Hi all,
The code was perfectly alright, just the package I was submitting had to be
the updated one (marked green below). The join happened but the output has
many duplicates (even though the *how *parameter is by default *inner*) -
Spark Submit:
/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_Stream_Join.py
Code:
from pyspark.sql import SparkSession
import time
from pyspark.sql.functions import split, col
class test:
spark = SparkSession.builder \
.appName("DirectKafka_Spark_Stream_Stream_Join") \
.getOrCreate()
table1_stream =
(spark.readStream.format("kafka").option("startingOffsets",
"earliest").option("kafka.bootstrap.servers",
"localhost:9092").option("subscribe", "test1").load())
table2_stream =
(spark.readStream.format("kafka").option("startingOffsets",
"earliest").option("kafka.bootstrap.servers",
"localhost:9092").option("subscribe", "test2").load())
query1 = table1_stream.select('value')\
.withColumn('value', table1_stream.value.cast("string")) \
.withColumn("ID", split(col("value"), ",").getItem(0)) \
.withColumn("First_Name", split(col("value"), ",").getItem(1)) \
.withColumn("Last_Name", split(col("value"), ",").getItem(2)) \
.drop('value')
query2 = table2_stream.select('value') \
.withColumn('value', table2_stream.value.cast("string")) \
.withColumn("ID", split(col("value"), ",").getItem(0)) \
.withColumn("Department", split(col("value"), ",").getItem(1)) \
.withColumn("Date_joined", split(col("value"), ",").getItem(2)) \
.drop('value')
joined_Stream = query1.join(query2, "Id")
a = query1.writeStream.format("console").start()
b = query2.writeStream.format("console").start()
c = joined_Stream.writeStream.format("console").start()
time.sleep(10)
a.awaitTermination()
b.awaitTermination()
c.awaitTermination()
Output -
+---+----------+---------+---------------+-----------+
| ID|First_Name|Last_Name| Department|Date_joined|
+---+----------+---------+---------------+-----------+
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 3| Tobit|Robardley| Accounting| 8/3/2006|
| 5| Reggy|Comizzoli|Human Resources| 8/15/2012|
| 5| Reggy|Comizzoli|Human Resources| 8/15/2012|
+---+----------+---------+---------------+-----------+
only showing top 20 rows
*Queries:*
*1) Why even after inner join, the join is doing a outer type?*
*2) Do we need to put awaitTermination on all the streams? Or putting only
on the input streams would suffice?*
*3) This code is not optimized, how to generically optimize streaming code?*
Thanks,
Aakash.
On Fri, Mar 16, 2018 at 3:23 PM, Aakash Basu <aakash.spark....@gmail.com>
wrote:
> Hi,
>
> *Thanks to Chris and TD* for perpetually supporting my endeavor. I ran
> the code with a little bit of tweak here and there, *it worked well in
> Spark 2.2.1* giving me the Deserialized values (I used withColumn in the
> writeStream section to run all SQL functions of split and cast).
>
> But, when I submit the same code in 2.3.0, I get an error which I couldn't
> find any solution of, on the internet.
>
>
>
>
>
> *Error: pyspark.sql.utils.StreamingQueryException: u'null\n=== Streaming
> Query ===\nIdentifier: [id = d956096e-42d2-493c-8b6c-125e3137c291, runId =
> cd25ec61-c6bb-436c-a93e-80814e1436ec]\nCurrent Committed Offsets:
> {}\nCurrent Available Offsets: {}\n\nCurrent State: INITIALIZING\nThread
> State: RUNNABLE'*
>
>
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>
> *Final code (for clearer understanding of where it may go wrong in 2.3.0)
> -from pyspark.sql import SparkSessionimport timefrom pyspark.sql.functions
> import split, colclass test: spark = SparkSession.builder \
> .appName("DirectKafka_Spark_Stream_Stream_Join") \ .getOrCreate()
> table1_stream = (spark.readStream.format("kafka").option("startingOffsets",
> "earliest").option("kafka.bootstrap.servers",
> "localhost:9092").option("subscribe", "test1").load()) query =
> table1_stream.select('value').withColumn('value',
> table1_stream.value.cast("string")) \ .withColumn("ID", split(col("value"),
> ",").getItem(0)) \ .withColumn("First_Name", split(col("value"),
> ",").getItem(1)) \ .withColumn("Last_Name", split(col("value"),
> ",").getItem(2)) \ .drop('value').writeStream.format("console").start()
> time.sleep(10) query.awaitTermination()# Code working in Spark 2.2.1#
> /home/kafka/Downloads/spark-2.2.1-bin-hadoop2.7/bin/spark-submit --packages
> org.apache.spark:spark-sql-kafka-0-10_2.11:2.1.0
> /home/aakashbasu/PycharmProjects/AllMyRnD/Kafka_Spark/Stream_Stream_Join.py#
> Code not working in Spark 2.3.0#
> /home/kafka/Downloads/spark-2.3.0-bin-hadoop2.7/bin/spark-submit --packages
> org.apache.spark:spark-sql-kafka-0-10_2.11:2.1.0
> /home/aakashbasu/PycharmProjects/AllMyRnD/Kafka_Spark/Stream_Stream_Join.py*
> 2) I'm getting the below output as expected, from the above code in 2.2.1.
> My query is, is there a way to get the header of a file being read and
> ensure header=True? (Or is it that for Structured Streaming, user has to
> provide headers explicitly all the time, as data shall always come in this
> structure [for Kafka] - topic, partition, offset, key, value, timestamp,
> timestampType; if so, then how to remove column headers explicitly from the
> data, as in the below table) I know it is a stream, and the data is fed in
> as messages, but still wanted experts to put some more light into it.
>
> +---+----------+---------+
> | ID|First_Name|Last_Name|
> +---+----------+---------+
> | Hi| null| null|
> | id|first_name|last_name|
> | 1| Kellyann| Moyne|
> | 2| Morty| Blacker|
> | 3| Tobit|Robardley|
> | 4| Wilona| Kells|
> | 5| Reggy|Comizzoli|
> | id|first_name|last_name|
> | 1| Kellyann| Moyne|
> | 2| Morty| Blacker|
> | 3| Tobit|Robardley|
> | 4| Wilona| Kells|
> | 5| Reggy|Comizzoli|
> | id|first_name|last_name|
> | 1| Kellyann| Moyne|
> | 2| Morty| Blacker|
> | 3| Tobit|Robardley|
> | 4| Wilona| Kells|
> | 5| Reggy|Comizzoli|
> | id|first_name|last_name|
> +---+----------+---------+
> only showing top 20 rows
>
>
> Any help?
>
> Thanks,
> Aakash.
>
> On Fri, Mar 16, 2018 at 12:54 PM, sagar grover <sagargrove...@gmail.com>
> wrote:
>
>>
>> With regards,
>> Sagar Grover
>> Phone - 7022175584
>>
>> On Fri, Mar 16, 2018 at 12:15 AM, Aakash Basu <aakash.spark....@gmail.com
>> > wrote:
>>
>>> Awesome, thanks for detailing!
>>>
>>> Was thinking the same, we've to split by comma for csv while casting
>>> inside.
>>>
>>> Cool! Shall try it and revert back tomm.
>>>
>>> Thanks a ton!
>>>
>>> On 15-Mar-2018 11:50 PM, "Bowden, Chris" <chris.bow...@microfocus.com>
>>> wrote:
>>>
>>>> To remain generic, the KafkaSource can only offer the lowest common
>>>> denominator for a schema (topic, partition, offset, key, value, timestamp,
>>>> timestampType). As such, you can't just feed it a StructType. When you are
>>>> using a producer or consumer directly with Kafka, serialization and
>>>> deserialization is often an orthogonal and implicit transform. However, in
>>>> Spark, serialization and deserialization is an explicit transform (e.g.,
>>>> you define it in your query plan).
>>>>
>>>>
>>>> To make this more granular, if we imagine your source is registered as
>>>> a temp view named "foo":
>>>>
>>>> SELECT
>>>>
>>>> split(cast(value as string), ',')[0] as id,
>>>>
>>>> split(cast(value as string), ',')[1] as name
>>>>
>>>> FROM foo;
>>>>
>>>>
>>>> Assuming you were providing the following messages to Kafka:
>>>>
>>>> 1,aakash
>>>>
>>>> 2,tathagata
>>>>
>>>> 3,chris
>>>>
>>>>
>>>> You could make the query plan less repetitive. I don't believe Spark
>>>> offers from_csv out of the box as an expression (although CSV is well
>>>> supported as a data source). You could implement an expression by reusing a
>>>> lot of the supporting CSV classes which may result in a better user
>>>> experience vs. explicitly using split and array indices, etc. In this
>>>> simple example, casting the binary to a string just works because there is
>>>> a common understanding of string's encoded as bytes between Spark and Kafka
>>>> by default.
>>>>
>>>>
>>>> -Chris
>>>> ------------------------------
>>>> *From:* Aakash Basu <aakash.spark....@gmail.com>
>>>> *Sent:* Thursday, March 15, 2018 10:48:45 AM
>>>> *To:* Bowden, Chris
>>>> *Cc:* Tathagata Das; Dylan Guedes; Georg Heiler; user
>>>>
>>>> *Subject:* Re: Multiple Kafka Spark Streaming Dataframe Join query
>>>>
>>>> Hey Chris,
>>>>
>>>> You got it right. I'm reading a *csv *file from local as mentioned
>>>> above, with a console producer on Kafka side.
>>>>
>>>> So, as it is a csv data with headers, shall I then use from_csv on the
>>>> spark side and provide a StructType to shape it up with a schema and then
>>>> cast it to string as TD suggested?
>>>>
>>>> I'm getting all of your points at a very high level. A little more
>>>> granularity would help.
>>>>
>>>> *In the slide TD just shared*, PFA, I'm confused at the point where he
>>>> is casting the value as string. Logically, the value shall consist of all
>>>> the entire data set, so, suppose, I've a table with many columns, *how
>>>> can I provide a single alias as he did in the groupBy. I missed it there
>>>> itself. Another question is, do I have to cast in groupBy itself? Can't I
>>>> do it directly in a select query? The last one, if the steps are followed,
>>>> can I then run a SQL query on top of the columns separately?*
>>>>
>>>> Thanks,
>>>> Aakash.
>>>>
>>>>
>>>> On 15-Mar-2018 9:07 PM, "Bowden, Chris" <chris.bow...@microfocus.com>
>>>> wrote:
>>>>
>>>> You need to tell Spark about the structure of the data, it doesn't know
>>>> ahead of time if you put avro, json, protobuf, etc. in kafka for the
>>>> message format. If the messages are in json, Spark provides from_json out
>>>> of the box. For a very simple POC you can happily cast the value to a
>>>> string, etc. if you are prototyping and pushing messages by hand with a
>>>> console producer on the kafka side.
>>>>
>>>> ________________________________________
>>>> From: Aakash Basu <aakash.spark....@gmail.com>
>>>> Sent: Thursday, March 15, 2018 7:52:28 AM
>>>> To: Tathagata Das
>>>> Cc: Dylan Guedes; Georg Heiler; user
>>>> Subject: Re: Multiple Kafka Spark Streaming Dataframe Join query
>>>>
>>>> Hi,
>>>>
>>>> And if I run this below piece of code -
>>>>
>>>>
>>>> from pyspark.sql import SparkSession
>>>> import time
>>>>
>>>> class test:
>>>>
>>>>
>>>> spark = SparkSession.builder \
>>>> .appName("DirectKafka_Spark_Stream_Stream_Join") \
>>>> .getOrCreate()
>>>> # ssc = StreamingContext(spark, 20)
>>>>
>>>> table1_stream =
>>>> (spark.readStream.format("kafka").option("startingOffsets",
>>>> "earliest").option("kafka.bootstrap.servers",
>>>> "localhost:9092").option("subscribe", "test1").load())
>>>>
>>>> table2_stream = (
>>>> spark.readStream.format("kafka").option("startingOffsets",
>>>> "earliest").option("kafka.bootstrap.servers",
>>>>
>>>> "localhost:9092").option("subscribe",
>>>>
>>>> "test2").load())
>>>>
>>>> joined_Stream = table1_stream.join(table2_stream, "Id")
>>>> #
>>>> # joined_Stream.show()
>>>>
>>>> # query =
>>>> table1_stream.writeStream.format("console").start().awaitTermination()
>>>> # .queryName("table_A").format("memory")
>>>> # spark.sql("select * from table_A").show()
>>>> time.sleep(10) # sleep 20 seconds
>>>> # query.stop()
>>>> # query
>>>>
>>>>
>>>> # /home/kafka/Downloads/spark-2.2.1-bin-hadoop2.7/bin/spark-submit
>>>> --packages org.apache.spark:spark-sql-kafka-0-10_2.11:2.1.0
>>>> Stream_Stream_Join.py
>>>>
>>>>
>>>>
>>>>
>>>> I get the below error (in Spark 2.3.0) -
>>>>
>>>> Traceback (most recent call last):
>>>> File
>>>> "/home/aakashbasu/PycharmProjects/AllMyRnD/Kafka_Spark/Stream_Stream_Join.py",
>>>> line 4, in <module>
>>>> class test:
>>>> File
>>>> "/home/aakashbasu/PycharmProjects/AllMyRnD/Kafka_Spark/Stream_Stream_Join.py",
>>>> line 19, in test
>>>> joined_Stream = table1_stream.join(table2_stream, "Id")
>>>> File "/home/kafka/Downloads/spark-2.3.0-bin-hadoop2.7/python/lib/
>>>> pyspark.zip/pyspark/sql/dataframe.py", line 931, in join
>>>> File "/home/kafka/Downloads/spark-2.3.0-bin-hadoop2.7/python/lib/
>>>> py4j-0.10.6-src.zip/py4j/java_gateway.py", line 1160, in __call__
>>>> File "/home/kafka/Downloads/spark-2.3.0-bin-hadoop2.7/python/lib/
>>>> pyspark.zip/pyspark/sql/utils.py", line 69, in deco
>>>> pyspark.sql.utils.AnalysisException: u'USING column `Id` cannot be
>>>> resolved on the left side of the join. The left-side columns: [key, value,
>>>> topic, partition, offset, timestamp, timestampType];'
>>>>
>>>> Seems, as per the documentation, they key and value are deserialized as
>>>> byte arrays.
>>>>
>>>> I am badly stuck at this step, not many materials online, with steps to
>>>> proceed on this, too.
>>>>
>>>> Any help, guys?
>>>>
>>>> Thanks,
>>>> Aakash.
>>>>
>>>>
>>>> On Thu, Mar 15, 2018 at 7:54 PM, Aakash Basu <
>>>> aakash.spark....@gmail.com<mailto:aakash.spark....@gmail.com>> wrote:
>>>> Any help on the above?
>>>>
>>>> On Thu, Mar 15, 2018 at 3:53 PM, Aakash Basu <
>>>> aakash.spark....@gmail.com<mailto:aakash.spark....@gmail.com>> wrote:
>>>> Hi,
>>>>
>>>> I progressed a bit in the above mentioned topic -
>>>>
>>>> 1) I am feeding a CSV file into the Kafka topic.
>>>> 2) Feeding the Kafka topic as readStream as TD's article suggests.
>>>> 3) Then, simply trying to do a show on the streaming dataframe, using
>>>> queryName('XYZ') in the writeStream and writing a sql query on top of it,
>>>> but that doesn't show anything.
>>>> 4) Once all the above problems are resolved, I want to perform a
>>>> stream-stream join.
>>>>
>>>> The CSV file I'm ingesting into Kafka has -
>>>>
>>>> id,first_name,last_name
>>>> 1,Kellyann,Moyne
>>>> 2,Morty,Blacker
>>>> 3,Tobit,Robardley
>>>> 4,Wilona,Kells
>>>> 5,Reggy,Comizzoli
>>>>
>>>>
>>>> My test code -
>>>>
>>>>
>>>> from pyspark.sql import SparkSession
>>>> import time
>>>>
>>>> class test:
>>>>
>>>>
>>>> spark = SparkSession.builder \
>>>> .appName("DirectKafka_Spark_Stream_Stream_Join") \
>>>> .getOrCreate()
>>>> # ssc = StreamingContext(spark, 20)
>>>>
>>>> table1_stream =
>>>> (spark.readStream.format("kafka").option("startingOffsets",
>>>> "earliest").option("kafka.bootstrap.servers",
>>>> "localhost:9092").option("subscribe", "test1").load())
>>>>
>>>> # table2_stream = (spark.readStream.format("kafka").option("
>>>> kafka.bootstrap.servers", "localhost:9092").option("subscribe",
>>>> "test2").load())
>>>>
>>>> # joined_Stream = table1_stream.join(table2_stream, "Id")
>>>> #
>>>> # joined_Stream.show()
>>>>
>>>> query = table1_stream.writeStream.form
>>>> at("console").queryName("table_A").start() # .format("memory")
>>>> # spark.sql("select * from table_A").show()
>>>> # time.sleep(10) # sleep 20 seconds
>>>> # query.stop()
>>>> query.awaitTermination()
>>>>
>>>>
>>>> # /home/kafka/Downloads/spark-2.2.1-bin-hadoop2.7/bin/spark-submit
>>>> --packages org.apache.spark:spark-sql-kafka-0-10_2.11:2.1.0
>>>> Stream_Stream_Join.py
>>>>
>>>>
>>>> The output I'm getting (whereas I simply want to show() my dataframe) -
>>>>
>>>> +----+--------------------+-----+---------+------+----------
>>>> ----------+-------------+
>>>> | key| value|topic|partition|offset|
>>>> timestamp|timestampType|
>>>> +----+--------------------+-----+---------+------+----------
>>>> ----------+-------------+
>>>> |null|[69 64 2C 66 69 7...|test1| 0| 5226|2018-03-15
>>>> 15:48:...| 0|
>>>> |null|[31 2C 4B 65 6C 6...|test1| 0| 5227|2018-03-15
>>>> 15:48:...| 0|
>>>> |null|[32 2C 4D 6F 72 7...|test1| 0| 5228|2018-03-15
>>>> 15:48:...| 0|
>>>> |null|[33 2C 54 6F 62 6...|test1| 0| 5229|2018-03-15
>>>> 15:48:...| 0|
>>>> |null|[34 2C 57 69 6C 6...|test1| 0| 5230|2018-03-15
>>>> 15:48:...| 0|
>>>> |null|[35 2C 52 65 67 6...|test1| 0| 5231|2018-03-15
>>>> 15:48:...| 0|
>>>> +----+--------------------+-----+---------+------+----------
>>>> ----------+-------------+
>>>>
>>>> 18/03/15 15:48:07 INFO StreamExecution: Streaming query made progress: {
>>>> "id" : "ca7e2862-73c6-41bf-9a6f-c79e533a2bf8",
>>>> "runId" : "0758ddbd-9b1c-428b-aa52-1dd40d477d21",
>>>> "name" : "table_A",
>>>> "timestamp" : "2018-03-15T10:18:07.218Z",
>>>> "numInputRows" : 6,
>>>> "inputRowsPerSecond" : 461.53846153846155,
>>>> "processedRowsPerSecond" : 14.634146341463415,
>>>> "durationMs" : {
>>>> "addBatch" : 241,
>>>> "getBatch" : 15,
>>>> "getOffset" : 2,
>>>> "queryPlanning" : 2,
>>>> "triggerExecution" : 410,
>>>> "walCommit" : 135
>>>> },
>>>> "stateOperators" : [ ],
>>>> "sources" : [ {
>>>> "description" : "KafkaSource[Subscribe[test1]]",
>>>> "startOffset" : {
>>>> "test1" : {
>>>> "0" : 5226
>>>> }
>>>> },
>>>> "endOffset" : {
>>>> "test1" : {
>>>> "0" : 5232
>>>> }
>>>> },
>>>> "numInputRows" : 6,
>>>> "inputRowsPerSecond" : 461.53846153846155,
>>>> "processedRowsPerSecond" : 14.634146341463415
>>>> } ],
>>>> "sink" : {
>>>> "description" : "org.apache.spark.sql.executio
>>>> n.streaming.ConsoleSink@3dfc7990"
>>>> }
>>>> }
>>>>
>>>> P.S - If I add the below piece in the code, it doesn't print a DF of
>>>> the actual table.
>>>>
>>>> spark.sql("select * from table_A").show()
>>>>
>>>> Any help?
>>>>
>>>>
>>>> Thanks,
>>>> Aakash.
>>>>
>>>> On Thu, Mar 15, 2018 at 10:52 AM, Aakash Basu <
>>>> aakash.spark....@gmail.com<mailto:aakash.spark....@gmail.com>> wrote:
>>>> Thanks to TD, the savior!
>>>>
>>>> Shall look into it.
>>>>
>>>> On Thu, Mar 15, 2018 at 1:04 AM, Tathagata Das <
>>>> tathagata.das1...@gmail.com<mailto:tathagata.das1...@gmail.com>> wrote:
>>>> Relevant: https://databricks.com/blog/2018/03/13/introducing-stream-st
>>>> ream-joins-in-apache-spark-2-3.html
>>>>
>>>> This is true stream-stream join which will automatically buffer delayed
>>>> data and appropriately join stuff with SQL join semantics. Please check it
>>>> out :)
>>>>
>>>> TD
>>>>
>>>>
>>>>
>>>> On Wed, Mar 14, 2018 at 12:07 PM, Dylan Guedes <djmggue...@gmail.com
>>>> <mailto:djmggue...@gmail.com>> wrote:
>>>> I misread it, and thought that you question was if pyspark supports
>>>> kafka lol. Sorry!
>>>>
>>>> On Wed, Mar 14, 2018 at 3:58 PM, Aakash Basu <
>>>> aakash.spark....@gmail.com<mailto:aakash.spark....@gmail.com>> wrote:
>>>> Hey Dylan,
>>>>
>>>> Great!
>>>>
>>>> Can you revert back to my initial and also the latest mail?
>>>>
>>>> Thanks,
>>>> Aakash.
>>>>
>>>> On 15-Mar-2018 12:27 AM, "Dylan Guedes" <djmggue...@gmail.com<mailto:d
>>>> jmggue...@gmail.com>> wrote:
>>>> Hi,
>>>>
>>>> I've been using the Kafka with pyspark since 2.1.
>>>>
>>>> On Wed, Mar 14, 2018 at 3:49 PM, Aakash Basu <
>>>> aakash.spark....@gmail.com<mailto:aakash.spark....@gmail.com>> wrote:
>>>> Hi,
>>>>
>>>> I'm yet to.
>>>>
>>>> Just want to know, when does Spark 2.3 with 0.10 Kafka Spark Package
>>>> allows Python? I read somewhere, as of now Scala and Java are the languages
>>>> to be used.
>>>>
>>>> Please correct me if am wrong.
>>>>
>>>> Thanks,
>>>> Aakash.
>>>>
>>>> On 14-Mar-2018 8:24 PM, "Georg Heiler" <georg.kf.hei...@gmail.com<mai
>>>> lto:georg.kf.hei...@gmail.com>> wrote:
>>>> Did you try spark 2.3 with structured streaming? There watermarking and
>>>> plain sql might be really interesting for you.
>>>> Aakash Basu <aakash.spark....@gmail.com<mailto:
>>>> aakash.spark....@gmail.com>> schrieb am Mi. 14. März 2018 um 14:57:
>>>> Hi,
>>>>
>>>> Info (Using):
>>>> Spark Streaming Kafka 0.8 package
>>>> Spark 2.2.1
>>>> Kafka 1.0.1
>>>>
>>>> As of now, I am feeding paragraphs in Kafka console producer and my
>>>> Spark, which is acting as a receiver is printing the flattened words, which
>>>> is a complete RDD operation.
>>>>
>>>> My motive is to read two tables continuously (being updated) as two
>>>> distinct Kafka topics being read as two Spark Dataframes and join them
>>>> based on a key and produce the output. (I am from Spark-SQL background,
>>>> pardon my Spark-SQL-ish writing)
>>>>
>>>> It may happen, the first topic is receiving new data 15 mins prior to
>>>> the second topic, in that scenario, how to proceed? I should not lose any
>>>> data.
>>>>
>>>> As of now, I want to simply pass paragraphs, read them as RDD, convert
>>>> to DF and then join to get the common keys as the output. (Just for R&D).
>>>>
>>>> Started using Spark Streaming and Kafka today itself.
>>>>
>>>> Please help!
>>>>
>>>> Thanks,
>>>> Aakash.
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>
>
