Elasticsearch: Beyond Just Search

While “search” is in the name, Elasticsearch is much more than a search engine. Let’s break down what it is, when to use it, and how to get started.

What is Elasticsearch?

At its core, Elasticsearch is a distributed, RESTful analytics engine. Think of it like a very fast, document-oriented database that excels at:

• Full-text search
• Log analysis
• Metrics aggregation
• Application monitoring

When to Use Elasticsearch

1. Complex Search Requirements

   • Need fuzzy matching
   • Multiple language support
   • Faceted search
   • Geospatial queries

2. Analytics and Metrics

   • Log analysis
   • Performance monitoring
   • Business intelligence
   • Real-time dashboards

3. Large-Scale Data

   • Billions of documents
   • Terabytes of data
   • Need for real-time results

Key Concepts

Documents and Indices

Index (like a database)
├── Document 1 (like a row)
│   ├── Field 1: value
│   ├── Field 2: value
│   └── Field 3: value
├── Document 2
│   ├── Field 1: value
│   └── Field 2: value
└── Document 3
    └── Field 1: value

Basic Operations

Index a Document

// Using the @elastic/elasticsearch client
const document = {
  title: 'Understanding Elasticsearch',
  tags: ['search', 'database', 'tutorial'],
  views: 1000,
  published: true,
};

await client.index({
  index: 'blog-posts',
  document,
});

Search Documents

// Basic search
const result = await client.search({
  index: 'blog-posts',
  query: {
    match: {
      title: 'elasticsearch tutorial',
    },
  },
});

// With filters
const result = await client.search({
  index: 'blog-posts',
  query: {
    bool: {
      must: [{ match: { title: 'elasticsearch' } }],
      filter: [{ term: { published: true } }, { range: { views: { gte: 100 } } }],
    },
  },
});

Common Patterns

1. Search-as-You-Type

// Create an index with autocomplete
await client.indices.create({
  index: 'products',
  body: {
    mappings: {
      properties: {
        name: {
          type: 'text',
          fields: {
            suggest: {
              type: 'completion',
            },
          },
        },
      },
    },
  },
});

// Use it
const suggestions = await client.search({
  index: 'products',
  suggest: {
    product_suggestions: {
      prefix: 'ela', // User input
      completion: {
        field: 'name.suggest',
      },
    },
  },
});

2. Faceted Search

// Get counts by category
const result = await client.search({
  index: 'products',
  aggs: {
    categories: {
      terms: { field: 'category.keyword' },
    },
    avg_price: {
      avg: { field: 'price' },
    },
  },
});

3. Geospatial Search

// Find stores near a location
const result = await client.search({
  index: 'stores',
  query: {
    geo_distance: {
      distance: '10km',
      location: {
        lat: 40.7128,
        lon: -74.006,
      },
    },
  },
});

Best Practices

1. Index Design

   • One index per use case
   • Use aliases for zero-downtime reindexing
   • Plan your mappings carefully

2. Performance

   • Use filters before queries
   • Limit field indexing
   • Use scroll API for large result sets

3. Reliability

   • Always use multiple nodes
   • Regular backups
   • Monitor cluster health

Common Pitfalls

1. Memory Usage

   DON'T: Store everything as keyword
   ┌────────────────┐
   │ field: keyword │ → Huge memory usage
   └────────────────┘
   
   DO: Use text with sub-fields
   ┌───────────────────────┐
   │ field: text          │
   │   └─ keyword: subset │ → Efficient
   └───────────────────────┘
   

2. Query Design

   • Avoid deep pagination
   • Don’t use scripts in high-volume queries
   • Be careful with wildcard queries

3. Data Modeling

   • Denormalize when it makes sense
   • But don’t go overboard with nested objects

Bottom Line

1. Use Elasticsearch when:

   • Complex search is crucial
   • You need real-time analytics
   • Traditional DBs struggle with scale

2. Don’t use Elasticsearch when:

   • ACID compliance is required
   • You need complex transactions
   • Your data is highly relational

Remember: Elasticsearch excels at search and analytics but isn’t a replacement for your primary database. Use it alongside your existing data store for best results.