Rate Limiting is a common requirement for any API service to protect itself from malicious or accidental abuse. Aperture provides a powerful policy engine that can be used to implement rate limiting using the Rate Limiter Component and Flow Classifier.
In this blog post, we will specifically examine how to implement rate limiting for GraphQL queries. Let us begin by discussing what GraphQL is and why rate limiting on GraphQL queries is required.
The FluxNinja team had the opportunity to demo Aperture open source at the November 2022 edition of the Kubernetes Pune meetup, organized at Slack (Salesforce) India’s office.
Kubernetes Pune is a group for all who want to learn and share experiences about Kubernetes. This meetup group is for all skill levels, from beginners to experienced professionals. Every month, we meet and discuss various aspects of the Kubernetes ecosystem, such as service discovery, load balancing, networking, storage, and more.
Highly available and reliable Services are a hallmark of any thriving business in today’s digital economy. As a Service owner, it is important to ensure that your Services stay within SLAs. But when bugs make it into production or user traffic surges unexpectedly, services can slow down under a large volume of requests and fail. If not addressed in time, such failures tend to cascade across your infrastructure, sometimes resulting in a complete outage.
At FluxNinja, we believe that adaptive concurrency limits are the most effective way to ensure services are protected and continue to perform within SLAs.
Today, FluxNinja is emerging from stealth mode to announce Aperture - the first open source flow control and reliability management platform for modern web applications.
