Thank you Richard for responding.
I am able to run it successfully by using row.getMap but since I have to
update the map I wanted to use the HashMap api. Is there a way I can use
that? And I am surprised it worked in first case where I am creating
Dataset from list of rows but fails in the Map function.
Thanks
Ankur
On Fri, Jan 27, 2017 at 12:15 PM, Richard Xin <richardxin...@yahoo.com>
wrote:
> try
> Row newRow = RowFactory.create(row.getString(0), row.getString(1),
> row.getMap(2));
>
>
>
> On Friday, January 27, 2017 10:52 AM, Ankur Srivastava <
> ankur.srivast...@gmail.com> wrote:
>
>
> + DEV Mailing List
>
> On Thu, Jan 26, 2017 at 5:12 PM, Ankur Srivastava <
> ankur.srivast...@gmail.com> wrote:
>
> Hi,
>
> I am trying to map a Dataset with rows which have a map attribute. When I
> try to create a Row with the map attribute I get cast errors. I am able to
> reproduce the issue with the below sample code. The surprising thing is
> with same schema I am able to create a dataset from the List of rows.
>
> I am on Spark 2.0 and scala 2.11
>
> public static void main(String[] args) {
> StructType schema = new StructType().add("src", DataTypes.StringType)
> .add("dst", DataTypes.StringType)
> .add("freq", DataTypes.createMapType( DataTypes.StringType,
> DataTypes.IntegerType));
> List<Row> inputData = new ArrayList<>();
> inputData.add(RowFactory.creat e("1", "2", new HashMap<>()));
> SparkSession sparkSession = SparkSession
> .builder()
> .appName("IPCountFilterTest")
> .master("local")
> .getOrCreate();
>
> Dataset<Row> out = sparkSession.createDataFrame( inputData, schema);
> out.show();
>
> Encoder<Row> rowEncoder = RowEncoder.apply(schema);
> out.map((MapFunction<Row, Row>) row -> {
> Row newRow = RowFactory.create(row. getString(0), row.getString(1),
> new HashMap<String, Integer>());
>
> //Row newRow = RowFactory.create(row. getString(0), row.getString(1),
> row.getJavaMap(2));
>
> return newRow;
> }, rowEncoder).show();
> }
>
> Below is the error:
>
> 17/01/26 17:05:30 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID
> 0)
> java.lang.RuntimeException: java.util.HashMap is not a valid external type
> for schema of map<string,int>
> at org.apache.spark.sql.catalyst. expressions.GeneratedClass$
> GeneratedIterator.processNext( Unknown Source)
> at org.apache.spark.sql. execution.BufferedRowIterator.
> hasNext(BufferedRowIterator. java:43)
> at org.apache.spark.sql. execution. WholeStageCodegenExec$$
> anonfun$8$$anon$1.hasNext( WholeStageCodegenExec.scala: 370)
> at org.apache.spark.sql. execution.SparkPlan$$anonfun$
> 4.apply(SparkPlan.scala:246)
> at org.apache.spark.sql. execution.SparkPlan$$anonfun$
> 4.apply(SparkPlan.scala:240)
> at org.apache.spark.rdd.RDD$$ anonfun$mapPartitionsInternal$
> 1$$anonfun$apply$24.apply(RDD. scala:784)
> at org.apache.spark.rdd.RDD$$ anonfun$mapPartitionsInternal$
> 1$$anonfun$apply$24.apply(RDD. scala:784)
> at org.apache.spark.rdd. MapPartitionsRDD.compute(
> MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD. computeOrReadCheckpoint(RDD. scala:319)
> at org.apache.spark.rdd.RDD. iterator(RDD.scala:283)
> at org.apache.spark.scheduler. ResultTask.runTask(ResultTask. scala:70)
> at org.apache.spark.scheduler. Task.run(Task.scala:85)
> at org.apache.spark.executor. Executor$TaskRunner.run( Executor.scala:274)
> at java.util.concurrent. ThreadPoolExecutor.runWorker(
> ThreadPoolExecutor.java:1142)
> at java.util.concurrent. ThreadPoolExecutor$Worker.run(
> ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread. java:745)
> 17/01/26 17:05:30 WARN TaskSetManager: Lost task 0.0 in stage 0.0 (TID 0,
> localhost): java.lang.RuntimeException: java.util.HashMap is not a valid
> external type for schema of map<string,int>
> at org.apache.spark.sql.catalyst. expressions.GeneratedClass$
> GeneratedIterator.processNext( Unknown Source)
> at org.apache.spark.sql. execution.BufferedRowIterator.
> hasNext(BufferedRowIterator. java:43)
> at org.apache.spark.sql. execution. WholeStageCodegenExec$$
> anonfun$8$$anon$1.hasNext( WholeStageCodegenExec.scala: 370)
> at org.apache.spark.sql. execution.SparkPlan$$anonfun$
> 4.apply(SparkPlan.scala:246)
> at org.apache.spark.sql. execution.SparkPlan$$anonfun$
> 4.apply(SparkPlan.scala:240)
> at org.apache.spark.rdd.RDD$$ anonfun$mapPartitionsInternal$
> 1$$anonfun$apply$24.apply(RDD. scala:784)
> at org.apache.spark.rdd.RDD$$ anonfun$mapPartitionsInternal$
> 1$$anonfun$apply$24.apply(RDD. scala:784)
> at org.apache.spark.rdd. MapPartitionsRDD.compute(
> MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD. computeOrReadCheckpoint(RDD. scala:319)
> at org.apache.spark.rdd.RDD. iterator(RDD.scala:283)
> at org.apache.spark.scheduler. ResultTask.runTask(ResultTask. scala:70)
> at org.apache.spark.scheduler. Task.run(Task.scala:85)
> at org.apache.spark.executor. Executor$TaskRunner.run( Executor.scala:274)
> at java.util.concurrent. ThreadPoolExecutor.runWorker(
> ThreadPoolExecutor.java:1142)
> at java.util.concurrent. ThreadPoolExecutor$Worker.run(
> ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread. java:745)
>
>
> Thanks
> Ankur
>
>
>
>
>
