Python: Apache Spark 공부 예제 (pyspark)

Apache Spark

v3.0.1 기준

Apache Spark™ - Unified Analytics Engine for Big Data

txt나 csv 불러오기

        
python
        

            
        
     
from pyspark.sql import SparkSession

# Spark v3.0.1
spark = SparkSession.builder.master("local").appName("SparkSQL").getOrCreate()
spark.sparkContext.setLogLevel("ERROR")

# CSV
lines = spark.read.option("header", True).csv("./data/customer_orders.csv")

# TXT
lines = spark.sparkContext.textFile("./data/fake_person.txt")

# TXT with schema
from pyspark.sql.types import StructType, StructField, IntegerType, StringType


schema = StructType(
    [StructField("id", IntegerType(), True), StructField("name", StringType(), True),]
)

names = spark.read.option("sep", " ").schema(schema).csv("./data/Marvel-names.txt")

 

책 txt 읽고 가장 많이 나온 단어 확인하기

regex

        
python
        

            
        
     
import re as regex

from pyspark.sql import SparkSession


def normalizeWords(text):
    return regex.compile(r"\W+", regex.UNICODE).split(text)


# Spark v3.0.1
spark = SparkSession.builder.master("local").appName("WordCounter").getOrCreate()
spark.sparkContext.setLogLevel("ERROR")

lines = spark.sparkContext.textFile("./data/DaddyLongLeg_book_korean.txt")
words = lines.flatMap(normalizeWords)
wordCounts = words.countByValue()

# mostFreqWord = max(wordCounts, key=lambda x: wordCounts[x])
# print(f"{mostFreqWord}: {wordCounts[mostFreqWord]} times")

for word, count in sorted(wordCounts.items(), key=lambda x: x[1]):
    cleanWord = word.encode("utf-8", "ignore")
    if count > 10:
        print(cleanWord.decode("utf-8"), count)

spark.stop()

""" 결과

...
한 293
보냄 301
총 305
이 317
있는 320
제가 341

"""

 

SQL 데이터 프레임 df 이용

        
python
        

            
        
     
from pyspark.sql import SparkSession

# Spark v3.0.1
spark = SparkSession.builder.master("local").appName("SparkSQL").getOrCreate()
spark.sparkContext.setLogLevel("ERROR")

people = (
    spark.read.option("header", True)
    .option("inferSchema", True)
    .csv("./data/fakefriends-header.csv")
)

print("Inferred schema:")
people.printSchema()

print("Name column:")
people.select("name").show()

print("Filter age < 21:")
people.filter(people.age < 21).show()

print("Group by age:")
people.groupBy("age").count().show()

print("Make everyone + 10y older:")
people.select(people.name, people.age + 10).show()

""" 결과

...
Filter age < 21:
+------+-------+---+-------+
|userID|   name|age|friends|
+------+-------+---+-------+
|    21|  Miles| 19|    268|
...

"""

 

SQL 명령어 사용

        
python
        

            
        
     
from pyspark.sql import Row, SparkSession


def parseLine(line):
    return Row(
        id=int(line[0]),
        name=str(line[1].encode("utf-8")),
        age=int(line[2]),
        numFriends=int(line[3]),
    )


# Spark v3.0.1
spark = SparkSession.builder.master("local").appName("SparkSQL").getOrCreate()
spark.sparkContext.setLogLevel("ERROR")

lines = spark.read.option("header", True).csv("./data/fakefriends-header.csv")
people = lines.rdd.map(parseLine)

schemaPeople = spark.createDataFrame(people).cache()
schemaPeople.createOrReplaceTempView("people")  # db name

# Option 1
teens = spark.sql("SELECT * FROM people WHERE age >= 13 AND age <= 19")
for teen in teens.collect():
    print(teen.name, teen.age, teen.numFriends)

# Option 2
schemaPeople.groupBy("age").count().orderBy("age").show()

spark.stop()

""" 결과

b'Miles' 19 268
b'Beverly' 19 269
b'Brunt' 19 5
...

+---+-----+
|age|count|
+---+-----+
| 18|    8|
...

"""

 

schema 스키마 지정

        
python
        

            
        
     
from pyspark.sql import SparkSession
from pyspark.sql.types import (
    StructType,
    StructField,
    StringType,
    IntegerType,
    FloatType,
)


# Spark v3.0.1
spark = SparkSession.builder.master("local").appName("MinTemp").getOrCreate()
spark.sparkContext.setLogLevel("ERROR")

schema = StructType(
    [
        StructField("stationID", StringType(), True),  # True for allowing no value
        StructField("date", IntegerType(), True),
        StructField("measure_type", StringType(), True),
        StructField("temperature", FloatType(), True),
    ]
)

df = spark.read.schema(schema).csv("./data/1800_weather.csv")
df.printSchema()

 

Sale

        
python
        

            
        
     
# Load the data into a Spark DataFrame
df = spark.read.csv("sales_data.csv", header=True)

# Convert the data type of a column to a different type
df = df.withColumn("total_sales", df["total_sales"].cast("double"))

# Create a new column based on the values of other columns
df = df.withColumn("profit", df["total_sales"] - df["cost"])

# Filter rows based on a complex condition
filtered_df = df.filter((df.region == "North America") & (df.profit > 1000))

# Group the data by multiple columns and calculate the sum of a column
grouped_df = df.groupBy("region", "product_type").sum("total_sales")

# Sort the data by multiple columns in descending order
sorted_df = df.orderBy(df.region.desc(), df.total_sales.asc())

# Save the DataFrame to a Parquet file
df.write.parquet("sales.parquet")

공부 @Github

Alfex4936/Spark-Studies