Developer Productivity Articles

  • diagram-of-linkedin-services-and-the-lix-engine

    Making the LinkedIn experimentation engine 20x faster

    January 3, 2020

    Co-authors: Alexander Ivaniuk, Jingbang Liu At LinkedIn, we like to say that experimentation is in our blood because no production release at the company happens without experimentation; by “experimentation,” we typically mean “A/B testing.” The company relies on employees to make decisions by analyzing data. Experimentation is a data-driven foundation of the...

  • build-cache-improvement-chart

    Productivity at scale: How we improved build time with Gradle build cache

    October 1, 2019

    Editor's Note: This is the second in a series of posts describing how we improved productivity at scale—both in terms of lines of code and number of engineers—at LinkedIn. In our first post of the #ProductivityAtScale series, we shared details on how we improved build time by 400%. This post covers how we continue to improve productivity with Gradle build cache....

  • language-adoption

    Evaluating Language Adoption At Scale

    August 20, 2019

    When adopting a new language at scale, there are many different factors to consider because things can change dramatically. For many, choosing a language can arguably rely on personal preference, but at LinkedIn, we have a Foundation team tasked with evaluating the impact of such fundamental technical decisions. Recently, we underwent the process of evaluating...

  • PartitionConsumer-objects-distribution

    Auto-Tuning Pinot Real-Time Consumption

    July 11, 2019

    Pinot, a scalable distributed columnar OLAP data store developed at LinkedIn, delivers real-time analytics for site-facing use cases...

  • High-level-architecture

    Expediting Data Fixes and Data Migrations

    July 9, 2019

    With over 630 million members, the LinkedIn platform delivers thousands of features that individually serve and store large amounts of...

  • proml1

    Scaling Machine Learning Productivity at LinkedIn

    January 3, 2019

    Co-authors: Joel Young, Bee-Chung Chen, Bo Long, Marius Seritan, and Priyanka Gariba   The rate at which artificial intelligence (AI)...