You could have a really large window.

From: Aakash Basu <aakash.spark....@gmail.com>
Date: Monday, April 16, 2018 at 10:56 AM
To: "Lalwani, Jayesh" <jayesh.lalw...@capitalone.com>
Cc: spark receiver <spark.recei...@gmail.com>, Panagiotis Garefalakis 
<panga...@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.lalw...@capitalone.com<mailto:jayesh.lalw...@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....@gmail.com<mailto:aakash.spark....@gmail.com>>
Date: Monday, April 16, 2018 at 4:52 AM
To: "Lalwani, Jayesh" 
<jayesh.lalw...@capitalone.com<mailto:jayesh.lalw...@capitalone.com>>
Cc: spark receiver <spark.recei...@gmail.com<mailto:spark.recei...@gmail.com>>, 
Panagiotis Garefalakis <panga...@gmail.com<mailto:panga...@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.lalw...@capitalone.com<mailto:jayesh.lalw...@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.recei...@gmail.com<mailto:spark.recei...@gmail.com>>
Date: Friday, April 13, 2018 at 11:49 PM
To: Aakash Basu <aakash.spark....@gmail.com<mailto:aakash.spark....@gmail.com>>
Cc: Panagiotis Garefalakis <panga...@gmail.com<mailto:panga...@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....@gmail.com<mailto:aakash.spark....@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 
<panga...@gmail.com<mailto:panga...@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....@gmail.com<mailto:aakash.spark....@gmail.com>> wrote:
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: 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 reader of this message is not the intended recipient, you are hereby 
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