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Performance
83 posts about Performance. Every summary links to the original.
Grab ·
How We Cut GrabFood.com’s Page JavaScript Asset Sizes by 3x
GrabFood.com faced high cloud costs while serving over 1 TB of weekly network egress across 175 million requests. To minimize egress and improve page performance, the engineering team audited and reduced their webpack dependencies using tools such as webpack-bundle-analyzer and dependency-cruiser. Their strategies included lazy loading rarely used libraries, unifying duplicate modules under single entry points, and substituting utility libraries like axios with native browser Web APIs. Additionally, altering implementation approaches, such as replacing client-side signed JWT cookie encoding with plain JSON strings, eliminated heavy Node dependencies like crypto. These optimizations reduced JavaScript static assets from 750 KB to 250 KB, decreased CloudFront costs by 20%, and accelerated build times by 3.6x.
Gibson ChengGrab ·
App Modularisation at Scale
Grab transitioned its monolithic mobile application into a modular architecture to resolve increasing code conflicts, slow releases, and difficult team collaboration. The team decomposed the single module by establishing base infrastructure modules, shared UI and utility libraries, discrete feature modules, and bridge kit modules for inter-module communication. Dependency injection using Dagger ties these components together in the main app module while preventing feature modules from directly depending on one another. The architecture spans over 1,000 modules across the app, with more than 200 modules in the Grab Financial Group payments domain where over 95% of modules build in under 15 seconds. This approach accelerated Gradle CI and local builds through parallel compilation and caching, though it increased Gradle sync times, IDE memory usage, and configuration maintenance overhead.
Amar JainGrab ·
Debugging High Latency Due to Context Leaks
Market-Store, Grab's feature store for real-time machine learning features, experienced latency spikes from under 200 milliseconds to 2 seconds as traffic grew. Metrics and logs showed no direct correlation to API issues, but heap profiling with PPROF revealed continuously increasing memory held by child contexts. Further analysis tracked the leak to an update in Grab's open-source Async Library, which switched background contexts to uncancelled task contexts for worker runners. Because parent contexts maintained references to these uncancelled child contexts, the garbage collector could not reclaim their memory. This progressive memory exhaustion directly degraded API latency.
Sourabh SumanGrab ·
How We Improved Agent Chat Efficiency with Machine Learning
Agent typing time represented a large portion of Grab's chat support journey, and 85% of messages were still free typed because agents customized static templates to fit their personal style. To accelerate typing across multilingual markets without robotic templates, Grab built SmartChat, a machine learning feature that provides contextual sentence completion. The team opted for a lightweight seq2seq architecture using single-layered GRU encoder-decoders in TensorFlow instead of bulky attention models to keep model latency under 100ms. The user interface was implemented in React using a content-editable div with inline typeahead suggestions activated via keyboard shortcuts.
Suman AnandGrab ·
How Grab Leveraged Performance Marketing Automation to Improve Conversion Rates by 30%
Grab faced operational bottlenecks managing direct-response Google Ads campaigns across thousands of ad groups due to its hyperlocal marketing across Southeast Asian markets. To eliminate the manual burden of tracking and updating ad creatives, the team built CARA, an in-house automation tool deployed on AWS serverless compute. CARA utilizes standardized file naming conventions to map assets to specific campaigns and connects with Google Ads and YouTube APIs to detect and replace low-performing assets. During an experimental rollout across more than 8,000 active ad groups, CARA replaced nearly 2,000 underperforming creatives. The automated asset replacement workflow produced an 18% to 30% increase in clickthrough and conversion rates.
Sc NgGrab ·
Serving Driver-partners Data at Scale Using Mirror Cache
Grab's Drivers Data service handles up to 10,000 requests per second during peak hours to supply driver information across backend microservices. The original setup used MySQL with Redis and standalone in-memory local caches, but yielded a low 25% local cache hit rate due to traffic patterns characterized by high burst frequency for individual drivers alongside redundant database calls across nodes. To solve this, the team developed Mirror Cache, an in-memory caching system that pairs Dgraph's Ristretto library with an asynchronous gRPC replication layer to mirror updates across cluster nodes. The replicator batches updates within the same AWS availability zone and forwards data to single nodes across zones to minimize transfer overhead. Production deployment increased the in-memory cache hit rate to approximately 75% and reduced direct MySQL queries by 5%.
Indrajit SarkarGrab ·
Trident - Real-time Event Processing at Scale
Trident serves as Grab's internal real-time event-processing and workflow automation engine, driving user campaigns, rewards, and notifications across multiple business lines. To handle peak loads exceeding 2,000 events per second without duplicate execution, the system consumes decoupled Kafka streams and enforces exactly-once semantics using Redis and MySQL deduplication checks. Processing efficiency relies on server autoscaling aligned with Kafka partition counts, combined with dynamic goroutine allocation per consumer. To minimize rule evaluation overhead, Trident indexes active campaigns into an in-memory hash map by event type, cutting processing time by at least 90%. Furthermore, condition evaluation is optimized through lazy loading and a weighted sorting algorithm that checks low-cost in-memory data prior to executing expensive database queries or external service calls.
Jie ZhangGrab ·
Optimally Scaling Kafka Consumer Applications
Grab's Coban platform runs Golang-based stream processing pipelines on Kubernetes, servicing roughly 400 billion events weekly from Kafka. The initial Horizontal Pod Autoscaler setup caused resource waste and uneven load distribution across Kafka partitions during scale-in and scale-out events. To resolve this, Grab moved to a fixed pod count matching the topic's partition count and adopted Vertical Pod Autoscaling, reducing resource usage versus requests by approximately 45%. The team also introduced Kubernetes priority classes to segment latency-sensitive workloads onto On-Demand nodes and non-critical jobs onto Spot instances. Additionally, overprovisioning via low-priority placeholder pods managed by Cluster Proportional Autoscaler enabled rapid pod rescheduling and reduced deployment delays.
Shubham BadkurGrab ·
Uncovering the Truth Behind Lua and Redis Data Consistency
Grab experienced replica CPU usage spikes following service deployments in their master/replica Redis cluster, which caused failovers to spike to 100% CPU. Investigation revealed that a post-deployment Lua monitor script executed separately on both nodes and relied on non-deterministic HGETALL key ordering. Redis encodes hash objects as either ziplists or hashtables, and restoring from an RDB snapshot initializes small hashes as ziplists even if the master previously converted them to hashtables. This encoding discrepancy caused key ordering to diverge, preventing secondary data from deleting correctly and bloating dataset sizes. Grab resolved the issue by sorting the outputs of HKEYS and HGETALL within the Lua script to guarantee deterministic execution across nodes.
Allen WangGrab ·
Tackling UI Test Execution Time Imbalance for Xcode Parallel Testing
Parallel test execution in Xcode can suffer from test time imbalance when tasks finish at significantly different times across parallel simulator workers. Analysis of Xcode scheduling logs shows that the runner groups tests by test class and dispatches all tests from the same class to a single simulator. Attempts to customize the suite by swizzling XCTestSuite fail because made-up suites initialize only after tests are dispatched. To overcome this grouping constraint, unique tokens or test names are appended to the class name component in `-only-testing` command-line arguments. This trick forces Xcode to treat each test as an independent class, successfully distributing individual tests across separate workers.
Ngoc Thuyen TrinhGrab ·
Returning 575 Terabytes of Storage Space to Our Users
Android Vitals data revealed that 15.7% of Grab users had less than 1GB of free device storage and uninstalled the app at 1.2 times the normal rate. To understand on-device storage consumption, the team instrumented session launches using the Android StorageManager API to collect binary size, cache folder size, and total footprint metrics. Analysis showed unusually large cache sizes driven by orphaned cache folders from discontinued third-party libraries, including an image library replacement of Picasso by Glide. An automated cleanup routine deployed in app updates purged legacy cache directories upon launch. This mechanism reclaimed 575 terabytes of junk data across more than 13 million devices, averaging 40MB per user.
Lucas NelaupeGrab ·
How We Prevented App Performance Degradation from Sudden Ride Demand Spikes
Grab experienced severe system strain when sudden localized spikes in ride demand, triggered by events like heavy rain or concert dismissals, coincided with driver shortages. These localized bursts overloaded the platform and degraded the experience for users outside the affected areas. To mitigate this, engineers created the Spampede filter, a circuit-breaker mechanism placed at the start of the booking pipeline. The filter converts pickup locations into Geohash Integer buckets and partitions time using Unix timestamps, tracking unfulfilled requests in Redis with atomic increments and time-to-live expirations. When unallocated requests exceed configured thresholds within a specific bucket, the system immediately short-circuits new incoming bookings to protect overall platform stability.
Corey ScottGrab ·
Journey to a Faster Everyday Superapp Where Every Millisecond Counts
Grab undertook an initiative to reduce startup time and improve time to interactive (TTI) on its passenger mobile app. Because local benchmarks failed to simulate real device and network conditions, the team instrumented code in production across 8–9 million daily users to capture p50 and p95 metrics. Initial gains came from caching service tiles between sessions and removing a startup animation, saving four seconds. Architectural changes followed, including converting iOS dynamic frameworks to static linking and merging others, while Android initialisation was refactored with Kotlin coroutines. Replacing a heavy third-party analytics library with an internal experimentation platform yielded further startup reductions.
Renu YadavGrab ·
Driving Southeast Asia Forward Through People-Focused Design
Designing digital products for Southeast Asia requires tailoring user experiences to unique regional constraints and consumer behaviors across diverse populations. Users in the region often operate low-end mobile hardware on congested networks while carefully rationing prepaid mobile data. Grab addresses these challenges by designing comprehensively for non-ideal UI stacks, implementing loading skeletons, and replacing heavy video tutorials with lightweight SVG animations to minimize bandwidth consumption. Furthermore, product teams adapt to mobile-only environments by prioritizing phone number and one-time-password registrations while avoiding legacy desktop-era iconography. Visual accessibility is validated by testing UI readability on dimmed, low-resolution screens under bright ambient sunlight.
Philip MadeleyGrab ·
Griffin, an Anti-fraud Risk Rule Engine Making Billions of Predictions Daily
Grab's Trust/Identity/Safety team built Griffin, an in-house anti-fraud risk rule engine designed to process billions of daily predictions across multiple business verticals. Initially, Grab managed fraud rules directly within backend service code, but escalating rule complexity, tight rule interdependencies, and translation gaps between data scientists and developers caused deployment delays and misfiring errors. To overcome the limitations and steep learning curves of third-party engines like Drools, the team separated the workflow into data orchestration and rule-based prediction. Griffin enables analysts and data scientists to author Python-based rules directly via a web portal and reload updated logic into memory without manual developer intervention. By eliminating I/O during rule evaluation and leveraging Gunicorn multi-processing, the engine handles over 100,000 queries per second at peak on six EC2 instances with single-prediction latencies under six milliseconds.
Muqi LiGrab ·
Using Grab’s Trust Counter Service to Detect Fraud Successfully
Grab's Trust Platform team built the Counter service to detect fraud across business verticals like transportation, food, and payments. The platform replaces manual, multi-week engineering cycles with a self-service UI where data analysts can define and experiment with counters independently. Operating on an asynchronous ingestion and synchronous transaction model, the architecture evaluates incoming stream data, enriches it via internal services, and persists aggregated signals to ScyllaDB through Grab-Stats. A multi-bucket strategy partitions queries into fifteen-minute, hourly, and daily granularities to maintain low-latency query aggregations across wide time ranges under strict SLAs.
Chao WangGrab ·
How We Built a Logging Stack at Grab
Grab needed a scalable logging platform to replace slow, fragmented systems that hindered debugging across their growing service fleet. Generating 25TB of daily logs, the team built a horizontally scalable Elasticsearch cluster configured via Ansible and monitored with Datadog. Although the initial proof of concept assigned all node roles (ingest, coordinator, master, and data) to every machine, operating at scale introduced major challenges with JVM heap exhaustion and cluster stability. The team resolved memory pressure and performance bottlenecks by tuning circuit breakers, lowering field data cache limits, adjusting shard allocations based on segment memory, and disabling translog compression during shard transfers.
Daniel KasenGrab ·
Recipe for Building a Widget: How We Helped to “Peak-Shift” Demand by Helping Passengers Understand Travel Trends
Transport demand spikes during regular commuting hours often outpace driver availability, resulting in passenger wait times and fare surges. To mitigate these imbalances, Grab created the Travel Trends Widget for its mobile feed to redistribute ride requests toward off-peak windows. The widget uses machine learning forecasting to present historical supply-demand patterns alongside pricing trends for the upcoming two hours. To handle anticipated high query rates across millions of database entries, engineers periodically load precomputed trend data into an in-memory data structure rather than querying the database per request. The feature rolled out to feeds in Singapore and Jakarta within four weeks of initial development.
Lara PuReum YimGrab ·
A Lean and Scalable Data Pipeline to Capture Large Scale Events and Support Experimentation Platform
Controlled online experimentation across diverse product verticals requires tracking interactions across systems to prevent local optimizations from causing global degradation. Grab built a batch data pipeline to capture, ingest, and process petabytes of event data to support its experimentation platform and analytics stakeholders. The architecture loads ingested event data from Amazon S3, transforms and sorts it, and writes partitioned output back to S3 with metadata registered in Apache Hive. Using Apache Spark on AWS Elastic MapReduce with Apache Airflow for orchestration, the system handles roughly 400,000 incoming events per second. The data is partitioned by event type and ingestion time and stored in Apache ORC format to streamline query workloads and reduce retrieval overhead.
Oscar CassettiGrab ·
Grab Senior Data Scientist Liuqin Yang Wins Beale-Orchard-Hays Prize
Grab Senior Data Scientist Dr. Liuqin Yang, Professor Defeng Sun, and Professor Kim-Chuan Toh received the 2018 Beale-Orchard-Hays Prize for their research paper introducing SDPNAL+. The software employs a majorised semismooth Newton-CG augmented Lagrangian method to solve large-scale semidefinite programming problems with nonnegative constraints. While traditional methods struggled beyond matrix dimensions of 2,000 and 5,000 constraints, SDPNAL+ successfully scales to matrix dimensions of 9,261 and over 12 million constraints. In benchmark testing, the software solved a problem on a desktop PC in 1.5 hours that required 122 hours on a 56-core CPU and 128-GPU cluster using a traditional solver. Grab implements these optimisation techniques to accelerate its passenger-driver allocation algorithms by hundreds of times.
Yang Liuqin