Riemann
jaeger
Riemann | jaeger | |
---|---|---|
10 | 94 | |
4,213 | 19,409 | |
0.1% | 0.7% | |
6.2 | 9.7 | |
4 months ago | 7 days ago | |
Clojure | Go | |
Eclipse Public License 1.0 | 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.
Riemann
-
Is it a good idea to write logs into Kafka from Go services?
This is fine- we do something similar using riemann.
- What killed Haskell, could kill Rust, too (2020)
-
Every Simple Language Will Eventually End Up Turing Complete
"It can't go into infinite loop" is utterly irrelevant. Over last maybe 15 years I've used a bunch of apps that just used their own programming language (from simple DSL to "just write exactly how the app is supposed to handle data") and literally not a single time has that become a problem.
-
How important is Observability for SRE?
Metrics are measurements of something about your system. They are numeric values, over an interval of time, usually with associated metadata (e.g., timestamp, name). They can be raw, calculated, or aggregated over a period of time. They can come from a variety of sources like servers or APIs. Metrics are structured by default and can be stored in open source systems like Prometheus and Riemann or in off-the-shelf solutions like Amazon CloudWatch and Azure Monitor. These optimized storage systems allow you to perform queries, create alerts, and store them for long periods of time.
- A monitoring system where the agents connect to the server?
-
Is Clojure the right tool for the job?
Reason #1 - Riemann https://riemann.io/
-
Do You Know Where Lisp Is Used Nowadays?
Riemann is a tool for distributed system monitoring. It aggregates events from user servers and applications, combines them into a stream and transmits them for further processing or storage. Greater flexibility and fault-tolerance make Riemann different from other similar systems. Moreover, it’s written in Clojure almost completely. The code is available on GitHub and is distributed under Eclipse Public License 1.0.
- Riemann – A Network Monitoring System
-
Mirabelle, a stream processing tool for monitoring inspired by Riemann, release v0.1.0
I did a new release today of Mirabelle, a stream procesing tool heavily inspired by Riemann. I also spent a lot of time on the documentation website if you want to try it, and also wrote an article today about an use case.
-
I want to quit my data analyst job and learn and become a Clojure developer
Consider dabbling in a project to get your feet wet first. You have a neat problem you want solved? Give it a shot. There an interesting open source project, fork it and tinker with the code. This will be tremendously educational both vocationally and will help you get a feel for if you'd like to work in clojure all the time. There are a lot of projects, but I chose https://github.com/riemann/riemann to read and try better to understand real world clojure.
jaeger
-
Observability with OpenTelemetry, Jaeger and Rails
Jaeger maps the flow of requests and data as they traverse a distributed system. These requests may make calls to multiple services, which may introduce their own delays or errors. https://www.jaegertracing.io/
-
Show HN: An open source performance monitoring tool
As engineers at past startups, we often had to debug slow queries, poor load times, inconsistent errors, etc... While tools like Jaegar [2] helped us inspect server-side performance, we had no way to tie user events to the traces we were inspecting. In other words, although we had an idea of what API route was slow, there wasn’t much visibility into the actual bottleneck.
This is where our performance product comes in: we’re rethinking a tracing/performance tool that focuses on bridging the gap between the client and server.
What’s unique about our approach is that we lean heavily into creating traces from the frontend. For example, if you’re using our Next.js SDK, we automatically connect browser HTTP requests with server-side code execution, all from the perspective of a user. We find this much more powerful because you can understand what part of your frontend codebase causes a given trace to occur. There’s an example here [3].
From an instrumentation perspective, we’ve built our SDKs on-top of OTel, so you can create custom spans to expand highlight-created traces in server routes that will transparently roll up into the flame graph you see in our UI. You can also send us raw OTel traces and manually set up the client-server connection if you want. [4] Here’s an example of what a trace looks like with a database integration using our Golang GORM SDK, triggered by a frontend GraphQL query [5] [6].
In terms of how it's built, we continue to rely heavily on ClickHouse as our time-series storage engine. Given that traces require that we also query based on an ID for specific groups of spans (more akin to an OLTP db), we’ve leveraged the power of CH materialized views to make these operations efficient (described here [7]).
To try it out, you can spin up the project with our self hosted docs [8] or use our cloud offering at app.highlight.io. The entire stack runs in docker via a compose file, including an OpenTelemetry collector for data ingestion. You’ll need to point your SDK to export data to it by setting the relevant OTLP endpoint configuration (ie. environment variable OTEL_EXPORTER_OTLP_LOGS_ENDPOINT [9]).
Overall, we’d really appreciate feedback on what we’re building here. We’re also all ears if anyone has opinions on what they’d like to see in a product like this!
[1] https://github.com/highlight/highlight/blob/main/LICENSE
[2] https://www.jaegertracing.io
[3] https://app.highlight.io/1383/sessions/COu90Th4Qc3PVYTXbx9Xe...
[4] https://www.highlight.io/docs/getting-started/native-opentel...
[5] https://static.highlight.io/assets/docs/gorm.png
[6] https://github.com/highlight/highlight/blob/1fc9487a676409f1...
[7] https://highlight.io/blog/clickhouse-materialized-views
[8] https://www.highlight.io/docs/getting-started/self-host/self...
[9] https://opentelemetry.io/docs/concepts/sdk-configuration/otl...
-
Kubernetes Ingress Visibility
For the request following, something like jeager https://www.jaegertracing.io/, because you are talking more about tracing than necessarily logging. For just monitoring, https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack would be the starting point, then it depends. Nginx gives metrics out of the box, then you can pull in the dashboard like https://grafana.com/grafana/dashboards/14314-kubernetes-nginx-ingress-controller-nextgen-devops-nirvana/ , or full metal with something like service mesh monitoring which would provably fulfil most of the requirements
-
Migrating to OpenTelemetry
Have you checked out Jaeger [1]? It is lightweight enough for a personal project, but featureful enough to really help "turn on the lightbulb" with other engineers to show them the difference between logging/monitoring and tracing.
[1] https://www.jaegertracing.io/
-
The Road to GraphQL At Enterprise Scale
From the perspective of the realization of GraphQL infrastructure, the interesting direction is "Finding". How to find the problem? How to find the bottleneck of the system? Distributed Tracing System (DTS) will help answer this question. Distributed tracing is a method of observing requests as they propagate through distributed environments. In our scenario, we have dozens of subgraphs, gateway, and transport layer through which the request goes. We have several tools that can be used to detect the whole lifecycle of the request through the system, e.g. Jaeger, Zipkin or solutions that provided DTS as a part of the solution NewRelic.
-
OpenTelemetry Exporters - Types and Configuration Steps
Jaeger is an open-source, distributed tracing system that monitors and troubleshoots the flow of requests through complex, microservices-based applications, providing a comprehensive view of system interactions.
-
Fault Tolerance in Distributed Systems: Strategies and Case Studies
However, ensuring fault tolerance in distributed systems is not at all easy. These systems are complex, with multiple nodes or components working together. A failure in one node can cascade across the system if not addressed timely. Moreover, the inherently distributed nature of these systems can make it challenging to pinpoint the exact location and cause of fault - that is why modern systems rely heavily on distributed tracing solutions pioneered by Google Dapper and widely available now in Jaeger and OpenTracing. But still, understanding and implementing fault tolerance becomes not just about addressing the failure but predicting and mitigating potential risks before they escalate.
-
Observability in Action Part 3: Enhancing Your Codebase with OpenTelemetry
In this article, we'll use HoneyComb.io as our tracing backend. While there are other tools in the market, some of which can be run on your local machine (e.g., Jaeger), I chose HoneyComb because of their complementary tools that offer improved monitoring of the service and insights into its behavior.
-
Building for Failure
The best way to do this, is with the help of tracing tools such as paid tools such as Honeycomb, or your own instance of the open source Jaeger offering, or perhaps Encore's built in tracing system.
-
Distributed Tracing and OpenTelemetry Guide
In this example, I will create 3 Node.js services (shipping, notification, and courier) using Amplication, add traces to all services, and show how to analyze trace data using Jaeger.
What are some alternatives?
Zabbix - Real-time monitoring of IT components and services, such as networks, servers, VMs, applications and the cloud.
Sentry - Developer-first error tracking and performance monitoring
Sensu
skywalking - APM, Application Performance Monitoring System
Nagios - Nagios Core
prometheus - The Prometheus monitoring system and time series database.
Flapjack - Monitoring notification routing + event processing system. For issues with the Flapjack packages, please see https://github.com/flapjack/omnibus-flapjack/
signoz - SigNoz is an open-source observability platform native to OpenTelemetry with logs, traces and metrics in a single application. An open-source alternative to DataDog, NewRelic, etc. 🔥 🖥. 👉 Open source Application Performance Monitoring (APM) & Observability tool
bosun - Time Series Alerting Framework
Pinpoint - APM, (Application Performance Management) tool for large-scale distributed systems.
Netdata - The open-source observability platform everyone needs
fluent-bit - Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX and Windows