Information out: search and analyze

• Elasticsearch Overview:

  • Elasticsearch serves as a document store and allows retrieval of documents and metadata.
  • Its true power lies in accessing a comprehensive suite of search capabilities built on the Apache Lucene search engine library.
  • Provides a coherent REST API for cluster management, indexing, and data search.
  • Supports various client languages: Java, JavaScript, Go, .NET, PHP, Perl, Python, or Ruby.

• Searching Data:

  • Elasticsearch REST APIs handle structured queries, full-text queries, and complex queries.
  • Structured queries resemble SQL constructs (e.g., searching gender and age fields).
  • Full-text queries find documents matching query strings, sorted by relevance.
  • Additional features: phrase searches, similarity searches, prefix searches, and autocomplete suggestions.
  • Supports high-performance geo and numerical queries for non-textual data.

• Query Languages:

  • Elasticsearch's comprehensive JSON-style Query DSL for search capabilities.
  • Construct SQL-style queries for native search and aggregation within Elasticsearch.
  • JDBC and ODBC drivers enable third-party applications to interact via SQL.

• Data Analysis:

  • Aggregations provide complex data summaries and insights.
  • Answer questions like needle count, average needle length, and manufacturer-specific metrics.
  • Analyze data in real time; reports and dashboards update dynamically.
  • Aggregations work alongside search requests, allowing simultaneous search, filtering, and analytics.
  • Machine learning features automate time series data analysis without specifying algorithms or models.
  • Detect anomalies, statistical rarity, and unusual behaviors.