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1. • • A table-valued function (TVF) is a function that returns a relation or a set of rows. There are two types of TVFs in Spark SQL: a TVF that can be specified in a FROM clause, e.g. range; a TVF that can be specified in SELECT/LATERAL VIEW clauses, e.g. explode.
     Reference:

     Table-valued Functions (TVF) - Spark 3.5.3 Documentation

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   What is a table-valued function in Spark SQL?

   • A table-valued function (TVF) is a function that returns a relation or a set of rows. There are two types of TVFs in Spark SQL: a TVF that can be specified in SELECT/LATERAL VIEW clauses, e.g. explode. containing rows in a range from 0 to end (exclusive) with step value 1. containing rows in a range from start to end (exclusive) with step value 1.

   Table-valued Functions (TVF) - Spark 3.5.0 Documentation

   spark.apache.org/docs/latest/sql-ref-syntax-qry-select-tvf.html

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   What are Spark SQL functions?

   • Spark SQL functions are a set of built-in functions provided by Apache Spark for performing various operations on DataFrame and Dataset objects in Spark SQL. These functions enable users to manipulate and analyze data within Spark SQL queries, providing a wide range of functionalities similar to those found in traditional SQL databases.

   SQL Built-in Functions in Spark - Spark By Examples

   sparkbyexamples.com/spark/spark-sql-functions/

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   What's the difference between a scalar function and a tabled-valued function?

   • Please also see: A scalar function returns a single value. It might not even be related to tables in your database. A tabled-valued function returns your specified columns for rows in your table meeting your selection criteria. An aggregate-valued function returns a calculation across the rows of a table -- for example summing values.

   Difference between scalar, table-valued, and aggregate functions in SQL

   stackoverflow.com/questions/34754663/difference-between-scalar-table-valued-and-aggregate-functions-in-sql-server

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   What is a table-valued function in SQL?

   • SQL user-defined table function. The range table-valued function. Any table-valued generator function, such as explode. Applies to: Databricks SQL Databricks Runtime 12.2 LTS and above. A table-valued function. A combination of one or more values, operators, and SQL functions that results in a value.

   Table-valued function (TVF) invocation | Databricks on AWS

   docs.databricks.com/en/sql/language-manual/sql-ref-syntax-qry-select-tvf.html

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   What is a window function in Spark SQL?

   • returns the population variance of the values in a column. Spark SQL window functions are used for performing calculations across a group of rows, known as a window, within a DataFrame. These functions allow users to perform aggregations, ranking, and analytical calculations over partitions of data defined by a window specification.

   SQL Built-in Functions in Spark - Spark By Examples

   sparkbyexamples.com/spark/spark-sql-functions/

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   What are collection functions in Spark SQL?

   • Collection functions in Spark SQL are used when working with array and map columns in DataFrames.These functions enable users to perform various operations on array and map columns efficiently, such as filtering, transforming, aggregating, and accessing elements. Creates a new array from the given input columns.

   SQL Built-in Functions in Spark - Spark By Examples

   sparkbyexamples.com/spark/spark-sql-functions/

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3. spark.apache.org › docs › latestTable-valued Functions (TVF) - Spark 3.5.3 Documentation

   spark.apache.org › docs › latest

   • Cached

   A table-valued function (TVF) is a function that returns a relation or a set of rows. There are two types of TVFs in Spark SQL: a TVF that can be specified in a FROM clause, e.g. range;

4. books.japila.pl › spark-sql-internals › table-valuedTable-Valued Functions - The Internals of Spark SQL

   books.japila.pl › spark-sql-internals › table-valued

   • Cached

   Table-Valued Functions (TFVs) are functions that return a table (as a LogicalPlan) that can be used anywhere that a regular (scalar) table is allowed. Table functions behave similarly to views, but, as functions in general, table functions accept parameters.

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6. stackoverflow.com › questions › 34754663Difference between scalar, table-valued, and aggregate ...

   stackoverflow.com › questions › 34754663

   Jan 13, 2016 · A tabled-valued function returns your specified columns for rows in your table meeting your selection criteria. An aggregate-valued function returns a calculation across the rows of a table -- for example summing values.

7. docs.databricks.com › en › sqlTable-valued function (TVF) invocation | Databricks on AWS

   docs.databricks.com › en › sql

   • Cached

   Invokes a function that returns a relation or a set of rows as a table-reference. A TVF can be a: SQL user-defined table function. The range table-valued function. Any table-valued generator function, such as explode. Applies to: Databricks SQL Databricks Runtime 12.2 LTS and above.

8. sparkbyexamples.com › spark › spark-sql-functionsSQL Built-in Functions in Spark - Spark By Examples

   sparkbyexamples.com › spark › spark-sql-functions

   • Cached

   Apr 22, 2024 · Spark SQL functions are a set of built-in functions provided by Apache Spark for performing various operations on DataFrame and Dataset objects in Spark SQL. These functions enable users to manipulate and analyze data within Spark SQL queries, providing a wide range of functionalities similar to those found in traditional SQL databases.

9. www.databricks.com › blog › 2021/10/20Introducing SQL User-Defined Functions | Databricks Blog

   www.databricks.com › blog › 2021/10/20

   • Cached

   Oct 20, 2021 · Learn more about Databricks’s new SQL UDF and how it makes UDFs within Spark SQL more performant, secure and versatile. Also includes tutorials on the use of scalar and table-valued functions.

10. sparkbyexamples.com › pyspark › pyspark-sql-functionsPySpark SQL Functions - Spark By Examples

    sparkbyexamples.com › pyspark › pyspark-sql-functions

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    May 16, 2024 · PySpark SQL Functions provide powerful functions for efficiently performing various transformations and computations on DataFrame columns within the PySpark environment. Leveraging these built-in functions offers several advantages.