dataloader VS Apache JMeter

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

Usually, I would let organic users be my load test. However, I am working on a project that has an anticipated load on a new-to-my-team stack, so I'm looking into ways to load test.

I've seen tools like k6 (https://k6.io/), Artillery (https://www.artillery.io), and JMeter (https://jmeter.apache.org/).

I've been using Artillery, but it's hard to visualize the results.

What do you use?

Apache JMeter: This tool is an open-source application built on Java, designed specifically to test load functionality and performance. Developed by the Apache Software Foundation, JMeter is versatile, able to simulate loads across a wide range of services and protocols such as HTTP, HTTPS, JDBC, LDAP, and SOAP. With an extensible core that can be tailored with plugins, it provides the flexibility needed for different testing scenarios. Its intuitive GUI makes it easy for testers to design test plans and visualize the results in various ways.

I'll use Apache JMeter to do this experiment:

Thanks for the tip. Hows that compare to this tool? https://jmeter.apache.org/

Apache JMeter: Download and install JMeter from the official website (https://jmeter.apache.org/). Java Development Kit (JDK): JMeter requires Java, so ensure you have the latest JDK installed on your system. Postman: Install Postman from the official website (https://www.postman.com/downloads/).

The test scenario consists of querying for descriptions of different offers. During the test, I will collect data on memory and GC parameters using jConsole. I will run the test scenario using jMeter, which additionally will allow me to measure response times.

FYI some of our people internally use Jmeter. https://jmeter.apache.org/

It's not flashy so it probably wont get the standard "we are going to milk you for data" plan

Issue #5682Pull request #717 - Open Model Thread Group: avoid skipping rows from CSV Data Set Config

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.

JMeter https://jmeter.apache.org