How to calculate total for each token in Elasticsearch












1















I have a request into Elastic



{  
"query":{
"bool":{
"must":[
{
"query_string":{
"query":"something1 OR something2 OR something3",
"default_operator":"OR"
}
}
],
"filter":{
"range":{
"time":{
"gte":date
}
}
}
}
}
}


I wanna calculate count for each token in all documents using elastic search in one request, for example:



something1: 26 documents
something2: 12 documents
something3: 1 documents









share|improve this question





























    1















    I have a request into Elastic



    {  
    "query":{
    "bool":{
    "must":[
    {
    "query_string":{
    "query":"something1 OR something2 OR something3",
    "default_operator":"OR"
    }
    }
    ],
    "filter":{
    "range":{
    "time":{
    "gte":date
    }
    }
    }
    }
    }
    }


    I wanna calculate count for each token in all documents using elastic search in one request, for example:



    something1: 26 documents
    something2: 12 documents
    something3: 1 documents









    share|improve this question



























      1












      1








      1








      I have a request into Elastic



      {  
      "query":{
      "bool":{
      "must":[
      {
      "query_string":{
      "query":"something1 OR something2 OR something3",
      "default_operator":"OR"
      }
      }
      ],
      "filter":{
      "range":{
      "time":{
      "gte":date
      }
      }
      }
      }
      }
      }


      I wanna calculate count for each token in all documents using elastic search in one request, for example:



      something1: 26 documents
      something2: 12 documents
      something3: 1 documents









      share|improve this question
















      I have a request into Elastic



      {  
      "query":{
      "bool":{
      "must":[
      {
      "query_string":{
      "query":"something1 OR something2 OR something3",
      "default_operator":"OR"
      }
      }
      ],
      "filter":{
      "range":{
      "time":{
      "gte":date
      }
      }
      }
      }
      }
      }


      I wanna calculate count for each token in all documents using elastic search in one request, for example:



      something1: 26 documents
      something2: 12 documents
      something3: 1 documents






      python-3.x elasticsearch elasticsearch-py






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Nov 20 '18 at 12:55









      Kamal

      1,6931920




      1,6931920










      asked Nov 20 '18 at 10:21









      Саша КоровійСаша Коровій

      204




      204
























          3 Answers
          3






          active

          oldest

          votes


















          1














          Assuming that the tokens are not akin to enumerations (i.e. constrained set of specific values, like state names, which would make a terms aggregation your best bet with the right mapping), I think the closest thing to what you want would be to use filters aggregation:



          POST your-index/_search
          {
          "query":{
          "bool":{
          "must":[
          {
          "query_string":{
          "query":"something1 OR something2 OR something3",
          "default_operator":"OR"
          }
          }
          ],
          "filter":{
          "range":{
          "time":{
          "gte":date
          }
          }
          }
          }
          },
          "aggs": {
          "token_doc_counts": {
          "filters" : {
          "filters" : {
          "something1" : {
          "bool": {
          "must": { "query_string" : { "query" : "something1" } },
          "filter": { "range": { "time": { "gte": date } } }
          }
          },
          "something2" : {
          "bool": {
          "must": { "query_string" : { "query" : "something2" } },
          "filter": { "range": { "time": { "gte": date } } }
          }
          },
          "something3" : {
          "bool": {
          "must": { "query_string" : { "query" : "something3" } },
          "filter": { "range": { "time": { "gte": date } } }
          }
          }
          }
          }
          }
          }
          }


          The response would look something like:



          {
          "took": 9,
          "timed_out": false,
          "_shards": ...,
          "hits": ...,
          "aggregations": {
          "token_doc_counts": {
          "buckets": {
          "something1": {
          "doc_count": 1
          },
          "something2": {
          "doc_count": 2
          },
          "something3": {
          "doc_count": 3
          }
          }
          }
          }
          }





          share|improve this answer
























          • Thanks! it`s work

            – Саша Коровій
            Nov 21 '18 at 11:01



















          0














          You can split your query into filters aggregation of three filters. For reference look here: https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-filters-aggregation.html






          share|improve this answer































            0














            What you would need to do, is to create a Copy_To field and have the mapping as shown below.



            Depending on the fields that your query_string queries, you need to include some or all of the fields with copy_to field.



            By default query_string searches all the fields, so you may need to specify copy_to for all the fields as shown in below mapping, where for sake of simplicity, I've created only three fields, title, field_2 and a third field content which would act as copied to field.



            Mapping



            PUT <your_index_name>
            {
            "mappings": {
            "mydocs": {
            "properties": {
            "title": {
            "type": "text",
            "copy_to": "content"
            },
            "field_2": {
            "type": "text",
            "copy_to": "content"
            },
            "content": {
            "type": "text",
            "fielddata": true
            }
            }
            }
            }
            }


            Sample Documents



            POST <your_index_name>/mydocs/1
            {
            "title": "something1",
            "field_2": "something2"
            }

            POST <your_index_name>/mydocs/2
            {
            "title": "something2",
            "field_2": "something3"
            }


            Query:



            You'd get the required document counts for the each and every token using the below aggregation query and I've made use of Terms Aggregation:



            POST <your_index_name>/_search
            {
            "size": 0,
            "query": {
            "query_string": {
            "query": "something1 OR something2 OR something3"
            }
            },
            "aggs": {
            "myaggs": {
            "terms": {
            "field": "content",
            "include" : ["something1","something2","something3"]
            }
            }
            }
            }


            Query Response:



            {
            "took": 7,
            "timed_out": false,
            "_shards": {
            "total": 5,
            "successful": 5,
            "skipped": 0,
            "failed": 0
            },
            "hits": {
            "total": 2,
            "max_score": 0,
            "hits":
            },
            "aggregations": {
            "myaggs": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
            {
            "key": "something2",
            "doc_count": 2
            },
            {
            "key": "something1",
            "doc_count": 1
            },
            {
            "key": "something3",
            "doc_count": 1
            }
            ]
            }
            }
            }


            Let me know if it helps!






            share|improve this answer























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              3 Answers
              3






              active

              oldest

              votes








              3 Answers
              3






              active

              oldest

              votes









              active

              oldest

              votes






              active

              oldest

              votes









              1














              Assuming that the tokens are not akin to enumerations (i.e. constrained set of specific values, like state names, which would make a terms aggregation your best bet with the right mapping), I think the closest thing to what you want would be to use filters aggregation:



              POST your-index/_search
              {
              "query":{
              "bool":{
              "must":[
              {
              "query_string":{
              "query":"something1 OR something2 OR something3",
              "default_operator":"OR"
              }
              }
              ],
              "filter":{
              "range":{
              "time":{
              "gte":date
              }
              }
              }
              }
              },
              "aggs": {
              "token_doc_counts": {
              "filters" : {
              "filters" : {
              "something1" : {
              "bool": {
              "must": { "query_string" : { "query" : "something1" } },
              "filter": { "range": { "time": { "gte": date } } }
              }
              },
              "something2" : {
              "bool": {
              "must": { "query_string" : { "query" : "something2" } },
              "filter": { "range": { "time": { "gte": date } } }
              }
              },
              "something3" : {
              "bool": {
              "must": { "query_string" : { "query" : "something3" } },
              "filter": { "range": { "time": { "gte": date } } }
              }
              }
              }
              }
              }
              }
              }


              The response would look something like:



              {
              "took": 9,
              "timed_out": false,
              "_shards": ...,
              "hits": ...,
              "aggregations": {
              "token_doc_counts": {
              "buckets": {
              "something1": {
              "doc_count": 1
              },
              "something2": {
              "doc_count": 2
              },
              "something3": {
              "doc_count": 3
              }
              }
              }
              }
              }





              share|improve this answer
























              • Thanks! it`s work

                – Саша Коровій
                Nov 21 '18 at 11:01
















              1














              Assuming that the tokens are not akin to enumerations (i.e. constrained set of specific values, like state names, which would make a terms aggregation your best bet with the right mapping), I think the closest thing to what you want would be to use filters aggregation:



              POST your-index/_search
              {
              "query":{
              "bool":{
              "must":[
              {
              "query_string":{
              "query":"something1 OR something2 OR something3",
              "default_operator":"OR"
              }
              }
              ],
              "filter":{
              "range":{
              "time":{
              "gte":date
              }
              }
              }
              }
              },
              "aggs": {
              "token_doc_counts": {
              "filters" : {
              "filters" : {
              "something1" : {
              "bool": {
              "must": { "query_string" : { "query" : "something1" } },
              "filter": { "range": { "time": { "gte": date } } }
              }
              },
              "something2" : {
              "bool": {
              "must": { "query_string" : { "query" : "something2" } },
              "filter": { "range": { "time": { "gte": date } } }
              }
              },
              "something3" : {
              "bool": {
              "must": { "query_string" : { "query" : "something3" } },
              "filter": { "range": { "time": { "gte": date } } }
              }
              }
              }
              }
              }
              }
              }


              The response would look something like:



              {
              "took": 9,
              "timed_out": false,
              "_shards": ...,
              "hits": ...,
              "aggregations": {
              "token_doc_counts": {
              "buckets": {
              "something1": {
              "doc_count": 1
              },
              "something2": {
              "doc_count": 2
              },
              "something3": {
              "doc_count": 3
              }
              }
              }
              }
              }





              share|improve this answer
























              • Thanks! it`s work

                – Саша Коровій
                Nov 21 '18 at 11:01














              1












              1








              1







              Assuming that the tokens are not akin to enumerations (i.e. constrained set of specific values, like state names, which would make a terms aggregation your best bet with the right mapping), I think the closest thing to what you want would be to use filters aggregation:



              POST your-index/_search
              {
              "query":{
              "bool":{
              "must":[
              {
              "query_string":{
              "query":"something1 OR something2 OR something3",
              "default_operator":"OR"
              }
              }
              ],
              "filter":{
              "range":{
              "time":{
              "gte":date
              }
              }
              }
              }
              },
              "aggs": {
              "token_doc_counts": {
              "filters" : {
              "filters" : {
              "something1" : {
              "bool": {
              "must": { "query_string" : { "query" : "something1" } },
              "filter": { "range": { "time": { "gte": date } } }
              }
              },
              "something2" : {
              "bool": {
              "must": { "query_string" : { "query" : "something2" } },
              "filter": { "range": { "time": { "gte": date } } }
              }
              },
              "something3" : {
              "bool": {
              "must": { "query_string" : { "query" : "something3" } },
              "filter": { "range": { "time": { "gte": date } } }
              }
              }
              }
              }
              }
              }
              }


              The response would look something like:



              {
              "took": 9,
              "timed_out": false,
              "_shards": ...,
              "hits": ...,
              "aggregations": {
              "token_doc_counts": {
              "buckets": {
              "something1": {
              "doc_count": 1
              },
              "something2": {
              "doc_count": 2
              },
              "something3": {
              "doc_count": 3
              }
              }
              }
              }
              }





              share|improve this answer













              Assuming that the tokens are not akin to enumerations (i.e. constrained set of specific values, like state names, which would make a terms aggregation your best bet with the right mapping), I think the closest thing to what you want would be to use filters aggregation:



              POST your-index/_search
              {
              "query":{
              "bool":{
              "must":[
              {
              "query_string":{
              "query":"something1 OR something2 OR something3",
              "default_operator":"OR"
              }
              }
              ],
              "filter":{
              "range":{
              "time":{
              "gte":date
              }
              }
              }
              }
              },
              "aggs": {
              "token_doc_counts": {
              "filters" : {
              "filters" : {
              "something1" : {
              "bool": {
              "must": { "query_string" : { "query" : "something1" } },
              "filter": { "range": { "time": { "gte": date } } }
              }
              },
              "something2" : {
              "bool": {
              "must": { "query_string" : { "query" : "something2" } },
              "filter": { "range": { "time": { "gte": date } } }
              }
              },
              "something3" : {
              "bool": {
              "must": { "query_string" : { "query" : "something3" } },
              "filter": { "range": { "time": { "gte": date } } }
              }
              }
              }
              }
              }
              }
              }


              The response would look something like:



              {
              "took": 9,
              "timed_out": false,
              "_shards": ...,
              "hits": ...,
              "aggregations": {
              "token_doc_counts": {
              "buckets": {
              "something1": {
              "doc_count": 1
              },
              "something2": {
              "doc_count": 2
              },
              "something3": {
              "doc_count": 3
              }
              }
              }
              }
              }






              share|improve this answer












              share|improve this answer



              share|improve this answer










              answered Nov 20 '18 at 22:22









              mike bmike b

              1563




              1563













              • Thanks! it`s work

                – Саша Коровій
                Nov 21 '18 at 11:01



















              • Thanks! it`s work

                – Саша Коровій
                Nov 21 '18 at 11:01

















              Thanks! it`s work

              – Саша Коровій
              Nov 21 '18 at 11:01





              Thanks! it`s work

              – Саша Коровій
              Nov 21 '18 at 11:01













              0














              You can split your query into filters aggregation of three filters. For reference look here: https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-filters-aggregation.html






              share|improve this answer




























                0














                You can split your query into filters aggregation of three filters. For reference look here: https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-filters-aggregation.html






                share|improve this answer


























                  0












                  0








                  0







                  You can split your query into filters aggregation of three filters. For reference look here: https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-filters-aggregation.html






                  share|improve this answer













                  You can split your query into filters aggregation of three filters. For reference look here: https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-filters-aggregation.html







                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered Nov 20 '18 at 11:01









                  Nishant SainiNishant Saini

                  1,5741018




                  1,5741018























                      0














                      What you would need to do, is to create a Copy_To field and have the mapping as shown below.



                      Depending on the fields that your query_string queries, you need to include some or all of the fields with copy_to field.



                      By default query_string searches all the fields, so you may need to specify copy_to for all the fields as shown in below mapping, where for sake of simplicity, I've created only three fields, title, field_2 and a third field content which would act as copied to field.



                      Mapping



                      PUT <your_index_name>
                      {
                      "mappings": {
                      "mydocs": {
                      "properties": {
                      "title": {
                      "type": "text",
                      "copy_to": "content"
                      },
                      "field_2": {
                      "type": "text",
                      "copy_to": "content"
                      },
                      "content": {
                      "type": "text",
                      "fielddata": true
                      }
                      }
                      }
                      }
                      }


                      Sample Documents



                      POST <your_index_name>/mydocs/1
                      {
                      "title": "something1",
                      "field_2": "something2"
                      }

                      POST <your_index_name>/mydocs/2
                      {
                      "title": "something2",
                      "field_2": "something3"
                      }


                      Query:



                      You'd get the required document counts for the each and every token using the below aggregation query and I've made use of Terms Aggregation:



                      POST <your_index_name>/_search
                      {
                      "size": 0,
                      "query": {
                      "query_string": {
                      "query": "something1 OR something2 OR something3"
                      }
                      },
                      "aggs": {
                      "myaggs": {
                      "terms": {
                      "field": "content",
                      "include" : ["something1","something2","something3"]
                      }
                      }
                      }
                      }


                      Query Response:



                      {
                      "took": 7,
                      "timed_out": false,
                      "_shards": {
                      "total": 5,
                      "successful": 5,
                      "skipped": 0,
                      "failed": 0
                      },
                      "hits": {
                      "total": 2,
                      "max_score": 0,
                      "hits":
                      },
                      "aggregations": {
                      "myaggs": {
                      "doc_count_error_upper_bound": 0,
                      "sum_other_doc_count": 0,
                      "buckets": [
                      {
                      "key": "something2",
                      "doc_count": 2
                      },
                      {
                      "key": "something1",
                      "doc_count": 1
                      },
                      {
                      "key": "something3",
                      "doc_count": 1
                      }
                      ]
                      }
                      }
                      }


                      Let me know if it helps!






                      share|improve this answer




























                        0














                        What you would need to do, is to create a Copy_To field and have the mapping as shown below.



                        Depending on the fields that your query_string queries, you need to include some or all of the fields with copy_to field.



                        By default query_string searches all the fields, so you may need to specify copy_to for all the fields as shown in below mapping, where for sake of simplicity, I've created only three fields, title, field_2 and a third field content which would act as copied to field.



                        Mapping



                        PUT <your_index_name>
                        {
                        "mappings": {
                        "mydocs": {
                        "properties": {
                        "title": {
                        "type": "text",
                        "copy_to": "content"
                        },
                        "field_2": {
                        "type": "text",
                        "copy_to": "content"
                        },
                        "content": {
                        "type": "text",
                        "fielddata": true
                        }
                        }
                        }
                        }
                        }


                        Sample Documents



                        POST <your_index_name>/mydocs/1
                        {
                        "title": "something1",
                        "field_2": "something2"
                        }

                        POST <your_index_name>/mydocs/2
                        {
                        "title": "something2",
                        "field_2": "something3"
                        }


                        Query:



                        You'd get the required document counts for the each and every token using the below aggregation query and I've made use of Terms Aggregation:



                        POST <your_index_name>/_search
                        {
                        "size": 0,
                        "query": {
                        "query_string": {
                        "query": "something1 OR something2 OR something3"
                        }
                        },
                        "aggs": {
                        "myaggs": {
                        "terms": {
                        "field": "content",
                        "include" : ["something1","something2","something3"]
                        }
                        }
                        }
                        }


                        Query Response:



                        {
                        "took": 7,
                        "timed_out": false,
                        "_shards": {
                        "total": 5,
                        "successful": 5,
                        "skipped": 0,
                        "failed": 0
                        },
                        "hits": {
                        "total": 2,
                        "max_score": 0,
                        "hits":
                        },
                        "aggregations": {
                        "myaggs": {
                        "doc_count_error_upper_bound": 0,
                        "sum_other_doc_count": 0,
                        "buckets": [
                        {
                        "key": "something2",
                        "doc_count": 2
                        },
                        {
                        "key": "something1",
                        "doc_count": 1
                        },
                        {
                        "key": "something3",
                        "doc_count": 1
                        }
                        ]
                        }
                        }
                        }


                        Let me know if it helps!






                        share|improve this answer


























                          0












                          0








                          0







                          What you would need to do, is to create a Copy_To field and have the mapping as shown below.



                          Depending on the fields that your query_string queries, you need to include some or all of the fields with copy_to field.



                          By default query_string searches all the fields, so you may need to specify copy_to for all the fields as shown in below mapping, where for sake of simplicity, I've created only three fields, title, field_2 and a third field content which would act as copied to field.



                          Mapping



                          PUT <your_index_name>
                          {
                          "mappings": {
                          "mydocs": {
                          "properties": {
                          "title": {
                          "type": "text",
                          "copy_to": "content"
                          },
                          "field_2": {
                          "type": "text",
                          "copy_to": "content"
                          },
                          "content": {
                          "type": "text",
                          "fielddata": true
                          }
                          }
                          }
                          }
                          }


                          Sample Documents



                          POST <your_index_name>/mydocs/1
                          {
                          "title": "something1",
                          "field_2": "something2"
                          }

                          POST <your_index_name>/mydocs/2
                          {
                          "title": "something2",
                          "field_2": "something3"
                          }


                          Query:



                          You'd get the required document counts for the each and every token using the below aggregation query and I've made use of Terms Aggregation:



                          POST <your_index_name>/_search
                          {
                          "size": 0,
                          "query": {
                          "query_string": {
                          "query": "something1 OR something2 OR something3"
                          }
                          },
                          "aggs": {
                          "myaggs": {
                          "terms": {
                          "field": "content",
                          "include" : ["something1","something2","something3"]
                          }
                          }
                          }
                          }


                          Query Response:



                          {
                          "took": 7,
                          "timed_out": false,
                          "_shards": {
                          "total": 5,
                          "successful": 5,
                          "skipped": 0,
                          "failed": 0
                          },
                          "hits": {
                          "total": 2,
                          "max_score": 0,
                          "hits":
                          },
                          "aggregations": {
                          "myaggs": {
                          "doc_count_error_upper_bound": 0,
                          "sum_other_doc_count": 0,
                          "buckets": [
                          {
                          "key": "something2",
                          "doc_count": 2
                          },
                          {
                          "key": "something1",
                          "doc_count": 1
                          },
                          {
                          "key": "something3",
                          "doc_count": 1
                          }
                          ]
                          }
                          }
                          }


                          Let me know if it helps!






                          share|improve this answer













                          What you would need to do, is to create a Copy_To field and have the mapping as shown below.



                          Depending on the fields that your query_string queries, you need to include some or all of the fields with copy_to field.



                          By default query_string searches all the fields, so you may need to specify copy_to for all the fields as shown in below mapping, where for sake of simplicity, I've created only three fields, title, field_2 and a third field content which would act as copied to field.



                          Mapping



                          PUT <your_index_name>
                          {
                          "mappings": {
                          "mydocs": {
                          "properties": {
                          "title": {
                          "type": "text",
                          "copy_to": "content"
                          },
                          "field_2": {
                          "type": "text",
                          "copy_to": "content"
                          },
                          "content": {
                          "type": "text",
                          "fielddata": true
                          }
                          }
                          }
                          }
                          }


                          Sample Documents



                          POST <your_index_name>/mydocs/1
                          {
                          "title": "something1",
                          "field_2": "something2"
                          }

                          POST <your_index_name>/mydocs/2
                          {
                          "title": "something2",
                          "field_2": "something3"
                          }


                          Query:



                          You'd get the required document counts for the each and every token using the below aggregation query and I've made use of Terms Aggregation:



                          POST <your_index_name>/_search
                          {
                          "size": 0,
                          "query": {
                          "query_string": {
                          "query": "something1 OR something2 OR something3"
                          }
                          },
                          "aggs": {
                          "myaggs": {
                          "terms": {
                          "field": "content",
                          "include" : ["something1","something2","something3"]
                          }
                          }
                          }
                          }


                          Query Response:



                          {
                          "took": 7,
                          "timed_out": false,
                          "_shards": {
                          "total": 5,
                          "successful": 5,
                          "skipped": 0,
                          "failed": 0
                          },
                          "hits": {
                          "total": 2,
                          "max_score": 0,
                          "hits":
                          },
                          "aggregations": {
                          "myaggs": {
                          "doc_count_error_upper_bound": 0,
                          "sum_other_doc_count": 0,
                          "buckets": [
                          {
                          "key": "something2",
                          "doc_count": 2
                          },
                          {
                          "key": "something1",
                          "doc_count": 1
                          },
                          {
                          "key": "something3",
                          "doc_count": 1
                          }
                          ]
                          }
                          }
                          }


                          Let me know if it helps!







                          share|improve this answer












                          share|improve this answer



                          share|improve this answer










                          answered Nov 20 '18 at 15:50









                          KamalKamal

                          1,6931920




                          1,6931920






























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