背景
JSON作为常用的数据格式，在消息中间件中用json做为消息格式也很常见。在flink table中消息可以理解为表的一行记录。所以对于一个消息队列中的一个topic来说，可以根据json数据格式映射成一张表。flink自身是支持json格式的，但是对于复杂格式支持不是太友好。笔者也是在flink table的应用中遇到了各种json格式，发布出来给大家看看，或有其他好的解析方式可留言探讨。
概述
下面3图可以很直观看出我理想的解析思想，左边是源json格式右边是table 格式
只有嵌套对象类json
单一嵌套数组类json
多嵌套数组类json
针对多嵌套数组的笔者的最终实现和图3有差别。选择的方式为扁平化的实现方式，后面根据条件去选择tablename拆分成2张表
实现 直接上源码。
package com.paic.phflink.core.util
import java.util
import org.apache.flink.api.common.typeinfo.TypeInformation
import org.apache.flink.api.java.typeutils.RowTypeInfo
import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.ObjectMapper
import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.node.{ArrayNode, JsonNodeFactory, ObjectNode}
import org.apache.flink.types.Row
import scala.collection.JavaConverters._
import scala.collection.mutable
/**
* 给定JSON串，和目标schema描述，生成对于的 Row
*/
object JsonNodeUtil {
def main(args: Array[String]):Unit = {
// objects_test()
// oneList_test()
twoList_test()
// oneList_no_flatMap_test()
// twoList_no_flatmap_test()
}
val objects =
"""
{"a":"1","b":{"c":"2","d":"3"},"e":{"f":"4","g":"5"}}
""".stripMargin
.getBytes()
val oneList =
"""
{"a":"1","b":[{"c":"2","d":"3"},{"c":"4","d":"5"}],"e":"6","f":{"g":"7","h":"8"}}
""".stripMargin
.getBytes()
val twoList =
"""
{"a":"1","b":[{"c":"2","d":"3"},{"c":"4","d":"5"}],"e":"6","f":[{"g":"7","h":"8"},{"g":"9","h":"10"}],"i":{"j":"11","k":"12"}}
""".stripMargin
.getBytes()
/**
* 嵌套对象打平 和嵌套list一个不打平一个打平
*/
def twoList_no_flatmap_test() = {
val objectMapper =new ObjectMapper()
val json =twoList
val map =new util.LinkedHashMap[String, String]()
map.put("a", TableSchemaUtil.STRING)
map.put("b", TableSchemaUtil.OBJECT_ARRAY)
map.put("b_c", TableSchemaUtil.STRING)
map.put("b_d", TableSchemaUtil.STRING)
map.put("e", TableSchemaUtil.STRING)
map.put("f", TableSchemaUtil.OBJECT_ARRAY)
map.put("f_g", TableSchemaUtil.STRING)
map.put("f_h", TableSchemaUtil.STRING)
map.put("i_j", TableSchemaUtil.STRING)
map.put("i_k", TableSchemaUtil.STRING)
map.put("tableName", TableSchemaUtil.STRING)
val rows:util.ArrayList[Row] =getRows(objectMapper,json,map)
rows
}
/**
* 嵌套对象打平 和嵌套list不打平
*/
def oneList_no_flatMap_test() = {
val objectMapper =new ObjectMapper
val json =oneList
val map =new util.LinkedHashMap[String, String]()
map.put("a", TableSchemaUtil.STRING)
map.put("b", TableSchemaUtil.OBJECT_ARRAY)
map.put("b_c", TableSchemaUtil.STRING)
map.put("b_d", TableSchemaUtil.STRING)
map.put("e", TableSchemaUtil.STRING)
map.put("f_g", TableSchemaUtil.STRING)
map.put("f_h", TableSchemaUtil.STRING)
val rows:util.ArrayList[Row] =getRows(objectMapper,json,map)
rows
}
/**
* 嵌套对象 和嵌套多个list 打平
* 多个数组的一定要指定 一个 tableName 列
* map.put("tableName", TableSchemaUtil.STRING)
* 后续根据这个tableName 进行查询分表
* @return
*/
def twoList_test() = {
val objectMapper =new ObjectMapper
val json =twoList
val map =new util.LinkedHashMap[String, String]()
map.put("a", TableSchemaUtil.STRING)
map.put("b_c", TableSchemaUtil.STRING)
map.put("b_d", TableSchemaUtil.STRING)
map.put("e", TableSchemaUtil.STRING)
map.put("f_g", TableSchemaUtil.STRING)
map.put("f_h", TableSchemaUtil.STRING)
map.put("i_j", TableSchemaUtil.STRING)
map.put("i_k", TableSchemaUtil.STRING)
map.put("tableName", TableSchemaUtil.STRING)
val rows:util.ArrayList[Row] =getRows(objectMapper,json,map)
rows
}
/**
* 嵌套对象 和嵌套list 打平
* @return
*/
def oneList_test() = {
val objectMapper =new ObjectMapper
val json =oneList
val map =new util.LinkedHashMap[String, String]()
map.put("a", TableSchemaUtil.STRING)
map.put("b_c", TableSchemaUtil.STRING)
map.put("b_d", TableSchemaUtil.STRING)
map.put("e", TableSchemaUtil.STRING)
map.put("f_g", TableSchemaUtil.STRING)
map.put("f_h", TableSchemaUtil.STRING)
val rows:util.ArrayList[Row] =getRows(objectMapper,json,map)
rows
}
/**
* 嵌套对象打平
* @return
*/
def objects_test() = {
val objectMapper =new ObjectMapper
val json =objects
val map =new util.LinkedHashMap[String, String]()
map.put("a", TableSchemaUtil.STRING)
map.put("b_c", TableSchemaUtil.STRING)
map.put("b_d", TableSchemaUtil.STRING)
map.put("e_f", TableSchemaUtil.STRING)
map.put("e_g", TableSchemaUtil.STRING)
val rows:util.ArrayList[Row] =getRows(objectMapper,json,map)
rows
}
def getRows(objectMapper: ObjectMapper,json:Array[Byte],map:util.LinkedHashMap[String, String]) ={
val objectNode:ObjectNode = objectMapper.readValue(json,classOf[ObjectNode])
//最外层的数据
val rootColums =new ObjectNode(JsonNodeFactory.instance)
//嵌套的数组
val tables = mutable.LinkedHashMap[String, ArrayNode]()
val list =new util.ArrayList[Row]()
//解析json
parse(objectNode,rootColums,tables,"")
val saclaLinkMap = mutable.LinkedHashMap[String, String]()
map.asScala.foreach{case (k:String,v:String) => saclaLinkMap += (k -> v)}
//对json转化成 row的时候选择打平跳过
val feidMapScala = TableSchemaUtil.toFlinkType(saclaLinkMap)
val types: Array[TypeInformation[_]] = feidMapScala.map(_._2).toArray
val fieldNames: Array[String] = feidMapScala.map(_._1).toArray
val info =new RowTypeInfo(types,fieldNames)
val tableSize = tables.size
if(tableSize ==0 && rootColums.size() >0){
val row = JsonToRowUtil.convertRow(rootColums,info)
list.add(row)
}
//循环每个嵌套的数组 每个数组理解为一个表
for( (table,value)<- tables) {//循环每个表
//判断表是否需要打平
if(map.containsKey(table) && map.get(table).startsWith(TableSchemaUtil.OBJECT_ARRAY)){
//不需要打平就把list数据弄成一个row 数组
val tableLine = rootColums.deepCopy()//每一行初始化的ObjectNode
if(tableSize >1){//如果有多个table 就要加一列table 名字做区分
tableLine.put("tableName",table)//把每行数据都加一个table名字
}
tableLine.put(table,value)
val row = JsonToRowUtil.convertRow(tableLine,info)
list.add(row)
}else{
val tableLines = value.elements()//表中的所有行
while (tableLines.hasNext) {//循环每一行
val line = tableLines.next()//获取每一行
val child = line.fields()//每一行的所有列
val tableLine = rootColums.deepCopy()//每一行初始化的ObjectNode
if(tableSize >1){//如果有多个table 就要加一列table 名字做区分
tableLine.put("tableName",table)//把每行数据都加一个table名字
}
while (child.hasNext) {//循环每一列
val colum = child.next()//获取每一列
val columName = colum.getKey//列名
val filedFullName = table +"_" + columName//组合列名
val columValue = colum.getValue.asText()//列对应的值
// println(filedFullName, columValue)
val dataValue = colum.getValue.asText()
tableLine.put(filedFullName,dataValue)
}
val row = JsonToRowUtil.convertRow(tableLine,info)
list.add(row)
}
}
}
// returnJson.put("root",json)
list
}
def parse(objectNode: ObjectNode,
objectSchema:ObjectNode,
listSchema:mutable.LinkedHashMap[String, ArrayNode],
parentName:String =""
):Unit ={
val fieldNames = objectNode.fieldNames()
//得到第一层
while(fieldNames.hasNext){
val field = fieldNames.next()
var node = objectNode.get(field)
val filedFullName =if(parentName.length >0){
parentName+"_"+field
}else{
field
}
if(node.isObject){
parse(objectNode.`with`(field),objectSchema,listSchema,filedFullName)
}else if(node.isArray){
val list:ArrayNode = node.asInstanceOf[ArrayNode]
if(list.size() >0){
//获取第0个解析一下
//判断list里面是否还有嵌套，如果没有则直接去
if(false){
// node = list.get(0)
// parse(node,objectSchema,listSchema,filedFullName)
}else{
listSchema += (filedFullName -> list)
}
}else{//不为空则填充默认值
}
}else{
val dataValue = node.asText()
// println(filedFullName,dataValue)
objectSchema.put(filedFullName,dataValue.toString)
// val dataType = schema.get(filedFullName)
}
}
}
}
