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Engineering
Performance bottlenecks of Go application on Kubernetes with non-integer (floating) CPU allocation
At Grab, we have been running our Go based stream processing framework (SPF) on Kubernetes for several years. But as the number of SPF pipelines increases, we noticed some performance bottlenecks and other issues. Read to find out how this issue came about and how the Coban team resolved it with non-integer CPU allocation. -
Engineering
How we improved our iOS CI infrastructure with observability tools
After upgrading to Xcode 13.1, we noticed a few issues such as instability of the CI tests and high CPU utilisation. Read to find out how the Test Automation - Mobile team investigated these issues and resolved them by integrating observability tools into our iOS CI development process. -
Engineering
2.3x faster using the Go plugin to replace Lua virtual machine
The Talaria open-source project has made significant improvements by replacing Lua VM with the Go plugin resulting in 2.3x faster performance and memory usage reduction. Talaria is a time-series database designed for Big Data systems used to process millions of transactions and connections daily at Grab, requiring scalable data-driven decision-making. -
Engineering
Safer deployment of streaming applications
As Flink becomes more popular with real-time stream applications, we realise that Flink deployments are sometimes stressful and prone to errors. The Coban team deep dives into the issues with our existing Flink deployment process, possible mitigations, and the eventual solution to ensure safer deployments of Flink streaming applications. -
Engineering · Design
Message Center - Redesigning the messaging experience on the Grab superapp
Grab’s messaging feature was designed for two-party communications, but as our superapp grew to include more features, we became more aware of the limitations in our app. Read to find out how we redesigned the messaging experience to make it more extensible and future-proof. -
Engineering · Design
Evolution of quality at Grab
Testing is typically done after development is complete, which often results in bugs being discovered late in the process. Read to find out how Grab has improved its quality to scale and support the superapp experience. This evolution also brings a cultural shift for quality mindset in teams, enabling us to deliver faster with a better experience for our users. -
Engineering · Design
How OVO determined the right technology stack for their web-based projects
As companies grow in today's technology landscape, it often leads to a diverse set of technology stacks being used in different teams, which can lead to bigger problems in the future. Find out how the OVO team compared and analysed different technologies to find the one that best met their needs.

PII masking for privacy-grade machine learning
Data engineers at Grab work with large sets of data to build and train advanced machine learning models to continuously improve our user experience. However, as with any data-handling company, dealing with users' data may present a potential privacy risk as it contains Personally Identifiable Information (PII). Read this article to find out more about Grab’s mature privacy protective measures and how our data streaming team uses PII tagging and masking on data streaming pipelines to protect our users.
