Basic full-text search and filtering in Elasticsearch
editBasic full-text search and filtering in Elasticsearch
editThis 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
editYou’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
editCreate 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 |
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Multi-fields are used here to index |
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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
editNow 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
editFull-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
editThe 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 |
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 |
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The title contains both "Fluffy" and "Pancakes", matching our search terms exactly. |
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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
editSpecify 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
Specify a minimum number of terms to match
editUse 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
editWhen 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
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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. |
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The description contains "curry" and related terms like "vegetables", further increasing the document’s relevance. |
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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
editFiltering 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 |
The .keyword
suffix accesses the unanalyzed version of a field, enabling exact, case-sensitive matching. This works in two scenarios:
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When using dynamic mapping for text fields. Elasticsearch automatically creates a
.keyword
sub-field. -
When text fields are explicitly mapped with a
.keyword
sub-field. For example, we explicitly mapped thecategory
field in Step 1 of this tutorial.
Search for posts within a date range
editOften 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" } } } }
Find exact matches
editSometimes 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);
The |
Avoid using the term
query for text
fields because they are transformed by the analysis process.
Step 6: Combine multiple search criteria
editA 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 main course
- Should contain "curry" or "spicy" in the title or description
- 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: { "category.keyword": "Main Course", }, }, { term: { tags: "vegetarian", }, }, { range: { rating: { gte: 4.5, }, }, }, ], should: [ { 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": { "category.keyword": "Main Course" } }, { "term": { "tags": "vegetarian" } }, { "range": { "rating": { "gte": 4.5 } } } ], "should": [ { "multi_match": { "query": "curry spicy", "fields": ["title^2", "description"] } }, { "range": { "date": { "gte": "now-1M/d" } } } ], "must_not": [ { "term": { "category.keyword": "Dessert" } } ] } } }
The |
Example response
{ "took": 1, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped": 0, "failed": 0 }, "hits": { "total": { "value": 1, "relation": "eq" }, "max_score": 7.9835095, "hits": [ { "_index": "cooking_blog", "_id": "2", "_score": 7.9835095, "_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. |
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While the description also contains matching terms, only the best matching field’s score is used by default. |
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The recipe was published within the last month, satisfying our recency preference. |
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The "Main Course" category matches our |
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The "vegetarian" tag satisfies another |
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The rating of 4.6 meets our minimum rating requirement of 4.5. |
Learn more
editThis 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:
- Elasticsearch basics — Search and analyze data: Understand all your options for searching and analyzing data in Elasticsearch.
- Text analysis: Understand how text is processed for full-text search.
-
Search your data: Learn about more advanced search techniques using the
_search
API, including semantic search.