Building a Kubernetes platform at Pinterest

Why Kubernetes? Over the years, 300 million Pinners have saved more than 200 billion Pins on Pinterest across more than 4 billion boards. To serve this vast user base and content pool…

An update on Pixie, Pinterest’s recommendation system

We recently welcomed engineers to our offices for our annual Mad Science Fair, where I shared the latest on Pixie, an advanced graph-based recommendation system. Here’s a recap of that talk, and you can find the video here. As Pinterest grows to more…

Bottoms-Up: How the Pinterest growth team decentralizes team structure

On the growth team at Pinterest, we describe our structure as “bottoms-up”, meaning ideas and responsibilities flow throughout the team in a way that provides a lot of autonomy to work on the projects each person wants to tackle, across a variety of roles. The…

API Profiling at Pinterest

When I walked into Pinterest on the first day of my internship and learned I’d be focusing on profiling the API Gateway service — the core backend service of the Pinterest product — my only thought was “What is profiling?”. Profiling is often shoved aside…

Hybrid Search: Building a textual and visual discovery experience at Pinterest

As a visual discovery engine, Pinterest helps you discover ideas even when you don’t know what you’re looking for, or have the words to describe it. Using a mix of technology (including visual search) and community input (thanks to the 175B+ Pins…

Building the Pinterest app for Windows 10

Solving a user problem As we focus on developing ways to help Pinners find inspiration across all interests, locations and platforms, we knew we had to improve the experience for Windows. Accessing Pinterest from desktop requires using a browser, often times with numerous tabs…

One year in at Pinterest and looking ahead as we grow

A little over a year ago, I joined Pinterest as the SVP of Technology. The world looks different than I thought it would around my first “Pinniversary”, but the opportunities before us are more apparent and abundant than ever before. For me personally, I’m…

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…

Automated cluster management and recovery for Rocksplicator

Rocksplicator at Pinterest Rocksplicator (github) was open sourced by Pinterest one and half years ago. It was designed to solve common problems of building large-scale online stateful data systems on commodity hardware, such as RocksDB replication and request routing. Technical details about how Rocksplicator solves…

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 ads indexing works at Pinterest

Introduction Ads indexing is a data processing pipeline that builds servable ad documents for the ads delivering stack at Pinterest, including ads targeting, retrieval, ranking and auction. The pipeline converts ad campaigns in the raw format to the servable format via a heavy computational…

Introducing Pinqueue3.0, Pinterest’s next-gen content moderation platform

In 2016, we reached a milestone of launching Pinqueue2.0, which has since become the go-to content moderation tool for the engineering, community, and data science teams. One of our top priorities at Pinterest is fostering a safe and trustworthy experience for all…

User state-based notification volume optimization

Notifications play an important role in helping Pinterest’s 335M+ monthly active users discover and engage with inspiring content. It’s crucial to send the right number of notifications with high quality content. If we send too few notifications, Pinners might miss out on great opportunities…

Keeping Related Pins fresh

If you’ve ever heard someone mention “the Pinterest Rabbit Hole,” they’re likely referring to Related Pins, the ideas you see below a Pin you’ve tapped. Related Pins is a feed of content relevant to the closeup Pin and accounts for 40 percent of engagement…

Adventures in big data wonderland: Going down the Pinterest Path

Have you ever started looking for plants on Pinterest, only to end up shopping for pillows instead? Ever find yourself layers of Pins deep into finding a new idea, not sure of how you got there or where you started? When Pinners are in…

GokuL: Extending time series data storage to serve beyond one day

At Pinterest, developers rely heavily on Statsboard to monitor their systems and get alerted when issues happen. Last year, we introduced our time series database (aka Goku) which stores and serves the most recent one day’s…

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…

Setting Up Snowflake Secondary Roles with Tableau

At Pinterest, we heavily utilize Tableau for our enterprise analytics. In an effort to secure access of sensitive data to the appropriate business units, we moved the enterprise data into a Snowflake data warehouse. Using Snowflake as a data platform, we can create and manage…

The top Pinterest Engineering blog posts of 2018

The most-read posts of the year Three-day no-meeting schedule for engineers A one year PWA retrospective Supporting React Native How Pinterest runs Kafka at scale How switching our domain structure unlocked international growth PinSage: A new graph convolutional neural network for web-scale recommender systems Looking inside the technology that powers Pinterest Building a more inclusive way to search Building a real-time user action counting system for ads Goku: Building a scalable and high performant time series database system We’ll be back in 2019 with more updates from the team. Happy New Year!

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…

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…

Understanding the product cycle of discovery to purchase on Pinterest

Pinners have always used Pinterest for shopping, as they often come in a planning mindset and are exploring products and…

What we learned from an iOS app OOMs incident

In early 2020, we started seeing significantly elevated out-of-memory (OOM) crashes in the Pinterest iOS app. That incident resulted in a declined Crash-Free Users Rate (CFUR), from the previous 99% to 96%, which was a steep drop. What was going on? We improved…

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…

Virtual onboarding in the time of COVID-19: The new hire’s new normal

Cindy Xinyi Zhang | Data Scientist, Experimentation and Metrics Science On March 2nd, exactly one week before my full-time position at Pinterest started…