graphql-spec
jaeger
graphql-spec | jaeger | |
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37 | 94 | |
14,230 | 19,544 | |
0.2% | 1.4% | |
5.8 | 9.7 | |
about 1 month ago | 2 days ago | |
Shell | Go | |
- | Apache License 2.0 |
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graphql-spec
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Show HN: REST Alternative to GraphQL and tRPC
GraphQL's first draft release was 8 years ago. [1]
It's first non-draft release was 5 years ago. [2]
It's first release under a community foundation was 2 years ago. [3]
[1] https://spec.graphql.org/July2015/
[2] https://github.com/graphql/graphql-spec/releases/tag/June201...
[3] https://github.com/graphql/graphql-spec/releases/tag/October...
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Intro to PostGraphile V5 (Part 3): Introspection and Abstraction
I'm a big believer in GraphQL (in fact, at time of writing I'm #2 contributor to the GraphQL spec itself) so it pains me that a tool I built doesn't always have easy ways to achieve the "versionless schema" design that GraphQL encourages when it comes to making significant breaking changes to your underlying database tables. (Personally, I think you should aim for your database schema itself to be versionless, but this is not always possible.) Of course you can build your PostGraphile schema over views instead of tables, but views have their own problems that I won't go into here…
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Migrating Netflix to GraphQL Safely
I created a proposal for Map type but didn’t make it through.
https://github.com/graphql/graphql-spec/pull/888
The issue with GraphQL is it tries to appease too many masters.
Similar to jsx. The language isn’t evolving.
The good thing is the spec is (almost) frozen, so there’s many implementations, the bad is it can encompass the flexibility of json schema can do.
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GraphQL Live Queries with live directive
Longer thread - Subscriptions RFC: Are Subscriptions and Live Queries the same thing?
https://github.com/graphql/graphql-spec/issues/284
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Ask HN: Tutorials Written with Heavy Dependencies
You’ve probably figured it out by now, but for others who may be in a similar position; GraphQL is a specification (with various implementations) and you can read up on the spec here: https://spec.graphql.org/
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GraphQL object schemas - how to represent (and query?) Graph (hierarchical objects) in GraphQL?
If you're asking whether GraphQL supports anonymous objects that can be arbitrarily nested then no, it doesn't.
- Union for an input to a mutation arg
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Thanks graphql, I hate it.
show this feature request some love https://github.com/graphql/graphql-spec/issues/174
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Deprecation Notice: GraphQL for Packages
* Performance: It's just hard to track down what makes an operation slow. The waterfall nature of resolvers is a big contributor
[1] https://github.com/graphql/graphql-spec/issues/488
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GraphQL error handling to the max with Typescript, codegen and fp-ts
:::note GraphQL Union is available for Types only, not for Inputs. However, the oneOf directive will bridge the gap in the future.
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
graphql-ws - Coherent, zero-dependency, lazy, simple, GraphQL over WebSocket Protocol compliant server and client.
prometheus - The Prometheus monitoring system and time series database.
Neo4j - Graphs for Everyone
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-shield - 🛡 A GraphQL tool to ease the creation of permission layer.
Pinpoint - APM, (Application Performance Management) tool for large-scale distributed systems.
gRPC - The C based gRPC (C++, Python, Ruby, Objective-C, PHP, C#)
fluent-bit - Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX and Windows