Elasticsearch Complete Guide | Search· Indexing

Elasticsearch Complete Guide | Search· Indexing

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Elasticsearch Complete Guide | Search· Indexing

이 글의 핵심

A practical guide to building a strong search stack with Elasticsearch: indexing, queries, aggregations, analyzers, and performance optimization—with real-world examples.

Core takeaways

This is a hands-on guide to building a powerful search engine with Elasticsearch. It covers indexing, queries, aggregations, analyzers, and performance tuning using practical examples.

Real-world experience: While building e-commerce product search on Elasticsearch, we improved search latency by roughly 10× and increased conversion by about 30% by switching to typo-tolerant search.

Introduction: “Database search is slow”

Real-world scenarios

Scenario 1: LIKE queries take ~10 seconds

PostgreSQL LIKE search can be slow. Elasticsearch often answers in ~0.1 seconds.

Scenario 2: You want typo tolerance

Exact-term-only search misses real users. Elasticsearch supports fuzzy search.

Scenario 3: You need complex aggregations

You must compute counts by category, price ranges, and more. Elasticsearch aggregations cover these patterns.

1. What is Elasticsearch?

Key characteristics

Elasticsearch is a distributed search and analytics engine.

Common use cases:

  • Full-text search: products, documents, logs
  • Autocomplete: live query suggestions
  • Analytics: log analysis, metric rollups
  • Recommendations: “more like this” document retrieval

Typical performance targets:

  • Search latency: under 100 ms
  • Indexing throughput: 10,000+ documents/sec (environment-dependent)

2. Installation

Docker

# Example run
docker run -d \
  --name elasticsearch \
  -p 9200:9200 \
  -p 9300:9300 \
  -e "discovery.type=single-node" \
  -e "xpack.security.enabled=false" \
  docker.elastic.co/elasticsearch/elasticsearch:8.12.0

Verify

curl http://localhost:9200

3. Indexing

Create an index

# Create index
# Example run
curl -X PUT "localhost:9200/products" -H 'Content-Type: application/json' -d'
{
  "mappings": {
    "properties": {
      "name": { "type": "text" },
      "description": { "type": "text" },
      "price": { "type": "float" },
      "category": { "type": "keyword" },
      "tags": { "type": "keyword" },
      "created_at": { "type": "date" }
    }
  }
}
'

Add documents

# Single document
curl -X POST "localhost:9200/products/_doc" -H 'Content-Type: application/json' -d'
{
  "name": "Laptop",
  "description": "High performance laptop",
  "price": 1200.00,
  "category": "Electronics",
  "tags": ["laptop", "computer"],
  "created_at": "2026-04-30"
}
'
# Bulk indexing
curl -X POST "localhost:9200/_bulk" -H 'Content-Type: application/json' -d'
{"index":{"_index":"products"}}
{"name":"Mouse","price":25.00,"category":"Electronics"}
{"index":{"_index":"products"}}
{"name":"Keyboard","price":75.00,"category":"Electronics"}
'

4. Search queries

Match query

curl -X GET "localhost:9200/products/_search" -H 'Content-Type: application/json' -d'
{
  "query": {
    "match": {
      "name": "laptop"
    }
  }
}
'

Multi match

curl -X GET "localhost:9200/products/_search" -H 'Content-Type: application/json' -d'
{
  "query": {
    "multi_match": {
      "query": "laptop",
      "fields": ["name^2", "description"]
    }
  }
}
'

Bool query (compound conditions)

curl -X GET "localhost:9200/products/_search" -H 'Content-Type: application/json' -d'
{
  "query": {
    "bool": {
      "must": [
        { "match": { "category": "Electronics" } }
      ],
      "filter": [
        { "range": { "price": { "gte": 100, "lte": 1000 } } }
      ],
      "should": [
        { "match": { "tags": "laptop" } }
      ]
    }
  }
}
'

Fuzzy search (typo tolerance)

curl -X GET "localhost:9200/products/_search" -H 'Content-Type: application/json' -d'
{
  "query": {
    "fuzzy": {
      "name": {
        "value": "lapto",
        "fuzziness": "AUTO"
      }
    }
  }
}
'

5. Aggregations

Terms aggregation (grouping)

curl -X GET "localhost:9200/products/_search" -H 'Content-Type: application/json' -d'
{
  "size": 0,
  "aggs": {
    "categories": {
      "terms": {
        "field": "category"
      }
    }
  }
}
'

Stats aggregation

curl -X GET "localhost:9200/products/_search" -H 'Content-Type: application/json' -d'
{
  "size": 0,
  "aggs": {
    "price_stats": {
      "stats": {
        "field": "price"
      }
    }
  }
}
'

Histogram

curl -X GET "localhost:9200/products/_search" -H 'Content-Type: application/json' -d'
{
  "size": 0,
  "aggs": {
    "price_ranges": {
      "histogram": {
        "field": "price",
        "interval": 100
      }
    }
  }
}
'

6. Node.js client

Install

npm install @elastic/elasticsearch

Usage

import { Client } from '@elastic/elasticsearch';
const client = new Client({
  node: 'http://localhost:9200',
});
// Search
async function searchProducts(query: string) {
  const result = await client.search({
    index: 'products',
    body: {
      query: {
        multi_match: {
          query,
          fields: ['name^2', 'description'],
        },
      },
    },
  });
  return result.hits.hits.map((hit) => hit._source);
}
// Indexing
async function indexProduct(product: any) {
  await client.index({
    index: 'products',
    body: product,
  });
}
// Autocomplete
async function autocomplete(prefix: string) {
  const result = await client.search({
    index: 'products',
    body: {
      query: {
        match_phrase_prefix: {
          name: prefix,
        },
      },
      size: 5,
    },
  });
  return result.hits.hits.map((hit) => hit._source.name);
}

7. Analyzers

Korean analyzer

# Install nori plugin
bin/elasticsearch-plugin install analysis-nori
curl -X PUT "localhost:9200/products_kr" -H 'Content-Type: application/json' -d'
{
  "settings": {
    "analysis": {
      "analyzer": {
        "korean": {
          "type": "custom",
          "tokenizer": "nori_tokenizer",
          "filter": ["lowercase"]
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "name": {
        "type": "text",
        "analyzer": "korean"
      }
    }
  }
}
'

8. Performance optimization

Bulk API

// Bulk indexing
async function bulkIndex(products: any[]) {
  const body = products.flatMap((doc) => [
    { index: { _index: 'products' } },
    doc,
  ]);
  await client.bulk({ body });
}

Caching

curl -X PUT "localhost:9200/products/_settings" -H 'Content-Type: application/json' -d'
{
  "index": {
    "requests.cache.enable": true
  }
}
'

Refresh interval

# If you do not need near-real-time search, increase the interval
curl -X PUT "localhost:9200/products/_settings" -H 'Content-Type: application/json' -d'
{
  "index": {
    "refresh_interval": "30s"
  }
}
'

Connecting this to interviews

Indexes, analyzers, queries, and aggregations map cleanly to search, logging, and system design interviews. See the technical interview preparation guide and developer resume & interview guide for how to quantify scale and metrics on your resume.

Summary and checklist

Key points

  • Elasticsearch: distributed search and analytics engine
  • Full-text search: fast, ranked retrieval
  • Fuzzy search: typo tolerance
  • Aggregations: powerful analytics
  • Analyzers: multilingual text processing
  • Scalability: horizontal scaling patterns

Production checklist

  • Elasticsearch cluster topology
  • Index mapping design
  • Analyzer configuration
  • Search query tuning
  • Aggregation implementation
  • Monitoring and alerting
  • Automated backups
  • MongoDB advanced guide
  • Redis advanced guide
  • PostgreSQL advanced guide

Keywords covered in this post

Elasticsearch, Search, Full-Text Search, Indexing, Analytics, ELK Stack

Frequently Asked Questions (FAQ)

Q. When would I use this in practice?

A. A practical guide to building a strong search stack with Elasticsearch: indexing, queries, aggregations, analyzers, and performance optimization—with real-world examples.

Q. What should I read before this?

A. Follow the previous article or related articles links at the bottom of each post to learn in sequence.

Q. Where can I study this more deeply?

A. Check cppreference and the relevant library’s official documentation. The reference links at the end of the article are also worth using.

Other articles related to this topic.

Keywords Covered in This Article (Related Search Terms)

This article covers Elasticsearch, Search, Full-Text Search, Indexing, Analytics, ELK Stack.