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…

Using machine learning to auto-organize boards

While planning one’s life might look different these days, our insights tell us people are saving ideas for the future more than ever. Last month, for instance, Pinterest had a 60% increase in the number…

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…

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…

The evolution of search at Pinterest

When Pinterest first launched it provided a new way for people to collect and organize ideas from around the web. What started as a place to save though, quickly became a unique service for discovery where Pinners could draw inspiration from each other, brands and…

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…

PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest

As we build a visual discovery engine that powers 2B+ Pins, it’s crucial to understand user interests and preferences in…

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…

Empowering Pinterest data scientists and machine learning engineers with PySpark

Data scientists and machine learning engineers at Pinterest found themselves hitting major challenges with existing tools. Hive and Presto were readily accessible tools for large scale data transformations, but complex logic…

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…

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…

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…

How we designed our Continuous Integration System to be more than 50% Faster

Earlier this year, the Engineering Productivity team at Pinterest published a blog called How a one line change decreased our clone times by 99%. In that post, we described how a simple Git configuration sped up clone times in one of our…

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…

How Pinterest Supercharged its Growth Team With Experiment Idea Review

Growth teams need to be organized bottom-up to scale well The Pinterest Growth team has over 100 members, and we’ve run thousands of experiments over the years. It’s difficult to run that many experiments and still maintain a high success rate over time. We’ve found the traditional growth team model of team leads…

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…

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 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…

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…

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…

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…

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.

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…

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…

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…