ClickHouse VS lnav

Yes, we are working on it! :) Taking some of the learnings from current experimental JSON Object datatype, we are now working on what will become the production-ready implementation. Details here: https://github.com/ClickHouse/ClickHouse/issues/54864

Variant datatype is already available as experimental in 24.1, Dynamic datatype is WIP (PR almost ready), and JSON datatype is next up. Check out the latest comment on that issue with how the Dynamic datatype will work: https://github.com/ClickHouse/ClickHouse/issues/54864#issuec...

In our repository, I've set up a few hard limits: each translation unit cannot spend more than a certain amount of memory for compilation and a certain amount of CPU time, and the compiled binary has to be not larger than a certain size.

When these limits are reached, the CI stops working, and we have to remove the bloat: https://github.com/ClickHouse/ClickHouse/issues/61121

Although these limits are too generous as of today: for example, the maximum CPU time to compile a translation unit is set to 1000 seconds, and the memory limit is 5 GB, which is ridiculously high.

I have a project dedicated to this topic: https://github.com/ClickHouse/ClickBench

It is important to explain the limitations of a benchmark, provide a methodology, and make it reproducible. It also has to be simple enough, otherwise it will not be realistic to include a large number of participants.

I'm also collecting all database benchmarks I could find: https://github.com/ClickHouse/ClickHouse/issues/22398

ClickHouse: A fast open-source column-oriented database management system. ClickHouse is designed for real-time analytics on large datasets and excels in high-speed data insertion and querying, making it ideal for real-time monitoring and reporting.

Today we're going to create an UDF (User-defined Function) in Golang that can be run inside Clickhouse query, this function will parse uuid v1 and return timestamp of it since Clickhouse doesn't have this function for now. Inspired from the python version with TabSeparated delimiter (since it's easiest to parse), UDF in Clickhouse will read line by line (each row is each line, and each text separated with tab is each column/cell value):

For the third, examples here might be analytics plugins in specialized databases like Clickhouse, data-transformations in places like your ETL pipeline using Airflow or Fivetran, or special integrations in your authentication workflow with Auth0 hooks and rules.

Online analytical processing (OLAP) databases like Apache Druid, Apache Pinot, and ClickHouse shine in addressing user-initiated analytical queries. You might write a query to analyze historical data to find the most-clicked products over the past month efficiently using OLAP databases. When contrasting with streaming databases, they may not be optimized for incremental computation, leading to challenges in maintaining the freshness of results. The query in the streaming database focuses on recent data, making it suitable for continuous monitoring. Using streaming databases, you can run queries like finding the top 10 sold products where the “top 10 product list” might change in real-time.

Proton is a lightweight streaming processing "add-on" for ClickHouse, and we are making these delta parts as standalone as possible. Meanwhile contributing back to the ClickHouse community can also help a lot.

Please check this PR from the proton team: https://github.com/ClickHouse/ClickHouse/pull/54870

curl https://clickhouse.com/ | sh

But I continue to find garbage in some of our CI scripts.

Here is an example: https://github.com/ClickHouse/ClickHouse/pull/58794/files

The right way is to:

- always pin versions of all packages;

The code base seems like a good reference as a small Python project.

My fav option in this class of apps: https://lnav.org/ It lets you use journalctl with pipes as requested here: https://github.com/Textualize/toolong/issues/4

For local development, I cannot recommend lnav[1] enough. Discovering this tool was a game changer in my day to day life. Adding comments, filtering in/out, prettify and analyse distribution is hard to live without now.

I don't think a browser tool would fit in my workflow. I need to pipe the output to the tool.

[1] https://lnav.org/

It looks like they run an SSH server inside a Docker container defined by this Dockerfile [1]. This uses the ForceCommand directive in the sshd_config file to ensure that a specific command is run when a user connects (rather than the user connecting directly to a shell).

Depending on whether the user connects as the `playground` or `tutorial1` user they interact with a bash script that is either [2] or [3].

[1]: https://github.com/tstack/lnav/blob/master/demo/Dockerfile

[2]: https://github.com/tstack/lnav/blob/master/docs/tutorials/pl...

[3]: https://github.com/tstack/lnav/blob/master/docs/tutorials/tu...

This is really pretty - I do really wish for a good rust replacement for lnav[1] someday.

1: https://lnav.org/

> I also don't see something else I might want: a way to have a different "view" for certain log messages; maybe to switch between filtering/viewing particular ones, maybe to just have line-format be conditional based on the detected format.

Have a look at the following comment on an issue that might be similar to what you're thinking of:

https://github.com/tstack/lnav/issues/1065#issuecomment-1602...

> I guess I can sort of do this based on `module-field`? but I might want it lighter-weight/finer-grained than that.

Unfortunately, the "module-field" does not work for JSON logs at the moment. It's something I should really fix.

Ultimately, lnav has existed for almost two decades now and I use it every day. So, it's always seeing improvements. If you're having a problem with it, file an issue on github. I don't always get around quickly to fixing other folks feature requests / issues, but it tends to happen eventually.

Thanks.