At Yelp, we embrace innovation and thrive on exploring new possibilities. With our consumers’ ever growing appetite for data, we recently revisited how we could load data into Redshift more efficiently. In this blog post, we explore how DBT can be used seamlessly with Redshift Spectrum to read data from Data Lake into Redshift to significantly reduce runtime, resolve data quality issues, and improve developer productivity. Starting Point Our method of loading batch data into Redshift had been effective for years, but we continually sought improvements. We primarily used Spark jobs to read S3 data and publish it to our...

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In 2019, Yelp’s Core Android team led an effort to boost navigation performance in Yelp’s Consumer app. We switched from building screens with multiple separate activities to using fragments inside a single activity. In this blog post, we’ll cover our solution, how we approached the migration and share learnings from along the way as well as performance wins. Where we started circa 2018 Navigating between screens in an Android app is often when the app and device are under the most strain. The new screen and its dependencies are quickly created, which can lead to slow or frozen frames. Prior...

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The Yelp Reservations service (yelp_res) is the service that powers reservations on Yelp. It was acquired along with Seatme in 2013, and is a Django service and webapp. It powers the reservation backend and logic for Yelp Guest Manager, our iPad app for restaurants, and handles diner and partner flows that create reservations. Along with that, it serves a web UI and backend API for our Yelp Reservations app, which has been superseded by Yelp Guest Manager but is still used by many of our restaurant customers. This service was built using a DB-centric architecture, and uses a “DB sync”...

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Machine Learning Feature Stores ML Feature Store at Yelp Many of Yelp’s core capabilities such as business search, ads, and reviews are powered by Machine Learning (ML). In order to ensure these capabilities are well supported, we have built a dedicated ML platform. One of the pillars of this infrastructure is the Feature Store, which is a centralized data store for ML Features that are the input of ML models. Having a centralized dedicated datastore for ML Features serves a number of purposes: Data Quality and Data Governance Feature discovery Improved operational efficiency Availability of Features in every required environment...

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Sessions, Where Everything Started For the past few years, Yelp has been using dbt as one of the tools to develop data products that power data marts, which are one stop shops for high visibility dashboards pertaining to top level business metrics. One of the key data products that’s owned by my team, Clickstream Analytics, is the Sessions Data Mart. This product is our in-house solution to understand what consumers do during their session interaction with Yelp products and provide insights on top of it. This blog post will walk you through how dbt is used as an important test...

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We’re excited to announce that multi-metric horizontal autoscaling is available for all services at Yelp. This allows us to scale services using multiple metrics, such as the number of in-flight requests and CPU utilization, rather than relying on a single metric. We expect this to provide us with better resilience and faster recovery during outages. This year, PaaSTA (Yelp’s platform-as-a-service, which we use to manage all of the applications running on our infrastructure) turns eleven years old! The first commit was on August 20th, 2013, and the first public commit was on October 22nd, 2015. That’s over half of Yelp’s...

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This is the next installment of our blog series on improving our autoscaling infrastructure. In the previous blog posts (Open-sourcing Clusterman, Recycling kubernetes nodes) we explained the architecture and inner-working of Clusterman. This time we are discussing how attribute based instance selection in the autoscaling group has helped us make our infrastructure more reliable and cost effective, while also decreasing the operation overhead. This will also cover how these changes enabled us to migrate from Clusterman to Karpenter. (Spoiler alert: Karpenter blog post is coming soon!) Motivation At Yelp we run most of our workload on AWS spot instances, and...

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One of Yelp’s top priorities is the trust and safety of our users. Yelp’s platform is most well-known for its reviews, and its moderation practices have been recognised in academic research for mitigating misinformation and building consumer trust. In addition to reviews, Yelp’s Trust and Safety team takes significant measures when it comes to protecting its users from inappropriate material posted through other content types. This blog post discusses how Yelp protects its users from inappropriate content in videos. Videos at Yelp Recently, Yelp revamped its review experience by giving users the ability to upload videos alongside their review text....

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