Any help? Need urgent help. Someone please clarify the doubt?
---------- Forwarded message ---------- From: Aakash Basu <aakash.spark....@gmail.com> Date: Mon, Apr 2, 2018 at 1:01 PM Subject: [Structured Streaming Query] Calculate Running Avg from Kafka feed using SQL query To: user <firstname.lastname@example.org>, "Bowden, Chris" < chris.bow...@microfocus.com> Hi, This is a very interesting requirement, where I am getting stuck at a few places. *Requirement* - Col1 Col2 1 10 2 11 3 12 4 13 5 14 *I have to calculate avg of col1 and then divide each row of col2 by that avg. And, the Avg should be updated with every new data being fed through Kafka into Spark Streaming.* *Avg(Col1) = Running Avg* *Col2 = Col2/Avg(Col1)* *Queries* *-* *1) I am currently trying to simply run a inner query inside a query and print Avg with other Col value and then later do the calculation. But, getting error.* Query - select t.Col2 , (Select AVG(Col1) as Avg from transformed_Stream_DF) as myAvg from transformed_Stream_DF t Error - pyspark.sql.utils.StreamingQueryException: u'Queries with streaming sources must be executed with writeStream.start(); Even though, I already have writeStream.start(); in my code, it is probably throwing the error because of the inner select query (I think Spark is assuming it as another query altogether which require its own writeStream.start. Any help? *2) How to go about it? *I have another point in mind, i.e, querying the table to get the avg and store it in a variable. In the second query simply pass the variable and divide the second column to produce appropriate result. But, is it the right approach? *3) Final question*: How to do the calculation over the entire data and not the latest, do I need to keep appending somewhere and repeatedly use it? My average and all the rows of the Col2 shall change with every new incoming data. *Code -* from pyspark.sql import SparkSession import time from pyspark.sql.functions import split, col class test: spark = SparkSession.builder \ .appName("Stream_Col_Oper_Spark") \ .getOrCreate() data = spark.readStream.format("kafka") \ .option("startingOffsets", "latest") \ .option("kafka.bootstrap.servers", "localhost:9092") \ .option("subscribe", "test1") \ .load() ID = data.select('value') \ .withColumn('value', data.value.cast("string")) \ .withColumn("Col1", split(col("value"), ",").getItem(0)) \ .withColumn("Col2", split(col("value"), ",").getItem(1)) \ .drop('value') ID.createOrReplaceTempView("transformed_Stream_DF") aggregate_func = spark.sql( "select t.Col2 , (Select AVG(Col1) as Avg from transformed_Stream_DF) as myAvg from transformed_Stream_DF t") # (Col2/(AVG(Col1)) as Col3)") # -----------For Console Print----------- query = aggregate_func \ .writeStream \ .format("console") \ .start() # .outputMode("complete") \ # -----------Console Print ends----------- query.awaitTermination() # /home/kafka/Downloads/spark-2.3.0-bin-hadoop2.7/bin/spark-submit --packages org.apache.spark:spark-sql-kafka-0-10_2.11:2.3.0 /home/aakashbasu/PycharmProjects/AllMyRnD/Kafka_Spark/Stream_Col_Oper_Spark.py Thanks, Aakash.