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Elastic Docs ›Elasticsearch Guide [8.15] ›Quick starts

Basic full-text search and filtering in Elasticsearch

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Basic full-text search and filtering in Elasticsearch

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This is a hands-on introduction to the basics of full-text search with Elasticsearch, also known as lexical search, using the _search API and Query DSL. You’ll also learn how to filter data, to narrow down search results based on exact criteria.

In this scenario, we’re implementing a search function for a cooking blog. The blog contains recipes with various attributes including textual content, categorical data, and numerical ratings.

The goal is to create search queries that enable users to:

  • Find recipes based on ingredients they want to use or avoid
  • Discover dishes suitable for their dietary needs
  • Find highly-rated recipes in specific categories
  • Find recent recipes from their favorite authors

To achieve these goals we’ll use different Elasticsearch queries to perform full-text search, apply filters, and combine multiple search criteria.

Requirements

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You’ll need a running Elasticsearch cluster, together with Kibana to use the Dev Tools API Console. Run the following command in your terminal to set up a single-node local cluster in Docker:

curl -fsSL https://elastic.co/start-local | sh

Step 1: Create an index

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Create the cooking_blog index to get started:

const response = await client.indices.create({
  index: "cooking_blog",
});
console.log(response);
PUT /cooking_blog

Now define the mappings for the index:

const response = await client.indices.putMapping({
  index: "cooking_blog",
  properties: {
    title: {
      type: "text",
      analyzer: "standard",
      fields: {
        keyword: {
          type: "keyword",
          ignore_above: 256,
        },
      },
    },
    description: {
      type: "text",
      fields: {
        keyword: {
          type: "keyword",
        },
      },
    },
    author: {
      type: "text",
      fields: {
        keyword: {
          type: "keyword",
        },
      },
    },
    date: {
      type: "date",
      format: "yyyy-MM-dd",
    },
    category: {
      type: "text",
      fields: {
        keyword: {
          type: "keyword",
        },
      },
    },
    tags: {
      type: "text",
      fields: {
        keyword: {
          type: "keyword",
        },
      },
    },
    rating: {
      type: "float",
    },
  },
});
console.log(response);
PUT /cooking_blog/_mapping
{
  "properties": {
    "title": {
      "type": "text",
      "analyzer": "standard", 
      "fields": { 
        "keyword": {
          "type": "keyword",
          "ignore_above": 256 
        }
      }
    },
    "description": {
      "type": "text",
      "fields": {
        "keyword": {
          "type": "keyword"
        }
      }
    },
    "author": {
      "type": "text",
      "fields": {
        "keyword": {
          "type": "keyword"
        }
      }
    },
    "date": {
      "type": "date",
      "format": "yyyy-MM-dd"
    },
    "category": {
      "type": "text",
      "fields": {
        "keyword": {
          "type": "keyword"
        }
      }
    },
    "tags": {
      "type": "text",
      "fields": {
        "keyword": {
          "type": "keyword"
        }
      }
    },
    "rating": {
      "type": "float"
    }
  }
}

The standard analyzer is used by default for text fields if an analyzer isn’t specified. It’s included here for demonstration purposes.

Multi-fields are used here to index text fields as both text and keyword data types. This enables both full-text search and exact matching/filtering on the same field. Note that if you used dynamic mapping, these multi-fields would be created automatically.

The ignore_above parameter prevents indexing values longer than 256 characters in the keyword field. Again this is the default value, but it’s included here for for demonstration purposes. It helps to save disk space and avoid potential issues with Lucene’s term byte-length limit.

Full-text search is powered by text analysis. Text analysis normalizes and standardizes text data so it can be efficiently stored in an inverted index and searched in near real-time. Analysis happens at both index and search time. This tutorial won’t cover analysis in detail, but it’s important to understand how text is processed to create effective search queries.

Step 2: Add sample blog posts to your index

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Now you’ll need to index some example blog posts using the Bulk API. Note that text fields are analyzed and multi-fields are generated at index time.

const response = await client.bulk({
  index: "cooking_blog",
  refresh: "wait_for",
  operations: [
    {
      index: {
        _id: "1",
      },
    },
    {
      title: "Perfect Pancakes: A Fluffy Breakfast Delight",
      description:
        "Learn the secrets to making the fluffiest pancakes, so amazing you won't believe your tastebuds. This recipe uses buttermilk and a special folding technique to create light, airy pancakes that are perfect for lazy Sunday mornings.",
      author: "Maria Rodriguez",
      date: "2023-05-01",
      category: "Breakfast",
      tags: ["pancakes", "breakfast", "easy recipes"],
      rating: 4.8,
    },
    {
      index: {
        _id: "2",
      },
    },
    {
      title: "Spicy Thai Green Curry: A Vegetarian Adventure",
      description:
        "Dive into the flavors of Thailand with this vibrant green curry. Packed with vegetables and aromatic herbs, this dish is both healthy and satisfying. Don't worry about the heat - you can easily adjust the spice level to your liking.",
      author: "Liam Chen",
      date: "2023-05-05",
      category: "Main Course",
      tags: ["thai", "vegetarian", "curry", "spicy"],
      rating: 4.6,
    },
    {
      index: {
        _id: "3",
      },
    },
    {
      title: "Classic Beef Stroganoff: A Creamy Comfort Food",
      description:
        "Indulge in this rich and creamy beef stroganoff. Tender strips of beef in a savory mushroom sauce, served over a bed of egg noodles. It's the ultimate comfort food for chilly evenings.",
      author: "Emma Watson",
      date: "2023-05-10",
      category: "Main Course",
      tags: ["beef", "pasta", "comfort food"],
      rating: 4.7,
    },
    {
      index: {
        _id: "4",
      },
    },
    {
      title: "Vegan Chocolate Avocado Mousse",
      description:
        "Discover the magic of avocado in this rich, vegan chocolate mousse. Creamy, indulgent, and secretly healthy, it's the perfect guilt-free dessert for chocolate lovers.",
      author: "Alex Green",
      date: "2023-05-15",
      category: "Dessert",
      tags: ["vegan", "chocolate", "avocado", "healthy dessert"],
      rating: 4.5,
    },
    {
      index: {
        _id: "5",
      },
    },
    {
      title: "Crispy Oven-Fried Chicken",
      description:
        "Get that perfect crunch without the deep fryer! This oven-fried chicken recipe delivers crispy, juicy results every time. A healthier take on the classic comfort food.",
      author: "Maria Rodriguez",
      date: "2023-05-20",
      category: "Main Course",
      tags: ["chicken", "oven-fried", "healthy"],
      rating: 4.9,
    },
  ],
});
console.log(response);
POST /cooking_blog/_bulk?refresh=wait_for
{"index":{"_id":"1"}}
{"title":"Perfect Pancakes: A Fluffy Breakfast Delight","description":"Learn the secrets to making the fluffiest pancakes, so amazing you won't believe your tastebuds. This recipe uses buttermilk and a special folding technique to create light, airy pancakes that are perfect for lazy Sunday mornings.","author":"Maria Rodriguez","date":"2023-05-01","category":"Breakfast","tags":["pancakes","breakfast","easy recipes"],"rating":4.8}
{"index":{"_id":"2"}}
{"title":"Spicy Thai Green Curry: A Vegetarian Adventure","description":"Dive into the flavors of Thailand with this vibrant green curry. Packed with vegetables and aromatic herbs, this dish is both healthy and satisfying. Don't worry about the heat - you can easily adjust the spice level to your liking.","author":"Liam Chen","date":"2023-05-05","category":"Main Course","tags":["thai","vegetarian","curry","spicy"],"rating":4.6}
{"index":{"_id":"3"}}
{"title":"Classic Beef Stroganoff: A Creamy Comfort Food","description":"Indulge in this rich and creamy beef stroganoff. Tender strips of beef in a savory mushroom sauce, served over a bed of egg noodles. It's the ultimate comfort food for chilly evenings.","author":"Emma Watson","date":"2023-05-10","category":"Main Course","tags":["beef","pasta","comfort food"],"rating":4.7}
{"index":{"_id":"4"}}
{"title":"Vegan Chocolate Avocado Mousse","description":"Discover the magic of avocado in this rich, vegan chocolate mousse. Creamy, indulgent, and secretly healthy, it's the perfect guilt-free dessert for chocolate lovers.","author":"Alex Green","date":"2023-05-15","category":"Dessert","tags":["vegan","chocolate","avocado","healthy dessert"],"rating":4.5}
{"index":{"_id":"5"}}
{"title":"Crispy Oven-Fried Chicken","description":"Get that perfect crunch without the deep fryer! This oven-fried chicken recipe delivers crispy, juicy results every time. A healthier take on the classic comfort food.","author":"Maria Rodriguez","date":"2023-05-20","category":"Main Course","tags":["chicken","oven-fried","healthy"],"rating":4.9}

Step 3: Perform basic full-text searches

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Full-text search involves executing text-based queries across one or more document fields. These queries calculate a relevance score for each matching document, based on how closely the document’s content aligns with the search terms. Elasticsearch offers various query types, each with its own method for matching text and relevance scoring.

match query

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The match query is the standard query for full-text, or "lexical", search. The query text will be analyzed according to the analyzer configuration specified on each field (or at query time).

First, search the description field for "fluffy pancakes":

const response = await client.search({
  index: "cooking_blog",
  query: {
    match: {
      description: {
        query: "fluffy pancakes",
      },
    },
  },
});
console.log(response);
GET /cooking_blog/_search
{
  "query": {
    "match": {
      "description": {
        "query": "fluffy pancakes" 
      }
    }
  }
}

By default, the match query uses OR logic between the resulting tokens. This means it will match documents that contain either "fluffy" or "pancakes", or both, in the description field.

At search time, Elasticsearch defaults to the analyzer defined in the field mapping. In this example, we’re using the standard analyzer. Using a different analyzer at search time is an advanced use case.

Example response 
{
  "took": 0,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": { 
    "total": {
      "value": 1,
      "relation": "eq"
    },
    "max_score": 1.8378843, 
    "hits": [
      {
        "_index": "cooking_blog",
        "_id": "1",
        "_score": 1.8378843, 
        "_source": {
          "title": "Perfect Pancakes: A Fluffy Breakfast Delight", 
          "description": "Learn the secrets to making the fluffiest pancakes, so amazing you won't believe your tastebuds. This recipe uses buttermilk and a special folding technique to create light, airy pancakes that are perfect for lazy Sunday mornings.", 
          "author": "Maria Rodriguez",
          "date": "2023-05-01",
          "category": "Breakfast",
          "tags": [
            "pancakes",
            "breakfast",
            "easy recipes"
          ],
          "rating": 4.8
        }
      }
    ]
  }
}

The hits object contains the total number of matching documents and their relation to the total. Refer to Track total hits for more details about the hits object.

max_score is the highest relevance score among all matching documents. In this example, we only have one matching document.

_score is the relevance score for a specific document, indicating how well it matches the query. Higher scores indicate better matches. In this example the max_score is the same as the _score, as there is only one matching document.

The title contains both "Fluffy" and "Pancakes", matching our search terms exactly.

The description includes "fluffiest" and "pancakes", further contributing to the document’s relevance due to the analysis process.

Require all terms in a match query

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Specify the and operator to require both terms in the description field. This stricter search returns zero hits on our sample data, as no document contains both "fluffy" and "pancakes" in the description.

const response = await client.search({
  index: "cooking_blog",
  query: {
    match: {
      description: {
        query: "fluffy pancakes",
        operator: "and",
      },
    },
  },
});
console.log(response);
GET /cooking_blog/_search
{
  "query": {
    "match": {
      "description": {
        "query": "fluffy pancakes",
        "operator": "and"
      }
    }
  }
}
Example response 
{
  "took": 0,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 0,
      "relation": "eq"
    },
    "max_score": null,
    "hits": []
  }
}

Specify a minimum number of terms to match

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Use the minimum_should_match parameter to specify the minimum number of terms a document should have to be included in the search results.

Search the title field to match at least 2 of the 3 terms: "fluffy", "pancakes", or "breakfast". This is useful for improving relevance while allowing some flexibility.

const response = await client.search({
  index: "cooking_blog",
  query: {
    match: {
      title: {
        query: "fluffy pancakes breakfast",
        minimum_should_match: 2,
      },
    },
  },
});
console.log(response);
GET /cooking_blog/_search
{
  "query": {
    "match": {
      "title": {
        "query": "fluffy pancakes breakfast",
        "minimum_should_match": 2
      }
    }
  }
}

Step 4: Search across multiple fields at once

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When users enter a search query, they often don’t know (or care) whether their search terms appear in a specific field. A multi_match query allows searching across multiple fields simultaneously.

Let’s start with a basic multi_match query:

const response = await client.search({
  index: "cooking_blog",
  query: {
    multi_match: {
      query: "vegetarian curry",
      fields: ["title", "description", "tags"],
    },
  },
});
console.log(response);
GET /cooking_blog/_search
{
  "query": {
    "multi_match": {
      "query": "vegetarian curry",
      "fields": ["title", "description", "tags"]
    }
  }
}

This query searches for "vegetarian curry" across the title, description, and tags fields. Each field is treated with equal importance.

However, in many cases, matches in certain fields (like the title) might be more relevant than others. We can adjust the importance of each field using field boosting:

const response = await client.search({
  index: "cooking_blog",
  query: {
    multi_match: {
      query: "vegetarian curry",
      fields: ["title^3", "description^2", "tags"],
    },
  },
});
console.log(response);
GET /cooking_blog/_search
{
  "query": {
    "multi_match": {
      "query": "vegetarian curry",
      "fields": ["title^3", "description^2", "tags"] 
    }
  }
}

The ^ syntax applies a boost to specific fields:

  • title^3: The title field is 3 times more important than an unboosted field
  • description^2: The description is 2 times more important

  • tags: No boost applied (equivalent to ^1)

    These boosts help tune relevance, prioritizing matches in the title over the description, and matches in the description over tags.

Learn more about fields and per-field boosting in the multi_match query reference.

Example response 
{
  "took": 0,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 1,
      "relation": "eq"
    },
    "max_score": 7.546015,
    "hits": [
      {
        "_index": "cooking_blog",
        "_id": "2",
        "_score": 7.546015,
        "_source": {
          "title": "Spicy Thai Green Curry: A Vegetarian Adventure", 
          "description": "Dive into the flavors of Thailand with this vibrant green curry. Packed with vegetables and aromatic herbs, this dish is both healthy and satisfying. Don't worry about the heat - you can easily adjust the spice level to your liking.", 
          "author": "Liam Chen",
          "date": "2023-05-05",
          "category": "Main Course",
          "tags": [
            "thai",
            "vegetarian",
            "curry",
            "spicy"
          ], 
          "rating": 4.6
        }
      }
    ]
  }
}

The title contains "Vegetarian" and "Curry", which matches our search terms. The title field has the highest boost (^3), contributing significantly to this document’s relevance score.

The description contains "curry" and related terms like "vegetables", further increasing the document’s relevance.

The tags include both "vegetarian" and "curry", providing an exact match for our search terms, albeit with no boost.

This result demonstrates how the multi_match query with field boosts helps users find relevant recipes across multiple fields. Even though the exact phrase "vegetarian curry" doesn’t appear in any single field, the combination of matches across fields produces a highly relevant result.

The multi_match query is often recommended over a single match query for most text search use cases, as it provides more flexibility and better matches user expectations.

Step 5: Filter and find exact matches

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Filtering allows you to narrow down your search results based on exact criteria. Unlike full-text searches, filters are binary (yes/no) and do not affect the relevance score. Filters execute faster than queries because excluded results don’t need to be scored.

This bool query will return only blog posts in the "Breakfast" category.

const response = await client.search({
  index: "cooking_blog",
  query: {
    bool: {
      filter: [
        {
          term: {
            "category.keyword": "Breakfast",
          },
        },
      ],
    },
  },
});
console.log(response);
GET /cooking_blog/_search
{
  "query": {
    "bool": {
      "filter": [
        { "term": { "category.keyword": "Breakfast" } }  
      ]
    }
  }
}

Note the use of category.keyword here. This refers to the keyword multi-field of the category field, ensuring an exact, case-sensitive match.

The .keyword suffix accesses the unanalyzed version of a field, enabling exact, case-sensitive matching. This works in two scenarios:

  1. When using dynamic mapping for text fields. Elasticsearch automatically creates a .keyword sub-field.
  2. When text fields are explicitly mapped with a .keyword sub-field. For example, we explicitly mapped the category field in Step 1 of this tutorial.

Search for posts within a date range

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Often users want to find content published within a specific time frame. A range query finds documents that fall within numeric or date ranges.

const response = await client.search({
  index: "cooking_blog",
  query: {
    range: {
      date: {
        gte: "2023-05-01",
        lte: "2023-05-31",
      },
    },
  },
});
console.log(response);
GET /cooking_blog/_search
{
  "query": {
    "range": {
      "date": {
        "gte": "2023-05-01", 
        "lte": "2023-05-31" 
      }
    }
  }
}

Greater than or equal to May 1, 2023.

Less than or equal to May 31, 2023.

Find exact matches

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Sometimes users want to search for exact terms to eliminate ambiguity in their search results. A term query searches for an exact term in a field without analyzing it. Exact, case-sensitive matches on specific terms are often referred to as "keyword" searches.

Here you’ll search for the author "Maria Rodriguez" in the author.keyword field.

const response = await client.search({
  index: "cooking_blog",
  query: {
    term: {
      "author.keyword": "Maria Rodriguez",
    },
  },
});
console.log(response);
GET /cooking_blog/_search
{
  "query": {
    "term": {
      "author.keyword": "Maria Rodriguez" 
    }
  }
}

The term query has zero flexibility. For example, here the queries maria or maria rodriguez would have zero hits, due to case sensitivity.

Avoid using the term query for text fields because they are transformed by the analysis process.

Step 6: Combine multiple search criteria

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A bool query allows you to combine multiple query clauses to create sophisticated searches. In this tutorial scenario it’s useful for when users have complex requirements for finding recipes.

Let’s create a query that addresses the following user needs:

  • Must be a vegetarian recipe
  • Should contain "curry" or "spicy" in the title or description
  • Should be a main course
  • Must not be a dessert
  • Must have a rating of at least 4.5
  • Should prefer recipes published in the last month 
const response = await client.search({
  index: "cooking_blog",
  query: {
    bool: {
      must: [
        {
          term: {
            tags: "vegetarian",
          },
        },
        {
          range: {
            rating: {
              gte: 4.5,
            },
          },
        },
      ],
      should: [
        {
          term: {
            category: "Main Course",
          },
        },
        {
          multi_match: {
            query: "curry spicy",
            fields: ["title^2", "description"],
          },
        },
        {
          range: {
            date: {
              gte: "now-1M/d",
            },
          },
        },
      ],
      must_not: [
        {
          term: {
            "category.keyword": "Dessert",
          },
        },
      ],
    },
  },
});
console.log(response);
GET /cooking_blog/_search
{
  "query": {
    "bool": {
      "must": [
        { "term": { "tags": "vegetarian" } },
        {
          "range": {
            "rating": {
              "gte": 4.5
            }
          }
        }
      ],
      "should": [
        {
          "term": {
            "category": "Main Course"
          }
        },
        {
          "multi_match": {
            "query": "curry spicy",
            "fields": [
              "title^2",
              "description"
            ]
          }
        },
        {
          "range": {
            "date": {
              "gte": "now-1M/d"
            }
          }
        }
      ],
      "must_not": [ 
        {
          "term": {
            "category.keyword": "Dessert"
          }
        }
      ]
    }
  }
}

The must_not clause excludes documents that match the specified criteria. This is a powerful tool for filtering out unwanted results.
Example response 
{
  "took": 1,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 1,
      "relation": "eq"
    },
    "max_score": 7.444513,
    "hits": [
      {
        "_index": "cooking_blog",
        "_id": "2",
        "_score": 7.444513,
        "_source": {
          "title": "Spicy Thai Green Curry: A Vegetarian Adventure", 
          "description": "Dive into the flavors of Thailand with this vibrant green curry. Packed with vegetables and aromatic herbs, this dish is both healthy and satisfying. Don't worry about the heat - you can easily adjust the spice level to your liking.", 
          "author": "Liam Chen",
          "date": "2023-05-05",
          "category": "Main Course", 
          "tags": [ 
            "thai",
            "vegetarian", 
            "curry",
            "spicy"
          ],
          "rating": 4.6 
        }
      }
    ]
  }
}

The title contains "Spicy" and "Curry", matching our should condition. With the default best_fields behavior, this field contributes most to the relevance score.

While the description also contains matching terms, only the best matching field’s score is used by default.

The recipe was published within the last month, satisfying our recency preference.

The "Main Course" category satisfies another should condition.

The "vegetarian" tag satisfies a must condition, while "curry" and "spicy" tags align with our should preferences.

The rating of 4.6 meets our minimum rating requirement of 4.5.

Learn more

edit

This tutorial introduced the basics of full-text search and filtering in Elasticsearch. Building a real-world search experience requires understanding many more advanced concepts and techniques. Here are some resources once you’re ready to dive deeper:

« Index and search data using Elasticsearch APIs Analyze eCommerce data with aggregations using Query DSL »