我已经从SQL Server中检索了一个表,其中包含300万条记录。
前10条记录:
+---------+-------------+----------+
|ACCOUNTNO|VEHICLENUMBER|CUSTOMERID|
+---------+-------------+----------+
| 10003014| MH43AJ411| 20000000|
| 10003014| MH43AJ411| 20000001|
| 10003015| MH12GZ3392| 20000002|
| 10003016| GJ15Z8173| 20000003|
| 10003018| MH05AM902| 20000004|
| 10003019| GJ15CD7657| 20001866|
| 10003019| MH02BY7774| 20000005|
| 10003019| MH02DG7774| 20000933|
| 10003019| GJ15CA7387| 20001865|
| 10003019| GJ15CB9601| 20001557|
+---------+-------------+----------+
only showing top 10 rows
这里ACCOUNTNO
是唯一的,相同的ACCOUNTNO
可能有一个以上的VEHICLENUMBER
,对于每辆车,我们可能相对于该CUSTOMERID
具有唯一的VEHICLENUMBER
我想导出为JSON格式。
这是我实现输出的代码:
package com.issuer.pack2.spark
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql._
object sqltojson {
def main(args:Array[String])
{
System.setProperty("hadoop.home.dir", "C:/winutil/")
val conf = new SparkConf().setAppName("SQLtoJSON").setMaster("local[*]")
val sc = new SparkContext(conf)
val sqlContext = new SQLContext(sc)
import sqlContext.implicits._
val jdbcSqlConnStr = "jdbc:sqlserver://192.168.70.88;databaseName=ISSUER;user=bhaskar;password=welcome123;"
val jdbcDbTable = "[HISTORY].[TP_CUSTOMER_PREPAIDACCOUNTS]"
val jdbcDF = sqlContext.read.format("jdbc").options(Map("url" -> jdbcSqlConnStr,"dbtable" -> jdbcDbTable)).load()
// jdbcDF.show(10)
jdbcDF.registerTempTable("tp_customer_account")
val res01 = sqlContext.sql("SELECT ACCOUNTNO, VEHICLENUMBER, CUSTOMERID FROM tp_customer_account GROUP BY ACCOUNTNO, VEHICLENUMBER, CUSTOMERID ORDER BY ACCOUNTNO ")
// res01.show(10)
res01.coalesce(1).write.json("D:/res01.json")
}
}
我得到的输出:
{"ACCOUNTNO":10003014,"VEHICLENUMBER":"MH43AJ411","CUSTOMERID":20000001}
{"ACCOUNTNO":10003014,"VEHICLENUMBER":"MH43AJ411","CUSTOMERID":20000000}
{"ACCOUNTNO":10003015,"VEHICLENUMBER":"MH12GZ3392","CUSTOMERID":20000002}
{"ACCOUNTNO":10003016,"VEHICLENUMBER":"GJ15Z8173","CUSTOMERID":20000003}
{"ACCOUNTNO":10003018,"VEHICLENUMBER":"MH05AM902","CUSTOMERID":20000004}
{"ACCOUNTNO":10003019,"VEHICLENUMBER":"MH02BY7774","CUSTOMERID":20000005}
{"ACCOUNTNO":10003019,"VEHICLENUMBER":"GJ15CA7387","CUSTOMERID":20001865}
{"ACCOUNTNO":10003019,"VEHICLENUMBER":"GJ15CD7657","CUSTOMERID":20001866}
{"ACCOUNTNO":10003019,"VEHICLENUMBER":"MH02DG7774","CUSTOMERID":20000933}
{"ACCOUNTNO":10003019,"VEHICLENUMBER":"GJ15CB9601","CUSTOMERID":20001557}
{"ACCOUNTNO":10003019,"VEHICLENUMBER":"GJ15CD7387","CUSTOMERID":20029961}
{"ACCOUNTNO":10003019,"VEHICLENUMBER":"GJ15CF7747","CUSTOMERID":20009020}
{"ACCOUNTNO":10003019,"VEHICLENUMBER":"GJ15CB727","CUSTOMERID":20000008}
{"ACCOUNTNO":10003019,"VEHICLENUMBER":"GJ15CA7837","CUSTOMERID":20001223}
{"ACCOUNTNO":10003019,"VEHICLENUMBER":"GJ15CD7477","CUSTOMERID":20001690}
{"ACCOUNTNO":10003020,"VEHICLENUMBER":"MH01AX5658","CUSTOMERID":20000006}
{"ACCOUNTNO":10003021,"VEHICLENUMBER":"GJ15AD727","CUSTOMERID":20000007}
{"ACCOUNTNO":10003023,"VEHICLENUMBER":"GU15PP7567","CUSTOMERID":20000009}
{"ACCOUNTNO":10003024,"VEHICLENUMBER":"GJ15CA7567","CUSTOMERID":20000010}
{"ACCOUNTNO":10003025,"VEHICLENUMBER":"GJ5JB9312","CUSTOMERID":20000011}
但是我想要这样的JSON格式输出:
我为上面表格的前三条记录手动编写了JSON(可能是我设计错误,我希望ACCOUNTNO
应该是唯一的)。
{
"ACCOUNTNO":10003014,
"VEHICLE": [
{ "VEHICLENUMBER":"MH43AJ411", "CUSTOMERID":20000000},
{ "VEHICLENUMBER":"MH43AJ411", "CUSTOMERID":20000001}
],
"ACCOUNTNO":10003015,
"VEHICLE": [
{ "VEHICLENUMBER":"MH12GZ3392", "CUSTOMERID":20000002}
]
}
那么,如何使用Spark代码实现这种JSON格式?我需要帮助。衷心感谢。
答案 0 :(得分:1)
您可以执行以下操作(不建议使用{{1}代替registerTempTable
,因为createOrReplaceTempView
已过时)
registerTempTable
您应该获得所需的输出为
jdbcDF.createGlobalTempView("tp_customer_account")
val res01 = sqlContext.sql("SELECT ACCOUNTNO, collect_list(struct(`VEHICLENUMBER`, `CUSTOMERID`)) as VEHICLE FROM tp_customer_account GROUP BY ACCOUNTNO ORDER BY ACCOUNTNO ")
res01.coalesce(1).write.json("D:/res01.json")
使用 spark scala API ,您可以执行以下操作:
{"ACCOUNTNO":"10003014","VEHICLE":[{"VEHICLENUMBER":"MH43AJ411","CUSTOMERID":"20000000"},{"VEHICLENUMBER":"MH43AJ411","CUSTOMERID":"20000001"}]}
{"ACCOUNTNO":"10003015","VEHICLE":[{"VEHICLENUMBER":"MH12GZ3392","CUSTOMERID":"20000002"}]}
{"ACCOUNTNO":"10003016","VEHICLE":[{"VEHICLENUMBER":"GJ15Z8173","CUSTOMERID":"20000003"}]}
{"ACCOUNTNO":"10003018","VEHICLE":[{"VEHICLENUMBER":"MH05AM902","CUSTOMERID":"20000004"}]}
{"ACCOUNTNO":"10003019","VEHICLE":[{"VEHICLENUMBER":"GJ15CD7657","CUSTOMERID":"20001866"},{"VEHICLENUMBER":"MH02BY7774","CUSTOMERID":"20000005"},{"VEHICLENUMBER":"MH02DG7774","CUSTOMERID":"20000933"},{"VEHICLENUMBER":"GJ15CA7387","CUSTOMERID":"20001865"},{"VEHICLENUMBER":"GJ15CB9601","CUSTOMERID":"20001557"}]}
您应该得到与sql方式相同的答案。
我希望答案会有所帮助。