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Databases · College · College and University

Elasticsearch and Search Engines

Inverted indexes, analyzers, relevance scoring with BM25, mappings, shards, query DSL and the ELK stack.

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35 basic cards for College Databases. It opens with "What is an inverted index?" and "Why are inverted indexes efficient for text search?". Study it alongside the other College sets below.

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What is an inverted index?
Show answerA data structure that maps terms to the documents and positions where they appear, enabling fast full-text search
Why are inverted indexes efficient for text search?
Show answerThey store a mapping from each term to its documents and positions, allowing direct lookup without scanning all documents
What is a postings list?
Show answerA list of documents and position information for a specific term in an inverted index
What is term vocabulary in inverted indexes?
Show answerThe collection of all unique terms extracted and indexed from documents
What is an analyzer in Elasticsearch?
Show answerA sequence of character filters, a tokenizer, and token filters that transforms text into searchable tokens
Name the three components of an Elasticsearch analyzer
Show answerCharacter filters, tokenizer, and token filters
What does a tokenizer do in an analyzer?
Show answerSplits text into individual tokens based on rules, producing a stream of tokens for further processing
What is a token filter's role in an analyzer?
Show answerModifies, adds, or removes tokens produced by the tokenizer, such as lowercasing, removing stopwords, or stemming
What does a character filter do?
Show answerPreprocesses input text before tokenization by removing or replacing specific characters or patterns
What does BM25 stand for?
Show answerBest Matching 25, a probabilistic relevance framework for ranking search results
How does BM25 handle term frequency?
Show answerIt increases scores for more frequent terms but with diminishing returns, so extra occurrences have marginal impact
What is inverse document frequency (IDF)?
Show answerA measure of how unique a term is across all documents, giving higher weight to rare terms and lower weight to common terms
How does BM25 normalize for document length?
Show answerIt prevents longer documents from gaining unfair relevance advantage by normalizing scores relative to document length
What is the default relevance scoring algorithm in Elasticsearch?
Show answerBM25, a probabilistic algorithm that ranks documents by relevance to a query
What is a mapping in Elasticsearch?
Show answerA definition specifying the structure of documents in an index, including field names, types, and analysis settings

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