Returns the first argument-based logarithm of the second argument.

Make DayTimeIntervalType duration from days, hours, mins and secs.

Make interval from years, months, weeks, days, hours, mins and secs.

Create timestamp from years, months, days, hours, mins, secs and timezone fields.

Create the current timestamp with local time zone from years, months, days, hours, mins, secs and timezone fields.

Create local date-time from years, months, days, hours, mins, secs fields.

Make year-month interval from years, months.

returns the average of the values in a group.

Alias for [DataFrame::unpivot]

Computes the min value for each numeric column for each group.

Returns number of months between dates date1 and date2.

Returns a [DataFrameNaFunctions] for handling missing values.

Returns the negative value.

create default DataFrame based on a spark session and initial logical plan

Create a new StructType from a vector of [StructField]

Create a new DataFrameReader with a [SparkSession]

Create a new Logical Plan from an initial [spark::Relation]

Returns the ntile group id (from 1 to n inclusive) in an ordered window partition.

Returns a new [DataFrame] by skiping the first n rows

Set many input options based on an iterator of (key/value pairs) for the underlying data source

Set many input options based on an iterator of (key/value pairs) for the underlying data source

Returns a new [DataFrame] sorted by the specified column(s).

Partitions the output by the given columns on the file system

Returns the exact percentile(s) of numeric column expr at the given percentage(s) with value range in [0.0, 1.0].

Returns the approximate percentile of the numeric column col which is the smallest value in the ordered col values (sorted from least to greatest) suc

Build the Spark [spark::Plan] for a [spark::command::CommandType]

Returns the value of the first argument raised to the power of the second argument.

Generates a random column with independent and identically distributed (i.i.d.) samples uniformly distributed in [0.0, 1.0).

Generates a column with independent and identically distributed (i.i.d.) samples from the standard normal distribution.

Recovers all the partitions of the given table and updates the catalog.

Invalidates and refreshes all the cached data (and the associated metadata) for any DataFrame that contains the given data source path.

Invalidates and refreshes all the cached data (and the associated metadata) for any DataFrame that contains the given data source path.

Validate a connect string for a remote Spark Session String must conform to the [Spark Documentation](https://github.com/apache/spark/blob/master/con

Remove a tag previously added to be assigned to all the operations started by this thread in this session.

Returns a new [DataFrame] partitioned by the given partitioning expressions.

Create a multi-dimensional rollup for the current DataFrame using the specified columns, and returns a [GroupedData] object

Round the given value to scale decimal places using HALF_UP rounding mode if scale >= 0 or at integral part when scale < 0.

Returns True when the logical query plans inside both DataFrames are equal and therefore return the same results.

Schema of the stream in DDL format (e.g. `"name string, age int"`)

Parses a CSV string and infers its schema in DDL format

Parses a JSON string and infers its schema in DDL format

Returns a hash code of the logical query plan against this [DataFrame].

Splits a string into arrays of sentences, where each sentence is an array of words

Generate a sequence of integers from start to stop, incrementing by step.

Change the reattachable execute value

Sorts the input array in ascending or descending order according to the natural ordering of the array elements.

Sorts the output in each bucket by the given columns on the file system

Returns a new [DataFrame] with each partition sorted by the specified column(s).

Returns Spark session that created this DataFrame.

Splits str around matches of the given pattern.

Returns a DataFrameStatFunctions for statistic functions.