Range Queries Example in Elasticsearch

Elasticsearch is a powerful search and analytics engine, widely used for exploring large volumes of structured and unstructured data. One of its most useful features is the ability to perform range queries—a query type that lets you filter documents based on a range of values. This is particularly useful for working with dates, numeric values, and price ranges. In this blog post, we'll explore how Elasticsearch handles range queries for these data types and share some best practices for optimizing their use.

What Are Range Queries?

A range query in Elasticsearch allows you to search for documents where a field’s value falls within a specific range. These queries are especially helpful when you want to find data within boundaries, such as:

Events between specific dates.
Products priced within a budget.
Numbers above or below a threshold.

The range query is part of Elasticsearch’s Query DSL (Domain Specific Language) and supports the following relational operators:

gte (greater than or equal to)
gt (greater than)
lte (less than or equal to)
lt (less than)

Here’s a basic example of a range query:

{
  "query": {
    "range": {
      "price": {
        "gte": 100,
        "lte": 500
      }
    }
  }
}

This query retrieves documents where the price field is between 100 and 500 inclusive

Handling Dates in Range Queries

Storing and Querying Dates

Elasticsearch treats dates as a special type and supports a variety of formats such as ISO 8601 (YYYY-MM-DD) and custom formats. When storing date fields, ensure the correct format is specified in the index mapping. Here’s an example mapping for a date field:

"mappings": {
  "properties": {
    "event_date": {
      "type": "date",
      "format": "yyyy-MM-dd"
    }
  }
}

Performing Date Range Queries

To filter documents by dates, you can use the range query with date fields. For instance:

{
  "query": {
    "range": {
      "event_date": {
        "gte": "2023-01-01",
        "lte": "2023-12-31"
      }
    }
  }
}

This query retrieves documents with an event_date in the year 2023.

Using Relative Dates

Elasticsearch also allows relative date filtering with keywords like now for the current timestamp:

{
  "query": {
    "range": {
      "event_date": {
        "gte": "now-30d/d", 
        "lte": "now/d"
      }
    }
  }
}

This query fetches documents from the last 30 days. The /d ensures the date range is rounded down to the nearest day.

Handling Numeric Values

Number Fields

Numeric fields in Elasticsearch include types like integer, long, float, and double. Ensure the field is correctly mapped for optimal performance:

"mappings": {
  "properties": {
    "rating": {
      "type": "float"
    }
  }
}

Performing Numeric Range Queries

Here’s an example of querying for products with a rating between 4.0 and 5.0:

{
  "query": {
    "range": {
      "rating": {
        "gte": 4.0,
        "lte": 5.0
      }
    }
  }
}

Numeric range queries are widely used for filtering metrics like product ratings, temperature readings, and other continuous values.

Handling Prices

Price Fields and Currencies

Prices are typically stored as numeric fields (e.g., float or double) in Elasticsearch. If you work with multiple currencies, you may need to include a currency field alongside the price to filter and convert values accordingly.

Price Range Query Example

Here’s an example that fetches products priced between $50 and $200:

{
  "query": {
    "range": {
      "price": {
        "gte": 50,
        "lte": 200
      }
    }
  }
}

For more complex scenarios involving discounts or tax calculations, you can use Elasticsearch’s scripting capabilities or preprocess your data during indexing.

Best Practices for Range Queries

Optimize Index Mappings Define explicit mappings for date, numeric, and price fields. Avoid using dynamic mappings for critical fields to ensure performance and accuracy.
Use Filters When Appropriate For range conditions that don't affect relevance scoring, use the filter context instead of query. Filters are faster as they don’t calculate relevance scores.

{
  "query": {
    "bool": {
      "filter": [
        {
          "range": {
            "price": {
              "gte": 50,
              "lte": 200
            }
          }
        }
      ]
    }
  }
}

Consider Data Cardinality
High-cardinality fields (fields with many unique values) like timestamps and prices can affect query performance. Ensure these fields are properly indexed.
Leverage Caching
Filters in Elasticsearch are automatically cached. Repeated range filters on high-traffic fields can benefit from this behavior.
Test and Monitor
Use Elasticsearch’s profiling tools to analyze query performance. Identify bottlenecks and optimize field mappings or queries accordingly.

Conclusion

Range queries are an essential part of Elasticsearch’s querying capabilities, enabling precise filtering of dates, numbers, and prices. By correctly defining index mappings, using filters, and adhering to best practices, you can make the most of this feature and deliver fast, accurate search results. Whether you're building an e-commerce platform, a data monitoring dashboard, or a time-series analysis tool, mastering range queries will enhance your Elasticsearch skills.