jaeger
apollo-server
Our great sponsors
jaeger | apollo-server | |
---|---|---|
94 | 66 | |
19,370 | 13,658 | |
1.3% | 0.2% | |
9.7 | 9.2 | |
6 days ago | 6 days ago | |
Go | TypeScript | |
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 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.
apollo-server
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React Server Components Example with Next.js
Another interesting point is that executing fetches on the server can allow developers to more easily leverage caching. Next.js already handles caching out-of-the-box and Iām curious to see if the wider adoption of RSC reduces the need to combine React with solutions like Apollo Server and Apollo Client. While there are other benefits to these tools, RSC could provide similar caching behavior without the need to invest in a GraphQL solution.
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Building Scalable GraphQL Microservices With Node.js and Docker: A Comprehensive Guide
There are several GraphQL server implementations, however, for this tutorial, we'll utilize Apollo GraphQL's Apollo Server, a lightweight and flexible JavaScript server that makes it easy to build GraphQL APIs.
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Launch HN: Serra (YC S23) ā Open-source, Python-based dbt alternative
As I mentioned, their main GraphQL server package is[1], so that's where the confusion came from. Thanks.
[1] https://github.com/apollographql/apollo-server/blob/9817bc47...
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Who moved my error codes? Adding error types to your GoLang GraphQL Server
While working on this blog post, I learned that Apollo Server, the most popular GraphQL server for typescript, uses a similar method for adding error codes to GraphQL. It even lets you add custom errors. Hopefully, someday other GraphQL server projects will follow them. Until then, weāve got a strong indication we took the right approach.
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Zero to Serverless Car Insurance - Part 2
GraphQL is just a schema, there are many different implementations of a GraphQL server, AppSync being one of them. I mentioned Apollo server in this series as well.
- How can i do query directives or executable directives?
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How we migrated to Apollo Server 4
After some head-scratching, I opened an issue on Apollo Serverās GitHub repository. There, Apollo Server contributor @āglasser shared a helpful suggestion: why not invoke our AuthPlugin from Apollo Serverās context function? Throwing from context would ensure we can control the HTTP status response without having to introduce more methods and error checks to our AuthPlugin (like unexpectedErrorProcessingRequest). With that suggestion in mind, we rewrote our AuthPlugin as follows:
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why would a developer choose nodejs over c#.net for backend?
Apollo as a middleware in Express.js, actually.
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Using Postman and Postman Interceptor to authenticate a session cookie based GraphQL API
Apollo Server 3 Cookie Issue #5775
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Custom API server with basic CRUD ā Apollo, GraphQL & MongoDB
Lastly, instead of writing our API core ourselves, we'll be using the star of this episodeā---āApollo Server (a.k.a. GraphQL server). It has detailed documentation available here.
What are some alternatives?
Sentry - Developer-first error tracking and performance monitoring
mercurius - Implement GraphQL servers and gateways with Fastify
skywalking - APM, Application Performance Monitoring System
graphql-mesh - The Graph of Everything - Federated architecture for any API service
prometheus - The Prometheus monitoring system and time series database.
nestjs-graphql - GraphQL (TypeScript) module for Nest framework (node.js) š·
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
graphql-yoga - š§ Rewrite of a fully-featured GraphQL Server with focus on easy setup, performance & great developer experience. The core of Yoga implements WHATWG Fetch API and can run/deploy on any JS environment.
Pinpoint - APM, (Application Performance Management) tool for large-scale distributed systems.
express-graphql - Create a GraphQL HTTP server with Express.
fluent-bit - Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX and Windows
graphql-ws - Coherent, zero-dependency, lazy, simple, GraphQL over WebSocket Protocol compliant server and client.