tup VS ClickHouse

Whenever looking at one these, I think back to the obscure but interesting "tup":

“How is it so awesome? In a typical build system, the dependency arrows go down. Although this is the way they would naturally go due to gravity, it is unfortunately also where the enemy's gate is. This makes it very inefficient and unfriendly. In tup, the arrows go up.”

https://gittup.org/tup/

Once upon a time, you could roll your own of this using `tup` which might have my favorite "how it works" in the readme:

How is it so awesome?

In a typical build system, the dependency arrows go down. Although this is the way they would naturally go due to gravity, it is unfortunately also where the enemy's gate is. This makes it very inefficient and unfriendly. In tup, the arrows go up. This is obviously true because it rhymes. See how the dependencies differ in make and tup:

[ Make vs. Tup ]

See the difference? The arrows go up. This makes it very fast.

https://gittup.org/tup/

Also has a whitepaper: https://gittup.org/tup/build_system_rules_and_algorithms.pdf

Ten years ago, I used reStructuredText and its support for LaTeX math and syntax highlighting. I used tup (tup monitor -a -f) to take care of running rst2html on save.

I might be showing my ignorance here, but this just sounds like Tup? https://gittup.org/tup/

I agree. While I like the idea of tup (https://gittup.org/tup/ -- the first "forward" build system I remember hearing of), writing a makefile is easy enough that thinking about the problem upside-down doesn't offer a compelling reason to switch.

Ptrace is one option for tracing dependencies, but it comes with a performance hit. A low-level alternative would be ftrace (https://lwn.net/Articles/608497/) or dtrace (https://en.wikipedia.org/wiki/DTrace).

Tup uses LD_PRELOAD (or equivalent) to intercept calls to C file i/o functions. On OSX it looks DYLD_INSERT_LIBRARIES would be the equivalent.

* order-only prerequisites - X must happen before Y if it's happening but a change in X doesn't trigger Y

This is just a small selection and there are missing things (like how to handle rules that affect multiple targets).

It's all horrible and complex because like a lot of languages there's a manual listing the features but not much in the way of motivations for how or why you'd use them so you have to find that out by painful experience.

It's also very difficult to address the warts and problems in (GNU) make because it's so critical to the build systems of so many packages that any breaking change could end up being a disaster for 1000s of packages used in your favorite linux distribution or even bits of Android and so on.

So it's in a very constrained situation BECAUSE of it's "popularity".

Make is also not a good way to logically describe your build/work - something like Meson would be better - where you can describe on the one hand what a "program" model was as a kind of class or interface and on the other an implementation of the many nasty operating system specific details of how to build an item of that class or type.

Make has so many complex possible ways of operating (sometimes not all needed) that it can be hard to think about.

The things that Make can do end up slowing it down as a parser such that for large builds the time to parse the makefile becomes significant.

Make uses a dependency tree - when builds get large one starts to want an Inverted Dependency Tree. i.e. instead of working out what the aim of the build is and therefore what subcomponents need to be checked for changes we start with what changed and that gives us a list of actions that have to be taken. This sidesteps parsing of a huge makefile with a lot of build information in it that is mostly not relevant at all to the things that have changed. TUP is the first tool I know about that used this approach and having been burned hard by make and ninja when it comes to parsing huge makefiles (ninja is better but still slow) I think TUP's answer is the best https://gittup.org/tup/

You might enjoy Tup[1] if you've not checked it out before.

[1]: https://gittup.org/tup/

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;