Engineering

Find out how the Trust, Identity, Safety, and Security (TISS) team improved machine learning predictions with Archivist, an in-house built solution.

Read more about our long awaited piece on the automation work we have made through integration and hermeticity.

Read to find out how Customer Support Experience's Phoenix live chat team improved agent chat efficiency with machine learning.

Read to find out how Grab's Performance Marketing team leveraged on automation to improve conversion rates.

Learn how Grab is investigating and reducing service binary size for Golang projects.

Read how we built our in-house workforce routing system at Grab.

Find out how a team at Grab used Mirror Cache, an in-memory local caching solution, to serve driver-partners data efficiently.

Find out where the messages and rewards come from, that arrive on your Grab app. Walk through scaling and processing optimisations that achieve tremendous throughput.

Learn how Grab stores driver locations and how these locations are used to find nearby drivers around you.

Learn how we planned and started migrating our superapp to Bazel at Grab.

Read how we built Grab's single source of truth for fare storage and management. In this post, we explain how we used the Event Sourcing pattern to build our fare data store.

Learn how we maintain our libraries and prevent defect leaks in our Grab Passenger app.

Read this deep dive on our Kubernetes infrastructure setup for Grab's stream processing framework.

Continuous Delivery is the principle of delivering software often, everyday. Read more to find out how we implemented continuous delivery at Grab.

Redis does not guarantee the consistency between master and its replica nodes when Lua scripts are used. Read more to find out why and how to guarantee data consistency.

This blog post discusses how Grab's DataGateway plays a key role in supporting hundreds of users in our entire Presto ecosystem - from managing user access, cluster selection, workload distribution, and many more.

This is the second post on the Go module series, which highlights Grab’s experience working with Go modules in a multi-module monorepo. Here, we discuss the additional solutions for addressing dependency issues, as well as cover automatic upgrades.

This blog post shares how we designed and implemented an Apache Airflow-based scheduling and orchestration platform for teams across Grab.

This blog post shares our learnings from building our very own chat platform for the web.

This post is the first in a series of blogs about Grab’s experience with Go modules in a multi-module monorepo. Here, we discuss the challenges we faced along the way and the solutions we came up with.

This blog post introduces how we use Xcode parallel testing to balance test execution time and improve the parallelism of our systems. We also share how we overcame a challenge that prevented us from running the tests efficiently.

This blog explains how we measured and reduced our app's storage footprint on user devices.

This blog highlights Grab's latest GPS trajectory dataset - its content, format, applications, and how you can access the dataset for your research purpose.

This blog addresses how engineers overcame the challenges Grab faced during the initial days due to sudden spike in ride demand.

This article details our journey building and deploying an event sourcing platform in Go, building a stream processing framework over it, and then scaling it (reliably and efficiently) to service over 300 billion events a week.

This post narrates the journey of our performance improvement efforts on the Grab passenger app. It highlights how we were able to reduce the time spent starting the app by more than 60%, while preventing regressions introduced by new features.

Do you know how we get early feedback on any breaking changes? Read through our blog to find out how Marionette, an in-house simulation platform, detects breaking changes in booking workflows. It even generates resources for running simulations and facilitates the testing of microservices powering our driver and passenger apps.

Are you curious how we quickly enabled our partners to self-service using our platform? Have you wondered how some teams at Grab implemented domain-driven development while using Golang? Read this blog post to know more.

This blog highlights Grab’s high-performance risk rule engine that automates the creation of rules to detect fraudulent activities with minimal efforts by engineers.

This blog introduces Grab’s Trust Counter service for detecting fraud. It explains how the solution was designed so that different stakeholders like data analysts and data scientists can use the Counter service without any manual intervention from engineers. The Counter service provides a reliable data feed to the data science world.

Curious about what a Principal Engineer role at Grab entails? Our Principal Engineers' responsibilities range from solving complex problems, taking care of the system-level architecture, collaborating with cross-functional teams, providing mentorship, and more.

Introducing Trailblazer, the Data Engineering team’s solution to implementing change data capture of all upstream databases. In this article, we introduce the reason why we needed to move away from periodic batch ingestion towards a real time solution and show how we achieved this through an end to end streaming pipeline.

This blog post explains what we did to solve our inhouse logging problem around the lack of visualizations and metrics for our service logs.

This blog post explains why and how we came up with a machine learning model serving platform to accelerate the use of machine learning in Grab.

This blog post describes how we used React Native to optimize the Grab PAX app.

This blog post describes Grab’s post-mortem findings for the outage caused by the Redis Cluster failure.

Read our blog to know how Loki, a dynamic mock server, makes local box testing of mobile apps easy, repeatable, and exhaustive. It supports both HTTP and TCP protocols and can provide dynamic runtime responses.

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.

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.

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.

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.

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!

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.

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.

This blog post focuses on the lessons we learned while building our batch data pipeline.

Grab designs fault-tolerant systems that can withstand failures allowing us to continuously provide our consumers with the many services they expect from us.

In this article, we focus on TalariaDB, a distributed, highly available, and low latency time-series database that stores real-time data. For example, logs, metrics, and click streams generated by mobile apps and backend services that use Grab's Experimentation Platform SDK. It "stalks" the real-time data feed and only keeps the last one hour of data.

Grab designs fault-tolerant systems that can withstand failures allowing us to continuously provide our consumers with the many services they expect from us.

At Grab, we practice chaos engineering by intentionally introducing failures in a service or component in the overall business flow. But the failed’ service is not the experiment’s focus. We’re interested in testing the services dependent on that failed service.

Grab’s feature toggle SDK provides a dynamic feature toggle capability to our engineering, data, product, and even business teams. Feature toggles also let teams modify system behaviour without changing code. Developers use the feature flags to keep new features hidden until product and marketing teams are ready to share and to run experiments (A/B tests) by dynamically changing feature toggles for specific users, rides, etc.

Sustaining quality in fast paced development is a challenge. At Grab, we use Mockers - a tool to expand the scope of local box testing. It helps us overcome testing challenges in a microservice architecture.

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.

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?

Disaster strikes when you do not configure timeout values properly. In this post, we dive into the details of how timeouts work with Ruby on Rails and Databases.

The meltdown attack reported recently had far reaching implications in terms of security as well as performance. This post is a quick rundown of what performance impacts we noted as well as how we went on to mitigate them.

Hiring the first five good engineers in your team requires a different approach to hiring the first twenty good engineers. The approach to designing this process will be even more different, when you want to hire to scale up to a 100 Engineers... or even to 300.

At Grab we take pride in creating solutions that impact millions of people in Southeast Asia and as they say, with great power comes great responsibility. As an app with 55 million downloads and 1.2 million drivers, it's our responsibility to keep our systems up-and-running. Any downtime causes drivers to miss earning and passengers to miss their appointments.

If you are in a fast growing tech startup, you're probably actively interviewing and hiring engineers to scale teams. My question to you is, what hiring strategy are you using when interviewing engineering warriors?

For the longest time, security has been at the center of our priorities. There’s nothing more self-evident about the trust our millions of driving partners and consumers put in Grab. We strive every day to build the best tools available to ensure their data stays secure.

Caching is arguably the most important and widely used technique in computer industry, from CPU to Facebook live videos, cache is everywhere.

Grab is Southeast Asia (SEA)’s leading transportation platform and our mission is to drive SEA forward, leveraging on the latest technology and the talented people we have in the company. As of May 2017, we handle 2.3 million rides daily and we are growing and hiring at a rapid scale. To keep up with Grab’s phenomenal growth, our web team and web platforms have to grow as well. Fortunately, or unfortunately, at Grab, the web team has been keeping up with the latest best practices and has incorporated the modern JavaScript ecosystem in our web apps.

This article is part two of a two-part series. In this article, we will talk about RFC 6724 (3484), how DNS resolution works in Go and Cgo, and finally explaining why disabling IPv6 also disables the sorting of IP Addresses.

My name is Arul Kumaravel, VP of Engineering at Grab. Grab's mission is to drive Southeast Asia (SEA) forwards. Today I would like to share with you how AWS is helping us with this mission.

This article is part one of a two-part series. In this article we explain the ELB 5XX errors which we experience without an apparent reason. We walk you through our investigative process and show you our immediate solution to this production issue. In the second article, we will explain why the non-intuitive immediate solution works and how we eventually found a more permanent solution.

A year ago, the data volumes at Grab were much lower than the volume we currently use for data-driven analytics. We had a simple and robust infrastructure in place to gather, process and store data to be consumed by numerous downstream applications, while supporting the requirements for data science and analytics.

This is the second part of our series "Deep Dive into iOS Automation at Grab", where we will cover how we manage continuous delivery. As a common solution to Apple developer account device whitelist limitation, we use an enterprise account to distribute beta apps internally. There are 4 build configurations per target.

This is the first part of our series "Deep Dive Into iOS Automation At Grab", where we will cover testing automation in the iOS team. Over the past two years at Grab, the iOS passenger app team has grown from 3 engineers in Singapore to 20 globally. Back then, each one of us was busy shipping features and had no time to set up a proper automation process.

One of Grab's more popular caching solutions is Redis (often in the flavour of the misleadingly named ElastiCache), and for most cases, it works. Except for that time it didn't. Follow our story as we investigate how Redis deals with consistency on key expiration.

Working at Grab will be the “most challenging yet rewarding opportunity” any employee will ever encounter.

Grab steadily attracts a diverse set of engineers from around the world in its three R&D centres: Singapore, Seattle, and Beijing. Right now, half of Grab’s top leadership team is made up of women and we have attracted people from five continents to work together on solving the biggest challenges for Southeast Asia.

Grab has been transitioning from a Rails + NodeJS stack to a full Golang Service Oriented Architecture. To contribute to a single common code base, we wanted to transfer engineers working on the Rails server powering our passenger app APIs to other Go teams.

While working on Grab's Common Data Service (CDS), there was the need to implement client side load balancing between CDS clients and servers. However, I kept encountering persistent connection issues with Elastic Load Balance (ELB).

Perhaps one of the biggest missed opportunities in Tech in recent history is UX. Somehow, UX became the domain of Product Designers and User Interface Designers. While they definitely are the right people to be thinking about web pages, mobile app screens and so on, we've missed a huge part of what we engineers work on every day: SDKs and APIs.

Grab adopts a Service-Oriented Architecture (SOA) to rapidly develop and deploy new feature services. One of the drawbacks of such a design is that team members find it hard to help with debugging production issues that inevitably arise in services belonging to other stakeholders.

Here at the Grab Engineering team, we have built our entire backend stack on top of Amazon Web Services (AWS). Over time, it was inevitable that some habits have started to form when perceiving our backend monitoring statistics.