jaeger
Gin
Our great sponsors
jaeger | Gin | |
---|---|---|
94 | 152 | |
19,409 | 75,469 | |
1.5% | 1.4% | |
9.7 | 8.5 | |
about 24 hours ago | 4 days ago | |
Go | Go | |
Apache License 2.0 | MIT License |
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.
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 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...
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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.
Gin
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How to Build and Document a Go REST API with Gin and Go-Swagger
Now let’s define the functions that will be called whenever a request hits our API. All the functions will be referencing the context provided by the Gin web framework. Paste the following code below the sample slice we just added to api.go:
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Password-less Login in Go from Scratch
We will be using Gorilla Mux. As per their last update, they have a new group of maintainers, and their repos have shown activity to confirm that. The tutorial can be easily replicated in any other framework or library as well. So, while we will be using Gorilla Mux, you can try to replicate it in Gin or Fiber as well.
- Autenticação com Golang e AWS Cognito
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Implementing JWT Authentication in a Golang Application
Now, let's dive into the fun part – creating our basic ToDo application using the powerful Gin framework. This section will walk you through the steps, breaking down the code into manageable snippets.
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Build a Serverless GenAI solution with Lambda, DynamoDB, LangChain and Amazon Bedrock
Thanks to the AWS Lambda Web Adapter, the application built as a (good old) REST/HTTP API using a familiar library (in this case, Gin.
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From Django or Flask to Sponge: How to Easily Develop High-Performance Web Services with Golang
Excellent Performance: Sponge is built on the gin framework, providing outstanding performance for web service development.
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Uploading and Serving Images from MongoDB in Golang
In this blog, we will delve into the fascinating realm of handling images in a Golang application, leveraging the power of the Gin framework for RESTful API development, MongoDB as a robust NoSQL database, and the mongo-driver library for seamless interaction with MongoDB. To store images efficiently, we'll explore the intricacies of GridFS, a specification within MongoDB for storing large files as separate chunks.
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Building RESTful API with Hexagonal Architecture in Go
It uses Gin as the HTTP framework and PostgreSQL as the database with pgx as the driver and Squirrel as the query builder. It also utilizes Redis as the caching layer with go-redis as the client.
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Different CORS settings for different paths?
I have created an application with Go in Gin-Gonic. In my frontend (Nuxt3/TypeScript) I always get a CORS error:
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Rapid Prototyping of Design-First APIs in Go
We use Gin web framework https://gin-gonic.com for the routing, Gin provides a balance between performance, ease of use and extensibility making it a preferred choice for building and running web applications in Go.
What are some alternatives?
Sentry - Developer-first error tracking and performance monitoring
Fiber - ⚡️ Express inspired web framework written in Go
skywalking - APM, Application Performance Monitoring System
mux - A powerful HTTP router and URL matcher for building Go web servers with 🦍
prometheus - The Prometheus monitoring system and time series database.
chi - lightweight, idiomatic and composable router for building Go HTTP services
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
Echo - High performance, minimalist Go web framework
Pinpoint - APM, (Application Performance Management) tool for large-scale distributed systems.
Beego - beego is an open-source, high-performance web framework for the Go programming language.
fluent-bit - Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX and Windows
Iris - The fastest HTTP/2 Go Web Framework. New, modern and easy to learn. Fast development with Code you control. Unbeatable cost-performance ratio :rocket: