Split dataset based on column values in spark

问题

I am trying to split the Dataset into different Datasets based on Manufacturer column contents. It is very slow
Please suggest a way to improve the code, so that it can execute faster and reduce the usage of Java code.

    List<Row> lsts= countsByAge.collectAsList();

        for(Row lst:lsts){
             String man=lst.toString();
             man = man.replaceAll("[\\p{Ps}\\p{Pe}]", "");
             Dataset<Row> DF = src.filter("Manufacturer='"+man+"'");
             DF.show();

        }

The Code, Input and Output Datasets are as shown below.

    package org.sparkexample;
    import org.apache.parquet.filter2.predicate.Operators.Column;
    import org.apache.spark.SparkConf;
    import org.apache.spark.api.java.JavaSparkContext;
    import org.apache.spark.sql.Dataset;
    import org.apache.spark.sql.RelationalGroupedDataset;
    import org.apache.spark.sql.Row;
    import org.apache.spark.sql.SQLContext;
    import org.apache.spark.sql.SparkSession;

    import java.util.Arrays;
    import java.util.List;

    import org.apache.spark.api.java.JavaPairRDD;
    import org.apache.spark.api.java.JavaRDD;
            public class GroupBy {

                public static void main(String[] args) {
                    System.setProperty("hadoop.home.dir", "C:\\winutils");
                    JavaSparkContext sc = new JavaSparkContext(new SparkConf().setAppName("SparkJdbcDs").setMaster("local[*]"));
                    SQLContext sqlContext = new SQLContext(sc);
                    SparkSession spark = SparkSession.builder().appName("split datasets").getOrCreate();
                    sc.setLogLevel("ERROR");

                    Dataset<Row> src= sqlContext.read()
                                .format("com.databricks.spark.csv")
                                .option("header", "true")
                                .load("sample.csv");


                    Dataset<Row> unq_manf=src.select("Manufacturer").distinct();
                    List<Row> lsts= unq_manf.collectAsList();

                    for(Row lst:lsts){
                         String man=lst.toString();
                         man = man.replaceAll("[\\p{Ps}\\p{Pe}]", "");
                         Dataset<Row> DF = src.filter("Manufacturer='"+man+"'");
                         DF.show();

                }
            }
        }

        INPUT TABLE-
        +------+------------+--------------------+---+
        |ItemID|Manufacturer|       Category name|UPC|
        +------+------------+--------------------+---+
        |   804|         ael|Brush & Broom Han...|123|
        |   805|         ael|Wheel Brush Parts...|124|
        |   813|         ael|      Drivers Gloves|125|
        |   632|        west|       Pipe Wrenches|126|
        |   804|         bil|     Masonry Brushes|127|
        |   497|        west|   Power Tools Other|128|
        |   496|        west|   Power Tools Other|129|
        |   495|         bil|           Hole Saws|130|
        |   499|         bil|    Battery Chargers|131|
        |   497|        west|   Power Tools Other|132|
        +------+------------+--------------------+---+

        OUTPUT-
        +------------+
        |Manufacturer|
        +------------+
        |         ael|
        |        west|
        |         bil|
        +------------+

        +------+------------+--------------------+---+
        |ItemID|Manufacturer|       Category name|UPC|
        +------+------------+--------------------+---+
        |   804|         ael|Brush & Broom Han...|123|
        |   805|         ael|Wheel Brush Parts...|124|
        |   813|         ael|      Drivers Gloves|125|
        +------+------------+--------------------+---+

        +------+------------+-----------------+---+
        |ItemID|Manufacturer|    Category name|UPC|
        +------+------------+-----------------+---+
        |   632|        west|    Pipe Wrenches|126|
        |   497|        west|Power Tools Other|128|
        |   496|        west|Power Tools Other|129|
        |   497|        west|Power Tools Other|132|
        +------+------------+-----------------+---+

        +------+------------+----------------+---+
        |ItemID|Manufacturer|   Category name|UPC|
        +------+------------+----------------+---+
        |   804|         bil| Masonry Brushes|127|
        |   495|         bil|       Hole Saws|130|
        |   499|         bil|Battery Chargers|131|
        +------+------------+----------------+---+

Thank You

回答1:

You have two choice in this case:

1. First you have to collect unique manufacturer values and then map over resulting array:

   val df = Seq(("HP", 1), ("Brother", 2), ("Canon", 3), ("HP", 5)).toDF("k", "v")    
   val brands = df.select("k").distinct.collect.flatMap(_.toSeq)
   val BrandArray = brands.map(brand => df.where($"k" <=> brand))
   BrandArray.foreach { x =>
   x.show()
   println("---------------------------------------")
   }
   

2. You can also save the data frame based on manufacturer.

   df.write.partitionBy("hour").saveAsTable("parquet")

来源：https://stackoverflow.com/questions/42645836/split-dataset-based-on-column-values-in-spark