Advanced Elasticsearch Eğitimi


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Eğitim Detayları
2 Gün (12 Saat)
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Kadıköy
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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