The Quest to Understand Metric Movements
Pinterest Engineering
by Pinterest Engineering
4d ago
Charles Wu, Software Engineer | Isabel Tallam, Software Engineer | Franklin Shiao, Software Engineer | Kapil Bajaj, Engineering Manager Overview Suppose you just saw an interesting rise or drop in one of your key metrics. Why did that happen? It’s an easy question to ask, but much harder to answer. One of the key difficulties in finding root causes for metric movements is that these causes can come in all shapes and sizes. For example, if your metric dashboard shows users experiencing higher latency as they scroll through their home feed, then that could be caused by anything from an OS u ..read more
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Advancements in Embedding-Based Retrieval at Pinterest Homefeed
Pinterest Engineering
by Pinterest Engineering
1w ago
Zhibo Fan | Machine Learning Engineer, Homefeed Candidate Generation; Bowen Deng | Machine Learning Engineer, Homefeed Candidate Generation; Hedi Xia | Machine Learning Engineer, Homefeed Candidate Generation; Yuke Yan | Machine Learning Engineer, Homefeed Candidate Generation; Hongtao Lin | Machine Learning Engineer, ATG Applied Science; Haoyu Chen | Machine Learning Engineer, ATG Applied Science; Dafang He | Machine Learning Engineer, Homefeed Relevance; Jay Adams | Principal Engineer, Pinner Curation & Growth; Raymond Hsu | Engineering Manager, Homefeed CG Product Enablement; James Li ..read more
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Establishing a Large Scale Learned Retrieval System at Pinterest
Pinterest Engineering
by Pinterest Engineering
1w ago
Bowen Deng | Machine Learning Engineer, Homefeed Candidate Generation; Zhibo Fan | Machine Learning Engineer, Homefeed Candidate Generation; Dafang He | Machine Learning Engineer, Homefeed Relevance; Ying Huang | Machine Learning Engineer, Curation; Raymond Hsu | Engineering Manager, Homefeed CG Product Enablement; James Li | Engineering Manager, Homefeed Candidate Generation; Dylan Wang | Director, Homefeed Relevance; Jay Adams | Principal Engineer, Pinner Curation & Growth Introduction At Pinterest, our mission is to bring everyone the inspiration to create a life they love. Finding ..read more
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How Optimizing Memory Management with LMDB Boosted Performance on Our API Service
Pinterest Engineering
by Pinterest Engineering
1M ago
Angel Vargas | Software Engineer, API Platform; Swati Kumar | Software Engineer, API Platform; Chris Bunting | Engineering Manager, API Platform The inside of the Pinterest lobby in Mexico City, showing a patterned ceiling, a receptionist deck with a plant on it, a light above it, and a gallery of images of pins you’d find on Pinterest, behind it. To the left, a glowing Pinterest P sign hovers in front of a glass wall. NGAPI, the API platform for serving all first party client API requests, requires optimized system performance to ensure a high success rate of requests and allow for ..read more
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Simplify Pinterest Conversion Tracking with NPM Packages
Pinterest Engineering
by Pinterest Engineering
1M ago
Juan Benavides Nanni; SDET II | Pinterest conversions are critical for businesses looking to optimize their campaigns and track the performance of their advertisements. By leveraging Pinterest’s Conversion API and Conversion Tag, advertisers can gain deeper insights into user behavior and fine-tune their marketing efforts. To make this process seamless for developers, we’ve created two NPM packages: pinterest-conversions-server and pinterest-conversions-client. These packages simplify the integration of Pinterest’s Conversion API and Conversion Tag, offering robust solutions for server-si ..read more
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How Pinterest Leverages Honeycomb to Enhance CI Observability and  Improve CI Build Stability
Pinterest Engineering
by Pinterest Engineering
2M ago
How Pinterest Leverages Honeycomb to Enhance CI Observability and Improve CI Build Stability Oliver Koo | Staff Software Engineer Optimizing Mobile Builds and Continuous Integration Observability at Pinterest with Honeycomb At Pinterest, our mobile infrastructure is core to delivering a high-quality experience for our users. In this blog, I’ll showcase how the Pinterest Mobile Builds team is leveraging Honeycomb (starting in 2021) to enhance observability and performance in our mobile builds and continuous integration (CI) workflows. Building a Data-Driven Approach to Observability Our mo ..read more
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Change Data Capture at Pinterest
Pinterest Engineering
by Pinterest Engineering
3M ago
Liang Mou; Staff Software Engineer, Logging Platform | Elizabeth (Vi) Nguyen; Software Engineer I, Logging Platform | In today’s data-driven world, businesses need to process and analyze data in real-time to make informed decisions. Change Data Capture (CDC) is a crucial technology that enables organizations to efficiently track and capture changes in their databases. In this blog post, we’ll explore what CDC is, why it’s important, and our journey of implementing Generic CDC solutions for all online databases at Pinterest. What is Change Data Capture? CDC is a set of software desig ..read more
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Resource Management with Apache YuniKorn™ for Apache Spark™ on AWS EKS at Pinterest
Pinterest Engineering
by Pinterest Engineering
4M ago
Yongjun Zhang; Staff Software Engineer | William Tom; Staff Software Engineer | Sandeep Kumar; Software Engineer | Monarch, Pinterest’s Batch Processing Platform, was initially designed to support Pinterest’s ever-growing number of Apache Spark and MapReduce workloads at scale. During Monarch’s inception in 2016, the most dominant batch processing technology around to build the platform was Apache Hadoop YARN. Now, eight years later, we have made the decision to move off of Apache Hadoop and onto our next generation Kubernetes (K8s) based platform. These are some of the key issues we aim ..read more
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Ray Batch Inference at Pinterest (Part 3)
Pinterest Engineering
by Pinterest Engineering
4M ago
Alex Wang; Software Engineer I | Lei Pan; Software Engineer II | Raymond Lee; Senior Software Engineer | Saurabh Vishwas Joshi; Senior Staff Software Engineer | Chia-Wei Chen; Senior Software Engineer | Introduction In Part 1 of our blog series, we discussed why we chose to use Ray(™) as a last mile data processing framework and how it enabled us to solve critical business problems. In Part 2 of our blog series, we described how we were able to integrate Ray(™) into our existing ML infrastructure. In this blog post, we will discuss a second type of popular application of Ray(™) at Pintere ..read more
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Feature Caching for Recommender Systems w/ Cachelib
Pinterest Engineering
by Pinterest Engineering
5M ago
Li Tang; Sr. Software Engineer | Saurabh Vishwas Joshi; Sr. Staff Software Engineer | Zhiyuan Zhang; Sr. Manager, Engineering | At Pinterest, we operate a large-scale online machine learning inference system, where feature caching plays a critical role to achieve optimal efficiency. In this blog post, we will discuss our decision to adopt Cachelib project by Meta Open Source (“Cachelib”) and how we have built a high-throughput, flexible feature cache by leveraging and expanding upon the capabilities of Cachelib. Background Recommender systems are fundamental to Pinterest’s mission to inspire u ..read more
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