Aggregation with terms and cardinality deliver different results


(Alexander Zerr) #1

I have big trouble with cardinality and term aggregation. Different aggregation over my index with 2700k entries deliver me diverse count/sum. Is it possible to get the correct count from ES like MySql or is ES not the best store to handle my data.

My ES Version is 6.0.1

This is my query with some aggregation, where i use different techniques to aggregate my dataset

Unique sessions

  1. cardinality = 44689
  2. terms + script > sum = 44678

Day histogram with unique sessions

  1. date_histogram > terms + script > sum = 44724
  2. date_histogram > cardinality = 44723

I don't known where is my mistake and why ES show me different result.

GET campaign-1/_search?filter_path=hits.total,aggregations.dayPerformance.buckets.dayCount,aggregations.dayPerformance.buckets.key_as_string,aggregations.uniqueSession,aggregations.totalCount,aggregations.totalTermCount,aggregations.dayPerformance.buckets.uniqueCount,aggregations.totalCount2
{
  "size": 0,
  "query": {
    "bool": {
      "must": [
        {
          "match_all": {}
        },
        {
          "term": {
            "payout.term": 1
          }
        },
        {
          "range": {
            "createdAt": {
              "gte": "1524787200000",
              "lte": "1525564740000",
              "format": "epoch_millis"
            }
          }
        },
        {
          "term": {
            "environment": "LIVE"
          }
        }
      ],
      "must_not": [
        {
          "term": {
            "event": ""
          }
        },
        {
          "term": {
            "session": ""
          }
        },
        {
          "term": {
            "event": "View"
          }
        }
      ]
    }
  },
  "aggs": {
    "dayPerformance": {
      "date_histogram": {
        "field": "createdAt",
        "interval": "day"
      },
      "aggs": {
        "uniqueInteractions": {
          "terms": {
            "field": "session",
            "size": 100000,
            "shard_size": 1000
          },
          "aggs": {
            "forCount": {
              "bucket_script": {
                "buckets_path": {
                  "count": "_count"
                },
                "script": "1"
              }
            }
          }
        },
        "dayCount": {
          "sum_bucket": {
            "buckets_path": "uniqueInteractions>forCount"
          }
        },
        "uniqueCount": {
          "cardinality": {
            "field": "session",
            "precision_threshold": 40000
          }
        }
      }
    },
    "uniqueSession": {
      "cardinality": {
        "field": "session",
        "precision_threshold": 40000
      }
    },
    "totalCount": {
      "sum_bucket": {
        "buckets_path": "dayPerformance>dayCount"
      }
    },
    "totalCount2": {
      "sum_bucket": {
        "buckets_path": "dayPerformance>uniqueCount"
      }
    },
    "uniqueSessionTerm": {
      "terms": {
        "field": "session",
        "size": 10000000,
        "shard_size": 15
      },
      "aggs": {
        "forCount": {
          "bucket_script": {
            "buckets_path": {
              "count": "_count"
            },
            "script": "1"
          }
        }
      }
    },
    "totalTermCount": {
      "sum_bucket": {
        "buckets_path": "uniqueSessionTerm>forCount"
      }
    }
  }
}

And this is my Response:

{
  "hits": {
    "total": 55835
  },
  "aggregations": {
    "uniqueSession": {
      "value": 44689
    },
    "dayPerformance": {
      "buckets": [
        {
          "key_as_string": "2018-04-27T00:00:00.000Z",
          "uniqueCount": {
            "value": 4387
          },
          "dayCount": {
            "value": 4387
          }
        },
        {
          "key_as_string": "2018-04-28T00:00:00.000Z",
          "uniqueCount": {
            "value": 14
          },
          "dayCount": {
            "value": 14
          }
        },
        {
          "key_as_string": "2018-04-29T00:00:00.000Z",
          "uniqueCount": {
            "value": 850
          },
          "dayCount": {
            "value": 850
          }
        },
        {
          "key_as_string": "2018-04-30T00:00:00.000Z",
          "uniqueCount": {
            "value": 1839
          },
          "dayCount": {
            "value": 1840
          }
        },
        {
          "key_as_string": "2018-05-01T00:00:00.000Z",
          "uniqueCount": {
            "value": 8487
          },
          "dayCount": {
            "value": 8487
          }
        },
        {
          "key_as_string": "2018-05-02T00:00:00.000Z",
          "uniqueCount": {
            "value": 5875
          },
          "dayCount": {
            "value": 5874
          }
        },
        {
          "key_as_string": "2018-05-03T00:00:00.000Z",
          "uniqueCount": {
            "value": 10923
          },
          "dayCount": {
            "value": 10925
          }
        },
        {
          "key_as_string": "2018-05-04T00:00:00.000Z",
          "uniqueCount": {
            "value": 11473
          },
          "dayCount": {
            "value": 11472
          }
        },
        {
          "key_as_string": "2018-05-05T00:00:00.000Z",
          "uniqueCount": {
            "value": 875
          },
          "dayCount": {
            "value": 875
          }
        }
      ]
    },
    "totalCount": {
      "value": 44724
    },
    "totalCount2": {
      "value": 44723
    },
    "totalTermCount": {
      "value": 44678
    }
  }
}

(Mark Harwood) #2

See FAB principle which is one of those "pick 2 of 3" conundrums. In a Big distributed system you need to pick between completely Accurate or Fast responses. Aggregations are tuned for FB but if you really need complete accuracy there are ways of breaking up single requests into many slower calls. It's always going to involve a trade-off whatever technology you use.


Unique count on a term more than documents? Is that possible?
(system) #3

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