[jira] [Updated] (SPARK-16664) Spark 1.6.2 - Persist call on Data frames with more than 200 columns is wiping out the data.

[Sean Owen (JIRA)]

 [ 
https://issues.apache.org/jira/browse/SPARK-16664?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Sean Owen updated SPARK-16664:
--
Fix Version/s: 1.6.3

> 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
>  Components: Spark Core
>Affects Versions: 1.6.2
>Reporter: Satish Kolli
>Assignee: Wesley Tang
>Priority: Blocker
> Fix For: 1.6.3, 2.0.1, 2.1.0
>
>
> 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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[jira] [Updated] (SPARK-16664) Spark 1.6.2 - Persist call on Data frames with more than 200 columns is wiping out the data.

[Sean Owen (JIRA)]

 [ 
https://issues.apache.org/jira/browse/SPARK-16664?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Sean Owen updated SPARK-16664:
--
Fix Version/s: (was: 1.6.3)

> 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
>  Components: Spark Core
>Affects Versions: 1.6.2
>Reporter: Satish Kolli
>Assignee: Wesley Tang
>Priority: Blocker
> Fix For: 2.0.1, 2.1.0
>
>
> 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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[jira] [Updated] (SPARK-16664) Spark 1.6.2 - Persist call on Data frames with more than 200 columns is wiping out the data.

[Sean Owen (JIRA)]

 [ 
https://issues.apache.org/jira/browse/SPARK-16664?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Sean Owen updated SPARK-16664:
--
Assignee: Wesley Tang

> 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
>  Components: Spark Core
>Affects Versions: 1.6.2
>Reporter: Satish Kolli
>Assignee: Wesley Tang
>Priority: Blocker
> Fix For: 1.6.3, 2.0.1, 2.1.0
>
>
> 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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[jira] [Updated] (SPARK-16664) Spark 1.6.2 - Persist call on Data frames with more than 200 columns is wiping out the data.

[Sean Owen (JIRA)]

 [ 
https://issues.apache.org/jira/browse/SPARK-16664?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Sean Owen updated SPARK-16664:
--
Target Version/s: 2.0.1

> 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
>  Components: Spark Core
>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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[jira] [Updated] (SPARK-16664) Spark 1.6.2 - Persist call on Data frames with more than 200 columns is wiping out the data.

[Satish Kolli (JIRA)]

 [ 
https://issues.apache.org/jira/browse/SPARK-16664?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Satish Kolli updated SPARK-16664:
-
Description: 
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}

  was:
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
   


// test value
assert(testDf.persist.take(1).apply(0).toSeq(100).asInstanceOf[Long] == 100)
sc.stop()
  }
}
{code}


> 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
>  Components: Spark Core
>Affects Versions: 1.6.2
>Reporter: Satish Kolli
>Priority: Blocker
>
> Calling persist on a data frame with more than 200 columns is removing 

[jira] [Updated] (SPARK-16664) Spark 1.6.2 - Persist call on Data frames with more than 200 columns is wiping out the data.

[Satish Kolli (JIRA)]

 [ 
https://issues.apache.org/jira/browse/SPARK-16664?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Satish Kolli updated SPARK-16664:
-
Description: 
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
   


// test value
assert(testDf.persist.take(1).apply(0).toSeq(100).asInstanceOf[Long] == 100)
sc.stop()
  }
}
{code}

  was:
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}


> 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
>  Components: Spark Core
>Affects Versions: 1.6.2
>Reporter: Satish Kolli
>Priority: Blocker
>
> Calling persist on a data frame with more than 200 columns is removing the 

[jira] [Updated] (SPARK-16664) Spark 1.6.2 - Persist call on Data frames with more than 200 columns is wiping out the data.

[Satish Kolli (JIRA)]

 [ 
https://issues.apache.org/jira/browse/SPARK-16664?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Satish Kolli updated SPARK-16664:
-
Component/s: Spark Core

> 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
>  Components: Spark Core
>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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