Ad Metrics Quality and Testing on Mobile

Every day at Pinterest, we help our advertising partners reach Pinners through different ad formats, and we provide them with metrics and analytics on how their ads are performing. Maintaining our partners’ trust and delivering value is critical, so the metrics we expose…

The Dead Code Society

On June 1, 2018, engineer Chris Lloyd created the #dead-code-society Slack channel to be “a sombre place for remembering [---]-only diffs.” Messages are prefixed by a :tombstone: and include links to diffs that contain only file or line deletions. The channel averages a little over one message per day, but they frequently come in bursts as part of refactoring projects and clean-up efforts. As Pinterest has grown as a company, our software unpinning has grown with it. We have hundreds of software engineers producing thousands of changes per week. The net result is an upward trend in total lines of code with a significant portion of our software having been (re)written in just the last few years. The #dead-code-society has helped balance that trend by celebrating the retirement of code from active service. Retired code remains in source control history to be remembered and consulted, but it no longer occupies a prominent place in our workspaces, build systems, or cognitive periphery. Less is more. Carpe diem.

Pinterest + ktlint = ❤

It’s been nearly a year since Pinterest’s Android codebase became Kotlin-first, and about two years since we adopted ktlint for Kotlin linting and formatting. Today, we’re sharing that Pinterest has officially taken ownership over the project. Initially, we chose ktlint because of its…

A Pinterest Engineering guide to technical interviews

Technical interviews are the key component to landing an engineering role. Everything from preparation to asking the right questions is important for a successful interview. In this post, Pinterest employees from Recruiting and Engineering share tips and tricks for…

Cómo redujimos el tamaño de la aplicación de Pinterest para iOS en más de un 30% / 50 MB

This blog was originally published in English. To read the English version, click here. Todos sabemos que el tamaño de las aplicaciones es importante (el tamaño de descarga[1] y el tamaño de instalación local[2]). Además, existe una correlación…

How we built rearranging Pins

Listening to user feedback is essential to building a product Pinners love and use to discover and save ideas every day. Often, their feature requests present interesting technical challenges for our engineers. For example, today we launched the ability to…

PinText: A Multitask Text Embedding System in Pinterest

Word embeddings have been actively studied and developed by researchers and practitioners in the machine learning community since the neural network language model was proposed. Researchers not only proposed principled algorithms and open-source code, but they also released pre-trained embedding models with public…

Using Kafka to throttle QPS on MySQL shards in bulk write APIs

At Pinterest, backend core services are in charge of various operations on pins, boards, and users from both Pinners and internal services. While Pinners’ operations are identified as online traffic because of real-time response, internal traffic is identified as offline because processing is…

Shallow Mirror

Henry Cai | Software Engineer, Data Engineering Pinterest uses Kafka as the backbone for data transportation. As a part of our infrastructure, Kafka MirrorMaker v1 is used to replicate traffic among different Kafka clusters spread across multiple regions. Kafka MirrorMaker is a powerful tool to replicate/aggregate data between different Kafka…

The Fantastic Fixathon

Engineers at Pinterest are always focused on doing what’s best for the product, but working in a codebase that’s clean, well-maintained, and relatively bug-free transforms engineering tasks from being “just work” to enjoyable opportunities to flex our skills. We’re also enriched as engineers, both technically and otherwise, when we take…

Interest Taxonomy: A knowledge graph management system for content understanding at Pinterest

Interest Taxonomy at Pinterest We recently began rolling out the beta version of Pinterest Trends, a new tool that gives a view of the top U.S. search terms on Pinterest within the past 12 months, and a look into when search terms peak for…

Multi-task Learning and Calibration for Utility-based Home Feed Ranking

Home feed is one of the most important surfaces at Pinterest that drives a significant portion of engagement from the more than 400+ million people who visit each month. From a…

Partitioning Pinlater for Fun(?) and Profit(!)

Contained within the November 2015 announcement of the open-sourcing of Pinlater, our asynchronous job execution system, was a description of some of the improvements that had been made to the system since the original 2014 blog post describing its creation. Notable among…

Introducing inclusive beauty searches for mobile

This week, we’re announcing skin tone ranges is coming to iOS (followed by Android) so Pinners can discover relevant hair and makeup ideas. Over the coming weeks, when Pinners search for beauty ideas on their iPhone or iPad app, a palette representing a range of skin tones will appear. The palette enables them to personalized Pins and videos they see. By combining the latest in machine learning and inclusive product development, we’re able to directly respond to Pinner feedback and build a more useful product. Last year, nearly 60% of the top 100 search terms for skin-related searches involved a tone, such as dark skin, pale skin, and olive skin, which showed us women of all skin types wanted a way to customize their searches. Keep an eye out for a deeper look at the design process behind the product. Acknowledgements: Emma Herold, Lulu Cheng, Long Cheng, Yixue Li, Larkin Brown, Candice Morgan, Xixia Wang, and Laksh Bhasin.

How to Review Code as a Junior Developer

“Just ask questions.” That was the piece of advice that completely changed my perspective on code reviews. I first started as a Software Engineer at Pinterest on the Shopping Partner Experience team right out of college. I focused on my personal learning by reading documentation…

Supporting React Native at Pinterest

Early last year, a small group of engineers started investigating the possibility of adopting React Native at Pinterest. We’ve been following the ecosystem with interest since it was first open-sourced in 2015. Since then, a robust community has developed around it, and it’s been…

Driving Shopping Upsells from Pinterest Search

Pinterest’s mission is to bring everyone the inspiration to create a life they love. Shopping is at the core of Pinterest’s mission by helping Pinners find and purchase the products they like. Oftentimes when Pinners want to buy…

Open sourcing Querybook, Pinterest’s collaborative big data hub

Charlie Gu | Tech Lead, Analytics Platform, Lena Ryoo | Software Engineer, Analytics Platform, and Justin Mejorada-Pier | Engineering Manager, Analytics Platform With more than 300 billion Pins, Pinterest is powering an ever-growing and unique dataset that maps interests, ideas…

Optimizing Kafka for the cloud

Takeaways Developing locality aware systems and balancing algorithms can help substantially reduce cost Making Kafka Producer and Consumer rack aware helps efficiently route traffic Data-driven engineering decision making is critical to continuously improve systems One of the fundamental principles when operating in the…

Launching your career as an apprentice engineer at Pinterest

In 2018, after more than a decade focused on environmental sustainability, I decided to start a new career as a software engineer. I enrolled in an immersive software development course in NYC, and a year later I was headed to a software engineering apprenticeship…

Unifying visual embeddings for visual search at Pinterest

Throughout the years, Pinterest has launched a variety of visual search products powered by computer vision to help people discover new ideas to try and products to buy. We started in 2015 with our visual cropping tool, allowing users to search within…

HierTCN: Deep learning models for dynamic recommendations and inferring user interests

As we build a visual discovery engine, it’s crucial to understand user intent and serve relevant content through recommender systems that constantly learn from a cross-section of data to dynamically predict the next idea a Pinner will love. However, we found…

How Pinterest runs Kafka at scale

Pinterest runs one of the largest Kafka deployments in the cloud. We use Apache Kafka extensively as a message bus to transport data and to power real-time streaming services, ultimately helping more than 250 million Pinners around the world discover and do what…

Understanding product attributes for shoppers

Every day, people come to Pinterest to shop and find ideas in categories like home and fashion, and it’s our responsibility to show them the most relevant and inspiring products. We measure relevance in a few different dimensions, but most importantly…

Using graph algorithms to optimize Kafka operations, Part 1

Pinterest uses Kafka across the board for data movement. The Logging Platform team alone manages over 3,000 Kafka brokers transporting trillions of messages per day to help bring more than 320 million monthly active Pinners the inspiration to create a life they love. …