- 1 year ago
PySpark Parse Kafka csv delimited data to columns using from_csv
To parse Kafka CSV-delimited data into columns using PySpark's from_csv
function, you can follow these steps:
- Import the required modules:
pythonfrom pyspark.sql import SparkSession
from pyspark.sql.functions import from_json, col
from pyspark.sql.types import StructType, StructField, StringType
- Create a SparkSession:
pythonspark = SparkSession.builder \
.appName("KafkaCSVParser") \
.getOrCreate()
- Define the schema for the CSV data:
pythonschema = StructType([
StructField("col1", StringType(), True),
StructField("col2", StringType(), True),
# Add more StructFields for additional columns if needed
])
- Read the Kafka stream into a DataFrame:
pythonkafka_df = spark \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", "localhost:9092") \
.option("subscribe", "your_topic_name") \
.load()
Replace "localhost:9092"
with the appropriate Kafka server address and port, and "your_topic_name"
with the actual topic name you want to consume from.
- Parse the CSV data using
from_csv
and apply the schema:
pythonparsed_df = kafka_df \
.select(from_json(col("value").cast("string"), schema).alias("data")) \
.select("data.*")
Here, col("value").cast("string")
converts the binary value column to a string, and from_json
function parses the CSV string into a struct column called "data". Finally, .select("data.*")
selects the individual columns from the "data" struct column.
- Start the streaming query:
pythonquery = parsed_df \
.writeStream \
.format("console") \
.start()
query.awaitTermination()
This example writes the parsed data to the console for demonstration purposes. You can replace .format("console")
with the desired output sink, such as a file, database, or another Kafka topic.
Make sure you have the necessary dependencies, such as the Kafka and PySpark libraries, properly installed and accessible in your Python environment.
Adjust the code according to your specific requirements, such as adding additional columns to the schema or customizing the output sink.