In this blog post, we highlight how phone number masking helps build consumer trust in the services marketplace at Yelp, decreases the friction in communication with service professionals, and allows for seamless switching between the Yelp app and a user’s phone. We present a high level overview of our in-house phone masking system and dive into the details of the engineering challenge of optimizing the usage of proxy phone number resources at Yelp’s scale. Background Every year, millions of requests for quotes, consultations or other messages are sent to businesses on Yelp. Those users choose to use Yelp to connect...

Continue reading

Yelp develops two major applications, Yelp & Yelp for Business, for Web (Desktop & Mobile), iOS, and Android platforms. That’s eight unique clients! Keeping a fresh, consistent UI on all these clients is a major challenge. Server-driven UI (SDUI) has become a standard industry technique for managing UI on multiple platforms. At Yelp, many product teams created SDUI frameworks for their features. Though successful, these frameworks were expensive to develop and maintain, and no single SDUI framework supported all our clients. In late 2021, we began building a unified SDUI framework called CHAOS or “Content Hosting Architecture with Optimization Strategies”....

Continue reading

What is Engineering Effectiveness Metrics (EE Metrics)? EE Metrics was envisioned as a hub that helps teams manage their technical debt. EE Metrics provides every team with a detailed web page that contains information about technical debt that needs to be addressed. It also serves as a platform to highlight top engineering initiatives at the organization level. EE Metrics empowers infrastructure teams to surface important migrations or metrics that could improve the health of software projects. Organization-wide migrations of technologies can often be difficult to surface and keep track of. General EE Metrics lifecycle Figure 1: Diagram showing how EE...

Continue reading

Yelp’s mission is to connect consumers with great local businesses by giving them access to reliable and useful information. Consumer trust is one of our top priorities, which is why we make significant investments in technology and human moderation to protect the integrity and quality of content on Yelp. As a platform for user-generated content, we rely on our community of users and business owners to help report reviews that they believe may violate our Terms of Service and Content Guidelines. Our User Operations team investigates flagged content and, if it’s found to be in violation of our policies, may...

Continue reading

Yelp relies heavily on streaming to synchronize enormous volumes of data in real time. This is facilitated by Yelp’s underlying data pipeline infrastructure, which manages the real-time flow of millions of messages originating from a plethora of services. This blog post covers how we leverage Yelp’s extensive streaming infrastructure to build robust data abstractions for our offline and streaming data consumers. We will use Yelp’s Business Properties ecosystem (explained in the upcoming sections) as an example. Key terminology Let’s start by covering certain key terms used throughout the post: Offline systems - data warehousing platforms such as AWS Redshift or...

Continue reading

While we once used Elasticsearch at Yelp, we have since built a replacement called Nrtsearch. The benefits and motivations of this switch can be found in our blog post: Nrtsearch: Yelp’s Fast, Scalable and Cost Effective Search Engine. However in this blog post, we will discuss the motivations behind building Nrtsearch Coordinator - a gateway for Nrtsearch clusters. We will also go over how Nrtsearch Coordinator adds sharding logic to Nrtsearch, handles scatter-gather queries, and adds support for dark/live launching cluster improvements. Motivations We traditionally used a gateway to call Elasticsearch, which provides metrics, isolation rate-limiting per client, and geo...

Continue reading

At Yelp, Apache Spark and JupyterHub are heavily used for batch processing and interactive use-cases, such as in building feature models, conducting ad-hoc data analysis, sharing templates, making on-boarding materials, creating visualizations, and producing sales reports. Our initial deployments of Jupyter at Yelp were iPython notebooks managed at an individual level. Later on when Jupyterlab was released (2018), our notebook ecosystem was extended to Jupyter Servers running on dev boxes, which was managed by individual engineering teams. Over time with growing use-cases and data-flow, this introduced unnecessary version variability, became error-prone due to the number of manual steps, caused config...

Continue reading

Merging code safely can be quite time consuming for busy repositories. A common method is to test and merge branches serially, and one at a time, in order to ensure the safety of the main branch. However, this method does not scale well when many developers want to merge code at the same time. In this blog post, you’ll see how we’ve sped up code merging at Yelp by creating a batched merge queue system! Why Merge Queues? In our blog post about Gondola, our frontend Platform as a Service (PaaS), we talked about the benefits of moving to a...

Continue reading