examples
jaeger
examples | jaeger | |
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16 | 94 | |
64 | 19,499 | |
- | 1.1% | |
2.6 | 9.7 | |
4 months ago | 1 day ago | |
Ruby | Go | |
Apache License 2.0 | Apache License 2.0 |
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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.
examples
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Observability at KubeCon + CloudNativeCon Europe 2024 in Paris
Honeycomb
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Tracing: Structured Logging, but better in every way
I haven't used anything else, but I'll gladly shill for https://honeycomb.io.
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Keeping up with my cat's š© using a RaspberryPi
With all of this in place I went a step further and added Opentelemetry to track the stats of how often the routine was being triggered on Honeycomb.
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Anyone having say 1PB of MySQL data? What efficient storage solution are you using.
Events can be used in many meaningful ways. The Event subsystem of B is pretty much a co-evolution of what honeycomb.io offers, but implemented completely differently - it is on bare-metal, and hence a lot cheaper. Because of that, B never subsampled, but always kept a full low of all events anywhere, no exceptions.
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āPeople used to take me seriously. Then I became a software vendorā
It should be noted that this is a very oblique ad for http://honeycomb.io. That in no way impugns the content of the post, and in fact, it's given the content of the post that I feel compelled to point out that, ultimately, this is an ad. Because what is sales and advertising, anyway? It's just a way to get you to buy a product, and you can't do that if you've never even heard about the product. I'm not currently in the market for an observability solution (something something splunk) but if I were, I've now heard of them.
The question though, is why does money ruin everything? The naive questions of an open source zealot to a proprietary software salesperson are one thing, but since we all need money to live, why does money being part of the equation (eg if someone was getting paid to post here) ruin things? Would a "donate to open source" button be more successful if the donate text is "buy me a coffee", "buy me a beer", or "upgrade my beer from Coors light"?
https://xkcd.com/2347/ was and is true, and if we don't figure out a way to change that, I don't really see a future for open source.
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Does anyone else use scatterplots of events?
Very cool to see honeycomb.io is doing that. I'm about to embark on my distributed tracing learning journey, this makes me want to try honeycomb right away.
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Is there a beginners guide to adding observability to your applications?
Caveat: I work for a vendor in the O11y space (https://honeycomb.io) as a Developer Advocate, however, this advice is generic, not specific to our platform.
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KubeCon North America 2022: A Retrospective
I spent Day 2 at the Colony Club to attend OTel Unplugged. This event was sponsored by Lightstep, Honeycomb, New Relic, Splunk, Dynatrace, Crowdstrike, and NGINX. I came into the event not knowing what to expect. I can sometimes clamp up when Iām around folks that I donāt know, but because I was helping with the event check-in, I got to say hello to a number of the attendees, which helped break the ice. And it turns out that there were a lot of names that I recognized from my work in the OTel community, and it was nice to connect in person with folks whom Iād only previously met through Slack or Zoom.
- The four pillars of data observability: metrics, metadata, lineage, and logs
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Honeycomb, Python, and I: an OpenTelemetry Horror Story (With a Happy Ending)
It's no surprise that my apps are mostly written using Sanic as I'm pretty involved with the project. I've been wanting to start testing honeycomb out as well, so it seemed the perfect opportunity to try out.
jaeger
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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/
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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 wasnāt much visibility into the actual bottleneck.
This is where our performance product comes in: weāre rethinking a tracing/performance tool that focuses on bridging the gap between the client and server.
Whatās unique about our approach is that we lean heavily into creating traces from the frontend. For example, if youāre 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. Thereās an example here [3].
From an instrumentation perspective, weāve 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] Hereās 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), weāve 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. Youāll 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, weād really appreciate feedback on what weāre building here. Weāre also all ears if anyone has opinions on what theyād 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...
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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
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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/
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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.
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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.
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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.
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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.
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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.
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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?
metriql - The metrics layer for your data. Join us at https://metriql.com/slack
Sentry - Developer-first error tracking and performance monitoring
otel-cli - OpenTelemetry command-line tool for sending events from shell scripts & similar environments
skywalking - APM, Application Performance Monitoring System
nx-go - š Nx plugin to use Go in a Nx Workspace
prometheus - The Prometheus monitoring system and time series database.
nxpansion
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. š„ š„. š Open source Application Performance Monitoring (APM) & Observability tool
keptn - Cloud-native application life-cycle orchestration. Keptn automates your SLO-driven multi-stage delivery and operations & remediation of your applications.
Pinpoint - APM, (Application Performance Management) tool for large-scale distributed systems.
hyperdx - Resolve production issues, fast. An open source observability platform unifying session replays, logs, metrics, traces and errors powered by Clickhouse and OpenTelemetry.
fluent-bit - Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX and Windows