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From Aakash Basu <aakash.spark....@gmail.com>
Subject Re: [Structured Streaming Query] Calculate Running Avg from Kafka feed using SQL query
Date Mon, 09 Apr 2018 08:15:24 GMT
Hey Felix,

I've already tried with

.format("memory")
   .queryName("tableName")

but, still, it doesn't work for the second query. It just stalls the
program expecting new data for the first query.

Here's my code -

from pyspark.sql import SparkSession
from pyspark.sql.functions import split

spark = SparkSession \
    .builder \
    .appName("StructuredNetworkWordCount") \
    .getOrCreate()

data = spark \
    .readStream \
    .format("socket") \
    .option("header","true") \
    .option("host", "localhost") \
    .option("port", 9998) \
    .load("csv")


id_DF = data.select(split(data.value, ",").getItem(0).alias("col1"),
split(data.value, ",").getItem(1).alias("col2"))

id_DF.createOrReplaceTempView("ds")

df = spark.sql("select avg(col1) as aver from ds")
query2 = df \
    .writeStream \
    .format("memory") \
    .queryName("ABCD") \
    .outputMode("complete") \
    .trigger(processingTime='5 seconds') \
    .start()

wordCounts = spark.sql("Select col1, col2, col2/(select aver from
ABCD) col3 from ds")

query = wordCounts \
    .writeStream \
    .format("console") \
    .trigger(processingTime='5 seconds') \
    .start()

spark.streams.awaitAnyTermination()


Here's my data -

1,2
3,4
5,6
7,8
9,10
11,12
13,14


What do you thing the problem maybe?


Thanks in adv,
Aakash.

On Fri, Apr 6, 2018 at 9:55 PM, Felix Cheung <felixcheung_m@hotmail.com>
wrote:

> 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.raj@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.raj@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>, "Bowden, Chris" <
> chris.bowden@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.
>
>

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