Any help on the above? On Thu, Mar 15, 2018 at 3:53 PM, Aakash Basu <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.format("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.execution.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> > 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> wrote: >> >>> Relevant: https://databricks.com/blog/2018/03/13/introducing >>> -stream-stream-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> >>> 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> 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> 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> 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> >>>>>>> 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> 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. >>>>>>>>> >>>>>>>> >>>>>> >>>> >>> >> >