QueryDB

  We had been facing issue with using spark-sftp Jar while downloading a remote SFTP file and creating Dataframe. Code being executed: val df = spark.read.format("com.springml.spark.sftp") .option("host", HOST) .option("port", PORT) .option("username", UN) .option("password", PWD) .option("fileType", "csv") .option("inferSchema", "true") .option("header", "true") .load(FILENAME) Error response: org.apache.spark.sql.AnalysisException: Path does not exist: wasb://... at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary$1.apply(DataSource.scala:612) at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary$1.apply(DataSource.scala:595) at scala.collection.TraversableLike$$anonfun$flatMap$1.ap...

Spark on Hadoop works on concept of data locality, such that code is tried to send to data rather then shuffling data to code. We specify following resources when creating a Spark Job -  executor cores executor memory driver cores driver memory number of executors Above property in general determines number of executors and each executor is kind of JVM process with specified cores and memory to use. The executors connects back to your driver program. Now the driver can send them commands.  Data is distributed on HDFS in blocks, and an input split kind of determine size of a data partition. YARN tries to collocate executors as close to data as possible to minimize network transfer of data. A task is a command sent from the driver to an executor by serializing your Function object. The executor deserializes the command, and executes it on a partition. The number of the Spark tasks in a single stage equals to the number of RDD partitions. Now, "executor cores" or " spark.ex...