Pyspark Structured streaming processing

Question

I am trying to make a structured streaming application with spark the main idea is to read from a kafka source, process the input, write back to another topic. i have successfully made spark read and write from and to kafka however my problem is with the processing part. I have tried the foreach function to capture every row and process it before writing back to kafka however it always only does the foreach part and never writes back to kafka. If i however remove the foreach part from the writestream it would continue writing but now i lost my processing.

if anyone can give me an example on how to do this with an example i would be extremely grateful.

here is my code

spark = SparkSession \
.builder \
.appName("StructuredStreamingTrial") \
.getOrCreate()
df = spark \
  .readStream \
  .format("kafka") \
  .option("kafka.bootstrap.servers", "localhost:9092") \
  .option("subscribe", "KafkaStreamingSource") \
  .load()

ds = df \
  .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")\
  .writeStream \
  .outputMode("update") \
  .format("kafka") \
  .option("kafka.bootstrap.servers", "localhost:9092") \
  .option("topic", "StreamSink") \
  .option("checkpointLocation", "./testdir")\
  .foreach(foreach_function)
  .start().awaitTermination()

and the foreach_function simply is

def foreach_function(df):
    try:
        print(df)
    except:
        print('fail')
    pass 

Answer 1

Processing the data before writing into Kafka sink in Pyspark based Structured Streaming API,we can easily handle with UDF function for any kind of complex transformation .

example code is in below . This code is trying to read the JSON format message Kafka topic and parsing the message to convert the message from JSON into CSV format and rewrite into another topic. You can handle any processing transformation in place of 'json_formatted' function .

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
from pyspark.streaming import StreamingContext
from pyspark.sql.column import Column, _to_java_column
from pyspark.sql.functions import col, struct
from pyspark.sql.functions import udf
import json
import csv
import time
import os

#  Spark Streaming context :

spark = SparkSession.builder.appName('pda_inst_monitor_status_update').getOrCreate()
sc = spark.sparkContext
ssc = StreamingContext(sc, 20)


#  Creating  readstream DataFrame :

df = spark \
  .readStream \
  .format("kafka") \
  .option("kafka.bootstrap.servers", "localhost:9092") \
  .option("subscribe", "KafkaStreamingSource") \
  .load()

df1 = df.selectExpr( "CAST(value AS STRING)")

df1.registerTempTable("test")


def json_formatted(s):
    val_dict = json.loads(s)
    return str([
                    val_dict["after"]["ID"]
                ,   val_dict["after"]["INST_NAME"]
                ,   val_dict["after"]["DB_UNIQUE_NAME"]
                ,   val_dict["after"]["DBNAME"]
                ,   val_dict["after"]["MON_START_TIME"]
                ,   val_dict["after"]["MON_END_TIME"]
                ]).strip('[]').replace("'","").replace('"','')

spark.udf.register("JsonformatterWithPython", json_formatted)

squared_udf = udf(json_formatted)
df1 = spark.table("test")
df2 = df1.select(squared_udf("value"))



#  Declaring the Readstream Schema DataFrame :

df2.coalesce(1).writeStream \
   .writeStream \
   .outputMode("update") \
   .format("kafka") \
   .option("kafka.bootstrap.servers", "localhost:9092") \
   .option("topic", "StreamSink") \
   .option("checkpointLocation", "./testdir")\
   .start()

ssc.awaitTermination()