Engineering

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 customers 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 customers 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 behavior 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 customers put in Grab. We strive everyday 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 in 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 everyday: 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.