First draft

Author: KOTungseth

get-started/elasticsearch.md

@@ -0,0 +1,162 @@
+---
+products:
+  - id: elasticsearch
+applies_to:
+  stack:
+---
+
+# {{es}} overview [elasticsearch-overview]
+
+{{es}} is a distributed datastore that ingests, indexes, and manages various types of data in near real-time, making them both searchable and analyzable. Built on Apache Lucene, {{es}} scales horizontally across multiple nodes to handle large data volumes while maintaining fast query performance.
+
+At its core, {{es}} solves the problem of making large amounts of data quickly searchable. Whether you're building a product search for an e-commerce site, implementing semantic search with AI, or analyzing log data, {{es}} provides the foundation for these use cases through its powerful indexing and query capabilities.
+
+## Distributed architecture [elasticsearch-distributed-architecture]
+
+{{es}} distributes data across multiple nodes in a cluster. Each node holds a portion of the data in shards, which are self-contained indexes that can be stored on any node. 
+
+This distribution enables:
+
+* Horizontal scaling: Add more nodes to increase capacity
+* High availability: Data is replicated across nodes to prevent loss
+* Parallel processing: Queries execute across shards simultaneously
+
+## Near real-time indexing [elasticsearch-near-real-time-indexing]
+
+When you send documents to Elasticsearch, they become searchable within about one second. This near real-time capability makes Elasticsearch suitable for applications that require immediate data availability, such as:
+
+* Live dashboards showing current system metrics
+* Product catalogs that update as inventory changes
+* User-generated content that appears in search results immediately
+
+## Schema-on-write with dynamic mapping [elasticsearch-schema-on-write-with-dynamic-mapping]
+
+Elasticsearch automatically detects field types when you index documents. If you send a document with a price field containing 29.99, Elasticsearch infers it's a floating-point number. You can also define explicit mappings to control exactly how data is stored and indexed. 
+
+Mappings are important for:
+
+* Optimizing storage and query performance
+* Enabling specific search features (like autocomplete or geo-search)
+* Ensuring data consistency across documents
+
+## Vector capabilities [elasticsearch-vector-capabilities]
+
+Elasticsearch serves as a vector database for AI and machine learning applications. It stores dense vector embeddings alongside traditional text and numeric data, enabling:
+
+* Semantic search: Find content by meaning rather than exact keywords
+* Hybrid search: Combine keyword and vector search for best results
+* RAG systems: Provide relevant context to large language models
+
+## How Elasticsearch works [how-elasticsearch-works]
+
+### Data flow [elasticsearch-data-flow]
+
+1. Ingestion: Data enters Elasticsearch through the REST API, client libraries, or integrations
+2. Analysis: Text is processed through analyzers (tokenization, stemming, etc.)
+3. Indexing: Documents are stored in shards with inverted indexes for fast retrieval
+4. Querying: Search requests are distributed to relevant shards and results are merged
+5. Response: Results are returned, typically in milliseconds
+
+### Storage model [elasticsearch-storage-model]
+
+Elasticsearch stores data in indices, which are collections of documents with similar characteristics. Each document is a JSON object with fields. 
+
+For example:
+
+```console
+{
+  "product_id": "abc123",
+  "name": "Wireless Headphones",
+  "price": 79.99,
+  "category": "Electronics",
+  "in_stock": true,
+  "description": "High-quality wireless headphones with noise cancellation"
+}
+```
+
+Under the hood, Elasticsearch creates inverted indexes that map each unique term to the documents containing it, enabling fast full-text search.
+
+### Query execution [elasticsearch-query-execution]
+
+When you search, Elasticsearch:
+
+1. Parses your query (e.g., "wireless headphones under $100")
+2. Determines which shards might contain matching documents
+3. Executes the query on each relevant shard in parallel
+4. Scores results by relevance
+5. Merges and sorts results from all shards
+6. Returns the top results
+7. This distributed query execution is why Elasticsearch can search petabytes of data in milliseconds.
+
+## Use cases [elasticsearch-use-cases]
+
+Elasticsearch excels in scenarios requiring fast search and analysis across large datasets.
+
+### Full-text and hybrid search [elasticsearch-full-text-hybrid-search]
+
+* E-commerce product catalogs: Fast product discovery with filters, facets, and autocomplete
+* Enterprise knowledge bases: Search across documents, wikis, and databases with permission controls
+* Content platforms: Search articles, videos, and user-generated content by relevance
+
+### AI-powered applications [elasticsearch-ai-powered-applications]
+
+* Semantic search: Find documents by meaning using vector embeddings from models like BERT or OpenAI
+* Chatbots and RAG systems: Retrieve relevant context from knowledge bases to enhance LLM responses
+* Recommendation engines: Surface similar items based on vector similarity
+
+### Geospatial search [elasticsearch-geospatial-search]
+
+* Location-based services: Find nearby restaurants, stores, or services
+* Delivery routing: Optimize routes based on geographic data
+* Geofencing: Trigger actions when users enter specific areas
+
+### Analytics and monitoring [elasticsearch-analytics-monitoring]
+
+* Log analytics: Centralize and analyze application and system logs
+* Security analytics: Detect threats and anomalies in security events
+* Business metrics: Analyze user behavior, sales trends, and KPIs
+
+## When to use Elasticsearch [when-to-use-elasticsearch]
+
+Use Elasticsearch when you need:
+
+* Fast search across large volumes of text, numeric, or vector data
+* Complex queries with filters, aggregations, and relevance scoring
+* Near real-time data availability (seconds, not minutes)
+* Scalability to handle growing data volumes
+* Flexibility to handle various data types and evolving schemas
+
+## Architecture considerations [elasticsearch-architecture-considerations]
+
+### Deployment options [elasticsearch-deployment-options]
+
+* Elasticsearch Serverless: Fully managed, auto-scaling deployment (recommended for new projects)
+* Elastic Cloud: Managed Elasticsearch with more configuration control
+* Self-managed: Install and operate Elasticsearch yourself (requires expertise)
+
+### Cluster sizing [elasticsearch-cluster-sizing]
+
+* Small deployments: 3-5 nodes for development and small production use cases
+* Medium deployments: 10-20 nodes for moderate data volumes and query loads
+* Large deployments: 50+ nodes for high-volume production systems
+
+### Data modeling best practices [elasticsearch-data-modeling-best-practices]
+
+* One document type per index: Keep related data together
+* Denormalize data: Include related information in documents to avoid "joins"
+* Use appropriate field types: Match data types to query patterns
+* Plan for growth: Consider time-based indices for logs and events
+
+## Next steps [elasticsearch-next-steps]
+
+Ready to try Elasticsearch? Here's how to get started:
+
+* Get started with Elasticsearch - Run your first queries in 5 minutes
+* Tutorial: Build a search application - Create a full-featured search experience
+* Understanding Elasticsearch architecture - Deep dive into distributed systems concepts
+
+For specific use cases:
+
+* Implementing semantic search - Add AI-powered search
+* Building geospatial applications - Work with location data
+* Analyzing logs and metrics - Set up observability