Is your input data partitioned? How much memory have you assigned to your 
executor? Have you looked at how much time is being spent in GC in the 
executor? Is Spark spilling the data into disk?

It is likely that the partition is too big. Spark tries to read the whole 
partition into the memory of one executor node.  If the partition is too big, 
it might be causing Spark to run out of memory. One of the side effects of how 
the JVM does garbage collection is that when applications use too much memory, 
they just might run very slowly instead of crashing.

If the problem is that the partition is too big, increasing executor memory, or 
reducing size of partition will do the trick
´╗┐On 4/13/18, 1:03 PM, "lsn24" <lekshmi.s...@gmail.com> wrote:

    Hello,
    
     We are running into issues while trying to process fixed length files using
    spark.
    
    The approach we took is as follows:
    
    1. Read the .bz2 file  into a dataset from hdfs using
    spark.read().textFile() API.Create a temporary view.
    
         Dataset<String> rawDataset = sparkSession.read().textFile(filePath);
         rawDataset.createOrReplaceTempView(tempView);
    
    2. Run a sql query on the view, to slice and dice the data the way we need
    it (using substring).
    
     (SELECT 
                         TRIM(SUBSTRING(value,1 ,16)) AS record1 ,
                         TRIM(SUBSTRING(value,17 ,8)) AS record2 ,
                         TRIM(SUBSTRING(value,25 ,5)) AS record3 ,
                         TRIM(SUBSTRING(value,30 ,16)) AS record4 ,
                         CAST(SUBSTRING(value,46 ,8) AS BIGINT) AS record5 , 
                         CAST(SUBSTRING(value,54 ,6) AS BIGINT) AS record6 , 
                         CAST(SUBSTRING(value,60 ,3) AS BIGINT) AS record7 , 
                         CAST(SUBSTRING(value,63 ,6) AS BIGINT) AS record8 , 
                         TRIM(SUBSTRING(value,69 ,20)) AS record9 ,
                         TRIM(SUBSTRING(value,89 ,40)) AS record10 ,
                         TRIM(SUBSTRING(value,129 ,32)) AS record11 ,
                         TRIM(SUBSTRING(value,161 ,19)) AS record12,
                         TRIM(SUBSTRING(value,180 ,1)) AS record13 ,
                         TRIM(SUBSTRING(value,181 ,9)) AS record14 ,
                         TRIM(SUBSTRING(value,190 ,3)) AS record15 ,
                         CAST(SUBSTRING(value,193 ,8) AS BIGINT) AS record16 , 
                         CAST(SUBSTRING(value,201 ,8) AS BIGINT) AS record17 
                         FROM tempView)
    
    3.Write the output of sql query to a parquet file.
         loadDataset.write().mode(SaveMode.Append).parquet(outputDirectory);
    
    Problem :
    
      The step #2 takes a longer time , if the length of line is ~2000
    characters. If each line in the file is only 1000 characters then it takes
    only 4 minutes to process 20 million lines. If we increase the line length
    to 2000 characters it takes 20 minutes to process 20 million lines.
    
    
    Is there a better way in spark to parse fixed length lines?
    
    
    *Note: *Spark version we use is 2.2.0 and we are using  Spark with Java.
    
    
    
    
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