At Yelp, we train many machine learning models on different schedules. Applied machine learning teams all have their own set of Spark-based training batches, scripts, and configurations. Over time, these diverged, leading to duplicated code, subtle inconsistencies, and a growing maintenance burden. Yelp’s Core Machine Learning Team has developed excellent tooling across our ML ecosystem over the years: feature stores for reproducible data, a unified training library for neural networks and gradient-boosted trees, seamless Spark integration, and MLflow services for model tracking and deployment. But there was still one key piece missing right in the middle: a standardized way to...

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The Evolution of Support: From Fixed Phrases to Conversation At Yelp, delivering responsive and accurate customer support is a core priority. For years, our legacy Customer Success (CS) Chatbot provided support by guiding users through a static support experience. Users either navigated a 2-step menu tree or typed a query that was matched against a fixed set of phrases to retrieve an answer. While functional, the legacy chatbot had a key limitation: its reliance on rigid matching meant that if a query didn’t fit the menu structure or precisely match a known phrase, the user wouldn’t be able to get...

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Introduction In large analytics environments, data teams often struggle to answer deceptively simple questions, like who their stakeholders are and how their data is being used. At Yelp, we address this by visualizing access patterns, plotting time-based partition key values against access event timestamps. These visualizations reveal distinct usage signatures – ad hoc queries, daily batch jobs, and periodic backfills – allowing data owners to understand their stakeholders and use cases. This deeper insight into data usage has enabled high-impact platform initiatives including migrating thousands of tables to Apache Iceberg format and identifying storage efficiencies which reduced the cost of...

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Over the years, Webpack has remained the bundler of choice for many JS projects, including here at Yelp. While it has served us well, its speed has increasingly become a bottleneck as our monorepo continues to grow. Fortunately, a bunch of new build tools (Vite, Parcel, Rspack, etc.) have emerged in recent years. Each of these tools promises different ways of improving performance and developer experience. In this blog post, we’ll walk through how we migrated our monorepo builds from Webpack to Rspack and achieved an approximately 50% reduction in build time. Why Rspack Our team has been closely observing...

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If you’ve read our earlier post, you already know about CHAOS—the server-driven UI (SDUI) framework we built at Yelp that powers our dynamic views. Until now, we’ve explored its architecture, backend implementation, and component model. In this post, we’ll dive into how we integrated CHAOS with Yelp’s cross-platform design system, Cookbook, and the auto-generated bridge library, Konbini. Introduction to Cookbook At Yelp, we support two major applications across our Web, iOS, and Android platforms: Yelp and Yelp for Business. This results in six different variations, which makes it challenging to maintain a unified experience. To address this challenge, we created...

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The Database Reliability Engineering team at Yelp seamlessly upgraded more than a thousand Cassandra nodes with zero downtime. This post takes you behind the scenes of our upgrade strategy, from planning sessions to flawless rollouts. Background Motivation Apache Cassandra is a distributed wide-column NoSQL datastore and is used widely at Yelp for storing both primary and derived data. Yelp orchestrates Cassandra clusters on Kubernetes with the help of operators, as explained in our operator overview post. Upgrading from Cassandra 3.11 to 4.1 offered several observability and reliability improvements, in addition to performance gains. Based on public benchmarks, we expected to...

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Introduction Users have access to a wealth of information on Yelp business pages – from reviews and photos to structured information, menus, and Ask the Community feature on the business page, a single business page can be an ocean of content. At the same time, user expectations have evolved: people now expect immediate, direct answers. Sifting through dozens of reviews to find a simple fact can be time-consuming. Fortunately, advances in Large Language Models (LLMs) have given us a new set of tools, allowing us to tackle information retrieval and summarization tasks that were prohibitively complex just a few years...

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Introduction Modern advertising platforms are fast-paced and interconnected: even small adjustments can have ripple effects on how ads are shown, how budgets are spent, and the value advertisers get from their ad spend. At Yelp, Ad Budget Allocation means splitting each campaign’s spend between on‑platform inventory (our website, mobile site, and app) and off‑platform inventory (the Yelp Ad Network). We optimize this split to meet advertisers’ performance goals while growing overall revenue. Due to the complexity of the budget allocation system and its feedback loop, even small changes can lead to unexpected system‑wide effects. To help us safely evaluate changes,...

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