dataloader VS NATS

When I began working with GraphQL, I had concerns about the N+1 query problem. In my research, I came across the DataLoader pattern and its implementation on GitHub. While I explored various examples of its usage, I still struggled to grasp how it operates internally. Join me in delving a bit deeper into GraphQL DataLoader! :)

DataLoader: it is the standard solution to N+1 problem.

Additionally, you can use DataLoader or similar tools to optimize data fetching and avoid over-fetching or under-fetching data. Ultimately, performance and load tests ensure that your GraphQL API delivers optimal performance, meets response time expectations, and provides a smooth experience for users, even under heavy loads.

The most common practice is to turn N+1 into 1+1 using dataloaders (https://github.com/graphql/dataloader for JS, there are equivalents for most implementations). The N resolvers invoke a single batched loader which receives a list of keys and returns a list of values.

Let's take Payload, for example. Surprise, surprise. We have a relationship field, and it can store IDs to other related documents which are seamlessly merged in when you retrieve documents from the DB. We leverage the dataloader pattern to batch together all "populations" required for a given query, returning them all super fast and with as few separate queries to the DB as possible. We actually even outperform SQL-based frameworks quite a bit. In a purely relational test, we were 3x faster than Directus and 7x faster than Strapi while both were running Postgres, and we were on MongoDB.

Also, have a look at Facebook's Dataloader[0].

[0] https://github.com/graphql/dataloader

In the next article, I'm going to implement a GraphQL server with dataloader using the tools we introduced.

The one example of using Scoped dependency that comes to my mind, it's HTTP request level caching for libs like dataloader.

you would fetch these 1000 rows via dataloader that batches all requests for this relation to a single query... solving the n+1 issue

Several message brokers, such as NATS and database queues, are not supported by OpenTelemetry (OTel) SDKs. This article will guide you on how to use context propagation explicitly with these message queues.

https://nats.io/ (Tracker removed)

> Connective Technology for Adaptive Edge & Distributed Systems

> An Introduction to NATS - The first screencast

I guess I don't need to know what it is

During our interview, we referred to NATS quite a few times! If you want to learn more about it, Sebastian suggests this tutorial series.

Pueue dumps the state of the queue to the disk as JSON every time the state changes, so when you have a lot of queued jobs this results in considerable disk io. I actually changed it to compress the state file via zstd which helped quite a bit but then eventually just moved on to running NATS [1] locally.

[1] https://nats.io/

Imagine you have an AI-powered personal alerting chat assistant that interacts using up-to-date data. Whether it's a big move in the stock market that affects your investments, any significant change on your shared SharePoint documents, or discounts on Amazon you were waiting for, the application is designed to keep you informed and alert you about any significant changes based on the criteria you set in advance using your natural language. In this post, we will learn how to build a full-stack event-driven weather alert chat application in Python using pretty cool tools: Streamlit, NATS, and OpenAI. The app can collect real-time weather information, understand your criteria for alerts using AI, and deliver these alerts to the user interface.

Why wasn't NATS[1] used ?

Written in Go, single-binary deployment... there's a lot to love about NATS !

[1]https://nats.io/

require nats.io

About cost, see [1]. Also, S3 prices have been increasing and there's been a bunch of alternative offers for object store from other companies. I think people in here (HN) comment often about increasing costs of AWS offerings.

Distributed systems and consensus are inherently hard problem, but there are a lot of implementations that you can study (like Etcd that you mention, or NATS [2], which I've been playing with and looks super cool so far :-p) if you want to understand the internals, on top of many books and papers released.

Again, I never said it was "easy" to build distributed systems, I just don't think there's any esoteric knowledge to what S3 provides.

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1: https://en.wikipedia.org/wiki/Economies_of_scale

2: https://nats.io/

I am in a project were nats.io is used. Someone thought, it would be a great idea to link data in an event with data in a response using the response channel name.