express-graphql
jaeger
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express-graphql | jaeger | |
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14 | 94 | |
6,386 | 19,409 | |
- | 1.3% | |
6.1 | 9.7 | |
about 1 year ago | about 2 hours ago | |
TypeScript | Go | |
MIT License | 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.
express-graphql
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How to define schema once and have server code and client code typed? [Typescript]
It looked a little janky but it actually worked fine. But then I needed file uploads. Something graphql-zeus does not support. So I had to create a small wrapper for the SDK created so that it worked. And then also express-graphql, the server I was using, was deprecated.
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Apollo Server v4 Breaking Changes. Time to move away?
This seems like a minor deal unless you're not using Express as your web framework. And an important note here is that Express GraphQL is being deprecated by the GraphQL Foundation. So if you're using Express for a GraphQL API, you should move away from it anyway.
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Learn How to Build a GraphQL API in Node.js Using Apollo Server
You might have seen other GraphQL server solutions where the schema is implemented by using a more programmatic approach. Here is an example of how schemas are implemented using the express-graphql library. (link: https://github.com/graphql/express-graphql)
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With Cube GraphQL API, you can query data warehouses like BigQuery and dozens of SQL-enabled databases like Postgres using GraphQL
I'm not sure where the "8MB limit" comes from but, indeed, there are issues like this where, in some implementations, the response size is limited to 100KB: https://github.com/graphql/express-graphql/issues/346
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What's your experience with Go and GraphQL? Learning Go coming from Node
With Node I used express-graphql as opposed to something like Apollo because it's lighter and less heavy on resources compared to Apollo.
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a first look at graphQL helix
Daniel Rearden listed the following reasons pushing him to create Helix, believing that these factors were absent from popular solutions like Apollo Server, express-graphql and Mercurius:
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how to deploy a graphQL server with docker and fly
Express GraphQL is a library for building production ready GraphQL HTTP middleware. Despite the emphasis on Express in the repo name, you can create a GraphQL HTTP server with any HTTP web framework that supports connect styled middleware. This includes Connect itself, Express and Restify.
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GraphQL - Diving Deep
If you are using Node.js there are a lot of implementations of GraphQL servers with a few being express-graphql, apollo-server, mercurius, graphql-helix and more. And if you are using other languages, you can see a great list here
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What is the best way to set up a GraphQL server?
express-graphql GitHub Repository
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REST vs. gRPC vs. GraphQL
Not sure about other libraries but it's certainly not the case for express-graphql on node.js. You can tie the graphql endpoint to any method you like. On the site I work on, we have a graphql endpoint that only accepts GET requests in production and is thus cacheable by Cloudfront. In dev and staging we accept all requests so that GraphIQL works.
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.
What are some alternatives?
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.
Sentry - Developer-first error tracking and performance monitoring
Hasura - Blazing fast, instant realtime GraphQL APIs on your DB with fine grained access control, also trigger webhooks on database events.
skywalking - APM, Application Performance Monitoring System
Prisma - Next-generation ORM for Node.js & TypeScript | PostgreSQL, MySQL, MariaDB, SQL Server, SQLite, MongoDB and CockroachDB
prometheus - The Prometheus monitoring system and time series database.
graphql-yoga - 🧘 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.
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
graphql-helix - A highly evolved GraphQL HTTP Server 🧬
Pinpoint - APM, (Application Performance Management) tool for large-scale distributed systems.
TypeGraphQL - Create GraphQL schema and resolvers with TypeScript, using classes and decorators!
fluent-bit - Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX and Windows