我们正在尝试为其中一个字符串数据类型的列进行聚合（求和）。我正在使用下面的查询。我有两个Druid服务器在那里有相同的数据。一个是暗示，另一个是从Ambari安装。在暗指它是工作的，我们得到了预期的输出，但从DruidAmbari我得到零作为以下查询的输出。下面是我的输入Kafka规范（我的AmbariDruid服务器和暗示以及）

{"type": "kafka",
  "dataSchema": {"dataSource": "DRUID_RAIN","parser": {"type": "string",  "parseSpec": {    "format": "json",    "timestampSpec": {      "column": "DATE_TIME","format": "auto"},"flattenSpec": {"fields": [{ "type": "path","name": "deviceType","expr": "$.ENVIRONMENT.deviceType"},{ "type": "path","name": "NS","expr":"$.ENVIRONMENT.NS"},
{"type": "path","name": "latitude","expr": "$.ENVIRONMENT.latitude"},{ "type": "path","name": "TIME","expr": "$.ENVIRONMENT.TIME"},{ "type": "path","name": "tenantCode","expr": "$.ENVIRONMENT.tenantCode"},{ "type": "path","name": "deviceName","expr": "$.ENVIRONMENT.deviceName"},{ "type": "path","name": "MAC","expr": "$.ENVIRONMENT.MAC"},{ "type": "path","name": "DATE","expr": "$.ENVIRONMENT.DATE"},{ "type": "path","name": "RAIN","expr": "$.ENVIRONMENT.RAIN"},
{ "type": "path","name": "MESSAGE_ID","expr": "$.ENVIRONMENT.MESSAGE_ID"},{ "type": "path","name": "tenantId","expr": "$.ENVIRONMENT.tenantId"},{ "type": "path","name": "zoneId","expr": "$.ENVIRONMENT.zoneId"},{ "type": "path","name": "DATE_TIME","expr": "$.ENVIRONMENT.DATE_TIME"},{ "type": "path","name": "zoneName","expr": "$.ENVIRONMENT.zoneName" }, { "type": "path","name": "longitude","expr": "$.ENVIRONMENT.longitude"},{ "type": "path","name": "STATUS","expr": "$.ENVIRONMENT.STATUS"}]},"dimensionsSpec": {"dimensions":["deviceType","NS","latitude","TIME","tenantCode","deviceName","MAC","DATE","RAIN","MESSAGE_ID","tenantId","zoneId","DATE_TIME","zoneName","longitude","STATUS"]}}},"metricsSpec": [ ],"granularitySpec": {  "type": "uniform",  "segmentGranularity": "DAY",  "queryGranularity": {"type": "none"},"rollup": true,  "intervals": null},"transformSpec": {  "filter": null,  "transforms": []}},"tuningConfig": {"type": "kafka","maxRowsInMemory": 1000000,"maxBytesInMemory": 0,"maxRowsPerSegment": 5000000,"maxTotalRows": null,"intermediatePersistPeriod": "PT10M","maxPendingPersists": 0,"indexSpec": {  "bitmap": {    "type": "concise"},  "dimensionCompression": "lz4",  "metricCompression": "lz4","longEncoding": "longs"},"buildV9Directly": true,"reportParseExceptions": false,"handoffConditionTimeout": 0,"resetOffsetAutomatically": false,"segmentWriteOutMediumFactory": null,"workerThreads": null,"chatThreads": null,"chatRetries": 8,"httpTimeout": "PT10S","shutdownTimeout": "PT80S","offsetFetchPeriod": "PT30S","intermediateHandoffPeriod": "P2147483647D","logParseExceptions": false,"maxParseExceptions": 2147483647,"maxSavedParseExceptions": 0,"skipSequenceNumberAvailabilityCheck": false},
  "ioConfig": {"topic": "rain_out","replicas": 2,"taskCount": 1,"taskDuration": "PT5S","consumerProperties": {  "bootstrap.servers": "XXXX:6667,XXXX:6667,XXXX:6667"},"pollTimeout": 100,"startDelay": "PT10S","period": "PT30S","useEarliestOffset": true,"completionTimeout": "PT20S","lateMessageRejectionPeriod": null,"earlyMessageRejectionPeriod": null,"stream": "env_out","useEarliestSequenceNumber": true,"type": "kafka"},
  "context": null,
  "suspended": false}

下面是我的问题，我写了什么

{
  "queryType": "groupBy",
  "dataSource": "DRUID_RAIN",
  "granularity": "hour",
  "dimensions": [
    "zoneName",
    "deviceName"
  ],

  "limitSpec": {
    "type": "default",
    "limit": 5000,
    "columns": [
      "zoneName",
      "deviceName"
    ]
  },
  "aggregations": [
    {
      "type": "doubleSum",
      "name": "RAIN",
      "fieldName": "RAIN"
    }
  ],
  "intervals": [
    "2020-10-27T18:30:00.000/2020-10-28T18:30:00.000"
  ],
  "context": {
    "skipEmptyBuckets": "true"
  }
}

rain列的输出总是得到“0”。我没有从 Postman 或Druid数据库的命令行得到期望的总和。但是使用implie，我得到的是相同查询的精确输出。