veneur
odigos
veneur | odigos | |
---|---|---|
2 | 40 | |
1,714 | 3,024 | |
0.1% | 1.1% | |
3.5 | 9.8 | |
about 1 month ago | about 14 hours ago | |
Go | Go | |
MIT License | Apache License 2.0 |
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
odigos
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Setup odigos in Ubuntu
Welcome 👋 to this blog. Did you want to use odigos on your Ubuntu machine but don't know how to start? Then this blog will definitely help you and it also helps you to understand the basic aspect of the odigos project.
- Open Source Distributed Tracing Through eBPF
- Odigos v0.1.82 - Open-source instant distributed tracing without code changes
- Odigos – Language Agnostic Auto-Instrumentation
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OpenTelemetry in 2023
Disclaimer: I am one of the maintainers
Many comments complain about the complexity of using OpenTelemetry, I recommend checking out Odigos, an open-source project which makes working with OpenTelemetry much easier: https://github.com/keyval-dev/odigos
We combine OpenTelemetry and eBPF to instantly generate distributed traces without any code changes.
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OpenObserve: Elasticsearch/Datadog alternative in Rust.. 140x lower storage cost
Check it out here: https://github.com/keyval-dev/odigos
- Odigos v0.1.5 - Managing OpenTelemetry using Kubernetes labels
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Instantly Generate and Send OpenTelemetry data to AWS S3
Hi, sure here is the link to Odigos: https://github.com/keyval-dev/odigos We do not replace Grafana or any other monitoring vendor. We build them a better pipeline with higher quality signals (distributed tracing). They still do the visualization, it will just display better information.
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Automatic Instrumentation As A Kubernetes Virtual Device
Check out mor about Odigos, our open source project at https://github.com/keyval-dev/odigos
- Extending Containers with Kubernetes Device Plugin
What are some alternatives?
opstrace - The Open Source Observability Distribution
opentelemetry-js - OpenTelemetry JavaScript Client
cortex - A horizontally scalable, highly available, multi-tenant, long term Prometheus.
openobserve-chart - Simplified Helm chart for single-node OpenObserve
Cortex - Cortex: a Powerful Observable Analysis and Active Response Engine
openobserve - 🚀 10x easier, 🚀 140x lower storage cost, 🚀 high performance, 🚀 petabyte scale - Elasticsearch/Splunk/Datadog alternative for 🚀 (logs, metrics, traces, RUM, Error tracking, Session replay).
loki - Like Prometheus, but for logs.
opentelemetry-java-instrumentation - OpenTelemetry auto-instrumentation and instrumentation libraries for Java
influxdb-apply - Define InfluxDB users and databases with a yaml file.
b3-propagation - Repository that describes and sometimes implements B3 propagation
opentelemetry-proto - OpenTelemetry protocol (OTLP) specification and Protobuf definitions