我使用一个avro值转换，它生成如下模式（这只是一个子集，因为它太大了）

{
  "type": "record",
  "name": "Envelope",
  "namespace": "mssql.dbo.InvTR_T",
  "fields": [
    {
      "name": "before",
      "type": [
        "null",
        {
          "type": "record",
          "name": "Value",
          "fields": [
            {
              "name": "InvTR_ID",
              "type": "int"
            },
            {
              "name": "Type_CH",
              "type": "string"
            },
            {
              "name": "CalcType_CH",
              "type": "string"
            },
            {
              "name": "ER_CST_ID",
              "type": "int"
            },
            {
              "name": "ER_REQ_ID",
              "type": "int"
            },
            {
              "name": "Vendor_ID",
              "type": "int"
            },
            {
              "name": "VendInv_VC",
              "type": "string"
            },
            {
              "name": "Status_CH",
              "type": "string"
            },
            {
              "name": "Stage_TI",
              "type": {
                "type": "int",
                "connect.type": "int16"
              }
            },
            {
              "name": "CheckOut_ID",
              "type": [
                "null",
                "int"
              ],
              "default": null
            },
            {
              "name": "ReCalcCk_LG",
              "type": "boolean"
            },
            {
              "name": "ReCalcAll_LG",
              "type": "boolean"
            },
            {
              "name": "PatMatch_LG",
              "type": "boolean"
            },
            {
              "name": "DocPatOvRd_LG",
              "type": "boolean"
            },
            {
              "name": "Locked_LG",
              "type": [
                "null",
                "boolean"
              ],
              "default": null
            },
            {
              "name": "SegErrFlag_LG",
              "type": "boolean"
            },
            {
              "name": "Hold_LG",
              "type": "boolean"
            },
            {
              "name": "Reason_ID",
              "type": [
                "null",
                {
                  "type": "int",
                  "connect.type": "int16"
                }
              ],
              "default": null
            },
            {
              "name": "HoldCom_VC",
              "type": [
                "null",
                "string"
              ],
              "default": null
            },
            {
              "name": "AllSegFin_LG",
              "type": "boolean"
            },
            {
              "name": "InvAmt_MN",
              "type": {
                "type": "bytes",
                "scale": 4,
                "precision": 19,
                "connect.version": 1,
                "connect.parameters": {
                  "scale": "4",
                  "connect.decimal.precision": "19"
                },
                "connect.name": "org.apache.kafka.connect.data.Decimal",
                "logicalType": "decimal"
              }

当我运行以下命令来创建一个流时

CREATE STREAM stream_invtr_t_json   WITH (KAFKA_TOPIC='InvTR_T', VALUE_FORMAT='AVRO');

然后我描述了那个流，模式是一种非常奇怪的格式。我想使用ksql来过滤特定的信息并适当地分散这些事件。但是，我不能从kafka topic=>ksql stream=>kafka topic=>sink开始。如果我从那条流中创建一个新的主题，并尝试将它消化到一个Flume中，我会得到

Expected Envelope for transformation, passing it unchanged

然后一个关于pk缺失的错误。我试图删除展开转换，只是为了看看它会如何出来，并收到了错误的。

BEFORE  | STRUCT<INVTR_ID INTEGER, TYPE_CH VARCHAR(STRING), CALCTYPE_CH VARCHAR(STRING), ER_CST_ID INTEGER, ER_REQ_ID INTEGER, VENDOR_ID INTEGER, VENDINV_VC VARCHAR(STRING), STATUS_CH VARCHAR(STRING), STAGE_TI INTEGER, CHECKOUT_ID INTEGER, RECALCCK_LG BOOLEAN, RECALCALL_LG BOOLEAN, PATMATCH_LG BOOLEAN, DOCPATOVRD_LG BOOLEAN, LOCKED_LG BOOLEAN, SEGERRFLAG_LG BOOLEAN, HOLD_LG BOOLEAN, REASON_ID INTEGER, HOLDCOM_VC VARCHAR(STRING), ALLSEGFIN_LG BOOLEAN, INVDATE_DT BIGINT, SHIPDATE_DT BIGINT, PDTERMS_CH VARCHAR(STRING), PMTDUE_DT BIGINT, PMTTERMS_VC VARCHAR(STRING), BILLTERMS_CH VARCHAR(STRING), JOINT_LG BOOLEAN, COMMENT_VC VARCHAR(STRING), SOURCE_CH VARCHAR(STRING), ADDBY_ID VARCHAR(STRING), ADDED_DT BIGINT, CHGBY_ID VARCHAR(STRING), CHGED_DT BIGINT, APPROVED_LG BOOLEAN, MULTIPO_VC VARCHAR(STRING), PRVAUDITED_INVTR_ID INTEGER, PRVVENDOR_ID INTEGER, TRANSITDAYS_SI INTEGER, SHIP_NUM_VC VARCHAR(STRING), PRVTRANSITDAYS_SI INTEGER, PRVJOINT_LG BOOLEAN, CLONEDFROM_INVTR_ID INTEGER, LASTCALC_DT BIGINT, TMSFMANUAL_LG BOOLEAN, FRTRATERSOURCE_CH VARCHAR(STRING), ACTPICKUP_DT BIGINT, ROUTVEND_SI INTEGER, CALCVRSN_TI INTEGER, VENDORRANK_SI INTEGER, SEQ_SI INTEGER, PRVAUDITED_DT BIGINT, FRTRATERBATCHTYPE_CH VARCHAR(STRING), CURRENCY_TYPE_CD VARCHAR(STRING), EXCHANGE_DT BIGINT, EXCHANGE_RATE_LOCKED_LG BOOLEAN, EXCHANGE_DT_LOCKED_LG BOOLEAN, CUSTAPPROVED_LG BOOLEAN, FRTRATERMATCH_INVTR_ID INTEGER, CRC_INVOICE_LG BOOLEAN, RG_ROUTVEND_SI INTEGER, RG_PRVVE