ClickHouse VS highlight

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;

[2] https://app.highlight.io/error-tags

[2] https://github.com/highlight/highlight/tree/main

We have an SDK request here: https://github.com/highlight/highlight/issues/4225

We don't have a particular leaning towards javascript, but haven't gotten to PHP yet. We're definitely open to contributors, but otherwise, we can hopefully get to this in the coming months.

Hi Hacker News! We’re Jay and Vadim from Highlight.io (https://highlight.io). We’re building a truly open source [1] observability platform for modern web applications. We posted some of our tools to HN in recent months [2][3]. Today, we’re excited to formally launch the project, share more about where we’re going, and of course, poll the community for some feedback.

A bit of background: Vadim and I have worked at quite a few startups at this point, and a recurring challenge we’ve faced was tracing usability issues on the frontend to downstream errors and logs on the server. Understanding the real reason behind customer issues was always a chaotic juggling of multiple tools. With the rise of "frontend-forward" frameworks such as NextJS, which blur the boundary between the client and server, the complexity of tracing these issues is only growing.

This is where Highlight.io comes in: our product bridges the gap between client and server to give you a holistic view of your entire application.

At its core, Highlight.io has three main “products”: Session Replay, Error Monitoring, and Logging. The novelty here is not in each product but in how they are connected. For example, in Highlight.io it’s very easy to click from a given error to the associated user session where it is thrown [4], and from a given error, you can easily inspect all of the logs that fired leading up to it. Ensuring that all of our products work together seamlessly with little to no effort is a core principle of our product strategy. If you’re using a common framework [5], for example, we’ll automatically link your frontend sessions with backend errors and logs. No agents, configuring facets, or anything else, It just works.

We depend on several open source projects that help us move quickly. OpenTelemetry (OTEL) [6] is one of them, which helps us with maintainability, i.e. for every language that we support, we only maintain a thin wrapper around its respective OTEL SDK. OTEL is also a great way to enable the community to contribute, and we’re already seeing traction in this space (ie. an open source contributor built a wrapper for a Java SDK [7]).

rrweb [8] is another project we leverage heavily for our session replay product. It drives our ability to record and replay the DOM to visualize user flows in the frontend. We’ve had the privilege to work closely with the rrweb team to ship improvements, and we’re now actively sponsoring the project [9].

ClickHouse [10] has recently become a loved database on our team, as we historically used Opensearch for search-heavy workloads and started to hit growing pains with ingest throughput. We recently rolled it out for our logging product [3] and plan to replace our sessions and errors (and upcoming tracing work) with the database as well.

From a business perspective, Highlight.io is open source under the Apache 2.0 license, and we make money with our hosted product [11]. For the hosted product, you can set billing caps for each offering and we don’t charge for seats. At this point, we have 100+ companies paying for our product (some of which are large enterprises), and thousands of sole developers use Highlight.io every week.

On our roadmap [12] for the future includes metrics, tracing, release management and more. We also are launching several updates this week on our launch week page [13].

Overall, we’re excited to be sharing Highlight.io with the world, and Vadim and I are particularly excited to get some feedback from the HN community. Please give us a test-drive at https://app.highlight.io and let us know what you think. We would love to learn about what you wish you had in an observability product as well as any other experiences and ideas in this space. We look forward to hearing from you!

https://gitlab.com/hyperlink-academy/app https://github.com/highlight/highlight https://github.com/calcom/cal.com https://github.com/Nutlope/roomGPT

I'd be curious to hear how this compares to

https://qryn.metrico.in

and

https://github.com/highlight/highlight

(There are some interesting comparisons/comments vs signoiz in sibling threads).

We only scratched the surface of vanilla-extract here, so check out the documentation if you’re interested in learning more. We’ll continue to share about how we are leveraging it to build the Highlight design system, and all our code is open source if you’re interested in exploring our usage more. All the code for the examples in this article are also available for anyone to fork and play around with as well.