# Performance > 125 posts about Performance, summarised, each linking to the original. ## Articles ### [How Grab Leveraged Performance Marketing Automation to Improve Conversion Rates by 30%](https://yomu.fyi/post/how-grab-leveraged-performance-marketing-automation-to-improve-convers.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Sc Ng - Published: Mar 22, 2021 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. ### [Supabase Beta January 2021](https://yomu.fyi/post/supabase-beta-january-2021.md) - Company: [Supabase](https://yomu.fyi/company/supabase.md) - Author: Paul Copplestone - Published: Feb 2, 2021 Supabase released a collection of platform updates during January 2021 covering database tools, authentication integrations, regional availability, and infrastructure performance. Client libraries added support for PostgREST exact, planned, and estimated count queries to eliminate counting friction commonly experienced in platforms like Firebase. Authentication capabilities expanded to include Facebook and Azure login providers, an in-dashboard GoTrue audit trail, a helper function for user emails, and a prebuilt React Auth Widget. The platform also enabled new database hosting regions in London and Sydney while transitioning storage to GP3 for improved throughput. Furthermore, migrating API subdomains to Route53 alongside custom Let's Encrypt certificates yielded read performance benchmark gains of up to twelve percent. ### [Serving Driver-partners Data at Scale Using Mirror Cache](https://yomu.fyi/post/serving-driver-partners-data-at-scale-using-mirror-cache.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Indrajit Sarkar - Published: Jan 26, 2021 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%. ### [Trident - Real-time Event Processing at Scale](https://yomu.fyi/post/trident-real-time-event-processing-at-scale.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Jie Zhang - Published: Jan 13, 2021 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. ### [Supabase Beta December 2020](https://yomu.fyi/post/supabase-beta-december-2020.md) - Company: [Supabase](https://yomu.fyi/company/supabase.md) - Author: Paul Copplestone - Published: Jan 2, 2021 Supabase transitioned to Beta following ten months of development concentrated on performance, security, and reliability. The platform introduced inline table documentation updates, storing descriptions directly as PostgreSQL comments and exposing them through OpenAPI specifications. Developers can now utilize a table view featuring immediate realtime update reflection alongside pagination for handling large datasets. Performance measurements show a 26 percent increase to 1460 requests per second in regions supporting AWS Graviton instances. Furthermore, the update introduces a new region in Sao Paulo, editing support for native Postgres arrays in the grid editor and side panel, and broader compatibility for custom database types. ### [Making the Supabase Dashboard Supa-fast](https://yomu.fyi/post/making-the-supabase-dashboard-supa-fast.md) - Company: [Supabase](https://yomu.fyi/company/supabase.md) - Author: Inian Parameshwaran - Published: Dec 13, 2020 Adding features to the Supabase single-page application dashboard risked performance regressions due to increasing JavaScript bundle sizes. To establish a baseline, the engineering team introduced next-bundle-analyzer and tracked Real User Monitoring alongside Core Web Vitals using Sentry. Optimization efforts focused on pruning dependencies, which included swapping Moment.js for day-js, replacing Joi with ajv, reverting crypto-js to version 3.3.0, and moving the 388 KB zxcvbn password module to a backend API. The team also implemented Next.js dynamic imports for heavy components like spreadsheet parsing, removed legacy server-side props to unlock Automatic Static Optimization, and configured long cache headers on assets. These architectural adjustments brought Core Web Vitals within recommended thresholds and lowered client-side page transition payloads below 200 KB of JavaScript. ### [Postgres Views](https://yomu.fyi/post/postgres-views.md) - Company: [Supabase](https://yomu.fyi/company/supabase.md) - Author: Paul Copplestone - Published: Nov 18, 2020 Postgres views serve as query shortcuts that execute underlying SQL statements upon retrieval without generating new tables or persisting duplicate data. By encapsulating complex multi-table joins, standard views provide query consistency across applications, simplify repetitive calls, improve logical schema organization, and enhance security by restricting sensitive columns. In contrast, materialized views physically store query results on disk, dramatically reducing read latency for heavy queries spanning millions of rows. Because materialized views introduce the trade-off of stale data, administrators must periodically run the refresh command based on workload tolerances for use cases like analytics and internal dashboards. Materialized views should not substitute query optimization, as underlying query efficiency remains essential. ### [Optimally Scaling Kafka Consumer Applications](https://yomu.fyi/post/optimally-scaling-kafka-consumer-applications.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Shubham Badkur - Published: Oct 13, 2020 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. ### [Supabase Alpha September 2020](https://yomu.fyi/post/supabase-alpha-september-2020.md) - Company: [Supabase](https://yomu.fyi/company/supabase.md) - Author: Paul Copplestone - Published: Oct 3, 2020 Seven months into development, Supabase announced a series of platform updates across authentication, database tooling, and client libraries. The release introduced OAuth logins supporting Bitbucket, GitHub, GitLab, and Google, alongside table cloning and one-click Postgres extension management. In the SQL editor, users can now save favorite queries and access locally stored query histories directly within the browser. The web dashboard adopted Next.js automatic static optimization for improved responsiveness, while postgrest-js migrated to TypeScript and an isomorphic gotrue-js TypeScript library was built for Netlify GoTrue integration. Supabase is prioritizing a transition from Alpha to Beta by tracking open-source tool performance in a dedicated benchmarks repository. ### [Uncovering the Truth Behind Lua and Redis Data Consistency](https://yomu.fyi/post/uncovering-the-truth-behind-lua-and-redis-data-consistency.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Allen Wang - Published: Sep 7, 2020 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. ### [The Reformer - Pushing the limits of language modeling](https://yomu.fyi/post/the-reformer-pushing-the-limits-of-language-modeling.md) - Company: huggingface.co - Author: Patrick von Platen - Published: Jul 3, 2020 Standard transformer models hit memory bottlenecks on long sequence modeling tasks due to the quadratic asymptotic memory complexity of global self-attention and oversized positional embedding matrices. The Reformer architecture overcomes these constraints to train sequences of up to half a million tokens using under 8GB of RAM. It re-engineers transformer operations using local and Locality Sensitive Hashing self-attention, chunked feed forward layers, reversible residual layers, and axial positional encodings. In empirical benchmarks using google/reformer-crime-and-punishment, axial positional encodings reduce the model parameter count from over 136 million to approximately 2.58 million by factorizing the positional dimensions. This architectural change cuts inference memory consumption from 959 MB down to 447 MB for evaluated benchmark workloads. ### [Tackling UI Test Execution Time Imbalance for Xcode Parallel Testing](https://yomu.fyi/post/tackling-ui-test-execution-time-imbalance-for-xcode-parallel-testing.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Ngoc Thuyen Trinh - Published: Mar 16, 2020 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. ### [Returning 575 Terabytes of Storage Space to Our Users](https://yomu.fyi/post/returning-575-terabytes-of-storage-space-to-our-users.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Lucas Nelaupe - Published: Feb 25, 2020 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. ### [How We Prevented App Performance Degradation from Sudden Ride Demand Spikes](https://yomu.fyi/post/how-we-prevented-app-performance-degradation-from-sudden-ride-demand-s.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Corey Scott - Published: Jan 8, 2020 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. ### [Journey to a Faster Everyday Superapp Where Every Millisecond Counts](https://yomu.fyi/post/journey-to-a-faster-everyday-superapp-where-every-millisecond-counts.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Renu Yadav - Published: Dec 26, 2019 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. ### [Driving Southeast Asia Forward Through People-Focused Design](https://yomu.fyi/post/driving-southeast-asia-forward-through-people-focused-design.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Philip Madeley - Published: Nov 5, 2019 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. ### [Griffin, an Anti-fraud Risk Rule Engine Making Billions of Predictions Daily](https://yomu.fyi/post/griffin-an-anti-fraud-risk-rule-engine-making-billions-of-predictions.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Muqi Li - Published: Oct 28, 2019 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. ### [Using Grab’s Trust Counter Service to Detect Fraud Successfully](https://yomu.fyi/post/using-grab-s-trust-counter-service-to-detect-fraud-successfully.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Chao Wang - Published: Oct 21, 2019 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. ### [How We Built a Logging Stack at Grab](https://yomu.fyi/post/how-we-built-a-logging-stack-at-grab.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Daniel Kasen - Published: Jul 31, 2019 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. ### [Recipe for Building a Widget: How We Helped to “Peak-Shift” Demand by Helping Passengers Understand Travel Trends](https://yomu.fyi/post/recipe-for-building-a-widget-how-we-helped-to-peak-shift-demand-by-hel.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Lara PuReum Yim - Published: Mar 7, 2019 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. [Newer posts](https://yomu.fyi/topic/performance/page/5.md) · [Older posts](https://yomu.fyi/topic/performance/page/7.md)