Search

Match All Query

Retrieve all documents from an index.

{
  "match_all": {}
}

Match Query

Search for documents containing a specific term across fields.

{
  "match": {
    "field_name": "search_term"
  }
}

Multi-Match Query

Search for a term across multiple fields with customizable weights.

{
  "multi_match": {
    "query": "search_term",
    "fields": ["field1", "field2"]
  }
}

Bool Query

Combine various query clauses using boolean logic.

{
  "bool": {
    "must": [],
    "should": [],
    "must_not": [],
    "filter": []
  }
}

Term Query

Match documents containing an exact term in a field.

Note: Don’t use term level queries on text fields

  • Use keyword mapping instead of text mapping. Term level queries are not analyzed and are therefore used for exact matching.
  • Term level queries are used for exact matching(filtering)
  • Term level queries are not analyzed
    • The search value is used exactly as is for inverted index lookups
    • Searches are therefore case sensitive
    • The entire terms must macth
      • “Nike” doesn’t match “Nike, Inc.”
      • A quesry for “nike” workds fine, but “Nike” doesn’t match anything
  • Can be used with multiple data types
    • Don’t use them for text data type
{
  "term": {
    "field_name": "exact_term"
  }
}

Examples

  1. Keywords
GET /products/_search
{
  "query": {
    "term": {
      "tags.keyword": "Vegtable"
    }
  }
}

GET /products/_search
{
  "query": {
    "term": {
      "tags.keyword": {
        "value": "vegtable",
        "case_insensitive": true
      }
    }
  }
}
  1. Booleans
GET /products/_search
{
  "query": {
    "term": {
      "is_active": true
    }
  }
}
  1. Numbers
GET /products/_search
{
  "query": {
    "term": {
      "is_stock": 1
    }
  }
}
  1. Dates
GET /products/_search
{
  "query": {
    "term": {
      "created": "2022/08/06"
    }
  }
}
  1. Timestamps
GET /products/_search
{
  "query": {
    "term": {
      "created": "2022/08/06 19:42:40
    }
  }
}

Terms Query

Search for documents with a field matching multiple values.

{
  "terms": {
    "field_name": ["term1", "term2"]
  }
}

Examples

  1. This query matches products tagged as either “Soup” or “Milk” or both.
GET /products/_search
{
  "query": {
    {
      "terms": {
        "tags.keyword": ["Soup", "Milk"]
      }
    }
  }
}

Range Query

Filter documents within a specified numeric or date range.

{
  "range": {
    "field_name": {
      "gte": "min_value",
      "lte": "max_value"
    }
  }
}

Prefix Query

Find documents with a field starting with a specific prefix.

{
  "prefix": {
    "field_name": "prefix_term"
  }
}

Wildcard Query

Search for documents with fields matching a wildcard pattern.

{
  "wildcard": {
    "field_name": "wildcard_term"
  }
}

Fuzzy Query

Retrieve documents with terms similar to a given term.

{
  "fuzzy": {
    "field_name": {
      "value": "fuzzy_term",
      "fuzziness": "auto"
    }
  }
}

Match Phrase Query

Locate documents containing an exact phrase.

{
  "match_phrase": {
    "field_name": "exact_phrase"
  }
}

Match Phrase Prefix Query

Find documents with a phrase prefix.

{
  "match_phrase_prefix": {
    "field_name": "prefix_of_phrase"
  }
}

Exists Query

Filter documents with a specific field present.

{
  "exists": {
    "field": "field_name"
  }
}

Missing Query

Filter documents with a specific field absent.

{
  "bool": {
    "must_not": {
      "exists": {
        "field": "field_name"
      }
    }
  }
}

Script Query

Use custom scripts to filter documents.

{
  "script": {
    "script": {
      "source": "doc['field_name'].value > threshold"
    }
  }
}

Nested Query

Search within nested objects within documents.

{
  "nested": {
    "path": "nested_field",
    "query": {
      "match": {
        "nested_field.field_name": "value"
      }
    }
  }
}

Regexp Query

Find documents with fields matching a regular expression.

{
  "regexp": {
    "field_name": "regex_pattern"
  }
}

Common Terms Query

Search with stop words using controlled term frequency.

{
  "common": {
    "field_name": {
      "query": "search_term",
      "cutoff_frequency": 0.001
    }
  }
}

GeoShape Query

Search for documents with shapes within a specified geometry.

{
  "geo_shape": {
    "field_name": {
      "shape": {
        "type": "polygon",
        "coordinates": [[ [lon1, lat1], [lon2, lat2], ... ]]
      },
      "relation": "within"
    }
  }
}

GeoDistance Query

Filter documents within a specified distance from a point.

{
  "geo_distance": {
    "distance": "10km",
    "field_name": {
      "lat": lat_value,
      "lon": lon_value
    }
  }
}

GeoBoundingBox Query

Filter documents within a specified bounding box.

{
  "geo_bounding_box": {
    "field_name": {
      "top_left": {
        "lat": lat1,
        "lon": lon1
      },
      "bottom_right": {
        "lat": lat2,
        "lon": lon2
      }
    }
  }
}

More Like This Query

Retrieve documents similar to a given text.

{
  "more_like_this": {
    "fields": ["field1", "field2"],
    "like": ["text to match"],
    "min_term_freq": 1,
    "min_doc_freq": 1
  }
}

Percolate Query

Match documents against stored queries.

{
  "percolate": {
    "field": "query_field",
    "document": {
      "field1": "value1",
      "field2": "value2"
    }
  }
}

Has Parent Query

Search for child documents based on parent’s query.

{
  "has_parent": {
    "parent_type": "parent_type",
    "query": {
      "match": {
        "field_name": "value"
      }
    }
  }
}

Has Child Query

Search for parent documents based on child’s query.

{
  "has_child": {
    "type": "child_type",
    "query": {
      "match": {
        "field_name": "value"
      }
    }
  }
}

Aggregations

Compute summary statistics and analytics on data.

{
  "aggs": {
    "agg_name": {
      "aggregation_type": {
        "field": "field_name"
      }
    }
  }
}

Function Score Query

Modify query scores using functions.

{
  "function_score": {
    "query": {
      "match": {
        "field_name": "search_term"
      }
    },
    "functions": [
      {
        "gauss": {
          "field_name": {
            "origin": "center_point",
            "scale": "scale_value"
          }
        }
      }
    ],
    "score_mode": "sum"
  }
}

Terms Set Query

Match documents based on a set of terms.

{
  "terms_set": {
    "field_name": {
      "terms": ["term1", "term2"],
      "minimum_should_match_field": "minimum_should_match_field"
    }
  }
}

Parent ID Query

Retrieve parent documents based on child’s parent ID.

{
  "parent_id": {
    "type": "parent_type",
    "id": "parent_document_id"
  }
}

Rank Feature Query

Modify scores based on rank feature values.

{
  "rank_feature": {
    "field": "field_name",
    "saturation": {
      "pivot": 10
    }
  }
}

Field Value Factor Function

Modify scores based on field values using factors.

{
  "function_score": {
    "query": {
      "match_all": {}
    },
    "functions": [
      {
        "field_value_factor": {
          "field": "field_name",
          "factor": 1.2,
          "modifier": "log1p"
        }
      }
    ]
  }
}

Span Near Query

Search for terms within a specified proximity.

{
  "span_near": {
    "clauses": [
      { "span_term": { "field_name": "term1" } },
      { "span_term": { "field_name": "term2" } }
    ],
    "slop": 2,
    "in_order": true
  }
}

Boosting Query

Combine positive and negative queries with a boost factor.

{
  "boosting": {
    "positive": {
      "match": { "field_name": "positive_term" }
    },
    "negative": {
      "match": { "field_name": "negative_term" }
    },
    "negative_boost": 0.5
  }
}
Edit this page on GitHub
Metric →