Advanced Elasticsearch Eğitimi

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Eğitim Detayları

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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

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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