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…

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…

Geo-blocking media content on Pinterest

Pinterest engineers are tasked with showing Pinners the best ideas related to their interests, which includes taking into account signals like location. As part of these efforts, we built a video and image platform to enable a…

Goku: Building a scalable and high performant time series database system

Co-authored by Jinghan Xu, Jian Wang & Tian-Ying Chang, Engineers At Pinterest, developers rely on Statsboard to monitor their systems and discover issues. A reliable and efficient monitoring system is very important for development velocity. Historically, we’ve used OpenTSDB to ingest and…

Four lessons in making Pinterest faster on Android

As engineers, we intuitively know that a faster app is a better experience for Pinners, but we also have proof that when web pages load faster, user growth improves. Last year, we doubled down on performance across our platforms so…

Pin2Interest: A scalable system for content classification

Content understanding is at the core of any great recommendation system. By knowing what each Pin is about, Pinterest can connect Pinners with the most relevant and inspiring content that best matches their interests. Taxonomy of interests The Interest Taxonomy captures popular concepts that appear within Pinterest…

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…

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…

People Behind the Product: Meet Rahim Daya, Head of International Product & Toronto Lead

Rahim Daya, Toronto-native and Pinployee of nearly 4 years, leads Pinterest’s International Product as well as the Tech team in Toronto. Ahead of our virtual Pinterest Engineering: Meet the Toronto Team event, get to know Rahim and learn more about…

Managing Videos on Android

Video launched on the Pinterest Android app in 2016 with the goal of bringing a seamless video experience to the app. This included the ability to support multiple videos per screen, while dynamically controlling their playback state by automatically pausing them when they’ve scrolled…

Detecting image similarity using Spark, LSH and TensorFlow

As a visual platform, the ability to learn from images to understand our content is important. In order to detect near-duplicate images we use the NearDup system, a Spark- and TensorFlow-based pipeline. At the core of the pipeline is a Spark implementation of batch…

Looking inside the technology that powers Pinterest

Pinterest began as a small startup, and has grown to a company that serves +175B Pins to +250M people. While we have one of the largest datasets online, we have 600+ engineers, and so technology needs to be accessible for each person to play their part in…

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…

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…

Real-time experiment analytics at Pinterest using Apache Flink

At Pinterest, we run thousands of experiments every day, and rely mostly on daily metrics to evaluate performance. The daily pipelines can take 10+ hours to run and sometimes are delayed, which has created some inconvenience in verifying the setup of…

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…

Fighting spam with Guardian, a real-time analytics and rules engine

As the Trust & Safety team, one of our major responsibilities is protecting Pinners from spam. Without protections, spam could potentially be all over Pinterest since spammers can script and generate activity at a rate much higher than actual users. One of…

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!

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…

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…

Building Expanded Targeting for Pinterest Ads

With more than 300 million people coming to Pinterest each month, Pinterest offers unique opportunities for advertisers to get their products in front of people with commercial intent. Similar to other platforms, Pinterest offers a variety of advertising targeting products¹. Advertisers can…

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…

PinalyticsDB: A Time Series Database on top of Hbase

PinalyticsDB is Pinterest’s proprietary time series database. At Pinterest, we rely on PinalyticsDB as a backend for storing and visualizing thousands of time series reports such as the sample case below, segmented by country. PinalyticsDB was built several years ago on…

User retention at Pinterest: tackling an ambiguous problem

User retention is an important but nebulous idea. It’s hard to acquire users and even harder to help them feel at home through an engaging and relevant experience. So when some of those users start to leave the platform, your first thought is…

Pinterest Flink Deployment Framework

Background At Pinterest, stream processing allows us to unlock value from real time data for pinners and partners. The Stream Processing Platform team is working on building a reliable and scalable platform to support many critical streaming applications including real-time experiment analytics…