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Components of indexing and searching

Short answer

Indexing components acquire raw content, build documents with fields, analyze text (tokenize, lower-case, remove stop words, stem), and store terms in an inverted index. Searching components include a query UI, parsing/analyzing the query, looking up terms in the inverted index, ranking hits by relevance (e.g., TF-IDF), and rendering results with snippets and highlighting.

Explain the components of Indexing and searching. (Describe the typical components involved in a search application.)

A search application lets users find relevant documents in a large collection quickly. Instead of scanning every document, it builds an index in advance and answers queries from that index. The typical components are as follows.

 Documents -> Acquire -> Build Document -> Analyze -> Index
                                                       |
 User -> Query UI -> Build Query -> Run Query -> Render Results

Indexing components

  1. Acquire raw content: collect the data to be searched from files, web pages (a crawler), databases, emails, etc. Tools: Nutch, Solr DIH, custom crawlers.
  2. Build documents: convert raw content into units called documents, each having fields (title, body, author, date). Text is extracted from PDF, HTML, Word etc. Fields can be boosted by importance.
  3. Analyze documents: the text is broken into tokens by an analyzer: tokenizing, lower-casing, stop-word removal (the, is, of), stemming (running -> run), synonyms.
  4. Index documents: the tokens are stored in an inverted index, which maps each term to the list of documents (and positions) containing it. It is written to disk by an index writer.

Example inverted index:

TermPostings (doc ids)
big1, 3
data1, 2, 3
hadoop2

Searching components

  1. Search user interface: the box where the user enters a query, and the page that shows results (with facets, paging, highlighting). It should be simple and fast.
  2. Build query: the user's text is parsed by a query parser into a structured query (terms, phrases, Boolean AND/OR/NOT, wildcard, ranges). The same analyzer is applied to query text.
  3. Run query (search query): the index searcher looks up the terms in the inverted index, finds the matching documents, and scores/ranks them (TF-IDF, BM25) by relevance.
  4. Render results: the top-ranked documents are shown with title, snippet, highlighted terms, and links, with sorting and pagination.

Administration components

  • Index management: update, delete, merge segments, optimise, replicate and back up the index.
  • Analytics and monitoring: logs, query statistics, to improve relevance.

Examples of platforms: Apache Lucene (library), Solr and Elasticsearch (servers built on Lucene).

✓ Answer checked by Claude Opus 5.5 (high effort)

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