Advanced Elasticsearch Eğitimi

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

• 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

Suggesters

• 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

Highlighting

• 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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