spark-daria

Spark helper methods to maximize developer productivity.

Setup

Option 1

Fetch the JAR file from Maven.

resolvers += "Spark Packages Repo" at "http://dl.bintray.com/spark-packages/maven"

libraryDependencies += "mrpowers" % "spark-daria" % "2.2.0_0.12.0"

Option 2

Add the sbt-spark-package plugin to your application. The spark-daria releases are maintained in Spark Packages.
Update your build.sbt file: spDependencies += "mrpowers/spark-daria:2.2.0_0.12.0"

Accessing spark-daria versions for different Spark versions

Message me if you need spark-daria to be compiled with a different Spark version and I'll help you out 😉

✅ DataFrameValidator

Custom transformations often make assumptions about the presence or absence of columns in the DataFrame. It's important to document these dependencies in the code and provide users with descriptive error messages if assumptions are not met.

Here's an example of a custom transformation that uses the validatePresenceOfColumns validation.

import com.github.mrpowers.spark.daria.sql.DataFrameValidator

object MyTransformations extends DataFrameValidator {

  def withStandardizedPersonInfo(df: DataFrame): DataFrame = {
    val requiredColNames = Seq("name", "age")
    validatePresenceOfColumns(df, requiredColNames)
    // some transformation code
  }

}

`validatePresenceOfColumns()`

Validates if columns are included in a DataFrame. This code will error out:

val sourceDF = Seq(
  ("jets", "football"),
  ("nacional", "soccer")
).toDF("team", "sport")

val requiredColNames = Seq("team", "sport", "country", "city")

validatePresenceOfColumns(sourceDF, requiredColNames)

This is the error message:

com.github.mrpowers.spark.daria.sql.MissingDataFrameColumnsException: The [country, city] columns are not included in the DataFrame with the following columns [team, sport]

`validateSchema()`

Validates the schema of a DataFrame. This code will error out:

val sourceData = List(
  Row(1, 1),
  Row(-8, 8),
  Row(-5, 5),
  Row(null, null)
)

val sourceSchema = List(
  StructField("num1", IntegerType, true),
  StructField("num2", IntegerType, true)
)

val sourceDF = spark.createDataFrame(
  spark.sparkContext.parallelize(sourceData),
  StructType(sourceSchema)
)

val requiredSchema = StructType(
  List(
    StructField("num1", IntegerType, true),
    StructField("num2", IntegerType, true),
    StructField("name", StringType, true)
  )
)

validateSchema(sourceDF, requiredSchema)

This is the error message:

com.github.mrpowers.spark.daria.sql.InvalidDataFrameSchemaException: The [StructField(name,StringType,true)] StructFields are not included in the DataFrame with the following StructFields [StructType(StructField(num1,IntegerType,true), StructField(num2,IntegerType,true))]

`validateAbsenceOfColumns()`

Validates columns are not included in a DataFrame. This code will error out:

val sourceDF = Seq(
  ("jets", "football"),
  ("nacional", "soccer")
).toDF("team", "sport")

val prohibitedColNames = Seq("team", "sport", "country", "city")

validateAbsenceOfColumns(sourceDF, prohibitedColNames)

This is the error message:

com.github.mrpowers.spark.daria.sql.ProhibitedDataFrameColumnsException: The [team, sport] columns are not allowed to be included in the DataFrame with the following columns [team, sport]

😍 Creating DataFrames

Spark provides two methods for creating DataFrames:

createDataFrame is verbose
toDF doesn't provide enough control for customizing the schema

spark-daria defined a createDF method that allows for the terse syntax of toDF and the control of createDataFrame.

spark.createDF(
  List(
    ("bob", 45),
    ("liz", 25),
    ("freeman", 32)
  ), List(
    ("name", StringType, true),
    ("age", IntegerType, false)
  )
)

The createDF method can also be used with lists of Row and StructField objects.

spark.createDF(
  List(
    Row("bob", 45),
    Row("liz", 25),
    Row("freeman", 32)
  ), List(
    StructField("name", StringType, true),
    StructField("age", IntegerType, false)
  )
)

🔗 Chaining UDFs and SQL functions

The ColumnExt class monkey patches the org.apache.spark.sql.Column class, so SQL functions and user defined functions can be chained (relevant blog post).

`chain()`

The chain method takes a org.apache.spark.sql.functions function as an argument and can be used as follows:

val wordsDf = Seq(
  ("Batman  "),
  ("  CATWOMAN"),
  (" pikachu ")
).toDF("word")

val actualDf = wordsDf.withColumn(
  "cleaned_word",
  col("word").chain(lower).chain(trim)
)

actualDf.show()

+----------+------------+
|      word|cleaned_word|
+----------+------------+
|  Batman  |      batman|
|  CATWOMAN|    catwoman|
|  pikachu |     pikachu|
+----------+------------+

`chainUDF()`

The chainUDF method takes the name of a user defined function as an argument and can be used as follows:

def appendZ(s: String): String = {
  s"${s}Z"
}

spark.udf.register("appendZUdf", appendZ _)

def prependA(s: String): String = {
  s"A${s}"
}

spark.udf.register("prependAUdf", prependA _)

val hobbiesDf = Seq(
  ("dance"),
  ("sing")
).toDF("word")

val actualDf = hobbiesDf.withColumn(
  "fun",
  col("word").chainUDF("appendZUdf").chainUDF("prependAUdf")
)

actualDf.show()

+-----+-------+
| word|    fun|
+-----+-------+
|dance|AdanceZ|
| sing| AsingZ|
+-----+-------+

Using `chain()` and `chainUDF()` together

The chain and chainUDF methods can be used together as follows:

def appendZ(s: String): String = {
  s"${s}Z"
}

spark.udf.register("appendZUdf", appendZ _)

val wordsDf = Seq(
  ("Batman  "),
  ("  CATWOMAN"),
  (" pikachu ")
).toDF("word")

val actualDf = wordsDf.withColumn(
  "cleaned_word",
  col("word").chain(lower).chain(trim).chainUDF("appendZUdf")
)

actualDf.show()

+----------+------------+
|      word|cleaned_word|
+----------+------------+
|  Batman  |     batmanZ|
|  CATWOMAN|   catwomanZ|
|  pikachu |    pikachuZ|
+----------+------------+

Column Extensions

`isFalsy`

isFalsy returns true if a column is null or false and false otherwise.

Suppose you start with the following sourceDF:

+------+
|is_fun|
+------+
|  true|
| false|
|  null|
+------+

Run the isFalsy method:

val actualDF = sourceDF.withColumn("is_fun_falsy", col("is_fun").isFalsy)

Here are the contents of actualDF:

+------+------------+
|is_fun|is_fun_falsy|
+------+------------+
|  true|       false|
| false|        true|
|  null|        true|
+------+------------+

isFalsy can also be used on StringType columns.

Suppose you have the following sourceDF DataFrame.

+-----------+
|animal_type|
+-----------+
|        dog|
|        cat|
|       null|
+-----------+

Run the isFalsy method:

val actualDF = sourceDF.withColumn(
  "animal_type_falsy",
  col("animal_type").isFalsy
)

Here are the contents of actualDF:

+-----------+-----------------+
|animal_type|animal_type_falsy|
+-----------+-----------------+
|        dog|            false|
|        cat|            false|
|       null|             true|
+-----------+-----------------+

`isTruthy`

isTruthy returns false if a column is null or false and true otherwise.

Suppose you start with the following sourceDF:

+------+
|is_fun|
+------+
|  true|
| false|
|  null|
+------+

Run the isTruthy method:

val actualDF = sourceDF.withColumn("is_fun_truthy", col("is_fun").isTruthy)

Here are the contents of actualDF:

+------+-------------+
|is_fun|is_fun_truthy|
+------+-------------+
|  true|         true|
| false|        false|
|  null|        false|
+------+-------------+

`isNullOrBlank`

The isNullOrBlank method returns true if a column is null or blank and false otherwise.

Suppose you start with the following sourceDF:

+-----------+
|animal_type|
+-----------+
|        dog|
|       null|
|         ""|
|     "    "|
+-----------+

Run the isNullOrBlank method:

val actualDF = sourceDF.withColumn(
  "animal_type_is_null_or_blank",
  col("animal_type").isNullOrBlank
)

Here are the contents of actualDF:

+-----------+----------------------------+
|animal_type|animal_type_is_null_or_blank|
+-----------+----------------------------+
|        dog|                       false|
|       null|                        true|
|         ""|                        true|
|     "    "|                        true|
+-----------+----------------------------+

`isNotNullOrBlank`

The isNotNullOrBlank method returns true if a column is not null or blank and false otherwise.

Suppose you start with the following sourceDF:

+-------------+
|employee_name|
+-------------+
|         John|
|         null|
|           ""|
|       "    "|
+-------------+

Run the isNotNullOrBlank method:

val actualDF = sourceDF.withColumn(
  "employee_name_is_not_null_or_blank",
  col("employee_name").isNotNullOrBlank
)

Here are the contents of actualDF:

+-------------+----------------------------------+
|employee_name|employee_name_is_not_null_or_blank|
+-------------+----------------------------------+
|         John|                              true|
|         null|                             false|
|           ""|                             false|
|       "    "|                             false|
+-------------+----------------------------------+

`isNotIn`

The isNotIn method returns true if a column element is not in a list and false otherwise. It's the opposite of the isin method.

Suppose you start with the following sourceDF:

+-----+
|stuff|
+-----+
|  dog|
|shoes|
|laces|
| null|
+-----+

Run the isNotIn method:

val footwearRelated = Seq("laces", "shoes")

val actualDF = sourceDF.withColumn(
  "is_not_footwear_related",
  col("stuff").isNotIn(footwearRelated: _*)
)

Here are the contents of actualDF:

+-----+-----------------------+
|stuff|is_not_footwear_related|
+-----+-----------------------+
|  dog|                   true|
|shoes|                  false|
|laces|                  false|
| null|                   null|
+-----+-----------------------+

`nullBetween`

The built in between doesn't work well when one of the bounds is undefined. nullBetween is more useful when you have "less than or equal to" or "greater than or equal to" logic embedded in your upper and lower bounds. For example, if the lower bound is null and the upper bound is 15, nullBetween will interpret that as "all values below 15".

Let's compare the between and nullBetween methods with a code snipped and the outputted DataFrame.

val actualDF = sourceDF.withColumn(
  "between",
  col("age").between(col("lower_bound"), col("upper_bound"))
).withColumn(
  "nullBetween",
  col("age").nullBetween(col("lower_bound"), col("upper_bound"))
)

+-----------+-----------+---+-------+-----------+
|lower_bound|upper_bound|age|between|nullBetween|
+-----------+-----------+---+-------+-----------+
|         10|         15| 11|   true|       true|
|         17|       null| 94|   null|       true|
|       null|         10|  5|   null|       true|
+-----------+-----------+---+-------+-----------+

DataFrame Extensions

`printSchemaInCodeFormat`

Spark has a printSchema method to print the schema of a DataFrame and a schema method that returns a StructType object.

The Dataset#schema method can be easily converted into working code for small DataFrames, but it can be a lot of manual work for DataFrames with a lot of columns.

The printSchemaInCodeFormat DataFrame extension prints the DataFrame schema as a valid StructType object.

Suppose you have the following sourceDF:

+--------+--------+---------+
|    team|   sport|goals_for|
+--------+--------+---------+
|    jets|football|       45|
|nacional|  soccer|       10|
+--------+--------+---------+

sourceDF.printSchemaInCodeFormat() will output the following rows in the console:

StructType(
  List(
    StructField("team", StringType, true),
    StructField("sport", StringType, true),
    StructField("goals_for", IntegerType, true)
  )
)

`composeTransforms`

Uses function composition to run a list of DataFrame transformations.

def withGreeting()(df: DataFrame): DataFrame = {
  df.withColumn("greeting", lit("hello world"))
}

def withCat(name: String)(df: DataFrame): DataFrame = {
  df.withColumn("cats", lit(s"$name meow"))
}

val transforms = List(
  withGreeting()(_),
  withCat("sandy")(_)
)

sourceDF.composeTransforms(transforms)

`reorderColumns`

Reorders the columns in a DataFrame.

val actualDF = sourceDF.reorderColumns(
  Seq("greeting", "team", "cats")
)

The actualDF will have the greeting column first, then the team column then the cats column.

`containsColumn`

sourceDF.containsColumn("team")

Returns true if sourceDF contains a column named "team" and false otherwise.

`trans`

Enables you to specify the columns that should be added / removed by a custom transformations and errors out if the columns the columns that are actually added / removed are different.

val actualDF = sourceDF
  .trans(
    CustomTransform(
      transform = ExampleTransforms.withGreeting(),
      addedColumns = Seq("greeting"),
      requiredColumns = Seq("something")
    )
  )
  .trans(
    CustomTransform(
      transform = ExampleTransforms.withCat("spanky"),
      addedColumns = Seq("cats")
    )
  )
  .trans(
    CustomTransform(
      transform = ExampleTransforms.dropWordCol(),
      removedColumns = Seq("word")
    )
  )

DataFrame Helpers

`twoColumnsToMap`

N.B. This method uses collect and should only be called on small DataFrames.*

Converts two columns in a DataFrame to a Map.

Suppose we have the following sourceDF:

+-----------+---------+
|     island|fun_level|
+-----------+---------+
|    boracay|        7|
|long island|        9|
+-----------+---------+

Let's convert this DataFrame to a Map with island as the key and fun_level as the value.

val actual = DataFrameHelpers.twoColumnsToMap[String, Integer](
  sourceDF,
  "island",
  "fun_level"
)

println(actual)

// Map(
//   "boracay" -> 7,
//   "long island" -> 9
// )

`columnToArray`

N.B. This method uses collect and should only be called on small DataFrames.*

This function converts a column to an array of items.

Suppose we have the following sourceDF:

+---+
|num|
+---+
|  1|
|  2|
|  3|
+---+

Let's convert the num column to an Array of values. Let's run the code and view the results.

val actual = DataFrameHelpers.columnToArray[Int](sourceDF, "num")

println(actual)

// Array(1, 2, 3)

`toArrayOfMaps`

N.B. This method uses collect and should only be called on small DataFrames.*

Converts a DataFrame to an array of Maps.

Suppose we have the following sourceDF:

+----------+-----------+---------+
|profession|some_number|pay_grade|
+----------+-----------+---------+
|    doctor|          4|     high|
|   dentist|         10|     high|
+----------+-----------+---------+

Run the code to convert this DataFrame into an array of Maps.

val actual = DataFrameHelpers.toArrayOfMaps(sourceDF)

println(actual)

Array(
  Map("profession" -> "doctor", "some_number" -> 4, "pay_grade" -> "high"),
  Map("profession" -> "dentist", "some_number" -> 10, "pay_grade" -> "high")
)

⚡ sql.functions

Spark has a ton of SQL functions and spark-daria is meant to fill in any gaps.

`singleSpace`

val actualDF = sourceDF.withColumn(
  "some_string_single_spaced",
  singleSpace(col("some_string"))
)

Replaces all multispaces with single spaces (e.g. changes "this has some" to "this has some".

`removeAllWhitespace`

val actualDF = sourceDF.withColumn(
  "some_string_without_whitespace",
  removeAllWhitespace(col("some_string"))
)

Removes all whitespace in a string (e.g. changes "this has some" to "thishassome".

You can also use this function with a colName string argument.

val actualDF = sourceDF.withColumn(
  "some_string_without_whitespace",
  removeAllWhitespace(col("some_string"))
)

`antiTrim`

val actualDF = sourceDF.withColumn(
  "some_string_anti_trimmed",
  antiTrim(col("some_string"))
)

Removes all inner whitespace, but doesn't delete leading or trailing whitespace (e.g. changes " this has some " to " thishassome ".

`removeNonWordCharacters`

val actualDF = sourceDF.withColumn(
  "some_string_remove_non_word_chars",
  removeNonWordCharacters(col("some_string"))
)

Removes all non-word characters from a string, excluding whitespace (e.g. changes " ni!!ce h^^air person " to " nice hair person ").

`exists`

Scala has an Array#exists function that works like this:

Array(1, 2, 5).exists(_ % 2 == 0) // true

Suppose we have the following sourceDF:

+---------+
|     nums|
+---------+
|[1, 4, 9]|
|[1, 3, 5]|
+---------+

We can use the spark-daria exists function to see if there are even numbers in the arrays in the nums column.

val actualDF = sourceDF.withColumn(
  "nums_has_even",
  exists[Int]((x: Int) => x % 2 == 0).apply(col("nums"))
)

actualDF.show()

+---------+-------------+
|     nums|nums_has_even|
+---------+-------------+
|[1, 4, 9]|         true|
|[1, 3, 5]|        false|
+---------+-------------+

`forall`

Scala has an Array#forall function that works like this:

Array("catdog", "crazy cat").forall(_.contains("cat")) // true

Suppose we have the following sourceDF:

+------------+
|       words|
+------------+
|[snake, rat]|
|[cat, crazy]|
+------------+

We can use the spark-daria forall function to see if all the strings in an array contain the string "cat".

val actualDF = sourceDF.withColumn(
  "all_words_begin_with_c",
  forall[String]((x: String) => x.startsWith("c")).apply(col("words"))
)

actualDF.show()

+------------+----------------------+
|       words|all_words_begin_with_c|
+------------+----------------------+
|[snake, rat]|                 false|
|[cat, crazy]|                  true|
+------------+----------------------+

`multiEquals`

Returns true if multiple columns are equal to a value.

Suppose we have the following sourceDF:

+---+---+
| s1| s2|
+---+---+
|cat|cat|
|cat|dog|
|pig|pig|
+---+---+

We can use the multiEquals function to see if multiple columns are equal to "cat".

val actualDF = sourceDF.withColumn(
  "are_s1_and_s2_cat",
  multiEquals[String]("cat", col("s1"), col("s2"))
)

actualDF.show()

+---+---+-----------------+
| s1| s2|are_s1_and_s2_cat|
+---+---+-----------------+
|cat|cat|             true|
|cat|dog|            false|
|pig|pig|            false|
+---+---+-----------------+

`yeardiff`

There is a datediff function that calculates the number of days between two dates, but there isn't a yeardiff function that calculates the number of years between two dates.

The com.github.mrpowers.spark.daria.sql.functions.yeardiff function fills the gap. Let's see how it works!

Suppose we have the following testDf

+--------------------+--------------------+
|      first_datetime|     second_datetime|
+--------------------+--------------------+
|2016-09-10 00:00:...|2001-08-10 00:00:...|
|2016-04-18 00:00:...|2010-05-18 00:00:...|
|2016-01-10 00:00:...|2013-08-10 00:00:...|
|                null|                null|
+--------------------+--------------------+

We can run the yeardiff function as follows:

import com.github.mrpowers.spark.daria.sql.functions._

val actualDf = testDf
  .withColumn("num_years", yeardiff(col("first_datetime"), col("second_datetime")))

actualDf.show()

Console output:

+--------------------+--------------------+------------------+
|      first_datetime|     second_datetime|         num_years|
+--------------------+--------------------+------------------+
|2016-09-10 00:00:...|2001-08-10 00:00:...|15.095890410958905|
|2016-04-18 00:00:...|2010-05-18 00:00:...| 5.923287671232877|
|2016-01-10 00:00:...|2013-08-10 00:00:...| 2.419178082191781|
|                null|                null|              null|
+--------------------+--------------------+------------------+

`truncate`

sourceDF.withColumn(
  "some_string_truncated",
  truncate(col("some_string"), 3)
)

Truncates the "some_string" column to only have three characters.

🔱 sql.transformations

SQL transformations take a DataFrame as an argument and return a DataFrame. They are suitable arguments for the Dataset#transform method.

It's convenient to work with DataFrames that have snake_case column names. Column names with spaces make it harder to write SQL queries.

`sortColumns`

The sortColumns transformation sorts the columns in a DataFrame alphabetically.

Suppose you start with the following sourceDF:

+-----+---+-----+
| name|age|sport|
+-----+---+-----+
|pablo|  3| polo|
+-----+---+-----+

Run the code:

val actualDF = sourceDF.transform(sortColumns())

Here’s the actualDF:

+---+-----+-----+
|age| name|sport|
+---+-----+-----+
|  3|pablo| polo|
+---+-----+-----+

`snakeCaseColumns`

spark-daria defines a com.github.mrpowers.spark.daria.sql.transformations.snakeCaseColumns transformation to convert all the column names to snake_case.

import com.github.mrpowers.spark.daria.sql.transformations._

val sourceDf = Seq(
  ("funny", "joke")
).toDF("A b C", "de F")

val actualDf = sourceDf.transform(snakeCaseColumns)

actualDf.show()

Console output:

+-----+----+
|a_b_c|de_f|
+-----+----+
|funny|joke|
+-----+----+

`multiRegexpReplace`

val actualDF = sourceDF.transform(
  transformations.multiRegexpReplace(
    List(col("person"), col("phone")),
    "cool",
    "dude"
  )
)

Replaces all "cool" strings in the person and phone columns with the string "dude".

`bulkRegexpReplace`

val actualDF = sourceDF.transform(
  transformations.bulkRegexpReplace(
    "cool",
    "dude"
  )
)

Replaces all "cool" strings in all the sourceDF columns of StringType with the string "dude".

`truncateColumns`

val columnLengths: Map[String, Int] = Map(
  "person" -> 2,
  "phone" -> 3
)

sourceDF.transform(
  truncateColumns(columnLengths)
)

Limits the "person" column to 2 characters and the "phone" column to 3 characters.

ETL

spark-daria can be used as a lightweight framework for running ETL analyses in Spark.

You can define EtlDefinitions, group them in a collection, and run the etls via jobs.

Components of an ETL

An ETL starts with a DataFrame, runs a series of transformations (filter, custom transformations, repartition), and writes out data.

The EtlDefinition class is generic and can be molded to suit all ETL situations. For example, it can read a CSV file from S3, run transformations, and write out Parquet files on your local filesystem.

Code example

This snippet creates a DataFrame and writes it out as a CSV file in your local filesystem.

val sourceDF = spark.createDF(
  List(
    ("bob", 14),
    ("liz", 20)
  ), List(
    ("name", StringType, true),
    ("age", IntegerType, true)
  )
)

def someTransform()(df: DataFrame): DataFrame = {
  df.withColumn("cool", lit("dude"))
}

def someWriter()(df: DataFrame): Unit = {
  val path = new java.io.File("./tmp/example").getCanonicalPath
  df.repartition(1).write.csv(path)
}

val etlDefinition = new EtlDefinition(
  name =  "example",
  sourceDF = sourceDF,
  transform = someTransform(),
  write = someWriter(),
  hidden = false
)

etlDefinition.process()

In production applications, it's more likely that you'll use Spark DataFrame readers to create the sourceDF (e.g. spark.read.parquet("some_s3_path")).

Example production use case

You can define a collection of ETL definitions in a Databricks notebook and create a Slack command that runs an EtlDefinition on command from Slack.

👭 👬 👫 Contribution Criteria

We are actively looking for contributors to add functionality that fills in the gaps of the Spark source code.

To get started, fork the project and submit a pull request. Please write tests!

After submitting a couple of good pull requests, you'll be added as a contributor to the project.

Continued excellence will be rewarded with push access to the master branch.

Name		Name	Last commit message	Last commit date
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project		project
scripts		scripts
src		src
.gitignore		.gitignore
.travis.yml		.travis.yml
LICENSE		LICENSE
README.md		README.md
build.sbt		build.sbt
daria.png		daria.png

License

xdreamcode/spark-daria

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spark-daria

Setup

✅ DataFrameValidator

validatePresenceOfColumns()

validateSchema()

validateAbsenceOfColumns()

😍 Creating DataFrames

🔗 Chaining UDFs and SQL functions

chain()

chainUDF()

Using chain() and chainUDF() together

Column Extensions

isFalsy

isTruthy

isNullOrBlank

isNotNullOrBlank

isNotIn

nullBetween

DataFrame Extensions

printSchemaInCodeFormat

composeTransforms

reorderColumns

containsColumn

trans

DataFrame Helpers

twoColumnsToMap

columnToArray

toArrayOfMaps

⚡ sql.functions

singleSpace

removeAllWhitespace

antiTrim

removeNonWordCharacters

exists

forall

multiEquals

yeardiff

truncate

🔱 sql.transformations

sortColumns

snakeCaseColumns

multiRegexpReplace

bulkRegexpReplace

truncateColumns

ETL

Components of an ETL

Code example

Example production use case

👭 👬 👫 Contribution Criteria

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`validatePresenceOfColumns()`

`validateSchema()`

`validateAbsenceOfColumns()`

`chain()`

`chainUDF()`

Using `chain()` and `chainUDF()` together

`isFalsy`

`isTruthy`

`isNullOrBlank`

`isNotNullOrBlank`

`isNotIn`

`nullBetween`

`printSchemaInCodeFormat`

`composeTransforms`

`reorderColumns`

`containsColumn`

`trans`

`twoColumnsToMap`

`columnToArray`

`toArrayOfMaps`

`singleSpace`

`removeAllWhitespace`

`antiTrim`

`removeNonWordCharacters`

`exists`

`forall`

`multiEquals`

`yeardiff`

`truncate`

`sortColumns`

`snakeCaseColumns`

`multiRegexpReplace`

`bulkRegexpReplace`

`truncateColumns`

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