mercurius VS zipkin

GraphQL Gateway is primarily responsible for serving GraphQL queries to consumers. It takes a query from a client, breaks it into smaller sub-queries, and executes that plan by proxying calls to the appropriate downstream subgraphs. When we started our journey, there was only Apollo Federation in the arena, and we used it. Still, now you can look at other options (e.g. Mercurius, Conductor, Hot Chocolate, Wundergraph, Hasura Remote Schemas), compare benchmarks and decide what's important and preferable for your needs. The Gateway provides a unified API for consumers while giving backend engineers flexibility and service isolation.

If you're using Fastify with Mercurius as your GraphQL adapter, you may be looking for some advanced usages. In this article, we'll explore a real world example with Dynamic GQL queries with Mercurius.

Loader: it is a built-in DataLoader-Like solution that is quick to set up and use.

In this post I will show you how to implement NestJS😻 with GraphQL in code first mode, using Mercurius and the "platform" to Fastify.

What this does is to overwrite or augment the types exposed by the pointed module, and can be used (for example) when relying on autogenerated code. One interesting case of this is GraphQL to TypeScript code generation, and how this is integrated with the amazing Mercurius library (made by some of my colleagues at NearForm! 😜).

When moving away from Apollo Server, and you're looking for a replacement built with JavaScript or TypeScript, let me give you some options. If you want to keep building your GraphQL API schema first, you might want to consider Mercurius (which relies on Fastify) or GraphQL Yoga. If you're going to build your GraphQL API code or resolver first, have a look at TypeGraphQL or Nexus. Alternatively, there are great GraphQL-as-a-Service solutions such as StepZen in case you no longer want to build, maintain and host your own GraphQL API.

Let's start with data. We live in amazing times and it's really easy and cheap (or free) to get started with storing and working with data online. Take for example a PlanetScale MySQL-compatible database, Fastify Node.js Server, Prisma database mapper and a GraphQL connector like Mercurius and you have an entire backend stack. For this example we assume you already have a backend or you want to connect to a 3rd party database like the GitHub GraphQL API.

In this chapter, we assume a basic understanding of GraphQL and focus on how to work with the built-in @nestjs/graphql module. The GraphQLModule can be configured to use Apollo server (with the @nestjs/apollo driver) and Mercurius (with the @nestjs/mercurius). We provide official integrations for these proven GraphQL packages to provide a simple way to use GraphQL with Nest. You can also build your own dedicated driver (read more on that here).

Why would I want to use Apollo Client? So far in my career I have used some server graphql scaffolding (webonyx/graphql-php for PHP and mercurius for Node) and just used the fetch API (or whatever ajax API around XMLHttpRequest) against that server with the body being an object with

Only for people who are clueless. Apollo server is probably the worst node.js server to use for your graphql schema. It's terribly slow. You should look into https://mercurius.dev

When choosing distributed tracing tools, considerations include your technology stack, business requirements, and monitoring complexity. Zipkin, SkyWalking, and OpenTelemetry are popular distributed tracing solutions, each with its unique features.

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.

Zipkin is a distributed tracing system used for tracking and analyzing how requests move through complex systems, especially in setups with many interconnected services, known as microservices.

Distributed Tracing: Middleware for distributed tracing like Jaeger and Zipkin helps monitor and trace requests as they flow through multiple microservices, aiding in debugging, performance optimization, and understanding the system's behavior.

For microservice tracing, you might want to look at Zipkin [0], or OpenTelemetry [1]

[0] https://zipkin.io/

I’ve not used a self-hosted solution before, but here’s one I found. https://zipkin.io/

> IMO the reason these vendors can and do charge so much is not because telemetry software is hard.

I always saw it as "they are charging for their polished UI/experience"

The UI of https://zipkin.io/ versus DataDog is kind of... not really in the same ballpark?

There are the zipkin https://zipkin.io/ and jaeger https://www.jaegertracing.io/ packages/components you can use both have quickstarts if you consider that to be a beginner's guide.

Zipkin, which was developed by Twitter, is an open source tool for distributed tracing that can also be used to troubleshoot latency issues in your application. While Zipkin is Java-based, py_zipkin is an implementation for Python.