本文介紹了有什么辦法可以扁平化電光流媒體中的嵌套JSON嗎？的處理方法，對大家解決問題具有一定的參考價值，需要的朋友們下面隨著小編來一起學習吧！
問題描述
我已經編寫了一個數據集火花作業(批處理)代碼來扁平化數據，運行正常，但當我嘗試在火花流作業中使用相同的代碼片段時，它拋出以下錯誤
必須使用WriteStream.start()；
執行具有流來源的查詢
那么，有什么方法可以在流作業中展平嵌套的JSON嗎？
樣本輸入嵌套JSON-
{
"name":" Akash",
"age":26,
"watches":{
"name":"Apple",
"models":[
"Apple Watch Series 5",
"Apple Watch Nike"
]
},
"phones":[
{
"name":" Apple",
"models":[
"iphone X",
"iphone XR",
"iphone XS",
"iphone 11",
"iphone 11 Pro"
]
},
{
"name":" Samsung",
"models":[
"Galaxy Note10",
"Galaxy Note10+",
"Galaxy S10e",
"Galaxy S10",
"Galaxy S10+"
]
},
{
"name":" Google",
"models":[
"Pixel 3",
"Pixel 3a"
]
}
]
}
預期輸出。
output after falttening
下面是代碼片段。
private static org.apache.spark.sql.Dataset flattenJSONdf(
org.apache.spark.sql.Dataset<org.apache.spark.sql.Row> ds) {
org.apache.spark.sql.types.StructField[] fields = ds.schema().fields();
java.util.List<String> fieldsNames = new java.util.ArrayList<>();
for (org.apache.spark.sql.types.StructField s : fields) {
fieldsNames.add(s.name());
}
for (int i = 0; i < fields.length; i++) {
org.apache.spark.sql.types.StructField field = fields[i];
org.apache.spark.sql.types.DataType fieldType = field.dataType();
String fieldName = field.name();
if (fieldType instanceof org.apache.spark.sql.types.ArrayType) {
java.util.List<String> fieldNamesExcludingArray = new java.util.ArrayList<String>();
for (String fieldName_index : fieldsNames) {
if (!fieldName.equals(fieldName_index))
fieldNamesExcludingArray.add(fieldName_index);
}
java.util.List<String> fieldNamesAndExplode = new java.util.ArrayList<>(
fieldNamesExcludingArray);
String s = String.format("explode_outer(%s) as %s", fieldName,
fieldName);
fieldNamesAndExplode.add(s);
String[] exFieldsWithArray = new String[fieldNamesAndExplode
.size()];
org.apache.spark.sql.Dataset exploded_ds = ds
.selectExpr(fieldNamesAndExplode
.toArray(exFieldsWithArray));
// explodedDf.show();
return flattenJSONdf(exploded_ds);
} else if (fieldType instanceof org.apache.spark.sql.types.StructType) {
String[] childFieldnames_struct = ((org.apache.spark.sql.types.StructType) fieldType)
.fieldNames();
java.util.List<String> childFieldnames = new java.util.ArrayList<>();
for (String childName : childFieldnames_struct) {
childFieldnames.add(fieldName + "." + childName);
}
java.util.List<String> newfieldNames = new java.util.ArrayList<>();
for (String fieldName_index : fieldsNames) {
if (!fieldName.equals(fieldName_index))
newfieldNames.add(fieldName_index);
}
newfieldNames.addAll(childFieldnames);
java.util.List<org.apache.spark.sql.Column> renamedStrutctCols = new java.util.ArrayList<>();
for (String newFieldNames_index : newfieldNames) {
renamedStrutctCols.add(new org.apache.spark.sql.Column(
newFieldNames_index.toString())
.as(newFieldNames_index.toString()
.replace(".", "_")));
}
scala.collection.Seq renamedStructCols_seq = scala.collection.JavaConverters
.collectionAsScalaIterableConverter(renamedStrutctCols)
.asScala().toSeq();
org.apache.spark.sql.Dataset ds_struct = ds
.select(renamedStructCols_seq);
return flattenJSONdf(ds_struct);
}
}
return ds;
}
推薦答案
Note代碼位于scala&；我已使用Spark Structured Streaming 。
可以使用org.apache.spark.sql.functions.explode函數展平數組列。請檢查以下代碼。
import org.apache.spark.sql.types._
val schema = DataType.fromJson("""{"type":"struct","fields":[{"name":"age","type":"long","nullable":true,"metadata":{}},{"name":"name","type":"string","nullable":true,"metadata":{}},{"name":"phones","type":{"type":"array","elementType":{"type":"struct","fields":[{"name":"models","type":{"type":"array","elementType":"string","containsNull":true},"nullable":true,"metadata":{}},{"name":"name","type":"string","nullable":true,"metadata":{}}]},"containsNull":true},"nullable":true,"metadata":{}},{"name":"watches","type":{"type":"struct","fields":[{"name":"models","type":{"type":"array","elementType":"string","containsNull":true},"nullable":true,"metadata":{}},{"name":"name","type":"string","nullable":true,"metadata":{}}]},"nullable":true,"metadata":{}}]}""").asInstanceOf[StructType]
// schema: org.apache.spark.sql.types.StructType = StructType(StructField(age,LongType,true), StructField(name,StringType,true), StructField(phones,ArrayType(StructType(StructField(models,ArrayType(StringType,true),true), StructField(name,StringType,true)),true),true), StructField(watches,StructType(StructField(models,ArrayType(StringType,true),true), StructField(name,StringType,true)),true))
val streamDF = spark.readStream.format("json").schema(schema).load("/tmp/jdata")
// streamDF: org.apache.spark.sql.DataFrame = [age: bigint, name: string ... 2 more fields]
streamDF
.withColumn("watches_models",explode($"watches.models")).withColumn("watches_name",$"watches.name")
.withColumn("phones_models",explode($"phones.models")).withColumn("phones_models",explode($"phones_models"))
.withColumn("phones_name",explode($"phones.name"))
.drop("watches","phones")
.writeStream
.format("console")
.outputMode("append")
.start()
.awaitTermination()
-------------------------------------------
Batch: 0
-------------------------------------------
+---+------+--------------------+------------+--------------+-----------+
|age| name| watches_models|watches_name| phones_models|phones_name|
+---+------+--------------------+------------+--------------+-----------+
| 26| Akash|Apple Watch Series 5| Apple| iphone X| Apple|
| 26| Akash|Apple Watch Series 5| Apple| iphone X| Samsung|
| 26| Akash|Apple Watch Series 5| Apple| iphone X| Google|
| 26| Akash|Apple Watch Series 5| Apple| iphone XR| Apple|
| 26| Akash|Apple Watch Series 5| Apple| iphone XR| Samsung|
| 26| Akash|Apple Watch Series 5| Apple| iphone XR| Google|
| 26| Akash|Apple Watch Series 5| Apple| iphone XS| Apple|
| 26| Akash|Apple Watch Series 5| Apple| iphone XS| Samsung|
| 26| Akash|Apple Watch Series 5| Apple| iphone XS| Google|
| 26| Akash|Apple Watch Series 5| Apple| iphone 11| Apple|
| 26| Akash|Apple Watch Series 5| Apple| iphone 11| Samsung|
| 26| Akash|Apple Watch Series 5| Apple| iphone 11| Google|
| 26| Akash|Apple Watch Series 5| Apple| iphone 11 Pro| Apple|
| 26| Akash|Apple Watch Series 5| Apple| iphone 11 Pro| Samsung|
| 26| Akash|Apple Watch Series 5| Apple| iphone 11 Pro| Google|
| 26| Akash|Apple Watch Series 5| Apple| Galaxy Note10| Apple|
| 26| Akash|Apple Watch Series 5| Apple| Galaxy Note10| Samsung|
| 26| Akash|Apple Watch Series 5| Apple| Galaxy Note10| Google|
| 26| Akash|Apple Watch Series 5| Apple|Galaxy Note10+| Apple|
| 26| Akash|Apple Watch Series 5| Apple|Galaxy Note10+| Samsung|
+---+------+--------------------+------------+--------------+-----------+
only showing top 20 rows
這篇關于有什么辦法可以扁平化電光流媒體中的嵌套JSON嗎？的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，
