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Hybrid search with post-filtering

Introduced 2.13

You can perform post-filtering on hybrid search results by providing the post_filter parameter in your query.

The post_filter clause is applied after the search results have been retrieved. Post-filtering is useful for applying additional filters to the search results without impacting the scoring or the order of the results.

Post-filtering does not impact aggregation results.

To filter all subqueries during query execution instead of filtering the final results, use a common filter. For more information, see Hybrid search with pre-filtering.

Example: Faceted search with post-filtering

Post-filtering is commonly used in faceted search, in which the UI displays aggregation counts (such as brand, color, and size filters) alongside search results. Using a post_filter keeps the aggregation counts based on the full unfiltered query while filtering only the displayed hits.

Consider an index containing product documents:

{
  "name": "Nike Air Max",
  "brand": "Nike",
  "color": "Red",
  "size": 10,
  "price": 120,
  "category": "Running Shoes"
}

A user searches for “running shoes”, and the application constructs a query containing aggregations for brand, color, and size:

POST /products/_search
{
  "query": {
    "match": {
      "category": "running shoes"
    }
  },
  "aggs": {
    "brands": {
      "terms": { "field": "brand.keyword" }
    },
    "colors": {
      "terms": { "field": "color.keyword" }
    },
    "sizes": {
      "terms": { "field": "size" }
    }
  }
}

The response returns hits from all brands:

Nike Air Max
Nike Pegasus
Adidas Adizero
Puma Velocity
...

The response also returns aggregations that include counts for every brand, color, and size:

Brands:  Nike (120), Adidas (80), Puma (45)
Colors:  Black (90), White (70), Red (55)
Sizes:   8 (40), 9 (60), 10 (85)

The aggregations are typically displayed as facet filters in the UI. When a user selects a specific brand (for example, Nike) to filter results, using a pre-filter would exclude non-Nike documents before aggregations are computed, causing other brands to disappear from the facet counts.

With post_filter, the query and aggregations run on the full result set. The filter is applied only to the displayed hits:

POST /products/_search
{
  "query": {
    "match": {
      "category": "running shoes"
    }
  },
  "aggs": {
    "brands": {
      "terms": { "field": "brand.keyword" }
    },
    "colors": {
      "terms": { "field": "color.keyword" }
    }
  },
  "post_filter": {
    "term": { "brand.keyword": "Nike" }
  }
}

The hits contain only Nike products, but the aggregations still reflect the full unfiltered query:

Brands:  Nike (120), Adidas (80), Puma (45)
Colors:  Black (90), White (70), Red (55)

All brand options remain visible in the facet, allowing the user to switch brands or compare counts without removing the filter.

How post-filtering affects search results and scoring

Post-filtering can significantly change the final search results and document scores. Consider the following scenarios.

Single-query scenario

Consider a query that returns the following results:

• Query results before normalization: [d2: 5.0, d4: 3.0, d1: 2.0]
• Normalized scores: [d2: 1.0, d4: 0.33, d1: 0.0]

After applying a post-filter to the initial query results, the results are as follows:

• Post-filter matches [d2, d4]
• Resulting scores: [d2: 1.0, d4: 0.0]

Note how document d4’s score changes from 0.33 to 0.0 after applying the post-filter.

Multiple-query scenario

Consider a query with two subqueries:

• Query 1 results: [d2: 5.0, d4: 3.0, d1: 2.0]
• Query 2 results: [d1: 1.0, d5: 0.5, d4: 0.25]
• Normalized scores:
  • Query 1: [d2: 1.0, d4: 0.33, d1: 0.0]
  • Query 2: [d1: 1.0, d5: 0.33, d4: 0.0]
• Combined initial scores: [d2: 1.0, d1: 0.5, d5: 0.33, d4: 0.165]

After applying a post-filter to the initial query results, the results are as follows:

• Post-filter matches [d2, d4]
• Resulting scores:
  • Query 1: [d2: 5.0, d4: 3.0]
  • Query 2: [d4: 0.25]
• Normalized scores:
  • Query 1: [d2: 1.0, d4: 0.0]
  • Query 2: [d4: 1.0]
• Combined final scores: [d2: 1.0, d4: 0.5]

Observe that:

• Document d2’s score remains unchanged.
• Document d4’s score has changed.