Indexing data - 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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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 each word appears.

It's called inverted because it inverts a document-centric data structure (document -> words) to a keyword-centric data structure (word -> documents).

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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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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, ...

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ocpodariu

Alt account of @ocp. I use it to stash ideas about software engineering

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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, ...

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