Administrar videos en Android

La función de video se lanzó en la aplicación de Pinterest para Android en 2016 a fin de ofrecer una experiencia de video completamente integrada en la aplicación. Esto incluía la capacidad de admitir varios videos por pantalla…

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.) …

El fantástico Fixathon

Los ingenieros de Pinterest siempre están enfocados en hacer lo mejor para el producto, pero trabajar en una base de código limpia, con buen mantenimiento y relativamente libre de errores transforma las tareas de ingeniería de ser “solo trabajo” a oportunidades agradables para…

Calidad y pruebas de las métricas publicitarias en dispositivos móviles

En Pinterest, todos los días ayudamos a nuestros socios de publicidad a llegar a los usuarios a través de diferentes formatos de anuncios y les ofrecemos métricas y análisis sobre el rendimiento de sus anuncios. Mantener la confianza…

Three-day no-meeting schedule for engineers

About 100 days ago, our product engineering team began experimenting with a three-day no-meeting schedule for individual contributors on our teams. It’s not a big revelation that software development requires long stretches of uninterrupted time to focus. As Pinterest has grown…

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…

Automating Shop The Look on Pinterest

Every day, people come to Pinterest to discover new ideas — and when they find a product they want to buy, it should be easy to purchase it. In home decor specifically, millions of people come to Pinterest to find inspiration. That’s why in 2017…

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…

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…

How we reduced Pinterest’s iOS app size by 30+% / 50MB

We all know app size (download size[1] and local install size[2]) matters, and there is a correlation between app size and customer engagement. Many times, people make decisions about using software based on size, and even pay for bandwidth by the megabyte. Not…

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…

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…

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…

Privacy Power-Up: Upgrading Pin Links from HTTP to HTTPS

HTTPS should be everywhere. Your website likely runs over HTTPS, giving your users a secure, encrypted connection, but what about all the outbound links? Can you guarantee that they all use HTTPS? In this post, we describe the steps we took…

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…

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…

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…

Pin it to Win it: Our First Virtual Pinternship

When our University Recruitment team started planning for the 2020 Pinterns to arrive, our goal was to welcome them to Pinterest and provide them with a memorable experience. As the pandemic quickly changed everyone’s plans, this core goal remained the same and we adapted our strategy to accommodate the…

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…

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…

Growth engineering meetup

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

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…

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…

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 a more inclusive way to search

Every day, tens of millions of people search for ideas on Pinterest, whether it’s a recipe to cook for dinner that night or a new hairstyle to try over the weekend. According to a Pinterest study, 70 percent of people use Pinterest to…