Snap Step 1: [Spark's Compression Handling]

By default, Spark can automatically handle compressed files, including formats like .gz, .bz2, .zip, etc.

Specifically:

1. Spark can automatically detect and process compressed files without explicit decompression settings
2. For .csv.gz files, you can read them directly using spark.read.csv() method just like regular CSV files
3. Spark automatically selects the appropriate decompression codec based on the file extension

Here's how to use it:

# Directly read compressed CSV file
df = spark.read \\\\
    .option("header", "true") \\\\
    .csv("path/to/your/file.csv.gz")

# Or read compressed files from an entire directory
df = spark.read \\\\
    .option("header", "true") \\\\
    .csv("path/to/directory/*.csv.gz")

This is a convenient feature of Spark that makes handling large-scale compressed data files simple. You don't need to decompress files before reading them - Spark handles this process automatically.

Snap Step 2: [Shell Script for NYC Taxi Data Download Meanings]

Let's break down this shell script line by line:

Sub Snap Step 1: Error Handle & Variable

set -e

• Sets a shell option that exits immediately if any command returns a non-zero status
• This is a safety practice to prevent executing further commands after an error occurs

TAXI_TYPE=$1 # "yellow"
YEAR=$2 # 2020

• Assigns the first command-line argument to TAXI_TYPE variable
• $1 represents the first script parameter (expected to be "yellow")
• Assigns the second command-line argument to YEAR variable