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Elasticsearch

Build fast, relevant search and analytics: inverted indexes, mappings and analyzers, the Query DSL, aggregations, autocomplete and vector search, plus the operations that keep a cluster healthy.

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आप क्या सीखेंगे

  • Explain how Elasticsearch stores and searches data with inverted indexes, shards and replicas.
  • Design explicit mappings and analyzers so text and structured fields are searchable the right way.
  • Write Query DSL searches that combine full-text relevance with fast filters.
  • Build aggregations, facets, pagination, highlighting, autocomplete and vector search.
  • Operate clusters safely with aliases, lifecycle policies, reindexing, security and performance tuning.

पाठ्यक्रम

Elasticsearch Foundations

  1. What Elasticsearch Is
  2. The Inverted Index
  3. Cluster, Node, Index, Shard and Replica

Indexing and Mappings

  1. Document CRUD and the Bulk API
  2. Field Types: text, keyword and Friends
  3. Dynamic Mapping Pitfalls and Explicit Mappings

Text Analysis

  1. How Analyzers Work
  2. Custom Analyzers: Synonyms, Stemming and Folding
  3. Debugging With the _analyze API

The Query DSL

  1. Full-Text Queries: match, multi_match, match_phrase
  2. The bool Query and Term-Level Queries
  3. Relevance: BM25, Boosting and function_score

Aggregations

  1. Bucket Aggregations: terms, date_histogram, range
  2. Metric Aggregations: avg, percentiles, cardinality
  3. Faceted Search With post_filter

Search Features

  1. Pagination: from/size, search_after and Point in Time
  2. Highlighting and Autocomplete
  3. Vector Search and Hybrid Retrieval

Operating a Cluster

  1. Cluster Health, Shard Sizing and Replicas
  2. Aliases, ILM and Data Streams
  3. Reindex and Zero-Downtime Mapping Changes

Elasticsearch in Production

  1. Performance Tuning
  2. Security
  3. Keeping Elasticsearch in Sync With the Database
  4. An Elasticsearch Production Checklist