Re: [Structured Streaming Query] Calculate Running Avg from Kafka feed using SQL query

[Felix Cheung]

Instead of write to console you need to write to memory for it to be queryable


 .format("memory")
   .queryName("tableName")
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#output-sinks

________________________________
From: Aakash Basu <aakash.spark....@gmail.com>
Sent: Friday, April 6, 2018 3:22:07 AM
To: user
Subject: Fwd: [Structured Streaming Query] Calculate Running Avg from Kafka 
feed using SQL query

Any help?

Need urgent help. Someone please clarify the doubt?


---------- Forwarded message ----------
From: Aakash Basu 
<aakash.spark....@gmail.com<mailto: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 <user@spark.apache.org<mailto:user@spark.apache.org>>, "Bowden, Chris" 
<chris.bow...@microfocus.com<mailto: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.