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From "Lalwani, Jayesh" <Jayesh.Lalw...@capitalone.com>
Subject Re: [Structured Streaming] More than 1 streaming in a code
Date Mon, 16 Apr 2018 15:23:22 GMT
You could have a really large window.

From: Aakash Basu <aakash.spark.raj@gmail.com>
Date: Monday, April 16, 2018 at 10:56 AM
To: "Lalwani, Jayesh" <Jayesh.Lalwani@capitalone.com>
Cc: spark receiver <spark.receiver@gmail.com>, Panagiotis Garefalakis <pangaref@gmail.com>,
user <user@spark.apache.org>
Subject: Re: [Structured Streaming] More than 1 streaming in a code

If I use timestamp based windowing, then my average will not be global average but grouped
by timestamp, which is not my requirement. I want to recalculate the avg of entire column,
every time a new row(s) comes in and divide the other column with the updated avg.
Let me know, in-case you or anyone else has any soln. for this.

On Mon, Apr 16, 2018 at 7:52 PM, Lalwani, Jayesh <Jayesh.Lalwani@capitalone.com<mailto:Jayesh.Lalwani@capitalone.com>>
wrote:
You could do it if you had a timestamp in your data.  You can use windowed operations to divide
a value by it’s own average over a window. However, in structured streaming, you can only
window by timestamp columns. You cannot do windows aggregations on integers.

From: Aakash Basu <aakash.spark.raj@gmail.com<mailto:aakash.spark.raj@gmail.com>>
Date: Monday, April 16, 2018 at 4:52 AM
To: "Lalwani, Jayesh" <Jayesh.Lalwani@capitalone.com<mailto:Jayesh.Lalwani@capitalone.com>>
Cc: spark receiver <spark.receiver@gmail.com<mailto:spark.receiver@gmail.com>>,
Panagiotis Garefalakis <pangaref@gmail.com<mailto:pangaref@gmail.com>>, user <user@spark.apache.org<mailto:user@spark.apache.org>>

Subject: Re: [Structured Streaming] More than 1 streaming in a code

Hey Jayesh and Others,
Is there then, any other way to come to a solution for this use-case?

Thanks,
Aakash.

On Mon, Apr 16, 2018 at 8:11 AM, Lalwani, Jayesh <Jayesh.Lalwani@capitalone.com<mailto:Jayesh.Lalwani@capitalone.com>>
wrote:
Note that what you are trying to do here is join a streaming data frame with an aggregated
streaming data frame. As per the documentation, joining an aggregated streaming data frame
with another streaming data frame is not supported


From: spark receiver <spark.receiver@gmail.com<mailto:spark.receiver@gmail.com>>
Date: Friday, April 13, 2018 at 11:49 PM
To: Aakash Basu <aakash.spark.raj@gmail.com<mailto:aakash.spark.raj@gmail.com>>
Cc: Panagiotis Garefalakis <pangaref@gmail.com<mailto:pangaref@gmail.com>>, user
<user@spark.apache.org<mailto:user@spark.apache.org>>
Subject: Re: [Structured Streaming] More than 1 streaming in a code

Hi Panagiotis ,

Wondering you solved the problem or not? Coz I met the same issue today. I’d appreciate
 so much if you could paste the code snippet  if it’s working .

Thanks.


在 2018年4月6日,上午7:40,Aakash Basu <aakash.spark.raj@gmail.com<mailto:aakash.spark.raj@gmail.com>>
写道:

Hi Panagiotis,
I did that, but it still prints the result of the first query and awaits for new data, doesn't
even goes to the next one.
Data -

$ nc -lk 9998

1,2
3,4
5,6
7,8
Result -

-------------------------------------------
Batch: 0
-------------------------------------------
+----+
|aver|
+----+
| 3.0|
+----+

-------------------------------------------
Batch: 1
-------------------------------------------
+----+
|aver|
+----+
| 4.0|
+----+


Updated 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")

df.createOrReplaceTempView("abcd")

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

query2 = df \
    .writeStream \
    .format("console") \
    .outputMode("complete") \
    .trigger(processingTime='5 seconds') \
    .start()

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

spark.streams.awaitAnyTermination()

Thanks,
Aakash.

On Fri, Apr 6, 2018 at 4:18 PM, Panagiotis Garefalakis <pangaref@gmail.com<mailto:pangaref@gmail.com>>
wrote:
Hello Aakash,

When you use query.awaitTermination you are pretty much blocking there waiting for the current
query to stop or throw an exception. In your case the second query will not even start.
What you could do instead is remove all the blocking calls and use spark.streams.awaitAnyTermination
instead (waiting for either query1 or query2 to terminate). Make sure you do that after the
query2.start call.

I hope this helps.

Cheers,
Panagiotis

On Fri, Apr 6, 2018 at 11:23 AM, Aakash Basu <aakash.spark.raj@gmail.com<mailto:aakash.spark.raj@gmail.com>>
wrote:
Any help?
Need urgent help. Someone please clarify the doubt?

---------- Forwarded message ----------
From: Aakash Basu <aakash.spark.raj@gmail.com<mailto:aakash.spark.raj@gmail.com>>
Date: Thu, Apr 5, 2018 at 3:18 PM
Subject: [Structured Streaming] More than 1 streaming in a code
To: user <user@spark.apache.org<mailto:user@spark.apache.org>>
Hi,
If I have more than one writeStream in a code, which operates on the same readStream data,
why does it produce only the first writeStream? I want the second one to be also printed on
the console.
How to do that?


from pyspark.sql import SparkSession
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")

    df = spark.sql("select avg(col1) as aver from transformed_Stream_DF")

    df.createOrReplaceTempView("abcd")

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


    # -----------------------#

    query1 = df \
        .writeStream \
        .format("console") \
        .outputMode("complete") \
        .trigger(processingTime='3 seconds') \
        .start()

    query1.awaitTermination()
    # -----------------------#

    query2 = wordCounts \
        .writeStream \
        .format("console") \
        .trigger(processingTime='3 seconds') \
        .start()

    query2.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,com.databricks:spark-csv_2.10:1.0.3
/home/aakashbasu/PycharmProjects/AllMyRnD/Kafka_Spark/Stream_Col_Oper_Spark.py

Thanks,
Aakash.





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________________________________

The information contained in this e-mail is confidential and/or proprietary to Capital One
and/or its affiliates and may only be used solely in performance of work or services for Capital
One. The information transmitted herewith is intended only for use by the individual or entity
to which it is addressed. If the reader of this message is not the intended recipient, you
are hereby notified that any review, retransmission, dissemination, distribution, copying
or other use of, or taking of any action in reliance upon this information is strictly prohibited.
If you have received this communication in error, please contact the sender and delete the
material from your computer.

________________________________________________________

The information contained in this e-mail is confidential and/or proprietary to Capital One
and/or its affiliates and may only be used solely in performance of work or services for Capital
One. The information transmitted herewith is intended only for use by the individual or entity
to which it is addressed. If the reader of this message is not the intended recipient, you
are hereby notified that any review, retransmission, dissemination, distribution, copying
or other use of, or taking of any action in reliance upon this information is strictly prohibited.
If you have received this communication in error, please contact the sender and delete the
material from your computer.
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