Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
# Purpose: Batch read Kafka topic, aggregate sales and orders by country,
# and output to console and Amazon S3 as CSV
# Author: Gary A. Stafford
# Date: 2021-09-22
import os
import boto3
import pyspark.sql.functions as F
from ec2_metadata import ec2_metadata
from pyspark.sql import SparkSession
from pyspark.sql.types import StructField, StructType, IntegerType, \
StringType, FloatType, TimestampType
from pyspark.sql.window import Window
topic_input = "pagila.sales.spark.streaming"
os.environ['AWS_DEFAULT_REGION'] = ec2_metadata.region
ssm_client = boto3.client("ssm")
def main():
params = get_parameters()
spark = SparkSession \
.builder \
.appName("kafka-batch-sales") \
.getOrCreate()
df_sales = read_from_kafka(spark, params)
summarize_sales(params, df_sales)
def read_from_kafka(spark, params):
options_read = {
"kafka.bootstrap.servers":
params["kafka_servers"],
"subscribe":
topic_input,
"startingOffsets":
"earliest",
"endingOffsets":
"latest",
"kafka.ssl.truststore.location":
"/tmp/kafka.client.truststore.jks",
"kafka.security.protocol":
"SASL_SSL",
"kafka.sasl.mechanism":
"AWS_MSK_IAM",
"kafka.sasl.jaas.config":
"software.amazon.msk.auth.iam.IAMLoginModule required;",
"kafka.sasl.client.callback.handler.class":
"software.amazon.msk.auth.iam.IAMClientCallbackHandler"
}
df_sales = spark.read \
.format("kafka") \
.options(**options_read) \
.load()
return df_sales
def summarize_sales(params, df_sales):
schema = StructType([
StructField("payment_id", IntegerType(), False),
StructField("customer_id", IntegerType(), False),
StructField("amount", FloatType(), False),
StructField("payment_date", TimestampType(), False),
StructField("city", StringType(), True),
StructField("district", StringType(), True),
StructField("country", StringType(), False),
])
window = Window.partitionBy("country").orderBy("amount")
window_agg = Window.partitionBy("country")
df_output = df_sales \
.selectExpr("CAST(value AS STRING)") \
.select(F.from_json("value", schema=schema).alias("data")) \
.select("data.*") \
.withColumn("row", F.row_number().over(window)) \
.withColumn("orders", F.count(F.col("amount")).over(window_agg)) \
.withColumn("sales", F.sum(F.col("amount")).over(window_agg)) \
.where(F.col("row") == 1).drop("row") \
.select("country", (F.format_number(F.col("sales"), 2)).alias("sales"), "orders") \
.coalesce(1) \
.orderBy(F.regexp_replace("sales", ",", "").cast("float"), ascending=False)
df_output \
.write \
.format("console") \
.option("numRows", 25) \
.option("truncate", False) \
.save()
df_output \
.write \
.csv(path=f"s3a://{params['kafka_demo_bucket']}/spark_output/sales_by_country",
header=True, sep="|") \
.mode("overwrite")
def get_parameters():
"""Load parameter values from AWS Systems Manager (SSM) Parameter Store"""
params = {
"kafka_servers": ssm_client.get_parameter(
Name="/kafka_spark_demo/kafka_servers")["Parameter"]["Value"],
"kafka_demo_bucket": ssm_client.get_parameter(
Name="/kafka_spark_demo/kafka_demo_bucket")["Parameter"]["Value"],
}
return params
if __name__ == "__main__":
main()
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment