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1. sparkbyexamples.com › pyspark › pyspark-string-functions-with-examplesPySpark String Functions with Examples - Spark By Examples

   sparkbyexamples.com › pyspark › pyspark-string-functions-with-examples

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   12 maj 2024 · In this article, I will explain the most used string functions I come across in my real-time projects with examples. When possible, try to leverage the functions from standard libraries (pyspark.sql.functions) as they are a little bit safer in compile-time, handle null, and perform better when compared to UDFs.

   • PySpark SparkContext Explained

     pyspark.SparkContext is an entry point to the PySpark...

2. sparkbyexamples.com › pyspark › pyspark-sql-with-examplesPySpark SQL Tutorial with Examples - Spark By Examples

   sparkbyexamples.com › pyspark › pyspark-sql-with-examples

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   7 maj 2024 · PySpark SQL Tutorial – The pyspark.sql is a module in PySpark that is used to perform SQL-like operations on the data stored in memory. You can either leverage using programming API to query the data or use the ANSI SQL queries similar to RDBMS.

3. stackoverflow.com › questions › 35870760Filtering a Pyspark DataFrame with SQL-like IN clause

   stackoverflow.com › questions › 35870760

   8 mar 2016 · I want to filter a Pyspark DataFrame with a SQL-like IN clause, as in sc = SparkContext() sqlc = SQLContext(sc) df = sqlc.sql('SELECT * from my_df WHERE field1 IN a') where a is the tuple (1, 2, ...

4. www.programcreek.com › python › examplePython pyspark.sql.SQLContext() Examples - ProgramCreek.com

   www.programcreek.com › python › example

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   pyspark.sql.SQLContext () Examples. The following are 21 code examples of pyspark.sql.SQLContext (). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

5. sparkbyexamples.com › pyspark › pyspark-sparkcontext-explainedPySpark SparkContext Explained - Spark By Examples

   sparkbyexamples.com › pyspark › pyspark-sparkcontext-explained

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   27 mar 2024 · pyspark.SparkContext is an entry point to the PySpark functionality that is used to communicate with the cluster and to create an RDD, accumulator, and broadcast variables. In this article, you will learn how to create PySpark SparkContext with examples.

6. spark.apache.org › docs › latestpyspark.SparkContext — PySpark 3.5.3 documentation - Apache Spark

   spark.apache.org › docs › latest

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   A SparkContext represents the connection to a Spark cluster, and can be used to create RDD and broadcast variables on that cluster. When you create a new SparkContext, at least the master and app name should be set, either through the named parameters here or through conf. Parameters. masterstr, optional.

7. people.eecs.berkeley.edu › ~jegonzal › pysparkpyspark.sql.context — PySpark master documentation

   people.eecs.berkeley.edu › ~jegonzal › pyspark

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   A SQLContext can be used create :class:`DataFrame`, register :class:`DataFrame` as tables, execute SQL over tables, cache tables, and read parquet files. :param sparkContext: The :class:`SparkContext` backing this SQLContext. :param sqlContext: An optional JVM Scala SQLContext.