Pinterest

Pinterest

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Pinterest is a social bookmarking site where users collect and share photos of their favorite events, interests and hobbies ... more

Decisions 5

James Man

Software Engineer at Pinterest

Shared insights
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FlaskFlaskPythonPythonReactReact
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One of our top priorities at Pinterest is fostering a safe and trustworthy experience for all Pinners. As Pinterest’s user base and ads business grow, the review volume has been increasing exponentially, and more content types require moderation support. To solve greater engineering and operational challenges at scale, we needed a highly-reliable and performant system to detect, report, evaluate, and act on abusive content and users and so we created Pinqueue.

Pinqueue-3.0 serves as a generic platform for content moderation and human labeling. Under the hood, Pinqueue3.0 is a Flask + React app powered by Pinterest’s very own Gestalt UI framework. On the backend, Pinqueue3.0 heavily relies on PinLater, a Pinterest-built reliable asynchronous job execution system, to handle the requests for enqueueing and action-taking. Using PinLater has significantly strengthened Pinqueue3.0’s overall infra with its capability of processing a massive load of events with configurable retry policies.

Hundreds of millions of people around the world use Pinterest to discover and do what they love, and our job is to protect them from abusive and harmful content. We’re committed to providing an inspirational yet safe experience to all Pinners. Solving trust & safety problems is a joint effort requiring expertise across multiple domains. Pinqueue3.0 not only plays a critical role in responsively taking down unsafe content, it also has become an enabler for future ML/automation initiatives by providing high-quality human labels. Going forward, we will continue to improve the review experience, measure review quality and collaborate with our machine learning teams to solve content moderation beyond manual reviews at an even larger scale.

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Ashish Singh

Tech Lead, Big Data Platform at Pinterest

To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

#BigData #AWS #DataScience #DataEngineering

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Jigar Shah

Security Software Engineer at Pinterest

Security team builds services, solutions and tools for teams within Pinterest to manage accesses of critical production resources as well as facilitate adding authentication, authorization and accounting within critical production micro-services. Issuing identities to > 130k AWS EC2 instances, using them to make authentication and authorization decisions high bandwidth critical traffic flow conditions while services communicate in a mesh, requires a great deal of performance and stability. GoLang provides exactly that. Also primary engineering skills in Security team need not to be fully familiar with complex programming logic requires in Java/Kotlin while avoiding the pitfalls on runtime failures or uncertain behavior using Python in production leads us to GoLang

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Jigar Shah

Security Software Engineer at Pinterest

A consistent plane across teams at Pinterest to achieve deploy orchestration using EC2 APIs underneath.

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Blog Posts 213

Nov 15 2022 at 1:23PM
by Pinterest Engineering
InfraLINEDID+3
Nov 4 2022 at 9:41PM
by Pinterest Engineering
UtilizeFigTransformers+5
Oct 28 2022 at 2:58PM
by Pinterest Engineering
ExoPlayerXMLAndroid SDK+4
Oct 26 2022 at 2:57PM
by Pinterest Engineering
DID+1

Open Source 66

This is a repository to implement the Google Tag Manager server-side tag template for Pinterest API for Conversions to be deployed into Google Community Template Gallery.
Google Tag ManagerSmarty+2
0 0
An SDK that makes it quick and easy to build applications with Pinterest API.
Python+1
0 1
This is the auto-generated code using OpenAPI generator. Generated code comprises HTTP requests to various v5 API endpoints.
OpenAPIPython+2
0 1
L10nMessages is a library that makes internationalization (i18n) and localization (l10n) of Java applications easy and safe.
Java+1
0 0

Tech Talks 17

by Pong Eksombatchai One of the primary engineering challenges at Pinterest is how to help people discover ideas they want to try, which means serving the right idea to the right person at the right time. While most other recommender systems have a small pool of possible candidates (like 100,000 film titles on a movie review site), Pinterest has to recommend from a catalog of more than 4+ billion ideas. To make it happen, we built Pixie, a flexible, graph-based system for making personalized recommendations in real-time. http://about.pinterest.com/ iTunes App Store: http://pin.it/VQ-xmlR Google Play: http://pin.it/bEYNSEA
by Ekrem Kocaguneli Welcome to Pinterest’s home-sweet-Home Feed. Ekrem starts by giving you the ins and outs of how Pinterest’s highly personalized Home Feed works, then explains how we use machine learning techniques to rank the Pins you find there and fully personalize the experience.​ http://about.pinterest.com/ iTunes App Store: http://pin.it/VQ-xmlR Google Play: http://pin.it/bEYNSEA
by Justin Mejorada-Pier & Charlie Gu In this talk, Justin and Charlie run through the challenges they faced while building in-house tools like DataHub (the primary way people run queries and mine data here at Pinterest). Tune in as they share the hard-won learnings they picked up along the way. http://about.pinterest.com/ iTunes App Store: http://pin.it/VQ-xmlR Google Play: http://pin.it/bEYNSEA
by Jenny Liu Learn how we built the web-scale recommender system that powers over 40% of user engagement on Pinterest. Jenny will discuss how the small but mighty team prioritized the simplest and highest-leverage solutions. She’ll also give a rundown of the many challenges and learnings that came up in the evolution of candidate generation, Memboost and ranking in our system.​ http://about.pinterest.com/ iTunes App Store: http://pin.it/VQ-xmlR Google Play: http://pin.it/bEYNSEA