Gatling VS dataloader

Gatling: An open-source load and performance testing tool primarily designed for web applications, Gatling utilizes a simple domain-specific language (DSL) for creating and maintaining test scripts. It supports HTTP/2 and allows recording and generation of scenarios directly from a browser. The tool also provides detailed performance reports that are easy to analyze.

Performance and load testing are essential parts of GraphQL API testing. It ensures APIs can handle expected traffic volumes and respond within acceptable timeframes. You can use tools like Apache JMeter or Gatling to generate realistic loads and evaluate the API's performance under different scenarios. Techniques like batched queries and caching can help mitigate this issue.

New to the .NET community and trying to learn! I have used tools such as Apache JMeter (Java), gatling.io (Java) and Locust (Python) that are decent full featured web perf frameworks. Typically these integrate well with your code, and can be run as part of your unit/integration tests and produce offline reports

Gatling , this is what we tested concurrency with. Setting up might take a while depending on your exp. but the tool is solid.

I used SpringBoot 3.0.2, GraalVM 22 (JVM mode), a MacOS 2,6 GHz 6-Core Intel Core i7, running 1000 users for 5 minutes. The idea was to test how memory consumption and CPU usage evolve. Below, I compared the footprint of these three solutions. I collected the total count of requests, throughput, memory consumption, and CPU usage using VisualVM and Gatling.

Load test your code to simulate a production load (you can use tools like k6 or Gatling)

k6 is our preferred javascript based cloud tester for light webservices. gattling if you have (a monolith with) workflows and need more formalized code to prove/audit your testing.

It's read as "Ochikisu" and origin is the machine gun manufacturer Hotchkiss because this tool does not have as much firepower as the Gatling.

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