Publishing v1.7.0

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Apache-Spark-Programming-with-Databricks/ASP 1.2 - Databricks Platform.py

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+# Databricks notebook source
+# MAGIC %md-sandbox
+# MAGIC 
+# MAGIC <div style="text-align: center; line-height: 0; padding-top: 9px;">
+# MAGIC <img src="https://databricks.com/wp-content/uploads/2018/03/db-academy-rgb-1200px.png" alt="Databricks Learning" style="width: 600px">
+# MAGIC </div>
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC # Spark SQL
+# MAGIC 
+# MAGIC Demonstrate fundamental concepts in Spark SQL using the DataFrame API.
+# MAGIC 
+# MAGIC ##### Objectives
+# MAGIC 1. Run a SQL query
+# MAGIC 1. Create a DataFrame from a table
+# MAGIC 1. Write the same query using DataFrame transformations
+# MAGIC 1. Trigger computation with DataFrame actions
+# MAGIC 1. Convert between DataFrames and SQL
+# MAGIC 
+# MAGIC ##### Methods
+# MAGIC - <a href="https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql.html#spark-session-apis" target="_blank">SparkSession</a>: `sql`, `table`
+# MAGIC - <a href="https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html" target="_blank">DataFrame</a>:
+# MAGIC - Transformations: `select`, `where`, `orderBy`
+# MAGIC - Actions: `show`, `count`, `take`
+# MAGIC - Other methods: `printSchema`, `schema`, `createOrReplaceTempView`
+
+# COMMAND ----------
+
+# MAGIC %run ./Includes/Classroom-Setup-SQL
+
+# COMMAND ----------
+
+# MAGIC %md ## Multiple Interfaces
+# MAGIC Spark SQL is a module for structured data processing with multiple interfaces. 
+# MAGIC 
+# MAGIC We can interact with Spark SQL in two ways:
+# MAGIC 1. Executing SQL queries
+# MAGIC 1. Working with the DataFrame API.
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC **Method 1: Executing SQL queries** 
+# MAGIC 
+# MAGIC This is how we interacted with Spark SQL in the previous lesson.
+
+# COMMAND ----------
+
+# MAGIC %sql
+# MAGIC SELECT name, price
+# MAGIC FROM products
+# MAGIC WHERE price < 200
+# MAGIC ORDER BY price
+
+# COMMAND ----------
+
+# MAGIC %md **Method 2: Working with the DataFrame API**
+# MAGIC 
+# MAGIC We can also express Spark SQL queries using the DataFrame API. 
+# MAGIC The following cell returns a DataFrame containing the same results as those retrieved above.
+
+# COMMAND ----------
+
+display(spark.table("products")
+ .select("name", "price")
+ .where("price < 200")
+ .orderBy("price"))
+
+# COMMAND ----------
+
+# MAGIC %md We'll go over the syntax for the DataFrame API later in the lesson, but you can see this builder design pattern allows us to chain a sequence of operations very similar to those we find in SQL. 
+
+# COMMAND ----------
+
+# MAGIC %md ## Query Execution
+# MAGIC We can express the same query using any interface. The Spark SQL engine generates the same query plan used to optimize and execute on our Spark cluster.
+# MAGIC 
+# MAGIC ![query execution engine](https://files.training.databricks.com/images/aspwd/spark_sql_query_execution_engine.png)
+# MAGIC 
+# MAGIC <img src="https://files.training.databricks.com/images/icon_note_32.png" alt="Note"> Resilient Distributed Datasets (RDDs) are the low-level representation of datasets processed by a Spark cluster. In early versions of Spark, you had to write <a href="https://spark.apache.org/docs/latest/rdd-programming-guide.html" target="_blank">code manipulating RDDs directly</a>. In modern versions of Spark you should instead use the higher-level DataFrame APIs, which Spark automatically compiles into low-level RDD operations.
+
+# COMMAND ----------
+
+# MAGIC %md ## Spark API Documentation
+# MAGIC 
+# MAGIC To learn how we work with DataFrames in Spark SQL, let's first look at the Spark API documentation. 
+# MAGIC The main Spark [documentation](https://spark.apache.org/docs/latest/) page includes links to API docs and helpful guides for each version of Spark. 
+# MAGIC 
+# MAGIC The [Scala API](https://spark.apache.org/docs/latest/api/scala/org/apache/spark/index.html) and [Python API](https://spark.apache.org/docs/latest/api/python/index.html) are most commonly used, and it's often helpful to reference the documentation for both languages. 
+# MAGIC Scala docs tend to be more comprehensive, and Python docs tend to have more code examples.
+# MAGIC 
+# MAGIC #### Navigating Docs for the Spark SQL Module
+# MAGIC Find the Spark SQL module by navigating to `org.apache.spark.sql` in the Scala API or `pyspark.sql` in the Python API. 
+# MAGIC The first class we'll explore in this module is the `SparkSession` class. You can find this by entering "SparkSession" in the search bar.
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC ## SparkSession
+# MAGIC The `SparkSession` class is the single entry point to all functionality in Spark using the DataFrame API. 
+# MAGIC 
+# MAGIC In Databricks notebooks, the SparkSession is created for you, stored in a variable called `spark`.
+
+# COMMAND ----------
+
+spark
+
+# COMMAND ----------
+
+# MAGIC %md The example from the beginning of this lesson used the SparkSession method `table` to create a DataFrame from the `products` table. Let's save this in the variable `productsDF`.
+
+# COMMAND ----------
+
+productsDF = spark.table("products")
+
+# COMMAND ----------
+
+# MAGIC %md 
+# MAGIC Below are several additional methods we can use to create DataFrames. All of these can be found in the <a href="https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.SparkSession.html" target="_blank">documentation</a> for `SparkSession`.
+# MAGIC 
+# MAGIC #### `SparkSession` Methods
+# MAGIC | Method | Description |
+# MAGIC | --- | --- |
+# MAGIC | sql | Returns a DataFrame representing the result of the given query | 
+# MAGIC | table | Returns the specified table as a DataFrame |
+# MAGIC | read | Returns a DataFrameReader that can be used to read data in as a DataFrame |
+# MAGIC | range | Create a DataFrame with a column containing elements in a range from start to end (exclusive) with step value and number of partitions |
+# MAGIC | createDataFrame | Creates a DataFrame from a list of tuples, primarily used for testing |
+
+# COMMAND ----------
+
+# MAGIC %md Let's use a SparkSession method to run SQL.
+
+# COMMAND ----------
+
+resultDF = spark.sql("""
+SELECT name, price
+FROM products
+WHERE price < 200
+ORDER BY price
+""")
+
+display(resultDF)
+
+# COMMAND ----------
+
+# MAGIC %md ## DataFrames
+# MAGIC Recall that expressing our query using methods in the DataFrame API returns results in a DataFrame. Let's store this in the variable `budgetDF`.
+# MAGIC 
+# MAGIC A **DataFrame** is a distributed collection of data grouped into named columns.
+
+# COMMAND ----------
+
+budgetDF = (spark.table("products")
+ .select("name", "price")
+ .where("price < 200")
+ .orderBy("price"))
+
+# COMMAND ----------
+
+# MAGIC %md We can use `display()` to output the results of a dataframe.
+
+# COMMAND ----------
+
+display(budgetDF)
+
+# COMMAND ----------
+
+# MAGIC %md The **schema** defines the column names and types of a dataframe.
+# MAGIC 
+# MAGIC Access a dataframe's schema using the `schema` attribute.
+
+# COMMAND ----------
+
+budgetDF.schema
+
+# COMMAND ----------
+
+# MAGIC %md View a nicer output for this schema using the `printSchema()` method.
+
+# COMMAND ----------
+
+budgetDF.printSchema()
+
+# COMMAND ----------
+
+# MAGIC %md ## Transformations
+# MAGIC When we created `budgetDF`, we used a series of DataFrame transformation methods e.g. `select`, `where`, `orderBy`. 
+# MAGIC 
+# MAGIC ```
+# MAGIC productsDF
+# MAGIC .select("name", "price")
+# MAGIC .where("price < 200")
+# MAGIC .orderBy("price")
+# MAGIC ```
+# MAGIC Transformations operate on and return DataFrames, allowing us to chain transformation methods together to construct new DataFrames. 
+# MAGIC However, these operations can't execute on their own, as transformation methods are **lazily evaluated**. 
+# MAGIC 
+# MAGIC Running the following cell does not trigger any computation.
+
+# COMMAND ----------
+
+(productsDF
+ .select("name", "price")
+ .where("price < 200")
+ .orderBy("price"))
+
+# COMMAND ----------
+
+# MAGIC %md ## Actions
+# MAGIC Conversely, DataFrame actions are methods that **trigger computation**. 
+# MAGIC Actions are needed to trigger the execution of any DataFrame transformations. 
+# MAGIC 
+# MAGIC The `show` action causes the following cell to execute transformations.
+
+# COMMAND ----------
+
+(productsDF
+ .select("name", "price")
+ .where("price < 200")
+ .orderBy("price")
+ .show())
+
+# COMMAND ----------
+
+# MAGIC %md Below are several examples of <a href="https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql.html#dataframe-apis" target="_blank">DataFrame</a> actions.
+# MAGIC 
+# MAGIC ### DataFrame Action Methods
+# MAGIC | Method | Description |
+# MAGIC | --- | --- |
+# MAGIC | show | Displays the top n rows of DataFrame in a tabular form |
+# MAGIC | count | Returns the number of rows in the DataFrame |
+# MAGIC | describe, summary | Computes basic statistics for numeric and string columns |
+# MAGIC | first, head | Returns the the first row |
+# MAGIC | collect | Returns an array that contains all rows in this DataFrame |
+# MAGIC | take | Returns an array of the first n rows in the DataFrame |
+
+# COMMAND ----------
+
+# MAGIC %md `count` returns the number of records in a DataFrame.
+
+# COMMAND ----------
+
+budgetDF.count()
+
+# COMMAND ----------
+
+# MAGIC %md `collect` returns an array of all rows in a DataFrame.
+
+# COMMAND ----------
+
+budgetDF.collect() 
+
+# COMMAND ----------
+
+# MAGIC %md ## Convert between DataFrames and SQL
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC `createOrReplaceTempView` creates a temporary view based on the DataFrame. The lifetime of the temporary view is tied to the SparkSession that was used to create the DataFrame.
+
+# COMMAND ----------
+
+budgetDF.createOrReplaceTempView("budget")
+
+# COMMAND ----------
+
+display(spark.sql("SELECT * FROM budget"))
+
+# COMMAND ----------
+
+# MAGIC %md
+# MAGIC # Spark SQL Lab
+# MAGIC 
+# MAGIC ##### Tasks
+# MAGIC 1. Create a DataFrame from the `events` table
+# MAGIC 1. Display the DataFrame and inspect its schema
+# MAGIC 1. Apply transformations to filter and sort `macOS` events
+# MAGIC 1. Count results and take the first 5 rows
+# MAGIC 1. Create the same DataFrame using a SQL query
+# MAGIC 
+# MAGIC ##### Methods
+# MAGIC - <a href="https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.SparkSession.html?highlight=sparksession" target="_blank">SparkSession</a>: `sql`, `table`
+# MAGIC - <a href="https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html" target="_blank">DataFrame</a> transformations: `select`, `where`, `orderBy`
+# MAGIC - <a href="https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html" target="_blank">DataFrame</a> actions: `select`, `count`, `take`
+# MAGIC - Other <a href="https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html" target="_blank">DataFrame</a> methods: `printSchema`, `schema`, `createOrReplaceTempView`
+
+# COMMAND ----------
+
+# MAGIC %md ### 1. Create a DataFrame from the `events` table
+# MAGIC - Use SparkSession to create a DataFrame from the `events` table
+
+# COMMAND ----------
+
+# TODO
+eventsDF = FILL_IN
+
+# COMMAND ----------
+
+# MAGIC %md ### 2. Display DataFrame and inspect schema
+# MAGIC - Use methods above to inspect DataFrame contents and schema
+
+# COMMAND ----------
+
+# TODO
+
+# COMMAND ----------
+
+# MAGIC %md ### 3. Apply transformations to filter and sort `macOS` events
+# MAGIC - Filter for rows where `device` is `macOS`
+# MAGIC - Sort rows by `event_timestamp`
+# MAGIC 
+# MAGIC <img src="https://files.training.databricks.com/images/icon_hint_32.png" alt="Hint"> Use single and double quotes in your filter SQL expression
+
+# COMMAND ----------
+
+# TODO
+macDF = (eventsDF
+ .FILL_IN
+)
+
+# COMMAND ----------
+
+# MAGIC %md ### 4. Count results and take first 5 rows
+# MAGIC - Use DataFrame actions to count and take rows
+
+# COMMAND ----------
+
+# TODO
+numRows = macDF.FILL_IN
+rows = macDF.FILL_IN
+
+# COMMAND ----------
+
+# MAGIC %md **CHECK YOUR WORK**
+
+# COMMAND ----------
+
+from pyspark.sql import Row
+
+assert(numRows == 1938215)
+assert(len(rows) == 5)
+assert(type(rows[0]) == Row)
+
+# COMMAND ----------
+
+# MAGIC %md ### 5. Create the same DataFrame using SQL query
+# MAGIC - Use SparkSession to run a SQL query on the `events` table
+# MAGIC - Use SQL commands to write the same filter and sort query used earlier
+
+# COMMAND ----------
+
+# TODO
+macSQLDF = spark.FILL_IN
+
+display(macSQLDF)
+
+# COMMAND ----------
+
+# MAGIC %md **CHECK YOUR WORK**
+# MAGIC - You should only see `macOS` values in the `device` column
+# MAGIC - The fifth row should be an event with timestamp `1592539226602157`
+
+# COMMAND ----------
+
+verify_rows = macSQLDF.take(5)
+assert (macSQLDF.select("device").distinct().count() == 1 and len(verify_rows) == 5 and verify_rows[0]['device'] == "macOS"), "Incorrect filter condition"
+assert (verify_rows[4]['event_timestamp'] == 1592539226602157), "Incorrect sorting"
+del verify_rows
+
+# COMMAND ----------
+
+# MAGIC %md ### Classroom Cleanup
+
+# COMMAND ----------
+
+# MAGIC %run ./Includes/Classroom-Cleanup
+
+# COMMAND ----------
+
+# MAGIC %md-sandbox
+# MAGIC &copy; 2022 Databricks, Inc. All rights reserved.<br/>
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the <a href="https://www.apache.org/">Apache Software Foundation</a>.<br/>
+# MAGIC <br/>
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