ecto VS ClickHouse

To ruin the party, deterministic UUID generation is exactly what UUID v5 is designed for. And since Ecto does not validate UUIDs against their specs, you might as well use uuid again and do:

To me this looks a lot like ecto https://github.com/elixir-ecto/ecto

Is there a significant difference?

Ecto database driver use-case

When it comes to building microservices, Go has the advantage of being easier to deploy and tighter integration with gRPC. On the other hand, Elixir will provide a more expressive layer to communicate with the database through Ecto.

May I ask why CouchDB though? Is it for the offline support?

Phoenix comes with its own database tool called Ecto[0] which is excellent, and it uses Postgres by default. If you're not intended to leverage CouchDB for offline support you should go Postgres without a second thought.

That said, I'm also curious about how to implement offline support with Phoenix in a nice and trivial way.

[0] https://github.com/elixir-ecto/ecto

The best would be to set aside at least 40 minutes of study a day. Being 20 minutes focused on the core of the language, solving problems and a website that can help you a lot and exercism. Another 20 minutes some of the core frameworks like: Phoenix, Ecto, Enum

This is a testiment to the value and productivity of Phoenix, but the resulting code is just basic CRUD. The views are tied 1:1 with their database-backed Ecto schemas. One thing to note is that Phoenix generates DDD-style contexts. This is unlike Rails, which would produce a typical ActiveRecord sprawl: bloated models directly being accessed and lazily queried across the entire application.

Ecto uses prewalk to count the number of interpolations within a given expression.

I've used my share of data access libraries and patterns (e.g. hibernate, activerecord, ecto, ...). The only time I've been happy is when I use raw SQL for non-dynamic SQL and a lightweight query builder for everything else.

I feel like I always run into some thing that at best isn't intuitive to express/read and at worse, cannot be expressed. If I remember correctly, when I was learning Elixir/Ecto, https://github.com/elixir-ecto/ecto/issues/1616 issue and the lack of lateral join support caused me issues.

Want to create a user?

"insert into users (id, name, status) values ($1, $2, $3)"

Our query builder takes pretty raw SQL fragments:

    q = Query.new()

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;