Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Relational LIKE '%...%' queries quickly fall short when building product catalogue search, autocomplete, log analytics or hybrid AI retrieval. Elasticsearch and OpenSearch solve these problems at scale, yet dynamic mapping mistakes, poor text analyzers or oversharding routinely degrade both search relevance and cluster stability.

This course covers Apache Lucene and distributed shard/replica internals, the text analysis pipeline (character filters, tokenizers, token filters, language/Turkish analyzers), explicit mappings (text vs keyword, nested objects), Query DSL and BM25 relevance tuning (boosting, fuzziness, function_score), Bucket/Metric Aggregations, hybrid k-NN vector + lexical search, and Index Lifecycle/State Management (ILM/ISM).

Who Should Attend?

  • Backend developers building e-commerce, document and in-app search experiences
  • DevOps and data engineers operating real-time log, observability and analytics clusters (ELK / OpenSearch)
  • AI and backend engineers combining BM25 lexical search with vector search for RAG pipelines
  • Prerequisite: familiarity with REST APIs and JSON

Key Takeaways

  • Design explicit mappings with the right text vs keyword fields, custom analyzers and index templates
  • Write precision-tuned Query DSL queries using bool combinations, multi_match, boosting, fuzziness and function_score
  • Implement fast autocomplete, typo tolerance, synonyms and faceted navigation
  • Run real-time analytical breakdowns across millions of documents using Metric, Bucket and Pipeline Aggregations
  • Size shards properly, execute zero-downtime reindexing with aliases and automate retention with ILM/ISM

Curriculum

Lucene Internals, Distributed Architecture and Indexing
  • Inverted Indexes, Doc Values, BKD Trees and immutable Lucene segments (refresh, flush, merge)
  • Cluster topology, node roles (master/cluster-manager, data, ingest), Primary and Replica Shards
  • High-throughput ingestion with the Bulk API, optimistic concurrency (_seq_no) and Index Aliases
Text Analysis Pipelines and Mapping Design
  • Anatomy of an Analyzer: Character Filters, Tokenizers (standard, ngram, edge_ngram) and Token Filters (lowercase, stemmer, synonym, asciifolding)
  • Configuring language-specific stemming and synonym graphs
  • Explicit Mappings: text vs keyword, multi-fields, the object vs nested array trap and runtime fields
Query DSL, BM25 Relevance Tuning and Autocomplete
  • Query Context (scored) vs Filter Context (cached bitsets) and bool clauses (must, should, filter, must_not)
  • Full-text queries: match, match_phrase, multi_match (best_fields, cross_fields) and BM25 internals
  • Typo tolerance with fuzziness, custom ranking via function_score, Highlighting and search_as_you_type
Aggregations, Hybrid Search (BM25 + Vectors) and Pagination
  • Metric, Bucket (terms, date_histogram, range, significant_terms) and Pipeline Aggregations
  • Building faceted navigation with aggregations and post_filter
  • HNSW vector indexes (dense_vector / knn_vector) and combining lexical + k-NN search with RRF
  • Avoiding deep from/size pagination costs with search_after and Point-in-Time (PIT)
Cluster Operations, Shard Sizing and Lifecycle Management (ILM/ISM)
  • Fixing oversharding, target shard sizing (10–50 GB) and zero-downtime schema changes via Reindex + Aliases
  • Data Streams, Rollover and Hot-Warm-Cold retention policies with ILM (Elasticsearch) and ISM (OpenSearch)
  • JVM Heap sizing rules, Circuit Breakers, Slow Logs and debugging queries with the _profile API

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