Growth engineering meetup

Synopsis of the talks Paid growth @ Airbnb Ganesh Venkataraman discusses the various channels and techniques…

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…

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…

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…

Improving HBase backup efficiency at Pinterest

Pinterest has one of the largest HBase production deployments in the industry. HBase is one of the foundational building blocks of our infrastructure and powers many of our critical services including our graph database (Zen), our general-purpose key-value store (UMS), our…

How switching our domain structure unlocked international growth

More than half of the 200 million monthly active users on Pinterest use our app outside the U.S. As we continue to make Pinterest even better for our global users, we moved our traffic to country code top-level domains (ccTLDs). An example of this…

Powering inclusive search & recommendations with our new visual skin tone model

To truly bring everyone the inspiration to create a life they love, Pinterest is committed to content diversity and to developing inclusive search and recommendation engines…

Refactoring Thrift schemas at Pinterest

The Pinterest tech stack consists of hundreds of microservices written in a variety of languages that use Apache Thrift to communicate with each other. Thrift schemas are used to define our service interfaces, constants and serialized data across all levels of the stack…

Powering Pinterest ads analytics with Apache Druid

The Change When we launched Promoted Pins in 2014, we chose Apache HBase as our database to store and serve all of our reporting metrics. At the beginning of our ads business, this was an appropriate choice because…

Faster Creator Content Distribution at Pinterest

At Pinterest, we are committed to building the best experience for creators to reach and inspire new audiences and for Pinners to easily discover the best ideas for them. We believe more diverse Pin formats and an efficient distribution system that builds a…

Pinterest + ktlint = ❤

Ha pasado casi un año desde que la base de código Android de Pinterest se convirtió en la prioridad de Kotlin, y aproximadamente dos años desde que adoptamos ktlint para el formato y lint de Kotlin. Hoy, compartimos que Pinterest se…

Secret management in multi-tenant environments

Knox, Pinterest’s open-source secret management service, now supports SPIFFE x509 identity documents as an authentication method. This update enables Knox to manage secrets in multi-tenant environments like Kubernetes. Knox is widely used across Pinterest’s infrastructure to provision secrets into production systems. In an…

Knit Con through an employee’s eyes

Originally published on Tobi’s personal blog. It’s not every day I get to spend two days with my coworkers making arepas, practicing Tai chi, learning how to plate dishes, and vibing to six different DJs. Two days immersing myself in the deepest caverns of…

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…

Building a real-time user action counting system for ads

The Pinterest ads team’s mission is to provide the best experience for both Pinners and advertisers. Our ads system is a real-time bidding system that delivers targeted ads based on a variety of attributes. One…

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…

Contextual relevance in ads ranking

Pinterest runs an ads marketplace that balances value for Pinners, partners, and the business. We initially used engagement prediction as our main method to balance the marketplace before eventually adding contextual relevance to increase Pinner value. We have three main surfaces for determining…

Building a dynamic and responsive Pinterest

In 2015, the majority of content on Pinterest was pregenerated for users prior to login. It was stored statically in HBase and served directly upon entering the service. (More details can be found in the blog post Building a smarter home feed.) …

A one year PWA retrospective

The idea of building a “Progressive Web App” (PWA) is not new, but its definition has changed with the emergence of key technologies like service workers. Now it’s finally possible to build great experiences in a mobile browser. Being an early adopter can…

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…

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…

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…

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…

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 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…