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

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

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

    <p>An array column</p> Signup and view all the answers

    How do you create a struct column in Spark SQL?

    <p>Using the struct function</p> Signup and view all the answers

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

    <p>To rename a column</p> Signup and view all the answers

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

    <p>All columns within the struct are selected</p> Signup and view all the answers

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

    <p>To create a new column with a boolean value</p> Signup and view all the answers

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

    <p>A dictionary with default values for filling nulls</p> Signup and view all the answers

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

    <p>To replace null values with a specific value</p> Signup and view all the answers

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

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

    What is the purpose of complex types in DataFrames?

    <p>To organize and structure data in a more meaningful way</p> Signup and view all the answers

    What is an example of a complex type in DataFrames?

    <p>A struct</p> Signup and view all the answers

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

    <p>To automatically infer the schema of the data</p> Signup and view all the answers

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

    <p>To register the DataFrame as a temporary view</p> Signup and view all the answers

    What is the purpose of the explode function in Spark?

    <p>To create one row per value in an array</p> Signup and view all the answers

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

    <p>Multiple rows with duplicated values</p> Signup and view all the answers

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

    <p>To split a string into an array of words</p> Signup and view all the answers

    How do you create a map in Spark?

    <p>Using the map function and key-value pairs of columns</p> Signup and view all the answers

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

    <p>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</p> Signup and view all the answers

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

    <p>To explode an array into multiple rows</p> Signup and view all the answers

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

    <p>Multiple rows with duplicated values</p> Signup and view all the answers

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

    <p>Define your own functions</p> Signup and view all the answers

    What type of input can UDFs take?

    <p>One or more columns as input</p> Signup and view all the answers

    In how many programming languages can you write UDFs?

    <p>In several different programming languages</p> Signup and view all the answers

    What happens by default when you create a UDF?

    <p>It is registered as a temporary function</p> Signup and view all the answers

    What is the purpose of registering a UDF with Spark?

    <p>To use it on all worker machines</p> Signup and view all the answers

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

    <p>It is serialized and transferred to all executor processes</p> Signup and view all the answers

    Match the complex types with their respective descriptions:

    <p>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</p> Signup and view all the answers

    Match the null value handling methods with their respective descriptions:

    <p>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</p> Signup and view all the answers

    Match the ordering methods with their respective descriptions:

    <p>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</p> Signup and view all the answers

    Match the following programming languages with their usage in Spark:

    <p>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</p> Signup and view all the answers

    Match the following Spark SQL functions with their descriptions:

    <p>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</p> Signup and view all the answers

    Match the following Spark SQL methods with their purposes:

    <p>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</p> Signup and view all the answers

    Match the following complex types in DataFrames with their descriptions:

    <p>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</p> Signup and view all the answers

    Match the following Spark SQL functions with their usage:

    <p>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</p> Signup and view all the answers

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

    <p>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</p> Signup and view all the answers

    Match the languages with their respective usage in UDFs:

    <p>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</p> Signup and view all the answers

    Match the concepts with their respective descriptions:

    <p>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</p> Signup and view all the answers

    Match the scenarios with their respective performance implications:

    <p>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</p> Signup and view all the answers

    Match the components with their respective roles:

    <p>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</p> Signup and view all the answers

    Match the following Spark SQL functions with their respective uses:

    <p>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</p> Signup and view all the answers

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

    <p>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</p> Signup and view all the answers

    Match the following Spark SQL operations with their respective effects:

    <p>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</p> Signup and view all the answers

    Match the following Spark SQL functions with their respective purposes:

    <p>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</p> Signup and view all the answers

    Match the following Spark SQL functions with their specific operations:

    <p>get_json_object = Extracts a specific value from a JSON field size = Calculates the size of an array column nvl = Returns the second value if the first value is NULL explode = Expands an array column into multiple rows</p> Signup and view all the answers

    Match the following DataFrame functions with their descriptions:

    <p>fill = Fills NULL values in a DataFrame with a specific value drop = Removes rows from a DataFrame where any value is NULL replace = Replaces specific values in a DataFrame with other values coalesce = Returns the first non-null value from a set of columns</p> Signup and view all the answers

    Match the following methods for handling NULL values with their purposes:

    <p>ifnull = Returns the second value if the first value is NULL nullif = Returns NULL if the two values are equal nvl2 = Returns the second value if the first is not NULL asc_nulls_first = Specifies that NULL values are sorted first in ascending order</p> Signup and view all the answers

    Match the following JSON-related functions with their respective actions:

    <p>from_json = Parses a JSON string into a specified StructType to_json = Converts a StructType into a JSON string json_tuple = Extracts multiple values from a JSON object to_timestamp = Converts a string to a timestamp with a specific format</p> Signup and view all the answers

    Match the following array functions with their specific outputs:

    <p>array_contains = Checks if an array column contains a specific value split = Splits a string column into an array based on a delimiter map = Creates a map from a set of key-value columns getField = Gets a value from a field within a struct</p> Signup and view all the answers

    Match the following Spark SQL functions with their respective operations:

    <p>lit = Converts a value to a Spark type crosstab = Calculates a cross-tabulation freqItems = Calculates frequent itemsets monotonically_increasing_id = Generates unique identifiers</p> Signup and view all the answers

    Match the following Spark SQL functions with their specific string operations:

    <p>lower = Converts a string column to lowercase upper = Converts a string column to uppercase trim = Removes leading and trailing spaces regexp_replace = Replaces occurrences of a specified pattern</p> Signup and view all the answers

    Match the following Spark SQL functions with their date operations:

    <p>date_add = Adds days to a date date_sub = Subtracts days from a date months_between = Calculates months difference between dates datediff = Calculates days difference between dates</p> Signup and view all the answers

    Match the following Boolean functions with their logical operations:

    <p>and = Combines two Boolean expressions with 'and' or = Combines two Boolean expressions with 'or' not = Negates a Boolean expression isin = Checks if a value is within a set</p> Signup and view all the answers

    Match the following DataFrame operations with their descriptions:

    <p>select = Selects specific columns in a DataFrame show = Displays the content of a DataFrame where = Filters DataFrame based on a condition withColumn = Adds a new column to a DataFrame</p> Signup and view all the answers

    Match the following statistical functions with their operations:

    <p>corr = Calculates correlation between two columns describe = Provides summary statistics for columns approxQuantile = Calculates approximate quantiles of a column stat = Provides statistical methods on DataFrame</p> Signup and view all the answers

    Match the following string transformations with their effects:

    <p>initcap = Capitalizes first letter of each word lpad = Adds padding to the left side rpad = Adds padding to the right side translate = Replaces specific characters in a string</p> Signup and view all the answers

    Match the following Spark SQL functions with their numerical operations:

    <p>pow = Raises a column to a power round = Rounds a numerical column to a decimal bround = Rounds down to the nearest integer leq = Checks if a column value is less than or equal</p> 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

    Spark SQL Functions

    • lit: Converts a value to a Spark type (e.g., Scala, Python, or SQL).
    • equalTo: Filters a DataFrame to include rows where a specified column matches a given value.
    • col: References a column in a DataFrame by its name.
    • select: Extracts specific columns from a DataFrame.
    • show: Displays the contents of a DataFrame.
    • where: Filters a DataFrame based on a given condition.
    • and: Combines two Boolean expressions using a logical "AND" operator.
    • or: Combines two Boolean expressions using a logical "OR" operator.
    • contains: Checks if a string column includes a specific substring.
    • isin: Checks if a column's value exists within a set of values.
    • withColumn: Adds a new column to a DataFrame.
    • expr: Evaluates a string expression as a Spark column.
    • not: Negates a Boolean expression.
    • leq: Determines if a column value is less than or equal to a specific value.
    • pow: Raises a column to a given power.
    • alias: Assigns a new name to a column or function.
    • round: Rounds a numerical column to a specified decimal place.
    • bround: Rounds a numerical column down to the nearest integer.
    • corr: Calculates the correlation between two columns.
    • stat: Provides statistical calculations on the DataFrame.
    • describe: Generates summary statistics for numerical columns.
    • approxQuantile: Calculates approximate quantiles of a column.
    • crosstab: Calculates a cross-tabulation between two columns.
    • freqItems: Determines frequent itemsets from a set of columns.
    • monotonically_increasing_id: Generates a monotonically increasing sequence of unique identifiers.
    • initcap: Capitalizes the first letter of each word in a string column.
    • lower: Converts a string column to lowercase.
    • upper: Converts a string column to uppercase.
    • lpad: Pads a string column on the left side with specified characters.
    • rpad: Pads a string column on the right side with specified characters.
    • ltrim: Removes leading spaces from a string column.
    • rtrim: Removes trailing spaces from a string column.
    • trim: Removes leading and trailing spaces from a string column.
    • regexp_replace: Replaces all occurrences of a pattern in a string column with another string.
    • translate: Replaces specific characters within a string column.
    • regexp_extract: Extracts a substring from a string column based on a regular expression.
    • date_add: Adds a specified number of days to a date column.
    • date_sub: Subtracts a specified number of days from a date column.
    • datediff: Calculates the difference between two dates in days.
    • months_between: Calculates the difference between two dates in months.
    • to_date: Converts a string to a date, optionally using a specific format.
    • to_timestamp: Converts a string to a timestamp, optionally using a specific format.
    • get_json_object: Extracts a specific value from a JSON field.
    • json_tuple: Extracts multiple values from a JSON object (single level of nesting).
    • to_json: Converts a StructType to a JSON string.
    • from_json: Parses a JSON string into a specified StructType.
    • struct: Creates a struct from a set of columns.
    • getField: Retrieves a value from a field within a struct.
    • split: Splits a string column into an array based on a delimiter.
    • size: Determines the size of an array column.
    • array_contains: Checks if an array column contains a specific value.
    • explode: Expands an array column into multiple rows, one for each element.
    • map: Creates a map from a set of key-value columns.
    • udf: Registers a user-defined function (UDF) for use in DataFrames.
    • coalesce: Returns the first non-null value from a set of columns.
    • ifnull: Returns the second value if the first value is NULL; otherwise, returns the first value.
    • nullif: Returns NULL if two values are equal; otherwise, returns the second value.
    • nvl: Returns the second value if the first value is NULL; otherwise, returns the first value.
    • nvl2: Returns the second value if the first value is not NULL; otherwise, returns the third value.
    • drop: Removes rows from a DataFrame where any value is NULL.
    • fill: Replaces NULL values in a DataFrame with a specific value.
    • replace: Replaces specific values in a DataFrame with other values.
    • asc_nulls_first: Sorts NULL values first in ascending order.
    • desc_nulls_first: Sorts NULL values first in descending order.
    • asc_nulls_last: Sorts NULL values last in ascending order.
    • desc_nulls_last: Sorts NULL values last in descending order.

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    Chapter 6 Working with Different Types of Data (PDF)

    Description

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

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