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...
ESPRESSO Articles
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- Topics:
- Performance,
- Data,
- ESPRESSO
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Co-authors: Christian Mathiesen and Jie Zhang Your LinkedIn profile is intended to be a representative picture of your professional life and career, and a key part of that picture is the skills you’ve acquired. In pursuit of our mission to create a place where everyone has access to opportunities based on the skills they have, we’re rolling out a new way to...
- Topics:
- Hadoop,
- Product Design,
- ESPRESSO
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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 master data hub that serves many important applications across LinkedIn,...
- Topics:
- Distributed Systems,
- ESPRESSO,
- infrastructure,
- Data
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Co-authors: Szymon Gizecki, Yu Li, Chinmaya Dattathri, Ethan Hall, Irina Issayeva, and Deep Majumder Introduction At LinkedIn, we...
- Topics:
- infrastructure,
- scale,
- ESPRESSO
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Espresso is LinkedIn's strategic distributed, fault-tolerant NoSQL database that powers many LinkedIn services. Espresso has a large...
- Topics:
- database,
- NYC Engineering,
- ESPRESSO
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Nuage (French for “cloud”) is what we call LinkedIn’s internal cloud management portal. It allows LinkedIn developers to quickly...
- Topics:
- Performance,
- ESPRESSO,
- LPS,
- Kafka,
- Voldemort