Mixing
How can I use spark to writeStream data from a kafka topic into hdfs?
0
I have been trying to get this code to work for hours:
val spark = SparkSession.builder()
.appName("Consumer")
.getOrCreate()
spark.readStream
.format("kafka")
.option("kafka.bootstrap.servers", url)
.option("subscribe", topic)
.load()
.select("value")
.writeStream
.format(fileFormat)
.option("path", filePath)
.option("checkpointLocation", "/tmp/checkpoint")
.start()
.awaitTermination()
it gives this exception:
Logical Plan:
Project [value#8]
+- StreamingExecutionRelation KafkaV2[Subscribe[MyTopic]], [key#7, value#8, topic#9, partition#10, offset#11L, timestamp#12, timestampType#13]
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:295)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)
Caused by: java.lang.ClassCastException: org.apache.spark.sql.execution.streaming.SerializedOffset cannot be cast to org.apache.spark.sql.sources.v2.reader.streaming.Offset
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1$$anonfun$apply$9.apply(MicroBatchExecution.scala:405)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1$$anonfun$apply$9.apply(MicroBatchExecution.scala:390)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at org.apache.spark.sql.execution.streaming.StreamProgress.foreach(StreamProgress.scala:25)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at org.apache.spark.sql.execution.streaming.StreamProgress.flatMap(StreamProgress.scala:25)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1.apply(MicroBatchExecution.scala:390)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1.apply(MicroBatchExecution.scala:390)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:389)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:133)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:117)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279)
I don't understand what's going on, I am simply trying to write data from a kafka topic into HDFS using spark streaming. Why is this so hard? And how can I do it?
I got the batching version to work just fine:
spark.read
.format("kafka")
.option("kafka.bootstrap.servers", url)
.option("subscribe", topic)
.load()
.selectExpr("CAST(value AS String)")
.write
.format(fileFormat)
.save(filePath)
scala apache-spark hadoop apache-kafka hdfs
edited Nov 20 '18 at 23:07
cricket_007
81.2k1142111
asked Nov 20 '18 at 20:24
hey_youhey_you
216111
add a comment |
0
I have been trying to get this code to work for hours:
val spark = SparkSession.builder()
.appName("Consumer")
.getOrCreate()
spark.readStream
.format("kafka")
.option("kafka.bootstrap.servers", url)
.option("subscribe", topic)
.load()
.select("value")
.writeStream
.format(fileFormat)
.option("path", filePath)
.option("checkpointLocation", "/tmp/checkpoint")
.start()
.awaitTermination()
it gives this exception:
Logical Plan:
Project [value#8]
+- StreamingExecutionRelation KafkaV2[Subscribe[MyTopic]], [key#7, value#8, topic#9, partition#10, offset#11L, timestamp#12, timestampType#13]
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:295)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)
Caused by: java.lang.ClassCastException: org.apache.spark.sql.execution.streaming.SerializedOffset cannot be cast to org.apache.spark.sql.sources.v2.reader.streaming.Offset
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1$$anonfun$apply$9.apply(MicroBatchExecution.scala:405)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1$$anonfun$apply$9.apply(MicroBatchExecution.scala:390)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at org.apache.spark.sql.execution.streaming.StreamProgress.foreach(StreamProgress.scala:25)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at org.apache.spark.sql.execution.streaming.StreamProgress.flatMap(StreamProgress.scala:25)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1.apply(MicroBatchExecution.scala:390)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1.apply(MicroBatchExecution.scala:390)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:389)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:133)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:117)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279)
I don't understand what's going on, I am simply trying to write data from a kafka topic into HDFS using spark streaming. Why is this so hard? And how can I do it?
I got the batching version to work just fine:
spark.read
.format("kafka")
.option("kafka.bootstrap.servers", url)
.option("subscribe", topic)
.load()
.selectExpr("CAST(value AS String)")
.write
.format(fileFormat)
.save(filePath)
scala apache-spark hadoop apache-kafka hdfs
edited Nov 20 '18 at 23:07
cricket_007
81.2k1142111
asked Nov 20 '18 at 20:24
hey_youhey_you
216111
Kafka Connect already does this, and is included in Kafka 0.10 and higher... Why write any code to do this?? confluent.io/connector/kafka-connect-hdfs
– cricket_007
Nov 20 '18 at 23:08
add a comment |
0
0
0
I have been trying to get this code to work for hours:
val spark = SparkSession.builder()
.appName("Consumer")
.getOrCreate()
spark.readStream
.format("kafka")
.option("kafka.bootstrap.servers", url)
.option("subscribe", topic)
.load()
.select("value")
.writeStream
.format(fileFormat)
.option("path", filePath)
.option("checkpointLocation", "/tmp/checkpoint")
.start()
.awaitTermination()
it gives this exception:
Logical Plan:
Project [value#8]
+- StreamingExecutionRelation KafkaV2[Subscribe[MyTopic]], [key#7, value#8, topic#9, partition#10, offset#11L, timestamp#12, timestampType#13]
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:295)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)
Caused by: java.lang.ClassCastException: org.apache.spark.sql.execution.streaming.SerializedOffset cannot be cast to org.apache.spark.sql.sources.v2.reader.streaming.Offset
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1$$anonfun$apply$9.apply(MicroBatchExecution.scala:405)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1$$anonfun$apply$9.apply(MicroBatchExecution.scala:390)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at org.apache.spark.sql.execution.streaming.StreamProgress.foreach(StreamProgress.scala:25)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at org.apache.spark.sql.execution.streaming.StreamProgress.flatMap(StreamProgress.scala:25)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1.apply(MicroBatchExecution.scala:390)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1.apply(MicroBatchExecution.scala:390)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:389)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:133)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:117)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279)
I don't understand what's going on, I am simply trying to write data from a kafka topic into HDFS using spark streaming. Why is this so hard? And how can I do it?
I got the batching version to work just fine:
spark.read
.format("kafka")
.option("kafka.bootstrap.servers", url)
.option("subscribe", topic)
.load()
.selectExpr("CAST(value AS String)")
.write
.format(fileFormat)
.save(filePath)
scala apache-spark hadoop apache-kafka hdfs
edited Nov 20 '18 at 23:07
cricket_007
81.2k1142111
asked Nov 20 '18 at 20:24
hey_youhey_you
216111
I have been trying to get this code to work for hours:
val spark = SparkSession.builder()
.appName("Consumer")
.getOrCreate()
spark.readStream
.format("kafka")
.option("kafka.bootstrap.servers", url)
.option("subscribe", topic)
.load()
.select("value")
.writeStream
.format(fileFormat)
.option("path", filePath)
.option("checkpointLocation", "/tmp/checkpoint")
.start()
.awaitTermination()
it gives this exception:
Logical Plan:
Project [value#8]
+- StreamingExecutionRelation KafkaV2[Subscribe[MyTopic]], [key#7, value#8, topic#9, partition#10, offset#11L, timestamp#12, timestampType#13]
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:295)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)
Caused by: java.lang.ClassCastException: org.apache.spark.sql.execution.streaming.SerializedOffset cannot be cast to org.apache.spark.sql.sources.v2.reader.streaming.Offset
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1$$anonfun$apply$9.apply(MicroBatchExecution.scala:405)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1$$anonfun$apply$9.apply(MicroBatchExecution.scala:390)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at org.apache.spark.sql.execution.streaming.StreamProgress.foreach(StreamProgress.scala:25)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at org.apache.spark.sql.execution.streaming.StreamProgress.flatMap(StreamProgress.scala:25)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1.apply(MicroBatchExecution.scala:390)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1.apply(MicroBatchExecution.scala:390)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:389)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:133)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:117)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279)
I don't understand what's going on, I am simply trying to write data from a kafka topic into HDFS using spark streaming. Why is this so hard? And how can I do it?
I got the batching version to work just fine:
spark.read
.format("kafka")
.option("kafka.bootstrap.servers", url)
.option("subscribe", topic)
.load()
.selectExpr("CAST(value AS String)")
.write
.format(fileFormat)
.save(filePath)
scala apache-spark hadoop apache-kafka hdfs
scala apache-spark hadoop apache-kafka hdfs
edited Nov 20 '18 at 23:07
cricket_007
81.2k1142111
asked Nov 20 '18 at 20:24
hey_youhey_you
216111
edited Nov 20 '18 at 23:07
cricket_007
81.2k1142111
asked Nov 20 '18 at 20:24
hey_youhey_you
216111
edited Nov 20 '18 at 23:07
cricket_007
81.2k1142111
edited Nov 20 '18 at 23:07
cricket_007
81.2k1142111
edited Nov 20 '18 at 23:07
cricket_007
81.2k1142111
81.2k1142111
asked Nov 20 '18 at 20:24
hey_youhey_you
216111
asked Nov 20 '18 at 20:24
hey_youhey_you
216111
asked Nov 20 '18 at 20:24
hey_youhey_you
216111
216111
Kafka Connect already does this, and is included in Kafka 0.10 and higher... Why write any code to do this?? confluent.io/connector/kafka-connect-hdfs
– cricket_007
Nov 20 '18 at 23:08
add a comment |
Kafka Connect already does this, and is included in Kafka 0.10 and higher... Why write any code to do this?? confluent.io/connector/kafka-connect-hdfs
– cricket_007
Nov 20 '18 at 23:08
Kafka Connect already does this, and is included in Kafka 0.10 and higher... Why write any code to do this?? confluent.io/connector/kafka-connect-hdfs
– cricket_007
Nov 20 '18 at 23:08
Kafka Connect already does this, and is included in Kafka 0.10 and higher... Why write any code to do this?? confluent.io/connector/kafka-connect-hdfs
– cricket_007
Nov 20 '18 at 23:08
add a comment |
2 Answers
2
active
oldest
votes
0
@happy You are encountering a known bug in structured streaming https://issues.apache.org/jira/browse/SPARK-25257
This is because the offset from disk is never deserialized and the fix will be merged in coming release
answered Nov 20 '18 at 23:26
bhavin tandelbhavin tandel
415
But it's fixed in Spark 2.4?
– cricket_007
Nov 21 '18 at 5:09
What fileFormat are you using ?
– bhavin tandel
Nov 21 '18 at 7:16
@cricket_007 I changed my spark version to 2.3.2 and everything started working!
– hey_you
Nov 21 '18 at 14:29
add a comment |
0
Everything started working after I changed my version of spark to 2.3.2.
answered Nov 21 '18 at 14:30
hey_youhey_you
216111
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
0
@happy You are encountering a known bug in structured streaming https://issues.apache.org/jira/browse/SPARK-25257
This is because the offset from disk is never deserialized and the fix will be merged in coming release
answered Nov 20 '18 at 23:26
bhavin tandelbhavin tandel
415
But it's fixed in Spark 2.4?
– cricket_007
Nov 21 '18 at 5:09
What fileFormat are you using ?
– bhavin tandel
Nov 21 '18 at 7:16
@cricket_007 I changed my spark version to 2.3.2 and everything started working!
– hey_you
Nov 21 '18 at 14:29
add a comment |
0
@happy You are encountering a known bug in structured streaming https://issues.apache.org/jira/browse/SPARK-25257
This is because the offset from disk is never deserialized and the fix will be merged in coming release
answered Nov 20 '18 at 23:26
bhavin tandelbhavin tandel
415
But it's fixed in Spark 2.4?
– cricket_007
Nov 21 '18 at 5:09
What fileFormat are you using ?
– bhavin tandel
Nov 21 '18 at 7:16
@cricket_007 I changed my spark version to 2.3.2 and everything started working!
– hey_you
Nov 21 '18 at 14:29
add a comment |
0
0
0
@happy You are encountering a known bug in structured streaming https://issues.apache.org/jira/browse/SPARK-25257
This is because the offset from disk is never deserialized and the fix will be merged in coming release
answered Nov 20 '18 at 23:26
bhavin tandelbhavin tandel
415
@happy You are encountering a known bug in structured streaming https://issues.apache.org/jira/browse/SPARK-25257
This is because the offset from disk is never deserialized and the fix will be merged in coming release
answered Nov 20 '18 at 23:26
bhavin tandelbhavin tandel
415
answered Nov 20 '18 at 23:26
bhavin tandelbhavin tandel
415
answered Nov 20 '18 at 23:26
bhavin tandelbhavin tandel
415
answered Nov 20 '18 at 23:26
bhavin tandelbhavin tandel
415
415
But it's fixed in Spark 2.4?
– cricket_007
Nov 21 '18 at 5:09
What fileFormat are you using ?
– bhavin tandel
Nov 21 '18 at 7:16
@cricket_007 I changed my spark version to 2.3.2 and everything started working!
– hey_you
Nov 21 '18 at 14:29
add a comment |
But it's fixed in Spark 2.4?
– cricket_007
Nov 21 '18 at 5:09
What fileFormat are you using ?
– bhavin tandel
Nov 21 '18 at 7:16
@cricket_007 I changed my spark version to 2.3.2 and everything started working!
– hey_you
Nov 21 '18 at 14:29
But it's fixed in Spark 2.4?
– cricket_007
Nov 21 '18 at 5:09
But it's fixed in Spark 2.4?
– cricket_007
Nov 21 '18 at 5:09
What fileFormat are you using ?
– bhavin tandel
Nov 21 '18 at 7:16
What fileFormat are you using ?
– bhavin tandel
Nov 21 '18 at 7:16
@cricket_007 I changed my spark version to 2.3.2 and everything started working!
– hey_you
Nov 21 '18 at 14:29
@cricket_007 I changed my spark version to 2.3.2 and everything started working!
– hey_you
Nov 21 '18 at 14:29
add a comment |
0
Everything started working after I changed my version of spark to 2.3.2.
answered Nov 21 '18 at 14:30
hey_youhey_you
216111
add a comment |
0
Everything started working after I changed my version of spark to 2.3.2.
answered Nov 21 '18 at 14:30
hey_youhey_you
216111
add a comment |
0
0
0
Everything started working after I changed my version of spark to 2.3.2.
answered Nov 21 '18 at 14:30
hey_youhey_you
216111
Everything started working after I changed my version of spark to 2.3.2.
answered Nov 21 '18 at 14:30
hey_youhey_you
216111
answered Nov 21 '18 at 14:30
hey_youhey_you
216111
answered Nov 21 '18 at 14:30
hey_youhey_you
216111
answered Nov 21 '18 at 14:30
hey_youhey_you
216111
216111
add a comment |
add a comment |
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Kafka Connect already does this, and is included in Kafka 0.10 and higher... Why write any code to do this?? confluent.io/connector/kafka-connect-hdfs
– cricket_007
Nov 20 '18 at 23:08