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https://issues.apache.org/jira/browse/SPARK-7032?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14562641#comment-14562641
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Lior Chaga commented on SPARK-7032:
-----------------------------------
[~viirya], the following modification to the code solves the problem with
dataframes:
{code}
DataFrame secondAggregated =
secondDataFrame.groupBy("key").sum("counter").withColumnRenamed("SUM(counter)",
"counter");
Row[] collectAgg = firstDataFrame.select("counter",
"key").except(secondAggregated.select("counter", "key")).collect();
{code}
or simply:
{code}
Row[] collectAgg = firstDataFrame.select("counter",
"key").except(secondAggregated.select("SUM(counter)", "key")).collect();
{code}
Thanks for you help!
> SparkSQL incorrect results when using UNION/EXCEPT with GROUP BY clause
> -----------------------------------------------------------------------
>
> Key: SPARK-7032
> URL: https://issues.apache.org/jira/browse/SPARK-7032
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.2.2, 1.3.1
> Reporter: Lior Chaga
>
> When using UNION/EXCEPT clause with GROUP BY clause in spark sql, results do
> not match expected.
> In the following example, only 1 record should be in first table and not in
> second (as when grouping by key field, the counter for key=1 is 10 in both
> tables).
> Each of the clauses by itself is working properly when running exclusively.
> {code}
> //import com.addthis.metrics.reporter.config.ReporterConfig;
> import org.apache.spark.SparkConf;
> import org.apache.spark.api.java.JavaRDD;
> import org.apache.spark.api.java.JavaSparkContext;
> import org.apache.spark.sql.api.java.JavaSQLContext;
> import org.apache.spark.sql.api.java.Row;
> import java.io.IOException;
> import java.io.Serializable;
> import java.util.ArrayList;
> import java.util.List;
> public class SimpleApp {
> public static void main(String[] args) throws IOException {
> SparkConf conf = new SparkConf().setAppName("Simple Application")
> .setMaster("local[1]");
> JavaSparkContext sc = new JavaSparkContext(conf);
> List<MyObject> firstList = new ArrayList<MyObject>(2);
> firstList.add(new MyObject(1, 10));
> firstList.add(new MyObject(2, 10));
> List<MyObject> secondList = new ArrayList<MyObject>(3);
> secondList.add(new MyObject(1, 4));
> secondList.add(new MyObject(1, 6));
> secondList.add(new MyObject(2, 8));
> JavaRDD<MyObject> firstRdd = sc.parallelize(firstList);
> JavaRDD<MyObject> secondRdd = sc.parallelize(secondList);
> JavaSQLContext sqlc = new JavaSQLContext(sc);
> sqlc.applySchema(firstRdd,
> MyObject.class).registerTempTable("table1");
> sqlc.sqlContext().cacheTable("table1");
> sqlc.applySchema(secondRdd,
> MyObject.class).registerTempTable("table2");
> sqlc.sqlContext().cacheTable("table2");
> List<Row> firstMinusSecond = sqlc.sql(
> "SELECT key, counter FROM table1 " +
> "EXCEPT " +
> "SELECT key, SUM(counter) FROM table2 " +
> "GROUP BY key ").collect();
> System.out.println("num of rows in first but not in second = [" +
> firstMinusSecond.size() + "]");
> sc.close();
> System.exit(0);
> }
> public static class MyObject implements Serializable {
> public MyObject(Integer key, Integer counter) {
> this.key = key;
> this.counter = counter;
> }
> private Integer key;
> private Integer counter;
> public Integer getKey() {
> return key;
> }
> public void setKey(Integer key) {
> this.key = key;
> }
> public Integer getCounter() {
> return counter;
> }
> public void setCounter(Integer counter) {
> this.counter = counter;
> }
> }
> }
> {code}
> Same example, give or take, with DataFrames - when not using groupBy works
> good, with groupBy I get 2 rows instead of 1:
> {code}
> SparkConf conf = new SparkConf().setAppName("Simple Application")
> .setMaster("local[1]");
> JavaSparkContext sc = new JavaSparkContext(conf);
> List<MyObject> firstList = new ArrayList<MyObject>(2);
> firstList.add(new MyObject(1, 10));
> firstList.add(new MyObject(2, 10));
> List<MyObject> secondList = new ArrayList<MyObject>(3);
> secondList.add(new MyObject(1, 10));
> secondList.add(new MyObject(2, 8));
> JavaRDD<MyObject> firstRdd = sc.parallelize(firstList);
> JavaRDD<MyObject> secondRdd = sc.parallelize(secondList);
> SQLContext sqlc = new SQLContext(sc);
> DataFrame firstDataFrame = sqlc.createDataFrame(firstRdd, MyObject.class);
> DataFrame secondDataFrame = sqlc.createDataFrame(secondRdd, MyObject.class);
> Row[] collect = firstDataFrame.except(secondDataFrame).collect();
> System.out.println("num of rows in first but not in second = [" +
> collect.length + "]");
> DataFrame secondAggregated = secondDataFrame.groupBy("key").sum("counter");
> Row[] collectAgg = firstDataFrame.except(secondAggregated).collect();
> System.out.println("num of rows in first but not in second = [" +
> collectAgg.length + "]"); // should be 1 row, but there are 2
> {code}
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