Convert window function from dot notation to SQL

We are going to add a column to a train schedule so that each row contains the number of minutes for the train to reach its next stop.

We have a dataframe df where df.columns == ['train_id', 'station', 'time'].
df is registered as a SQL table named schedule.
The following window function query uses dot notation. It gives a new dataframe dot_df.

window = Window.partitionBy('train_id').orderBy('time')
dot_df = df.withColumn('diff_min', 
                    (unix_timestamp(lead('time', 1).over(window),'H:m') 
                     - unix_timestamp('time', 'H:m'))/60)

Note the use of the unix_timestamp function, which is equivalent to the UNIX_TIMESTAMP SQL function.

Please be aware of the scaffolding in the sample code. Formatting the answer according to the scaffolding will ensure that your submitted answer is not erroneously rejected due to a formatting issue.

This exercise is part of the course

Introduction to Spark SQL in Python

Exercise instructions

Create a SQL query to obtain an identical result to dot_df. Please format the query according to the scaffolding (i.e., the placeholder underscores _____) .

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create a SQL query to obtain an identical result to dot_df
query = """
SELECT *, 
(____(____(time, 1) ____ (____ BY train_id ____ BY time),'H:m') 
 - ____(time, 'H:m'))/60 AS diff_min 
FROM schedule 
"""
sql_df = spark.sql(query)
sql_df.show()

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This exercise is part of the course

Introduction to Spark SQL in Python

AdvancedSkill Level

4.8+

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In this chapter you will learn how to create and query a SQL table in Spark. Spark SQL brings the expressiveness of SQL to Spark. You will also learn how to use SQL window functions in Spark. Window functions perform a calculation across rows that are related to the current row. They greatly simplify achieving results that are difficult to express using only joins and traditional aggregations. We'll use window functions to perform running sums, running differences, and other operations that are challenging to perform in basic SQL.

Exercise 1: Creating and querying a SQL table in Spark Exercise 2: Create a SQL table from a dataframe Exercise 3: Determine the column names of a table Exercise 4: Window function SQL Exercise 5: Running sums using window function SQL Exercise 6: Fix the broken query Exercise 7: Dot notation and SQL Exercise 8: Aggregation, step by step Exercise 9: Aggregating the same column twice Exercise 10: Aggregate dot SQL Exercise 11: Convert window function from dot notation to SQL

Current Exercise

In this chapter, you will be loading natural language text. Then you will apply a moving window analysis to find frequent word sequences.

Exercise 1: Loading natural language text Exercise 2: Loading a dataframe from a parquet file Exercise 3: Split and explode a text column Exercise 4: Using monotonically_increasing_id()Exercise 5: Moving window analysis Exercise 6: Creating context window feature data Exercise 7: Repartitioning the data Exercise 8: Common word sequences Exercise 9: What type of data is this Exercise 10: Finding common word sequences Exercise 11: Unique 5-tuples in sorted order Exercise 12: Most frequent 3-tuples per chapter

In the previous chapters you learned how to use the expressiveness of window function SQL. However, this expressiveness now makes it important that you understand how to properly cache dataframes and cache SQL tables. It is also important to know how to evaluate your application. You learn how to do do this using the Spark UI. You'll also learn a best practice for logging in Spark. Spark SQL brings with it another useful tool for tuning query performance issues, the query execution plan. You will learn how to use the execution plan for evaluating the provenance of a dataframe.

Exercise 1: Caching Exercise 2: Practice query plans 2 Exercise 3: Practicing caching: part 1 Exercise 4: Practicing caching: the SQL Exercise 5: Practicing caching: putting it all together Exercise 6: Caching and uncaching tables Exercise 7: The Spark UI Exercise 8: Spark UI storage tab Exercise 9: Inspecting cache in the Spark UI Exercise 10: Logging Exercise 11: Practice logging Exercise 12: Practice logging 2 Exercise 13: Query plans Exercise 14: Practice query plans Exercise 15: Practice reading query plans 2

Previous chapters provided you with the tools for loading raw text, tokenizing it, and extracting word sequences. This is already very useful for analysis, but it is also useful for machine learning. What you've learned now comes together by using logistic regression to classify text. By the conclusion of this chapter, you will have loaded raw natural language text data and used it to train a text classifier.

Exercise 1: Extract Transform Select Exercise 2: Practicing creating a UDF Exercise 3: Practicing array column Exercise 4: Creating feature data for classification Exercise 5: Creating a UDF for vector data Exercise 6: Applying a UDF to vector data Exercise 7: Transforming text to vector format Exercise 8: Text Classification Exercise 9: Label the data Exercise 10: Split the data Exercise 11: Train the classifier Exercise 12: Predicting and evaluating Exercise 13: Evaluate the classifier Exercise 14: Predict test data Exercise 15: Recap