Redis VS dataloader

In this article, we used a postgres database to store users’ data. We created endpoints using Gorilla Mux, and on top of those endpoints, we integrated Redis caching into the API response from a React application. Remember that Redis is a powerful tool and what we covered is just the tip of the iceberg. For more information, consider reading the docs at redis.io. You can also follow along with this article by watching this YouTube tutorial.

However, running and validating these HTTP servers can be a time-consuming and challenging task for instructors, especially as the number of students increases. To address this problem, an web application was developed which can accept Docker images created by students, run the image, provide environment variables for database connections and perform a set of HTTP requests to validate the implementation. This web-based application is created using the Django web framework, uses Redis for queue management and the Docker Python SDK to communicate with the Docker Daemon.

OpenTalk is a young project for creating online meeting software similar to Jitsi or BigBlueButton. It is a completely new development, and while it is not a fork of an existing open-source project, it integrates with other projects such as the Janus WebRTC server, Redis for volatile state, RabbitMQ for communication between server instances, and PostreSQL for persistent state.

Database reads are expensive and slow. There's a multitude of other reasons why caching is essential, but for the project I'm working on I'm using supabase and their free tier supports only a limited amount of bandwidth. To avoid any rate-limiting I added a Redis layer in front of it to reduce the number of queries that hit the database directly.

The AWS Lambda function had been written in Java and therefore the team looked at different Java-based Redis drivers and eventually decided to use Jedis, a Java client for Redis designed for performance and ease of use. One of the reasons they chose to use Jedis was that it proved to be a lightweight library. Instantiating the client connection takes somewhere between 200-600ms (depending on the lambda memory settings) which impacts the cold start of the lambda function.

Caching - while it's not possible to cache everything, there's always a large percentage of your website / app that can be cached for an hour or ten minutes or 1 day etc... - all depends on the type of content but the longer you can cache for without negatively effecting content quality - the better. A good caching server example would be redis : https://redis.io/ or https://memcached.org/

The code uses the codehooks-js library to handle routing and middleware for the API, and the node-fetch library to make HTTP requests to the OpenAI API. The code also uses the built-in codehooks.io Key-Value store to cache the response from the OpenAI API for 60 seconds, in order to reduce the number of requests made to the OpenAI API (If you want to create your own Node.js Express backend, you just need to replace this cache with Redis for example).

Redis is an in-memory database and an open-source streaming engine with an IMDB data structure server that supports multiple data sets and data streams. This open-source platform has high throughputs and lower bandwidth due to its in-memory features that lead to faster processing of big data in data science applications.

Quirrel is a popular open-source webhook framework that makes it easy to add webhook support to your app. It makes uses of redis under the hood.

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.

We knew that Payload was fast and we place a lot of emphasis on ensuring that it performs as quickly as possible, but with our recent addition of the dataloader pattern, our result surprised even us.

For Apollo, firstly we need to install the dataloader package.

GraphQL is designed around CRUD fetching patterns - so typically you would have a query per data type slice, and that query can be as complex as you like. In order to avoid N+1 queries, you should use dataloader: https://github.com/graphql/dataloader.

You can also join additional data to reference in deeper resolvers, but that's an antipattern AFAIK.

dataloader is a good utility that can batch multiple queries into one, and also cache the item if it's requested again. this example would be two queries (users then posts). works well with how resolvers are split up. https://github.com/graphql/dataloader

The second solution that can somewhat eliminate this problem is implementing grapql dataLoader(https://github.com/graphql/dataloader) which is also tricky to get right, and it will require you to change your resolvers.

Interesting, I've just been researching Dataloader implementations for Ruby and have between trying to decide between GraphQL::Dataloader, graphql-batch and BatchLoader. I'll give this a look as well. Can you also make API calls inside the loaders?

Will it be gateway for REST API or gateway for a number of GraphQL services (here you can expect n + 1 problem and it can be a challenge to design DataLoaders on your layers)? You also should consider here how authorization rules, throttling rates or caching timeouts should be handled. Since GraphQL uses only one endpoint REST route-specific rules should be transferred into GraphQL schema rules: you may need to write authorization, throttling or caching logic in a separate layer.