Reliable, scalable, and high performing solutions for common system level issues are essential for microservice success, and there is a Grab-wide initiative to provide those common solutions. As an important component of the initiative, we wrote a microservice called Quotas, a highly scalable API request rate limiting solution to mitigate the problems of service abuse and cascading service failures.

Grab Senior Data Scientist Dr. Liuqin Yang wins the 2018 Beale-Orchard-Hays Prize, the highest honor in Computational Mathematical Optimization. He has been recognised for his paper and the corresponding software SDPNAL+.

At Grab, we continuously strive to improve the user experience of our app for both our passengers and driver partners. To do that, we’re constantly experimenting, and in fact, many of the improvements we roll out to the Grab app are a direct result of successful experiments.

As we evolved from a single monolithic application to a microservices-based architecture, we were faced with a new challenge. How do we support exponential growth while maintaining consistency, coordination, and quality?

At Grab, we consistently strive to build a platform that delivers excellent user experience to both our Passengers and Driver-Partners. A major degradation to a seamless booking experience is the cancellation of that booking.