veneur
self-hosted
veneur | self-hosted | |
---|---|---|
2 | 29 | |
1,714 | 7,284 | |
0.1% | 1.5% | |
3.5 | 9.1 | |
about 1 month ago | 5 days ago | |
Go | Shell | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
veneur
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OpenTelemetry in 2023
This was the idea behind Stripe's Veneur project - spans, logs, and metrics all in the same format, "automatically" rolling up cardinality as needed - which I thought was cool but also that it would be very hard to get non-SRE developers on board with when I saw a talk about it a few years ago.
https://github.com/stripe/veneur
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Launch HN: Opstrace (YC S19) – open-source Datadog
One pain point with Prometheus is that is has relatively weak support for quantiles, histograms, and sets[1]:
- Histograms require manually specifying the distribution of your data, which is time-consuming, lossy, and can introduce significant error bands around your quantile estimates.
- Quantiles calculated via the Prometheus "summary" feature are specific to a given host, and not aggregatable, which is almost never what you want (you normally want to see e.g. the 95th percentile value of request latency for all servers of a given type, or all servers within a region). Quantiles can be calculated from histograms instead, but that requires a well-specified histogram and can be expensive at query time.
- As far as I know, Prometheus doesn't have any explicit support for unique sets. You can compute this at query time, but persisting and then querying high-cardinality data in this way is expensive.
Understanding the distribution of your data (rather than just averages) is arguably the most important feature you want from a monitoring dashboard, so the weak support for quantiles is very limiting.
Veneur[2] addresses these use-cases for applications that use DogStatsD[3] by using clever data structures for approximate histograms[4] and approximate sets[5], but I believe its integration with Prometheus is limited and currently only one-way - there is a CLI app to poll Prometheus metrics and push them into Veneur, but there's no output sink for Veneur to write to Prometheus (or expose metrics for a Prometheus instance to poll).
It would be extremely useful to have something similar for Prometheus, either by integrating with Veneur or implementing those data structures as an extension to Prometheus.
[1] https://prometheus.io/docs/practices/histograms/
[2] https://github.com/stripe/veneur
[3] https://docs.datadoghq.com/developers/dogstatsd/
[4] https://github.com/stripe/veneur#approximate-histograms
[5] https://github.com/stripe/veneur#approximate-sets
self-hosted
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Pydantic Logfire
I was responding to the One of the Sentry inconvenience is self-hosting: it relies on so many services it can be very complicated to maintain part, and also reminding readers that if they, too, hate companies that rug-pull their open source licenses, there is a band-aid for both parts
Compare https://github.com/getsentry/self-hosted/blob/9.1.2/docker-c... with https://github.com/getsentry/self-hosted/blob/24.4.2/docker-... for what life used to be like for running Sentry on-prem. It was awesome
It would take a ton of work to dig up the actual memory and CPU requirements of each one, but rest assured they're not zero, so every one of those services eats ram and requires TLC when, not if, they shit themselves. So, more parts == more headaches with all other things being equal
Then, I deeply appreciate that there are a whole spectrum of reactions to the various licensing schemes in use nowadays, and a bunch of folks don't care. I care, though, because I have gotten immense value from open source projects, and have contributed changes back to quite a few. It has been my life experience that any of those "source available" licenses usually are very hostile toward making local builds and if I can't build it to match how prod goes, then I can't test my fixes in my environment and then I can't contribute the PR with any faith
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Sentry new TOS to use data to train AI with no opt-out
This is the point where I will point out that you can self-host Sentry free of charge :) https://develop.sentry.dev/self-hosted/
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Low cost self-hosted bug reporting?
Sentry can be self hosted: https://develop.sentry.dev/self-hosted/
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FSL: A License for the Bazaar, Not the Cathedral
The people we're concerned about are not the hundreds of thousands of Sentry users, including those that self-host.
We're concerned about people who have taken the software for the purposes of competing directly against us, that hinders our ability to monetize the work. Monetizing the work helps us continue improving the software and distribute it for free use, benefitting those aforementioned real users (e.g. https://github.com/getsentry/self-hosted).
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Show HN: A open-source financial accounting alternative to QuickBooks
> I mean no slander or disrespect to anyone involved, but there was a DataDog alternative posted sometime in the last few weeks that had a docker-compose with like 15 containers in it.
Reminds me of Sentry: https://develop.sentry.dev/self-hosted/
This is their example docker-compose for self-hosting: https://github.com/getsentry/self-hosted/blob/master/docker-...
It has:
- exim4 (smtp)
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OpenTelemetry in 2023
> What should people use?
I recall Apache Skywalking being pretty good, especially for smaller/medium scale projects: https://skywalking.apache.org/
The architecture is simple, the performance is adequate, it doesn't make you spend days configuring it and it even supports various different data stores: https://skywalking.apache.org/docs/main/v9.0.0/en/setup/back...
The problems with it are that it isn't super popular (although has agents for most popular stacks), the docs could be slightly better and I recall them also working on a new UI so there is a little bit of churn: https://skywalking.apache.org/downloads/
Still better versus some of the other options when you need something that just works instead of spending a lot of time configuring something (even when that something might be superior in regards to the features): https://github.com/getsentry/self-hosted/blob/master/docker-...
Sentry is just the first thing that comes to mind (OpenTelemetry also isn't simpler due to how much it tries to do), but compare its complexity to Skywalking: https://github.com/apache/skywalking/blob/master/docker/dock...
I wish there was more self-hosted software like that out there, enough to address certain concerns in a simple way on day 1 and leave branching out to more complex options like OpenTelemetry once you have a separate team for that and the cash is rolling in.
- Why use application stacks script installers
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OpenObserve: Elasticsearch/Datadog alternative in Rust.. 140x lower storage cost
Sounds interesting!
Will you compare with qryn? Self-hosted sentry?
qryn.metrico.in/
https://develop.sentry.dev/self-hosted/
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Insufficient logging
I haven't done it in years, but technically sentry is able to be self hosted https://github.com/getsentry/self-hosted
- Cloud Native Alternative to Sentry?
What are some alternatives?
opstrace - The Open Source Observability Distribution
Sentry - Developer-first error tracking and performance monitoring
cortex - A horizontally scalable, highly available, multi-tenant, long term Prometheus.
Code-Server - VS Code in the browser
Cortex - Cortex: a Powerful Observable Analysis and Active Response Engine
apprise - Apprise - Push Notifications that work with just about every platform!
loki - Like Prometheus, but for logs.
zammad-docker-compose - Zammad Docker images for docker-compose
influxdb-apply - Define InfluxDB users and databases with a yaml file.
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
b3-propagation - Repository that describes and sometimes implements B3 propagation
JupyterLab - JupyterLab computational environment.