(Spark) Chapter 6: Data Transformation with Apache Spark (Match

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Questions and Answers

What is the purpose of the struct function in Spark SQL?

To aggregate data in a DataFrame

To filter rows in a DataFrame

To split a column into an array

To create a new column with a complex data type (correct)

How do you query a column within a struct in Spark SQL?

Using the dot syntax (e.g. complex.Description) (correct)

Using the getField method

Using the select method

Using the getColumn method

What is the purpose of the split function in Spark SQL?

To filter rows in a DataFrame

To create a new column with a complex data type

To split a column into an array (correct)

To aggregate data in a DataFrame

How do you select all columns within a struct in Spark SQL?

Using the <code>*</code> syntax Signup and view all the answers

What is the result of using the split function on a column in Spark SQL?

An array column Signup and view all the answers

How do you create a struct column in Spark SQL?

Using the struct function Signup and view all the answers

What is the purpose of the alias method in Spark SQL?

To rename a column Signup and view all the answers

What is the result of using the `complex.*` syntax in Spark SQL?

All columns within the struct are selected Signup and view all the answers

What is the purpose of the `not` function in the Scala code?

To create a new column with a boolean value Signup and view all the answers

What is the result of the `fill_cols_vals` variable in the Python code?

A dictionary with default values for filling nulls Signup and view all the answers

What is the purpose of the `replace` function in the Scala code?

To replace null values with a specific value Signup and view all the answers

What is the purpose of the `asc_nulls_last` function in the DataFrame?

To sort a DataFrame in descending order with null values last Signup and view all the answers

What is the purpose of complex types in DataFrames?

To organize and structure data in a more meaningful way Signup and view all the answers

What is an example of a complex type in DataFrames?

A struct Signup and view all the answers

What is the purpose of the `inferSchema` option in the example code?

To automatically infer the schema of the data Signup and view all the answers

What is the purpose of the `createOrReplaceTempView` method in the example code?

To register the DataFrame as a temporary view Signup and view all the answers

What is the purpose of the explode function in Spark?

To create one row per value in an array Signup and view all the answers

What is the result of using the explode function on a column of arrays?

Multiple rows with duplicated values Signup and view all the answers

In the example code, what is the purpose of the split function?

To split a string into an array of words Signup and view all the answers

How do you create a map in Spark?

Using the map function and key-value pairs of columns Signup and view all the answers

What is the difference between the explode function and the split function?

The explode function takes a column of arrays and creates one row per value, while the split function splits a string into an array of words Signup and view all the answers

What is the purpose of the lateral view in the SQL example?

To explode an array into multiple rows Signup and view all the answers

What is the result of using the explode function on a column of arrays in Spark?

Multiple rows with duplicated values Signup and view all the answers

What is one of the most powerful things you can do in Spark?

Define your own functions Signup and view all the answers

What type of input can UDFs take?

One or more columns as input Signup and view all the answers

In how many programming languages can you write UDFs?

In several different programming languages Signup and view all the answers

What happens by default when you create a UDF?

It is registered as a temporary function Signup and view all the answers

What is the purpose of registering a UDF with Spark?

To use it on all worker machines Signup and view all the answers

What happens to a UDF when you register it with Spark?

It is serialized and transferred to all executor processes Signup and view all the answers

Match the programming languages with their respective null value replacement code:

Scala = df.na.fill(fillColValues) Python = df.na.fill({"StockCode": 5, "Description" : "No Value"}) SQL = N/A R = N/A Signup and view all the answers

Match the null value handling methods with their respective descriptions:

drop = completely removing null values from the DataFrame fill = replacing null values with specific values replace = replacing values in a certain column according to their current value filter = selecting a subset of the DataFrame based on conditions Signup and view all the answers

Match the complex types with their respective descriptions:

structs = DataFrames within DataFrames arrays = collections of values of the same type maps = key-value pairs DataFrame = a collection of data organized into rows and columns Signup and view all the answers

Match the ordering methods with their respective descriptions:

asc_nulls_first = placing null values first in an ordered DataFrame desc_nulls_last = placing null values last in an ordered DataFrame asc = ordering in ascending order desc = ordering in descending order Signup and view all the answers

Match the following Spark SQL functions with their descriptions:

split = Splits a string column into an array of substrings size = Determines the length of an array column array_contains = Checks if an array column contains a specific value alias = Renames a column with a new name Signup and view all the answers

Match the following programming languages with their usage in Spark:

Scala = Used for writing Spark SQL functions and DataFrames Python = Used for writing PySpark applications and UDFs SQL = Used for querying DataFrames using Spark SQL JavaScript = Not used in Spark Signup and view all the answers

Match the following Spark SQL methods with their purposes:

selectExpr = Selects a column and applies an expression to it show = Displays the contents of a DataFrame alias = Renames a column with a new name size = Determines the length of an array column Signup and view all the answers

Match the following complex types in DataFrames with their descriptions:

array = A column of arrays of values struct = A column of structs with multiple fields map = A column of key-value pairs udf = A user-defined function Signup and view all the answers

Match the following Spark SQL functions with their usage:

array_contains = Checks if an array column contains a specific value split = Splits a string column into an array of substrings size = Determines the length of an array column explode = Explodes an array column into multiple rows Signup and view all the answers

Match the following data types with their descriptions:

Booleans = Used for styling web pages Numbers = Used for general-purpose programming Strings = A type of data used for client-side scripting Dates and timestamps = Used for Database queries Signup and view all the answers

Match the following Spark modules with their functions:

DataFrameStatFunctions = Used for handling null data DataFrameNaFunctions = Holds statistically related functions org.apache.spark.sql.functions = Used for creating a struct column Column = Holds a variety of general column-related methods Signup and view all the answers

Match the following Spark resources with their descriptions:

Dataset methods = Used for transforming data DataFrame methods = Holds a variety of general column-related methods org.apache.spark.sql.functions = Used for solving specific problems Column methods = Used for finding functions to transform data Signup and view all the answers

Match the programming languages with their respective execution process in Spark:

Scala or Java = Executed within the Java Virtual Machine (JVM) Python = Spark starts a Python process on the worker, serializes all of the data to a format that Python can understand Spark SQL = Executed directly by Spark UDF = Executed in a separate process Signup and view all the answers

Match the languages with their respective usage in UDFs:

Scala or Java = Can be used to write UDFs, executed within the JVM Python = Can be used to write UDFs, executed in a separate Python process Spark SQL = Cannot be used to write UDFs SQL = Cannot be used to write UDFs Signup and view all the answers

Match the concepts with their respective descriptions:

Code generation capabilities = A feature of Spark for built-in functions, not available for UDFs Serialization = The process of converting data into a format that can be understood by Python Optimization = A technique to improve performance by minimizing object creation JVM = The runtime environment for Scala and Java Signup and view all the answers

Match the scenarios with their respective performance implications:

Creating a lot of objects = May lead to performance issues Using built-in functions = No significant performance penalty Writing UDFs in Python = May lead to performance issues due to serialization and deserialization Using Scala or Java UDFs = No significant performance penalty Signup and view all the answers

Match the components with their respective roles:

Executor = Responsible for executing tasks in Spark Worker = Runs the Python process for Python UDFs JVM = Runs the Scala or Java UDFs Spark = Coordinates the execution of tasks Signup and view all the answers

Match the following Spark SQL functions with their respective uses:

get_json_object = To extract a specific JSON object from a column json_tuple = To parse a JSON string into a column struct = To create a new column with a nested structure explode = To split an array into multiple rows Signup and view all the answers

Match the following Spark SQL methods with their respective languages:

selectExpr = Scala and Python get_json_object = Scala only json_tuple = Python only createOrReplaceTempView = SQL only Signup and view all the answers

Match the following Spark SQL data types with their respective descriptions:

struct = A nested column with multiple fields array = A column with multiple values map = A column with key-value pairs JSON = A column with a JSON object Signup and view all the answers

Match the following Spark SQL operations with their respective effects:

inflate = To create a new column with a specific value explode = To split an array into multiple rows split = To split a string into multiple columns fill = To replace null values with a specific value Signup and view all the answers

Match the following Spark SQL functions with their respective purposes:

alias = To rename a column replace = To replace a specific value in a column asc_nulls_last = To sort a column in ascending order with null values last inferSchema = To infer the schema of a DataFrame Signup and view all the answers

Study Notes

Data Transformation Tools

All data transformation tools exist to transform rows of data from one format or structure to another.
These tools can create more rows or reduce the number of rows available.

Reading Data into a DataFrame

Data can be read into a DataFrame using Scala or Python.
The read.format() method is used to specify the format of the data (e.g. CSV).
The option() method is used to specify options such as headers and schema inference.
The load() method is used to specify the location of the data.

Data Schema

The data schema is the structure of the data.
The schema can be printed using the printSchema() method.
The schema includes information such as column names and data types.

Data Manipulation

Data can be manipulated using various methods such as withColumn() and filter().
The withColumn() method is used to add a new column to a DataFrame.
The filter() method is used to filter data based on certain conditions.

Replacing Null Values

Null values can be replaced using the fill() method.
The fill() method can be used to replace null values with a specified value.
The replace() method can also be used to replace specific values in a column.

Ordering

Data can be ordered using the asc() and desc() methods.
The asc() method is used to sort data in ascending order.
The desc() method is used to sort data in descending order.
The nulls_first() and nulls_last() methods can be used to specify how null values are handled.

Complex Types

Complex types are used to organize and structure data.
There are three types of complex types: structs, arrays, and maps.
Structs are similar to DataFrames within DataFrames.
Arrays are used to store multiple values in a single column.
Maps are used to store key-value pairs.

Structs

Structs can be created using the struct() function.
Structs can be used to wrap a set of columns in a query.
Structs can be queried using the dot syntax or the getField() method.

Arrays

Arrays can be created using the split() function.
The split() function is used to split a string into an array of values.
The explode() function is used to convert an array into a set of rows.

Maps

Maps can be created using the map() function.
Maps are used to store key-value pairs.

User-Defined Functions (UDFs)

UDFs are used to define custom functions in Spark.
UDFs can be used to write custom transformations using Python or Scala.
UDFs can take and return one or more columns as input.
UDFs are registered as temporary functions to be used in a specific SparkSession or Context.

Working with Different Types of Data

This chapter covers building expressions, working with various types of data, and handling null values and complex types.
Key places to find transformations:
- DataFrame (Dataset) methods
- Column methods
- org.apache.spark.sql.functions package
- SQL and DataFrame functions

Booleans, Numbers, Strings, Dates, and Timestamps

No specific details provided in the text, but these data types will be covered in this chapter.

Handling Null

Replacing null values using drop and fill methods
More flexible options for replacing null values using replace method
Example: replacing null values in a column with a specific value
Using na.fill to fill null values with a specified value

Complex Types

Structs: DataFrames within DataFrames, can be used to organize and structure data
Arrays: can be used to store and manipulate collections of values
Maps: can be used to store and manipulate key-value pairs
Example: using split function to create an array column from a string column

Working with Arrays

Determining the length of an array using the size function
Checking if an array contains a specific value using the array_contains function
Example: using array_contains to check if an array contains the value "WHITE"

Working with JSON

Spark has unique support for working with JSON data
Creating a JSON column using the selectExpr function
Using get_json_object to extract JSON objects from a JSON column
Using json_tuple to extract JSON objects from a JSON column with only one level of nesting
Example: using get_json_object and json_tuple to extract JSON objects from a JSON column

User-Defined Functions

UDFs can be written in Scala or Java and used within the JVM
UDFs can be written in Python, but will incur a performance penalty due to the need to serialize data and execute the function in a separate Python process
Example: using a UDF to perform a specific operation on a DataFrame

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Learn how to transform data formats and structures using Apache Spark, creating or reducing rows of data in the process.

(Spark) Chapter 6: Data Transformation with Apache Spark (Match | Mutiple Choice)

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Questions and Answers

What is the purpose of the struct function in Spark SQL?

How do you query a column within a struct in Spark SQL?

What is the purpose of the split function in Spark SQL?

How do you select all columns within a struct in Spark SQL?

What is the result of using the split function on a column in Spark SQL?

How do you create a struct column in Spark SQL?

What is the purpose of the alias method in Spark SQL?

What is the result of using the complex.* syntax in Spark SQL?

What is the purpose of the not function in the Scala code?

What is the result of the fill_cols_vals variable in the Python code?

What is the purpose of the replace function in the Scala code?

What is the purpose of the asc_nulls_last function in the DataFrame?

What is the purpose of complex types in DataFrames?

What is an example of a complex type in DataFrames?

What is the purpose of the inferSchema option in the example code?

What is the purpose of the createOrReplaceTempView method in the example code?

What is the purpose of the explode function in Spark?

What is the result of using the explode function on a column of arrays?

In the example code, what is the purpose of the split function?

How do you create a map in Spark?

What is the difference between the explode function and the split function?

What is the purpose of the lateral view in the SQL example?

What is the result of using the explode function on a column of arrays in Spark?

What is one of the most powerful things you can do in Spark?

What type of input can UDFs take?

In how many programming languages can you write UDFs?

What happens by default when you create a UDF?

What is the purpose of registering a UDF with Spark?

What happens to a UDF when you register it with Spark?

Match the programming languages with their respective null value replacement code:

Match the null value handling methods with their respective descriptions:

Match the complex types with their respective descriptions:

Match the ordering methods with their respective descriptions:

Match the following Spark SQL functions with their descriptions:

Match the following programming languages with their usage in Spark:

Match the following Spark SQL methods with their purposes:

Match the following complex types in DataFrames with their descriptions:

Match the following Spark SQL functions with their usage:

Match the following data types with their descriptions:

Match the following Spark modules with their functions:

Match the following Spark resources with their descriptions:

Match the programming languages with their respective execution process in Spark:

Match the languages with their respective usage in UDFs:

Match the concepts with their respective descriptions:

Match the scenarios with their respective performance implications:

Match the components with their respective roles:

Match the following Spark SQL functions with their respective uses:

Match the following Spark SQL methods with their respective languages:

Match the following Spark SQL data types with their respective descriptions:

Match the following Spark SQL operations with their respective effects:

Match the following Spark SQL functions with their respective purposes:

Study Notes

Data Transformation Tools

Reading Data into a DataFrame

Data Schema

Data Manipulation

Replacing Null Values

Ordering

Complex Types

Structs

Arrays

Maps

User-Defined Functions (UDFs)

Working with Different Types of Data

Booleans, Numbers, Strings, Dates, and Timestamps

Handling Null

Complex Types

Working with Arrays

Working with JSON

User-Defined Functions

Studying That Suits You

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What is the purpose of the `replace` function in the Scala code?

What is the purpose of the `asc_nulls_last` function in the DataFrame?

What is the purpose of the `inferSchema` option in the example code?

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