Elasticsearch

What is Elasticsearch?

Elasticsearch is a distributed search and analytics engine that provides near-real-time search and analysis of structured and unstructured data at scale. Built on Apache Lucene, Elasticsearch abstracts the complexities of full-text search with a simple RESTful API accessible via HTTP/JSON. Data is stored in inverted indexes optimized for fast retrieval, allowing searches across millions of documents in milliseconds. Elasticsearch's distributed architecture automatically shards data across nodes, replicates for fault tolerance, and routes queries to the appropriate shards for parallel execution.

Elasticsearch supports diverse use cases beyond simple text search—log and event data analysis, application performance monitoring (APM), security analytics (SIEM), business analytics, and geospatial searches. Its powerful aggregation framework enables complex analytics like histograms, percentiles, statistical calculations, and nested aggregations. With machine learning capabilities (anomaly detection, forecasting), Elasticsearch can automatically identify unusual patterns in time-series data. The Elastic Stack ecosystem—Elasticsearch (storage/search), Logstash (data ingestion), Kibana (visualization), and Beats (lightweight shippers)—provides a complete observability and search platform.

Core Features and Capabilities

Search and Indexing

• Full-text search - Relevance scoring, phrase matching, fuzzy search, wildcards
• Near-real-time indexing - Documents searchable within 1 second of indexing
• Inverted indexes - Optimized data structures for fast text retrieval
• Analyzers - Tokenization, stemming, stop words, custom language processing
• Multi-field mapping - Index same field multiple ways (text + keyword)
• Nested and parent-child documents - Model complex relationships
• Highlighting - Return matching text snippets with search results
• Suggesters - Autocomplete, spell correction, phrase suggestions

Distributed Architecture

• Horizontal scalability - Add nodes to increase capacity and throughput
• Automatic sharding - Distribute data across primary and replica shards
• Cluster coordination - Master-eligible nodes manage cluster state
• Cross-cluster search - Query across multiple Elasticsearch clusters
• Snapshot and restore - Backup indexes to cloud storage (S3, GCS, Azure)
• Rolling upgrades - Update clusters without downtime
• Shard allocation awareness - Control data placement for redundancy
• Index lifecycle management - Automate rollover, shrink, delete policies

Analytics and Aggregations

• Metrics aggregations - Sum, avg, min, max, percentiles, cardinality
• Bucket aggregations - Terms, histograms, date ranges, filters
• Pipeline aggregations - Derivative, moving average, cumulative sum
• Geospatial aggregations - Geo-distance, geo-bounds, geo-centroid
• SQL interface - Query Elasticsearch using SQL syntax
• Machine learning - Anomaly detection, forecasting, outlier detection
• Runtime fields - Compute fields on-the-fly during search
• Transforms - Create aggregated views of data for reporting

Elasticsearch for AI/ML Applications

Elasticsearch plays important roles in AI/ML workflows:

• Log analytics for ML systems - Monitor training jobs, track errors, analyze performance
• Feature search - Full-text search over feature descriptions in feature stores
• Vector search - Store and search embeddings with k-NN (k-nearest neighbors)
• Training data exploration - Search and filter large datasets for ML training
• Model monitoring - Track prediction distributions, detect data drift
• Anomaly detection - Built-in ML for identifying unusual patterns in metrics
• Recommendation systems - Combine text search with collaborative filtering
• Document retrieval for RAG - Hybrid search (keyword + semantic) for retrieval-augmented generation

Use Cases and Applications

• Application search - Website search, e-commerce product search, content search
• Log and event data analytics - Centralized logging, troubleshooting, forensics
• Application performance monitoring (APM) - Trace requests, identify bottlenecks
• Security analytics (SIEM) - Threat detection, security event correlation
• Business analytics - Sales dashboards, customer behavior analysis
• Observability - Metrics, logs, and traces for infrastructure monitoring
• Geospatial analysis - Location-based search, proximity queries
• Time-series data - IoT sensor data, financial market data
• Enterprise search - Search across documents, wikis, databases
• Recommendation engines - Content recommendations based on user behavior

Elasticsearch vs Other Search Solutions

Compared to Solr (another Lucene-based search engine), Elasticsearch offers simpler setup, better scalability defaults, and a more modern API design. Solr has more traditional search features and stronger support for complex document schemas, while Elasticsearch excels at log analytics and near-real-time use cases. For relational database full-text search (PostgreSQL full-text search, MySQL FULLTEXT indexes), Elasticsearch provides superior relevance scoring, distributed search, and analytics capabilities at the cost of maintaining a separate system.

Compared to specialized vector databases (Pinecone, Weaviate), Elasticsearch's vector search capabilities are newer and may not match specialized systems for pure semantic search. However, Elasticsearch excels at hybrid search—combining traditional keyword search with vector similarity for more accurate retrieval. For applications requiring both full-text and semantic search (like RAG systems), Elasticsearch provides a unified solution without managing multiple databases.

Getting Started with Elasticsearch

Install Elasticsearch locally with Docker (`docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:8.11.0`) or package managers. Verify installation: `curl localhost:9200` returns cluster information. Index a document: `curl -X POST localhost:9200/products/_doc/1 -H 'Content-Type: application/json' -d '{"name":"Laptop","price":999}'`. Search: `curl localhost:9200/products/_search?q=laptop`. Use official clients for programming languages (Python: elasticsearch-py, Node.js: @elastic/elasticsearch, Java: elasticsearch-java).

For production, Elastic Cloud (managed service) handles infrastructure, backups, security, and scaling. Alternatively, self-hosted deployments require configuring cluster with at least 3 master-eligible nodes, setting up security (TLS, authentication), configuring index lifecycle policies, and monitoring with Kibana or Elastic Observability. Start with single-node for development, then scale to multi-node clusters for production workloads. Elastic provides extensive documentation, training courses, and community forums for learning.

Integration with 21medien Services

21medien implements Elasticsearch for AI application backends requiring advanced search capabilities. We deploy Elasticsearch for full-text search over documentation, hybrid search for RAG systems (combining keyword and vector search), and log analytics for ML pipeline monitoring. Our team provides Elasticsearch consulting, architecture design (cluster sizing, index design, shard strategy), performance tuning (query optimization, indexing throughput), and managed operations. We specialize in Elasticsearch for semantic search applications, observability platforms for AI systems, and building custom search experiences for AI-powered applications. We help clients integrate Elasticsearch with their existing data infrastructure, design optimal mapping strategies, and implement production-ready search solutions.

Pricing and Access

Elasticsearch is open-source with multiple license options. Basic features are free (Apache 2.0 and Elastic License 2.0), while advanced features (machine learning, security, alerting) require subscriptions. Elastic Cloud (managed service) pricing: Standard tier ~$95/month (4GB RAM, 120GB storage), Gold tier ~$109/month (adds alerting, ML), Platinum/Enterprise tiers ~$125-175+/month (adds advanced security, SIEM). Storage costs ~$0.125/GB-month, data transfer charges apply. Self-hosting costs are infrastructure only (~$100-2000/month for small to medium clusters). For AI workloads, budget $200-1000/month for development environments, $1000-5000/month for production search applications, $5000-20,000+/month for large-scale observability or SIEM deployments with ML features.