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...
Search results for "Espresso"
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- Topics:
- infrastructure,
- ESPRESSO,
- Open Source
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In this blog post, we’ll share how we migrated Espresso, LinkedIn’s distributed data store, to a new Netty4-based framework and achieved a large performance and capacity gain throughout the Espresso system as a result. In the larger scheme, this is particularly important since Espresso is a primary data hub that serves many important applications across LinkedIn...
- Topics:
- Distributed Systems,
- ESPRESSO,
- infrastructure,
- Data
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Espresso is LinkedIn's strategic distributed, fault-tolerant NoSQL database that powers many LinkedIn services. Espresso has a large production footprint at LinkedIn, with close to a hundred clusters in use, storing about 420 terabytes of Source of Truth (SoT) data and handling more than two million queries per second at peak load. This post discusses our...
- Topics:
- database,
- NYC Engineering,
- ESPRESSO
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For the last several years, internal infrastructure at LinkedIn has been built around a self-service model, enabling developers to...
- Topics:
- Stream Processing,
- Data Streams,
- Open Source
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Co-Authors: Estella Pham and Guanlin Lu At peak, LinkedIn serves over 4.8 million member profiles per second. The number of requests...
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Co-authors: Hunter Lee and Dru Pollini LinkedIn was built to help professionals achieve more in their careers, and every day millions...
- Topics:
- Apache Helix,
- Distributed Systems,
- ESPRESSO,
- Data