火花PSV文件到数据帧的转换错误

Question

我正在使用的Spark版本是2.0+，我要做的只是将管道（|）分隔的值文件读入Dataframe，然后像查询一样运行SQL。 我也尝试了逗号分隔文件。 我正在使用spark-shell与spark进行交互，我已经下载了spark-csv jar并使用--packages选项运行spark-shell将其导入到会话中。 已成功导入。

import spark.implicits._
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql._
val session = 
SparkSession.builder().appName("test").master("local").getOrCreate()
    val df = session.read.format("com.databricks.spark.csv").option("header", "true").option("mode", "DROPMALFORMED").load("testdata.txt");

WARN Hive: Failed to access metastore. This class should not accessed in runtime.
apache.hadoop.hive.ql.metadata.HiveException: java.lang.RuntimeException: Unable to instantiate org.apache.hadoop.hi
 at org.apache.hadoop.hive.ql.metadata.Hive.getAllDatabases(Hive.java:1236)
 at org.apache.hadoop.hive.ql.metadata.Hive.reloadFunctions(Hive.java:174)
 at org.apache.hadoop.hive.ql.metadata.Hive.<clinit>(Hive.java:166)
 at org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:503)
 at org.apache.spark.sql.hive.client.HiveClientImpl.<init>(HiveClientImpl.scala:171)
 at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
 at sun.reflect.NativeConstructorAccessorImpl.newInstance(Unknown Source)
 at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(Unknown Source)
 at java.lang.reflect.Constructor.newInstance(Unknown Source)
 at org.apache.spark.sql.hive.client.IsolatedClientLoader.createClient(IsolatedClientLoader.scala:258)
 at org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:359)
 at org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:263)
 at org.apache.spark.sql.hive.HiveSharedState.metadataHive$lzycompute(HiveSharedState.scala:39)

Answer 1

您可以将psv文件直接加载到RDD中，然后根据需要将其拆分，然后可以在其上应用架构。 这是java示例。

import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;

public class RDDtoDF_Update {
    public static void main(final  String[] args) throws Exception {

        SparkSession spark = SparkSession
                .builder()
                .appName("RDDtoDF_Updated")
                .master("local[2]")
                .config("spark.some.config.option", "some-value")
                .getOrCreate();

        StructType schema = DataTypes
                .createStructType(new StructField[] {
                        DataTypes.createStructField("eid", DataTypes.IntegerType, false),
                        DataTypes.createStructField("eName", DataTypes.StringType, false),
                        DataTypes.createStructField("eAge", DataTypes.IntegerType, true),
                        DataTypes.createStructField("eDept", DataTypes.IntegerType, true),
                        DataTypes.createStructField("eSal", DataTypes.IntegerType, true),
                        DataTypes.createStructField("eGen", DataTypes.StringType,true)});


        String filepath = "F:/Hadoop/Data/EMPData.txt";
        JavaRDD<Row> empRDD = spark.read()
                .textFile(filepath)
                .javaRDD()
                .map(line -> line.split("\t"))
                .map(r -> RowFactory.create(Integer.parseInt(r[0]), r[1].trim(),Integer.parseInt(r[2]),
                        Integer.parseInt(r[3]),Integer.parseInt(r[4]),r[5].trim() ));


        Dataset<Row> empDF = spark.createDataFrame(empRDD, schema);
        empDF.groupBy("edept").max("esal").show();

谢谢。