workerd VS ClickHouse

Standards bodies only standardize things after they've been proven to work. You can't standardize a new idea before offering it to the market. It's hard enough to get just one vendor to experiment with an idea (it literally took me years to convince everyone inside Cloudflare that we should build Durable Objects). Getting N competing vendors to agree on it -- before anything has been proven in the market -- is simply not possible.

But the Durable Objects API is not complicated and there's nothing stopping competing platforms from building a compatible product if they want. Much of the implementation is open source, even. In fact, if you build an app on DO but decide you don't want to host it on Cloudflare, you can self-host it on workerd:

https://github.com/cloudflare/workerd

In any case, I welcome this initiative with my open hands and look forward all the cool apps that people will now build with this!

[1] https://pyodide.org/

[2] https://github.com/cloudflare/workerd/blob/main/docs/pyodide...

[3] https://github.com/cloudflare/workerd/pull/1875

For ref:

- https://blog.cloudflare.com/workerd-open-source-workers-runt...

- https://github.com/cloudflare/workerd

workerd

I think this is for people who want to run their own cloudflare workers (sort of) and since nobody wants to run full node for that, they want a small runtime that just executes js/wasm in an isolated way. But I wonder why they don't tell me how I can be sure that this is safe or how it's safe. Surely I can't just trust them and it explicitly mentions that it still has file IO so clearly there is still work I need to do customize the isolation further. But then they don't show any info on that core usecase. But then that's probably because they don't really want you to use this to run it on your own, they are selling you on running things on their edge platform called "Wasmer Edge". So that's probably why this is so light on information.. the motivation isn't to get you to use this yourself, just to use this their hosted edge platform. But then I wonder why I wouldn't just use https://github.com/cloudflare/workerd which is also open source. Surely that is fast enough? If not then it should show some benchmarks?

i love how the main reference for workerd can be just one capnp file.

https://github.com/cloudflare/workerd/blob/main/src/workerd/...

this changed my world how i think about computing on the web.

if there was just a good enough js library as for lua and you could directly send capnp messages to workerd instead of always going through files. I guess one day i have to relearn c++ and understand how the internals actually work.

A significant chunk of it is open source: https://github.com/cloudflare/workerd/

Some of the configuration files for applications wind up being an entire language unto themselves, e.g., https://github.com/cloudflare/workerd/blob/1b5057f2bfcfedf146f6f79ff04e99903d55412b/src/workerd/io/compatibility-date.capnp

Edge runtimes are very good alternatives to containers that shouldn't be dismissed for "not being containers". They're often faster, more scalable, and cheaper than containers. Them being so lightweight also enable a "nanoservice architecture" – being able to run every service on a single computer instead of running different services on different computers and having to deal with network latency and unreliability.

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;