# Architecture
> 182 posts about Architecture, summarised, each linking to the original.

## Articles

### [How Partition Access Visualizations Reduced our Data Lake S3 Cost by 33%](https://yomu.fyi/post/how-partition-access-visualizations-reduced-our-data-lake-s3-cost-by-3.md)
- Company: [Yelp](https://yomu.fyi/company/yelp.md)
- Author: Nick Del Nano, Data Streaming
- Published: May 21, 2026

Data teams at Yelp struggled to identify table consumers and track how datasets were accessed across their petabyte-scale data lake. To solve this, the team built a usage attribution system using Amazon S3 server access logs to visualize time-based partition keys against access timestamps and IAM roles. These visualizations revealed distinctive patterns for daily batch jobs, backfills, and ad hoc queries, guiding migrations of thousands of tables to Apache Iceberg format. Furthermore, Yelp implemented a Default Access Retention strategy gated by IAM bucket policies to prevent accidental queries from resetting S3 Intelligent Tiering transitions. Combining these granular usage insights with targeted S3 storage classes reduced Yelp's S3 storage costs by 33 percent.


### [Scaling Airbnb’s identity graph with a unified knowledge graph infrastructure](https://yomu.fyi/post/scaling-airbnb-s-identity-graph-with-a-unified-knowledge-graph-infrast.md)
- Company: [Airbnb](https://yomu.fyi/company/airbnb.md)
- Author: Lucen Zhao
- Published: May 19, 2026

Airbnb migrated its massive identity graph from a third-party graph database vendor to a unified, in-house knowledge graph platform to solve long-tail latency and scaling bottlenecks. Built on JanusGraph with AWS DynamoDB for persistence and OpenSearch for indexing, the new architecture decouples storage operations from graph traversal logic. The migration improved P99 query latency, eliminated routine instance reboots, and supported a tenfold increase in write throughput.


### [From latency to instant: Modernizing GitHub Issues navigation performance](https://yomu.fyi/post/from-latency-to-instant-modernizing-github-issues-navigation-performan.md)
- Company: [Github](https://yomu.fyi/company/github.md)
- Author: Natalie Guevara
- Published: May 14, 2026

GitHub Issues addressed navigation latency by shifting workloads to the client using a local-first, stale-while-revalidate architecture. To evaluate perceived delays, the team tracked user transitions through Highest Priority Content thresholds, aiming for instant rendering in under 200 milliseconds. The core implementation added a persistent client-side cache using IndexedDB, paired with a synchronous in-memory tier to serve hot issue payloads without asynchronous overhead. A selective preheating mechanism resolves missing cache entries for high-intent links without overburdening backend capacity with redundant requests. Following broad rollout, the proportion of instant React soft navigations increased from 4% to approximately 22%, yielding an overall cache-hit ratio of around 33%.


### [From SSH to REST: A Security-Driven Modernization of Slack’s EMR Data Pipelines](https://yomu.fyi/post/from-ssh-to-rest-a-security-driven-modernization-of-slack-s-emr-data-p.md)
- Company: [Slack](https://yomu.fyi/company/slack.md)
- Author: Mahendran Vasagam
- Published: May 5, 2026

Slack's data platform accumulated over 700 SSH-based Airflow operators executing jobs directly on AWS Elastic MapReduce clusters, creating broad security surfaces and operational instability. Stateful SSH connections frequently failed when Kubernetes pods restarted, leaving orphaned zombie processes and preventing infrastructure modernization such as migrating EMR clusters to child accounts. To eliminate SSH access, Slack adopted a REST-based architecture using Quarry, a gateway providing unified REST APIs and token authentication across compute engines. For non-Hadoop CLI workloads, Slack leveraged YARN Distributed Shell to execute arbitrary shell scripts stored in Amazon S3 within managed, isolated YARN containers. The phased migration achieved zero downtime across eight data regions, unlocking reliable monitoring, simplified onboarding, and future initiatives like Spark on Kubernetes.


### [Data Mesh at Grab (Part II): The foundational tools behind certification](https://yomu.fyi/post/data-mesh-at-grab-part-ii-the-foundational-tools-behind-certification.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Aezo Teo
- Published: Apr 30, 2026

Grab operationalizes its Signals Marketplace data mesh through integrated platforms designed for continuous data certification and observability. The central metadata management platform, Hubble, extends open-source DataHub to model metadata as an event-driven graph and expose search, lineage, ownership, and data contracts. An automated certification engine built on the DataHub Actions framework continuously evaluates metadata changes, classifying assets into Uncertified, Certified, CertifiedPlus, or Revoked states. Genchi serves as the data quality observability layer, using Temporal and Kafka to run checks for freshness, volume completeness, schema stability, and semantic rules. To eliminate false-positive alerts caused by decoupled cron schedules, Genchi integrates with the Lighthouse monitoring service to trigger quality tests immediately upon pipeline completion.


### [How Yelp Keeps Server-Driven UI Consistent Across Four Platforms](https://yomu.fyi/post/how-yelp-keeps-server-driven-ui-consistent-across-four-platforms.md)
- Company: [Yelp](https://yomu.fyi/company/yelp.md)
- Author: Radu Comaneci, Software Engineer
- Published: Apr 22, 2026

Yelp maintains two major applications across Web, iOS, and Android, which previously led to fragmented server-driven UI implementations with mismatched backend component representations. To unify its server-driven UI framework, CHAOS, with its cross-platform design system, Cookbook, Yelp built an automated bridge library called Konbini. Konbini reads centralized JSON interface definitions through Jenkins pipelines to automatically generate synchronized libraries for Python, Kotlin, Swift, and TypeScript. These generated libraries handle component serialization on the backend and deserialization on client platforms. To maintain backward compatibility when component interfaces introduce breaking changes, client requests pass versioned spec identifiers, prompting the backend to execute migration methods that downcast newer component models to older supported versions.


### [Managing context in long-run agentic applications](https://yomu.fyi/post/managing-context-in-long-run-agentic-applications.md)
- Company: [Slack](https://yomu.fyi/company/slack.md)
- Author: Dominic Marks
- Published: Apr 13, 2026

Long-running multi-agent systems struggle with context management because accumulating raw message histories degrades inference quality, increases latency, and exceeds context window limits. In a collaborative security investigation platform, passing unrestricted history can also introduce confirmation bias across specialized agents. To maintain coherence across unbounded rounds of investigation, the system eliminates raw message history carryover between invocations. Instead, it coordinates agents through three structured context channels: a Director's Journal for orchestration memory, a Critic's Review that scores findings to filter hallucinations, and a Critic's Timeline of validated chronological events. This architecture provides agents with tailored context without overwhelming their inference capacity.


### [Improving storage efficiency in Magic Pocket, our immutable blob store](https://yomu.fyi/post/improving-storage-efficiency-in-magic-pocket-our-immutable-blob-store.md)
- Company: [Dropbox](https://yomu.fyi/company/dropbox.md)
- Author: Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura,Mike White,Kevin Altschuler,Facundo Agriel
- Published: Apr 2, 2026

After introducing a direct erasure-coding service, Dropbox encountered severe storage fragmentation in its immutable blob store, Magic Pocket, due to a large number of under-filled volumes. Because their legacy compaction strategy was only designed to top off near-full volumes, the team created new multi-strategy compaction methods to aggressively consolidate sparse volumes. These approaches leverage dynamic programming and streaming pipelines to rapidly reclaim unused disk capacity at exabyte scale.


### [Building Biz Ask Anything: From Prototype to Product](https://yomu.fyi/post/building-biz-ask-anything-from-prototype-to-product.md)
- Company: [Yelp](https://yomu.fyi/company/yelp.md)
- Author: Maria Christoforaki, Group Tech Lead; Shree Shalini Pusapati, Software Engineer
- Published: Mar 27, 2026

Yelp expanded Yelp Assistant to business pages by developing a question-answering architecture that extracts concise, evidence-backed answers from reviews, photos, and structured facts. The production system replaces static snapshots with near-real-time streaming pipelines for reviews and structured properties alongside weekly batch ingestion for menus and website data. For each incoming query, the system evaluates intent, retrieves relevant context through keyword and embedding searches, and constructs targeted prompts with dynamically selected few-shot examples. Synthesized answers are augmented with supporting visuals and citations before being streamed token-by-token to users. Generating suggested questions directly from specific business content rather than generic categories improved user engagement by roughly fifty percent and reduced unanswerable queries by twenty-six percent.


### [How Slack Rebuilt Notifications 📣](https://yomu.fyi/post/how-slack-rebuilt-notifications.md)
- Company: [Slack](https://yomu.fyi/company/slack.md)
- Author: Frances Coronel
- Published: Mar 19, 2026

Slack redesigned its legacy notification architecture to address user confusion, conflicting desktop and mobile preference systems, and tight coupling between notification content and push delivery. The engineering team unified four separate preference models into a single framework with three channel options: All new posts, Mentions, and Mute. To ensure safe rollback and backwards compatibility without direct database mutations, Slack implemented a read-time translation strategy alongside a new desktop\_push\_enabled preference and auto-saving cross-platform React UI components. This decoupling of activity from push delivery resulted in a fivefold sustained increase in settings engagement, reduced reliance on per-channel overrides, and eliminated synchronization mismatches across mobile and desktop clients.


### [From firefighting to building: How AI agents restored our team’s core productivity](https://yomu.fyi/post/from-firefighting-to-building-how-ai-agents-restored-our-team-s-core-p.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Sneh Agrawal
- Published: Mar 19, 2026

Grab's Analytics Data Warehouse team spent roughly 40% of their engineering bandwidth answering repetitive questions, tracing data lineage, and handling basic pipeline enhancement requests across more than 15,000 tables. To eliminate these manual investigative bottlenecks, the team implemented a multi-agent AI architecture using FastAPI, LangGraph, Redis, and PostgreSQL. Incoming requests route through two dedicated pathways: an enhancement pipeline for generating code changes and an investigation pipeline for diagnosing data anomalies. Specialized agents interact with underlying engines like Trino, GitLab, and observability platforms to query data, trace transformations, and check ongoing incidents before synthesizing findings. This system automates the context-gathering process within minutes while maintaining human-in-the-loop review for merge requests and production changes.


### [How Reblogs Work](https://yomu.fyi/post/how-reblogs-work.md)
- Company: engineering.tumblr.com
- Author: engineering
- Published: Feb 20, 2026

Tumblr reblog trees expand from an original root post into branching reblog trails as users reshare and comment on content. Storing these trails traditionally using relational database joins would require numerous queries per post on trails that extend thousands of levels deep. To maintain a constant query load of one query per post, Tumblr copies reblog trail content on every reblog rather than fetching intermediate posts at runtime. Older posts stored this copied content within deeply nested HTML blockquotes relying solely on permalink URLs, which caused trails to break whenever blogs changed their names. With the Neue Post Format, Tumblr moved to a JSON array structure that embeds trail content alongside immutable references to each blog and post, reducing broken trails while preserving single-query post loading.


### [From deployment slop to production reality: How BriX bridges the gap with enterprise-grade AI infrastructure](https://yomu.fyi/post/from-deployment-slop-to-production-reality-how-brix-bridges-the-gap-wi.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Sneh Agrawal
- Published: Jan 16, 2026

Internal AI prototypes frequently fail enterprise rollouts due to diverging versions, security oversights, hardcoded credentials, and infrastructure bottlenecks. BriX addresses this deployment gap by turning AI rollout into a configuration-driven platform rather than an engineering rewrite. Built on a synchronous streaming architecture, it routes user prompts through a React frontend using Server-Sent Events, a FastAPI gateway, and LangGraph orchestration. The platform integrates model switching, centralized prompt locks, and standardized Model Context Protocols for governed enterprise data access.


### [How Grab is accelerating growth with real-time personalization using Customer Data Platform scenarios](https://yomu.fyi/post/how-grab-is-accelerating-growth-with-real-time-personalization-using-c.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Saubhagya Awaneesh
- Published: Dec 18, 2025

Grab previously relied on daily batch attribute updates in its Customer Data Platform, which created engineering bottlenecks and hindered time-sensitive engagement opportunities. To address this limitation, the team introduced Scenarios, a self-serve real-time personalization capability embedded within the platform. The architecture processes real-time event triggers from Grab's Scribe platform using Apache Flink, enriches incoming events with historical context from StarRocks, and evaluates pre-trained machine learning classifiers. Computed outputs sync to Kafka streams or Amphawa, an internal feature store powered by AWS DynamoDB, maintaining end-to-end latencies under fifteen seconds. Across more than a dozen production deployments, including real-time interventions for subscription abandonment within fifteen minutes, the platform achieved over a 3% conversion uplift compared to batch campaigns.


### [Streamlining Security Investigations with Agents](https://yomu.fyi/post/streamlining-security-investigations-with-agents.md)
- Company: [Slack](https://yomu.fyi/company/slack.md)
- Author: Dominic Marks
- Published: Dec 1, 2025

Slack's Security Engineering team handles billions of daily security events and needed a reliable way to streamline on-call alert triage. An initial prototype relying on a single 300-word prompt produced inconsistent results and frequently reached spurious conclusions without properly challenging assumptions. To gain precise control, the team decomposed the workflow into chained model invocations with structured JSON outputs organized across three agent personas: a Director, four domain experts, and a Critic. Domain experts gather raw evidence through tool calls, the Critic evaluates finding quality and synthesizes a timeline, and the Director steers investigation phases using tiered model costs. The multi-agent system enables engineers to supervise investigations via a real-time dashboard while uncovering emergent issues like credential exposures across process ancestry chains.


### [SpellVault’s evolution: Beyond LLM apps, towards the agentic future](https://yomu.fyi/post/spellvault-s-evolution-beyond-llm-apps-towards-the-agentic-future.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Felix Haryanto Lie
- Published: Nov 21, 2025

Grab developed SpellVault as an internal no-code platform to democratize the creation of AI applications backed by Retrieval-Augmented Generation (RAG) and plugin integrations. To advance beyond static retrieval and linear input-output processing, the platform transitioned from its legacy executor to a graph-based execution model supporting branching, looping, and ReAct agent patterns. Capabilities like Python code execution and internal repository searching were unbundled from the prompt builder and consolidated alongside user plugins into unified Native and Community Built Tools. The platform also introduced a drag-and-drop deterministic workflow designer, automated task scheduling, and support for the Model Context Protocol (MCP).


### [Modernising Grab’s model serving platform with NVIDIA Triton Inference Server](https://yomu.fyi/post/modernising-grab-s-model-serving-platform-with-nvidia-triton-inference.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Daniel Tai
- Published: Oct 21, 2025

Grab's machine learning serving platform, Catwalk, experienced mounting technical debt, elevated latency, and rising costs from maintaining disparate inference engines for various frameworks. To address these limitations, the team adopted NVIDIA Triton Inference Server to establish a unified engine, starting with an ONNX migration. They built a custom Triton manager component featuring a proxy layer to translate legacy API requests alongside a server manager that handles model downloads, verification, configuration, and health checks. Within ten days, over half of online deployments migrated seamlessly without requiring client code modifications. The transition reduced p90 latency from 120ms to 20ms on large transformer models and cut average infrastructure spend by approximately 20 percent across evaluated services.


### [User foundation models for Grab](https://yomu.fyi/post/user-foundation-models-for-grab.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Abhinav Rai
- Published: Sep 26, 2025

Traditional recommendation systems at Grab relied on siloed, manually engineered features that struggled to capture sequential interaction data and cross-service user intent across its superapp ecosystem. To resolve these limitations, Grab developed a custom user foundation model built on a transformer architecture designed to learn from both tabular profile data and time-series clickstream interactions. The architecture unifies diverse data modalities—including categorical identifiers, numerical values, text, and geographical coordinates—by framing inputs as key-value token pairs. Custom positional embeddings and attention masks ensure the model processes tabular attributes as unordered sets while maintaining the sequential order of time-series events. The pre-trained model generates holistic user embeddings and supports direct fine-tuning across downstream tasks such as fraud detection, churn prediction, and advertisement optimization.


### [Data mesh at Grab part I: Building trust through certification](https://yomu.fyi/post/data-mesh-at-grab-part-i-building-trust-through-certification.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Chun Rong Phang
- Published: Aug 19, 2025

Rapid business growth across multiple verticals led Grab's centralized data engineering model to become an unscalable bottleneck, resulting in duplicate pipelines, ambiguous ownership, and broken downstream dependencies. To resolve these issues, the organization initiated a data mesh journey called Signals Marketplace that decentralizes data management and treats data as a product. A central data certification system establishes formal data contracts covering schemas, SLAs, freshness, and retention, while assigning clear Business Data Owners and Technical Data Owners. Breaches in contract guarantees automatically generate Data Production Incident tickets to enforce accountability and root-cause fixes. Consequently, 75% of internal queries now target certified assets, redundant tables saw a 400% year-over-year deprecation increase, and the total number of top-used datasets dropped by over 58%.


### [Grab's service mesh evolution: From Consul to Istio](https://yomu.fyi/post/grab-s-service-mesh-evolution-from-consul-to-istio.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Hilman Kurniawan
- Published: Jul 16, 2025

Grab operated over 1,000 microservices across hybrid infrastructure using Consul alongside a fallback mechanism called Catcher. Single-point-of-failure vulnerabilities in Consul servers and limited support for multi-cluster operations prompted an evaluation of alternative mesh technologies, ultimately leading to the selection of Istio. Grab avoided the standard single-control-plane-per-cluster pattern by deploying multiple external control planes in dedicated Kubernetes clusters arranged in active-active pairs. Migration began in Q4 2024, shifting traffic across AWS and GCP while handling both HTTP and gRPC protocols with gradual traffic-shifting and rollback mechanisms.


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