ESPRESSO Articles

  • The impact of slow NFS on data systems

    June 23, 2020

    Espresso is LinkedIn's defacto NoSQL database solution. It is an online, distributed, fault-tolerant database that powers most of LinkedIn’s applications including member profiles, InMail (LinkedIn's member-to-member messaging system), sections of the main LinkedIn homepage, our mobile applications, and more. Since Espresso caters to many critical features, its...

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    How we reduced latency and cost-to-serve by merging two systems

    April 22, 2020

    Co-authors: Xiang Zhang, Estella Pham, and Ke Wu Identity services are critical systems that serve data on profile and member settings to help power many other applications at LinkedIn. In this blog post, we’ll share how we merged two layers of the identity services that handle more than half a million queries per second (QPS) that drove a 10% reduction in...

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    How we improved latency through projection in Espresso

    March 5, 2020

    Co-authors: Xiang Zhang and Chuck Jerian Espresso is LinkedIn’s document-oriented, highly available, and timeline-consistent distributed datastore. Clients, such as services fetching profile data, read a subset fields of whole documents from Espresso in different use cases for reasons related to performance and costs. For example, one use case is reading only a...

  • Skill-Assessments-Example

    The building blocks of LinkedIn Skill Assessments

    September 17, 2019

    Co-authors: Christian Mathiesen and Jie Zhang Your LinkedIn profile is intended to be a representative picture of your professional...

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    Improving performance and capacity for Espresso with new...

    June 27, 2019

    In this blog post, we’ll share how we migrated Espresso, LinkedIn’s distributed data store, to a new Netty4-based framework and...

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    Managing Software Dependency at Scale

    September 6, 2018

    Co-authors: Szymon Gizecki, Yu Li, Chinmaya Dattathri, Ethan Hall, Irina Issayeva, and Deep Majumder Introduction At LinkedIn, we have...