Espresso is the database that we designed to power our member profiles, feed, recommendations, and hundreds of other Linkedin applications that handle large amounts of data and need both high performance and reliability. As Espresso continued to expand in support of our 950M+ member base, the number of network connections that it needed began to drive scalability and resiliency challenges. To address these challenges, we migrated to HTTP/2. With the initial Netty based implementation, we observed a 45% degradation in throughput which we needed to analyze then correct. In this post,  […]

Solving Espresso’s scalability and performance challenges to support our member base

Co-Authors: Estella Pham and Guanlin Lu At peak, LinkedIn serves over 1.4 million member profiles per second. The number of requests to our storage infrastructure doubles every year. In the past, we addressed latency, throughput and cost issues by migrating off Oracle onto Espresso, an open-source document platform, and adding more nodes. We are now at the point where some of the core components are straining under the increasing load, and we can no longer address scaling concerns with the node addition strategy. Instead, we chose to introduce Couchbase as a centralized storage tier […]

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