ClickHouse VS Plausible Analytics

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;

I think a single Google Analytics alternative is pretty hard to pick considering that GA can be used to very much varying extents.

For simple and "detailed enough" insights, I enjoyed using Plausible (https://plausible.io/) in the past.

For more in depth analytics that give you a detailed view into your own product, PostHog.com seems to be by far the best and most popular option out there.

I could do the same exercise with Google Analytics and Google Tag Manager, but luckily I don't need to, since Plausible already did. A piece of advice, rip out Google Analytics and use Plausible instead. It first of all doesn't destroy your website, and secondly it doesn't violate the GDPR - So you can embed it on your site without having to warn your visitors about that they're being spied on by Google.

Also, currently we are using https://plausible.io/ for analytics. No other bugs.

I just swapped out Google Analytics with Plausible for AINIRO.IO. It’s only been a week, but so far I am super jazzed about it. First of all, Plausible doesn’t use cookies, so I can completely drop all cookie disclaimers and popups I had because of GDPR. Second of all, the site scores significantly better on load time. This results in a 10x better user experience for my website visitors, while making sure the website is still 100% conforming to GDPR laws.

No clue what you mean, browser cache might even clear itself without you doing anything manually. This thing makes no sense.

Nowhere ever did it say Tech Demo anywhere, not in the HN headline, not on the page itself. No, thanks. And even as a tech demo, there is nothing impressive going in. It is stores shit to local storage, I guess. Lol, I just looked this up, and it was in Firefox on 2009 already? WHAT? https://developer.mozilla.org/en-US/docs/Web/API/Window/loca... I never used it myself directly, but I remember reading about some API that kind of is the new version of cookies that can store more and better and I think that is it. 2009, I would swear what I think about was newer, maybe I am mixing something up, maybe not.

It has unnecessarily tracking from the comment above, not sure if it even sends all your notes to https://plausible.io, and I do not care. For me, this fails as a tech demo or whatever the fuck It's supposed to be. Sorry to not get all excited about everything posted here. In 2009 it for sure would ;)

If you already use Posthog, Web Analytics has been in Public Beta for quite some time.[1]

If I remember correctly, CloudFlare Analytics does not need you to register your domain with them. I personally feel keeping domain registration coupled with your DNS provider is not a good idea.

Plausible[2] has an Open Source self-hostable version but is not so updated in sync with their SaaS version.

Umami[3] is another simple, clean one. And, of course, as many have suggested, Matomo is the other well-established one. If you want to avoid maintaining a hosting routine, a lot do the hosting out of the box these days. PikaPods[4] was good when I tried and played around for a while.

1. https://posthog.com/docs/web-analytics

2. https://github.com/plausible/analytics

3. https://umami.is

4. https://www.pikapods.com

Plausible - Open Source Alternative to Google Analytics

There are many good, lightweight, and open-source alternatives to Google Analytics, such as Plausible, Matomo, Fathom, Simple Analytics, and so on. Many of these options are open-source, and can be self-hosted.

You log the IP address, referrer, user agent and the requested page URL but you don't set a unique cookie to identify the user.

This still gets you plenty of actionable analytics information: where geographically people are located (via GeoIP), what pages are most popular, what platforms (including desktop vs mobile) people are using.

I've been using https://plausible.io for analytics on a bunch of my sites for a couple of years now and I honestly don't miss the extra level of detail I got from cookie-based analytics I've used in the past.