Concourse
prometheus
Concourse | prometheus | |
---|---|---|
47 | 382 | |
7,181 | 52,843 | |
0.4% | 0.9% | |
9.0 | 9.9 | |
1 day ago | 2 days ago | |
Go | Go | |
Apache License 2.0 | 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.
Concourse
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Elm 2023, a year in review
Ableton ⬩ Acima ⬩ ACKO ⬩ ActiveState ⬩ Adrima ⬩ AJR International ⬩ Alma ⬩ Astrosat ⬩ Ava ⬩ Avetta ⬩ Azara ⬩ Barmenia ⬩ Basiq ⬩ Beautiful Destinations ⬩ BEC Systems ⬩ Bekk ⬩ Bellroy ⬩ Bendyworks ⬩ Bernoulli Finance ⬩ Blue Fog Training ⬩ BravoTran ⬩ Brilliant ⬩ Budapest School ⬩ Buildr ⬩ Cachix ⬩ CalculoJuridico ⬩ CareRev ⬩ CARFAX ⬩ Caribou ⬩ carwow ⬩ CBANC ⬩ CircuitHub ⬩ CN Group CZ ⬩ CoinTracking ⬩ Concourse CI ⬩ Consensys ⬩ Cornell Tech ⬩ Corvus ⬩ Crowdstrike ⬩ Culture Amp ⬩ Day One ⬩ Deepgram ⬩ diesdas.digital ⬩ Dividat ⬩ Driebit ⬩ Drip ⬩ Emirates ⬩ eSpark ⬩ EXR ⬩ Featurespace ⬩ Field 33 ⬩ Fission ⬩ Flint ⬩ Folq ⬩ Ford ⬩ Forsikring ⬩ Foxhound Systems ⬩ Futurice ⬩ FörsäkringsGirot ⬩ Generative ⬩ Genesys ⬩ Geora ⬩ Gizra ⬩ GWI ⬩ HAMBS ⬩ Hatch ⬩ Hearken ⬩ hello RSE ⬩ HubTran ⬩ IBM ⬩ Idein ⬩ Illuminate ⬩ Improbable ⬩ Innovation through understanding ⬩ Insurello ⬩ iwantmyname ⬩ jambit ⬩ Jobvite ⬩ KOVnet ⬩ Kulkul ⬩ Logistically ⬩ Luko ⬩ Metronome Growth Systems ⬩ Microsoft ⬩ MidwayUSA ⬩ Mimo ⬩ Mind Gym ⬩ MindGym ⬩ Next DLP ⬩ NLX ⬩ Nomalab ⬩ Nomi ⬩ NoRedInk ⬩ Novabench ⬩ NZ Herald ⬩ Permutive ⬩ Phrase ⬩ PINATA ⬩ PinMeTo ⬩ Pivotal Tracker ⬩ PowerReviews ⬩ Practle ⬩ Prima ⬩ Rakuten ⬩ Roompact ⬩ SAVR ⬩ Scoville ⬩ Scrive ⬩ Scrivito ⬩ Serenytics ⬩ Smallbrooks ⬩ Snapview ⬩ SoPost ⬩ Splink ⬩ Spottt ⬩ Stax ⬩ Stowga ⬩ StructionSite ⬩ Studyplus For School ⬩ Symbaloo ⬩ Talend ⬩ Tallink & Silja Line ⬩ Test Double ⬩ thoughtbot ⬩ Travel Perk ⬩ TruQu ⬩ TWave ⬩ Tyler ⬩ Uncover ⬩ Unison ⬩ Veeva ⬩ Vendr ⬩ Verity ⬩ Vnator ⬩ Vy ⬩ W&W Interaction Solutions ⬩ Watermark ⬩ Webbhuset ⬩ Wejoinin ⬩ Zalora ⬩ ZEIT.IO ⬩ Zettle
- The worst thing about Jenkins is that it works
- Show HN: Togomak – declarative pipeline orchestrator based on HCL and Terraform
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GitHub Actions could be so much better
> Why bother, when Dagger caches everything automatically?
The fear with needing to run `npm ci` (or better, `pnpm install`) before running dagger is on the amount of time required to get this step to run. Sure, in the early days, trying out toy examples, when the only dependencies are from dagger upstream, very little time at all. But what happens when I start pulling more and more dependencies from the Node ecosystem to build the Dagger pipeline? Your documentation includes examples like pulling in `@google-cloud/run` as a dependency: https://docs.dagger.io/620941/github-google-cloud#step-3-cre... and similar for Azure: https://docs.dagger.io/620301/azure-pipelines-container-inst... . The more dependencies brought in - the longer `npm ci` is going to take on GitHub Actions. And it's pretty predictable that, in a complicated pipeline, the list of dependencies is going to get pretty big - at least a dependency per infrastructure provider we use, plus inevitably all the random Node dependencies that work their way into any Node project, like eslint, dotenv, prettier, testing dependencies... I think I have a reasonable fear that `npm ci` just for the Dagger pipeline will hit multiple minutes, and then developers who expect linting and similar short-run jobs to finish within 30 seconds are going to wonder why they're dealing with this overhead.
It's worth noting that one of Concourse's problems was, even with webhooks setup for GitHub to notify Concourse to begin a build, Concourse's design required it to dump the contents of the webhook and query the GitHub API for the same information (whether there were new commits) before starting a pipeline and cloning the repository (see: https://github.com/concourse/concourse/issues/2240 ). And that was for a CI/CD system where, for all YAML's faults, for sure one of its strengths is that it doesn't require running `npm ci`, with all its associated slowness. So please take it on faith that, if even a relatively small source of latency like that was felt in Concourse, for sure the latency from running `npm ci` will be felt, and Dagger's users (DevOps) will be put in an uncomfortable place where they need to defend the choice of Dagger from their users (developers) who go home and build a toy example on AlternateCI which runs what they need much faster.
> I will concede that Dagger’s clustering capabilities are not great yet
Herein my argument. It's not that I'm not convinced that building pipelines in a general-purpose programming language is a better approach compared to YAML, it's that building pipelines is tightly coupled with the infrastructure that runs the pipelines. One aspect of that is scaling up compute to meet the requirements dictated by the pipeline. But another aspect is that `npm ci` should not be run before submitting the pipeline code to Dagger, but after submitting the pipeline code to Dagger. Dagger should be responsible for running `npm ci`, just like Concourse was responsible for doing all the interpolation of the `((var))` syntax (i.e. you didn't need to run some kind of templating before submitting the YAML to Concourse). If Dagger is responsible for running `npm ci` (really, `pnpm install`), then it can maintain its own local pnpm store / pipeline dependency caching, which would be much faster, and overcome any shortcomings in the caching system of GitHub Actions or whatever else is triggering it.
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We built the fastest CI in the world. It failed
> Imagine you live in a world where no part of the build has to repeat unless the changes actually impacted it. A world in which all builds happened with automatic parallelism. A world in which you could reproduce very reliably any part of the build on your laptop.
That sounds similar to https://concourse-ci.org/
I quite like it, but it never seemed to gain traction outside of Cloud Foundry.
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Ask HN: What do you use to run background jobs?
I used Concourse[0] for a while. No real complaints, the visibility is nice but the functionality isn't anything new.
[0] https://concourse-ci.org/
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How to host React/Next "Cheaply" with a global audience? (NGO needs help)
We run https://concourse-ci.org/ on our own hardware at our office. (as a side note, running your own hardware, you realise just how abysmally slow most cloud servers are.)
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What are some good self-hosted CI/CD tools where pipeline steps run in docker containers?
Concourse: https://concourse-ci.org
- JSON vs XML
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Cicada - Build CI pipelines using TypeScript
We use https://concourse-ci.org/ at the moment and have been reasonably happy with it, however it only has support for linux containers at the moment, no windows containers. (MacOS doesn't have a containers primitive yet unfortunately)
prometheus
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Release Radar · April 2024 Edition: Major updates from the open source community
It's like Prometheus, but for logs. Okay it's not really to do with the Norse or Greek gods, instead Loki is a horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by the open source project Prometheus. Built by Grafana Labs, Loki is designed for ease of use. Instead of indexing the contents of the logs, Loki provides a set of labels for each log stream. The latest update includes query acceleration with Bloom filters, native OTel support, Helm charts, and more. Check out the changelog for all the major changes and deprecations.
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Fivefold Slower Compared to Go? Optimizing Rust's Protobuf Decoding Performance
WriteRequest::timeseries is a vector (https://github.com/prometheus/prometheus/blob/main/prompb/re...) and
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Tools for frontend monitoring with Prometheus
Developers widely use Prometheus as a system for operational monitoring and alerting for their projects. Here is a list of tools for monitoring frontend services with Prometheus.
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The power of the CLI with Golang and Cobra CLI
Just to give an example of the power of Go for CLI builds, you may have already used or at least heard of Docker, Kubernetes, Prometheus, Terraform, but what do they all have in common? They all have a large part of their usability via CLI and are developed in Go 🐿.
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On Implementation of Distributed Protocols
Distributed system administrators need mechanisms and tools for monitoring individual nodes in order to analyze the system and promptly detect anomalies. Developers also need effective mechanisms for analyzing, diagnosing issues, and identifying bugs in protocol implementations. Logging, tracing, and collecting metrics are common observability techniques to allow monitoring and obtaining diagnostic information from the system; most of the explored code bases use these techniques. OpenTelemetry and Prometheus are popular open-source monitoring solutions, which are used in many of the explored code bases.
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Golang: out-of-box backpressure handling with gRPC, proven by a Grafana dashboard
Setting up monitoring for a system, especially one involving GRPC communication, provides crucial visibility into its operations. In this guide, we walked through the steps to instrument both a GRPC server and client with Prometheus metrics, exposed those metrics via an HTTP endpoint, and visualized them using Grafana. The Docker-Compose setup simplified the deployment of both Prometheus and Grafana, ensuring a streamlined process.
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Monitoring, Observability, and Telemetry Explained
Alerting and Notification: Select a tool with flexible alerting mechanisms to proactively detect anomalies or deviations from defined thresholds. Consider asking questions like "Does this tool offer customizable alerting options and support notification channels that suit our team's communication preferences?" A tool like Prometheus provides robust alerting capabilities.
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Observability at KubeCon + CloudNativeCon Europe 2024 in Paris
Prometheus
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Top 5 Docker Container Monitoring Tools in 2024
Prometheus is an open-source monitoring and alerting toolkit. It is designed to monitor highly dynamic containerized systems, making it an excellent choice for monitoring Docker containers and Kubernetes clusters.
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Install and Setup Grafana & Prometheus on Ubuntu 20.04 | 22.04/EC2
wget https://github.com/prometheus/prometheus/releases/download/v2.46.0/prometheus-2.46.0.linux-amd64.tar.gz
What are some alternatives?
drone - Gitness is an Open Source developer platform with Source Control management, Continuous Integration and Continuous Delivery. [Moved to: https://github.com/harness/gitness]
metrics-server - Scalable and efficient source of container resource metrics for Kubernetes built-in autoscaling pipelines.
GitlabCi
skywalking - APM, Application Performance Monitoring System
woodpecker - Woodpecker is a simple yet powerful CI/CD engine with great extensibility.
Jolokia - JMX on Capsaicin
Jenkins - A static site for the Jenkins automation server
Telegraf - The plugin-driven server agent for collecting & reporting metrics.
Jenkins - Jenkins automation server
JavaMelody - JavaMelody : monitoring of JavaEE applications
Buildbot - Python-based continuous integration testing framework; your pull requests are more than welcome!
Glowroot - Easy to use, very low overhead, Java APM