What is Elasticsearch?
Elasticsearch is a distributed, RESTful search and analytics engine built on Apache Lucene. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.
10K+
Queries/sec per node
<50ms
Search latency
TB
Index capacity per node
99.9%
Search availability
Key Features & Use Cases
Core Capabilities
Near Real-time Search
Fast indexing and search capabilities
Distributed Architecture
Automatic sharding and replication
Rich Query DSL
Complex queries with aggregations
RESTful API
JSON over HTTP interface
Common Use Cases
Application and website search
Log and event data analysis
Business intelligence and analytics
Infrastructure and security monitoring
Geospatial data analysis
Core Concepts
Index
A collection of documents with similar characteristics
Key Features
- Schema mapping
- Configurable settings
- Multiple shards
- Custom analyzers
Example
products, users, logs-2024-01, articles🧮 Elasticsearch Cluster Calculator
Cluster Configuration
Performance Metrics
Index Size29297 GB
Search QPS900
Indexing Rate12,000 docs/sec
Fault Tolerance1 failures
Can survive 1 node failure(s)
Search Patterns & Use Cases
Full-Text Search
Search across text content with relevance scoring
Query Example
{"match": {"description": {"query": "wireless headphones", "fuzziness": "AUTO"}}}✅ Advantages
- Natural language queries
- Typo tolerance
- Relevance scoring
- Highlighting
⚠️ Considerations
- Index size overhead
- Analysis complexity
- Language-specific needs
Architecture Patterns
Search-First Architecture
Primary Data Store
Elasticsearch as the main database for search-heavy applications
Use case: Content management, catalogs, documentation
Search-Only Layer
Dedicated search index with data synced from primary database
Use case: E-commerce, user directories, content discovery
Analytics Engine
Time-series data analysis and real-time dashboards
Use case: Log analysis, metrics, business intelligence
Integration Patterns
Change Data Capture
Real-time sync from database changes to search index
Database → Debezium → Kafka → Logstash → ESDual Write Pattern
Application writes to both primary DB and search index
App → PostgreSQL + ElasticsearchEvent-Driven Sync
Event-based eventual consistency with message queues
App → Events → Queue → Index Worker🏢 Real-world Implementations
GitHub: Code Search
• 100+ million repositories indexed
• Complex code syntax analysis
• Real-time repository updates
• Advanced filtering and facets
Pattern: Specialized analyzers for code, real-time indexing pipeline
Airbnb: Search & Discovery
• 7+ million listings globally
• Geo-spatial search with filters
• ML-powered ranking signals
• Real-time availability updates
Pattern: Geo queries, ML ranking, faceted search with dynamic pricing
Netflix: Content Discovery
• Personalized content search
• Multi-language content indexing
• Real-time viewing analytics
• A/B testing search algorithms
Pattern: Personalization, multi-language, analytics-driven optimization
Uber: Operational Analytics
• Real-time trip and driver analytics
• Fraud detection and monitoring
• Operational dashboards
• Log aggregation across services
Pattern: Time-series analysis, real-time monitoring, multi-service logging
💡 Key Takeaways
- • Index Design: Plan your mapping and analyzers carefully - changes require reindexing
- • Shard Strategy: Right-size shards (10-50GB) and plan for growth patterns
- • Query Performance: Use filters over queries when possible, cache frequent searches
- • Data Modeling: Denormalize for search performance, consider parent-child relationships
- • Monitoring: Watch cluster health, query latency, and indexing performance
- • Scaling: Horizontal scaling through sharding, not just adding more nodes
📝 Elasticsearch Mastery Quiz
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