话题公司 › pinterest


Pinterest(中文译名:缤趣),是一个网络与手机的应用程序,可以让用户利用其平台作为个人创意及项目工作所需的视觉探索工具,同时也有人把它视为一个图片分享类的社交网站,用户可以按主题分类添加和管理自己的图片收藏,并与好友分享。其使用的网站布局为瀑布流(Pinterest-style layout)。

Pinterest由美国加州帕罗奥图的一个名为Cold Brew Labs的团队营运,创办人为Ben Silbermann、 Paul Sciarra 及 Evan Sharp。2010年正式上线。“Pinterest”是由“Pin”及“interest”两个字组成,在社交网站中的访问量仅次于Facebook、Youtube、VKontakte以及Twitter。

The Field Guide to Non-Engagement Signals

Pinterest发布了一份关于非参与度信号的“领域指南”,旨在帮助在线平台更好地平衡用户参与度与内容质量,并实现用户需求的多样化。该指南提供了对于非参与度信号的应用和决策指导,特别是在调整情绪健康、保护用户权益等方面具有实际可行性。它强调了优化用户参与度对于长期用户留存的重要性,并指出通过使用质量指标和调查反馈等非参与度信号可以进一步提高用户留存率。此外,指南还提到通过使用生成人工智能来扩展内容质量信号的可能性,以及各个平台可以根据指南对自身进行分析和改进的方法。如果您有兴趣加入这项工作,可以了解更多信息并签署“Inspired Internet Pledge”。

How we built Text-to-SQL at Pinterest


LinkSage: GNN-based Pinterest Off-site Content Understanding

Adopted by Pinterest multiple user facing surfaces, Ads, and Board.

Improving Efficiency Of Goku Time Series Database at Pinterest (Part 2)

Goku is a time series database used by Pinterest for monitoring and alerting services. It offers pre-aggregation as an optimization technique for reducing query latency and cardinality. Users can enable pre-aggregation for metrics experiencing high latency or hitting cardinality limits. The Goku team provides users with tag combination distribution for the metric, allowing them to choose the tags they want to preserve in the pre-aggregated time series. After consuming data points from Kafka, the Goku Short Term host checks if the time series qualifies for pre-aggregation. If it does, the data is entered into an in-memory data structure that records various aggregations. Additionally, 5 aggregated data points are emitted for the time series. Goku Root handles query requests and modifies the metric name to query the right time series. The success story mentions a metric with high cardinality that achieved lower latencies after enabling pre-aggregation. Goku has onboarded over 50 use cases for pre-aggregation.

Migrating Policy Delivery Engines with (almost) Nobody Knowing

Several years ago, Pinterest had a short incident due to oversights in the policy delivery engine. This engine is the technology that ensures a policy document written by a developer and checked into source control is fully delivered to the production system evaluating that policy, similar to OPAL. This incident began a multi-year journey for our team to rethink policy delivery and migrate hundreds of policies to a new distribution model. We shared details about our former policy delivery system in a conference talk from Kubecon 2019.

At a high level, there are three important architectural decisions we’d like to bring attention to for this story.

Handling Online-Offline Discrepancy in Pinterest Ads Ranking System

At Pinterest, our mission is to bring everyone the inspiration to create a life they love. People often come to Pinterest when they are considering what to do or buy next. Understanding this evolving user journey while balancing across multiple objectives is crucial to bring the best experience to Pinterest users and is supported by multiple recommendation models, with each providing real-time inference with an overall latency of 200–300 milliseconds. In particular, our machine learning powered ads ranking systems are trying to understand users’ engagement and conversion intent and promote the right ads to the right user at the right time. Our engineers are constantly discovering new algorithms and new signals to improve the performance of our machine learning models. A typical development cycle involves offline model training to realize offline model metric gains and then online A/B experiments to quantify online metric movements. However, it is not uncommon that offline metric gains do not translate into online business metric wins. In this blog, we will focus on some online and offline discrepancies and development cycle learnings we have observed in Pinterest ads conversion models, as well as some of the key platform investments Pinterest has made to minimize such discrepancies.

Evolution of Ads Conversion Optimization Models at Pinterest

A Journey from GBDT to Multi-Task Ensemble DNN.

Building Pinterest’s new wide column database using RocksDB

Rockstorewidecolumn 是一个大规模用户序列项目,用于存储用户事件数据。该项目已在Pinterest公司使用了两年多,并得到了广泛应用。它能够处理每秒数百万次请求,存储了PB级数据。用户可以根据用户ID、事件类型和事件数量进行查询,并返回每个事件类型的最新N个事件。该项目的成功应用提高了用户参与度。

A Glimpse into the Redesigned Goku-Ingestor vNext at Pinterest

Better performance, lower cost and less code complexity.

Improving Efficiency Of Goku Time Series Database at Pinterest (Part — 1)

Goku是Pinterest的内部时间序列数据库,用于监控和设置警报。他们改变了数据写入方式和摄取模型,采用基于拉取的、分片感知的摄取模型,并引入了Goku side Kafka。他们还使用本地磁盘和S3替代了EFS作为持久化数据和备份。这些改变使得GokuS的恢复时间从90-120分钟缩短到不到40分钟,提供了高效的查询路由。GokuL利用RocksDB进行时间序列数据存储,使用分层存储的方式,将较小和较新的SST文件在低层进行压缩,存储为较大和较旧的SST文件在高层。GokuL集群存储并提供超过一天的旧数据,这些数据的保留时间为1年。具体的数据分层策略和存储集群信息可以在GokuL博客成本降低博客中找到。

Running Unified PubSub Client in Production at Pinterest

At Pinterest, data is ingested and transported at petabyte scale every day, bringing inspiration for our users to create a life they love. A central component of data ingestion infrastructure at Pinterest is our PubSub stack, and the Logging Platform team currently runs deployments of Apache Kafka and MemQ. Over the years, operational experience has taught us that our customers and business would greatly benefit from a unified PubSub interface that the platform team owns and maintains, so that application developers can focus on application logic instead of spending precious hours debugging client-server connectivity issues. Value-add features on top of the native clients can also help us achieve more ambitious goals for dev velocity, scalability, and stability. For these reasons, and others detailed in our original PubSub Client blog post, our team has decided to invest in building, productionalizing, and most recently open-sourcing PubSub Client (PSC).

In the 1.5 years since our previous blog post, PSC has been battle-tested at large scale in Pinterest with notably positive feedback and results. From dev velocity and service stability improvements to seamless migrations from native client to PSC, we would like to share some of our findings from running a unified PubSub client library in production.

pincompute: A Kubernetes Backed General Purpose Compute Platform for Pinterest

Modern compute platforms are foundational to accelerating innovation and running applications more efficiently. At Pinterest, we are evolving our compute platform to provide an application-centric and fully managed compute API for the 90th percentile of use cases. This will accelerate innovation through platform agility, scalability, and a reduced cost of keeping systems up to date, and will improve efficiency by running our users’ applications on Kubernetes-based compute. We refer to this next generation compute platform as PinCompute, and our multi-year vision is for PinCompute to run the most mission critical applications and services at Pinterest.

PinCompute aligns with the Platform as a Service (PaaS) cloud computing model, in that it abstracts away the undifferentiated heavy lifting of managing infrastructure and Kubernetes and enables users to focus on the unique aspects of their applications. PinCompute evolves Pinterest architecture with cloud-native principles, including containers, microservices, and service mesh, reduces the cost of keeping systems up to date by providing and managing immutable infrastructure, operating system upgrades, and graviton instances, and delivers costs savings by applying enhanced scheduling capabilities to large multi-tenant Kubernetes clusters, including oversubscription, bin packing, resource tiering, and trough usage.

In this article, we discuss the PinCompute primitives, architecture, control plane and data plane capabilities, and showcase the value that PinCompute has delivered for innovation and efficiency at Pinterest.

Bring Your Own Algorithm to Anomaly Detection

In this blog, we present a pragmatic way of integrating analytics, written in Python, with our distributed anomaly detection platform, written in Java. The approach here could be generalized to integrate processing done in one language/paradigm into a platform in another language/paradigm.

Lessons from debugging a tricky direct memory leak

To support metrics reporting for ads from external advertisers and real-time ad budget calculations at Pinterest, we run streaming pipelines using Apache Flink. These jobs have guaranteed an overall 99th percentile availability to our users; however, every once in a while some tasks get hit with nasty direct out-of-memory (OOM) errors on multiple operators.

Training Foundation Improvements for Closeup Recommendation Ranker

Pinterest’s mission is- to bring everyone the inspiration to create a life they love. The closeup team helps with this mission by providing a feed of relevant and context-and-user-aware recommendations when a Pinner closes up on any Pin.

The recommendations are powered by innovative and cutting-edge machine learning technologies. We have published a detailed blog post of its modeling architecture. While adopting the newest architectures improves a model’s capabilities, building a solid training foundation stabilizes the model and further up-levels the model’s potential.

Training foundations cover a lot of aspects, from training preparation (training data logging, feature freshness, sampling strategies, hyperparameter tuning, etc), to training efficiency optimization (distributed training, model refreshes, GPU training, etc), to post training validation (offline replay, etc).

Building for Inclusivity: The Technical Blueprint of Pinterest’s Multidimensional Diversification

Pinterest’s mission as a company is to bring everyone the inspiration to create a life they love. “Everyone” has been the north star for our Inclusive AI and Inclusive Product teams. These teams work together to ensure algorithmic fairness, inclusive design, and representation are an integral part of our platform and product experience.

Our commitment is evidenced by our history of building products that champion inclusivity. In 2018, Pinterest announced the skin tone signal and skin tone ranges. In 2020, we announced the integration of skin tone ranges into Try on for Beauty. In 2021, we announced hair pattern search. In early 2023, we announced how we have been using our skin tone signal to shape our recommendations to increase skin tone representation across several surfaces. Now, we are expanding the latter to also include body type representation in fashion related results across search and closeup recommendations (AKA related feeds).

首页 - Wiki
Copyright © 2011-2024 iteam. Current version is 2.123.4. UTC+08:00, 2024-04-19 17:48
浙ICP备14020137号-1 $访客地图$