qryn VS ClickHouse

Thanks for mentioning qryn! We are a non-corporate alternative and feature full ingestion compatibility with DataDog (including Cloudflare emitters, etc), Loki, Prometheus, Tempo, Elastic & others for both on-prem (https://qryn.dev) and Cloud (https://qryn.cloud) deployments, without the killer price tag.

Note: in qryn s3/r2 are as close to /dev/null as it gets!

qryn and vector get along very well! We use it all the time for testing and developing qryn and qryn.cloud and most of our users love it! But we're just as compatible with Loki/LogQL, Influx protocol for metrics and logs, Elastic Bulk, Prometheus for metrics, opentelemetry for everything... and more coming!

Feel free to open an issue on our repository if you end up trying it and/or would like us to help out!

https://qryn.dev

> *it would be nice to use ClickHouse as a Prometheus backend*

Well... that's already possible and it works great! As you might know https://qryn.dev turns ClickHouse into a powerful Prometheus *remote_write* backend and the GO/cloud version supports full PromQL queries off ClickHouse transparently (the JS/Node version transpiles to LogQL instead) and from a performance point of view its well on par with Prometheus, Mimir and Victoriametrics in our internal benchmarks (including Clickhouse as part of the resource set) with millions of inserts/s and broad client compatibility. Same for Logs (LogQL) and Traces (Tempo)

Disclaimer: I work on qryn

Thanks for mentioning our project! qryn (formerly cloki) is currently more focused on the polyglot factor and trying to unify logs, metrics and telemetry on a single stateless platform, easy to scale without hundreds of services and moving parts. At this stage, its a lightweight Grafana Cloud alternative just requiring clickhouse - no sidecar databases, redis, or plugins needed, and no new query languages or rules to learn. Latest info is at https://qryn.dev

cloki can be used to read metrics out of any CH table so it should work fine.

we also just introduced experimental support for ingesting OTLP/ZIPKIN spans and a tempo-compatible API in cloki, looking for testers to validate this feature:

https://github.com/lmangani/cLoki/wiki/Tempo-Tracing#clickho...

Internally trace spans are stored as tagged JSON logs, meaning they are available from both Loki and Tempo APIs and can be used from pretty much any visualization, too!

There's also cLoki. It's a new project that puts a Loki gateway over a ClickHouse backend store. We're looking at it and plan a presentation from the author(s) at the next ClickHouse SF Bay Area Meetup.

https://github.com/lmangani/cLoki

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;