How LinkedIn Uses Caching
14 engineering articles about Caching from LinkedIn's engineering team
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Morteza Ramezani
17 min read
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The article discusses how LinkedIn reduced the upload of Apache Spark application dependencies by 99% through the implementation of a user-level caching mechanism.
LinkedIn Engineering Team
10 min read
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This article discusses performance optimization strategies for Android apps, focusing on specific techniques that can significantly reduce app startup and page load times.
The article discusses how LinkedIn addresses manageability challenges at scale through its cloud management service, Nuage.
The article announces the release of Samza 1. 0, a distributed stream processing framework developed at LinkedIn, highlighting its significant features and improvements.
Jagadish Venkatraman
10 min read
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The article discusses Nuage, LinkedIn's cloud management team, which is designed to scale data systems management effectively.
The article discusses the development of LinkedIn Lite, a server-side rendered Progressive Web App (PWA) designed to enhance user experience in emerging markets.
Gopal Venkatesan
9 min read
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The article discusses the evolution of Couchbase at LinkedIn, detailing its transition from Memcached to Couchbase as a standard caching platform.
The article discusses the REACH program at LinkedIn, aimed at integrating software engineers from non-traditional backgrounds into the tech workforce.
Shalini Agarwal
6 min read
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The article discusses FollowFeed, LinkedIn's new feed infrastructure designed to enhance performance and relevance for its users.
The article discusses the importance of building polite and secure applications, emphasizing how thoughtful design can enhance user experience and address common security concerns in software devel...
Erran Berger
3 min read
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The article outlines the evolution of LinkedIn's architecture and scaling strategies from its inception in 2003 to its modern service-oriented architecture.
The article discusses LinkedIn's transition from server-side templates like JSPs to client-side templates using dust.
This article discusses best practices for scaling Java applications using distributed caching techniques.
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