Performance - Deepstash
Machine Learning With Google

Learn more about computerscience with this collection

Understanding machine learning models

Improving data analysis and decision-making

How Google uses logic in machine learning

Machine Learning With Google

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Performance

Performance

  • Fast full-text search - Because it's built on top of Lucene
  • Near real-time indexing - It takes under 1s for newly indexed documents to show up in searches
  • High performance & Fault tolerance - Each index is split into shards, that are distributed and replicated across servers. This enables Elasticsearch to process large volumes of data in parallel and remain available in case of hardware failure.

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Common use cases

Common use cases

  • Adding search to an application or website
  • Logging and log analytics
  • Application performance monitoring
  • Infrastructure monitoring

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Elasticsearch and Lucene

Elasticsearch and Lucene

Elasticsearch is a distributed search and analytics engine, built on top of Lucene. It provides a JSON-based REST API and supports sharding, replication, and cluster management, for high availability.

Lucene is a high-performance search engine library, writ...

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Indexing data

Indexing data

When you add data to Elasticsearch, it is analyzed (parsed, normalized, and enriched), and stored into an inverted index.

The inverted index is a data structure designed for fast full-text searches. It keeps track of all unique words and in which documents ...

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