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A DataFrame can be transformed by changing the order of columns based on the values in rows.
A DataFrame can be transformed by changing the order of columns based on the values in rows.
False (B)
The most common DataFrame transformations involve changing multiple columns at once.
The most common DataFrame transformations involve changing multiple columns at once.
False (B)
DataFrames can be created directly from raw data sources.
DataFrames can be created directly from raw data sources.
True (A)
Transforming a DataFrame always involves adding or removing rows or columns.
Transforming a DataFrame always involves adding or removing rows or columns.
The expr function cannot parse transformations from a string
The expr function cannot parse transformations from a string
Columns are a superset of expression functionality
Columns are a superset of expression functionality
The logical tree representation of a Spark expression is a cyclic graph
The logical tree representation of a Spark expression is a cyclic graph
Col("someCol") + 5 is a valid expression in Spark
Col("someCol") + 5 is a valid expression in Spark
The expr function is only used to create DataFrame column references
The expr function is only used to create DataFrame column references
The sortWithinPartitions method can be used to globally sort a DataFrame by a specific column.
The sortWithinPartitions method can be used to globally sort a DataFrame by a specific column.
The limit method can be used to extract a random sample from a DataFrame.
The limit method can be used to extract a random sample from a DataFrame.
Repartitioning a DataFrame always results in a reduction of the number of partitions.
Repartitioning a DataFrame always results in a reduction of the number of partitions.
The orderBy method must be used in conjunction with the limit method to extract the top N rows from a DataFrame.
The orderBy method must be used in conjunction with the limit method to extract the top N rows from a DataFrame.
The coalesce method is used to increase the number of partitions in a DataFrame.
The coalesce method is used to increase the number of partitions in a DataFrame.
Repartitioning a DataFrame is a cost-free operation.
Repartitioning a DataFrame is a cost-free operation.
The filter df.filter(col("count") < 2) is not equivalent to the SQL query SELECT * FROM dfTable WHERE count < 2 LIMIT 2.
The filter df.filter(col("count") < 2) is not equivalent to the SQL query SELECT * FROM dfTable WHERE count < 2 LIMIT 2.
Chaining multiple filters sequentially in Spark can lead to improved performance due to the optimized filter ordering.
Chaining multiple filters sequentially in Spark can lead to improved performance due to the optimized filter ordering.
The filter df.where(col("count") < 2).where(col("ORIGIN_COUNTRY_NAME") =!= "Croatia") is equivalent to the SQL query SELECT * FROM dfTable WHERE count < 2 OR ORIGIN_COUNTRY_NAME!= "Croatia" LIMIT 2.
The filter df.where(col("count") < 2).where(col("ORIGIN_COUNTRY_NAME") =!= "Croatia") is equivalent to the SQL query SELECT * FROM dfTable WHERE count < 2 OR ORIGIN_COUNTRY_NAME!= "Croatia" LIMIT 2.
The collect method in Spark is used to iterate over the entire dataset partition-by-partition in a serial manner.
The collect method in Spark is used to iterate over the entire dataset partition-by-partition in a serial manner.
Calling the collect method on a large dataset can crash the driver.
Calling the collect method on a large dataset can crash the driver.
The show(2) method is used to display the first 2 rows of the filtered DataFrame.
The show(2) method is used to display the first 2 rows of the filtered DataFrame.
The take method in Spark only works with a Long count.
The take method in Spark only works with a Long count.
The show method in Spark is used to collect all data from the entire DataFrame.
The show method in Spark is used to collect all data from the entire DataFrame.
The collect method and toLocalIterator method in Spark have the same functionality.
The collect method and toLocalIterator method in Spark have the same functionality.
Using toLocalIterator can be more expensive than using collect because it operates on a one-by-one basis.
Using toLocalIterator can be more expensive than using collect because it operates on a one-by-one basis.
What is the primary purpose of creating a temporary view in Spark?
What is the primary purpose of creating a temporary view in Spark?
What is the advantage of using Spark's implicits in Scala?
What is the advantage of using Spark's implicits in Scala?
How can a DataFrame be created on the fly in Spark?
How can a DataFrame be created on the fly in Spark?
What is the difference between the createDataFrame method and the toDF method in Spark?
What is the difference between the createDataFrame method and the toDF method in Spark?
Why is using the toDF method on a Seq type not recommended for production use cases?
Why is using the toDF method on a Seq type not recommended for production use cases?
How can a DataFrame be created from a JSON file in Spark?
How can a DataFrame be created from a JSON file in Spark?
What is the primary purpose of the select method in DataFrames?
What is the primary purpose of the select method in DataFrames?
What is the purpose of the show method in Spark?
What is the purpose of the show method in Spark?
What is the purpose of the StructType in PySpark?
What is the purpose of the StructType in PySpark?
What is the difference between the select and selectExpr methods in DataFrames?
What is the difference between the select and selectExpr methods in DataFrames?
What is the purpose of the org.apache.spark.sql.functions package in DataFrames?
What is the purpose of the org.apache.spark.sql.functions package in DataFrames?
How can you create a DataFrame from a manual schema in PySpark?
How can you create a DataFrame from a manual schema in PySpark?
What is the purpose of the Row class in PySpark?
What is the purpose of the Row class in PySpark?
What are the three tools that can be used to solve the vast majority of transformation challenges in DataFrames?
What are the three tools that can be used to solve the vast majority of transformation challenges in DataFrames?
What is the purpose of using backticks in the given Scala and Python code snippets?
What is the purpose of using backticks in the given Scala and Python code snippets?
How can Spark be made case sensitive?
How can Spark be made case sensitive?
What is the purpose of the 'selectExpr' method in Spark?
What is the purpose of the 'selectExpr' method in Spark?
How can columns with reserved characters or keywords in their names be referred to in Spark?
How can columns with reserved characters or keywords in their names be referred to in Spark?
What is the purpose of the 'createOrReplaceTempView' method in Spark?
What is the purpose of the 'createOrReplaceTempView' method in Spark?
How can columns be removed from a DataFrame in Spark?
How can columns be removed from a DataFrame in Spark?
What is the primary difference between using collect and toLocalIterator to collect data to the driver?
What is the primary difference between using collect and toLocalIterator to collect data to the driver?
When using collect or toLocalIterator, what is the main risk of crashing the driver?
When using collect or toLocalIterator, what is the main risk of crashing the driver?
What is the main benefit of using show with a specified number of rows?
What is the main benefit of using show with a specified number of rows?
What is the main difference between take and collect?
What is the main difference between take and collect?
What is the main limitation of using collect or toLocalIterator?
What is the main limitation of using collect or toLocalIterator?
What is the main benefit of using DataFrames in Spark?
What is the main benefit of using DataFrames in Spark?
When should you avoid using collect or toLocalIterator?
When should you avoid using collect or toLocalIterator?
What is the main consequence of using collect or toLocalIterator on a large dataset?
What is the main consequence of using collect or toLocalIterator on a large dataset?
What is a schema in a DataFrame?
What is a schema in a DataFrame?
When is it a good idea to define a schema manually?
When is it a good idea to define a schema manually?
What is the purpose of schema-on-read?
What is the purpose of schema-on-read?
What can be a potential issue with schema-on-read?
What can be a potential issue with schema-on-read?
What is the result of running spark.read.format("json").load("/data/flight-data/json/2015-summary.json").schema in Scala?
What is the result of running spark.read.format("json").load("/data/flight-data/json/2015-summary.json").schema in Scala?
Why is it important to define a schema manually when working with untyped data sources?
Why is it important to define a schema manually when working with untyped data sources?
What is the advantage of using schema-on-read for ad hoc analysis?
What is the advantage of using schema-on-read for ad hoc analysis?
What is the difference between schema-on-read and defining a schema manually?
What is the difference between schema-on-read and defining a schema manually?
Study Notes
DataFrame Transformations
- DataFrame transformations can be broken down into several core operations:
- Adding rows or columns
- Removing rows or columns
- Transforming a row into a column (or vice versa)
- Changing the order of rows based on the values in columns
Creating DataFrames
- DataFrames can be created from raw data sources
- Expressions can be used to create DataFrames, where an expression is a column reference
- The
exprfunction can parse transformations and column references from a string and can be passed into further transformations - Columns and transformations of columns compile to the same logical plan as parsed expressions
DataFrame Operations
- The
sortWithinPartitionsmethod can be used to sort DataFrames - The
limitmethod can be used to restrict what you extract from a DataFrame - The
repartitionmethod can be used to partition the data according to some frequently filtered columns - The
coalescemethod can be used to reduce the number of partitions
Filtering DataFrames
- The
filtermethod can be used to filter DataFrames - The
wheremethod can be used to filter DataFrames - Multiple filters can be chained together using the
wheremethod - Spark automatically performs all filtering operations at the same time, regardless of the filter ordering
Collecting DataFrames
- The
collectmethod can be used to collect all data from the entire DataFrame - The
takemethod can be used to select the first N rows - The
showmethod can be used to print out a number of rows nicely - The
toLocalIteratormethod can be used to collect rows to the driver as an iterator, allowing for iteration over the entire dataset partition-by-partition in a serial manner
Schemas
- A schema defines the column names and types of a DataFrame
- Schemas can be defined explicitly or let a data source define the schema (called schema-on-read)
- Deciding whether to define a schema prior to reading in data depends on the use case
- Defining schemas manually can be useful in production Extract, Transform, and Load (ETL) scenarios, especially when working with untyped data sources like CSV and JSON
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Description
Understand how to create expressions in SparkSQL using the expr function and how it differs from column references created with the col function. Learn about performing transformations on columns and parsing expressions from strings.
