Advanced Elasticsearch Eğitimi

Advanced Elasticsearch Eğitimi Advanced Elasticsearch Eğitimi
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Eğitim Detayları
2 Gün (12 Saat)
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Eğitim İçeriği

Ön Koşul: Elasticsearch Fundamentals eğitimi alınması tavsiye edilir

Relevancy tuning

  • Analysis: stopwords, synonyms, ngrams and shingles and their alternatives
  • Using the Reindex API when mappings need to be changed
  • A deep look into BM25
  • Multi-match query: choosing between best fields, most fields and cross fields modes
  • Tweaking the score with the function score query
  • Lab
    • Using the letter tokenizer as an option for URL matching
    • Using ngrams to tolerate typos
    • Using shingles to match compound words
    • Implement hashtag search via the word delimiter token filter
    • Searching across multiple fields
    • Boosting documents based on date and number of views
    • Typo tolerance without using ngrams
    • Reducing the impact of common words without using stopwords

Advanced aggregations

  • Finding trends and outliers with the significant terms aggregation
  • Cheaper and more representative results with the sampler aggregation
  • Field collapsing with the top hits aggregations
  • Pipeline aggregations; moving averages
  • Lab
    • Checking trends the significant terms aggregation
    • Show the latest hit per category
    • Using the moving average aggregation

Working with relational data

  • Arrays and objects; why they offer the best performance and when they fail
  • Nested documents
  • Nested queries; using inner hits
  • Parent-child relations
  • Denormalizing and application-side joins
  • Deciding on which feature/technique to use
  • Lab
    • Model a one-to-one relationship
    • Model a query-heavy one-to-many relationship
    • Model an update-heavy one-to-many relationship
    • Model a many-to-many relationship


  • Percolator basics
  • Configuring mappings for percolation
  • Using routing, filters, sorting and aggregations with the Percolator Query
  • Lab
    • Using Percolator to trigger alerts
    • Using metadata to filter and aggregate matching queries


  • Overview of types and requests
  • Term vs. phrase suggester
  • How the phrase suggester collects candidates
  • Using a shingle field to score candidate phrases
  • Completion vs context suggesters
  • Completion suggesters vs prefix queries
  • Mapping for completion suggesters
  • Weights and fuzzy matches
  • Payloads for instant-search kind of autocomplete
  • Lab
    • Using the term suggester to suggest single word corrections
    • Using the phrase suggester against a shingle field for multi-word suggestions
    • Using a separate index for autocomplete
    • Using the _suggest endpoint instead of _search
    • Boosting suggestions via static weights
    • Add fuzzy support for suggestions
    • Filtering suggestions
    • Using metadata for ranking suggestions (terms, location)

Geo-spatial search

  • Basics: geo-point and geo-shape types
  • How shape matching is done via geohashes
  • Distance, distance range and bounding box queries
  • Lab
    • Indexing geo-points and searching them via bounding box and polygon queries
    • Filtering and aggregating geo-points by distance
    • Matching a shape against a point


  • How the default highlighter works
  • Common highlighter options: size, order and number of fragments
  • Postings highlighter: overhead, use-cases, mapping
  • Fast vector highlighter: using term vectors for extra flexibility
  • Lab
    • Selecting fields to highlight and disabling _source from the response
    • Choosing highlight tags, number of fragments, their size and order
    • Using the postings highlighter for long natural language fields
    • Using the fast vector highlighter for multi-fields


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