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Yelp
Platform for discovering and reviewing local businesses including restaurants, services, shops, and other businesses.
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Yelp ·
Building Menu Vision: Real-Time Dish Recognition
Yelp developed Menu Vision to help diners recognize menu items and view corresponding user-generated photos and reviews in real time using their phone cameras. The feature evolved from a two-day hackathon Android prototype into a production system deployed across iOS and Android. Client devices perform all optical character recognition, text cleanup, and dish matching locally using native machine learning frameworks, while dish metadata is prefetched upon opening a restaurant page. Backend pipelines combine partner data, reviews, and photo captions into a Cassandra database, filtering exclusively for dishes with photos. To handle optical character recognition errors and name variations, the matching pipeline runs exact matching, bidirectional substring matching, and Jaro-Winkler similarity matching.
Arpitha Dudi, Growth Tech LeadYelp ·
Migrating a Large Flow Monorepo to TypeScript
Yelp migrated a 1.4-million-line JavaScript monorepo from Flow to TypeScript after community support and package ecosystem tooling shifted toward TypeScript. To maintain continuous type safety without code freezes, engineers converted the codebase package by package starting at the deepest internal dependencies. The toolchain used flowts for code conversion and flowgen to turn generated TypeScript declaration files back into Flow-compatible headers for downstream consumers. Webcore dogfooded the tooling across 90 packages, automated tracking through internal metrics, and assisted feature teams with bottleneck conversions. Completed in February 2026, the migration increased overall type coverage from 83.15% to 96.44% and unlocked TypeScript-only tooling.
Shawn Walton, Software EngineerYelp ·
Migrating from Apollo Tooling to GraphQL Codegen at Yelp
Yelp migrated its frontend React monorepo from the deprecated apollo-tooling to GraphQL Codegen to ensure ongoing maintenance and build reliability. Because the monorepo spans over 500 packages with thousands of generated type files and custom nested type imports, the Client Data team prioritized a transparent migration that avoided codebase-wide changes. The team rejected approaches requiring syntax updates or altered import paths, choosing instead to configure GraphQL Codegen and contribute upstream plugin changes so output structures and naming conventions matched the legacy output. Migrating packages incrementally through small pull requests enabled continuous feedback while allowing developers across dozens of teams to maintain their workflows without editing existing code. Completing the migration to GraphQL Codegen v6 simplified Yelp's build tooling, eliminated deprecated dependencies, and left upstream compatibility enhancements available for the wider open-source community.
Igor Kusakov, Software EngineerYelp ·
Training Orchestrator: Unifying Model Training at Yelp
Yelp developed Training Orchestrator to standardize machine learning pipeline execution and eliminate divergent training scripts across its applied engineering teams. Previously, monolithic Spark scripts coupled training logic directly to cluster runtimes, preventing local testing, slowing debug cycles, and causing inconsistent configuration drift across projects. The new framework introduces a declarative configuration layer backed by Pydantic models, enabling static type checking and step schema validation before runtime execution starts. Training workflows are defined as modular step hierarchies that the orchestration engine compiles into directed acyclic graphs for execution in topological order. By injecting Spark and MLflow contexts into steps, the architecture ensures reproducible tracking, automated Slack alerts, and parity across local development and production environments.
Ying Wang and Nathan Sponberg, Software EngineerYelp ·
Beyond the Menu Tree: How Yelp Built a Smarter Customer Success Chatbot with AI
Yelp transitioned from a legacy customer support chatbot that relied on rigid phrase matching and static menus to a retrieval-augmented generation system. The updated architecture classifies incoming queries using a large language model to route users into one of five specialized workflows, including QA, Billing, Refund, Cancel, and Review. For general QA, the system uses FAISS to search an in-memory vectorstore built exclusively from article metadata such as titles, summaries, and headers rather than raw article text. Retrieved articles are supplied to the language model as prompt context, followed by automated validations for safety, character limits, and hallucinated hyperlinks. In production A/B testing, this metadata-driven design doubled the chatbot resolution rate while achieving approximately 94% recall@5.
Lina Lee, Machine Learning Engineer; Nelson Lee, Engineering ManagerYelp ·
How Partition Access Visualizations Reduced our Data Lake S3 Cost by 33%
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.
Nick Del Nano, Data StreamingYelp ·
Optimizing Our Build Times by Migrating from Webpack to Rspack
As Yelp's JavaScript monorepo expanded over time, Webpack build speeds became a significant engineering bottleneck. To resolve this without rewriting complex configurations, the team selected Rspack for its direct compatibility with existing Webpack setups and plugins. Yelp implemented an adapter pattern to generate Rspack configurations dynamically from original Webpack files, enabling a staged opt-in rollout across development tools and CI environments. Following the initial migration, engineers addressed performance issues in barrel files using automated codemods to replace star re-exports and import-then-export patterns with named re-exports. Combined with Rspack's portable persistent caching, the migration reduced integration build times by approximately 52 percent and warm cache builds by up to 80 percent.
Benson Pan, Software EngineerYelp ·
How Yelp Keeps Server-Driven UI Consistent Across Four Platforms
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.
Radu Comaneci, Software EngineerYelp ·
Zero downtime Upgrade: Yelp’s Cassandra 4.x Upgrade Story
Yelp's Database Reliability Engineering team upgraded over a thousand Cassandra nodes from version 3.11 to 4.1 without downtime. The upgrade delivered reliability, observability, and performance improvements, including Java 11 support, configurable guardrails, and fixed incremental repairs. To avoid the high costs, consistency compromises, and data streaming delays of standing up separate datacenters, the team selected an in-place rolling upgrade strategy. The rollout orchestrated staged node updates alongside compatible versions of the Stargate proxy and custom data pipeline connectors without requiring client code modifications. Although Stargate 2.x introduced regressions on range and multi-partition queries that required downgrading to 1.x, the finished migration achieved up to a 58% reduction in p99 latency across key clusters.
Mark Surnin and Muhammad Junaid Muzammil, Software EngineerYelp ·
Building Biz Ask Anything: From Prototype to Product
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.
Maria Christoforaki, Group Tech Lead; Shree Shalini Pusapati, Software Engineer