using the jdbc driver?
Related Clojure, DuckDB/Ducklake tool: https://github.com/o11ylite/o11ylite
I did consider tmducken in the beginning.
(Disclaimer: I maintain o11ylite)
Impressive, you can really do a lot on a single node when it comes to big-data queries nowadays, I agree too many jump straight to a Spark cluster or something similar when you can just write a small script on a single node.
> Developing such a high quality power tool in such an open manner is honorable.
Credit where credit is due, I would say their efforts are more than just "honorable", I could easily prefix that with an "extremely" and maybe add in a ", most excellent" afterwards.
Couldn't agree more, and the use of Clojure is the cherry on top.
At Cronitor we use ClickHouse, but we're leaving it behind for our next product and building directly on Parquet and DuckDB.
We think the future of observability in the AI age is self-hosted directly on NVMe backed by cheap and limitless object storage. I don't want to send customer conversations and agent thoughts to a giant multi-tenant borg SaaS database like Sentry or BetterStack.
i dont really into languages now. llm has solved this abstraction "perfectly".
most of my query now are llm generated. i created custom api to connect llm to any databases im currently using.
I’m not so sure - architecture matters, efficiency matters, extensibility matters, and all are impacted by language choice.
If we had developed our tech in Python we’d be a world of hurt now as we scale.
You understand this is more than that right? This is saying, next time you need to analyze a big dataset, tell your AI to load it in DuckDB, and if it needs to run a query that SQL cannot do, tell it to do what they did in this blog, and it'll have the query run using Clojure super fast.
It's not really about querying a DB from Clojure, well not in the boring sense of how do I run some SQL over data in an existing DB.
What this is supposed to replace is say a big Spark Cluster for processing/querying large datasets.
These takes are not interesting, relevant, or helpful.
Then perhaps this isn't for you.