We had some trouble doing the math on a column with dataframes even if the method is readily there.
We kept getting an error that the column was not a numeric value.
After a bit of reading I figured that I needed to use a UDF to transform the string column to a numeric column.
package com.cinq.experience;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.SQLContext;
import org.apache.spark.sql.api.java.UDF1;
import org.apache.spark.sql.types.DataTypes;
public class DataFrameAvg {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("DataFrameAvg").setMaster("local");
JavaSparkContext jsc = new JavaSparkContext(conf);
SQLContext sqlContext = new SQLContext(jsc);
DataFrame df = sqlContext.read()
.format("com.databricks.spark.csv")
.option("header", "true")
.load("numericdata.csv");
df.registerTempTable("allData");
df.show();
sqlContext.udf().register("toInt", new UDF1() {
public Integer call(String s) throws Exception {
System.out.println("Parsing: " + s);
return Integer.parseInt(s);
}
}, DataTypes.IntegerType);
DataFrame withNumber = sqlContext.sql("SELECT toInt(number) from allData");
withNumber.groupBy().avg("c0").show();
}
}
and the content of the numericdata.csv is very simple:
name,number person1,53 person2,42 person3,27 person4,15 person5,24 person6,30 person7,33 person8,36
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