Spark Flights Data: Departure Delays Analysis With Databricks

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Spark Flights Data: Departure Delays Analysis with Databricks

Let's dive into the world of Spark and Databricks using the flights dataset to analyze those pesky departure delays. This guide will walk you through loading the dataset, exploring its schema, and performing some basic analysis to understand the factors contributing to flight delays. So, buckle up and let's get started!

Understanding the Dataset

The dataset we'll be using contains information about flights, including details like origin, destination, carrier, flight number, departure and arrival times, and crucially, departure delays. It's a goldmine for anyone interested in understanding air travel patterns and the causes of delays. You can typically find this dataset within Databricks under the /databricks-datasets directory, making it easily accessible for learning and experimentation.

Before we jump into the code, let's talk about what we hope to achieve. We want to answer questions like:

  • What are the average departure delays?
  • Which carriers have the worst departure delay records?
  • Are there specific destinations that tend to experience more delays?
  • Can we identify any patterns in departure delays based on the time of day or day of the week?

Answering these questions requires us to use Spark's powerful data processing capabilities to manipulate and analyze the dataset efficiently. We’ll be using Spark SQL, which allows us to interact with the data using SQL-like queries, making the analysis more intuitive.

Accessing the Databricks Datasets

First things first, we need to access the dataset within Databricks. The dbfs:/databricks-datasets/learning-spark-v2/flights/departuredelays.csv path is where this CSV file resides. We're going to read this CSV file into a Spark DataFrame, which is a distributed collection of data organized into named columns. Think of it as a table in a database, but spread across multiple machines for faster processing. Spark DataFrames provide a rich set of functions for querying, filtering, aggregating, and transforming data.

Setting Up Your Spark Environment

Before you start, make sure you have a Databricks cluster running with a Spark environment configured. You'll need to create a notebook attached to your cluster where you can write and execute your Spark code. Now, let's get our hands dirty with some code.

Reading the CSV into a DataFrame

Here’s the code snippet to read the CSV file into a Spark DataFrame:

from pyspark.sql.types import *
from pyspark.sql.functions import *

delaySchema = StructType([
 StructField("date", StringType(), True),
 StructField("delay", IntegerType(), True),
 StructField("distance", IntegerType(), True),
 StructField("origin", StringType(), True),
 StructField("destination", StringType(), True)
])

df = spark.read.format("csv")\
 .option("header", "true")\
 .schema(delaySchema)\
 .load("dbfs:/databricks-datasets/learning-spark-v2/flights/departuredelays.csv")

df.createOrReplaceTempView("departureDelays")

Let's break down what this code does:

  • from pyspark.sql.types import * and from pyspark.sql.functions import *: Import necessary Spark SQL types and functions.
  • delaySchema = StructType(...): This defines the schema of our CSV file. It specifies the names and data types of each column. Defining a schema is optional, but it's a good practice because it helps Spark optimize data processing and ensures data consistency.
  • spark.read.format("csv"): This tells Spark to use the CSV data source.
  • .option("header", "true"): This indicates that the first row of the CSV file contains the column headers.
  • .schema(delaySchema): This applies the defined schema to the DataFrame.
  • .load("dbfs:/databricks-datasets/learning-spark-v2/flights/departuredelays.csv"): This specifies the path to the CSV file.
  • df.createOrReplaceTempView("departureDelays"): This creates a temporary view named "departureDelays" that you can use to query the data using SQL.

Displaying the Data

To get a quick glimpse of the data, you can use the show() method:

df.show(10)

This will display the first 10 rows of the DataFrame, allowing you to verify that the data was loaded correctly.

Analyzing Departure Delays using Spark SQL

Now that we have the data loaded into a DataFrame, we can start analyzing it using Spark SQL. We'll use the temporary view we created earlier to execute SQL queries against the data.

Average Departure Delays

Let's start by calculating the average departure delay:

df.select(mean("delay")).alias("AverageDelay").show()

This code selects the delay column, calculates the mean, aliases the resulting column as "AverageDelay", and then displays the result.

Delays by Origin

To find out which origin airports have the highest average departure delays, you can use the following query:

spark.sql("""
SELECT origin, avg(delay) AS AverageDelay
FROM departureDelays
GROUP BY origin
ORDER BY AverageDelay DESC
""").show(10)

This query groups the data by origin, calculates the average delay for each origin, orders the results in descending order of average delay, and then displays the top 10 origins with the highest average delays.

Delays by Destination

Similarly, to analyze delays by destination:

spark.sql("""
SELECT destination, avg(delay) AS AverageDelay
FROM departureDelays
GROUP BY destination
ORDER BY AverageDelay DESC
""").show(10)

This will show you the destinations that tend to have the worst departure delays.

Delays by Carrier

To analyze delays by carrier, you’ll first need to join this dataset with another dataset containing carrier information. Assuming you have a carriers table with a Code and Description column, you can perform a join. Since the provided data does not contain carrier information, let's use the origin:

spark.sql("""
SELECT origin, avg(delay) AS AverageDelay
FROM departureDelays
GROUP BY origin
ORDER BY AverageDelay DESC
""").show(10)

Analyzing Time-Based Patterns

To analyze departure delays based on the time of day, you'll need to extract the hour from the date column and group the data accordingly. This involves some more advanced data manipulation.

from pyspark.sql.functions import hour

df_with_hour = df.withColumn("hour", hour(df["date"]))

df_with_hour.createOrReplaceTempView("departureDelaysWithHour")

spark.sql("""
SELECT hour, avg(delay) AS AverageDelay
FROM departureDelaysWithHour
GROUP BY hour
ORDER BY hour
""").show(24)

This code adds a new column called hour to the DataFrame, extracts the hour from the date column using the hour() function, creates a temporary view, and then calculates the average delay for each hour of the day.

Advanced Analysis and Visualization

These are just a few basic examples of what you can do with the flights dataset and Spark SQL. You can perform more advanced analysis by combining multiple datasets, using more complex SQL queries, and creating visualizations to better understand the data.

Visualizing the Results

To create visualizations, you can use libraries like Matplotlib or Seaborn in combination with Spark. First, you would collect the results of your Spark queries into a Pandas DataFrame, and then use Matplotlib or Seaborn to create plots and charts.

import matplotlib.pyplot as plt
import pandas as pd

delays_by_origin = spark.sql("""
SELECT origin, avg(delay) AS AverageDelay
FROM departureDelays
GROUP BY origin
ORDER BY AverageDelay DESC
LIMIT 10
""").toPandas()

plt.figure(figsize=(12, 6))
plt.bar(delays_by_origin["origin"], delays_by_origin["AverageDelay"])
plt.xlabel("Origin Airport")
plt.ylabel("Average Delay (minutes)")
plt.title("Top 10 Origin Airports with Highest Average Departure Delays")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

This code calculates the average delay for the top 10 origin airports, collects the results into a Pandas DataFrame, and then creates a bar chart using Matplotlib.

Conclusion

In this guide, we've covered the basics of loading and analyzing the flights dataset using Spark and Databricks. We've explored how to use Spark SQL to query the data, calculate average departure delays, analyze delays by origin and destination, and identify time-based patterns. By leveraging Spark's powerful data processing capabilities, you can gain valuable insights into air travel patterns and the factors contributing to flight delays. This is a great starting point for anyone looking to learn more about big data analysis with Spark!

Remember, this is just the beginning. There’s so much more you can do with this dataset and Spark. Keep experimenting, exploring, and learning, and you’ll be amazed at what you can discover!