sqlite_orm VS ClickHouse

Obligatory "how does it compare to" https://github.com/fnc12/sqlite_orm ?

I had some success with sqlite_orm, but more generally I'd just recommend to pick a lib that fit your needs and read its examples/docs.

For SQLite, I like sqlite_orm

This one thing I feel like C++ has lacked in: a good, generic, SQL ORM. There is sqlite_orm which is really nice, but only for SQLite. Anyone have any other suggestions?

Probably the most popular ORM for modern C++ is https://github.com/fnc12/sqlite_orm. I've never used it personally. But if you configure SQLite to disable all the barriers and all synchronisation, I think you'll find it goes very, very quickly even with the ORM layer in between.

Hey folks. I've posted my new `SQLiteORM` lib today here on GitHub https://github.com/fnc12/sqlite-orm-swift . It is the same lib I've written with C++ before https://github.com/fnc12/sqlite_orm . It uses keypath instead of `Codable` protocol to serialize and deserialize objects. If you need to use SQLite database inside your Swift project and you hate writing SQL queries using raw strings then you may like this lib.

I was leaning towards SQLite (along with sqlite_orm ) since I thought my data might be more relational and I used to work with databases a lot more. And I can use a SQLite browser app to view my data. But I'm wondering if doing serialization with a library like cerial or Boost's would be better.

For the brief time I've used sqlite_orm, I've really liked it: https://github.com/fnc12/sqlite_orm

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;