graphql-jit VS jaeger

In the end like all performance questions you'll have to measure it. Persisted queries, and even more extreme approaches, like a GraphQL JIT (https://github.com/zalando-incubator/graphql-jit) can remove a lot of the overhead.

We faced something similar. To improve GraphQL performance, we use graphql-jit. We turned off all other tracing that datadog turns on by default. Then, we then wrote a custom tracer to connect graphql-jit to dd-trace. Hopefully this same pattern works for you!

GraphQL JIT – GraphQL execution using a JIT compiler\ (0 comments)

https://github.com/zalando-incubator/graphql-jit#compile-a-q...

so I guess the difference is instead of having totally generic functions that parse the query every time, instead you can call a factory function up front to get a specialised function for a particular query

and V8 JIT can make that faster

JS has always been special & excellent because it allows multiphase programming. That one coild just add more code has been unique versus the world of statuc languages, where maybe youcd have some tool to generate stubs for Thrift or gRPC, then compule that in to your program. Which works ok, if and only if you know ahead of time what schemas you might need. Im these cases, you have to give up on stub code & start using more interprtted systems.

The advantages in js have been colossal, often an easy order of magnitude win to compile selectors & queries & other fast inner loop bits into jit compiled code versus having userland interpretters. Some of thd rete engines enjoy this. Just today there's graphql-jit. https://github.com/zalando-incubator/graphql-jit/blob/main/s...

Everything you say is full of doubt & scorn & skepticism. I just cant imagine living im such a world where each system had to know fully well ahead of time each type of data it might want to interact with, might have to have that precompiled & baked in. Thatcs a shitty miserable world that looks like the hell JS plucked us out of, by being a flexible, dynamic language that could load in routines & change it's behavior over time. Screw that dark hell world.

By using useParserCache we make sure to parse every unique operation only once. By using useValidationCache we make sure to validate every unique operation only once. By using useGraphQLJit we replace the default execute function with a just-in-time implementation.

One thing to understand is that, if you are using Node.js graphql-js would typically be the underlying implementation of all libraries and ultimately everything would get converted to JS/TS objects typically an AST ultimately making all these as abstractions on top of the existing way to define schemas. Note that the implementation can differ a bit in other languages or even within Node.js if you are using other ways of implementation like graphql-jit

Could you provide more context? the GraphQLJit plugin is a lightweight wrapper around graphql-jit. Could you share your previous datadog/graphql setup? Would love to dig in and find a proper solution!

Customizable runtime. Use custom GraphQL implementation such as graphql-jit or rolling our own for performance and cutting-edge features.

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/

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...

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

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/

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.

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.

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.

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.

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.

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.