Co-authors: Andy Li and Hongbin Wu Indexing plays the key role in modern search engines for fast and accurate information retrieval, and the ability to swiftly build indexes is crucial for LinkedIn to provide up to date information, such as candidates to recruiters, job posts to members, etc. In some instances, such as if a member profile is missing and...
Distributed Systems Articles
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- Topics:
- Spark,
- Data,
- Distributed Systems
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Co-authors: Konstantin V. Shvachko, Chen Liang, and Simbarashe Dzinamarira LinkedIn runs its big data analytics on Hadoop. During the last five years, the analytics infrastructure has experienced tremendous growth, almost doubling every year in data size, compute workloads, and in all other dimensions. It recently reached two important milestones. LinkedIn now...
- Topics:
- scale,
- Hadoop,
- Distributed Systems,
- infrastructure
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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 of people use our products to make connections, discover new opportunities and get better at what they do. An important part of our mission is helping people to find other professionals who are interested in the same things they...
- Topics:
- Apache Helix,
- Distributed Systems,
- ESPRESSO,
- Data
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We regularly play host to a series of meetups here at the LinkedIn office in the Empire State Building. Open to the community, these...
- Topics:
- NYC Engineering,
- Distributed Systems,
- events
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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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Pinot is an open source, scalable distributed OLAP data store that entered the Apache Incubation recently. Developed at LinkedIn, it...
- Topics:
- Pinot,
- Distributed Systems,
- Data,
- Open Source