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...
Open Source Articles
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- Topics:
- infrastructure,
- ESPRESSO,
- Open Source
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One measure of a successful network is uptime - providing consistent, reliable service for members and customers. If there are frequent connection errors or downtime notifications, it becomes difficult to deliver an experience where people can connect and interact with ease. When faced with uptime challenges, being able to quickly escalate issues to network...
- Topics:
- infrastructure,
- Architecture,
- Open Source
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For the last several years, internal infrastructure at LinkedIn has been built around a self-service model, enabling developers to onboard themselves with minimal support. We have various user experiences that let application teams provision their own resources and infrastructure, generally by filling out forms or using command-line tools. For example,...
- Topics:
- Stream Processing,
- Data Streams,
- Open Source
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Co-authors: Jonathan Hung, Pei-Lun Liao, Lijuan Zhang, Abin Shahab, Keqiu Hu TensorFlow is one of the most popular frameworks we use...
- Topics:
- TensorFlow,
- Open Source
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With the widespread adoption of Rest.li since its inception in 2013, LinkedIn has built thousands of microservices to enable the...
- Topics:
- rest.li,
- Data,
- Open Source
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Co-authors: Mimi Chen, Calvin Lei, and Amit Yadav Background LinkedIn’s mission is to connect the world’s professionals to make them...
- Topics:
- Data,
- Open Source