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Lakehouse/config/spark-jobs/kafka_to_s3.py
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2026-05-19 23:12:41 +02:00

35 lines
1.1 KiB
Python

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, current_timestamp
spark = SparkSession.builder \
.appName("KafkaToS3") \
.config("spark.hadoop.fs.s3a.endpoint", "http://10.0.20.111:9020") \
.config("spark.hadoop.fs.s3a.access.key", "AKIA38FD4BA7FC1FB43C") \
.config("spark.hadoop.fs.s3a.secret.key", "IK7M3ro+CWb7f4OyNKdK1W2SCvMwJTrPX1NBDwsj") \
.config("spark.hadoop.fs.s3a.path.style.access", "true") \
.config("spark.hadoop.fs.s3a.connection.ssl.enabled", "false") \
.getOrCreate()
spark.sparkContext.setLogLevel("WARN")
df = spark.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", "10.0.21.36:9092") \
.option("subscribe", "test-lakehouse") \
.option("startingOffsets", "latest") \
.load()
output = df.select(
col("key").cast("string"),
col("value").cast("string"),
current_timestamp().alias("timestamp")
)
query = output.writeStream \
.outputMode("append") \
.format("console") \
.start()
print("Streaming started. Press Ctrl+C to stop.")
query.awaitTermination()