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https://issues.apache.org/jira/browse/SPARK-19629?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15869848#comment-15869848
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Sean Owen commented on SPARK-19629:
-----------------------------------

I don't believe that is guaranteed, or should be. For example, in general, it 
could be better to choose to construe many small physical partitions as one.

> Partitioning of Parquet is not considered correctly at loading in local[X] 
> mode
> -------------------------------------------------------------------------------
>
>                 Key: SPARK-19629
>                 URL: https://issues.apache.org/jira/browse/SPARK-19629
>             Project: Spark
>          Issue Type: Bug
>          Components: Input/Output, Spark Core
>    Affects Versions: 2.0.0, 2.1.0
>         Environment: Tested using docker run 
> gettyimages/spark:1.6.1-hadoop-2.6 and
> docker run gettyimages/spark:2.1.0-hadoop-2.7.
>            Reporter: Navige
>            Priority: Minor
>
> Running the following two examples will lead to different results depending 
> on whether the code is run using Spark 1.6 or Spark 2.1. 
> h1.What does the example do?
> - The code creates an exemplary dataframe with random data. 
> - The dataframe is repartitioned and stored to disk. 
> - Then the dataframe is re-read from disk.
> - The number of partitions of the dataframe is considered.
> h1. What is the expected behaviour?
> The number of partitions specified when storing the dataframe should be the 
> same as when re-loading the dataframe from disk.
> On Spark 1.6 the partitioning is kept, i.e., the code example will return 10 
> partitions as specified using npartitions; on Spark 2.1 the number of 
> partitions will equal the number of local nodes specified when starting Spark 
> (using local[X] as master).
> h1.Minimal code example
> {code:none}
> # run on Spark 1.6
> import scala.util.Random
> import org.apache.spark.sql.types.{StructField, StructType, FloatType}
> import org.apache.spark.sql.Row
>  val rdd = sc.parallelize(Seq.fill(100)(Row(Seq(Random.nextFloat()): _*)))
> val df = sqlContext.createDataFrame(rdd, StructType(Seq(StructField("test", 
> FloatType))))
> val npartitions = 10
> df.repartition(npartitions).write.parquet("/tmp/test1")
> val read = sqlContext.read.parquet("/tmp/test1")
> assert(npartitions == read.rdd.getNumPartitions) //true on Spark 1.6
> {code}
> {code:none}
> # run on Spark 2.1
> import scala.util.Random
> import org.apache.spark.sql.types.{StructField, StructType, FloatType}
> import org.apache.spark.sql.Row
> val rdd = sc.parallelize(Seq.fill(100)(Row(Seq(Random.nextFloat()): _*)))
> val df = spark.sqlContext.createDataFrame(rdd, 
> StructType(Seq(StructField("test", FloatType))))
> val npartitions = 10
> df.repartition(npartitions).write.parquet("/tmp/test1")
> val read = spark.sqlContext.read.parquet("/tmp/test1")
> assert(npartitions == read.rdd.getNumPartitions) //false on Spark 2.1
> {code}



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