Tutorial: Load and transform data with Apache Spark Scala DataFrames

This tutorial shows you how to use the Apache Spark Scala DataFrame API in Databricks to load and transform U.S. city data.

By the end of this tutorial, you will understand what a DataFrame is and be familiar with the following tasks:

See also Apache Spark Scala API reference.

What is a DataFrame?

A DataFrame is a two-dimensional labeled data structure with columns of potentially different types. You can think of a DataFrame like a spreadsheet, a SQL table, or a dictionary of series objects. Apache Spark DataFrames provide a rich set of functions (select columns, filter, join, aggregate) that allow you to solve common data analysis problems efficiently.

Apache Spark DataFrames are an abstraction built on top of Resilient Distributed Datasets (RDDs). Spark DataFrames and Spark SQL use a unified planning and optimization engine, allowing you to get nearly identical performance across all supported languages on Databricks (Python, SQL, Scala, and R).

What is a Spark Dataset?

The Apache Spark Dataset API provides a type-safe, object-oriented programming interface. DataFrame is an alias for an untyped Dataset [Row]. See Dataset API.

Databricks documentation uses the term DataFrame, because it applies to Python, Scala, and R. See Example notebook: Scala Dataset aggregator.


To complete the following tutorial, you must meet the following requirements:


If you do not have cluster creation privileges, you can still complete most of the following steps as long as you have access to an existing cluster.

From the sidebar on the homepage, you access Databricks entities: the workspace browser, Catalog Explorer, workflows, and compute. Workspace is the root folder that stores Databricks assets like notebooks and libraries.

Step 1: Create a DataFrame with Scala

To learn how to navigate Databricks notebooks, see Databricks notebook interface and controls.

  1. Open a new notebook by clicking the New Icon icon.

  2. Copy and paste the following code into the empty notebook cell, then press Shift+Enter to run the cell. The following code example creates a DataFrame named df1 with city population data and displays its contents.

    // Create a data frame from the given data
    case class City(rank: Long, city: String, state: String, code: String, population: Long, price: Double)
    val df1 = Seq(new City(295, "South Bend", "Indiana", "IN", 101190, 112.9)).toDF

Step 2: Load data into a DataFrame from files

Add more city population data from the /databricks-datasets directory into df2.

To load data into DataFrame df2 from the data_geo.csv file, copy and paste the following code into the new empty notebook cell. Press Shift+Enter to run the cell.

You can load data from many supported file formats. The following example uses a dataset available in the /databricks-datasets directory, accessible from most workspaces. See Sample datasets.

// Create second dataframe from a file
val df2 = spark.read
  .option("header", "true")
  .option("inferSchema", "true")

Step 3: View and interact with your DataFrame

View and interact with your city population DataFrames using the following methods.

Combine DataFrames

Combine the contents of your first DataFrame df1 with DataFrame df2 containing the contents of data_geo.csv.

In the notebook, use the following example code to create a new DataFrame that adds the rows of one DataFrame to another using the union operation:

// Returns a DataFrame that combines the rows of df1 and df2
val df = df1.union(df2)

View the DataFrame

To view the U.S. city data in a tabular format, use the Databricks display() command in a notebook cell.


Filter rows in a DataFrame

Discover the five most populous cities in your data set by filtering rows, using .filter() or .where(). Use filtering to select a subset of rows to return or modify in a DataFrame. There is no difference in performance or syntax, as seen in the following example.

To select the most populous cities, continue to add new cells to your notebook and add the following code example:

// Filter rows using .filter()
val filtered_df = df.filter(df("rank") < 6)

// Filter rows using .where()
val filtered_df = df.where(df("rank") < 6)

Select columns from a DataFrame

Learn about which state a city is located in using the select() method. Select columns by passing one or more column names to .select(), as in the following example:

// Select columns from a DataFrame

val select_df = df.select("City", "State")

Create a subset DataFrame

Create a subset DataFrame with the ten cities with the highest population and display the resulting data. Combine select and filter queries to limit rows and columns returned, using the following code in your notebook:

// Create a subset Dataframe
val subset_df = df.filter(df("rank") < 11).select("City")

Step 4: Save the DataFrame

You can either save your DataFrame to a table or write the DataFrame to a file or multiple files.

Save the DataFrame to a table

Databricks uses the Delta Lake format for all tables by default. To save your DataFrame, you must have CREATE table privileges on the catalog and schema. The following example saves the contents of the DataFrame to a table named us_cities:

// Save DataFrame to a table

Most Spark applications work on large data sets and in a distributed fashion. Spark writes out a directory of files rather than a single file. Delta Lake splits the Parquet folders and files. Many data systems can read these directories of files. Databricks recommends using tables over file paths for most applications.

Save the DataFrame to JSON files

The following example saves a directory of JSON files:

// Write a DataFrame to a collection of files

// To overwrite an existing file, use the following:
// df.write.format("json").mode("overwrite").save("/tmp/json_data")

Read the DataFrame from a JSON file

// Read a DataFrame from a JSON file
val df3 = spark.read.format("json").json("/tmp/json_data")

Additional tasks: Run SQL queries in Scala

Spark DataFrames provide the following options to combine SQL with Scala. You can run the following code in the same notebook that you created for this tutorial.

Specify a column as a SQL query

The selectExpr() method allows you to specify each column as a SQL query, such as in the following example:

display(df.selectExpr("`rank`", "upper(city) as big_name"))

Import expr()

You can import org.apache.spark.sql.functions.expr to use SQL syntax anywhere a column would be specified, as in the following example:

import org.apache.spark.sql.functions.expr

display(df.select($"rank", expr("lower(city) as little_name")))

Run an arbitrary SQL query

You can use spark.sql() to run arbitrary SQL queries, as in the following example:

val query_df = spark.sql("SELECT * FROM us_cities")

Parameterize SQL queries

You can use Scala formatting to parameterize SQL queries, as in the following example:

// Define the table name
val table_name = "us_cities"

// Query the DataFrame using the constructed SQL query
val query_df = spark.sql(s"SELECT * FROM $table_name")

Example notebook: Scala Dataset aggregator

The following notebook shows how to work with Dataset aggregators.

Dataset aggregator notebook

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