mysql导入es nested数据 中文乱码

mysql 一张主表 一张子表 上传到ES一个索引 mysql查询时将子表数据转成json为es索引的一个字段 结果里面的中文全乱码了

SELECT p.guid, JSON_ARRAYAGG( JSON_OBJECT( 'guid', pn.guid, 'colorid', pn.colorid, 'sizeid', pn.sizeid, 'barcode', pn.barcode, 'num', pn.num, 'salesnum', pn.salesnum, 'no', pn.NO)) AS sku, p.uid, p.fid, p.DIRECTORY, p.brand, p.bh, p.NAME, p.factory, p.factory_name AS 'factoryName', p.fprice, p.cost_amount AS 'costAmount', p.price, p.discount, p.discount_price AS 'discountPrice', p.has_directory_discount AS 'hasDirectoryDiscount', p.member_discount AS 'memberDiscount', p.member_price AS 'memberPrice', p.dat, p.modify_date AS 'modifyDate', p.nw, p.unit, p.pdesc, p.image222, p.imgs, p.remark, p.ONLINE, p.opensales, p.opensales_time AS 'opensalesTime', p.stopsales_time AS 'stopsalesTime', p.opensales_user AS 'opensalesUser', p.num, p.salesnum, p.material, p.quality, p.safety_grade AS 'safetyGrade', p.exe_standard AS 'exeStandard', p.washing_method AS 'washingMethod', p.has_open_discount AS 'hasOpenDiscount', p.has_give_points AS 'hasGivePoints', p.has_online AS 'hasOnline', p.video_url AS 'videoUrl', p.web_category_id AS 'webCategoryId', p.web_category_name AS 'webCategoryName', p.good_appraise_nums AS 'goodAppraiseNums', p.total_appraise_grade AS 'totalAppraiseGrade', p.default_sort AS 'defaultSort', p.has_open_member_discount AS hasOpenMemberDiscount FROM t_product p LEFT JOIN t_product_num pn ON p.guid = pn.guid GROUP BY pn.guid

SQL语句 是能正常运行的 关键是JSON_ARRAYAGG( JSON_OBJECT( 'guid', pn.guid, 'colorid', pn.colorid, 'sizeid', pn.sizeid, 'barcode', pn.barcode, 'num', pn.num, 'salesnum', pn.salesnum, 'no', pn.NO)) AS sku

转为nested上传上去后 就会乱码

{
"job": {
"setting": {
"speed": {
"channel": 5
},
"errorLimit": {
"record": 0,
"percentage": 0.02
}
},
"content": [{
"reader": {
"encoding":"UTF-8",
"name": "mysqlreader",
"parameter": {
"username":
"password": "",

"connection": [{
"jdbcUrl": [
""
],
"querySql": [
"SELECT p.guid, JSON_ARRAYAGG( JSON_OBJECT( 'guid', pn.guid, 'colorid', pn.colorid, 'sizeid', pn.sizeid, 'barcode', pn.barcode, 'num', pn.num, 'salesnum', pn.salesnum, 'no', pn.NO)) AS sku, p.uid, p.fid, p.DIRECTORY, p.brand, p.bh, p.NAME, p.factory, p.factory_name AS 'factoryName', p.fprice, p.cost_amount AS 'costAmount', p.price, p.discount, p.discount_price AS 'discountPrice', p.has_directory_discount AS 'hasDirectoryDiscount', p.member_discount AS 'memberDiscount', p.member_price AS 'memberPrice', p.dat, p.modify_date AS 'modifyDate', p.nw, p.unit, p.pdesc, p.image222, p.imgs, p.remark, p.ONLINE, p.opensales, p.opensales_time AS 'opensalesTime', p.stopsales_time AS 'stopsalesTime', p.opensales_user AS 'opensalesUser', p.num, p.salesnum, p.material, p.quality, p.safety_grade AS 'safetyGrade', p.exe_standard AS 'exeStandard', p.washing_method AS 'washingMethod', p.has_open_discount AS 'hasOpenDiscount', p.has_give_points AS 'hasGivePoints', p.has_online AS 'hasOnline', p.video_url AS 'videoUrl', p.web_category_id AS 'webCategoryId', p.web_category_name AS 'webCategoryName', p.good_appraise_nums AS 'goodAppraiseNums', p.total_appraise_grade AS 'totalAppraiseGrade', p.default_sort AS 'defaultSort', p.has_open_member_discount AS hasOpenMemberDiscount FROM t_product p LEFT JOIN t_product_num pn ON p.guid = pn.guid GROUP BY pn.guid"
]
}]
}
},

"writer": {
"name": "elasticsearchwriter",
"parameter": {
"endpoint": "",
"accessId": "",
"accessKey": "",
"index": "fz_test_product",
"cleanup": false,
"discovery": false,
"column": [{
"name": "guid",
"type": "keyword"
},
{
"name": "sku",
"type": "nested"
},
{
"name": "uid",
"type": "keyword"
},
{
"name": "fid",
"type": "keyword"
},
{
"name": "directory",
"type": "text",
"analyzer": "standard"
},
{
"name": "brand",
"type": "text",
"analyzer": "standard"
},
{
"name": "bh",
"type": "keyword"
},
{
"name": "name",
"type": "text",
"analyzer": "standard"
},
{
"name": "factory",
"type": "keyword"
},
{
"name": "factoryName",
"type": "text",
"analyzer": "standard"
},
{
"name": "fprice",
"type": "double"
},
{
"name": "costAmount",
"type": "double"
},
{
"name": "price",
"type": "double"
},
{
"name": "discount",
"type": "double"
},
{
"name": "discountPrice",
"type": "double"
},
{
"name": "hasDirectoryDiscount",
"type": "integer"
},
{
"name": "memberDiscount",
"type": "double"
},
{
"name": "memberPrice",
"type": "double"
},
{
"name": "dat",
"type": "date"
},
{
"name": "modifyDate",
"type": "date",
"format": "yyyy-MM-dd HH:mm:ss"
},
{
"name": "nw",
"type": "double"
},
{
"name": "unit",
"type": "keyword"
},
{
"name": "pdesc",
"type": "text",
"analyzer": "standard"
},
{
"name": "image222",
"type": "keyword"
},
{
"name": "imgs",
"type": "keyword"
},
{
"name": "remark",
"type": "text",
"analyzer": "standard"
},
{
"name": "online",
"type": "integer"
},
{
"name": "opensales",
"type": "integer"
},
{
"name": "opensalesTime",
"type": "long"
},
{
"name": "stopsalesTime",
"type": "long"
},
{
"name": "opensalesUser",
"type": "keyword"
},
{
"name": "num",
"type": "integer"
},
{
"name": "salesnum",
"type": "integer"
},
{
"name": "material",
"type": "keyword"
},
{
"name": "quality",
"type": "keyword"
},
{
"name": "safetyGrade",
"type": "keyword"
},
{
"name": "exeStandard",
"type": "keyword"
},
{
"name": "washingMethod",
"type": "keyword"
},
{
"name": "hasOpenDiscount",
"type": "integer"
},
{
"name": "hasGivePoints",
"type": "integer"
},
{
"name": "hasOnline",
"type": "integer"
},
{
"name": "videoUrl",
"type": "keyword"
},
{
"name": "webCategoryId",
"type": "keyword"
},
{
"name": "webCategoryName",
"type": "keyword"
},
{
"name": "goodAppraiseNums",
"type": "keyword"
},
{
"name": "totalAppraiseGrade",
"type": "double"
},
{
"name": "defaultSort",
"type": "long"
},
{
"name": "hasOpenMemberDiscount",
"type": "integer"
}
]
}
}

}]
}
}

No source file or test is named. Start by reproducing the supplied MySQL JSON_ARRAYAGG query with the DataX mysqlreader and elasticsearchwriter configuration, then trace the import path for the nested sku field. Done means Chinese text in the nested data remains readable after import into Elasticsearch.

Written by the indexing model from the issue text.