jaeger
graphql-js
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jaeger | graphql-js | |
---|---|---|
94 | 26 | |
19,370 | 19,916 | |
1.3% | 0.3% | |
9.7 | 7.4 | |
5 days ago | 4 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 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.
graphql-js
- Understanding TTFB Latency in DJango - Seems absurdly slow after DB optimizations even locally
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Diving into Open-Source Development
To begin, I'm going to start with GraphQL. This repo is a JS-specific implementation for GraphQL, for which projects written in JS/TS can utilize to build an API for their web app. The reason why I chose this project is because I've always been intrigued by how GraphQl challenges the standard way of building an API, a.k.a REST APIs. I have very little knowledge about this project since I've never used it before at work or for my personal projects. I only have theoretical knowledge about it which I gained from watching YouTube videos. It also uses TypeScript which is fascinating because type safety is very important when building software considering it cleans out a lot of bugs early on before the software is shipped.
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How to define schema once and have server code and client code typed? [Typescript]
When I asked this in StackOverflow over a year ago I reached the solution of using graphql + graphql-zeus.
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Uncovering Frontend Data Aggregation: Our Encounter with BFF, GraphQL, and Hydration
In short, we chose not to pursue GraphQL due to some limitations with union types and a lack of support for maps. This is further detailed in this link: limitations.
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Exploring the Most Commonly Used Folder Names in Popular NPM Packages
benchmarks: This directory contains benchmark tests that help measure the performance of the package's code, these tests can be are very useful when experimenting with performance optimizations, and to ensure no slowdowns are introduced between releases. Example from graphql.
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Apollo federated graph is not presenting its schema to graphiql with fields sorted lexicographically
GraphiQL (and many other tools) relies on introspection query which AFAIK is not guaranteed to have any specific order (and many libs don't support it). Apollo Server is built on top of graphql-js and it relies on it for this functionality.
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What are popular ORMs for Node.js?
GraphQL.js + Knex.js + knex-types (TypeScript generator for Knex)
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Announcing GraphQL Yoga 2.0!
Yoga v2 supports some experimental GraphQL features such as @defer and @stream, allowing you to get a taste of the future of GraphQL (with compatible clients such as URQL).
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11 JavaScript Examples to Source Code That Reveal Design Patterns In Use
Visitors are used for many reasons like extensibility, plugins, printing an entire schema somewhere, etc.
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How not to learn GraphQL
support for @defer and @stream
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-jit - GraphQL execution using a JIT compiler
prometheus - The Prometheus monitoring system and time series database.
apollo-server - 🌍 Spec-compliant and production ready JavaScript GraphQL server that lets you develop in a schema-first way. Built for Express, Connect, Hapi, Koa, and more.
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
fastify-websocket - basic websocket support for fastify
Pinpoint - APM, (Application Performance Management) tool for large-scale distributed systems.
graphql-subscriptions - :newspaper: A small module that implements GraphQL subscriptions for Node.js
fluent-bit - Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX and Windows
graphql-code-generator - A tool for generating code based on a GraphQL schema and GraphQL operations (query/mutation/subscription), with flexible support for custom plugins.