Satish Kolli created SPARK-16664:
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Summary: Spark 1.6.2 - Persist call on Data frames with more than
200 columns is wiping out the data.
Key: SPARK-16664
URL: https://issues.apache.org/jira/browse/SPARK-16664
Project: Spark
Issue Type: Bug
Affects Versions: 1.6.2
Reporter: Satish Kolli
Priority: Blocker
Calling persist on a data frame with more than 200 columns is removing the data
from the data frame. This is an issue in Spark 1.6.2. Works with out any issues
in Spark 1.6.1
Following test case demonstrates problem. Please let me know if you need any
additional information. Thanks.
{code}
import org.apache.spark._
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.types._
import org.apache.spark.sql.{Row, SQLContext}
import org.scalatest.FunSuite
class TestSpark162_1 extends FunSuite {
test("test data frame with 200 columns") {
val sparkConfig = new SparkConf()
val parallelism = 5
sparkConfig.set("spark.default.parallelism", s"$parallelism")
sparkConfig.set("spark.sql.shuffle.partitions", s"$parallelism")
val sc = new SparkContext(s"local[3]", "TestNestedJson", sparkConfig)
val sqlContext = new SQLContext(sc)
// create dataframe with 200 columns and fake 200 values
val size = 200
val rdd: RDD[Seq[Long]] = sc.parallelize(Seq(Seq.range(0, size)))
val rowRdd: RDD[Row] = rdd.map(d => Row.fromSeq(d))
val schemas = List.range(0, size).map(a => StructField("name"+ a, LongType,
true))
val testSchema = StructType(schemas)
val testDf = sqlContext.createDataFrame(rowRdd, testSchema)
// test value
assert(testDf.persist.take(1).apply(0).toSeq(100).asInstanceOf[Long] == 100)
sc.stop()
}
test("test data frame with 201 columns") {
val sparkConfig = new SparkConf()
val parallelism = 5
sparkConfig.set("spark.default.parallelism", s"$parallelism")
sparkConfig.set("spark.sql.shuffle.partitions", s"$parallelism")
val sc = new SparkContext(s"local[3]", "TestNestedJson", sparkConfig)
val sqlContext = new SQLContext(sc)
// create dataframe with 201 columns and fake 201 values
val size = 201
val rdd: RDD[Seq[Long]] = sc.parallelize(Seq(Seq.range(0, size)))
val rowRdd: RDD[Row] = rdd.map(d => Row.fromSeq(d))
val schemas = List.range(0, size).map(a => StructField("name"+ a, LongType,
true))
val testSchema = StructType(schemas)
val testDf = sqlContext.createDataFrame(rowRdd, testSchema)
// test value
assert(testDf.persist.take(1).apply(0).toSeq(100).asInstanceOf[Long] == 100)
sc.stop()
}
}
{code}
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