Getting a clear count from a framework using Apache Spark

Question

I have data that looks like

+--------------+---------+-------+---------+
|       dataOne|OtherData|dataTwo|dataThree|
+--------------+---------|-------+---------+
|          Best|     tree|      5|      533|
|            OK|     bush|      e|     3535|
|           MEH|      cow|      -|     3353|
|           MEH|      oak|   none|       12|
+--------------+---------+-------+---------+

and I'm trying to get it out

+--------------+---------+
|       dataOne|    Count|
+--------------+---------|
|          Best|        1|
|            OK|        1|
|           Meh|        2|
+--------------+---------+

I have no problem getting the dataOne into a dataframe by itself and displaying its contents to make sure that I just grab the dataOne column.However, I cannot find the correct syntax to turn this sql query into the data I need. I tried to create this next framework from a temporary view created by the whole dataset

Dataset<Row> dataOneCount = spark.sql("select dataOne, count(*) from 
dataFrame group by dataOne");
dataOneCount.show();

But the spark The documentation, which I could only find on this, showed how to make this type of aggregation in spark category 1.6 earlier, so any help would be appreciated.

An error message appears here, but I checked the data and there is no indexing error there.

 java.lang.ArrayIndexOutOfBoundsException: 11

functions() countDistinct

Column countNum = countDistinct(dataFrame.col("dataOne"));
Dataset<Row> result = dataOneDataFrame.withColumn("count",countNum);
result.show();

dataOneDataFrame - dataFrame,

select dataOne from dataFrame

, , , , , / countDistinct

edit: , , ( )

Dataset<Row> dataFrame 

public static void main(String[] args) {


    SparkSession spark = SparkSession
            .builder()
            .appName("Log File Reader")
            .getOrCreate();

    //args[0] is the textfile location
    JavaRDD<String> logsRDD = spark.sparkContext()
            .textFile(args[0],1)
            .toJavaRDD();

    String schemaString = "dataOne OtherData dataTwo dataThree";

    List<StructField> fields = new ArrayList<>();
    String[] fieldName = schemaString.split(" ");


    for (String field : fieldName){
        fields.add(DataTypes.createStructField(field, DataTypes.StringType, true));
    }
    StructType schema = DataTypes.createStructType(fields);

    JavaRDD<Row> rowRDD = logsRDD.map((Function<String, Row>) record -> {
       String[] attributes = record.split(" ");
       return RowFactory.create(attributes[0],attributes[1],attributes[2],attributes[3]);
    });


    Dataset<Row> dF = spark.createDataFrame(rowRDD, schema);

    //first attempt
    dF.groupBy(col("dataOne")).count().show();

    //Trying with a sql statement
    dF.createOrReplaceTempView("view");
    dF.sparkSession().sql("select command, count(*) from view group by command").show();

, , RowFactory? , , , .

best tree 5 533
OK bush e 3535
MEH cow - 3353
MEH oak none 12

Answer 1

Scala . Java:

// Input data
val df = {
  import org.apache.spark.sql._
  import org.apache.spark.sql.types._
  import scala.collection.JavaConverters._

  val simpleSchema = StructType(
    StructField("dataOne", StringType) ::
    StructField("OtherData", StringType) ::
    StructField("dataTwo", StringType) ::
    StructField("dataThree", IntegerType) :: Nil)

  val data = List(
    Row("Best", "tree", "5", 533),
    Row("OK", "bush", "e", 3535),
    Row("MEH", "cow", "-", 3353),
    Row("MEH", "oak", "none", 12)
  )

  spark.createDataFrame(data.asJava, simpleSchema)
}

df.show

+-------+---------+-------+---------+
|dataOne|OtherData|dataTwo|dataThree|
+-------+---------+-------+---------+
|   Best|     tree|      5|      533|
|     OK|     bush|      e|     3535|
|    MEH|      cow|      -|     3353|
|    MEH|      oak|   none|       12|
+-------+---------+-------+---------+

df.groupBy(col("dataOne")).count().show()

+-------+-----+
|dataOne|count|
+-------+-----+
|    MEH|    2|
|   Best|    1|
|     OK|    1|
+-------+-----+

Java S3, :

$SPARK_HOME/bin/spark-submit \
  --class sparktest.FromStackOverflow \
  --packages "org.apache.hadoop:hadoop-aws:2.7.3" \
  target/scala-2.11/sparktest_2.11-1.0.0-SNAPSHOT.jar "s3a://my-bucket-name/sample.txt"