Show HN: OpenObserve – Elasticsearch/Datadog alternative | Hacker News Reader

Show HN: OpenObserve – Elasticsearch/Datadog alternative

github.com

198 pointsprabhatsharma97 comments

Hello folks,

We are launching OpenObserve. An open source Elasticsearch/Splunk/Datadog alternative written in rust and vue that is super easy to get started with and has 140x lower storage cost compared to elasticsearch. It offers logs, metrics, traces, dashboards, alerts, functions (run aws lambda like functions during ingestion and query to enrich, redact, transform, normalize and whatever else you want to do. Think redacting email IDs from logs, adding geolocation based on IP address, etc). You can do all of this from the UI, no messing up with configuration files.

OpenObserve can use local disk for storage in single node mode or s3/gcs/minio/azure blob or any s3 compatible store in HA mode.

We found that setting up observability often involved setting up 4 different tools (grafana for dashboarding, elasticsearch/loki/etc for logs, jaeger for tracing, thanos, cortex etc for metrics) and its not simple to do these things.

Here is a blog on why we built OpenObserve - https://openobserve.ai/blog/launching-openobserve.

We are in early days and would love to get feedback and suggestions.

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Hah, funny to see this on HN, I just tried this 3 days ago when I saw it being discussed on /r/selfhosted.

As someone running a homelab, and hadn't set up logging yet, it was a great find. I didn't have to learn and combine 3+ log technologies, it's just a single all-in-one monitoring server with web UI, dashboards, log filtering/search, etc. RAM usage of the Docker container was under 100MB.

Glad you liked it. Yeah, resource usage is so low that some folks are running it in their homelab even on a raspberry-pi.

How does this compare to Prometheus + Grafana + Loki?

We haven't compared resource usage against grafana stack. Rust is a lot more efficient than Go in general though.

The amount of times I've started building an ELK stack in my home lab just to get pissed and trash it! Will be giving this a go.

Once I wanted to use Data dog with our internal project. Unfortunately I couldn't use the trial, it ended when I was ill at home. So I asked to prolong the trial. Ohhh that was a big mistake. They came upon me with zoom meetings, with two persons from their side. Asking do many questions about everything made me angry. After our internal meeting we agreed that we will use another service. So I emailed Data dog that we will pass on their offer. And then emails started. I was like stalked about "let's do the meeting, tell us more for what do you need 3 weeks more of the trial .." and so on. That was worst corporate behaviour I experienced

Yeah, Reddit is filled with horror stories from Datadog. One thread here - https://www.reddit.com/r/devops/comments/zz4naq/datadog_i_do...

Datadog sales were so annoying I banned their entire domain in google apps.

Then they started sending me emails from their personal gmail accounts…

https://www.linkedin.com/posts/adrianmacneil_sdrs-are-the-fr...

What differentiates you from the grafana stack? Grafana labs now have solutions for every pillar of observability (metrics, logs, traces), and that whole stack also touts cheap ingest (due to minimal upfront indexing and cheap blob storage) as one of its biggest selling points.

Grafana stack is cool. It solved the problem around observability for the first time in a neat and light way. Think about it however, for someone setting up their observability stack they will need to setup 4 components: Grafana (dashboarding), Loki (logs), Mimir (Metrics) and Tempo (Traces). This is a lot of work and a lot of moving parts. It is also a lot of work to maintain and upgrade. Loki, Mimir and Tempo have their own querying languages and their own quirks. Good amount of stuff to learn. It is really hard for a small team to learn and maintain all of this. Junior engineers really struggle with this and seniors resent the time they have to spend on this. Loki also has issues around high cardinality, which is a problem for many teams.

We need something new and better.

We made sure to build a solution that is easy to setup and maintain. One single binary/container for single node setup and one stack to setup/upgrade/maintain for HA setup.

We also made sure that it is easy to use and learn. We allow standard SQL to be used for querying logs, metrics and traces - nothing extra to learn here. Metrics querying is supported using PromQL too. Drag and drop for creating panels and dashboards is there too :-).

Dashboarding is supported within the same stack. No need to setup Grafana or something else separately.

OpenObserve also offer functions that grafana stack does not offer. Give it a shot, you are going to love it.

Data is stored in open parquet format, allowing its usage even outside of OpenObserve if so desired.

Hope this helps.

One of the most annoying things of grafana is that panels can only be edited via the UI and the “changes” summary when you commit is near unusable with a bunch of auto generated garbage.

Having a simple language/YAML to define those panels and an easy way to preview them (eg paste in your file to preview the page, paste in a panel source to try it) in addition to editing via GUI and being able to copy changes back to revision control would be great

This is a good feedback. That would be true DaC (Dashboards as code). We already have plans to implement versioning of dashboards via git. Will see how to get DaC in place.

https://grafana.github.io/grafonnet-lib/

Grafana log handling also isn't the best, especially when compared to New Relic etc.

Loki itself is rock solid. The Grafana integration isn’t the best yeah, it doesn’t even have tabular output. What’s the whole point of doing structural logging if you can’t do that. Many similar low hanging fruit are missing that could easily make it so much more useful. Now every service need its own bespoke query, slowing down exploration.

Exactly. They could take a tip or two from Greylog

How does OpenObserve handle logs with high cardinality?

In Elasticsearch tuning these indices is one of the bigger operational burdens. Loki solves this by asking users to not put data in the indexed fields, instead brute force through it all.

Does OpenObserve have any other approach to this?

Loki solves the problem of high cardinality by saying - "don't have high cardinality".

OpenObserve use parquet as the storage format which is much more resilient to high cardinality. OpenObserve has page indexes and partitions that allow it to handle this very well.

Let's determine what is the "high cardinality" logs. Grafana Loki puts log messages with the same set of labels into a single "stream". For example, all the log messages with the labels {job="nginx", instance="host123"} go into a single stream. Loki stores every stream in a separate set of files and maintains in-memory index per each stream. So big number of streams in Loki lead to "high cardinality" issues such as performance degradation, high memory usage and increased storage load. That's why Loki developers recommend avoiding labels with big number of unique values such as ip, user_id, trace_id, etc.

I'm unsure what does "high cardinality" mean for ElasticSearch case. As I know it happily accepts log messages with high-cardinality labels such as ip, user_id or trace_id.

I don't know whether OpenObserve has any issues related to high cardinality.

P.S. I'm, as VictoriaMetrics core developer, confident that the upcoming log storage and analytics solution from VictoriaMetrics - VictoriaLogs - will be free from Loki-like high cardinality issues.

VictoriaMetrics core developer here.

OpenObserve looks very promising from the first sight! It prioritizes the same properties as VictoriaMetrics products:

- Easy setup and operation

- Low resource usage (CPU, RAM, storage)

- Fast performance

It would be interesting to compare the operation complexity, the performance and resource usage of OpenObserve for metrics vs VictoriaMetrics.

P.S. VictoriaMetrics is going to present its own log storage and log analytics solution - VictoriaLogs - at the upcoming Monitorama conference in Portland [1]. It will provide much smoother experience for ELK and Grafana Loki users, while requiring lower amounts of RAM, disk space and CPU [2].

[1] https://monitorama.com/2023/pdx.html

[2] https://youtu.be/Gu96Fj2l7ls

Sounds interesting!

Will you compare with qryn? Self-hosted sentry?

https://qryn.metrico.in/

https://develop.sentry.dev/self-hosted/

VictoriaLogs will be easier to setup and operate than qryn and self-hosted Sentry. Single-node VictoriaLogs is a single small self-contained statically linked binary with zero external dependencies. It runs with optimal settings out of the box on any hardware - from Raspberry PI to high-end servers with hundreds CPU cores and terabytes of RAM.

Additionally, VictoriaLogs will provide more user-friendly query language - LogsQL, which is easier to use than SQL at ClickHouse or the query language provided by Grafana Loki.

That's inaccurare: qryn does not require SQL at all . It offers LogQL, PromQL, Tempo and other customizable APIs natively by design without requiring the user to know/learn anything.

About the easier aspect that's true - qryn is designed to be an "overlay" on top of various backends such as ClickHouse and IOx (pros and cons for each up to the user) and to provide full granular data control to the underlying set (compliance, gdpr, etc) rather than an all-in-one solution with its own proprietary formats.

I'm looking forward to learning more about VictoriaLogs! You guys always come up with elegant solutions and I'm sure this will be no exception.

The folks at VictoriaMetrics have a track record of producing well-documented and easy-to-use software, and they're responsive.

It's easy to overlook these aspects but they will make all the difference to your team implementing the solution, so if you don't want to gamble, their products are a solid choice.

PS: I have no personal relation or connection with them. I am a user of VictoriaMetrics. Just want to point out things that matter but get ignored when choosing your software stack.

This looks great, I had some questions about the company itself if that's OK, I'm just curious to understand the background:

At the bottom of your GitHub project home page, you say the best way to join the project is to join a WeChat group (in Chinese text), but likely only a very small minority of us outside China use WeChat, so that may be a stumbling block if you are trying to encourage people outside Asia to contribute to the project.

Per https://openobserve.ai/about , the address at the bottom says San Francisco, California, but in the same page it says "headquartered in Bangalore, India". So where are you based out of?

Also curious what the relationship is between OpenObserve the open-source project and Zinc Labs, which is referenced in the website (but not in the GitHub project).

You can join Slack if you are outside of China, or wechat, if you are in China. We had some good early users whoBoth the options are available.

> headquartered in Bangalore, India This is embarrassing, just fixed it. We are a delaware based company, headquartered in San Francisco. Pure copy paste error.

Zinc Labs (Legal name) is the company behind the product OpenObserve.

How do you typo "San Francisco" into "Bangalore"? This seems extremely shady.

Copy and paste from where? Did you even build this product or just hire cheap labor out of India to build it?

Plus one on this. How did this get “typo”?

Please give the benefit of the doubt on HN.

This company created ZincSearch:

https://github.com/zincsearch/zincsearch

Prabhat is one of the core contributors/maintainers:

https://github.com/zincsearch/zincsearch/graphs/contributors

https://github.com/prabhatsharma

Also the negative insinuation of using “cheap” labor out of India to build the product is unnecessary. If you’re concerned about code quality, look at the code.

Assuming everyone working with devs in India is doing so cynically is not charitable.

I dont know why the headquarters was set as india versus SF but does it actually even matter?

> Did you even build this product or just hire cheap labor out of India to build it?

Assuming _only_ cheap labour exists in India is just plain ignorant. Even a discriminatory tone towards some of the outstanding engineers and hackers who live there. Please reconsider your biases, and ask questions in good faith.

It seems that the 140x lower storage cost comes from: 1. S3 (OO) vs EBS (ES): about 5x 2. No indexing: About 10x ? 3. No data duplication (due to using S3 I assume) in HA deployment: 3x

Is my math right? Or do you use something different for compression?

2 Orders of magnitude of storage saving is pretty impressive.

You are right.

Thanks! Best of luck! Looks like a solid product

I poked around the user guide[0] and discovered this:

> Currently OpenObserve support steams of type logs, in future streams types metrics & traces will be supported.

- are metrics and traces queryable yet? I admit, I feel a little misled, if only logs are supported for now that should be made more clear.

- do (or will) metric and trace queries use a similar SQL syntax as log search?

Finally... is there a demo? I would love to be able to try out the product without actually putting in the effort to set it up.

[0]: https://openobserve.ai/docs/user-guide/streams/

I should fix it. Metrics and traces are supported today. Logs and traces are advanced.

Metrics is still in its infancy though.

Here is a quick demo - https://www.youtube.com/watch?v=fZ-ErfMdF-o . Covers basics, logs, function, dashboards. Does not cover traces and metrics.

Logs, Metrics and traces are all queryable.

Logs, metrics and traces are all queryable using SQL. Metrics can additionally use PromQL for querying.

You could also try the cloud version of OpenObserve without setting it up yourself.

Interesting product, thank you for your effort, definitely want to give it a try!

For me, thought, setting up a system is not the primary pain point today. FWIW, signing up for a cloud service is not hard.

The problem starts at the ingestion point. I am writing my apps according to 12 factors and running them in docker containers. What I want is an agent companion that will collect logs and metrics from these apps and containers and forward to the system. Datadog and Grafana has that, do OpenObserve?

Also, interesting that in your quickstart tutorial you have a step of unzipping logs file before sending to the system. I would suggest the ability to send them in (g)zipped format, as it is an organic format of keeping logs.

Got your point. We think that there are 3 really good agents out there that can be leveraged by anyone to capture and send telemetry data. These are Vector, fluentbit and Otel-collector. Between these 3 they can handle pretty much anything. If there is something that needs to be handled that these cannot handle then its easy enough to build plugins for fluentbit and otel-collector.

We did not want to reinvent the wheel and recommend that you use one of these.

quickstart tutorial does not reflect what you will do in real life scenario.

You will want to learn how to use log forwarders like fluentbit and vector. These are the ones that you will generally use in real life. These log forwarders read the log files and send them to OpenObserve. Here is a blog on how to use fluentbit to capture kubernetes logs using fluentbit and send them to OpenObserve - https://openobserve.ai/blog/how-to-send-kubernetes-logs-usin...

We will add more tutorials and examples soon.

Hope this helps.

Hi SergeAx, I am building Odigos, which do exactly what you asked :) We combine OpenTelemetry and eBPF to automatically generate and deliver distributed traces, metrics and logs to Grafana, DataDog and other 15+ destinations. Check it out here: https://github.com/keyval-dev/odigos

Hi, great to meet another builder here on HN) Am I understand correctly that your product is like grafana_agent, but vendor-agnostic?

Was excited about this as I've been looking to host my own DataDog substitute for aggregating logs, alerts, searches, metrics, etc. Wanted to give this a shot but unfortunately unlike Grafana Loki, which is more complicated, it does not have clear enough documentation, or at least I couldn't find out whether it had a collection agent, to easily ingest logs and metrics to submit to the system.

With DataDog they have an easy to install agent that runs and ingests log data and can also handle (somewhat) for duplications due to disruptions.

Based on the OpenObserve documentation one essentially has to curl the logs themselves to the web service to submit, standardize on fluentd or equivalent, or tie into one of the other providers / agents.

I'm sure it's possible and that one of the services/software they mention briefly provides for this hopefully so I don't have to create my own agent, or ensure all services have a central logger that curls.

Not every system is on Cloudwatch (Linode, etc), and in many cases I'd like to have different ways to ingest and manage all the different logs one may produce. AWS EC2, Lambda, Linode, etc. Was really hoping for an easy server setup, plus agent, API, etc. Also, for systems you want to observe and not modify, such as 3rd party systems, would be nice to have an easy to add agent as to not have to modify or take ownership of those processes.

How does this compare to Parseable?

https://github.com/parseablehq/parseable

First guess is that the underlying storage / query layer is pretty similar (Parquet + Datafusion), but OpenObserve has more built in use cases?

As an aside, it’s awesome that Datafusion’s existence and maturity makes launching a product with scalable analytical reads 10x easier than before and cool to see so many projects integrating it

Parquet and data fusion are awesome. Parseable is a good project. Just has logs though. No HA either. They will get there though - I don't know anyone there, but they certainly need to if not there already.

Yes, OpenObserve has a lot more use cases than parseable and our focus on ease of use has allowed us to build far more features and join them in an elegant way making it easier to use it, than any other platform out there.

Give it a shot and let us know what you think.

We wish parseable team our best wishes.

Will definitely give this a try. I'm sick of fighting with Loki on upgrades and a recent migration that refuses to save to S3 properly. Exactly to your point of having to dive deep in so many components.

Intro video is private. I could not access it on YouTube.

https://openobserve.ai/docs/getting-started/

Thanks for pointing out. Fixed it.

Looked at this a couple of days ago and it looks impressive! However, it does saddens me to see the elasticsearch api compatibility being behind a license, I had hoped this was also going to be open after discovering the functionality existed in the first place.

I guess having the cake and eating it too is actually not feasible in this case.

With that said I will indeed have a go at this and test and see if I can replace elasticsearch in our stack.

Good job guys!

Is this because of the elastic search license or simply the project author chose to commercialize this functionality?

Elasticsearch compatibility is provided through a separate component. We believe that this should be a commercial solution. As a commercial entity we need to figure out ways of making money. This is one of them along with SaaS.

What is your use case?

Congrats on the launch! Elasticsearch/Splunk/Datadog, are expensive beasts. It's great to see an opensource alternative!

I wonder why you want to write your own database called zincsearch. This gets very hard at scale and with all edge cases.

Wouldn't be easier to use Cickhouse?

ZincSearch was an old product that we built. We don't use that in OpenObserve in any way. OpenObserve is built on top of awesome Apache arrow datafusion.

How long until you have SSO/SCIM/IDP support? Being able to provision and manage users and sign in via Okta will be really useful.

An ask, we are getting from a lot of users. We are trying to figure out the most sophisticated and easiest way. Goal is to be able to do it in next 2-3 months. We have looked at Dex, but have not finalized on anything yet.

Do you support OTLP endpoint for this? E.g. send metrics, traces, and logs in OpenTelemetry protocol? It’s a bit unclear on the site

Yes, OTLP is supported. You can use standard Opentelemetry SDKs with no changes required.

Check the docs - https://openobserve.ai/docs/ingestion/traces/opentelemetry/

May be intentional but fyi the Getting Started video from your docs won't run for me, gets "Video unavailable / This video is private". I think it would be useful introductory info, although I'm just a hobbyist not a very likely customer.

Thanks for pointing out. fixed.

That's traces? I was wondering if I could use https://github.com/open-telemetry/opentelemetry-js/tree/main...

Yes, you could use it. If you face any issues, let us know through github issues or on slack.

According to the docs you only have OpenTelemetry ingest support for traces, but not metrics and logs. Is that right?

No metrics and logs with OTLP yet. Only traces for now. Will have it soon for logs and metrics too.

That would be awesome. We have an app that exports telemetry and it's such a pain in the ass to try to test that feature locally (jaeger all-in-one is easy to run, but only supports traces)

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).

qryn developer here, thanks for the mention! We will be watching and following the evolution of this project as well as the established masters such as VictoriaMetrics with much admiration and curiosity. It's a busy space!

For qryn the vision remains "abstracting" our two-way polyglot Observability APIs transparently on top of multiple modern data backends (InfluxDB IOx/Flight is right next!) and being able to operate on both edge (light js) and core (fast go/rust) and provide users more choice and control as of how they can spend resources to store and leverage the data they collect in as many ways as possible, while transparently using the protocols, tools, agents and formats they already love and trust.

Inserting lots of Observability data really fast is relatively easy nowadays but reading the same data back from multiple APIs supporting multiple vendors formats is where qryn comes in strong without requiring new tools or plugins. We love to think of qryn as one of the many good vendor-locksmith tools for observability integrators out there who want more of out their data.

If anyone's curious you can try and benchmark qryn for free at https://qryn.cloud

*SigNoz

qryn, signoz and highlight are all built on top of clickhouse. I think they all have done a good job of leveraging clickhouse instead of building something from the ground up. That is a super smart thing.

We think that managing clickhouse is extra effort. Direct access to object storage is vital for cost reduction which is somewhat difficult in clickhouse. Small resource footprint is extremely valuable to many too. Simplicity of deployment is key to get started and many will use a product simply because they can. I know, I have used products that simply ran, and for no other reason.

Our best wishes to all the 3 teams.

> Use of SIMD instruction sets (AVX-512 and neon)

Have you done any comparison testing with various generations of hardware? AFAIK the latest generations don't down clock their scalar turbo that much (or at all), but Skylake definitely did.

We have done minimal performance testing for now. A lot of work is remaining in this area.

What metrics and tests do you have to support your petabyte scale claim?

We have run internal tests to ingest 1.2 petabayte of data in 24 hours period. We are making further improvements in performance and we will publish details that will allow you to replicate it yourself.

The documentation website is really nice, what did you use? Can you share the code? I have an OSS project, would like something similar!

used material for mkdocs - https://github.com/squidfunk/mkdocs-material . Its pretty neat.

How are you different from something like Signoz ?

I think Signoz is a good product. The team really understands that it you need all 3 - logs, metrics and traces - to effectively observe and troubleshoot applications and has done a good implementation.

Certain difference on how OpenObserve is different than Signoz:

1. Built in rust for high performance and memory safety. 2. Has its own data storage engine based on open specification of Apache Parquet. This allows the data to be used by other applications outside of OpenObserve if desired. 3. Use stateless nodes in architecture (You are going to love it from maintenance point of view). 4. Use of object storage for extreme scalability and low cost. 5. Requires far lower resources than Signoz. Can practically be run using a single binary/container. There are users ingesting terabytes of data on a single node. There are people running OpenObserve on raspberry-pi. 6. A lot easier to setup and maintain. 7. Ingest and query functions are awesome. Give them a shot.

Hi Prabhat, I am one of the maintainers at SigNoz. Nice to see OpenObserve's belief that the future of Observability should be OpenSource. We chose clickhouse rather than building a database as it takes multi-year effort to move a db to maturity and clickhouse has been battle-tested at Yandex, Uber and Cloudflare. Clickhouse also provides native integration with s3 and other blob storages. Our users have been using disk as hot storage for a week and moving the data to s3 after that. Tiered storage is really cool in terms of query performance.

We have also seen logs data of our users at a compression ratio of 30x/40x. We have published a logs benchmark (https://github.com/SigNoz/logs-benchmark) where the data is very high-cardinal (causing a compression factor of only 2.5x). Would love to see how does OpenObserve perform in that dataset someday.

Wishing you best for the journey ahead.

so if i understand docs correctly i can integrate it with existing prometheus setup to get eg. long term metrics with S3?

and this would allow me also to get all logs to the same place?

i see that graphana support in enterprise only feature, does it mean that long term metrics mirrored from prometheus are no longer available there?

is there any way to get just graphana plugin for self hosted deployment?

Yes, you understand it correctly that you can integrate existing prometheus with OpenObserve for long term storage of metrics with s3.

And yes you can get your logs there too.

You don't need grafana as OpenObserve has good dashboarding for metrics and logs built in. Still early days for OpenObserve - some features are missing and has some rough edges.

I am not sure what you mean by:

> does it mean that long term metrics mirrored from prometheus are no longer available there?

> is there any way to get just graphana plugin for self hosted deployment?

ping us on slack channel, If you are able to make friends with the team you can get anything :-) . Fair warning - We have found OpenObserve logs UI to be much smoother and advanced than grafana UI.

> I am not sure what you mean by:

> > does it mean that long term metrics mirrored from prometheus are no longer available there?

Ok maybe i didn't describe it clearly, what i mean is that long term metrics will be only available over OO, and when we use grafana and our team is already familiar with that then it's issue that for querying long term metrics we would need to use different tool/UI.

> We have found OpenObserve logs UI to be much smoother and advanced than grafana UI.

Grafana isn't very good with logs, but as far as I understand you are able to generate metrics from logs in OO (like errors/exceptions per hour or time since last error etc) and adding them to existing dashboard in grafana is quite important.

PS: overall OO looks very promising and I'm currently investigating self hosted product to aggregate logs and I'm for sure will be evaluating OO :)

Might I suggest a Helm chart so that people can just apply it/install it to their home test k8s cluster, etc.

And instructions on how to build a Docker image from sources. Looks nice, would love to try this out.

Here you go - https://github.com/openobserve/openobserve/blob/main/CONTRIB...

Helm chart is available for HA mode installation that allows you to use minio/s3/gcs etc - https://openobserve.ai/docs/ha_deployment/ .

For single node installations, you can use base k8s manifests that is available using instructions at - https://openobserve.ai/docs/quickstart/#self-hosted-installa... .

Mike created a helm chart for s ingle node installation too - https://github.com/mshade/openobserve-chart/

Do you have any benchmark tests with Loki?

Not yet. Will have it in some time.

> It is a drop-in replacement for Elasticsearch if you are just ingesting data using APIs and searching using kibana (Kibana is not supported nor required with OpenObserve. OpenObserve provides its own UI which does not require separate installation unlike kibana).

This seems contradictory. If OpenObserve doesn't support Kibana, how can it be a drop-in replacement for Elasticsearch queried using Kibana? Even if the OpenObserve UI has similar features, I can't just replace Elasticsearch with OpenObserve and keep the rest of my workflow the same, which I would consider a precondition to call something a drop-in replacement.

Said an alternate way, if Kibana drop in compat could be a thing, it would be easier for infra teams to do it as a drop in into existing stacks at bigger companies which makes it easier to steal market share of existing customers on the ES stack. There’s probably a non trivial amount of existing dashboards built and UX familiarity that would be a friction point without seamless compat.

Is Kibana support totally out of the question?

>Is Kibana support totally out of the question?

Actually no.

We already have a good amount of pieces in place to support kibana. 2-3 weeks of dev work and it will be ready. Just not the top priority right now though.

Wow that’s really cool. Great job!

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