---
title: "Performance"
description: "817 posts about Performance, summarised, each linking to the original."
---

# Performance
> 817 posts about Performance, summarised, each linking to the original.

## Articles

### [Android App Size at Scale with Project Bonsai](https://yomu.fyi/post/android-app-size-at-scale-with-project-bonsai.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Nguyen Van Minh
- Published: Mar 1, 2024
- Type: Problem & solution

Grab launched Project Bonsai to optimize the size of its Android superapp, which encompassed over four million lines of code across 1,500 modules. Because large app sizes negatively impact user acquisition on low-end devices with constrained bandwidth, the engineering team structured its optimization strategy around measurement, reduction, and containment. The team developed App Sizer, a custom CI-integrated tool that tracks binary compositions and module size contributions in Grafana. Initial reductions targeted Java and Kotlin dex files, resolving bloated transitive R classes by upgrading the Android Gradle Plugin to eliminate overly broad R8 retention rules. These initiatives achieved a 26% reduction in app download size while simultaneously decreasing overall disk footprint.


### [Enabling near real-time data analytics on the data lake](https://yomu.fyi/post/enabling-near-real-time-data-analytics-on-the-data-lake.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Shi Kai Ng
- Published: Feb 23, 2024
- Type: Problem & solution

Traditional data lake setups using Parquet on Hive metastores struggle with frequent updates and long pipeline intervals, introducing significant latency for ad hoc queries. Grab solved this by implementing Apache Hudi to support near real-time analytics across bounded relational databases and unbounded Kafka streams. For high-throughput sources, Flink streams Avro log files to Merge On Read tables and generates compaction plans for asynchronous Spark writers. Low-throughput workloads leverage Copy On Write tables, while relational database sources ingest binlogs via Flink Change Data Capture connectors. This architecture reduced analytics data latency to the minute level without overloading production databases and Kafka clusters.


### [The journey of building a comprehensive attribution platform](https://yomu.fyi/post/the-journey-of-building-a-comprehensive-attribution-platform.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Kang Huang
- Published: Feb 20, 2024
- Type: Problem & solution

Grab needed to modernize its marketing analytics from manual ad hoc queries and high data latency to a platform supporting real-time attribution for pricing models like cost per order. The engineering team initially deployed a pure stream-processing engine using Kappa architecture, Kafka, ScyllaDB, and Redis, which reduced latency from days to minutes and merged ads and promo touchpoints. However, stream-only processing faced high costs, out-of-order event issues, and difficulties running multi-touch models across longer historical windows. Grab transitioned to a Lambda architecture pairing Coban stream processing with Spark-based batch ETL and Amazon S3. This hybrid design separated real-time operational metrics from historical batch reporting, cutting real-time processing costs by approximately 25% while maintaining under 1% data discrepancy.


### [Horizontally scaling the Rails backend of Shop app with Vitess](https://yomu.fyi/post/horizontally-scaling-the-rails-backend-of-shop-app-with-vitess.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Jan 17, 2024
- Type: Problem & solution

Shop app’s hockey-stick growth pushed its Ruby on Rails backend and MySQL primary datastore toward capacity, with multi-terabyte storage, week-long schema migrations, and throttled background jobs. After splitting databases and scaling surrounding systems, the team adopted Vitess to shard and re-shard MySQL while retaining SQL compatibility and self-managed primary datastores. They organized user-owned data into a sharded “users” keyspace using user\_id, kept other data in an unsharded “global” keyspace, and built application-layer query verifiers to validate routing, distribution, and transaction correctness before migration. The move made schema migrations take hours instead of weeks, removed the reported capacity-driven throttling, and enabled further scaling by adding shards, while introducing concepts such as Sequences, Vindexes, and VSchemas that developers must learn.


### [Sliding window rate limits in distributed systems](https://yomu.fyi/post/sliding-window-rate-limits-in-distributed-systems.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Naveen Kumar Jakuva Premkumar
- Published: Dec 14, 2023
- Type: Problem & solution

Marketing communications across Grab's user base risked causing notification overload and consent revocations. To enforce personalized daily and weekly frequency caps across more than 270 million users, the team addressed segment membership storage and communication rate limiting. They adopted roaring bitmaps instead of Bloom filters to compactly store user segment data without hash collisions or costly rebuilds upon deletion. For frequency capping, they chose Amazon ElastiCache for Redis over DynamoDB, executing a sliding log rate limiting algorithm directly on the cluster using Lua scripts and sorted sets. Timestamps are stored as sorted set scores, and historical data is cleaned up via eviction ranges to prevent unbounded memory growth.


### [Getting Started with React Native Skia](https://yomu.fyi/post/getting-started-with-react-native-skia.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Dec 4, 2023
- Type: Tutorial

React Native Skia exposes Google’s cross-platform Skia 2D graphics engine to React Native, giving developers a canvas for custom shapes, visual effects, and dynamic interactions. The article explains when to choose it over react-native-svg or standard React Native Views: SVG is optimized for static SVGs, while Skia supplies primitives such as shaders, path operations, and image filters for animated experiences. It introduces the Cartesian canvas, basic Circle, Rect, and RoundedRect shapes, and custom paths created with SVG notation or imperative commands including addCircle, lineTo, and addPoly. Examples then combine Skia with react-native-reanimated shared values for UI-thread animation and react-native-gesture-handler for panning, before adding Blur and DashPathEffect; the article also notes web support through React Native Web and points to further examples.


### [Road localisation in GrabMaps](https://yomu.fyi/post/road-localisation-in-grabmaps.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Roxana Crisan
- Published: Nov 17, 2023
- Type: Problem & solution

Grab needed to localise nearly 30 million road segments across more than 10,000 area boundaries in Southeast Asia to attach hyperlocal attributes like language, driving side, and vehicle access rules. Testing direct geometric inclusion between complex road polylines and boundary polygons proved computationally prohibitive for daily map generation pipelines. To optimize this process, Grab introduced geohashes as rectangular proxies, precomputing geohash coverage across borders and road segments before joining the datasets in parallel. To resolve misclassification errors near borders without sacrificing performance, the team evaluated geohash coverage percentages and refined boundary-touching geohashes with polygon clipping. A laptop benchmark on a subset of data reduced runtime from 38 minutes with geometric intersection to 78 seconds using the geohash approximation.


### [Introducing Ruvy](https://yomu.fyi/post/introducing-ruvy.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Oct 18, 2023
- Type: Announcement

Ruvy is an open-source toolchain that takes Ruby code as input and produces a WebAssembly module whose exported \_start function executes that code. Built on ruby.wasm, it pre-initializes the Ruby virtual machine and includes Ruby files during the build, while avoiding the need to supply a Ruby script path as a WASI argument at runtime. In Wasmtime benchmarks, Ruvy’s instantiation and \_start execution took about 44.4–45.2 ms, compared with 55.8–56.9 ms for ruby.wasm plus wasi-vfs, and its Wasm-to-native compilation took about 440–457 ms versus 1.62–1.68 seconds. These results correspond to roughly 20% faster runtime performance and about 70% less compilation time in the reported tests. Ruvy currently requires users to install build dependencies and compile it because precompiled binaries are not shipped.


### [Stepping up marketing for advertisers: Scalable lookalike audience](https://yomu.fyi/post/stepping-up-marketing-for-advertisers-scalable-lookalike-audience.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: William Wu
- Published: Sep 22, 2023
- Type: Problem & solution

Grab's legacy lookalike audience generation platform suffered from long creation SLAs of two working days, high costs, and low weekly update frequencies. To resolve these bottlenecks, the engineering team designed an embedding-based platform powered by an in-memory retrieval service and automated update pipelines. The system creates audience representations by averaging constituent passenger embeddings, determining user membership through real-time cosine score thresholds. To eliminate feature store latency while fitting all embeddings into memory, a hash-based compression method cuts passenger embedding storage needs by roughly 90%. Consequently, audience availability dropped to within 15 minutes of campaign creation, audience generation costs fell by 98%, and ad impressions and clicks doubled.


### [Streamlining Grab's Segmentation Platform with faster creation and lower latency](https://yomu.fyi/post/streamlining-grab-s-segmentation-platform-with-faster-creation-and-low.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Jake Ng
- Published: Aug 15, 2023
- Type: Problem & solution

Grab's Segmentation Platform previously stored user-to-segment mappings across individual rows in ScyllaDB, causing write bottlenecks during segment creation and read latencies too high for downstream consumers. To resolve these performance limitations, the team transitioned to storing segments as Roaring Bitmaps saved as single blobs in object storage. This compression strategy splits 32-bit integer user IDs into chunks across array, bitmap, and run containers based on data density, reducing a one-million-member segment to under one megabyte. An accompanying client SDK manages segment retrieval, decoding, update notifications, and least-recently-used in-memory caching. Consequently, consumers such as Grab's communications platform achieved peak throughput of 15,000 queries per second with sub-millisecond p99 read latencies.


### [Coding the stars - an interactive constellation with Three.js and React Three Fiber](https://yomu.fyi/post/coding-the-stars-an-interactive-constellation-with-three-js-and-react.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: fsansalvadore
- Published: Aug 4, 2023
- Type: Tutorial

The article provides a tutorial for recreating Supabase Launch Week 8’s animated constellation in Next.js with Three.js and React Three Fiber (R3F). Each signup or user-generated ticket becomes a particle shaped into an “8,” with signups loaded from the \`lw8\_tickets\` table and later updates delivered through Supabase Realtime. The scene uses five-sided circle geometry, a white standard material with optional AdditiveBlending, an R3F Canvas, and a camera positioned at \`\[0, 0, 500\]\`. Particle movement is calculated with \`useFrame\`, elapsed time, sine and cosine functions, alternating loop direction, randomized speed and delay, and configurable offsets; a \`dat.gui\` debug mode exposes parameters such as particle count and shape dimensions.


### [pgvector: Fewer dimensions are better](https://yomu.fyi/post/pgvector-fewer-dimensions-are-better.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: gregnr,oli\_rice,egor\_romanov
- Published: Aug 3, 2023
- Type: Benchmark

Embeddings support semantic search, retrieval augmented generation, and clustering in pgvector, but high-dimensional vectors increase storage, memory, and distance-computation costs as datasets grow. The post explains how IVF indexes reduce the records examined and compares embedding models using Massive Text Embedding Benchmark results, noting little correlation between dimension count and performance. In a 224,482-vector Wikipedia dataset, all-MiniLM-L6-v2 with 384 dimensions delivered 78% higher queries per second than OpenAI text-embedding-ada-002 with 1536 dimensions at accuracy@10 of 0.99, while using 4GB rather than 7.5GB of RAM. The 384-dimensional gte-small model produced more than a 200% queries-per-second improvement over all-MiniLM-L6-v2 at the same accuracy, though model choice still depends on task performance, sequence length, language support, and operational costs.


### [Supabase Beta July 2023](https://yomu.fyi/post/supabase-beta-july-2023.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: ant\_wilson
- Published: Aug 2, 2023
- Type: Announcement

Supabase’s July 2023 update lists product releases, community content, and preparations for Launch Week 8, which was scheduled to begin the following week. The shipped changes include a new Android Kotlin client library, Login with Figma as an OAuth provider, Resend support as a custom SMTP provider, pgsodium 3.1.8, and PostgREST 11.1. A performance benchmark examines pgvector 0.4.0’s strengths and limitations, while noting that work on HNSW had started and was not far away. The update also features a Snowflake natural-language query application built with Python, Streamlit, Open AI, and Supabase Vector, plus tutorials, courses, community projects, and hiring announcements.


### [Sidekick’s Improved Streaming Experience](https://yomu.fyi/post/sidekick-s-improved-streaming-experience.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Aug 1, 2023
- Type: Explainer

Sidekick addresses two streaming problems in LLM chatbots: Markdown fragments can render awkwardly before syntax is complete, and tool-driven responses can delay visible output across multiple LLM roundtrips. Its solution combines a buffering Markdown parser with an event emitter that multiplexes the main response and asynchronously resolved content into one stream. A Node.js Transform stream processes Unicode characters through a finite-state machine, buffering ambiguous sequences such as emphasis, list markers, and links until they resolve or are flushed as raw text. The parser also recognizes card links, triggering asynchronous card-resolution tasks while Server-Sent Events carry named streams to the UI, which demultiplexes them into the main response and placeholders. The design uses Markdown as a human-readable transport between the LLM, backend, and frontend, allowing content to appear as it is produced.


### [Inference characteristics of Llama](https://yomu.fyi/post/inference-characteristics-of-llama.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Aman Sanger
- Published: Jul 20, 2023
- Type: Benchmark

Llama-2-70B is evaluated against GPT-3.5-turbo on serving cost and latency, with the comparison deliberately excluding GPT-4 and noting fine-tuning as a separate reason to choose Llama. The evaluation serves Llama on two 80-GB A100 GPUs in 16-bit precision, then uses transformer FLOP, memory-bandwidth, and KV-cache analysis to explain the difference between prompt processing and token generation. The measurements indicate that Llama-2 has worse completion-token pricing than GPT-3.5 on two A100s, while its prompt-token pricing is more than three times cheaper; eight A100s might improve pricing but introduce unacceptable latency. The resulting guidance favors GPT-3.5 for most workloads and open-source Llama models for prompt-heavy tasks such as classification or reranking, with batch processing and workloads lacking prompt tokens identified as additional cases where Llama may make sense.


### [Contributing support for a Wasm instruction to Winch](https://yomu.fyi/post/contributing-support-for-a-wasm-instruction-to-winch.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Jul 18, 2023
- Type: Tutorial

Adding a WebAssembly instruction to Winch, Wasmtime’s baseline non-optimizing compiler, requires coordinated changes across code generation, register management, ISA-specific emission, and tests. The contribution described adds visitor support, delegates through CodeGenContext and MacroAssembler, emits x86\_64 instructions through Cranelift-backed assembler methods, and registers the instruction for differential fuzzing. CodeGenContext keeps the value stack, register allocator, and function frame consistent, while spilling can move values to linear-memory offsets; MacroAssembler handles ISA differences, register constraints, and CPU feature fallbacks such as lzcnt versus bsr-based clz. Filetests inspect generated machine code without executing it, while differential fuzzing compares Winch results with another WebAssembly engine across randomly generated modules and inputs. The workflow also includes running fuzzing, incorporating draft-PR feedback, rebasing, and submitting the public Wasmtime pull request.


### [pgvector 0.4.0 performance](https://yomu.fyi/post/pgvector-0-4-0-performance.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: egor\_romanov,pavel
- Published: Jul 13, 2023
- Type: Benchmark

The post benchmarks pgvector 0.4.0 for approximate nearest-neighbor search and assesses whether PostgreSQL can support production-scale vector workloads. Using the ANN Benchmarks methodology, pre-warmed RAM, the dbpedia-entities-openai-1M dataset of 1 million 1,536-dimensional OpenAI embeddings, and Supabase ARM instances, it varies probes, lists, concurrency, and compute size. On an 8-core, 32 GB instance, pgvector reached 0.91 accuracy@10 at 380 QPS with 10 probes and 0.98 accuracy at 140 QPS with 40 probes; a 64-core, 256 GB server reached about 1,800 QPS at 0.91 accuracy. The results indicate predictable scaling, while higher lists can improve query speed at the cost of index-build time, and the roadmap identifies HNSW, product quantization, and parallel index builds as planned improvements.


### [What is new in PostgREST v11.1?](https://yomu.fyi/post/what-is-new-in-postgrest-v11-1.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: steve\_chavez
- Published: Jul 12, 2023
- Type: Announcement

PostgREST v11.1 is available on the Supabase platform with configuration and querying improvements, plus a small set of documented breaking changes from v10. It lets JWT-authenticated impersonated roles such as anon, authenticated, and service\_role receive ALTER ROLE settings, including statement timeouts, pg\_plan\_filter statement cost limits, and configurable transaction isolation levels. The release also supports bulk JSON inserts that preserve column defaults when defaultToNull is false, and adds any/all modifiers for several filters, including eq, like, ilike, and comparison filters. For HTTP clients outside Supabase libraries, Range is now ignored on non-GET requests and RPC requests no longer use the deprecated Prefer: params=multiple-objects header; existing Supabase projects can upgrade by pausing and unpausing.


### [Go module proxy at Grab](https://yomu.fyi/post/go-module-proxy-at-grab.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Jerry Ng
- Published: Jun 30, 2023
- Type: Problem & solution

Grab's 69.3 GiB multi-module Go monorepo caused commands like go get to take over 18 minutes as Git repeatedly traversed commit history, downloaded large worktrees, and overloaded their GitLab VCS infrastructure. To bypass direct VCS queries without losing automatic updates for external repositories, the team deployed the Athens Go module proxy configured in fallback network mode. They used the GOVCS environment variable to disable Git access specifically for the monorepo path, forcing Athens to fall back to its internal object storage when resolving monorepo modules. A dedicated CI pipeline pre-populates and refreshes the Athens cache whenever new monorepo modules are released. This setup reduced monorepo go get execution times to approximately 12 seconds and allowed a 70% scale-down of the Athens proxy cluster.


### [ChatGPT plugins now support Postgres & Supabase](https://yomu.fyi/post/chatgpt-plugins-now-support-postgres-supabase.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: egor\_romanov,paul\_copplestone
- Published: May 25, 2023
- Type: Tutorial

Retrieval plugins let ChatGPT access relevant content from private datasets by chunking documents, generating embeddings with OpenAI’s text-embedding-ada-002, and querying a vector database. The article describes Postgres and Supabase datastore implementations using pgvector, with a shared documents schema and a match\_page\_sections function that filters by source, author, document, and date before ranking vectors with inner product distance. It then gives a local example: downloading the Postgres 15 documentation as a PDF, uploading it through the plugin’s /upsert-file endpoint, and configuring environment variables for Supabase or Postgres. The example produced 7,904 document records, and an optional IVFFlat index can trade exact nearest-neighbor search for approximate search; after registering the local plugin in ChatGPT, users can ask questions answered from the documentation.


[Newer posts](https://yomu.fyi/topic/performance/page/29.md) · [Older posts](https://yomu.fyi/topic/performance/page/31.md)