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Scala vs Python vs Java :: Big Data processing with Apache Spark

Scala vs Python vs Java :: Big Data processing with Apache Spark I have tried to compare Python and Scala on various parameters like: performance, ease of using the language, integration using existing libraries, support for streaming use cases and of Apache Spark’s core capabilities. I did not try to evaluate Java for the following reasons: Java does not support the REPL command line feature (Read, Evaluate, Print, Loop) which is very extensively used to check if small code-snippets are working as expected.  Java is too verbose – It uses more lines of code, and displays more messages than needed. Scala on the JVM is way more powerful and cleaner than Java. SCALA PYTHON De-Facto language for Spark Scala is the 1st preferred language for Spark as Spark itself is written in Scala, so developers can dig deep into the Spark source code whenever required. New features of Spark are first av
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Filtering out Nulls and Headers in Scala/Spark

Filtering out Nulls and Headers in Scala/Spark Consider a contact_file in HDFS location  /User/VJ/testfile  which has a null record in a non-null field. Here, the last line has no value in the 'age' field so the requirement is to filter all such lines. id,fname,lname,age,designation 1, amarnath, jaiswal, 61, Businessman 2, prakash, yadav, 30, Developer 3, vishal, jaiswal, 32, Engineer 4, ravi, jaiswal,, Builder Solution: Usage of mapPartitionsWithIndex to drop the 1st iterator for 0th index will filter the header from your input file, while the Usage of != "" on the 4th field will filter out the 3rd line scala> sc.textFile("/User/VJ/testfile"). mapPartitionsWithIndex ((x,y) => if (x==0) y.drop(1) else y).filter(x=>x.split(",")(3) != "" ).take(5).foreach(println) Output: 1, amarnath, jaiswal, 61, Businessman 2, prakash, yadav, 30, Developer 3, vishal, jaiswal, 32, Engineer Thanks, Vishal.
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Apache Spark :: aggregateByKey explained :: CCA175 exam topic

Apache Spark :: aggregateByKey explained :: S ample question for Spark Developer's exam (Cloudera/Databricks) Scenario :   Sample Input Tuple 'ordersVJ' is in the form of (ItemId, RevenuePerItemId) as follows: ... .. (10,299.98) (20, 199.99 )  (20,250.0) (20,129.99) (40,49.98) -- Key = 40,  Value  = 49.98 (Input value type is  Float ) (40,299.95) (40,150.0) (40,199.92) (50,299.98) (50,299.95) ... .. Using the  aggregateByKey  Spark RDD API, find the total revenue and maximum revenue per ItemId. Desired output for ItemId 40 will be (40,( 699.85 , 299.95 )) Solution : The RDD way of achieving the result using aggregateByKey is a bit trickier and complex when you compare it with the Spark Dataframe or Spark SQL way of achieving the results. You need to have very good understanding of what is being passed in the function. The aggregateByKey function takes 2 argument. The desired output for ItemId 40 is in a (Key, Value) pair with value
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Apache Spark :: Error Resolution :: 'value join is not a member of org.apache.spark.rdd.RDD'

Apache Spark :: Error Resolution :: 'value join is not a member of org.apache.spark.rdd.RDD' ERROR DESCRIPTION Consider 2 Spark RDDs to be joined together.. Say, rdd1.first is in the form of (Int, Int, Float) = (1,957,299.98) while rdd2.first is something like (Int, Int) = (25876,1) where the join is supposed to take place on the 1st field from both the RDDs. scala> rdd1.join(rdd2)  --- results in an error <console>:**: error: value join is not a member of org.apache.spark.rdd.RDD[(Int, Int, Float)] REASON Both the RDDs should be in the form of a Key-Value pair. Here, rdd2 -- being in the form of (1,957,299.98) -- does not obey this rule.. While rdd1 -- which is in the form of (25876,1) -- does. RESOLUTION Convert the output of the 1st RDD from (1,957,299.98) to a Key-Value pair in the form of (1,(957,299.98)) before joining it with rdd2, as shown below: scala> val rdd1KV = rdd1.map(x=>(x.split(",")(1).toInt,(x.split(","
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Automated bash script to export all Hive DDLs from an existing environment at one go!

Exporting all Hive DDLs from an existing environment The below script scans through all the databases on your Hive system, and routes all the Create Table statements for all the tables to a file. This will be helpful when you need to set up a new environment based on the existing one. It has been tested on databases with over 500 tables. Steps to be performed are as follows: 1. Create a script, say  hive_ddls_export.sh  in the Hadoop box with HIVE CLI installed on it with the following content: #!/bin/bash databases =`hive -e "show databases;"` all_db_names =${databases} datetoday =`date +%Y-%m-%d_%H:%M:%S` touch dev_hive_ext_tables_$datetoday.sql chmod 744 dev_hive_ext_tables_$datetoday.sql for listofdatabases in $ all_db_names do   tables=`hive -e "use $listofdatabases;show tables;"`   all_tab_names=`echo "${tables}"`   echo " /****  Start DDLs for Tables in ${listofdatabases} ****/ " >> dev_hive_ext
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