学ぶ Joins in PySpark: Inner, Left, and Anti

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Joins combine two DataFrames on a common key. PySpark supports all standard join types – the most useful for analytical work are inner, left, and anti.

Setting Up Two DataFrames

To demonstrate joins, create a small airline names DataFrame alongside the flights data:


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import urllib.request
from pyspark.sql import SparkSession
from pyspark.sql import Row

urllib.request.urlretrieve(
    "https://staging-content-media-cdn.codefinity.com/courses/aa80ac56-0d50-49e8-9231-2c2374cd3e9d/flights.csv",
    "flights.csv"
)

spark = SparkSession.builder \
    .appName("Joins") \
    .master("local[*]") \
    .getOrCreate()

flights_df = spark.read.csv("flights.csv", header=True, inferSchema=True)

# Small reference DataFrame with full airline names
airlines_df = spark.createDataFrame([
    Row(IATA="AA", NAME="American Airlines"),
    Row(IATA="DL", NAME="Delta Air Lines"),
    Row(IATA="UA", NAME="United Airlines"),
    Row(IATA="WN", NAME="Southwest Airlines"),
    Row(IATA="AS", NAME="Alaska Airlines"),
])

Inner Join

Returns only rows where the key exists in both DataFrames:


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# Joining flights with airline names
inner_df = flights_df.join(airlines_df, flights_df["AIRLINE"] == airlines_df["IATA"], "inner")
inner_df.select("AIRLINE", "NAME", "ORIGIN_AIRPORT", "ARRIVAL_DELAY").show(5)

Left Join

Returns all rows from the left DataFrame, with nulls for unmatched rows from the right:


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left_df = flights_df.join(airlines_df, flights_df["AIRLINE"] == airlines_df["IATA"], "left")
left_df.select("AIRLINE", "NAME", "ORIGIN_AIRPORT").show(5)

Anti Join

Returns rows from the left DataFrame that have no match in the right – useful for finding orphaned records:


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# Flights whose airline code is not in the reference table
unmatched_df = flights_df.join(airlines_df, flights_df["AIRLINE"] == airlines_df["IATA"], "left_anti")
print(unmatched_df.select("AIRLINE").distinct().collect())

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