SQLite VS ClickHouse

If you are into alternative storage engines for SQLite, there is also an LSM (Log-Structured Merge-tree) extension in the main repository that is not announced nor documented but seems to work. It’s based on the SQLite 4 project.

https://github.com/sqlite/sqlite/tree/master/ext/lsm1

https://www.charlesleifer.com/blog/lsm-key-value-storage-in-...

The sqlite code base is really well done. Lots of documentation.

https://github.com/sqlite/sqlite

Especially the VM part: https://github.com/spandanb/learndb-py/blob/master/learndb/v...

Compare it with this: https://github.com/sqlite/sqlite/blob/master/src/vdbe.c

That's said, I'm curious how complete this LearnDB is. SQLite is hard to read not only it's old but also it covers a lot of SQL and following SQL spec makes hings complicated. SQLite has great test suite so it's nice if you run the suit against this implementation.

Correct, see the github mirror[1]. I don't know how well supported that feature is compared to main branch. If it was completely stable, then it would have already landed in the main stable branch. Clarity about the roadmap of that branch would be nice.

1. https://github.com/sqlite/sqlite/tree/begin-concurrent

Including comprehensive comments, documentation and tests in a codebase takes time and effort.

Failing to do so creates code that is very difficult to maintain or for someone new to the codebase to understand.

However, time and effort may not be what the organization wants to pay for, and individuals may view their own incomprehensible code as something like job security, as they can't be replaced by someone else easily.

As an example of complicated code that's still well-documented, the open-source sqlite code is a good example, about 1/4 of the B-tree file is comments, every time a variable is defined there's a short note explaining what it's used for, every function has a comment header that's comprehensive, such that someone new to the codebase could construct a map of how it all works fairly quickly. It's a good model for how to avoid the problem:

https://github.com/sqlite/sqlite/blob/master/src/btree.c

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;