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Data ScienceHow We Harnessed the Wisdom of Crowds to Improve Restaurant Location Accuracy
We questioned some of the estimates that our algorithm for calculating restaurant wait times was making, and found that the "errors" were actually useful to discover restaurants whose locations had been incorrectly registered in our system. By combining such error signals across multiple orders, we were able to identify correct restaurant locations and amend them to improve the experience for our consumers.
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EngineeringDesigning Resilient Systems Beyond Retries (Part 3): Architecture Patterns and Chaos Engineering
This post is the third of a three-part series on going beyond retries and circuit breakers to improve system resiliency. This whole series covers techniques and architectures that can be used as part of a strategy to improve resiliency. In this article, we will focus on architecture patterns and chaos engineering to reduce, prevent, and test resiliency.
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EngineeringDesigning Resilient Systems Beyond Retries (Part 2): Bulkheading, Load Balancing, and Fallbacks
This post is the second of a three-part series on going beyond retries to improve system resiliency. We’ve previously discussed about rate-limiting as a strategy to improve resiliency. In this article, we will cover these techniques: bulkheading, load balancing, and fallbacks.
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EngineeringDesigning Resilient Systems Beyond Retries (Part 1): Rate-Limiting
This post is the first of a three-part series on going beyond retries to improve system resiliency. In this series, we will discuss other techniques and architectures that can be used as part of a strategy to improve resiliency. To start off the series, we will cover rate-limiting.
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EngineeringContext Deadlines and How to Set Them
This blog post explains from the ground up a strategy for configuring timeouts and using context deadlines correctly, drawing from our experience developing microservices in a large scale and often turbulent network environment.
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Data Science · Engineering · Product · DesignRecipe for Building a Widget: How We Helped to “Peak-Shift” Demand by Helping Passengers Understand Travel Trends
We help to “peak-shift” demand by helping passengers understand travel trends with Grab’s data. Curious to know how we empower our passengers to make better travel decisions? Read on!
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EngineeringStructured Logging: The Best Friend You’ll Want When Things Go Wrong
This blog post describes how we built a structured logging framework that integrates well with our existing Elastic stack-based logging backend, allowing us to do logging better and more efficiently.
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EngineeringHow We Simplified Our Data Ingestion & Transformation Process
This blog post describes how Grab built a scalable data ingestion system and how we went from prototyping with Spark Streaming to running a production-grade data processing cluster written in Golang.
