Espresso is LinkedIn's defacto NoSQL database solution. It is an online, distributed, fault-tolerant database that powers most of LinkedIn’s applications including member profiles, InMail (LinkedIn's member-to-member messaging system), sections of the main LinkedIn homepage, our mobile applications, and more. Since Espresso caters to many critical features, its...
ESPRESSO Articles
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- Topics:
- Performance,
- ESPRESSO,
- site speed,
- SRE
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Co-authors: Xiang Zhang, Estella Pham, and Ke Wu Identity services are critical systems that serve data on profile and member settings to help power many other applications at LinkedIn. In this blog post, we’ll share how we merged two layers of the identity services that handle more than half a million queries per second (QPS) that drove a 10% reduction in...
- Topics:
- A/B Testing,
- Architecture,
- experimentation,
- Performance,
- ESPRESSO,
- T-REX,
- Data
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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...
- 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...
- 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...
- 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 have...
- Topics:
- infrastructure,
- scale,
- ESPRESSO