ogx360 VS ClickHouse

It's a diy design. You can buy premade versions or build your own. https://github.com/Ryzee119/ogx360

Not sure about the brook wingman adapter but the ogx360 does support the 8bitdo Bluetooth adapter according to the GitHub https://github.com/Ryzee119/ogx360

https://github.com/Ryzee119/ogx360 somewhere in the documentation it mentioned a button press combo to invert up and down

I'm not sure it would work unless you were able to split the DC controller port into one Wingman SD, and at that I don't believe the firmware would support multiple controllers om one Wingman. My thinking here is that the DC controller ports are separate and discrete and without a physical connection to each port it would never be able to identify controller 2,3 & 4. I believe we would need something similar to the ogx360 for the OG Xbox to accomplish what your looking for u/mune87

OGX360 which also requires an Xbox 360 wireless receiver

Yes, you can mod the Communicator to use a 3.5m jack. And that seems to work well as long as you don't put high impedance headphones on it. https://imgur.com/a/xisxrwS However yeah, you would have to find a Communicator first... I could see a "translator" being made though, like the ogx360 https://github.com/Ryzee119/ogx360 But instead of converting a controller, it pretends to be a Communicator and interfaces with normal usb and more ideally Bluetooth headsets. Not sure how / if any of that could be done. Just throwing the idea out there. Hard part would be stripping out what you can out of a full HID headset and piping that back into the xboxs simple XID in a way its happy with and still actually works. https://xboxdevwiki.net/Xbox_Input_Devices#Xbox_Communicator

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;