Introducing SenseiDB 1.0: an open-source, distributed, realtime, semi-structured database (link)

Sensei is a distributed data system that was built to support many product initiatives at LinkedIn, including the real-time faceted search in Signal and the news feed and tabs on the Homepage. It is the foundation of LinkedIn’s search and data infrastructure.

Sensei is both a search engine and a database. It is designed to query and navigate through documents that consist of (a) unstructured text and (b) well-formed and structured metadata.

Features

Some features and differentiators of Sensei:

  • Ability to consume high insert/updates while maintaining high query performance.
  • Support for complex queries via a query language (BQL) and a REST/JSON api.
  • Streaming updates from different Gateways such as JDBC, JMS, and Kafka.
  • Bootstrapping from Hadoop, e.g. Map-Reduce job to batch build index and push to Sensei clusters.
  • Ability plug-in custom and complex faceting logic such as the social graph.

Architecture

Fuente:
Introducing SenseiDB 1.0: an open-source, distributed, realtime, semi-structured database | LinkedIn Engineering.

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