cl-ppcre VS ClickHouse

I've never used cl-ppcre myself, but its docs[1] claim that it provides compile-time regexes:

> CL-PPCRE uses compiler macros to pre-compile scanners at load time if possible. This happens if the compiler can determine that the regular expression (no matter if it's a string or an S-expression) is constant at compile time and is intended to save the time for creating scanners at execution time (probably creating the same scanner over and over in a loop).

[1]: https://edicl.github.io/cl-ppcre/

A few weeks back Xach recommended cl-ppcre which i found educational.

For simple string processing, there are some functions in the language, that you can find listed here (for string-specific functions) and here (for more generic sequence-handling functions). For anything involving regular expressions, cl-ppcre is the way, in particular the split and register-groups-bind functions.

I must have a serious bug in my writing about this, because this was never about regex engines -- it's about literals and domain-specific sublanguages in general. Composing DSL programs by string concatenation is such a famous source of security bugs you see it in top-10 lists. I linked to the very similar example of a PEG parsing DSL.

But any regex engine that can work with a parse tree shows the same principle, e.g. https://edicl.github.io/cl-ppcre/#create-scanner2

Take a look at - https://github.com/edicl/cl-ppcre

And here's what I believe represents the reality of the situation... Stas was indeed tired of ASDF's changes. Now the nature of what changes to make is a matter of judgement of course, but in this case (I'm thinking of SBCL's bug report request to update ASDF: https://bugs.launchpad.net/sbcl/+bug/1826074), it would be a different matter altogether if the discussion was centered on how best to make the new ASDF work with SBCL, but the thread reads to me like a man who had to put up with too much breakage for the upteenth time. Now, if (for the sake of argument :D) the change was of the necessary kind -- think hardware changes or security issues -- I can still see myself feeling wronged, it's human to do so. Because I don't trust ASDF anymore or I feel as if they (or other people at each step of the process) have not shared enough of the burden. But from the discussions I have read (https://github.com/edicl/cl-ppcre/pull/30) what the ASDF maintainers want to change does not seem unreasonable and they are willing to share the burden. But let us say it's truly a 50/50 deadlock. Well then Linus is right, show us the code, who dares wins. And Stas certainly has enough on his plate. But that's why we must cooperate. You don't have to be a diplomat to know the difference when two people want to work together and when one party wants out. And this setting makes more sense when you read (https://bugs.launchpad.net/sbcl/+bug/1823442) where Stas honestly states he wants nothing more to do with ASDF. I don't think it's unreasonable to surmise there's a bit more going on here than plainly technical issues.

Could you just direct me to some existing discussions, in order to save time? I already read this one.

I plan to use the regular expressions with a cl-ppcre wrapper, also emulating various clojure regular expression operations. Similar to re21, which doesn't quite support the operations in the way I'd like (or match the clojure operations), and whose regular expression literal syntax is "#//".

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;