Chris identified the problem correctly. You need to parse out the json text from Kafka into separate columns before you can join them up. I walk through an example of this in my slides - https://www.slideshare.net/databricks/easy-scalable-fault-tolerant-stream-processing-with-structured-streaming-with-tathagata-das
On Thu, Mar 15, 2018 at 8:37 AM, 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.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< > 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- > 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 > <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<mailto: > 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. > > > > > > > >