我有一个CSV，其数据如下：
dept|emp_json|location
finance|{ "employee":[{"name":{"firstName":"John","lasteName":"Doe"},"address":{"street":"1234 West Broad St","unit":"8505","city":"Columbus"}},{"name":{"firstName":"Alex","lasteName":"Messi"},"address":{"street":"4321 North Meecham Rd","unit":"300","city":"Salinas"}}]}|OH
我能够读取文件并创建数据集并提取Json列：
Dataset<Row> empDetlsDS = sparkSession.read().option("header", "true").option(delimiter, "|").schema(mySchema).csv(inputCSVPath);
Dataset<Row> empDetlsJsonDocDS = empDetlsDS.select(emp_json);
我想展平JSON并创建一个输出数据集，其雇员数组中的行数采用以下格式：
dept |emp_name |emp_address |emp_city|location |
---------------------------------------------------------------
finance |John Doe |1234 West Broad St 8505 |Columbus|OH |
finance |Alex Messi |4321 North Meecham Rd 300|Salinas |OH |
-------------------------------------------------------------------
如果有人对使用Java和Spark有任何建议，请提供帮助。预先感谢。
答案 0 :(得分：0)
@tkkman 这是我所说的scala方式。 rdd方法已被弃用，现在建议使用DataSet方法，因此在Java中应该很简单
import spark.implicits._
import org.apache.spark.sql.functions._
val df = spark.read.option("delimiter","|").option("header","true").csv("/FileStore/tables/test.txt")
val jdf = spark.read.json(df.select("emp_json").rdd.map(_.toString)).select(explode($"employee").alias("emp"))
.select($"emp.name.firstName",$"emp.name.lasteName",$"emp.address.street",$"emp.address.unit",$"emp.address.city")
jdf.printSchema
jdf.withColumn("dept", lit("finance")).withColumn("city",lit("OH")).show(false)
+---------+---------+---------------------+----+----+-------+
|firstName|lasteName|street |unit|city|dept |
+---------+---------+---------------------+----+----+-------+
|John |Doe |1234 West Broad St |8505|OH |finance|
|Alex |Messi |4321 North Meecham Rd|300 |OH |finance|
+---------+---------+---------------------+----+----+-------+
答案 1 :(得分：0)
在Java中，您可以这样操作：
package net.jgp.books.sparkInAction.ch12.lab950CsvWithEmbdeddedJson;
import static org.apache.spark.sql.functions.concat;
import static org.apache.spark.sql.functions.explode;
import static org.apache.spark.sql.functions.lit;
import java.io.Serializable;
import org.apache.spark.api.java.function.MapFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
/**
* Ingesting a CSV with embedded JSON.
*
* @author jgp
*/
public class CsvWithEmbdeddedJsonApp implements Serializable {
private static final long serialVersionUID = 19711L;
/**
* Turns a Row into JSON. NOt very fail safe, but done to illustrate.
*
* @author jgp
*/
private final class Jsonifier
implements MapFunction<Row, String> {
private static final long serialVersionUID = 19712L;
@Override
public String call(Row r) throws Exception {
StringBuffer sb = new StringBuffer();
sb.append("{ \"dept\": \"");
sb.append(r.getString(0));
sb.append("\",");
String s = r.getString(1).toString();
if (s != null) {
s = s.trim();
if (s.charAt(0) == '{') {
s = s.substring(1, s.length() - 1);
}
}
sb.append(s);
sb.append(", \"location\": \"");
sb.append(r.getString(2));
sb.append("\"}");
return sb.toString();
}
}
/**
* main() is your entry point to the application.
*
* @param args
*/
public static void main(String[] args) {
CsvWithEmbdeddedJsonApp app = new CsvWithEmbdeddedJsonApp();
app.start();
}
/**
* The processing code.
*/
private void start() {
// Creates a session on a local master
SparkSession spark = SparkSession.builder()
.appName("Processing of invoices")
.master("local[*]")
.getOrCreate();
Dataset<Row> df = spark
.read()
.option("header", "true")
.option("delimiter", "|")
.csv("data/misc/csv_with_embedded_json.csv");
df.show(5, false);
df.printSchema();
Dataset<String> ds = df.map(
new Jsonifier(),
Encoders.STRING());
ds.show(5, false);
ds.printSchema();
Dataset<Row> dfJson = spark.read().json(ds);
dfJson.show(5, false);
dfJson.printSchema();
dfJson = dfJson
.withColumn("emp", explode(dfJson.col("employee")))
.drop("employee");
dfJson.show(5, false);
dfJson.printSchema();
dfJson = dfJson
.withColumn("emp_name",
concat(
dfJson.col("emp.name.firstName"),
lit(" "),
dfJson.col("emp.name.lasteName")))
.withColumn("emp_address",
concat(dfJson.col("emp.address.street"),
lit(" "),
dfJson.col("emp.address.unit")))
.withColumn("emp_city", dfJson.col("emp.address.city"))
.drop("emp");
dfJson.show(5, false);
dfJson.printSchema();
}
}
和往常一样，Java非常冗长:)-我没有抱怨。我留下了很多printSchema（）和show（）来说明构造过程。 Jsonifier类可以以一种更好，更通用的方式实现，但它给出了一个主意（如果需要，您也可以将其作为lambda来实现）。
输出为：
+-------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+
|dept |emp_json |location|
+-------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+
|finance|{ "employee":[{"name":{"firstName":"John","lasteName":"Doe"},"address":{"street":"1234 West Broad St","unit":"8505","city":"Columbus"}},{"name":{"firstName":"Alex","lasteName":"Messi"},"address":{"street":"4321 North Meecham Rd","unit":"300","city":"Salinas"}}]}|OH |
+-------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+
root
|-- dept: string (nullable = true)
|-- emp_json: string (nullable = true)
|-- location: string (nullable = true)
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|value |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|{ "dept": "finance", "employee":[{"name":{"firstName":"John","lasteName":"Doe"},"address":{"street":"1234 West Broad St","unit":"8505","city":"Columbus"}},{"name":{"firstName":"Alex","lasteName":"Messi"},"address":{"street":"4321 North Meecham Rd","unit":"300","city":"Salinas"}}], "location": "OH"}|
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
root
|-- value: string (nullable = true)
+-------+-------------------------------------------------------------------------------------------------------------+--------+
|dept |employee |location|
+-------+-------------------------------------------------------------------------------------------------------------+--------+
|finance|[[[Columbus, 1234 West Broad St, 8505], [John, Doe]], [[Salinas, 4321 North Meecham Rd, 300], [Alex, Messi]]]|OH |
+-------+-------------------------------------------------------------------------------------------------------------+--------+
root
|-- dept: string (nullable = true)
|-- employee: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- address: struct (nullable = true)
| | | |-- city: string (nullable = true)
| | | |-- street: string (nullable = true)
| | | |-- unit: string (nullable = true)
| | |-- name: struct (nullable = true)
| | | |-- firstName: string (nullable = true)
| | | |-- lasteName: string (nullable = true)
|-- location: string (nullable = true)
+-------+--------+------------------------------------------------------+
|dept |location|emp |
+-------+--------+------------------------------------------------------+
|finance|OH |[[Columbus, 1234 West Broad St, 8505], [John, Doe]] |
|finance|OH |[[Salinas, 4321 North Meecham Rd, 300], [Alex, Messi]]|
+-------+--------+------------------------------------------------------+
root
|-- dept: string (nullable = true)
|-- location: string (nullable = true)
|-- emp: struct (nullable = true)
| |-- address: struct (nullable = true)
| | |-- city: string (nullable = true)
| | |-- street: string (nullable = true)
| | |-- unit: string (nullable = true)
| |-- name: struct (nullable = true)
| | |-- firstName: string (nullable = true)
| | |-- lasteName: string (nullable = true)
+-------+--------+----------+-------------------------+--------+
|dept |location|emp_name |emp_address |emp_city|
+-------+--------+----------+-------------------------+--------+
|finance|OH |John Doe |1234 West Broad St 8505 |Columbus|
|finance|OH |Alex Messi|4321 North Meecham Rd 300|Salinas |
+-------+--------+----------+-------------------------+--------+
root
|-- dept: string (nullable = true)
|-- location: string (nullable = true)
|-- emp_name: string (nullable = true)
|-- emp_address: string (nullable = true)
|-- emp_city: string (nullable = true)
答案 2 :(得分：-1)
如果每个架构的json正确，则可以使用 explode 方法，然后使用点运算符选择想要的列。 （例如：emp_json.name，emp_json.address ...）
示例代码
val flatJSON = df.select（$“ dept”，explode（$“ emp_json”）。as（“ emp））
flatJSON.select（“ dept”，“ emp.name”，“ emp.address”）
答案 3 :(得分：-1)
检查一下：
scala> val df = spark.read.format("csv").option("header", "true").option("delimiter", "|").option("inferSchema","true").load("/tmp/stack/tkkman.csv")
df: org.apache.spark.sql.DataFrame = [dept: string, emp_json: string ... 1 more field]
scala> df.show(false)
+-------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+
|dept |emp_json |location|
+-------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+
|finance|{ "employee":[{"name":{"firstName":"John","lasteName":"Doe"},"address":{"street":"1234 West Broad St","unit":"8505","city":"Columbus"}},{"name":{"firstName":"Alex","lasteName":"Messi"},"address":{"street":"4321 North Meecham Rd","unit":"300","city":"Salinas"}}]}|OH |
+-------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+
scala> df.printSchema
root
|-- dept: string (nullable = true)
|-- emp_json: string (nullable = true)
|-- location: string (nullable = true)
scala> val jsonstr = """{ "employee":[{"name":{"firstName":"John","lasteName":"Doe"},"address":{"street":"1234 West Broad St","unit":"8505","city":"Columbus"}},{"name":{"firstName":"Alex","lasteName":"Messi"},"address":{"street":"4321 North Meecham Rd","unit":"300","city":"Salinas"}}]}"""
jsonstr: String = { "employee":[{"name":{"firstName":"John","lasteName":"Doe"},"address":{"street":"1234 West Broad St","unit":"8505","city":"Columbus"}},{"name":{"firstName":"Alex","lasteName":"Messi"},"address":{"street":"4321 North Meecham Rd","unit":"300","city":"Salinas"}}]}
scala> val dfj = spark.read.json(Seq(jsonstr).toDS)
dfj: org.apache.spark.sql.DataFrame = [employee: array<struct<address:struct<city:string,street:string,unit:string>,name:struct<firstName:string,lasteName:string>>>]
scala> dfj.show(false)
+-------------------------------------------------------------------------------------------------------------+
|employee |
+-------------------------------------------------------------------------------------------------------------+
|[[[Columbus, 1234 West Broad St, 8505], [John, Doe]], [[Salinas, 4321 North Meecham Rd, 300], [Alex, Messi]]]|
+-------------------------------------------------------------------------------------------------------------+
scala> dfj.schema
res51: org.apache.spark.sql.types.StructType = StructType(StructField(employee,ArrayType(StructType(StructField(address,StructType(StructField(city,StringType,true), StructField(street,StringType,true), StructField(unit,StringType,true)),true), StructField(name,StructType(StructField(firstName,StringType,true), StructField(lasteName,StringType,true)),true)),true),true))
scala> val sch_emp = dfj.schema
sch_emp: org.apache.spark.sql.types.StructType = StructType(StructField(employee,ArrayType(StructType(StructField(address,StructType(StructField(city,StringType,true), StructField(street,StringType,true), StructField(unit,StringType,true)),true), StructField(name,StructType(StructField(firstName,StringType,true), StructField(lasteName,StringType,true)),true)),true),true))
scala> val df2 = df.select(col("*"),from_json('emp_json,sch_emp).as("emp"))
df2: org.apache.spark.sql.DataFrame = [dept: string, emp_json: string ... 2 more fields]
scala> df2.select(explode($"emp.employee")).printSchema
root
|-- col: struct (nullable = true)
| |-- address: struct (nullable = true)
| | |-- city: string (nullable = true)
| | |-- street: string (nullable = true)
| | |-- unit: string (nullable = true)
| |-- name: struct (nullable = true)
| | |-- firstName: string (nullable = true)
| | |-- lasteName: string (nullable = true)
scala> df2.select(col("*"),explode($"emp.employee").as("emp2")).select('dept,concat($"emp2.name.firstName",lit(" "),$"emp2.name.lasteName").as("emp_name"),$"emp2.address.street" as "emp_address", $"emp2.address.city" as "emp_city", 'location).show(false)
+-------+----------+---------------------+--------+--------+
|dept |emp_name |emp_address |emp_city|location|
+-------+----------+---------------------+--------+--------+
|finance|John Doe |1234 West Broad St |Columbus|OH |
|finance|Alex Messi|4321 North Meecham Rd|Salinas |OH |
+-------+----------+---------------------+--------+--------+
scala>