openobserve VS superset

Lot of interesting OSS observability products coming out in recent years. One of the more impressive(and curious for many reasons) IMHO is OpenObserve: https://github.com/openobserve/openobserve .

As opposed to just a stack, they are implementing just about the whole backend shebang from scratch.

in case it matters to others, https://github.com/openobserve/openobserve/tree/v0.7.0 is the last Apache2 licensed copy before they went AGPL with 0.7.1

https://github.com/openobserve/openobserve/blob/v0.7.0/.env.... is some "onoz" for me, but just recently someone submitted https://github.com/aenix-io/etcd-operator to the CNCF sandbox so maybe things have gotten better around keeping that PoS alive

eCharts is awesome. We moved from plotly after using it for several months to echarts at https://github.com/openobserve/openobserve and are super happy.

Wouldn't make more sense to have the same observability stack on production and development? For instance, open-observe is also a single binary that provides UI for logs, metrics and traces, although every log producer would have to be properly configured and routing to it.

Another idea: maybe chrome dev-tools could be repurposed to display server logs instead of client logs, somehow [2].

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1: https://github.com/openobserve/openobserve

2: https://chromedevtools.github.io/devtools-protocol/

It doesn't read from files unfortunately, but https://openobserve.ai/ is very easy to set up locally (single binary) and send otel logs/metrics/traces to.

Here's how I run it locally for my little shovel project - https://github.com/bbkane/shovel#run-the-webapp-locally-with... .

Also linked from that README is an Ansible playbook to start OpenObserve as a systems service on a Linux VM.

Alternatively, see the shovel codebase I linked above for a "stdout" TracerProvider. You could do something like that to save to a file, and then use a tool to prettify the JSON. I have a small script to format json logs at https://github.com/bbkane/dotfiles/blob/2df9af5a9bbb40f2e101...

Implement a monitoring stack, or openobserve for an all-in-one package.

I would recommend you take a look at OpenObserve (https://github.com/openobserve/openobserve ). It's free and open source and can do all you asked and more with far lower resource utilization. It's the easiest to run of any log system that you can find. Can capture windows and linux logs. Also compresses them heavily (30-60x, YMMV). 100 GB ingested logs can be 3 GB stored.

I have used https://github.com/openobserve/openobserve in several hobby projects and liked it. It's an all-in-one solution. It's likely less featureful than many others but a single binary and everything in one place pulled me in and worked for me so far.

Not affiliated, I just like the tool.

A good one. A lot is being built on top of clickhouse. I can count at least 3 if not more (hyperdx, signoz and highlight) built on top of clickhouse now.

We at OpenObserve are solving the same problem but a bit differently. A much simpler solution that anyone can run using a single binary on their own laptop or in a cluster of hundreds of nodes backed by s3. Covers logs, metrics, traces, Session replay, RUM and error tracking are being released by end of the month) - https://github.com/openobserve/openobserve

Superset is absolutely phenomenal. I really hope Microsoft eventually releases all of their customizations they made to it internally to the OS community someday.

https://www.youtube.com/watch?v=RY0SSvSUkMA

https://github.com/apache/superset/discussions/20094

I recently ran a little shootout between Superset, Metabase, and Lightdash. All have nontrivial weaknesses but I ended up picking Lightdash.

Superset the best of them at _data visualization_ but I honestly found it almost useless for self-serve _BI_ by business users. This issue on how to do joins in Superset (with stalebot making a mess XD) is everything difficult about Superset for BI in a nutshell. https://github.com/apache/superset/issues/8645

Metabase is pretty great and it's definitely the right choice for a startup looking to get low cost BI set up. It still has a very table centric view, but feels built for _BI_ rather than visualization alone.

Lightdash has significant warts (YAML, pivoting being done in the frontend, no symmetric aggregates) but the Looker inspiration is obvious and it makes it easy to present _groups of tables_ to business users ready to rock. I liked Looker before Google acquired it. My business users are comfortable with star and snowflake schemas (not that they know those words) and it was easy to drop Lightdash on top of our existing data warehouse.

Or like not being able to delete a user without running some SQL:

https://github.com/apache/superset/issues/13345

Almostl instantly run into this issue setting up a test instance of Superset. And the issue has been around for years.

Are you interested in trying out Superset, but you're intimidated by the local setup process? Worry not! Superset needs some initial setup to install locally, but I've got a streamlined way to get started - using the makefile! This file contains a set of scripts to simplify the setup process.

Recently I discovered BigQuery public datasets - just over 200 datasets available for directly querying via SQL. I think this is a great thing! I can connect these direct to an analytics platform (we use Apache Superset which uses Python SQLAlchemy under the hood) for example and just start dashboarding.

if they don't want to pay for powerbi, can try something like https://superset.apache.org/

In today's fast-paced data-driven world, organizations must analyze data in real-time to make timely and informed decisions. Real-time data analytics enables businesses to gain valuable insights, respond to real-time events, and stay ahead of the competition. Also, the analytics engine must be capable of running analytical queries and returning results in real-time. In this article, we will explore how you can build a real-time data analytics solution using the open-source tools Redpanda a distributed streaming platform, Apache Superset, a data visualization, and a business intelligence platform, combined with RisingWave a streaming database.