Summaryorg.apache.spark.sql.functions是一个Object，提供了约两百多个函数。大部分函数与Hive的差不多。除UDF函数，均可在spark-sql中直接使用。

Functions. Spark SQL provides two function features to meet a wide range of user needs: built-in functions and user-defined functions (UDFs). Built-in functions are commonly used routines that Spark SQL predefines and a complete list of the functions can be found in the Built-in Functions API document.

Spark SQL sort functions are grouped as “sort_funcs” in spark SQL, these sort functions come handy when we want to perform any ascending and descending operations on columns. These are primarily used on the Sort function of the Dataframe or Dataset. asc () – ascending function In addition to the SQL interface, spark allows users to create custom user defined scalar and aggregate functions using Scala, Python and Java APIs. Please refer to scalar_functions and aggregate functions for more information. Syntax CREATE [OR REPLACE] [TEMPORARY] FUNCTION [IF NOT EXISTS] function_name AS class_name [resource_locations] Parameters OR REPLACE If specified, the resources for the function are reloaded. Apache Spark / Spark SQL Functions Spark SQL provides built-in standard Date and Timestamp (includes date and time) Functions defines in DataFrame API, these come in handy when we need to make operations on date and time. All these accept input as, Date type, Timestamp type or String.

Built-in functions This article presents the usages and descriptions of categories of frequently used built-in functions for aggregation, arrays and maps, dates and timestamps, and JSON data. cardinality(expr) - Returns the size of an array or a map. The function returns -1 if its input is null and spark.sql.legacy.sizeOfNull is set to true. If spark.sql.legacy.sizeOfNull is set to false, the function returns null for null input. By default, the spark.sql.legacy.sizeOfNull parameter is set to true. Examples: ROW_NUMBER in Spark assigns a unique sequential number (starting from 1) to each record based on the ordering of rows in each window partition. It is commonly used to deduplicate data.

Spark SQL functions make it easy to perform DataFrame analyses. This post will show you how to use the built-in Spark SQL functions and how to build your own SQL functions. Make sure to read Writing Beautiful Spark Code for a detailed overview of how to use SQL functions in production applications. Review of common functions

inline_outer(expr) - Explodes an array of structs into a table. Examples: > SELECT inline_outer(array(struct(1, 'a'), … Functions. Spark SQL provides two function features to meet a wide range of user needs: built-in functions and user-defined functions (UDFs).

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(Subset of) Standard Functions for Date and Time. Converts column to timestamp type (with an optional timestamp format) Converts current or specified time to Unix timestamp (in seconds) Generates time windows (i.e.

SPARK SQL FUNCTIONS. Spark comes over with the property of Spark SQL and it has many inbuilt functions that helps 3.
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Spark SQL provides a function broadcast to indicate that the dataset is smaller enough and should be broadcast def broadcast[T](df: Dataset[T]): Dataset[T] = { Dataset[T](df.sparkSession, PySpark PySpark expr () is a SQL function to execute SQL-like expressions and to use an existing DataFrame column value as an expression argument to Pyspark built-in functions. When executing Spark-SQL native functions, the data will stays in tungsten backend.

In particular, the inputs of an operator or function are not necessarily evaluated left-to-right or in any other fixed order. For example, logical AND and OR expressions do not have left-to-right “short-circuiting Window functions in Hive, Spark, SQL. What are window functions?
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You can access the standard functions using the following import statement. There are several functions associated with Spark for data processing such as custom transformation, spark SQL functions, Columns Function, User Defined functions known as UDF. Spark defines the dataset as data frames.

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sequence (start, stop, step) - Generates an array of elements from start to stop (inclusive), incrementing by step. The type of the returned elements is the same as the type of argument expressions. The start and stop expressions must resolve to the same type. Spark SQL map Functions Spark SQL map functions are grouped as “collection_funcs” in spark SQL along with several array functions. These map functions are useful when we want to concatenate two or more map columns, convert arrays of StructType entries to map column e.t.c Call an user-defined function. Example: import org.apache.spark.sql._ val df = Seq(("id1", 1), ("id2", 4), ("id3", 5)).toDF("id", "value") val spark = df.sparkSession spark.udf.register("simpleUDF", (v: Int) => v * v) df.select($"id", callUDF("simpleUDF", $"value")) When SQL config 'spark.sql.parser.escapedStringLiterals' is enabled, it fallbacks to Spark 1.6 behavior regarding string literal parsing.