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Unstructured data

As explained in Elasticsearch in Action, Second Edition

Unstructured data is data that does not conform to a fixed field-based or tabular schema, such as free-form text.

Unstructured data is free-form information that does not follow a fixed schema or fit neatly into table columns. It commonly centers on text, while also carrying dates, numbers, or facts within documents such as emails, social posts, photos, and other content. section 1.2.1 section 7.1.1

A search application treats unstructured data differently from exact-value fields. Rather than merely deciding whether a document matches, full-text search aims to return the documents most relevant to a user’s words and assigns scores based on how well they match. section 8.9 section 10.0

Searching a quotation

Given the quote below, searches for “imagination,” “knowledge,” or combinations such as “knowledge important” can return it. Default search behavior may miss related wording such as “passion,” misspellings such as “knowlege,” and abbreviations; analyzers can later be adjusted for synonyms, stemming, spelling mistakes, and similar variations. section 7.1.1

"quote": "Imagination is more important than knowledge"

Use this short text document to see why a full-text query can match its words yet fail to recognize alternate wording without additional analysis. section 7.1.1

For Elasticsearch search design, unstructured data usually implies these choices:

  • Use full-text queries rather than term-level queries when searching text fields expected to receive relevance scores. section 8.9
  • Build requests with Query DSL when constructing simple or advanced search criteria; it supports leaf and compound queries. section 8.9
  • Plan for real user input, including synonyms, acronyms, emojis, and lingo, instead of assuming every query repeats document wording exactly. section 7.1.1
  • Combine a transactional database with a search engine when transactional storage and search or analytics have distinct roles. section 1.2.3

Search engines are designed for unstructured text analysis, high query loads, and large amounts of text data. They are not a universal replacement for every storage system: large binary content such as videos or images may call for a dedicated binary store, and the right choice depends on the use case. section 1.2.3 section 1.4.5

How Elasticsearch in Action, Second Edition covers it

How deep it goes
Defines schema-free data and explains relevance-oriented querying, then defers analyzer configuration for synonyms, stemming, and spelling variations.
Examples
Uses a quotation about imagination and knowledge to contrast direct word matches with synonyms, misspellings, and abbreviations that miss.
What it assumes
Introduces the distinction from zero by contrasting free-form text with predefined schemas and exact structured searches.
What you can do after
Apply it via Elasticsearch full-text queries to retrieve and rank documents matching users’ text searches.
How it connects
Connects unstructured data with full-text search, relevance scoring, Query DSL, text analysis, and structured data.

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This explainer was generated by Manning's knowledge graph from the text of Elasticsearch in Action, Second Edition. The section links point to the parts of the book it draws on.