Elasticsearch
Elasticsearch is a distributed search and analytics engine that indexes data for searching, retrieval, and analysis.
Elasticsearch in Action, Second Edition
Elasticsearch is a distributed search and analytics engine for indexing, querying, and analyzing structured and unstructured data.
- How it is used
- Powers application, enterprise, and site search alongside analytics, observability, and security workloads.
- How deep it goes
- Introduces engine architecture, search modes, analytics, use cases, limits, and a first indexing request; defers request constituents to later treatment.
- Examples
- Uses an online bookstore and Kibana Dev Tools to index a document, inspect its JSON response, then search and aggregate loaded business data.
- What you can do after
- Apply it via Kibana or REST clients to index documents, run searches, and perform aggregations on prepared data.
Read how Elasticsearch in Action, Second Edition explains Elasticsearch →
Kafka for Architects
Elasticsearch is a distributed search and analytics engine that indexes and queries structured and unstructured data.
- How it is used
- Supports centralized, real-time log collection, indexing, searching, and dashboard-based analysis across multiple services.
- How deep it goes
- Defines the high-level logging flow and user interfaces but stops short of implementation details.
- Examples
- Uses multi-service application logs, Kafka-to-Elasticsearch transfer, REST queries, and Kibana dashboards.
- What you can do after
- Operate log analysis through Elasticsearch’s REST API or Kibana dashboards after events are indexed.
Learn Docker in a Month of Lunches, Second Edition
Elasticsearch is a distributed search and analytics engine for storing, searching, and analyzing structured and unstructured data.
- How it is used
- Stores centralized Docker container logs for later searching and inspection through Kibana.
- How deep it goes
- Walks through containerized setup, index selection, and Kibana data views without explaining Elasticsearch internals.
- Examples
- Uses timecheck and image-gallery containers, Fluentd tags, Docker commands, daily indexes, and Kibana Discover.
- What you can do after
- Operate/configure a containerized Elasticsearch logging stack and search collected records through Kibana.
Read how Learn Docker in a Month of Lunches, Second Edition explains Elasticsearch →
Effective Conversational AI
Elasticsearch is a distributed search and analytics engine that indexes and retrieves structured and unstructured data.
- How it is used
- Uses Elasticsearch as a candidate embedding-indexing solution for evaluating RAG responses.
- How deep it goes
- Compares Elasticsearch through dataset selection, embedding generation, indexing, querying, benchmarking, and trade-off analysis, without implementation detail.
- Examples
- Uses PharmaBot, medical articles, drug-interaction queries, vaccine-effect questions, and hypothetical configuration benchmarks.
- What you can do after
- Evaluate Elasticsearch in a RAG pipeline by comparing quality, latency, throughput, and resource-use results against alternatives.
Read how Effective Conversational AI explains Elasticsearch →
MongoDB 8.0 in Action, Third Edition
Elasticsearch is a distributed search and analytics engine for indexing, searching, and analyzing structured and unstructured data.
- How it is used
- Supports advanced full-text search, analytics, and searchable operational logs alongside a separate database system.
- How deep it goes
- Explains architecture, replication, and operational trade-offs but stops short of Elasticsearch commands, schema design, or query implementation.
- Examples
- Uses MongoDB collection replication, Monstache change streams, Atlas log delivery, and Grafana dashboards as concrete integration examples.
- What you can do after
- Evaluate Elasticsearch for advanced search or log analysis while accounting for synchronization, monitoring, resource, and consistency costs.
Read how MongoDB 8.0 in Action, Third Edition explains Elasticsearch →
Related concepts
- Kibanaused by · Kibana is an open-source web interface for searching, analyzing, visualizing, monitoring, and dashboarding data stored in Elasticsearch.
- ELK stackpart of · The ELK stack is a collection of Elasticsearch, Logstash, and Kibana components for ingesting, processing, searching, analyzing, and visualizing data.
- Luceneuses · Apache Lucene is an open-source Java library for indexing, searching, and retrieving textual and other content.
- document databasekind of · A document database is a nonrelational database that stores and retrieves records as self-contained documents, often with flexible or nested structures.
- fluentdused by · Fluentd is an open-source data collector that gathers, processes, transforms, and routes logs and other machine-generated event data through pluggable integrations.
- search enginekind of · A search engine is software that retrieves and presents information relevant to queries, typically by matching them against indexed content and ranking results.
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