Hacker News API VS ClickHouse

I built a plugin that allows Coze bots to make requests to the Hacker News API.

I recognise the huge amounts of effort involved in this and I applaud the moderators for keeping HN an interesting place to be.

That said, I think it's reasonable for us to have visibility on their manual interventions, and this could be easily surfaced via the Hacker News API (https://github.com/HackerNews/API), if the Story JSON included the values of "contro," "bury," and "gag" fields, which are currently opaque to users of the API

See https://medium.com/hacking-and-gonzo/how-hacker-news-ranking... for more discussion on terminology

Google probably stopped updating BigQuery when they started hosting live Hacker News data in Firebase: https://github.com/HackerNews/API

The live nature of the Firebase data is awesome, but the lack of ability to query is a loss.

Nice work! I did something similar with a personal project a few months ago using an open source llm. Also, not sure if you know, there is an api you can use. https://github.com/HackerNews/API

Hi HN, I made a live feed for viewing all of the new items on Hacker News in almost-realtime. It is a single, modern HTML file that doesn't use polling.

It works by establishing a websocket connection to the Hacker News Firebase database, to receive updates every time the HN server updates Firebase, which is about once every 30 seconds. This is very efficient, putting no load on HN's server and using minimal bandwidth.

To make the feed continuous despite the delay, it waits to display each item until exactly 30 seconds before displaying it. I think this is a fair tradeoff, it gives you a sense of how active HN is. For comparision, there are something like 6-7 thousand tweets every second.

Official HN Firebase API: https://github.com/HackerNews/API

Source code: https://github.com/jerbear2008/hn-live/blob/main/index.html

You could use the API: https://github.com/HackerNews/API

You can now get Hacker News data in real time from the Hacker News API powered by Firebase: https://github.com/HackerNews/API

This is great (real time!), but also kind of a pain (38+ millions individual http requests to get the whole thing).

Thankfully there's no authentication or apparent rate limiting. I fumbled my way through downloading the whole thing with curl. I screwed up a few times so made over 70 million requests in total.

Toy analysis of the data I downloaded here: https://public.tableau.com/app/profile/isna/viz/HackerNewsDa...

Get your submissions from this API

https://hacker-news.firebaseio.com/v0/user/ohjeez.json?print...

and then scan the "submitted" articles as described here

https://github.com/HackerNews/API

I have a crawler that sucks down all the posts from HN and then I read it into Pandas and write all sorts of queries. The boggle I have now is that I want to use the same system to (1) make sure YOShInOn never submits duplicate articles, and (2) have accurate vote and comment scores. (1) requires picking up articles as soon as possible, (2) requires waiting two weeks or so until the scores have settled down to what they are going to be. I guess I gotta come back and rescan things in 2 weeks so I have the right scores.

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;