QueryDB

  Cryptography, or cryptology is the practice and study of techniques for secure communication in the presence of adversarial behavior. More generally, cryptography is about constructing and analyzing protocols that prevent third parties or the public from reading private messages. This secures the information being transmitted in point to point communication. This at lowest level, is achieved by using data & mathematical algorithms. Cryptography prior to the modern age was effectively synonymous with encryption , converting readable information (plaintext) to unintelligible nonsense text (ciphertext), which can only be read by reversing the process (decryption). The sender of an encrypted (coded) message shares the decryption (decoding) technique only with intended recipients to preclude access from adversaries. Modern cryptography is heavily based on mathematical theory and computer science practice; cryptographic algorithms are designed around computational hardness assumptions,

  We had been writing to a Partitioned Hive Table and realized that data is being written has sub-folder. For ex- Refer Table definition as below -  Create table T1 ( name string, address string) Partitioned by (process_date string) stored as parquet location '/mytable/a/b/c/org=employee'; While writing to table HDFS path being written looks something like this -  /mytable/a/b/c/org=employee/ process_date=20220812/ org=employee The unnecessary addition of   org=employee after  process_date partition is because Hive Table has location consisting "=" operator, which Hive uses as syntax to determine partition column. Re-defining Table resolves above problem -  Create table T1 ( name string, address string) Partitioned by (process_date string) stored as parquet location '/mytable/a/b/c/employee';

  For a Structured Tabular Structure it is many a times required to transform Rows into Columns. This blog explains step by step process which can be executed as one SQL to achieve same.  Lets try to understand with help of below example: where -in , we want to implement / transform input Table into table structure mentioned as output. INPUT_TABLE   topic groupId batchTimeMs Partition  offset  Count  t1  g001  1658173779  0 123  122 t1 g001  1658173779  1 2231 100 t2 g001  1658173779  0 12 11 OUTPUT_TABLE   rowkey:key offset:0 count:0     offset:1  count:1  t1:g001:1658173779  123 122 2231 100 t2:g001:1658173779  12 11 NULL NULL   FIRST STEP - Concat Topic, GroupID, and BatchTimeMS to create RowKey  Create Columns - offsets:0, counts:0, offsets:1, counts:1. Such that Columns has value only when respective partition value matches with column name. SQL as below - select concat_ws(':', topic,groupId,batchTimeMs) as rowkey, case when partition='0' then offset else null en

  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