小贝子编程

根据基于嵌套ID的列表，将事件事例类相乘



我正在处理一个数据帧，并使用Event case类转换为Dataset[Event]。如何有嵌套的Id，我需要根据嵌套设备的扁平化来乘以事件：os。

我能够在Kafka事件级别返回case类Event。但不确定如何使事件成倍增加。

Kafka传入事件：

{
"partition": 1,
"key": "34768_20220203_MFETP501",
"offset": 1841543,
"createTime": 1646041475348,
"topic": "topic_int",
"publishTime": 1646041475344,
"errorCode": 0,
"userActions": {
"productId": "3MFETP501",
"createdDate": "2022-02-26T11:19:35.786Z",
"events": [
{
"GUID": "dbb1-f38b-f7f0-44af-90da-80179412f89c",
"eventDate": "2022-02-26T11:19:35.786Z",
"familyId": 2010,
"productTypeId": 1004678,
"serialID": "890479804",
"productName": "MFE Total Protection 2021 Family Pack",
"features": {
"mapping": [
{
"deviceId": 999795,
"osId": [
100
]
},
{
"deviceId": 987875
"osId": [
101
]
}
]
}
}
]
}
}

事件的预期输出用例类

Event("3MFETP501","1004678","2010","3MFETP501:890479804","MFE Total Protection 2021 Family Pack","999795_100", Map("targetId"->"999795_100") )
Event("3MFETP501","1004678","2010","3MFETP501:890479804","MFE Total Protection 2021 Family Pack","987875_100", Map("targetId"->"987875_100") )

case class Event(
productId: String,
familyId: String,
productTypeId: String,
key: String,
productName: String,
deviceOS:String, 
var featureMap: mutable.Map[String, String])


val finalDataset:Dataset[Event] = inputDataFrame.flatMap(
row=> {
val productId = row.getAs[String]("productId")
val userActions = row.getAs[Row]("userActions")
val userEvents:mutable.Seq[Row] = userActions.getAs[mutable.WrappedArray[Row]]("events")
val processedEvents:mutable.Seq[Row]= userEvents.map(
event=> 
val productTypeId = event.getAs[Int]("productTypeId")
val familyId = event.getAs[String]("familyId")
val features = activity.getAs[mutable.WrappedArray[Row]]("features")
val serialId = activity.getAs[String]("serialId")
val key =  productId+":"+serialId
val features = mutable.Map[String, String]().withDefaultValue(null)

val device_os_list=List("999795_100","987875_101")
//Feature Map is for every device_os ( example "targetId"->"999795_100") for 999795_100
if (familyId == 2010 )
{
val a: Option[List[String]] = flatten the deviceId,osId ..

a.get.map(i=>{
val key: String =  methodToCombinedeviceIdAndosId
val featureMapping: mutable.Map[String, String] = getfeatureMapForInvidualKey
Event(productId,productTypeId,familyId,key,productName,device_os,feature) ---> This is returning **List[Event]** 
})
}
else{
Event(productId,productTypeId,familyId,key,productName,device_os,feature)  --> This is returning **Event**. THIS WORKS
}

)
}
)

我并没有完全相同地实现它，但我认为理解逻辑并将其应用于您的案例是可能的。

我创建了类似于kafka.json的json文件，并在其中放入了如下代码(您的事件(：

[{
"partition": 1,
"key": "34768_20220203_MFETP501",
"offset": 1841543,
"createTime": 1646041475348,
"topic": "topic_int",
"publishTime": 1646041475344,
"errorCode": 0,
"userActions": {
"productId": "3MFETP501",
"createdDate": "2022-02-26T11:19:35.786Z",
"events": [
{
"GUID": "dbb1-f38b-f7f0-44af-90da-80179412f89c",
"eventDate": "2022-02-26T11:19:35.786Z",
"familyId": 2010,
"productTypeId": 1004678,
"serialID": "890479804",
"productName": "MFE Total Protection 2021 Family Pack",
"features": {
"mapping": [
{
"deviceId": 999795,
"osId": [
100
]
},
{
"deviceId": 987875,
"osId": [
101
]
}
]
}
}
]
}
}]

请在下面找到第一个基于flatMap和的循环的解决方案。

case class Event(
productId: String,
familyId: String,
productTypeId: String,
key: String,
productName: String,
deviceOS: String,
featureMap: Map[String, String])
import org.apache.spark.sql.{Dataset, Row, SparkSession}
import scala.collection.mutable
val spark = SparkSession
.builder
.appName("StructuredStreaming")
.master("local[*]")
.getOrCreate()
private val inputDataFrame = spark.read.option("multiline", "true").format("json").load("/absolute_path_to_kafka.json")

import spark.implicits._
val finalDataset: Dataset[Event] = inputDataFrame.flatMap(
row => {
val userActions = row.getAs[Row]("userActions")
val productId = userActions.getAs[String]("productId")
val userEvents = userActions.getAs[mutable.WrappedArray[Row]]("events")
for (event <- userEvents;
familyId = event.getAs[Int]("familyId").toString;
productTypeId = event.getAs[Int]("productTypeId").toString;
serialId = event.getAs[String]("serialID");
productName = event.getAs[String]("productName");
key = s"$productId:$serialId";
features = event.getAs[Row]("features");
mappings = features.getAs[mutable.WrappedArray[Row]]("mapping");
mappingRow <- mappings;
deviceId = mappingRow.getAs[Long]("deviceId");
osIds = mappingRow.getAs[mutable.WrappedArray[Long]]("osId");
osId <- osIds;
deviseOs = deviceId + "_" + osId
) yield Event(productId, familyId, productTypeId, key, productName, deviseOs, Map("target" -> (deviseOs)))
}
)
finalDataset.foreach(e => println(e))
//  Event(3MFETP501,2010,1004678,3MFETP501:890479804,MFE Total Protection 2021 Family Pack,999795_100,Map(target -> 999795_100))
//  Event(3MFETP501,2010,1004678,3MFETP501:890479804,MFE Total Protection 2021 Family Pack,987875_101,Map(target -> 987875_101))

此外，您还可以使用select、with Column，explose和concat函数来解决此任务。

case class Event(
productId: String,
familyId: String,
productTypeId: String,
key: String,
productName: String,
deviceOS: String,
featureMap: Map[String, String])
import org.apache.spark.sql.{Dataset, SparkSession}
import org.apache.spark.sql.functions.{col, explode, concat, lit, map}
val spark = SparkSession
.builder
.appName("StructuredStreaming")
.master("local[*]")
.getOrCreate()
private val inputDataFrame = spark.read.option("multiline", "true").format("json").load("/absolute_path_to_kafka.json")
val transformedDataFrame = inputDataFrame
.select(col("userActions.productId").as("productId"),
explode(col("userActions.events")).as("event"))
.select(col("productId"),
col("event.familyId").as("familyId"),
col("event.productTypeId").as("productTypeId"),
col("event.serialID").as("serialID"),
col("event.productName").as("productName"),
explode(col("event.features.mapping")).as("features")
)
.select(
col("productId"),
col("familyId"),
col("productTypeId"),
col("serialID"),
col("productName"),
col("features.deviceId").as("deviceId"),
explode(col("features.osId")).as("osId")
)
.withColumn("key", concat(col("productId"), lit(":"), col("serialID")))
.withColumn("deviceOS", concat(col("deviceId"), lit("_"), col("osId")))
.withColumn("featureMap", map(lit("target"), col("deviceOS")))
import spark.implicits._
private val result: Dataset[Event] = transformedDataFrame.as[Event]
result.foreach(e => println(e))
//  Event(3MFETP501,2010,1004678,3MFETP501:890479804,MFE Total Protection 2021 Family Pack,999795_100,Map(target -> 999795_100))
//  Event(3MFETP501,2010,1004678,3MFETP501:890479804,MFE Total Protection 2021 Family Pack,987875_101,Map(target -> 987875_101))

添加选项以根据字段的值one自定义响应。我将此处的替换为理解映射/平面映射，因此您可以根据类型返回一个或多个事件作为响应。此外，我还对json进行了一些自定义，以便在结果中显示更多的示例。

新json:

[{
"partition": 1,
"key": "34768_20220203_MFETP501",
"offset": 1841543,
"createTime": 1646041475348,
"topic": "topic_int",
"publishTime": 1646041475344,
"errorCode": 0,
"userActions": {
"productId": "3MFETP501",
"createdDate": "2022-02-26T11:19:35.786Z",
"events": [
{
"GUID": "dbb1-f38b-f7f0-44af-90da-80179412f89c",
"eventDate": "2022-02-26T11:19:35.786Z",
"familyId": 2010,
"productTypeId": 1004678,
"serialID": "890479804",
"productName": "MFE Total Protection 2021 Family Pack",
"features": {
"mapping": [
{
"deviceId": 999795,
"osId": [
100,
110
]
},
{
"deviceId": 987875,
"osId": [
101
]
}
]
}
},
{
"GUID": "1111-2222-f7f0-44af-90da-80179412f89c",
"eventDate": "2022-03-26T11:19:35.786Z",
"familyId": 2011,
"productTypeId": 1004679,
"serialID": "890479805",
"productName": "Product name",
"features": {
"mapping": [
{
"deviceId": 999796,
"osId": [
103
]
},
{
"deviceId": 987877,
"osId": [
104
]
}
]
}
}
]
}
}]

请在下面找到代码：

case class Event(
productId: String,
familyId: String,
productTypeId: String,
key: String,
productName: String,
deviceOS: String,
featureMap: Map[String, String])
import org.apache.spark.sql.{Dataset, SparkSession}
val spark = SparkSession
.builder
.appName("StructuredStreaming")
.master("local[*]")
.getOrCreate()
private val inputDataFrame = spark.read.option("multiline", "true").format("json").load("/absolute_path_to_kafka.json")
import spark.implicits._
val finalDataset: Dataset[Event] = inputDataFrame.flatMap(
row => {
val userActions = row.getAs[Row]("userActions")
val productId = userActions.getAs[String]("productId")
val userEvents = userActions.getAs[mutable.WrappedArray[Row]]("events")
for (event <- userEvents;
productTypeId = event.getAs[Int]("productTypeId").toString;
serialId = event.getAs[String]("serialID");
productName = event.getAs[String]("productName");
key = s"$productId:$serialId";
familyId = event.getAs[Int]("familyId").toString;
features = event.getAs[Row]("features");
mappings = features.getAs[mutable.WrappedArray[Row]]("mapping");
mappingRow <- mappings;
deviceId = mappingRow.getAs[Long]("deviceId");
osIds = mappingRow.getAs[mutable.WrappedArray[Long]]("osId");
osId <- osIds;
deviseOs = deviceId + "_" + osId
) yield Event(productId, familyId, productTypeId, key, productName, deviseOs, Map("target" -> deviseOs))
userEvents.flatMap(event => {
val productTypeId = event.getAs[Int]("productTypeId").toString
val serialId = event.getAs[String]("serialID")
val productName = event.getAs[String]("productName")
val key = s"$productId:$serialId"
val familyId = event.getAs[Long]("familyId")
if(familyId == 2010) {
val features = event.getAs[Row]("features")
val mappings = features.getAs[mutable.WrappedArray[Row]]("mapping")
mappings.flatMap(mappingRow => {
val deviceId = mappingRow.getAs[Long]("deviceId")
val osIds = mappingRow.getAs[mutable.WrappedArray[Long]]("osId")
osIds.map(osId => {
val devise_os = deviceId + "_" + osId
Event(productId, familyId.toString, productTypeId, key, productName, devise_os, Map("target" -> devise_os))
})
})
} else {
Seq(Event(productId, familyId.toString, productTypeId, key, productName, "default_defice_os", Map("target" -> "default_defice_os")))
}
})
}
)
finalDataset.foreach(e => println(e))
//  Event(3MFETP501,2010,1004678,3MFETP501:890479804,MFE Total Protection 2021 Family Pack,999795_100,Map(target -> 999795_100))
//  Event(3MFETP501,2010,1004678,3MFETP501:890479804,MFE Total Protection 2021 Family Pack,999795_110,Map(target -> 999795_110))
//  Event(3MFETP501,2010,1004678,3MFETP501:890479804,MFE Total Protection 2021 Family Pack,987875_101,Map(target -> 987875_101))
//  Event(3MFETP501,2011,1004679,3MFETP501:890479805,Product name,default_defice_os,Map(target -> default_defice_os))

由于这是在一行DataFrame下，返回Event case类，转换为DataSet。这里的问题是针对一个条件，我得到的是List[Event]和rest类型，我只得到Event类

仅供参考：这不是一个答案。但我的进一步尝试解决。

if (familyId == 2010 )
{
val a: Option[List[String]] = flatten the deviceId,osId ..

a.get.map(i=>{
val key: String =  methodToCombinedeviceIdAndosId
val featureMapping: mutable.Map[String, String] = getfeatureMapForInvidualKey
Event(productId,productTypeId,familyId,key,productName,device_os,feature) ---> This is returning List[Event]
})
}
else{
Event(productId,productTypeId,familyId,key,productName,device_os,feature)  --> This is returning Event
}

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