Elasticsearch中关于transform的一个问题？

背景：现在有一个业务，派件业务，业务员今天去派件（扫描产生一条派件记录），派件可能会有重复派件的情况，第二天再派送（记录被更新，以最新的派件操作为准）。现在需要分业务员按天统计每天的派件数量。
es版本：7.15.1
1、创建索引：

PUT t_test_001
{
  "settings": {
    "number_of_shards": 1,
    "number_of_replicas": 1
  },
  "mappings": {
    "properties": {
      "city_id": {
        "type": "long"
      },
      "city_name": {
        "type": "keyword"
      },
      "create_time": {
        "type": "date"
      },
      "push_date": {
        "type": "date"
      },
      "update_time": {
        "type": "date"
      }
    }
  }
}

2、插入测试数据

POST /t_test_001/_bulk
{ "index": {}}
{ "order_no" : 1,"employee":"张三",  "create_time" : "2021-12-06T08:00:00.000Z", "push_date" : "2021-12-06T08:00:00.000Z", "update_time" : "2021-12-06T08:00:00.000Z"}
{ "index": {}}
{ "order_no" : 2,"employee":"张三",  "create_time" : "2021-12-06T08:00:00.000Z", "push_date" : "2021-12-06T08:00:00.000Z", "update_time" : "2021-12-06T08:00:00.000Z"}
{ "index": {}}
{ "order_no" : 3,"employee":"张三",  "create_time" : "2021-12-07T00:00:00.000Z", "push_date" : "2021-12-07T00:00:00.000Z", "update_time" : "2021-12-07T00:00:00.000Z"}
{ "index": {}}
{ "order_no" : 4,"employee":"张三",  "create_time" : "2021-12-07T00:00:00.000Z", "push_date" : "2021-12-07T00:00:00.000Z", "update_time" : "2021-12-07T00:00:00.000Z"}
{ "index": {}}
{ "order_no" : 5,"employee":"王五",  "create_time" : "2021-12-06T08:00:00.000Z", "push_date" : "2021-12-06T08:00:00.000Z", "update_time" : "2021-12-06T08:00:00.000Z"}
{ "index": {}}
{ "order_no" : 6,"employee":"王五",  "create_time" : "2021-12-06T08:00:00.000Z", "push_date" : "2021-12-06T08:00:00.000Z", "update_time" : "2021-12-06T08:00:00.000Z"}
{ "index": {}}
{ "order_no" : 7,"employee":"王五",  "create_time" : "2021-12-07T00:00:00.000Z", "push_date" : "2021-12-07T00:00:00.000Z", "update_time" : "2021-12-07T00:00:00.000Z"}
{ "index": {}}
{ "order_no" : 8,"employee":"王五",  "create_time" : "2021-12-07T00:00:00.000Z", "push_date" : "2021-12-07T00:00:00.000Z", "update_time" : "2021-12-07T00:00:00.000Z"}

3、查询一下看看

GET /t_test_001/_search
{
  "size": 10
}

结果：

{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 8,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "t_test_001",
        "_type" : "_doc",
        "_id" : "GLztkn0BDKE3xmcewwIG",
        "_score" : 1.0,
        "_source" : {
          "order_no" : 1,
          "employee" : "张三",
          "create_time" : "2021-12-06T08:00:00.000Z",
          "push_date" : "2021-12-06T08:00:00.000Z",
          "update_time" : "2021-12-06T08:00:00.000Z"
        }
      },
      {
        "_index" : "t_test_001",
        "_type" : "_doc",
        "_id" : "Gbztkn0BDKE3xmcewwIG",
        "_score" : 1.0,
        "_source" : {
          "order_no" : 2,
          "employee" : "张三",
          "create_time" : "2021-12-06T08:00:00.000Z",
          "push_date" : "2021-12-06T08:00:00.000Z",
          "update_time" : "2021-12-06T08:00:00.000Z"
        }
      },
      {
        "_index" : "t_test_001",
        "_type" : "_doc",
        "_id" : "Grztkn0BDKE3xmcewwIG",
        "_score" : 1.0,
        "_source" : {
          "order_no" : 3,
          "employee" : "张三",
          "create_time" : "2021-12-07T00:00:00.000Z",
          "push_date" : "2021-12-07T00:00:00.000Z",
          "update_time" : "2021-12-07T00:00:00.000Z"
        }
      },
      {
        "_index" : "t_test_001",
        "_type" : "_doc",
        "_id" : "G7ztkn0BDKE3xmcewwIG",
        "_score" : 1.0,
        "_source" : {
          "order_no" : 4,
          "employee" : "张三",
          "create_time" : "2021-12-07T00:00:00.000Z",
          "push_date" : "2021-12-07T00:00:00.000Z",
          "update_time" : "2021-12-07T00:00:00.000Z"
        }
      },
      {
        "_index" : "t_test_001",
        "_type" : "_doc",
        "_id" : "HLztkn0BDKE3xmcewwIG",
        "_score" : 1.0,
        "_source" : {
          "order_no" : 5,
          "employee" : "王五",
          "create_time" : "2021-12-06T08:00:00.000Z",
          "push_date" : "2021-12-06T08:00:00.000Z",
          "update_time" : "2021-12-06T08:00:00.000Z"
        }
      },
      {
        "_index" : "t_test_001",
        "_type" : "_doc",
        "_id" : "Hbztkn0BDKE3xmcewwIG",
        "_score" : 1.0,
        "_source" : {
          "order_no" : 6,
          "employee" : "王五",
          "create_time" : "2021-12-06T08:00:00.000Z",
          "push_date" : "2021-12-06T08:00:00.000Z",
          "update_time" : "2021-12-06T08:00:00.000Z"
        }
      },
      {
        "_index" : "t_test_001",
        "_type" : "_doc",
        "_id" : "Hrztkn0BDKE3xmcewwIG",
        "_score" : 1.0,
        "_source" : {
          "order_no" : 7,
          "employee" : "王五",
          "create_time" : "2021-12-07T00:00:00.000Z",
          "push_date" : "2021-12-07T00:00:00.000Z",
          "update_time" : "2021-12-07T00:00:00.000Z"
        }
      },
      {
        "_index" : "t_test_001",
        "_type" : "_doc",
        "_id" : "H7ztkn0BDKE3xmcewwIG",
        "_score" : 1.0,
        "_source" : {
          "order_no" : 8,
          "employee" : "王五",
          "create_time" : "2021-12-07T00:00:00.000Z",
          "push_date" : "2021-12-07T00:00:00.000Z",
          "update_time" : "2021-12-07T00:00:00.000Z"
        }
      }
    ]
  }
}

4、创建一个transform，将数据按天、业务员  聚合

PUT _transform/t_test_transform
{
  "id": "t_test_transform",
  "source": {
    "index": [
      "t_test_001"
    ]
  },
  "dest": {
    "index": "t_test_x"
  },
  "frequency": "60s",
  "sync": {
    "time": {
      "field": "update_time",
      "delay": "60s"
    }
  },
  "pivot": {
    "group_by": {
      "employee": {
        "terms": {
          "field": "employee"
        }
      },
      "push_date": {
        "date_histogram": {
          "field": "push_date",
          "calendar_interval": "1d"
        }
      }
    },
    "aggregations": {
      "sum_all": {
        "value_count": {
          "field": "_id"
        }
      }
    }
  }
}

5、开启transform

POST _transform/t_test_transform/_start

6、查看transform转换的索引结果

GET /t_test_x/_search
{}

结果：如图，张三2021-12-06和07号各派送两单：

7、12月7号，订单order_no = 1的单子再次被张三派送；数据被更新

POST /t_test_001/_update/GLztkn0BDKE3xmcewwIG
{
  "doc": {
    "push_date": "2021-12-07T03:27:12.000Z",
    "update_time": "2021-12-07T03:27:12.000Z"
  }
}

注意模拟操作数据的真实性，更新时间在上一个检查点之后！

8、预期transfrom转换的结果是：张三12-6号的派单统计数据由2减少为1；12-7号的派单数据从2增加到3。

9、查询transform转换的索引结果

GET /t_test_x/_search
{}

结果：张三12-6号的派单统计数据为2没有减少，不符合预期；12-7号的派单数据为3，符合预期。

10，再查询一下原始数据：

GET /t_test_001/_search
{}

11、再统计一下数据：

GET /t_test_001/_search
{
  "size": 0,
  "aggs": {
    "employee": {
      "terms": {
        "field": "employee"
      },
      "aggs": {
        "push_date": {
          "date_histogram": {
            "field": "push_date",
            "calendar_interval": "1d"
          }
        }
      }
    }
  }
}

结果很显然：张三 12-06号配送量为1，12-07号配送量为3！！！而transform统计的结果，此时就错了！！！这个怎么理解呢？是他es的transform不支持这种场景数据变化的聚合，还是说这是一个bug呢？我理解，可能是因为考虑到性能的原因，es的transform在这种场景下是有这种问题的。

若有错误之处，望大家指正。谢谢。