Hello,
I'm using 4Gb for the driver memory. The checkpoint is between 1 Gb and 10
Gb depending if I'm reprocessing all the data from beginning or just
getting the latest offset from the real time processed. Is there any best
practice to be aware of with driver memory relating to checkpoint size ?
That stacktrace looks like an out of heap space on the driver while writing
checkpoint, not on the worker nodes. How much memory are you giving the
driver? How big are your stored checkpoints?
On Tue, Jul 28, 2015 at 9:30 AM, Nicolas Phung nicolas.ph...@gmail.com
wrote:
Hi,
After using
Hello,
I manage to read all my data back with skipping offset that contains a
corrupt message. I have one more question regarding messageHandler method
vs dstream.foreachRDD.map vs dstream.map.foreachRDD best practices. I'm
using a function to read the serialized message from kafka and convert it
It's really a question of whether you need access to the
MessageAndMetadata, or just the key / value from the message.
If you just need the key/value, dstream map is fine.
In your case, since you need to be able to control a possible failure when
deserializing the message from the
Yeah, I'm referring to that api.
If you want to filter messages in addition to catching that exception, have
your mesageHandler return an option, so the type R would end up being
Option[WhateverYourClassIs], then filter out None before doing the rest of
your processing.
If you aren't already
Hi Cody,
Thanks for your answer. I'm with Spark 1.3.0. I don't quite understand how
to use the messageHandler parameter/function in the createDirectStream
method. You are referring to this, aren't you ?
def createDirectStream[ K: ClassTag, V: ClassTag, KD : Decoder[K]: ClassTag
, VD :
Hi Cody,
Thanks for you help. It seems there's something wrong with some messages
within my Kafka topics then. I don't understand how, I can get bigger or
incomplete message since I use default configuration to accept only 1Mb
message in my Kafka topic. If you have any others informations or
I'd try logging the offsets for each message, see where problems start,
then try using the console consumer starting at those offsets and see if
you can reproduce the problem.
On Mon, Jul 20, 2015 at 2:15 AM, Nicolas Phung nicolas.ph...@gmail.com
wrote:
Hi Cody,
Thanks for you help. It seems
Yeah, in the function you supply for the messageHandler parameter to
createDirectStream, catch the exception and do whatever makes sense for
your application.
On Mon, Jul 20, 2015 at 11:58 AM, Nicolas Phung nicolas.ph...@gmail.com
wrote:
Hello,
Using the old Spark Streaming Kafka API, I got
Hello,
Using the old Spark Streaming Kafka API, I got the following around the
same offset:
kafka.message.InvalidMessageException: Message is corrupt (stored crc =
3561357254, computed crc = 171652633)
at kafka.message.Message.ensureValid(Message.scala:166)
at
Well, working backwards down the stack trace...
at java.nio.Buffer.limit(Buffer.java:275)
That exception gets thrown if the limit is negative or greater than
the buffer's capacity
at kafka.message.Message.sliceDelimited(Message.scala:236)
If size had been negative, it would have just returned
Not exactly the same issue, but possibly related:
https://issues.apache.org/jira/browse/KAFKA-1196
On Thu, Jul 16, 2015 at 12:03 PM, Cody Koeninger c...@koeninger.org wrote:
Well, working backwards down the stack trace...
at java.nio.Buffer.limit(Buffer.java:275)
That exception gets thrown
Hello,
When I'm reprocessing the data from kafka (about 40 Gb) with the new
Spark Streaming Kafka method createDirectStream, everything is fine
till a driver error happened (driver is killed, connection lost...).
When the driver pops up again, it resumes the processing with the
checkpoint in
13 matches
Mail list logo
