How LinkedIn Uses Elasticsearch
7 engineering articles about Elasticsearch from LinkedIn's engineering team
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The article discusses the evolution of metadata architectures, focusing on three generations of data discovery tools.
The article discusses LinkedIn's open sourcing of DataHub, a metadata search and discovery platform, detailing its development journey from WhereHows to DataHub.
ApacheAWSAzureDependency InjectionDockerElasticsearchGoogle CloudJSONKubernetesMicroservicesMongoDBMySQLNeo4jOracleSpring
Kerem Sahin
15 min read
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The article discusses LinkedIn's approach to mobile crash reporting, detailing the development of an internal tool to effectively capture and analyze crash data while addressing security concerns.
Ramanathan Muthukaruppan
9 min read
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The article discusses strategies employed by LinkedIn to reduce the Mean Time to Detect (MTTD) and Mean Time to Recovery (MTTR) for their private cloud management system, Nuage.
Gustaf Helgesson
7 min read
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The article discusses LinkedIn's transition to a more efficient self-service metrics collection system called Autometrics, detailing its growth, design challenges, and iterative improvements.
Stephen Bisordi
11 min read
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Apache Helix is a framework designed for developing distributed systems, addressing challenges such as scalability, fault tolerance, and partition management.
Kishore Gopalakrishna
10 min read
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The article discusses LinkedIn's new search architecture, Galene, which was developed to address the limitations of their previous search stack built on Lucene.
LinkedIn Engineering Team
19 min read
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