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This post has been edited by medmourad: Today, 03:34 PM

hive – Spark – how to rename a column in orc file (not table)

We a hive ORC table which is being populated with spark jobs. The problem is hive table has a column name “ABC”. however while loading on the job, we loaded ORC file with column “XYZ”.

problem:
some of the orc file has “ABC” column and some of the orc file has “XYZ” column. And hive schema has “ABC” column.

How do I merge both these columns into “ABC” using spark?

current Solution

  1. Create tempoary hive table.
  2. insert records from main table where abc is not null
  3. Change column name to xyz in main table and load temp table where xyz is null
  4. Change column name back to “ABC”
  5. insert overwrite main table with temp table.

regex – Spark Scala: SQL rlike vs Custom UDF

I’ve a scenario where 10K+ regular expressions are stored in a table along with various other columns and this needs to be joined against an incoming dataset. Initially I was using “spark sql rlike” method as below and it was able to hold the load until incoming record counts were less than 50K

PS: The regular expression reference data is a broadcasted dataset.

dataset.join(regexDataset.value, expr("input_column rlike regular_exp_column")

Then I wrote a custom UDF to transform them using Scala native regex search as below,

  1. Below val collects the reference data as Array of tuples.
val regexPreCalcArray: Array((Int, Regex)) = {
        regexDataset.value
            .select( "col_1", "regex_column")
            .collect
            .map(row => (row.get(0).asInstanceOf(Int),row.get(1).toString.r))
    }

Implementation of Regex matching UDF,

    def findMatchingPatterns(regexDSArray: Array((Int,Regex))): UserDefinedFunction = {
        udf((input_column: String) => {
            for {
                text <- Option(input_column)
                matches = regexDSArray.filter(regexDSValue => if (regexDSValue._2.findFirstIn(text).isEmpty) false else true)
                if matches.nonEmpty
            } yield matches.map(x => x._1).min
        }, IntegerType)
    }

Joins are done as below, where a unique ID from reference data will be returned from UDF in case of multiple regex matches and joined against reference data using unique ID to retrieve other columns needed for result,

dataset.withColumn("min_unique_id", findMatchingPatterns(regexPreCalcArray)($"input_column"))
.join(regexDataset.value, $"min_unique_id" === $"unique_id" , "left")

But this too gets very slow with skew in execution (1 executor task runs for a very long time) when record count spikes to 1M+, spark suggests not to use UDF as it would degrade the performance, any other best practises I should apply here? or any suggestions to do this efficiently would be very helpful.

Does using multiple columns in partitioning Spark DataFrame makes read slower?

I wonder if I use multiple columns while writing a Spark DataFrame in spark makes future read slower?
I know partitioning with critical columns on which I would filter in future will improve the performance while reading data, I don’t know if I use many columns in partitionBy does this reduce the performance or still, I could benefit from the higher performance?

(ordersDF
  .write
  .format("parquet")
  .mode("overwrite")
  .partitionBy("CustomerId", "OrderDate") # <----------- add many columns
  .save("/storage/Orders_parquet"))

Python – FileNotFound on Spark

I am running code in a cluster environment with the following code:

spark-submit --conf spark.executorEnv.LD_LIBRARY_PATH=$LD_LIBRARY_PATH --num-executors 10 
--executor-cores 5 --executor-memory 10G --files hdfs:///temp_folder/a.shp,
hdfs:///temp_folder/file1.txt, hdfs:///temp_folder/file2.txt test.py                         
hdfs:///temp_folder/data.csv output

This is a snapshot of the code wherever the files are accessed:

def functionSomething(_, bulkContent):


    file1, file2 = 'file1.txt', 'file2.txt'
    files_duo = open(file1, 'r').readlines()+ open(file, 'r').readlines()
    shapefile = 'a.shp'

if __name__=='__main__':

    sc = SparkContext()
    spark = SparkSession()

    data = sys.argv(-2)                            
    rdd = sc.textFile(data).mapPartitionsWithIndex(functionSomething)
    rdd.saveAsTextFile(sys.argv(-1))

However, when I run my code in the cluster, I keep getting the following error:

FileNotFoundError: (Errno 2) No such file or directory: 'hdfs:///temp_folder/file1.txt'

How should I address this problem?

The best way to make data from a Spark cluster available for queries and custom dashboards in a web app?

I have a Spark cluster that contains my customer's data. I would like to enable my customers to query their data via our admin dashboard and to create their own reports.

An important consideration is that the customer is technical.

The best option I can think of is embedding Jupyter Notebook or Zeppelin Notebook in the dashboard as this gives them direct access to their data.

What other options do I have?

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