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Elastic Docs ›Elasticsearch Guide [master] ›Aggregations ›Bucket aggregations

Multi Terms aggregation

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Multi Terms aggregation

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A multi-bucket value source based aggregation where buckets are dynamically built - one per unique set of values. The multi terms aggregation is very similar to the terms aggregation, however in most cases it will be slower than the terms aggregation and will consume more memory. Therefore, if the same set of fields is constantly used, it would be more efficient to index a combined key for this fields as a separate field and use the terms aggregation on this field.

The multi_term aggregations are the most useful when you need to sort by a number of document or a metric aggregation on a composite key and get top N results. If sorting is not required and all values are expected to be retrieved using nested terms aggregation or composite aggregations will be a faster and more memory efficient solution.

Example:

resp = client.search(
    index="products",
    aggs={
        "genres_and_products": {
            "multi_terms": {
                "terms": [
                    {
                        "field": "genre"
                    },
                    {
                        "field": "product"
                    }
                ]
            }
        }
    },
)
print(resp)
response = client.search(
  index: 'products',
  body: {
    aggregations: {
      genres_and_products: {
        multi_terms: {
          terms: [
            {
              field: 'genre'
            },
            {
              field: 'product'
            }
          ]
        }
      }
    }
  }
)
puts response
const response = await client.search({
  index: "products",
  aggs: {
    genres_and_products: {
      multi_terms: {
        terms: [
          {
            field: "genre",
          },
          {
            field: "product",
          },
        ],
      },
    },
  },
});
console.log(response);
GET /products/_search
{
  "aggs": {
    "genres_and_products": {
      "multi_terms": {
        "terms": [{
          "field": "genre" 
        }, {
          "field": "product"
        }]
      }
    }
  }
}

multi_terms aggregation can work with the same field types as a terms aggregation and supports most of the terms aggregation parameters.

Response:

{
  ...
  "aggregations" : {
    "genres_and_products" : {
      "doc_count_error_upper_bound" : 0,  
      "sum_other_doc_count" : 0,          
      "buckets" : [                       
        {
          "key" : [                       
            "rock",
            "Product A"
          ],
          "key_as_string" : "rock|Product A",
          "doc_count" : 2
        },
        {
          "key" : [
            "electronic",
            "Product B"
          ],
          "key_as_string" : "electronic|Product B",
          "doc_count" : 1
        },
        {
          "key" : [
            "jazz",
            "Product B"
          ],
          "key_as_string" : "jazz|Product B",
          "doc_count" : 1
        },
        {
          "key" : [
            "rock",
            "Product B"
          ],
          "key_as_string" : "rock|Product B",
          "doc_count" : 1
        }
      ]
    }
  }
}

an upper bound of the error on the document counts for each term, see <<search-aggregations-bucket-multi-terms-aggregation-approximate-counts,below>

when there are lots of unique terms, Elasticsearch only returns the top terms; this number is the sum of the document counts for all buckets that are not part of the response

the list of the top buckets.

the keys are arrays of values ordered the same ways as expression in the terms parameter of the aggregation

By default, the multi_terms aggregation will return the buckets for the top ten terms ordered by the doc_count. One can change this default behaviour by setting the size parameter.

Aggregation Parameters

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The following parameters are supported. See terms aggregation for more detailed explanation of these parameters.

size

Optional. Defines how many term buckets should be returned out of the overall terms list. Defaults to 10.

shard_size

Optional. The higher the requested size is, the more accurate the results will be, but also, the more expensive it will be to compute the final results. The default shard_size is (size * 1.5 + 10).

show_term_doc_count_error

Optional. Calculates the doc count error on per term basis. Defaults to false

order

Optional. Specifies the order of the buckets. Defaults to the number of documents per bucket. The bucket terms value is used as a tiebreaker for buckets with the same document count.

min_doc_count

Optional. The minimal number of documents in a bucket for it to be returned. Defaults to 1.

shard_min_doc_count

Optional. The minimal number of documents in a bucket on each shard for it to be returned. Defaults to min_doc_count.

collect_mode

Optional. Specifies the strategy for data collection. The depth_first or breadth_first modes are supported. Defaults to breadth_first.

Script

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Generating the terms using a script:

resp = client.search(
    index="products",
    runtime_mappings={
        "genre.length": {
            "type": "long",
            "script": "emit(doc['genre'].value.length())"
        }
    },
    aggs={
        "genres_and_products": {
            "multi_terms": {
                "terms": [
                    {
                        "field": "genre.length"
                    },
                    {
                        "field": "product"
                    }
                ]
            }
        }
    },
)
print(resp)
response = client.search(
  index: 'products',
  body: {
    runtime_mappings: {
      'genre.length' => {
        type: 'long',
        script: "emit(doc['genre'].value.length())"
      }
    },
    aggregations: {
      genres_and_products: {
        multi_terms: {
          terms: [
            {
              field: 'genre.length'
            },
            {
              field: 'product'
            }
          ]
        }
      }
    }
  }
)
puts response
const response = await client.search({
  index: "products",
  runtime_mappings: {
    "genre.length": {
      type: "long",
      script: "emit(doc['genre'].value.length())",
    },
  },
  aggs: {
    genres_and_products: {
      multi_terms: {
        terms: [
          {
            field: "genre.length",
          },
          {
            field: "product",
          },
        ],
      },
    },
  },
});
console.log(response);
GET /products/_search
{
  "runtime_mappings": {
    "genre.length": {
      "type": "long",
      "script": "emit(doc['genre'].value.length())"
    }
  },
  "aggs": {
    "genres_and_products": {
      "multi_terms": {
        "terms": [
          {
            "field": "genre.length"
          },
          {
            "field": "product"
          }
        ]
      }
    }
  }
}

Response:

{
  ...
  "aggregations" : {
    "genres_and_products" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : [
            4,
            "Product A"
          ],
          "key_as_string" : "4|Product A",
          "doc_count" : 2
        },
        {
          "key" : [
            4,
            "Product B"
          ],
          "key_as_string" : "4|Product B",
          "doc_count" : 2
        },
        {
          "key" : [
            10,
            "Product B"
          ],
          "key_as_string" : "10|Product B",
          "doc_count" : 1
        }
      ]
    }
  }
}

Missing value

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The missing parameter defines how documents that are missing a value should be treated. By default if any of the key components are missing the entire document will be ignored but it is also possible to treat them as if they had a value by using the missing parameter.

resp = client.search(
    index="products",
    aggs={
        "genres_and_products": {
            "multi_terms": {
                "terms": [
                    {
                        "field": "genre"
                    },
                    {
                        "field": "product",
                        "missing": "Product Z"
                    }
                ]
            }
        }
    },
)
print(resp)
response = client.search(
  index: 'products',
  body: {
    aggregations: {
      genres_and_products: {
        multi_terms: {
          terms: [
            {
              field: 'genre'
            },
            {
              field: 'product',
              missing: 'Product Z'
            }
          ]
        }
      }
    }
  }
)
puts response
const response = await client.search({
  index: "products",
  aggs: {
    genres_and_products: {
      multi_terms: {
        terms: [
          {
            field: "genre",
          },
          {
            field: "product",
            missing: "Product Z",
          },
        ],
      },
    },
  },
});
console.log(response);
GET /products/_search
{
  "aggs": {
    "genres_and_products": {
      "multi_terms": {
        "terms": [
          {
            "field": "genre"
          },
          {
            "field": "product",
            "missing": "Product Z"
          }
        ]
      }
    }
  }
}

Response:

{
   ...
   "aggregations" : {
    "genres_and_products" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : [
            "rock",
            "Product A"
          ],
          "key_as_string" : "rock|Product A",
          "doc_count" : 2
        },
        {
          "key" : [
            "electronic",
            "Product B"
          ],
          "key_as_string" : "electronic|Product B",
          "doc_count" : 1
        },
        {
          "key" : [
            "electronic",
            "Product Z"
          ],
          "key_as_string" : "electronic|Product Z",  
          "doc_count" : 1
        },
        {
          "key" : [
            "jazz",
            "Product B"
          ],
          "key_as_string" : "jazz|Product B",
          "doc_count" : 1
        },
        {
          "key" : [
            "rock",
            "Product B"
          ],
          "key_as_string" : "rock|Product B",
          "doc_count" : 1
        }
      ]
    }
  }
}

Documents without a value in the product field will fall into the same bucket as documents that have the value Product Z.

Mixing field types

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When aggregating on multiple indices the type of the aggregated field may not be the same in all indices. Some types are compatible with each other (integer and long or float and double) but when the types are a mix of decimal and non-decimal number the terms aggregation will promote the non-decimal numbers to decimal numbers. This can result in a loss of precision in the bucket values.

Sub aggregation and sorting examples

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As most bucket aggregations the multi_term supports sub aggregations and ordering the buckets by metrics sub-aggregation:

resp = client.search(
    index="products",
    aggs={
        "genres_and_products": {
            "multi_terms": {
                "terms": [
                    {
                        "field": "genre"
                    },
                    {
                        "field": "product"
                    }
                ],
                "order": {
                    "total_quantity": "desc"
                }
            },
            "aggs": {
                "total_quantity": {
                    "sum": {
                        "field": "quantity"
                    }
                }
            }
        }
    },
)
print(resp)
response = client.search(
  index: 'products',
  body: {
    aggregations: {
      genres_and_products: {
        multi_terms: {
          terms: [
            {
              field: 'genre'
            },
            {
              field: 'product'
            }
          ],
          order: {
            total_quantity: 'desc'
          }
        },
        aggregations: {
          total_quantity: {
            sum: {
              field: 'quantity'
            }
          }
        }
      }
    }
  }
)
puts response
const response = await client.search({
  index: "products",
  aggs: {
    genres_and_products: {
      multi_terms: {
        terms: [
          {
            field: "genre",
          },
          {
            field: "product",
          },
        ],
        order: {
          total_quantity: "desc",
        },
      },
      aggs: {
        total_quantity: {
          sum: {
            field: "quantity",
          },
        },
      },
    },
  },
});
console.log(response);
GET /products/_search
{
  "aggs": {
    "genres_and_products": {
      "multi_terms": {
        "terms": [
          {
            "field": "genre"
          },
          {
            "field": "product"
          }
        ],
        "order": {
          "total_quantity": "desc"
        }
      },
      "aggs": {
        "total_quantity": {
          "sum": {
            "field": "quantity"
          }
        }
      }
    }
  }
}
{
  ...
  "aggregations" : {
    "genres_and_products" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : [
            "jazz",
            "Product B"
          ],
          "key_as_string" : "jazz|Product B",
          "doc_count" : 1,
          "total_quantity" : {
            "value" : 10.0
          }
        },
        {
          "key" : [
            "rock",
            "Product A"
          ],
          "key_as_string" : "rock|Product A",
          "doc_count" : 2,
          "total_quantity" : {
            "value" : 9.0
          }
        },
        {
          "key" : [
            "electronic",
            "Product B"
          ],
          "key_as_string" : "electronic|Product B",
          "doc_count" : 1,
          "total_quantity" : {
            "value" : 3.0
          }
        },
        {
          "key" : [
            "rock",
            "Product B"
          ],
          "key_as_string" : "rock|Product B",
          "doc_count" : 1,
          "total_quantity" : {
            "value" : 1.0
          }
        }
      ]
    }
  }
}
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