zero-one.geni.core — org.clojars.behrica/geni 0.0.42-CB.2

! clj

(! expr)

Params: (e: Column)

Result: Column

Inversion of boolean expression, i.e. NOT.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.497Z

% clj

(% left-expr right-expr)

Params: (other: Any)

Result: Column

Modulo (a.k.a. remainder) expression.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.822Z

& clj

(& left-expr right-expr)

Params: (other: Any)

Result: Column

Compute bitwise AND of this expression with another expression.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.878Z

&& clj

(&& & exprs)

Params: (other: Any)

Result: Column

Boolean AND.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.824Z

* clj

(* & exprs)

Params: (other: Any)

Result: Column

Multiplication of this expression and another expression.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.827Z

** clj

(** base exponent)

Params: (l: Column, r: Column)

Result: Column

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

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.520Z

+ clj

(+ & exprs)

Params: (other: Any)

Result: Column

Sum of this expression and another expression.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.829Z

- clj

(- & exprs)

Params: (other: Any)

Result: Column

Subtraction. Subtract the other expression from this expression.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.957Z

->col-array clj

(->col-array args)

Coerce a coll of coerceable values into a coll of columns.

->column clj multimethod

Params: (colName: String)

Result: Column

Returns a Column based on the given column name.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.258Z

->dataset clj multimethod

Create a Dataset from a path or a collection of records.

->date-col clj

(->date-col expr)

(->date-col expr date-format)

Params: (e: Column)

Result: Column

Converts the column into DateType by casting rules to DateType.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.616Z

->debug-string clj

Coerce to string useful for debugging.

->kebab-columns clj

(->kebab-columns dataset)

Returns a new Dataset with all columns renamed to kebab cases.

->schema clj

(->schema value)

Coerces plain Clojure data structures to a Spark schema.

(-> {:x [:short]
     :y [:string :int]
     :z {:a :float :b :double}}
    g/->schema
    g/->string)
=> StructType(
     StructField(x,ArrayType(ShortType,true),true),
     StructField(y,MapType(StringType,IntegerType,true),true),
     StructField(
       z,
       StructType(
         StructField(a,FloatType,true),
         StructField(b,DoubleType,true)
       ),
       true
     )
   )

->string clj

Coerce to string.

->timestamp-col clj

(->timestamp-col expr)

(->timestamp-col expr date-format)

Params: (s: Column)

Result: Column

Converts to a timestamp by casting rules to TimestampType.

A date, timestamp or string. If a string, the data must be in a format that can be cast to a timestamp, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

A timestamp, or null if the input was a string that could not be cast to a timestamp

2.2.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.623Z

->utc-timestamp clj

(->utc-timestamp expr)

Params: (ts: Column, tz: String)

Result: Column

Given a timestamp like '2017-07-14 02:40:00.0', interprets it as a time in the given time zone, and renders that time as a timestamp in UTC. For example, 'GMT+1' would yield '2017-07-14 01:40:00.0'.

A date, timestamp or string. If a string, the data must be in a format that can be cast to a timestamp, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

A string detailing the time zone ID that the input should be adjusted to. It should be in the format of either region-based zone IDs or zone offsets. Region IDs must have the form 'area/city', such as 'America/Los_Angeles'. Zone offsets must be in the format '(+|-)HH:mm', for example '-08:00' or '+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'. Other short names are not recommended to use because they can be ambiguous.

A timestamp, or null if ts was a string that could not be cast to a timestamp or tz was an invalid value

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.626Z

/ clj

(/ & exprs)

Params: (other: Any)

Result: Column

Division this expression by another expression.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.832Z

< clj

Params: (other: Any)

Result: Column

Less than.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.834Z

<= clj

Params: (other: Any)

Result: Column

Less than or equal to.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.836Z

<=> clj

Params: (other: Any)

Result: Column

Equality test that is safe for null values.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.838Z

= clj

Params: (other: Any)

Result: Column

Equality test.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.843Z

=!= clj

Params: (other: Any)

Result: Column

Inequality test.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.840Z

=== clj

Params: (other: Any)

Result: Column

Equality test.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.843Z

> clj

Params: (other: Any)

Result: Column

Greater than.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.845Z

>= clj

Params: (other: Any)

Result: Column

Greater than or equal to an expression.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.847Z

abs clj

(abs expr)

Params: (e: Column)

Result: Column

Computes the absolute value of a numeric value.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.169Z

acos clj

(acos expr)

Params: (e: Column)

Result: Column

inverse cosine of e in radians, as if computed by java.lang.Math.acos

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.171Z

add clj

(add cms item)

(add cms item cnt)

Params: (item: Any)

Result: Unit

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/util/sketch/CountMinSketch.html

Timestamp: 2020-10-19T01:56:26.095Z

add-months clj

(add-months expr months)

Params: (startDate: Column, numMonths: Int)

Result: Column

Returns the date that is numMonths after startDate.

A date, timestamp or string. If a string, the data must be in a format that can be cast to a date, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

The number of months to add to startDate, can be negative to subtract months

A date, or null if startDate was a string that could not be cast to a date

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.174Z

agg clj

(agg dataframe & args)

Params: (aggExpr: (String, String), aggExprs: (String, String)*)

Result: DataFrame

(Scala-specific) Aggregates on the entire Dataset without groups.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.739Z

agg-all clj

(agg-all dataframe agg-fn)

Aggregates on all columns of the entire Dataset without groups.

aggregate clj

(aggregate expr init merge-fn)

(aggregate expr init merge-fn finish-fn)

Params: (expr: Column, initialValue: Column, merge: (Column, Column) ⇒ Column, finish: (Column) ⇒ Column)

Result: Column

Applies a binary operator to an initial state and all elements in the array, and reduces this to a single state. The final state is converted into the final result by applying a finish function.

the input array column

the initial value

(combined_value, input_value) => combined_value, the merge function to merge an input value to the combined_value

combined_value => final_value, the lambda function to convert the combined value of all inputs to final result

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.177Z

alias clj multimethod

Column: Gives the column an alias.

Dataset: Returns a new Dataset with an alias set.

app-name clj

(app-name)

(app-name spark)

Params:

Result: String

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.487Z

approx-count-distinct clj

(approx-count-distinct expr)

(approx-count-distinct expr rsd)

Params: (e: Column)

Result: Column

(Since version 2.1.0) Use approx_count_distinct

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.742Z

approx-quantile clj

(approx-quantile dataframe col-or-cols probs rel-error)

Params: (col: String, probabilities: Array[Double], relativeError: Double)

Result: Array[Double]

Calculates the approximate quantiles of a numerical column of a DataFrame.

The result of this algorithm has the following deterministic bound: If the DataFrame has N elements and if we request the quantile at probability p up to error err, then the algorithm will return a sample x from the DataFrame so that the exact rank of x is close to (p * N). More precisely,

This method implements a variation of the Greenwald-Khanna algorithm (with some speed optimizations). The algorithm was first present in Space-efficient Online Computation of Quantile Summaries by Greenwald and Khanna.

the name of the numerical column

a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the minimum, 0.5 is the median, 1 is the maximum.

The relative target precision to achieve (greater than or equal to 0). If set to zero, the exact quantiles are computed, which could be very expensive. Note that values greater than 1 are accepted but give the same result as 1.

the approximate quantiles at the given probabilities

2.0.0

null and NaN values will be removed from the numerical column before calculation. If the dataframe is empty or the column only contains null or NaN, an empty array is returned.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/DataFrameStatFunctions.html

Timestamp: 2020-10-19T01:56:24.640Z

array clj

(array & exprs)

Params: (cols: Column*)

Result: Column

Creates a new array column. The input columns must all have the same data type.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.184Z

array-contains clj

(array-contains expr value)

Params: (column: Column, value: Any)

Result: Column

Returns null if the array is null, true if the array contains value, and false otherwise.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.185Z

array-distinct clj

(array-distinct expr)

Params: (e: Column)

Result: Column

Removes duplicate values from the array.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.186Z

array-except clj

(array-except left right)

Params: (col1: Column, col2: Column)

Result: Column

Returns an array of the elements in the first array but not in the second array, without duplicates. The order of elements in the result is not determined

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.188Z

array-intersect clj

(array-intersect left right)

Params: (col1: Column, col2: Column)

Result: Column

Returns an array of the elements in the intersection of the given two arrays, without duplicates.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.189Z

array-join clj

(array-join expr delimiter)

(array-join expr delimiter null-replacement)

Params: (column: Column, delimiter: String, nullReplacement: String)

Result: Column

Concatenates the elements of column using the delimiter. Null values are replaced with nullReplacement.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.194Z

array-max clj

(array-max expr)

Params: (e: Column)

Result: Column

Returns the maximum value in the array.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.195Z

array-min clj

(array-min expr)

Params: (e: Column)

Result: Column

Returns the minimum value in the array.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.197Z

array-position clj

(array-position expr value)

Params: (column: Column, value: Any)

Result: Column

Locates the position of the first occurrence of the value in the given array as long. Returns null if either of the arguments are null.

2.4.0

The position is not zero based, but 1 based index. Returns 0 if value could not be found in array.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.198Z

array-remove clj

(array-remove expr element)

Params: (column: Column, element: Any)

Result: Column

Remove all elements that equal to element from the given array.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.199Z

array-repeat clj

(array-repeat left right)

Params: (left: Column, right: Column)

Result: Column

Creates an array containing the left argument repeated the number of times given by the right argument.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.201Z

array-sort clj

(array-sort expr)

Params: (e: Column)

Result: Column

Sorts the input array in ascending order. The elements of the input array must be orderable. Null elements will be placed at the end of the returned array.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.202Z

array-type clj

(array-type val-type nullable)

Creates an ArrayType by specifying the data type of elements val-type and whether the array contains null values nullable.

array-union clj

(array-union left right)

Params: (col1: Column, col2: Column)

Result: Column

Returns an array of the elements in the union of the given two arrays, without duplicates.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.204Z

arrays-overlap clj

(arrays-overlap left right)

Params: (a1: Column, a2: Column)

Result: Column

Returns true if a1 and a2 have at least one non-null element in common. If not and both the arrays are non-empty and any of them contains a null, it returns null. It returns false otherwise.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.209Z

arrays-zip clj

(arrays-zip & exprs)

Params: (e: Column*)

Result: Column

Returns a merged array of structs in which the N-th struct contains all N-th values of input arrays.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.211Z

as clj multimethod

Column: Gives the column an alias.

Dataset: Returns a new Dataset with an alias set.

asc clj

(asc expr)

Params:

Result: Column

Returns a sort expression based on ascending order of the column.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.867Z

asc-nulls-first clj

(asc-nulls-first expr)

Params:

Result: Column

Returns a sort expression based on ascending order of the column, and null values return before non-null values.

2.1.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.869Z

asc-nulls-last clj

(asc-nulls-last expr)

Params:

Result: Column

Returns a sort expression based on ascending order of the column, and null values appear after non-null values.

2.1.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.870Z

ascii clj

(ascii expr)

Params: (e: Column)

Result: Column

Computes the numeric value of the first character of the string column, and returns the result as an int column.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.216Z

asin clj

(asin expr)

Params: (e: Column)

Result: Column

inverse sine of e in radians, as if computed by java.lang.Math.asin

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.219Z

assoc clj multimethod

Column: variadic version of map-concat.

Dataset: variadic version of with-column.

atan clj

(atan expr)

Params: (e: Column)

Result: Column

inverse tangent of e, as if computed by java.lang.Math.atan

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.221Z

atan-2 clj

(atan-2 expr-x expr-y)

Params: (y: Column, x: Column)

Result: Column

coordinate on y-axis

coordinate on x-axis

the theta component of the point (r, theta) in polar coordinates that corresponds to the point (x, y) in Cartesian coordinates, as if computed by java.lang.Math.atan2

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.233Z

atan2 clj

(atan2 expr-x expr-y)

Params: (y: Column, x: Column)

Result: Column

coordinate on y-axis

coordinate on x-axis

the theta component of the point (r, theta) in polar coordinates that corresponds to the point (x, y) in Cartesian coordinates, as if computed by java.lang.Math.atan2

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.233Z

base-64 clj

(base-64 expr)

Params: (e: Column)

Result: Column

Computes the BASE64 encoding of a binary column and returns it as a string column. This is the reverse of unbase64.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.236Z

base64 clj

(base64 expr)

Params: (e: Column)

Result: Column

Computes the BASE64 encoding of a binary column and returns it as a string column. This is the reverse of unbase64.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.236Z

between clj

(between expr lower-bound upper-bound)

Params: (lowerBound: Any, upperBound: Any)

Result: Column

True if the current column is between the lower bound and upper bound, inclusive.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.872Z

bin clj

(bin expr)

Params: (e: Column)

Result: Column

An expression that returns the string representation of the binary value of the given long column. For example, bin("12") returns "1100".

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.238Z

binary-files clj multimethod

Params: (path: String, minPartitions: Int)

Result: JavaPairRDD[String, PortableDataStream]

Read a directory of binary files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI as a byte array. Each file is read as a single record and returned in a key-value pair, where the key is the path of each file, the value is the content of each file.

For example, if you have the following files:

Do

then rdd contains

A suggestion value of the minimal splitting number for input data.

Small files are preferred; very large files but may cause bad performance.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.492Z

bit-size clj

(bit-size bloom)

Params: ()

Result: Long

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/util/sketch/BloomFilter.html

Timestamp: 2020-10-19T01:56:25.738Z

bitwise-and clj

(bitwise-and left-expr right-expr)

Params: (other: Any)

Result: Column

Compute bitwise AND of this expression with another expression.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.878Z

bitwise-not clj

(bitwise-not expr)

Params: (e: Column)

Result: Column

Computes bitwise NOT (~) of a number.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.239Z

bitwise-or clj

(bitwise-or left-expr right-expr)

Params: (other: Any)

Result: Column

Compute bitwise OR of this expression with another expression.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.879Z

bitwise-xor clj

(bitwise-xor left-expr right-expr)

Params: (other: Any)

Result: Column

Compute bitwise XOR of this expression with another expression.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.881Z

bloom-filter clj

(bloom-filter dataframe expr expected-num-items num-bits-or-fpp)

Params: (colName: String, expectedNumItems: Long, fpp: Double)

Result: BloomFilter

Builds a Bloom filter over a specified column.

name of the column over which the filter is built

expected number of items which will be put into the filter.

expected false positive probability of the filter.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/DataFrameStatFunctions.html

Timestamp: 2020-10-19T01:56:24.647Z

boolean clj

(boolean expr)

Casts the column to a boolean.

broadcast clj

(broadcast dataframe)

Params: (df: Dataset[T])

Result: Dataset[T]

Marks a DataFrame as small enough for use in broadcast joins.

The following example marks the right DataFrame for broadcast hash join using joinKey.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.240Z

bround clj

(bround expr)

Params: (e: Column)

Result: Column

Returns the value of the column e rounded to 0 decimal places with HALF_EVEN round mode.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.243Z

byte clj

(byte expr)

Casts the column to a byte.

cache clj

(cache dataframe)

Params: ()

Result: Dataset.this.type

Persist this Dataset with the default storage level (MEMORY_AND_DISK).

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.750Z

case clj

(case expr & clauses)

Returns a new Column imitating Clojure's case macro behaviour.

cast clj

(cast expr new-type)

Params: (to: DataType)

Result: Column

Casts the column to a different data type.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.885Z

cbrt clj

(cbrt expr)

Params: (e: Column)

Result: Column

Computes the cube-root of the given value.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.253Z

ceil clj

(ceil expr)

Params: (e: Column)

Result: Column

Computes the ceiling of the given value.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.255Z

checkpoint clj

(checkpoint dataframe)

(checkpoint dataframe eager)

Params: ()

Result: Dataset[T]

Eagerly checkpoint a Dataset and return the new Dataset. Checkpointing can be used to truncate the logical plan of this Dataset, which is especially useful in iterative algorithms where the plan may grow exponentially. It will be saved to files inside the checkpoint directory set with SparkContext#setCheckpointDir.

2.1.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.752Z

checkpoint-dir clj

(checkpoint-dir)

(checkpoint-dir spark)

Params:

Result: Optional[String]

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.509Z

clip clj

(clip expr low high)

Returns a new Column where values outside [low, high] are clipped to the interval edges.

coalesce clj multimethod

Column: Returns the first column that is not null, or null if all inputs are null.

Dataset: Returns a new Dataset that has exactly numPartitions partitions, when the fewer partitions are requested.

col clj multimethod

Params: (colName: String)

Result: Column

Returns a Column based on the given column name.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.258Z

col-regex clj

(col-regex dataframe col-name)

Params: (colName: String)

Result: Column

Selects column based on the column name specified as a regex and returns it as Column.

2.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.758Z

collect clj

(collect dataframe)

Params: ()

Result: Array[T]

Returns an array that contains all rows in this Dataset.

Running collect requires moving all the data into the application's driver process, and doing so on a very large dataset can crash the driver process with OutOfMemoryError.

For Java API, use collectAsList.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.759Z

collect-col clj

(collect-col dataframe col-name)

Returns a vector that contains all rows in the column of the Dataset.

collect-list clj

(collect-list expr)

Params: (e: Column)

Result: Column

Aggregate function: returns a list of objects with duplicates.

1.6.0

The function is non-deterministic because the order of collected results depends on the order of the rows which may be non-deterministic after a shuffle.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.261Z

collect-set clj

(collect-set expr)

Params: (e: Column)

Result: Column

Aggregate function: returns a set of objects with duplicate elements eliminated.

1.6.0

The function is non-deterministic because the order of collected results depends on the order of the rows which may be non-deterministic after a shuffle.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.263Z

collect-to-arrow clj

(collect-to-arrow rdd chunk-size out-dir)

Collects the dataframe on driver and exports it as arrow files. The data gets transfered by partition, and so each partions should be small enough to fit in heap space of the driver. Then the data is saved in chunks of chunk-size rows to disk as arrow files.

rdd Spark dataset chunk-size Number of rows each arrow file will have. Should be small enoungh to make data fit in heap space of driver. out-dir Output dir of arrow files

collect-vals clj

(collect-vals dataframe)

Returns the vector values of the Dataset collected.

column-names clj

(column-names dataframe)

Returns all column names as an array of strings.

columns clj

(columns dataframe)

Returns all column names as an array of keywords.

compatible? clj

(compatible? bloom other)

Params: (other: BloomFilter)

Result: Boolean

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/util/sketch/BloomFilter.html

Timestamp: 2020-10-19T01:56:25.740Z

concat clj

(concat & exprs)

Params: (exprs: Column*)

Result: Column

Concatenates multiple input columns together into a single column. The function works with strings, binary and compatible array columns.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.265Z

concat-ws clj

(concat-ws sep & exprs)

Params: (sep: String, exprs: Column*)

Result: Column

Concatenates multiple input string columns together into a single string column, using the given separator.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.267Z

cond clj

(cond & clauses)

Returns a new Column imitating Clojure's cond macro behaviour.

condp clj

(condp pred expr & clauses)

Returns a new Column imitating Clojure's condp macro behaviour.

conf clj

(conf)

(conf spark)

Params:

Result: SparkConf

Return a copy of this JavaSparkContext's configuration. The configuration cannot be changed at runtime.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.511Z

confidence clj

(confidence cms)

Params: ()

Result: Double

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/util/sketch/CountMinSketch.html

Timestamp: 2020-10-19T01:56:26.102Z

contains clj

(contains expr literal)

Params: (other: Any)

Result: Column

Contains the other element. Returns a boolean column based on a string match.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.888Z

conv clj

(conv expr from-base to-base)

Params: (num: Column, fromBase: Int, toBase: Int)

Result: Column

Convert a number in a string column from one base to another.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.268Z

corr clj multimethod

Column: Aggregate function: returns the Pearson Correlation Coefficient for two columns.

Datasate: Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.

cos clj

(cos expr)

Params: (e: Column)

Result: Column

angle in radians

cosine of the angle, as if computed by java.lang.Math.cos

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.272Z

cosh clj

(cosh expr)

Params: (e: Column)

Result: Column

hyperbolic angle

hyperbolic cosine of the angle, as if computed by java.lang.Math.cosh

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.275Z

count clj multimethod

Column: Aggregate function: returns the number of items in a group.

Dataset: Returns the number of rows in the Dataset.

RelationalGroupedDataset: Count the number of rows for each group.

count-distinct clj

(count-distinct & exprs)

Params: (expr: Column, exprs: Column*)

Result: Column

Aggregate function: returns the number of distinct items in a group.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.279Z

count-min-sketch clj

(count-min-sketch dataframe expr eps-or-depth confidence-or-width seed)

Params: (colName: String, depth: Int, width: Int, seed: Int)

Result: CountMinSketch

Builds a Count-min Sketch over a specified column.

name of the column over which the sketch is built

depth of the sketch

width of the sketch

random seed

a CountMinSketch over column colName

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/DataFrameStatFunctions.html

Timestamp: 2020-10-19T01:56:24.659Z

cov clj

(cov dataframe col-name1 col-name2)

Params: (col1: String, col2: String)

Result: Double

Calculate the sample covariance of two numerical columns of a DataFrame.

the name of the first column

the name of the second column

the covariance of the two columns.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/DataFrameStatFunctions.html

Timestamp: 2020-10-19T01:56:24.661Z

covar clj

(covar l-expr r-expr)

Params: (column1: Column, column2: Column)

Result: Column

Aggregate function: returns the sample covariance for two columns.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.284Z

covar-pop clj

(covar-pop l-expr r-expr)

Params: (column1: Column, column2: Column)

Result: Column

Aggregate function: returns the population covariance for two columns.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.282Z

covar-samp clj

(covar-samp l-expr r-expr)

Params: (column1: Column, column2: Column)

Result: Column

Aggregate function: returns the sample covariance for two columns.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.284Z

crc-32 clj

(crc-32 expr)

Params: (e: Column)

Result: Column

Calculates the cyclic redundancy check value (CRC32) of a binary column and returns the value as a bigint.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.285Z

crc32 clj

(crc32 expr)

Params: (e: Column)

Result: Column

Calculates the cyclic redundancy check value (CRC32) of a binary column and returns the value as a bigint.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.285Z

create-dataframe clj

(create-dataframe rows schema)

(create-dataframe spark rows schema)

Params: (rdd: RDD[A])

(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[A])

Result: DataFrame

Creates a DataFrame from an RDD of Product (e.g. case classes, tuples).

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/SparkSession.html

Timestamp: 2020-10-19T01:56:50.125Z

create-global-temp-view! clj

(create-global-temp-view! dataframe view-name)

Creates a global temporary view using the given name.

Global temporary view is cross-session. Its lifetime is the lifetime of the Spark application, i.e. it will be automatically dropped when the application terminates. It's tied to a system preserved database global_temp, and we must use the qualified name to refer a global temp view, e.g. SELECT * FROM global_temp.view1.

create-or-replace-global-temp-view! clj

(create-or-replace-global-temp-view! dataframe view-name)

Creates or replaces a global temporary view using the given name.

Global temporary view is cross-session. Its lifetime is the lifetime of the Spark application, i.e. it will be automatically dropped when the application terminates. It's tied to a system preserved database global_temp, and we must use the qualified name to refer a global temp view, e.g. SELECT * FROM global_temp.view1.

create-or-replace-temp-view! clj

(create-or-replace-temp-view! dataframe view-name)

Creates or replaces a local temporary view using the given name.

The lifetime of this temporary view is tied to the SparkSession that was used to create this Dataset.

create-spark-session clj

(create-spark-session
  {:keys [app-name master configs log-level checkpoint-dir]
   :or {app-name "Geni App" master "local[*]" configs {} log-level "WARN"}})

The entry point to programming Spark with the Dataset and DataFrame API.

create-temp-view! clj

(create-temp-view! dataframe view-name)

Creates a local temporary view using the given name.

Local temporary view is session-scoped. Its lifetime is the lifetime of the session that created it, i.e. it will be automatically dropped when the session terminates. It's not tied to any databases, i.e. we can't use db1.view1 to reference a local temporary view.

cross-join clj

(cross-join left right)

Params: (right: Dataset[_])

Result: DataFrame

Explicit cartesian join with another DataFrame.

Right side of the join operation.

2.1.0

Cartesian joins are very expensive without an extra filter that can be pushed down.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.770Z

crosstab clj

(crosstab dataframe col-name1 col-name2)

Params: (col1: String, col2: String)

Result: DataFrame

Computes a pair-wise frequency table of the given columns. Also known as a contingency table. The number of distinct values for each column should be less than 1e4. At most 1e6 non-zero pair frequencies will be returned. The first column of each row will be the distinct values of col1 and the column names will be the distinct values of col2. The name of the first column will be col1_col2. Counts will be returned as Longs. Pairs that have no occurrences will have zero as their counts. Null elements will be replaced by "null", and back ticks will be dropped from elements if they exist.

The name of the first column. Distinct items will make the first item of each row.

The name of the second column. Distinct items will make the column names of the DataFrame.

A DataFrame containing for the contingency table.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/DataFrameStatFunctions.html

Timestamp: 2020-10-19T01:56:24.664Z

cube clj

(cube dataframe & exprs)

Params: (cols: Column*)

Result: RelationalGroupedDataset

Create a multi-dimensional cube for the current Dataset using the specified columns, so we can run aggregation on them. See RelationalGroupedDataset for all the available aggregate functions.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.778Z

cube-root clj

(cube-root expr)

Params: (e: Column)

Result: Column

Computes the cube-root of the given value.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.253Z

cume-dist clj

(cume-dist)

Params: ()

Result: Column

Window function: returns the cumulative distribution of values within a window partition, i.e. the fraction of rows that are below the current row.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.286Z

current-date clj

(current-date)

Params: ()

Result: Column

Returns the current date as a date column.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.287Z

current-timestamp clj

(current-timestamp)

Params: ()

Result: Column

Returns the current timestamp as a timestamp column.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.288Z

cut clj

(cut expr bins)

Returns a new Column of discretised expr into the intervals of bins.

date-add clj

(date-add expr days)

Params: (start: Column, days: Int)

Result: Column

Returns the date that is days days after start

A date, timestamp or string. If a string, the data must be in a format that can be cast to a date, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

The number of days to add to start, can be negative to subtract days

A date, or null if start was a string that could not be cast to a date

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.295Z

date-diff clj

(date-diff l-expr r-expr)

Params: (end: Column, start: Column)

Result: Column

Returns the number of days from start to end.

Only considers the date part of the input. For example:

A date, timestamp or string. If a string, the data must be in a format that can be cast to a date, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

A date, timestamp or string. If a string, the data must be in a format that can be cast to a date, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

An integer, or null if either end or start were strings that could not be cast to a date. Negative if end is before start

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.304Z

date-format clj

(date-format expr date-fmt)

Params: (dateExpr: Column, format: String)

Result: Column

Converts a date/timestamp/string to a value of string in the format specified by the date format given by the second argument.

See Datetime Patterns for valid date and time format patterns

A date, timestamp or string. If a string, the data must be in a format that can be cast to a timestamp, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

A pattern dd.MM.yyyy would return a string like 18.03.1993

A string, or null if dateExpr was a string that could not be cast to a timestamp

1.5.0

IllegalArgumentException if the format pattern is invalid

Use specialized functions like year whenever possible as they benefit from a specialized implementation.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.297Z

date-sub clj

(date-sub expr days)

Params: (start: Column, days: Int)

Result: Column

Returns the date that is days days before start

A date, timestamp or string. If a string, the data must be in a format that can be cast to a date, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

The number of days to subtract from start, can be negative to add days

A date, or null if start was a string that could not be cast to a date

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.300Z

date-trunc clj

(date-trunc fmt expr)

Params: (format: String, timestamp: Column)

Result: Column

Returns timestamp truncated to the unit specified by the format.

For example, date_trunc("year", "2018-11-19 12:01:19") returns 2018-01-01 00:00:00

A date, timestamp or string. If a string, the data must be in a format that can be cast to a timestamp, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

A timestamp, or null if timestamp was a string that could not be cast to a timestamp or format was an invalid value

2.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.302Z

datediff clj

(datediff l-expr r-expr)

Params: (end: Column, start: Column)

Result: Column

Returns the number of days from start to end.

Only considers the date part of the input. For example:

A date, timestamp or string. If a string, the data must be in a format that can be cast to a date, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

A date, timestamp or string. If a string, the data must be in a format that can be cast to a date, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

An integer, or null if either end or start were strings that could not be cast to a date. Negative if end is before start

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.304Z

day-of-month clj

(day-of-month expr)

Params: (e: Column)

Result: Column

Extracts the day of the month as an integer from a given date/timestamp/string.

An integer, or null if the input was a string that could not be cast to a date

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.305Z

day-of-week clj

(day-of-week expr)

Params: (e: Column)

Result: Column

Extracts the day of the week as an integer from a given date/timestamp/string. Ranges from 1 for a Sunday through to 7 for a Saturday

An integer, or null if the input was a string that could not be cast to a date

2.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.306Z

day-of-year clj

(day-of-year expr)

Params: (e: Column)

Result: Column

Extracts the day of the year as an integer from a given date/timestamp/string.

An integer, or null if the input was a string that could not be cast to a date

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.307Z

dayofmonth clj

(dayofmonth expr)

Params: (e: Column)

Result: Column

Extracts the day of the month as an integer from a given date/timestamp/string.

An integer, or null if the input was a string that could not be cast to a date

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.305Z

dayofweek clj

(dayofweek expr)

Params: (e: Column)

Result: Column

Extracts the day of the week as an integer from a given date/timestamp/string. Ranges from 1 for a Sunday through to 7 for a Saturday

An integer, or null if the input was a string that could not be cast to a date

2.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.306Z

dayofyear clj

(dayofyear expr)

Params: (e: Column)

Result: Column

Extracts the day of the year as an integer from a given date/timestamp/string.

An integer, or null if the input was a string that could not be cast to a date

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.307Z

dec clj

(dec expr)

Returns an expression one less than expr.

decode clj

(decode expr charset)

Params: (value: Column, charset: String)

Result: Column

Computes the first argument into a string from a binary using the provided character set (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16'). If either argument is null, the result will also be null.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.309Z

default-min-partitions clj

(default-min-partitions)

(default-min-partitions spark)

Params:

Result: Integer

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.503Z

default-parallelism clj

(default-parallelism)

(default-parallelism spark)

Params:

Result: Integer

Default level of parallelism to use when not given by user (e.g. parallelize and makeRDD).

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.504Z

degrees clj

(degrees expr)

Params: (e: Column)

Result: Column

Converts an angle measured in radians to an approximately equivalent angle measured in degrees.

angle in radians

angle in degrees, as if computed by java.lang.Math.toDegrees

2.1.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.312Z

dense clj

(dense & values)

Params: (firstValue: Double, otherValues: Double*)

Result: Vector

Creates a dense vector from its values.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/linalg/Vectors$.html

Timestamp: 2020-10-19T01:56:35.334Z

dense-rank clj

(dense-rank)

Params: ()

Result: Column

Window function: returns the rank of rows within a window partition, without any gaps.

The difference between rank and dense_rank is that denseRank leaves no gaps in ranking sequence when there are ties. That is, if you were ranking a competition using dense_rank and had three people tie for second place, you would say that all three were in second place and that the next person came in third. Rank would give me sequential numbers, making the person that came in third place (after the ties) would register as coming in fifth.

This is equivalent to the DENSE_RANK function in SQL.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.313Z

depth clj

(depth cms)

Params: ()

Result: Int

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/util/sketch/CountMinSketch.html

Timestamp: 2020-10-19T01:56:26.103Z

desc clj

(desc expr)

Params:

Result: Column

Returns a sort expression based on the descending order of the column.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.890Z

desc-nulls-first clj

(desc-nulls-first expr)

Params:

Result: Column

Returns a sort expression based on the descending order of the column, and null values appear before non-null values.

2.1.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.891Z

desc-nulls-last clj

(desc-nulls-last expr)

Params:

Result: Column

Returns a sort expression based on the descending order of the column, and null values appear after non-null values.

2.1.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.893Z

describe clj

(describe dataframe & col-names)

Params: (cols: String*)

Result: DataFrame

Computes basic statistics for numeric and string columns, including count, mean, stddev, min, and max. If no columns are given, this function computes statistics for all numerical or string columns.

This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting Dataset. If you want to programmatically compute summary statistics, use the agg function instead.

Use summary for expanded statistics and control over which statistics to compute.

Columns to compute statistics on.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.780Z

disk-only clj

Flag for controlling the storage of an RDD.

DataFrame is stored only on disk and the CPU computation time is high as I/O involved.

disk-only-2 clj

Flag for controlling the storage of an RDD.

Same as disk-only storage level but replicate each partition to two cluster nodes.

dissoc clj multimethod

Column: Returns a map whose key is not in ks.

Dataset: variadic version of drop.

distinct clj

(distinct dataframe)

Params: ()

Result: Dataset[T]

Returns a new Dataset that contains only the unique rows from this Dataset. This is an alias for dropDuplicates.

2.0.0

Equality checking is performed directly on the encoded representation of the data and thus is not affected by a custom equals function defined on T.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.781Z

double clj

(double expr)

Casts the column to a double.

drop clj

(drop dataframe & col-names)

Params: (colName: String)

Result: DataFrame

Returns a new Dataset with a column dropped. This is a no-op if schema doesn't contain column name.

This method can only be used to drop top level columns. the colName string is treated literally without further interpretation.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.785Z

drop-duplicates clj

(drop-duplicates dataframe & col-names)

Params: ()

Result: Dataset[T]

Returns a new Dataset that contains only the unique rows from this Dataset. This is an alias for distinct.

For a static batch Dataset, it just drops duplicate rows. For a streaming Dataset, it will keep all data across triggers as intermediate state to drop duplicates rows. You can use withWatermark to limit how late the duplicate data can be and system will accordingly limit the state. In addition, too late data older than watermark will be dropped to avoid any possibility of duplicates.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.791Z

drop-na clj

(drop-na dataframe)

(drop-na dataframe min-non-nulls-or-cols)

(drop-na dataframe min-non-nulls cols)

Params: ()

Result: DataFrame

Returns a new DataFrame that drops rows containing any null or NaN values.

1.3.1

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/DataFrameNaFunctions.html

Timestamp: 2020-10-19T01:56:23.886Z

dtypes clj

(dtypes dataframe)

Params:

Result: Array[(String, String)]

Returns all column names and their data types as an array.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.792Z

element-at clj

(element-at expr value)

Params: (column: Column, value: Any)

Result: Column

Returns element of array at given index in value if column is array. Returns value for the given key in value if column is map.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.318Z

empty? clj

(empty? dataframe)

Params:

Result: Boolean

Returns true if the Dataset is empty.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.840Z

encode clj

(encode expr charset)

Params: (value: Column, charset: String)

Result: Column

Computes the first argument into a binary from a string using the provided character set (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16'). If either argument is null, the result will also be null.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.319Z

ends-with clj

(ends-with expr literal)

Params: (other: Column)

Result: Column

String ends with. Returns a boolean column based on a string match.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.898Z

estimate-count clj

(estimate-count cms item)

Params: (item: Any)

Result: Long

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/util/sketch/CountMinSketch.html

Timestamp: 2020-10-19T01:56:26.104Z

even? clj

(even? expr)

Returns true if expr is even, else false.

except clj

(except dataframe other)

Params: (other: Dataset[T])

Result: Dataset[T]

Returns a new Dataset containing rows in this Dataset but not in another Dataset. This is equivalent to EXCEPT DISTINCT in SQL.

2.0.0

Equality checking is performed directly on the encoded representation of the data and thus is not affected by a custom equals function defined on T.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.796Z

except-all clj

(except-all dataframe other)

Params: (other: Dataset[T])

Result: Dataset[T]

Returns a new Dataset containing rows in this Dataset but not in another Dataset while preserving the duplicates. This is equivalent to EXCEPT ALL in SQL.

2.4.0

Equality checking is performed directly on the encoded representation of the data and thus is not affected by a custom equals function defined on T. Also as standard in SQL, this function resolves columns by position (not by name).

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.798Z

exists clj

(exists expr predicate)

Params: (column: Column, f: (Column) ⇒ Column)

Result: Column

Returns whether a predicate holds for one or more elements in the array.

the input array column

col => predicate, the Boolean predicate to check the input column

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.322Z

exp clj

(exp expr)

Params: (e: Column)

Result: Column

Computes the exponential of the given value.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.324Z

expected-fpp clj

(expected-fpp bloom)

Params: ()

Result: Double

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/util/sketch/BloomFilter.html

Timestamp: 2020-10-19T01:56:25.739Z

explain clj multimethod

Column: Prints the expression to the console for debugging purposes.

Dataset: Prints the physical plan to the console for debugging purposes.

explode clj

(explode expr)

Params: (e: Column)

Result: Column

Creates a new row for each element in the given array or map column. Uses the default column name col for elements in the array and key and value for elements in the map unless specified otherwise.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.325Z

expm-1 clj

(expm-1 expr)

Params: (e: Column)

Result: Column

Computes the exponential of the given value minus one.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.329Z

expm1 clj

(expm1 expr)

Params: (e: Column)

Result: Column

Computes the exponential of the given value minus one.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.329Z

expr clj

(expr s)

Params: (expr: String)

Result: Column

Parses the expression string into the column that it represents, similar to Dataset#selectExpr.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.330Z

factorial clj

(factorial expr)

Params: (e: Column)

Result: Column

Computes the factorial of the given value.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.331Z

fill-na clj

(fill-na dataframe value)

(fill-na dataframe value cols)

Params: (value: Long)

Result: DataFrame

Returns a new DataFrame that replaces null or NaN values in numeric columns with value.

2.2.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/DataFrameNaFunctions.html

Timestamp: 2020-10-19T01:56:23.908Z

filter clj multimethod

Column: Returns an array of elements for which a predicate holds in a given array.

Dataset: Filters rows using the given condition.

first clj multimethod

Column: Aggregate function: returns the first value of a column in a group.

Dataset: Returns the first row.

first-vals clj

(first-vals dataframe)

Returns the vector values of the first row in the Dataset collected.

flatten clj

(flatten expr)

Params: (e: Column)

Result: Column

Creates a single array from an array of arrays. If a structure of nested arrays is deeper than two levels, only one level of nesting is removed.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.345Z

float clj

(float expr)

Casts the column to a float.

floor clj

(floor expr)

Params: (e: Column)

Result: Column

Computes the floor of the given value.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.347Z

forall clj

(forall expr predicate)

Params: (column: Column, f: (Column) ⇒ Column)

Result: Column

Returns whether a predicate holds for every element in the array.

the input array column

col => predicate, the Boolean predicate to check the input column

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.349Z

format-number clj

(format-number expr decimal-places)

Params: (x: Column, d: Int)

Result: Column

Formats numeric column x to a format like '#,###,###.##', rounded to d decimal places with HALF_EVEN round mode, and returns the result as a string column.

If d is 0, the result has no decimal point or fractional part. If d is less than 0, the result will be null.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.350Z

format-string clj

(format-string fmt & exprs)

Params: (format: String, arguments: Column*)

Result: Column

Formats the arguments in printf-style and returns the result as a string column.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.351Z

freq-items clj

(freq-items dataframe col-names)

(freq-items dataframe col-names support)

Params: (cols: Array[String], support: Double)

Result: DataFrame

Finding frequent items for columns, possibly with false positives. Using the frequent element count algorithm described in here, proposed by Karp, Schenker, and Papadimitriou. The support should be greater than 1e-4.

This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting DataFrame.

the names of the columns to search frequent items in.

The minimum frequency for an item to be considered frequent. Should be greater than 1e-4.

A Local DataFrame with the Array of frequent items for each column.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/DataFrameStatFunctions.html

Timestamp: 2020-10-19T01:56:24.676Z

from-csv clj

(from-csv expr schema)

(from-csv expr schema options)

Params: (e: Column, schema: StructType, options: Map[String, String])

Result: Column

Parses a column containing a CSV string into a StructType with the specified schema. Returns null, in the case of an unparseable string.

a string column containing CSV data.

the schema to use when parsing the CSV string

options to control how the CSV is parsed. accepts the same options and the CSV data source.

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.354Z

from-json clj

(from-json expr schema)

(from-json expr schema options)

Params: (e: Column, schema: StructType, options: Map[String, String])

Result: Column

(Scala-specific) Parses a column containing a JSON string into a StructType with the specified schema. Returns null, in the case of an unparseable string.

a string column containing JSON data.

the schema to use when parsing the json string

options to control how the json is parsed. Accepts the same options as the json data source.

2.1.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.372Z

from-unixtime clj

(from-unixtime expr)

(from-unixtime expr fmt)

Params: (ut: Column)

Result: Column

Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string representing the timestamp of that moment in the current system time zone in the yyyy-MM-dd HH:mm:ss format.

A number of a type that is castable to a long, such as string or integer. Can be negative for timestamps before the unix epoch

A string, or null if the input was a string that could not be cast to a long

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.375Z

get-checkpoint-dir clj

(get-checkpoint-dir)

(get-checkpoint-dir spark)

Params:

Result: Optional[String]

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.509Z

get-conf clj

(get-conf)

(get-conf spark)

Params:

Result: SparkConf

Return a copy of this JavaSparkContext's configuration. The configuration cannot be changed at runtime.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.511Z

get-field clj

(get-field expr field-name)

Params: (fieldName: String)

Result: Column

An expression that gets a field by name in a StructType.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.913Z

get-item clj

(get-item expr k)

Params: (key: Any)

Result: Column

An expression that gets an item at position ordinal out of an array, or gets a value by key key in a MapType.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.915Z

get-local-property clj

(get-local-property k)

(get-local-property spark k)

Params: (key: String)

Result: String

Get a local property set in this thread, or null if it is missing. See org.apache.spark.api.java.JavaSparkContext.setLocalProperty.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.512Z

get-persistent-rdds clj

(get-persistent-rdds)

(get-persistent-rdds spark)

Params:

Result: Map[Integer, JavaRDD[_]]

Returns a Java map of JavaRDDs that have marked themselves as persistent via cache() call.

This does not necessarily mean the caching or computation was successful.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.513Z

get-spark-home clj

(get-spark-home)

(get-spark-home spark)

Params: ()

Result: Optional[String]

Get Spark's home location from either a value set through the constructor, or the spark.home Java property, or the SPARK_HOME environment variable (in that order of preference). If neither of these is set, return None.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.518Z

greatest clj

(greatest & exprs)

Params: (exprs: Column*)

Result: Column

Returns the greatest value of the list of values, skipping null values. This function takes at least 2 parameters. It will return null iff all parameters are null.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.382Z

group-by clj

(group-by dataframe & exprs)

Params: (cols: Column*)

Result: RelationalGroupedDataset

Groups the Dataset using the specified columns, so we can run aggregation on them. See RelationalGroupedDataset for all the available aggregate functions.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.827Z

grouping clj

(grouping expr)

Params: (e: Column)

Result: Column

Aggregate function: indicates whether a specified column in a GROUP BY list is aggregated or not, returns 1 for aggregated or 0 for not aggregated in the result set.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.388Z

grouping-id clj

(grouping-id & exprs)

Params: (cols: Column*)

Result: Column

Aggregate function: returns the level of grouping, equals to

2.0.0

The list of columns should match with grouping columns exactly, or empty (means all the grouping columns).

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.390Z

hash clj

(hash & exprs)

Params: (cols: Column*)

Result: Column

Calculates the hash code of given columns, and returns the result as an int column.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.391Z

hash-code clj

(hash-code expr)

Params: ()

Result: Int

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.918Z

head clj

(head dataframe)

(head dataframe n-rows)

Params: (n: Int)

Result: Array[T]

Returns the first n rows.

1.6.0

this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.834Z

head-vals clj

(head-vals dataframe)

(head-vals dataframe n-rows)

Returns the vector values of the first n rows in the Dataset collected.

hex clj

(hex expr)

Params: (column: Column)

Result: Column

Computes hex value of the given column.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.393Z

hint clj

(hint dataframe hint-name & args)

Params: (name: String, parameters: Any*)

Result: Dataset[T]

Specifies some hint on the current Dataset. As an example, the following code specifies that one of the plan can be broadcasted:

2.2.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.835Z

hour clj

(hour expr)

Params: (e: Column)

Result: Column

Extracts the hours as an integer from a given date/timestamp/string.

An integer, or null if the input was a string that could not be cast to a date

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.394Z

hypot clj

(hypot left-expr right-expr)

Params: (l: Column, r: Column)

Result: Column

Computes sqrt(a2 + b2) without intermediate overflow or underflow.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.406Z

if clj

(if condition if-expr)

(if condition if-expr else-expr)

Params: (condition: Column, value: Any)

Result: Column

Evaluates a list of conditions and returns one of multiple possible result expressions. If otherwise is not defined at the end, null is returned for unmatched conditions.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.724Z

inc clj

(inc expr)

Returns an expression one greater than expr.

initcap clj

(initcap expr)

Params: (e: Column)

Result: Column

Returns a new string column by converting the first letter of each word to uppercase. Words are delimited by whitespace.

For example, "hello world" will become "Hello World".

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.407Z

input-file-name clj

(input-file-name)

Params: ()

Result: Column

Creates a string column for the file name of the current Spark task.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.408Z

input-files clj

(input-files dataframe)

Params:

Result: Array[String]

Returns a best-effort snapshot of the files that compose this Dataset. This method simply asks each constituent BaseRelation for its respective files and takes the union of all results. Depending on the source relations, this may not find all input files. Duplicates are removed.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.837Z

instr clj

(instr expr substr)

Params: (str: Column, substring: String)

Result: Column

Locate the position of the first occurrence of substr column in the given string. Returns null if either of the arguments are null.

1.5.0

The position is not zero based, but 1 based index. Returns 0 if substr could not be found in str.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.409Z

int clj

(int expr)

Casts the column to an int.

interquartile-range clj multimethod

Column: Aggregate function: returns the inter-quartile range of the values in a group.

RelationalGroupedDataset: Compute the inter-quartile range for each numeric columns for each group.

intersect clj

(intersect dataframe other)

Params: (other: Dataset[T])

Result: Dataset[T]

Returns a new Dataset containing rows only in both this Dataset and another Dataset. This is equivalent to INTERSECT in SQL.

1.6.0

Equality checking is performed directly on the encoded representation of the data and thus is not affected by a custom equals function defined on T.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.838Z

intersect-all clj

(intersect-all dataframe other)

Params: (other: Dataset[T])

Result: Dataset[T]

Returns a new Dataset containing rows only in both this Dataset and another Dataset while preserving the duplicates. This is equivalent to INTERSECT ALL in SQL.

2.4.0

Equality checking is performed directly on the encoded representation of the data and thus is not affected by a custom equals function defined on T. Also as standard in SQL, this function resolves columns by position (not by name).

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.839Z

iqr clj multimethod

Column: Aggregate function: returns the inter-quartile range of the values in a group.

RelationalGroupedDataset: Compute the inter-quartile range for each numeric columns for each group.

is-compatible clj

(is-compatible bloom other)

Params: (other: BloomFilter)

Result: Boolean

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/util/sketch/BloomFilter.html

Timestamp: 2020-10-19T01:56:25.740Z

is-empty clj

(is-empty dataframe)

Params:

Result: Boolean

Returns true if the Dataset is empty.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.840Z

is-in-collection clj

(is-in-collection expr coll)

Params: (values: Iterable[_])

Result: Column

A boolean expression that is evaluated to true if the value of this expression is contained by the provided collection.

Note: Since the type of the elements in the collection are inferred only during the run time, the elements will be "up-casted" to the most common type for comparison. For eg:

1. In the case of "Int vs String", the "Int" will be up-casted to "String" and the comparison will look like "String vs String".
2. In the case of "Float vs Double", the "Float" will be up-casted to "Double" and the comparison will look like "Double vs Double"

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.924Z

is-local clj

(is-local dataframe)

Params:

Result: Boolean

Returns true if the collect and take methods can be run locally (without any Spark executors).

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.843Z

is-nan clj

(is-nan expr)

Params:

Result: Column

True if the current expression is NaN.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.927Z

is-not-null clj

(is-not-null expr)

Params:

Result: Column

True if the current expression is NOT null.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.932Z

is-null clj

(is-null expr)

Params:

Result: Column

True if the current expression is null.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.933Z

is-streaming clj

(is-streaming dataframe)

Params:

Result: Boolean

Returns true if this Dataset contains one or more sources that continuously return data as it arrives. A Dataset that reads data from a streaming source must be executed as a StreamingQuery using the start() method in DataStreamWriter. Methods that return a single answer, e.g. count() or collect(), will throw an AnalysisException when there is a streaming source present.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.844Z

isin clj

(isin expr coll)

Params: (list: Any*)

Result: Column

A boolean expression that is evaluated to true if the value of this expression is contained by the evaluated values of the arguments.

Note: Since the type of the elements in the list are inferred only during the run time, the elements will be "up-casted" to the most common type for comparison. For eg:

1. In the case of "Int vs String", the "Int" will be up-casted to "String" and the comparison will look like "String vs String".
2. In the case of "Float vs Double", the "Float" will be up-casted to "Double" and the comparison will look like "Double vs Double"

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.936Z

jars clj

(jars)

(jars spark)

Params:

Result: List[String]

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.532Z

java-spark-context clj

(java-spark-context spark)

Converts a SparkSession to a JavaSparkContext.

join clj

(join left right expr)

(join left right expr join-type)

Params: (right: Dataset[_])

Result: DataFrame

Join with another DataFrame.

Behaves as an INNER JOIN and requires a subsequent join predicate.

Right side of the join operation.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.856Z

join-with clj

(join-with left right condition)

(join-with left right condition join-type)

Params: (other: Dataset[U], condition: Column, joinType: String)

Result: Dataset[(T, U)]

Joins this Dataset returning a Tuple2 for each pair where condition evaluates to true.

This is similar to the relation join function with one important difference in the result schema. Since joinWith preserves objects present on either side of the join, the result schema is similarly nested into a tuple under the column names _1 and _2.

This type of join can be useful both for preserving type-safety with the original object types as well as working with relational data where either side of the join has column names in common.

Right side of the join.

Join expression.

Type of join to perform. Default inner. Must be one of: inner, cross, outer, full, fullouter,full_outer, left, leftouter, left_outer, right, rightouter, right_outer.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.860Z

keys clj

(keys expr)

Params: (e: Column)

Result: Column

Returns an unordered array containing the keys of the map.

2.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.472Z

kurtosis clj

(kurtosis expr)

Params: (e: Column)

Result: Column

Aggregate function: returns the kurtosis of the values in a group.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.416Z

lag clj

(lag expr offset)

(lag expr offset default)

Params: (e: Column, offset: Int)

Result: Column

Window function: returns the value that is offset rows before the current row, and null if there is less than offset rows before the current row. For example, an offset of one will return the previous row at any given point in the window partition.

This is equivalent to the LAG function in SQL.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.421Z

last clj multimethod

Column: Aggregate function: returns the last value of the column in a group.

Dataset: Returns the last row.

last-day clj

(last-day expr)

Params: (e: Column)

Result: Column

Returns the last day of the month which the given date belongs to. For example, input "2015-07-27" returns "2015-07-31" since July 31 is the last day of the month in July 2015.

A date, timestamp or string. If a string, the data must be in a format that can be cast to a date, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

A date, or null if the input was a string that could not be cast to a date

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.431Z

last-vals clj

(last-vals dataframe)

Returns the vector values of the last row in the Dataset collected.

lead clj

(lead expr offset)

(lead expr offset default)

Params: (columnName: String, offset: Int)

Result: Column

Window function: returns the value that is offset rows after the current row, and null if there is less than offset rows after the current row. For example, an offset of one will return the next row at any given point in the window partition.

This is equivalent to the LEAD function in SQL.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.437Z

least clj

(least & exprs)

Params: (exprs: Column*)

Result: Column

Returns the least value of the list of values, skipping null values. This function takes at least 2 parameters. It will return null iff all parameters are null.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.439Z

length clj

(length expr)

Params: (e: Column)

Result: Column

Computes the character length of a given string or number of bytes of a binary string. The length of character strings include the trailing spaces. The length of binary strings includes binary zeros.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.440Z

levenshtein clj

(levenshtein left-expr right-expr)

Params: (l: Column, r: Column)

Result: Column

Computes the Levenshtein distance of the two given string columns.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.441Z

like clj

(like expr literal)

Params: (literal: String)

Result: Column

SQL like expression. Returns a boolean column based on a SQL LIKE match.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.939Z

limit clj

(limit dataframe n-rows)

Params: (n: Int)

Result: Dataset[T]

Returns a new Dataset by taking the first n rows. The difference between this function and head is that head is an action and returns an array (by triggering query execution) while limit returns a new Dataset.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.861Z

lit clj

(lit arg)

Params: (literal: Any)

Result: Column

Creates a Column of literal value.

The passed in object is returned directly if it is already a Column. If the object is a Scala Symbol, it is converted into a Column also. Otherwise, a new Column is created to represent the literal value.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.442Z

local? clj

(local? dataframe)

Params:

Result: Boolean

Returns true if the collect and take methods can be run locally (without any Spark executors).

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.843Z

locate clj

(locate substr expr)

Params: (substr: String, str: Column)

Result: Column

Locate the position of the first occurrence of substr.

1.5.0

The position is not zero based, but 1 based index. Returns 0 if substr could not be found in str.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.445Z

log clj

(log expr)

Params: (e: Column)

Result: Column

Computes the natural logarithm of the given value.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.449Z

log-10 clj

(log-10 expr)

Params: (e: Column)

Result: Column

Computes the logarithm of the given value in base 10.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.451Z

log-1p clj

(log-1p expr)

Params: (e: Column)

Result: Column

Computes the natural logarithm of the given value plus one.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.453Z

log-2 clj

(log-2 expr)

Params: (expr: Column)

Result: Column

Computes the logarithm of the given column in base 2.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.455Z

log10 clj

(log10 expr)

Params: (e: Column)

Result: Column

Computes the logarithm of the given value in base 10.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.451Z

log1p clj

(log1p expr)

Params: (e: Column)

Result: Column

Computes the natural logarithm of the given value plus one.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.453Z

log2 clj

(log2 expr)

Params: (expr: Column)

Result: Column

Computes the logarithm of the given column in base 2.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.455Z

long clj

(long expr)

Casts the column to a long.

lower clj

(lower expr)

Params: (e: Column)

Result: Column

Converts a string column to lower case.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.457Z

lpad clj

(lpad expr length pad)

Params: (str: Column, len: Int, pad: String)

Result: Column

Left-pad the string column with pad to a length of len. If the string column is longer than len, the return value is shortened to len characters.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.458Z

ltrim clj

(ltrim expr)

Params: (e: Column)

Result: Column

Trim the spaces from left end for the specified string value.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.460Z

map clj

(map & exprs)

Params: (cols: Column*)

Result: Column

Creates a new map column. The input columns must be grouped as key-value pairs, e.g. (key1, value1, key2, value2, ...). The key columns must all have the same data type, and can't be null. The value columns must all have the same data type.

2.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.461Z

map->dataset clj

(map->dataset map-of-values)

(map->dataset spark map-of-values)

Construct a Dataset from an associative map.

(g/show (g/map->dataset {:a [1 2], :b [3 4]}))
; +---+---+
; |a  |b  |
; +---+---+
; |1  |3  |
; |2  |4  |
; +---+---+

map-concat clj

(map-concat & exprs)

Params: (cols: Column*)

Result: Column

Returns the union of all the given maps.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.462Z

map-entries clj

(map-entries expr)

Params: (e: Column)

Result: Column

Returns an unordered array of all entries in the given map.

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.463Z

map-filter clj

(map-filter expr predicate)

Params: (expr: Column, f: (Column, Column) ⇒ Column)

Result: Column

Returns a map whose key-value pairs satisfy a predicate.

the input map column

(key, value) => predicate, the Boolean predicate to filter the input map column

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.465Z

map-from-arrays clj

(map-from-arrays key-expr val-expr)

Params: (keys: Column, values: Column)

Result: Column

Creates a new map column. The array in the first column is used for keys. The array in the second column is used for values. All elements in the array for key should not be null.

2.4

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.470Z

map-from-entries clj

(map-from-entries expr)

Params: (e: Column)

Result: Column

Returns a map created from the given array of entries.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.471Z

map-keys clj

(map-keys expr)

Params: (e: Column)

Result: Column

Returns an unordered array containing the keys of the map.

2.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.472Z

map-type clj

(map-type key-type val-type)

Creates a MapType by specifying the data type of keys key-type, the data type of values val-type, and whether values contain any null value nullable.

map-values clj

(map-values expr)

Params: (e: Column)

Result: Column

Returns an unordered array containing the values of the map.

2.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.473Z

map-zip-with clj

(map-zip-with left right merge-fn)

Params: (left: Column, right: Column, f: (Column, Column, Column) ⇒ Column)

Result: Column

Merge two given maps, key-wise into a single map using a function.

the left input map column

the right input map column

(key, value1, value2) => new_value, the lambda function to merge the map values

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.474Z

master clj

(master)

(master spark)

Params:

Result: String

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.532Z

max clj multimethod

Column: Aggregate function: returns the maximum value of the column in a group.

RelationalGroupedDataset: Compute the max value for each numeric columns for each group.

md-5 clj

(md-5 expr)

Params: (e: Column)

Result: Column

Calculates the MD5 digest of a binary column and returns the value as a 32 character hex string.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.478Z

md5 clj

(md5 expr)

Params: (e: Column)

Result: Column

Calculates the MD5 digest of a binary column and returns the value as a 32 character hex string.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.478Z

mean clj multimethod

Column: Aggregate function: returns the average of the values in a group.

RelationalGroupedDataset: Compute the average value for each numeric columns for each group.

median clj multimethod

Column: Aggregate function: returns the median range of the values in a group.

RelationalGroupedDataset: Compute the median range for each numeric columns for each group.

memory-and-disk clj

Flag for controlling the storage of an RDD.

The default behavior of the DataFrame or Dataset. In this Storage Level, The DataFrame will be stored in JVM memory as deserialized objects. When required storage is greater than available memory, it stores some of the excess partitions into a disk and reads the data from disk when it required. It is slower as there is I/O involved.

memory-and-disk-2 clj

Flag for controlling the storage of an RDD.

Same as memory-and-disk storage level but replicate each partition to two cluster nodes.

memory-and-disk-ser clj

Flag for controlling the storage of an RDD.

Same as memory-and-disk storage level difference being it serializes the DataFrame objects in memory and on disk when space not available.

memory-and-disk-ser-2 clj

Flag for controlling the storage of an RDD.

Same as memory-and-disk-ser storage level but replicate each partition to two cluster nodes.

memory-only clj

Flag for controlling the storage of an RDD.

memory-only-2 clj

Flag for controlling the storage of an RDD.

Same as memory-only storage level but replicate each partition to two cluster nodes.

memory-only-ser clj

Flag for controlling the storage of an RDD.

Same as memory-only but the difference being it stores RDD as serialized objects to JVM memory. It takes lesser memory (space-efficient) then memory-only as it saves objects as serialized and takes an additional few more CPU cycles in order to deserialize.

memory-only-ser-2 clj

Flag for controlling the storage of an RDD.

Same as memory-only-ser storage level but replicate each partition to two cluster nodes.

merge clj

(merge expr & ms)

Variadic version of map-concat.

merge-in-place clj

(merge-in-place bloom-or-cms other)

Params: (other: BloomFilter)

Result: BloomFilter

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/util/sketch/BloomFilter.html

Timestamp: 2020-10-19T01:56:25.741Z

merge-with clj

(merge-with left right merge-fn)

Params: (left: Column, right: Column, f: (Column, Column, Column) ⇒ Column)

Result: Column

Merge two given maps, key-wise into a single map using a function.

the left input map column

the right input map column

(key, value1, value2) => new_value, the lambda function to merge the map values

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.474Z

might-contain clj

(might-contain bloom item)

Params: (item: Any)

Result: Boolean

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/util/sketch/BloomFilter.html

Timestamp: 2020-10-19T01:56:25.742Z

min clj multimethod

Column: Aggregate function: returns the minimum value of the column in a group.

RelationalGroupedDataset: Compute the min value for each numeric columns for each group.

minute clj

(minute expr)

Params: (e: Column)

Result: Column

Extracts the minutes as an integer from a given date/timestamp/string.

An integer, or null if the input was a string that could not be cast to a date

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.483Z

mod clj

Params: (other: Any)

Result: Column

Modulo (a.k.a. remainder) expression.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.958Z

monotonically-increasing-id clj

(monotonically-increasing-id)

Params: ()

Result: Column

A column expression that generates monotonically increasing 64-bit integers.

The generated ID is guaranteed to be monotonically increasing and unique, but not consecutive. The current implementation puts the partition ID in the upper 31 bits, and the record number within each partition in the lower 33 bits. The assumption is that the data frame has less than 1 billion partitions, and each partition has less than 8 billion records.

As an example, consider a DataFrame with two partitions, each with 3 records. This expression would return the following IDs:

(Since version 2.0.0) Use monotonically_increasing_id()

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.744Z

month clj

(month expr)

Params: (e: Column)

Result: Column

Extracts the month as an integer from a given date/timestamp/string.

An integer, or null if the input was a string that could not be cast to a date

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.486Z

months-between clj

(months-between l-expr r-expr)

Params: (end: Column, start: Column)

Result: Column

Returns number of months between dates start and end.

A whole number is returned if both inputs have the same day of month or both are the last day of their respective months. Otherwise, the difference is calculated assuming 31 days per month.

For example:

A date, timestamp or string. If a string, the data must be in a format that can be cast to a timestamp, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

A date, timestamp or string. If a string, the data must be in a format that can cast to a timestamp, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

A double, or null if either end or start were strings that could not be cast to a timestamp. Negative if end is before start

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.490Z

name-value-seq->dataset clj

(name-value-seq->dataset map-of-values)

(name-value-seq->dataset spark map-of-values)

Construct a Dataset from an associative map.

(g/show (g/map->dataset {:a [1 2], :b [3 4]}))
; +---+---+
; |a  |b  |
; +---+---+
; |1  |3  |
; |2  |4  |
; +---+---+

nan? clj

(nan? expr)

Params:

Result: Column

True if the current expression is NaN.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.927Z

nanvl clj

(nanvl left-expr right-expr)

Params: (col1: Column, col2: Column)

Result: Column

Returns col1 if it is not NaN, or col2 if col1 is NaN.

Both inputs should be floating point columns (DoubleType or FloatType).

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.492Z

neg? clj

(neg? expr)

Returns true if expr is less than zero, else false.

negate clj

(negate expr)

Params: (e: Column)

Result: Column

Unary minus, i.e. negate the expression.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.494Z

next-day clj

(next-day expr day-of-week)

Params: (date: Column, dayOfWeek: String)

Result: Column

Returns the first date which is later than the value of the date column that is on the specified day of the week.

For example, next_day('2015-07-27', "Sunday") returns 2015-08-02 because that is the first Sunday after 2015-07-27.

A date, timestamp or string. If a string, the data must be in a format that can be cast to a date, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

Case insensitive, and accepts: "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"

A date, or null if date was a string that could not be cast to a date or if dayOfWeek was an invalid value

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.495Z

nlargest clj

(nlargest dataframe n-rows expr)

Return the Dataset with the first n-rows rows ordered by expr in descending order.

none clj

Flag for controlling the storage of an RDD.

No caching.

not clj

(not expr)

Params: (e: Column)

Result: Column

Inversion of boolean expression, i.e. NOT.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.497Z

not-null? clj

(not-null? expr)

Params:

Result: Column

True if the current expression is NOT null.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.932Z

nsmallest clj

(nsmallest dataframe n-rows expr)

Return the Dataset with the first n-rows rows ordered by expr in ascending order.

ntile clj

(ntile n)

Params: (n: Int)

Result: Column

Window function: returns the ntile group id (from 1 to n inclusive) in an ordered window partition. For example, if n is 4, the first quarter of the rows will get value 1, the second quarter will get 2, the third quarter will get 3, and the last quarter will get 4.

This is equivalent to the NTILE function in SQL.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.500Z

null-count clj

(null-count expr)

Aggregate function: returns the null count of a column.

null-rate clj

(null-rate expr)

Aggregate function: returns the null rate of a column.

null? clj

(null? expr)

Params:

Result: Column

True if the current expression is null.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.933Z

nunique clj

(nunique dataframe)

Count distinct observations over all columns in the Dataset.

odd? clj

(odd? expr)

Returns true if expr is odd, else false.

off-heap clj

Flag for controlling the storage of an RDD.

Off-heap refers to objects (serialised to byte array) that are managed by the operating system but stored outside the process heap in native memory (therefore, they are not processed by the garbage collector). Accessing this data is slightly slower than accessing the on-heap storage but still faster than reading/writing from a disk. The downside is that the user has to manually deal with managing the allocated memory.

order-by clj

(order-by dataframe & exprs)

Params: (sortCol: String, sortCols: String*)

Result: Dataset[T]

Returns a new Dataset sorted by the given expressions. This is an alias of the sort function.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.884Z

over clj

(over column window-spec)

Params: (window: WindowSpec)

Result: Column

Defines a windowing column.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.973Z

overlay clj

(overlay src rep pos)

(overlay src rep pos len)

Params: (src: Column, replace: Column, pos: Column, len: Column)

Result: Column

Overlay the specified portion of src with replace, starting from byte position pos of src and proceeding for len bytes.

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.503Z

partitions clj

(partitions dataframe)

Params:

Result: List[Partition]

Set of partitions in this RDD.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaRDD.html

Timestamp: 2020-10-19T01:56:48.891Z

percent-rank clj

(percent-rank)

Params: ()

Result: Column

Window function: returns the relative rank (i.e. percentile) of rows within a window partition.

This is computed by:

This is equivalent to the PERCENT_RANK function in SQL.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.504Z

persist clj

(persist dataframe)

(persist dataframe new-level)

Params: ()

Result: Dataset.this.type

Persist this Dataset with the default storage level (MEMORY_AND_DISK).

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.886Z

pi clj

The double value that is closer than any other to pi, the ratio of the circumference of a circle to its diameter.

pivot clj

(pivot grouped expr)

(pivot grouped expr values)

Params: (pivotColumn: String)

Result: RelationalGroupedDataset

Pivots a column of the current DataFrame and performs the specified aggregation.

There are two versions of pivot function: one that requires the caller to specify the list of distinct values to pivot on, and one that does not. The latter is more concise but less efficient, because Spark needs to first compute the list of distinct values internally.

Name of the column to pivot.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/RelationalGroupedDataset.html

Timestamp: 2020-10-19T01:56:23.317Z

pmod clj

(pmod left-expr right-expr)

Params: (dividend: Column, divisor: Column)

Result: Column

Returns the positive value of dividend mod divisor.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.505Z

pos? clj

(pos? expr)

Returns true if expr is greater than zero, else false.

posexplode clj

(posexplode expr)

Params: (e: Column)

Result: Column

Creates a new row for each element with position in the given array or map column. Uses the default column name pos for position, and col for elements in the array and key and value for elements in the map unless specified otherwise.

2.1.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.506Z

posexplode-outer clj

(posexplode-outer expr)

Params: (e: Column)

Result: Column

Creates a new row for each element with position in the given array or map column. Uses the default column name pos for position, and col for elements in the array and key and value for elements in the map unless specified otherwise.

2.1.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.506Z

pow clj

(pow base exponent)

Params: (l: Column, r: Column)

Result: Column

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

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.520Z

print-schema clj

(print-schema dataframe)

Params: ()

Result: Unit

Prints the schema to the console in a nice tree format.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.888Z

put clj

(put bloom item)

Params: (item: Any)

Result: Boolean

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/util/sketch/BloomFilter.html

Timestamp: 2020-10-19T01:56:25.746Z

qcut clj

(qcut expr num-buckets-or-probs)

Returns a new Column of discretised expr into equal-sized buckets based on rank or based on sample quantiles.

quantile clj multimethod

Column: Aggregate function: returns the quantile of the values in a group.

RelationalGroupedDataset: Compute the quantile for each numeric columns for each group.

quarter clj

(quarter expr)

Params: (e: Column)

Result: Column

Extracts the quarter as an integer from a given date/timestamp/string.

An integer, or null if the input was a string that could not be cast to a date

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.521Z

radians clj

(radians expr)

Params: (e: Column)

Result: Column

Converts an angle measured in degrees to an approximately equivalent angle measured in radians.

angle in degrees

angle in radians, as if computed by java.lang.Math.toRadians

2.1.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.523Z

rand clj

(rand)

(rand seed)

Params: (seed: Long)

Result: Column

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

1.4.0

The function is non-deterministic in general case.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.526Z

rand-nth clj

(rand-nth dataframe)

Returns a random row collected.

randn clj

(randn)

(randn seed)

Params: (seed: Long)

Result: Column

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

1.4.0

The function is non-deterministic in general case.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.528Z

random-choice clj

(random-choice choices)

(random-choice choices probs)

(random-choice choices probs seed)

Returns a new Column of a random sample from a given collection of choices.

random-exp clj

(random-exp)

(random-exp rate)

(random-exp rate seed)

Returns a new Column of draws from an exponential distribution.

random-int clj

(random-int)

(random-int low high)

(random-int low high seed)

Returns a new Column of random integers from low (inclusive) to high (exclusive).

random-norm clj

(random-norm)

(random-norm mu sigma)

(random-norm mu sigma seed)

Returns a new Column of draws from a normal distribution.

random-split clj

(random-split dataframe weights)

(random-split dataframe weights seed)

Params: (weights: Array[Double], seed: Long)

Result: Array[Dataset[T]]

Randomly splits this Dataset with the provided weights.

weights for splits, will be normalized if they don't sum to 1.

Seed for sampling. For Java API, use randomSplitAsList.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.892Z

random-uniform clj

(random-uniform)

(random-uniform low high)

(random-uniform low high seed)

Returns a new Column of draws from a uniform distribution.

range clj multimethod

Creates a Dataset with a single LongType column named id.

The Dataset contains elements in a range from start (default 0) to end (exclusive) with the given step (default 1).

If num-partitions is specified, the dataset will be distributed into the specified number of partitions. Otherwise, spark uses internal logic to determine the number of partitions.

rank clj

(rank)

Params: ()

Result: Column

Window function: returns the rank of rows within a window partition.

The difference between rank and dense_rank is that dense_rank leaves no gaps in ranking sequence when there are ties. That is, if you were ranking a competition using dense_rank and had three people tie for second place, you would say that all three were in second place and that the next person came in third. Rank would give me sequential numbers, making the person that came in third place (after the ties) would register as coming in fifth.

This is equivalent to the RANK function in SQL.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.529Z

rchoice clj

(rchoice choices)

(rchoice choices probs)

(rchoice choices probs seed)

Returns a new Column of a random sample from a given collection of choices.

rdd clj

(rdd dataframe)

Params:

Result: RDD[T]

Represents the content of the Dataset as an RDD of T.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.894Z

read-avro! clj multimethod

Loads an Avro file and returns the results as a DataFrame.

Spark's DataFrameReader options may be passed in as a map of options.

See: https://spark.apache.org/docs/latest/sql-data-sources.html

read-binary! clj multimethod

Loads a binary file and returns the results as a DataFrame.

Spark's DataFrameReader options may be passed in as a map of options.

See: https://spark.apache.org/docs/latest/sql-data-sources-binaryFile.html

read-csv! clj multimethod

Loads a CSV file and returns the results as a DataFrame.

Spark's DataFrameReader options may be passed in as a map of options.

See: https://spark.apache.org/docs/latest/sql-data-sources.html

read-edn! clj multimethod

Loads an EDN file and returns the results as a DataFrame.

read-jdbc! clj

(read-jdbc! options)

(read-jdbc! spark options)

Loads a database table and returns the results as a DataFrame.

Spark's DataFrameReader options may be passed in as a map of options.

See: https://spark.apache.org/docs/latest/sql-data-sources.html

read-json! clj multimethod

Loads a JSON file and returns the results as a DataFrame.

Spark's DataFrameReader options may be passed in as a map of options.

See: https://spark.apache.org/docs/latest/sql-data-sources.html

read-libsvm! clj multimethod

Loads a LIBSVM file and returns the results as a DataFrame.

Spark's DataFrameReader options may be passed in as a map of options.

See: https://spark.apache.org/docs/latest/sql-data-sources.html

read-parquet! clj multimethod

Loads a Parquet file and returns the results as a DataFrame.

Spark's DataFrameReader options may be passed in as a map of options.

See: https://spark.apache.org/docs/latest/sql-data-sources-parquet.html

read-table! clj multimethod

Reads a managed (hive) table and returns the result as a DataFrame.

read-text! clj multimethod

Loads a text file and returns the results as a DataFrame.

Spark's DataFrameReader options may be passed in as a map of options.

See: https://spark.apache.org/docs/latest/sql-data-sources.html

read-xlsx! clj multimethod

Loads an Excel file and returns the results as a DataFrame.

Example options:

{:header true :sheet "Sheet2"}

records->dataset clj

(records->dataset records)

(records->dataset spark records)

Construct a Dataset from a collection of maps.

(g/show (g/records->dataset [{:a 1 :b 2} {:a 3 :b 4}]))
; +---+---+
; |a  |b  |
; +---+---+
; |1  |2  |
; |3  |4  |
; +---+---+

regexp-extract clj

(regexp-extract expr regex idx)

Params: (e: Column, exp: String, groupIdx: Int)

Result: Column

Extract a specific group matched by a Java regex, from the specified string column. If the regex did not match, or the specified group did not match, an empty string is returned.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.530Z

regexp-replace clj

(regexp-replace expr pattern-expr replacement-expr)

Params: (e: Column, pattern: String, replacement: String)

Result: Column

Replace all substrings of the specified string value that match regexp with rep.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.532Z

relative-error clj

(relative-error cms)

Params: ()

Result: Double

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/util/sketch/CountMinSketch.html

Timestamp: 2020-10-19T01:56:26.106Z

remove clj

(remove dataframe expr)

Returns a new Dataset that only contains elements where func returns false.

rename-columns clj

(rename-columns dataframe rename-map)

Returns a new Dataset with a column renamed according to the rename-map.

rename-keys clj

(rename-keys expr kmap)

Same as transform-keys with a map arg.

repartition clj

(repartition dataframe & args)

Params: (numPartitions: Int)

Result: Dataset[T]

Returns a new Dataset that has exactly numPartitions partitions.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.901Z

repartition-by-range clj

(repartition-by-range dataframe & args)

Params: (numPartitions: Int, partitionExprs: Column*)

Result: Dataset[T]

Returns a new Dataset partitioned by the given partitioning expressions into numPartitions. The resulting Dataset is range partitioned.

At least one partition-by expression must be specified. When no explicit sort order is specified, "ascending nulls first" is assumed. Note, the rows are not sorted in each partition of the resulting Dataset.

Note that due to performance reasons this method uses sampling to estimate the ranges. Hence, the output may not be consistent, since sampling can return different values. The sample size can be controlled by the config spark.sql.execution.rangeExchange.sampleSizePerPartition.

2.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.904Z

replace clj

(replace expr lookup-map)

(replace expr from-value-or-values to-value)

Returns a new Column where from-value-or-values is replaced with to-value.

replace-na clj

(replace-na dataframe cols replacement)

Params: (col: String, replacement: Map[T, T])

Result: DataFrame

Replaces values matching keys in replacement map with the corresponding values.

name of the column to apply the value replacement. If col is "*", replacement is applied on all string, numeric or boolean columns.

value replacement map. Key and value of replacement map must have the same type, and can only be doubles, strings or booleans. The map value can have nulls.

1.3.1

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/DataFrameNaFunctions.html

Timestamp: 2020-10-19T01:56:23.927Z

resources clj

(resources)

(resources spark)

Params:

Result: Map[String, ResourceInformation]

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.550Z

reverse clj

(reverse expr)

Params: (e: Column)

Result: Column

Returns a reversed string or an array with reverse order of elements.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.534Z

rexp clj

(rexp)

(rexp rate)

(rexp rate seed)

Returns a new Column of draws from an exponential distribution.

rint clj

(rint expr)

Params: (e: Column)

Result: Column

Returns the double value that is closest in value to the argument and is equal to a mathematical integer.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.536Z

rlike clj

(rlike expr literal)

Params: (literal: String)

Result: Column

SQL RLIKE expression (LIKE with Regex). Returns a boolean column based on a regex match.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.977Z

rnorm clj

(rnorm)

(rnorm mu sigma)

(rnorm mu sigma seed)

Returns a new Column of draws from a normal distribution.

rollup clj

(rollup dataframe & exprs)

Params: (cols: Column*)

Result: RelationalGroupedDataset

Create a multi-dimensional rollup for the current Dataset using the specified columns, so we can run aggregation on them. See RelationalGroupedDataset for all the available aggregate functions.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.907Z

round clj

(round expr)

Params: (e: Column)

Result: Column

Returns the value of the column e rounded to 0 decimal places with HALF_UP round mode.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.539Z

row clj

(row & values)

Params: (values: Seq[Any])

Result: Row

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Row$.html

Timestamp: 2020-10-19T01:56:24.277Z

row-number clj

(row-number)

Params: ()

Result: Column

Window function: returns a sequential number starting at 1 within a window partition.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.540Z

rpad clj

(rpad expr length pad)

Params: (str: Column, len: Int, pad: String)

Result: Column

Right-pad the string column with pad to a length of len. If the string column is longer than len, the return value is shortened to len characters.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.541Z

rtrim clj

(rtrim expr)

Params: (e: Column)

Result: Column

Trim the spaces from right end for the specified string value.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.543Z

runif clj

(runif)

(runif low high)

(runif low high seed)

Returns a new Column of draws from a uniform distribution.

runiform clj

(runiform)

(runiform low high)

(runiform low high seed)

Returns a new Column of draws from a uniform distribution.

sample clj

(sample dataframe fraction)

(sample dataframe fraction with-replacement)

Params: (fraction: Double, seed: Long)

Result: Dataset[T]

Returns a new Dataset by sampling a fraction of rows (without replacement), using a user-supplied seed.

Fraction of rows to generate, range [0.0, 1.0].

Seed for sampling.

2.3.0

This is NOT guaranteed to provide exactly the fraction of the count of the given Dataset.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.913Z

sample-by clj

(sample-by dataframe expr fractions seed)

Params: (col: String, fractions: Map[T, Double], seed: Long)

Result: DataFrame

Returns a stratified sample without replacement based on the fraction given on each stratum.

stratum type

column that defines strata

sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as zero.

random seed

a new DataFrame that represents the stratified sample

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/DataFrameStatFunctions.html

Timestamp: 2020-10-19T01:56:24.694Z

sc clj

(sc)

(sc spark)

Params:

Result: SparkContext

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.550Z

schema-of-csv clj

(schema-of-csv expr)

(schema-of-csv expr options)

Params: (csv: String)

Result: Column

Parses a CSV string and infers its schema in DDL format.

a CSV string.

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.547Z

schema-of-json clj

(schema-of-json expr)

(schema-of-json expr options)

Params: (json: String)

Result: Column

Parses a JSON string and infers its schema in DDL format.

a JSON string.

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.554Z

second clj

(second expr)

Params: (e: Column)

Result: Column

Extracts the seconds as an integer from a given date/timestamp/string.

An integer, or null if the input was a string that could not be cast to a timestamp

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.555Z

select clj

(select dataframe & exprs)

Params: (cols: Column*)

Result: DataFrame

Selects a set of column based expressions.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.931Z

select-columns clj

(select-columns dataframe & exprs)

Params: (cols: Column*)

Result: DataFrame

Selects a set of column based expressions.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.931Z

select-expr clj

(select-expr dataframe & exprs)

Params: (exprs: String*)

Result: DataFrame

Selects a set of SQL expressions. This is a variant of select that accepts SQL expressions.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.933Z

select-keys clj

(select-keys expr ks)

Returns a map containing only those entries in map (expr) whose key is in ks.

sequence clj

(sequence start stop step)

Params: (start: Column, stop: Column, step: Column)

Result: Column

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

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.557Z

sha-1 clj

(sha-1 expr)

Params: (e: Column)

Result: Column

Calculates the SHA-1 digest of a binary column and returns the value as a 40 character hex string.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.558Z

sha-2 clj

(sha-2 expr n-bits)

Params: (e: Column, numBits: Int)

Result: Column

Calculates the SHA-2 family of hash functions of a binary column and returns the value as a hex string.

column to compute SHA-2 on.

one of 224, 256, 384, or 512.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.559Z

sha1 clj

(sha1 expr)

Params: (e: Column)

Result: Column

Calculates the SHA-1 digest of a binary column and returns the value as a 40 character hex string.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.558Z

sha2 clj

(sha2 expr n-bits)

Params: (e: Column, numBits: Int)

Result: Column

Calculates the SHA-2 family of hash functions of a binary column and returns the value as a hex string.

column to compute SHA-2 on.

one of 224, 256, 384, or 512.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.559Z

shape clj

(shape dataframe)

Returns a vector representing the dimensionality of the Dataset.

shift-left clj

(shift-left expr num-bits)

Params: (e: Column, numBits: Int)

Result: Column

Shift the given value numBits left. If the given value is a long value, this function will return a long value else it will return an integer value.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.560Z

shift-right clj

(shift-right expr num-bits)

Params: (e: Column, numBits: Int)

Result: Column

(Signed) shift the given value numBits right. If the given value is a long value, it will return a long value else it will return an integer value.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.562Z

shift-right-unsigned clj

(shift-right-unsigned expr num-bits)

Params: (e: Column, numBits: Int)

Result: Column

Unsigned shift the given value numBits right. If the given value is a long value, it will return a long value else it will return an integer value.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.563Z

short clj

(short expr)

Casts the column to a short.

show clj

(show dataframe)

(show dataframe options)

Params: (numRows: Int)

Result: Unit

Displays the Dataset in a tabular form. Strings more than 20 characters will be truncated, and all cells will be aligned right. For example:

Number of rows to show

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.945Z

show-vertical clj

(show-vertical dataframe)

(show-vertical dataframe options)

Displays the Dataset in a list-of-records form.

shuffle clj multimethod

Column: Returns a random permutation of the given array.

Dataset: Shuffles the rows of the Dataset.

signum clj

(signum expr)

Params: (e: Column)

Result: Column

Computes the signum of the given value.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.566Z

sin clj

(sin expr)

Params: (e: Column)

Result: Column

angle in radians

sine of the angle, as if computed by java.lang.Math.sin

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.568Z

sinh clj

(sinh expr)

Params: (e: Column)

Result: Column

hyperbolic angle

hyperbolic sine of the given value, as if computed by java.lang.Math.sinh

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.570Z

size clj

(size expr)

Params: (e: Column)

Result: Column

Returns length of array or map.

The function returns null for null input if spark.sql.legacy.sizeOfNull is set to false or spark.sql.ansi.enabled is set to true. Otherwise, the function returns -1 for null input. With the default settings, the function returns -1 for null input.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.571Z

skewness clj

(skewness expr)

Params: (e: Column)

Result: Column

Aggregate function: returns the skewness of the values in a group.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.574Z

slice clj

(slice expr start length)

Params: (x: Column, start: Int, length: Int)

Result: Column

Returns an array containing all the elements in x from index start (or starting from the end if start is negative) with the specified length.

the array column to be sliced

the starting index

the length of the slice

2.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.575Z

sort clj

(sort dataframe & exprs)

Params: (sortCol: String, sortCols: String*)

Result: Dataset[T]

Returns a new Dataset sorted by the given expressions. This is an alias of the sort function.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.884Z

sort-array clj

(sort-array expr)

(sort-array expr asc)

Params: (e: Column)

Result: Column

Sorts the input array for the given column in ascending order, according to the natural ordering of the array elements. Null elements will be placed at the beginning of the returned array.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.577Z

sort-within-partitions clj

(sort-within-partitions dataframe & exprs)

Params: (sortCol: String, sortCols: String*)

Result: Dataset[T]

Returns a new Dataset with each partition sorted by the given expressions.

This is the same operation as "SORT BY" in SQL (Hive QL).

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.950Z

soundex clj

(soundex expr)

Params: (e: Column)

Result: Column

Returns the soundex code for the specified expression.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.578Z

spark-conf clj

(spark-conf spark-session)

Params:

Result: SparkConf

Return a copy of this JavaSparkContext's configuration. The configuration cannot be changed at runtime.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.511Z

spark-context clj

(spark-context)

(spark-context spark)

Params:

Result: SparkContext

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.550Z

spark-home clj

(spark-home)

(spark-home spark)

Params: ()

Result: Optional[String]

Get Spark's home location from either a value set through the constructor, or the spark.home Java property, or the SPARK_HOME environment variable (in that order of preference). If neither of these is set, return None.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.518Z

spark-partition-id clj

(spark-partition-id)

Params: ()

Result: Column

Partition ID.

1.6.0

This is non-deterministic because it depends on data partitioning and task scheduling.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.579Z

spark-session clj

(spark-session dataframe)

Params:

Result: SparkSession

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.951Z

sparse clj

Params: (size: Int, indices: Array[Int], values: Array[Double])

Result: Vector

Creates a sparse vector providing its index array and value array.

vector size.

index array, must be strictly increasing.

value array, must have the same length as indices.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/linalg/Vectors$.html

Timestamp: 2020-10-19T01:56:35.350Z

split clj

(split expr pattern)

Params: (str: Column, pattern: String)

Result: Column

Splits str around matches of the given pattern.

a string expression to split

a string representing a regular expression. The regex string should be a Java regular expression.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.582Z

sql clj

(sql spark sql-text)

Executes a SQL query using Spark, returning the result as a DataFrame.

The dialect that is used for SQL parsing can be configured with 'spark.sql.dialect'.

(g/sql spark "SELECT * FROM my_table")

sql-context clj

(sql-context dataframe)

Params:

Result: SQLContext

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.952Z

sqr clj

(sqr expr)

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

sqrt clj

(sqrt expr)

Params: (e: Column)

Result: Column

Computes the square root of the specified float value.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.584Z

starts-with clj

(starts-with expr literal)

Params: (other: Column)

Result: Column

String starts with. Returns a boolean column based on a string match.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.979Z

std clj

(std expr)

Params: (e: Column)

Result: Column

Aggregate function: alias for stddev_samp.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.586Z

stddev clj

(stddev expr)

Params: (e: Column)

Result: Column

Aggregate function: alias for stddev_samp.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.586Z

stddev-pop clj

(stddev-pop expr)

Params: (e: Column)

Result: Column

Aggregate function: returns the population standard deviation of the expression in a group.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.593Z

stddev-samp clj

(stddev-samp expr)

Params: (e: Column)

Result: Column

Aggregate function: alias for stddev_samp.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.586Z

storage-level clj

(storage-level dataframe)

Params:

Result: StorageLevel

Get the Dataset's current storage level, or StorageLevel.NONE if not persisted.

2.1.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.954Z

str clj

(str expr)

Casts the column to a str.

streaming? clj

(streaming? dataframe)

Params:

Result: Boolean

Returns true if this Dataset contains one or more sources that continuously return data as it arrives. A Dataset that reads data from a streaming source must be executed as a StreamingQuery using the start() method in DataStreamWriter. Methods that return a single answer, e.g. count() or collect(), will throw an AnalysisException when there is a streaming source present.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.844Z

struct clj

(struct & exprs)

Params: (cols: Column*)

Result: Column

Creates a new struct column. If the input column is a column in a DataFrame, or a derived column expression that is named (i.e. aliased), its name would be retained as the StructField's name, otherwise, the newly generated StructField's name would be auto generated as col with a suffix index + 1, i.e. col1, col2, col3, ...

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.597Z

struct-field clj

(struct-field col-name data-type nullable)

Creates a StructField by specifying the name col-name, data type data-type and whether values of this field can be null values nullable.

struct-type clj

(struct-type & fields)

Creates a StructType with the given list of StructFields fields.

substring clj

(substring expr pos len)

Params: (str: Column, pos: Int, len: Int)

Result: Column

Substring starts at pos and is of length len when str is String type or returns the slice of byte array that starts at pos in byte and is of length len when str is Binary type

1.5.0

The position is not zero based, but 1 based index.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.599Z

substring-index clj

(substring-index expr delim cnt)

Params: (str: Column, delim: String, count: Int)

Result: Column

Returns the substring from string str before count occurrences of the delimiter delim. If count is positive, everything the left of the final delimiter (counting from left) is returned. If count is negative, every to the right of the final delimiter (counting from the right) is returned. substring_index performs a case-sensitive match when searching for delim.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.600Z

sum clj multimethod

Column: Aggregate function: returns the sum of all values in the given column.

RelationalGroupedDataset: Compute the sum for each numeric columns for each group.

sum-distinct clj

(sum-distinct expr)

Params: (e: Column)

Result: Column

Aggregate function: returns the sum of distinct values in the expression.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.604Z

summary clj

(summary dataframe & stat-names)

Params: (statistics: String*)

Result: DataFrame

Computes specified statistics for numeric and string columns. Available statistics are:

If no statistics are given, this function computes count, mean, stddev, min, approximate quartiles (percentiles at 25%, 50%, and 75%), and max.

This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting Dataset. If you want to programmatically compute summary statistics, use the agg function instead.

To do a summary for specific columns first select them:

See also describe for basic statistics.

Statistics from above list to be computed.

2.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.957Z

table->dataset clj

(table->dataset table col-names)

(table->dataset spark table col-names)

Construct a Dataset from a collection of collections.

(g/show (g/table->dataset [[1 2] [3 4]] [:a :b]))
; +---+---+
; |a  |b  |
; +---+---+
; |1  |2  |
; |3  |4  |
; +---+---+

tail clj

(tail dataframe n-rows)

Params: (n: Int)

Result: Array[T]

Returns the last n rows in the Dataset.

Running tail requires moving data into the application's driver process, and doing so with a very large n can crash the driver process with OutOfMemoryError.

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.959Z

tail-vals clj

(tail-vals dataframe n-rows)

Returns the vector values of the last n rows in the Dataset collected.

take clj

(take dataframe n-rows)

Params: (n: Int)

Result: Array[T]

Returns the first n rows in the Dataset.

Running take requires moving data into the application's driver process, and doing so with a very large n can crash the driver process with OutOfMemoryError.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.961Z

take-vals clj

(take-vals dataframe n-rows)

Returns the vector values of the first n rows in the Dataset collected.

tan clj

(tan expr)

Params: (e: Column)

Result: Column

angle in radians

tangent of the given value, as if computed by java.lang.Math.tan

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.607Z

tanh clj

(tanh expr)

Params: (e: Column)

Result: Column

hyperbolic angle

hyperbolic tangent of the given value, as if computed by java.lang.Math.tanh

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.610Z

time-window clj

(time-window time-expr duration)

(time-window time-expr duration slide)

(time-window time-expr duration slide start)

Params: (timeColumn: Column, windowDuration: String, slideDuration: String, startTime: String)

Result: Column

Bucketize rows into one or more time windows given a timestamp specifying column. Window starts are inclusive but the window ends are exclusive, e.g. 12:05 will be in the window [12:05,12:10) but not in [12:00,12:05). Windows can support microsecond precision. Windows in the order of months are not supported. The following example takes the average stock price for a one minute window every 10 seconds starting 5 seconds after the hour:

The windows will look like:

For a streaming query, you may use the function current_timestamp to generate windows on processing time.

The column or the expression to use as the timestamp for windowing by time. The time column must be of TimestampType.

A string specifying the width of the window, e.g. 10 minutes, 1 second. Check org.apache.spark.unsafe.types.CalendarInterval for valid duration identifiers. Note that the duration is a fixed length of time, and does not vary over time according to a calendar. For example, 1 day always means 86,400,000 milliseconds, not a calendar day.

A string specifying the sliding interval of the window, e.g. 1 minute. A new window will be generated every slideDuration. Must be less than or equal to the windowDuration. Check org.apache.spark.unsafe.types.CalendarInterval for valid duration identifiers. This duration is likewise absolute, and does not vary according to a calendar.

The offset with respect to 1970-01-01 00:00:00 UTC with which to start window intervals. For example, in order to have hourly tumbling windows that start 15 minutes past the hour, e.g. 12:15-13:15, 13:15-14:15... provide startTime as 15 minutes.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.732Z

to-byte-array clj

(to-byte-array cms)

Params: ()

Result: Array[Byte]

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/util/sketch/CountMinSketch.html

Timestamp: 2020-10-19T01:56:26.107Z

to-csv clj

(to-csv expr)

(to-csv expr options)

Params: (e: Column, options: Map[String, String])

Result: Column

(Java-specific) Converts a column containing a StructType into a CSV string with the specified schema. Throws an exception, in the case of an unsupported type.

a column containing a struct.

options to control how the struct column is converted into a CSV string. It accepts the same options and the json data source.

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.613Z

to-date clj

(to-date expr)

(to-date expr date-format)

Params: (e: Column)

Result: Column

Converts the column into DateType by casting rules to DateType.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.616Z

to-debug-string clj

Coerce to string useful for debugging.

to-df clj multimethod

Collection: alias for table->dataset.

Dataset: Converts this strongly typed collection of data to generic DataFrame with columns renamed.

to-json clj multimethod

Column: Converts a column containing a StructType, ArrayType or a MapType into a JSON string with the specified schema.

Dataset: Returns the content of the Dataset as a Dataset of JSON strings.

to-string clj

Coerce to string.

to-timestamp clj

(to-timestamp expr)

(to-timestamp expr date-format)

Params: (s: Column)

Result: Column

Converts to a timestamp by casting rules to TimestampType.

A date, timestamp or string. If a string, the data must be in a format that can be cast to a timestamp, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

A timestamp, or null if the input was a string that could not be cast to a timestamp

2.2.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.623Z

to-utc-timestamp clj

(to-utc-timestamp expr)

Params: (ts: Column, tz: String)

Result: Column

Given a timestamp like '2017-07-14 02:40:00.0', interprets it as a time in the given time zone, and renders that time as a timestamp in UTC. For example, 'GMT+1' would yield '2017-07-14 01:40:00.0'.

A date, timestamp or string. If a string, the data must be in a format that can be cast to a timestamp, such as yyyy-MM-dd or yyyy-MM-dd HH:mm:ss.SSSS

A string detailing the time zone ID that the input should be adjusted to. It should be in the format of either region-based zone IDs or zone offsets. Region IDs must have the form 'area/city', such as 'America/Los_Angeles'. Zone offsets must be in the format '(+|-)HH:mm', for example '-08:00' or '+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'. Other short names are not recommended to use because they can be ambiguous.

A timestamp, or null if ts was a string that could not be cast to a timestamp or tz was an invalid value

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.626Z

total-count clj

(total-count cms)

Params: ()

Result: Long

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/util/sketch/CountMinSketch.html

Timestamp: 2020-10-19T01:56:26.108Z

transform clj

(transform expr xform-fn)

Params: (column: Column, f: (Column) ⇒ Column)

Result: Column

Returns an array of elements after applying a transformation to each element in the input array.

the input array column

col => transformed_col, the lambda function to transform the input column

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.629Z

transform-keys clj

(transform-keys expr key-fn)

Params: (expr: Column, f: (Column, Column) ⇒ Column)

Result: Column

Applies a function to every key-value pair in a map and returns a map with the results of those applications as the new keys for the pairs.

the input map column

(key, value) => new_key, the lambda function to transform the key of input map column

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.630Z

transform-values clj

(transform-values expr key-fn)

Params: (expr: Column, f: (Column, Column) ⇒ Column)

Result: Column

Applies a function to every key-value pair in a map and returns a map with the results of those applications as the new values for the pairs.

the input map column

(key, value) => new_value, the lambda function to transform the value of input map column

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.638Z

translate clj

(translate expr match replacement)

Params: (src: Column, matchingString: String, replaceString: String)

Result: Column

Translate any character in the src by a character in replaceString. The characters in replaceString correspond to the characters in matchingString. The translate will happen when any character in the string matches the character in the matchingString.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.639Z

trim clj

(trim expr trim-string)

Params: (e: Column)

Result: Column

Trim the spaces from both ends for the specified string column.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.641Z

unbase-64 clj

(unbase-64 expr)

Params: (e: Column)

Result: Column

Decodes a BASE64 encoded string column and returns it as a binary column. This is the reverse of base64.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.702Z

unbase64 clj

(unbase64 expr)

Params: (e: Column)

Result: Column

Decodes a BASE64 encoded string column and returns it as a binary column. This is the reverse of base64.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.702Z

unbounded-following clj

Params:

Result: Long

Value representing the last row in the partition, equivalent to "UNBOUNDED FOLLOWING" in SQL. This can be used to specify the frame boundaries:

2.1.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/expressions/Window$.html

Timestamp: 2020-10-19T01:56:25.054Z

unbounded-preceding clj

Params:

Result: Long

Value representing the first row in the partition, equivalent to "UNBOUNDED PRECEDING" in SQL. This can be used to specify the frame boundaries:

2.1.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/expressions/Window$.html

Timestamp: 2020-10-19T01:56:25.055Z

unhex clj

(unhex expr)

Params: (column: Column)

Result: Column

Inverse of hex. Interprets each pair of characters as a hexadecimal number and converts to the byte representation of number.

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.703Z

union clj

(union & dataframes)

Params: (other: Dataset[T])

Result: Dataset[T]

Returns a new Dataset containing union of rows in this Dataset and another Dataset.

This is equivalent to UNION ALL in SQL. To do a SQL-style set union (that does deduplication of elements), use this function followed by a distinct.

Also as standard in SQL, this function resolves columns by position (not by name):

Notice that the column positions in the schema aren't necessarily matched with the fields in the strongly typed objects in a Dataset. This function resolves columns by their positions in the schema, not the fields in the strongly typed objects. Use unionByName to resolve columns by field name in the typed objects.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.974Z

union-by-name clj

(union-by-name & dataframes)

Params: (other: Dataset[T])

Result: Dataset[T]

Returns a new Dataset containing union of rows in this Dataset and another Dataset.

This is different from both UNION ALL and UNION DISTINCT in SQL. To do a SQL-style set union (that does deduplication of elements), use this function followed by a distinct.

The difference between this function and union is that this function resolves columns by name (not by position):

2.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.978Z

unix-timestamp clj

(unix-timestamp)

(unix-timestamp expr)

(unix-timestamp expr pattern)

Params: ()

Result: Column

Returns the current Unix timestamp (in seconds) as a long.

1.5.0

All calls of unix_timestamp within the same query return the same value (i.e. the current timestamp is calculated at the start of query evaluation).

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.710Z

unpersist clj

(unpersist dataframe)

(unpersist dataframe blocking)

Params: (blocking: Boolean)

Result: Dataset.this.type

Mark the Dataset as non-persistent, and remove all blocks for it from memory and disk. This will not un-persist any cached data that is built upon this Dataset.

Whether to block until all blocks are deleted.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.980Z

update clj multimethod

Column: transform-values with Clojure's assoc signature.

Dataset: with-column with Clojure's assoc signature.

upper clj

(upper expr)

Params: (e: Column)

Result: Column

Converts a string column to upper case.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.712Z

vals clj

(vals expr)

Params: (e: Column)

Result: Column

Returns an unordered array containing the values of the map.

2.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.473Z

value-counts clj

(value-counts dataframe)

Returns a Dataset containing counts of unique rows.

The resulting object will be in descending order so that the first element is the most frequently-occurring element.

var-pop clj

(var-pop expr)

Params: (e: Column)

Result: Column

Aggregate function: returns the population variance of the values in a group.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.714Z

var-samp clj

(var-samp expr)

Params: (e: Column)

Result: Column

Aggregate function: alias for var_samp.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.718Z

variance clj

(variance expr)

Params: (e: Column)

Result: Column

Aggregate function: alias for var_samp.

1.6.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.718Z

version clj

(version)

(version spark)

Params:

Result: String

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/api/java/JavaSparkContext.html

Timestamp: 2020-10-19T01:56:49.576Z

week-of-year clj

(week-of-year expr)

Params: (e: Column)

Result: Column

Extracts the week number as an integer from a given date/timestamp/string.

A week is considered to start on a Monday and week 1 is the first week with more than 3 days, as defined by ISO 8601

An integer, or null if the input was a string that could not be cast to a date

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.723Z

weekofyear clj

(weekofyear expr)

Params: (e: Column)

Result: Column

Extracts the week number as an integer from a given date/timestamp/string.

A week is considered to start on a Monday and week 1 is the first week with more than 3 days, as defined by ISO 8601

An integer, or null if the input was a string that could not be cast to a date

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.723Z

when clj

(when condition if-expr)

(when condition if-expr else-expr)

Params: (condition: Column, value: Any)

Result: Column

Evaluates a list of conditions and returns one of multiple possible result expressions. If otherwise is not defined at the end, null is returned for unmatched conditions.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.724Z

where clj multimethod

Column: Returns an array of elements for which a predicate holds in a given array.

Dataset: Filters rows using the given condition.

width clj

(width cms)

Params: ()

Result: Int

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/util/sketch/CountMinSketch.html

Timestamp: 2020-10-19T01:56:26.108Z

window clj

(window {:keys [partition-by order-by range-between rows-between]})

Utility functions for defining window in DataFrames.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/expressions/Window$.html

Timestamp: 2020-10-19T01:55:47.755Z

windowed clj

(windowed options)

Shortcut to create WindowSpec that takes a map as the argument.

Expected keys: [:partition-by :order-by :range-between :rows-between]

with-column clj

(with-column dataframe col-name expr)

Params: (colName: String, col: Column)

Result: DataFrame

Returns a new Dataset by adding a column or replacing the existing column that has the same name.

column's expression must only refer to attributes supplied by this Dataset. It is an error to add a column that refers to some other Dataset.

2.0.0

this method introduces a projection internally. Therefore, calling it multiple times, for instance, via loops in order to add multiple columns can generate big plans which can cause performance issues and even StackOverflowException. To avoid this, use select with the multiple columns at once.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.987Z

with-column-renamed clj

(with-column-renamed dataframe old-name new-name)

Params: (existingName: String, newName: String)

Result: DataFrame

Returns a new Dataset with a column renamed. This is a no-op if schema doesn't contain existingName.

2.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Dataset.html

Timestamp: 2020-10-19T01:56:20.988Z

write-avro! clj

(write-avro! dataframe path)

(write-avro! dataframe path options)

Writes an Avro file at the specified path.

Spark's DataFrameWriter options may be passed in as a map of options.

See: https://spark.apache.org/docs/latest/sql-data-sources.html

write-csv! clj

(write-csv! dataframe path)

(write-csv! dataframe path options)

Writes a CSV file at the specified path.

Spark's DataFrameWriter options may be passed in as a map of options.

See: https://spark.apache.org/docs/latest/sql-data-sources.html

write-edn! clj

(write-edn! dataframe path)

(write-edn! dataframe path options)

Writes an EDN file at the specified path.

write-jdbc! clj

(write-jdbc! dataframe options)

Writes a database table.

Spark's DataFrameWriter options may be passed in as a map of options.

See: https://spark.apache.org/docs/latest/sql-data-sources.html

write-json! clj

(write-json! dataframe path)

(write-json! dataframe path options)

Writes a JSON file at the specified path.

Spark's DataFrameWriter options may be passed in as a map of options.

See: https://spark.apache.org/docs/latest/sql-data-sources-json.html

write-libsvm! clj

(write-libsvm! dataframe path)

(write-libsvm! dataframe path options)

Writes a LIBSVM file at the specified path.

Spark's DataFrameWriter options may be passed in as a map of options.

See: https://spark.apache.org/docs/latest/sql-data-sources.html

write-parquet! clj

(write-parquet! dataframe path)

(write-parquet! dataframe path options)

Writes a Parquet file at the specified path.

Spark's DataFrameWriter options may be passed in as a map of options.

See: https://spark.apache.org/docs/latest/sql-data-sources-parquet.html

write-table! clj

(write-table! dataframe table-name)

(write-table! dataframe table-name options)

Writes the dataset to a managed (hive) table.

write-text! clj

(write-text! dataframe path)

(write-text! dataframe path options)

Writes a text file at the specified path.

Spark's DataFrameWriter options may be passed in as a map of options.

See: https://spark.apache.org/docs/latest/sql-data-sources.html

write-xlsx! clj

(write-xlsx! dataframe path)

(write-xlsx! dataframe path options)

Writes an Excel file at the specified path.

xxhash-64 clj

(xxhash-64 & exprs)

Params: (cols: Column*)

Result: Column

Calculates the hash code of given columns using the 64-bit variant of the xxHash algorithm, and returns the result as a long column.

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.733Z

xxhash64 clj

(xxhash64 & exprs)

Params: (cols: Column*)

Result: Column

Calculates the hash code of given columns using the 64-bit variant of the xxHash algorithm, and returns the result as a long column.

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.733Z

year clj

(year expr)

Params: (e: Column)

Result: Column

Extracts the year as an integer from a given date/timestamp/string.

An integer, or null if the input was a string that could not be cast to a date

1.5.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.734Z

zero? clj

(zero? expr)

Returns true if expr is zero, else false.

zip-with clj

(zip-with left right merge-fn)

Params: (left: Column, right: Column, f: (Column, Column) ⇒ Column)

Result: Column

Merge two given arrays, element-wise, into a single array using a function. If one array is shorter, nulls are appended at the end to match the length of the longer array, before applying the function.

the left input array column

the right input array column

(lCol, rCol) => col, the lambda function to merge two input columns into one column

3.0.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.737Z

zipmap clj

(zipmap key-expr val-expr)

Params: (keys: Column, values: Column)

Result: Column

Creates a new map column. The array in the first column is used for keys. The array in the second column is used for values. All elements in the array for key should not be null.

2.4

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/functions$.html

Timestamp: 2020-10-19T01:56:22.470Z

| clj

(| left-expr right-expr)

Params: (other: Any)

Result: Column

Compute bitwise OR of this expression with another expression.

1.4.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.879Z

|| clj

(|| & exprs)

Params: (other: Any)

Result: Column

Boolean OR.

1.3.0

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/sql/Column.html

Timestamp: 2020-10-19T01:56:19.994Z