How Pinterest Uses Caching

The article discusses Pinterest's journey in enhancing developer experience through the creation of PinConsole, an Internal Developer Platform built on Backstage.

The article discusses the Structured DataStore (SDS), a unified multi-model data management platform developed by Pinterest.

The article discusses Pinterest's implementation of feature caching in their recommender systems using Cachelib, an in-process caching engine developed by Meta Open Source.

The article discusses Pinterest's adoption of TiDB as a replacement for HBase, detailing the motivations, selection methodology, and the journey of integrating TiDB into their infrastructure.

The article discusses Pinterest's transition from HBase, its first NoSQL datastore, to a new serving architecture with a unified storage service.

The article discusses Pacer, Pinterest's new asynchronous computing platform designed to address the limitations of its predecessor, Pinlater.

This article discusses the process of migrating data from HBase to TiDB at Pinterest with zero downtime.

The article discusses Pinterest's advancements in distributed caching performance and efficiency, focusing on their memcached fleet's optimization strategies.

The article discusses Pinterest's transition to running a large end-to-end UI test suite before every commit in their Android and iOS repositories.

This article discusses how Pinterest scaled its ad corpus by 60 times through a partnership with Shopify and the implementation of a key-value store and memory optimizations in Go.

The article discusses the development of Ixia, a scalable near-real-time secondary indexing solution built on HBase at Pinterest.

The article discusses Pinterest's journey in scaling its Kubernetes platform, focusing on governance, resilience, and operability.

The article discusses how Pinterest has scaled its cache infrastructure to handle increasing demand from users.

The article discusses Pinterest's transition from using Apache HBase to Apache Druid for ads analytics, highlighting the challenges faced and the benefits of Druid's capabilities in handling comple...

The article discusses the ads indexing system at Pinterest, detailing its architecture, design, and implementation.

The article discusses the importance of upgrading outbound Pin links from HTTP to HTTPS to enhance user security on Pinterest.

PinalyticsDB is Pinterest's proprietary time series database built on HBase, designed to store and visualize a large volume of time series reports.

The article discusses the upgrade of Pinterest's operational metrics system, detailing the transition from the deprecated Ostrich library to an in-house solution called Pinterest StatsCollector.

The article discusses Goku, an in-house time series database developed by Pinterest to address the limitations of OpenTSDB as the company scales.

The article discusses Pinterest's upgrade of HBase from version 0. 94. 26 to 1. 2, emphasizing the importance of maintaining high performance and availability during the transition.

The article discusses how Pinterest engineers improved user growth through significant performance enhancements on their mobile web platform.

The article discusses PinQueue, a generic content review system developed by Pinterest to enhance manual moderation processes alongside machine learning.