Just out of curiosity, have you tried using separate group ids for the
separate streams?
On Oct 11, 2016 4:46 AM, "Matthias Niehoff" <matthias.nieh...@codecentric.de>
wrote:
> I stripped down the job to just consume the stream and print it, without
> avro deserialization. When I only consume one topic, everything is fine. As
> soon as I add a second stream it stucks after about 5 minutes. So I
> basically bails down to:
>
>
> val kafkaParams = Map[String, String](
> "bootstrap.servers" -> kafkaBrokers,
> "group.id" -> group,
> "key.deserializer" -> classOf[StringDeserializer].getName,
> "value.deserializer" -> classOf[BytesDeserializer].getName,
> "session.timeout.ms" -> s"${1 * 60 * 1000}",
> "request.timeout.ms" -> s"${2 * 60 * 1000}",
> "auto.offset.reset" -> "latest",
> "enable.auto.commit" -> "false"
> )
>
> def main(args: Array[String]) {
>
> def createStreamingContext(): StreamingContext = {
>
> val sparkConf = new SparkConf(true)
> .setAppName("Kafka Consumer Test")
> sparkConf.setMaster("local[*]")
>
> val ssc = new StreamingContext(sparkConf,
> Seconds(streaming_interval_seconds))
>
> // AD REQUESTS
> // ===========
> val serializedAdRequestStream = createStream(ssc, topic_adrequest)
> serializedAdRequestStream.map(record => record.value().get()).print(10)
>
> // VIEWS
> // ======
> val serializedViewStream = createStream(ssc, topic_view)
> serializedViewStream.map(record => record.value().get()).print(10)
>
> // // CLICKS
> // // ======
> // val serializedClickStream = createStream(ssc, topic_click)
> // serializedClickStream.map(record => record.value().get()).print(10)
>
> ssc
> }
>
> val streamingContext = createStreamingContext
> streamingContext.start()
> streamingContext.awaitTermination()
> }
>
>
> And in the logs you see:
>
>
> 16/10/10 14:02:26 INFO JobScheduler: Finished job streaming job 1476100944000
> ms.2 from job set of time 1476100944000 ms*16/10/10 14:02:26 *INFO
> JobScheduler: Total delay: 2,314 s for time 1476100944000 ms (execution:
> 0,698 s)*16/10/10 14:03:26 *INFO JobScheduler: Added jobs for time
> 1476100946000 ms
> 16/10/10 14:03:26 INFO MapPartitionsRDD: Removing RDD 889 from persistence
> list
> 16/10/10 14:03:26 INFO JobScheduler: Starting job streaming job 1476100946000
> ms.0 from job set of time 1476100946000 ms
>
>
> 2016-10-11 9:28 GMT+02:00 Matthias Niehoff <matthias.niehoff@codecentric.
> de>:
>
>> This Job will fail after about 5 minutes:
>>
>>
>> object SparkJobMinimal {
>>
>> //read Avro schemas
>> var stream = getClass.getResourceAsStream("/avro/AdRequestLog.avsc")
>> val avroSchemaAdRequest =
>> scala.io.Source.fromInputStream(stream).getLines.mkString
>> stream.close
>> stream = getClass.getResourceAsStream("/avro/AbstractEventLogEntry.avsc")
>> val avroSchemaEvent =
>> scala.io.Source.fromInputStream(stream).getLines.mkString
>> stream.close
>>
>>
>> val kafkaBrokers = "broker-0.kafka.mesos:9092"
>>
>> val topic_adrequest = "adserving.log.ad_request"
>> val topic_view = "adserving.log.view"
>> val topic_click = "adserving.log.click"
>> val group = UUID.randomUUID().toString
>> val streaming_interval_seconds = 2
>>
>> val kafkaParams = Map[String, String](
>> "bootstrap.servers" -> kafkaBrokers,
>> "group.id" -> group,
>> "key.deserializer" -> classOf[StringDeserializer].getName,
>> "value.deserializer" -> classOf[BytesDeserializer].getName,
>> "session.timeout.ms" -> s"${1 * 60 * 1000}",
>> "request.timeout.ms" -> s"${2 * 60 * 1000}",
>> "auto.offset.reset" -> "latest",
>> "enable.auto.commit" -> "false"
>> )
>>
>> def main(args: Array[String]) {
>>
>> def createStreamingContext(): StreamingContext = {
>>
>> val sparkConf = new SparkConf(true)
>> .setAppName("Kafka Consumer Test")
>> sparkConf.setMaster("local[*]")
>>
>>
>> val ssc = new StreamingContext(sparkConf,
>> Seconds(streaming_interval_seconds))
>>
>> // AD REQUESTS
>> // ===========
>> val serializedAdRequestStream = createStream(ssc, topic_adrequest)
>>
>> val adRequestStream = deserializeStream(serializedAdRequestStream,
>> avroSchemaAdRequest, record => AdRequestLog(record)).cache()
>> adRequestStream.print(10)
>>
>> // VIEWS
>> // ======
>>
>> val serializedViewStream = createStream(ssc, topic_view)
>> val viewStream = deserializeStream(serializedViewStream,
>> avroSchemaEvent, record => Event(record, EventType.View)).cache()
>> viewStream.print(10)
>>
>>
>> // CLICKS
>> // ======
>> val serializedClickStream = createStream(ssc, topic_click)
>> val clickEventStream = deserializeStream(serializedClickStream,
>> avroSchemaEvent, record => Event(record, EventType.Click)).cache()
>> clickEventStream.print(10)
>>
>> ssc
>> }
>>
>> val streamingContext = createStreamingContext
>> streamingContext.start()
>> streamingContext.awaitTermination()
>> }
>>
>> def createStream(ssc: StreamingContext, topic: String):
>> InputDStream[ConsumerRecord[String, Bytes]] = {
>> KafkaUtils.createDirectStream[String, Bytes](ssc,
>> LocationStrategies.PreferConsistent, ConsumerStrategies.Subscribe[String,
>> Bytes](Set(topic), kafkaParams))
>> }
>>
>> def deserializeStream[EventType: ClassTag](serializedAdRequestStream:
>> InputDStream[ConsumerRecord[String, Bytes]], avroSchema: String,
>> recordMapper: GenericRecord => EventType): DStream[EventType] = {
>> serializedAdRequestStream.mapPartitions {
>> iteratorOfMessages =>
>> val schema: Schema = new Schema.Parser().parse(avroSchema)
>> val recordInjection = GenericAvroCodecs.toBinary(schema)
>> iteratorOfMessages.map(message => {
>> recordInjection.invert(message.value().get())
>>
>> }).filter(_.isSuccess).map(_.get.asInstanceOf[GenericRecord]).map(recordMapper)
>> }
>> }
>> }
>>
>>
>> 2016-10-10 17:42 GMT+02:00 Matthias Niehoff <
>> matthias.nieh...@codecentric.de>:
>>
>>> Yes, without commiting the data the consumer rebalances.
>>> The job consumes 3 streams process them. When consuming only one stream
>>> it runs fine. But when consuming three streams, even without joining them,
>>> just deserialize the payload and trigger an output action it fails.
>>>
>>> I will prepare code sample.
>>>
>>> 2016-10-07 3:35 GMT+02:00 Cody Koeninger <c...@koeninger.org>:
>>>
>>>> OK, so at this point, even without involving commitAsync, you're
>>>> seeing consumer rebalances after a particular batch takes longer than
>>>> the session timeout?
>>>>
>>>> Do you have a minimal code example you can share?
>>>>
>>>> On Tue, Oct 4, 2016 at 2:18 AM, Matthias Niehoff
>>>> <matthias.nieh...@codecentric.de> wrote:
>>>> > Hi,
>>>> > sry for the late reply. A public holiday in Germany.
>>>> >
>>>> > Yes, its using a unique group id which no other job or consumer group
>>>> is
>>>> > using. I have increased the session.timeout to 1 minutes and set the
>>>> > max.poll.rate to 1000. The processing takes ~1 second.
>>>> >
>>>> > 2016-09-29 4:41 GMT+02:00 Cody Koeninger <c...@koeninger.org>:
>>>> >>
>>>> >> Well, I'd start at the first thing suggested by the error, namely
>>>> that
>>>> >> the group has rebalanced.
>>>> >>
>>>> >> Is that stream using a unique group id?
>>>> >>
>>>> >> On Wed, Sep 28, 2016 at 5:17 AM, Matthias Niehoff
>>>> >> <matthias.nieh...@codecentric.de> wrote:
>>>> >> > Hi,
>>>> >> >
>>>> >> > the stacktrace:
>>>> >> >
>>>> >> > org.apache.kafka.clients.consumer.CommitFailedException: Commit
>>>> cannot
>>>> >> > be
>>>> >> > completed since the group has already rebalanced and assigned the
>>>> >> > partitions
>>>> >> > to another member. This means that the time between subsequent
>>>> calls to
>>>> >> > poll() was longer than the configured session.timeout.ms, which
>>>> >> > typically
>>>> >> > implies that the poll loop is spending too much time message
>>>> processing.
>>>> >> > You
>>>> >> > can address this either by increasing the session timeout or by
>>>> reducing
>>>> >> > the
>>>> >> > maximum size of batches returned in poll() with max.poll.records.
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.kafka.clients.consumer.internals.ConsumerCoordina
>>>> tor$OffsetCommitResponseHandler.handle(ConsumerCoordinator.java:578)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.kafka.clients.consumer.internals.ConsumerCoordina
>>>> tor$OffsetCommitResponseHandler.handle(ConsumerCoordinator.java:519)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.kafka.clients.consumer.internals.AbstractCoordina
>>>> tor$CoordinatorResponseHandler.onSuccess(AbstractCoordinator.java:679)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.kafka.clients.consumer.internals.AbstractCoordina
>>>> tor$CoordinatorResponseHandler.onSuccess(AbstractCoordinator.java:658)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.kafka.clients.consumer.internals.RequestFuture$1.
>>>> onSuccess(RequestFuture.java:167)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.kafka.clients.consumer.internals.RequestFuture.fi
>>>> reSuccess(RequestFuture.java:133)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.kafka.clients.consumer.internals.RequestFuture.co
>>>> mplete(RequestFuture.java:107)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.kafka.clients.consumer.internals.ConsumerNetworkC
>>>> lient$RequestFutureCompletionHandler.onComplete(ConsumerNetw
>>>> orkClient.java:426)
>>>> >> > at org.apache.kafka.clients.NetworkClient.poll(NetworkClient.ja
>>>> va:278)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.kafka.clients.consumer.internals.ConsumerNetworkC
>>>> lient.clientPoll(ConsumerNetworkClient.java:360)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.kafka.clients.consumer.internals.ConsumerNetworkC
>>>> lient.poll(ConsumerNetworkClient.java:224)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.kafka.clients.consumer.internals.ConsumerNetworkC
>>>> lient.poll(ConsumerNetworkClient.java:201)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.kafka.clients.consumer.KafkaConsumer.pollOnce(Kaf
>>>> kaConsumer.java:998)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaCo
>>>> nsumer.java:937)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.kafka010.DirectKafkaInputDStream.
>>>> latestOffsets(DirectKafkaInputDStream.scala:169)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.kafka010.DirectKafkaInputDStream.
>>>> compute(DirectKafkaInputDStream.scala:196)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCom
>>>> pute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCom
>>>> pute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341)
>>>> >> > at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCom
>>>> pute$1$$anonfun$1.apply(DStream.scala:340)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCom
>>>> pute$1$$anonfun$1.apply(DStream.scala:340)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.DStream.createRDDWithLoca
>>>> lProperties(DStream.scala:415)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCom
>>>> pute$1.apply(DStream.scala:335)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCom
>>>> pute$1.apply(DStream.scala:333)
>>>> >> > at scala.Option.orElse(Option.scala:289)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.DStream.getOrCompute(DStr
>>>> eam.scala:330)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.MapPartitionedDStream.com
>>>> pute(MapPartitionedDStream.scala:37)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCom
>>>> pute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCom
>>>> pute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341)
>>>> >> > at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCom
>>>> pute$1$$anonfun$1.apply(DStream.scala:340)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCom
>>>> pute$1$$anonfun$1.apply(DStream.scala:340)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.DStream.createRDDWithLoca
>>>> lProperties(DStream.scala:415)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCom
>>>> pute$1.apply(DStream.scala:335)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCom
>>>> pute$1.apply(DStream.scala:333)
>>>> >> > at scala.Option.orElse(Option.scala:289)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.DStream.getOrCompute(DStr
>>>> eam.scala:330)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.dstream.ForEachDStream.generateJo
>>>> b(ForEachDStream.scala:48)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DSt
>>>> reamGraph.scala:117)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DSt
>>>> reamGraph.scala:116)
>>>> >> > at
>>>> >> >
>>>> >> > scala.collection.TraversableLike$$anonfun$flatMap$1.apply(Tr
>>>> aversableLike.scala:241)
>>>> >> > at
>>>> >> >
>>>> >> > scala.collection.TraversableLike$$anonfun$flatMap$1.apply(Tr
>>>> aversableLike.scala:241)
>>>> >> > at
>>>> >> >
>>>> >> > scala.collection.mutable.ResizableArray$class.foreach(Resiza
>>>> bleArray.scala:59)
>>>> >> > at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.sca
>>>> la:48)
>>>> >> > at
>>>> >> > scala.collection.TraversableLike$class.flatMap(TraversableLi
>>>> ke.scala:241)
>>>> >> > at scala.collection.AbstractTraversable.flatMap(Traversable.sca
>>>> la:104)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.DStreamGraph.generateJobs(DStream
>>>> Graph.scala:116)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3
>>>> .apply(JobGenerator.scala:248)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3
>>>> .apply(JobGenerator.scala:246)
>>>> >> > at scala.util.Try$.apply(Try.scala:192)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.scheduler.JobGenerator.generateJo
>>>> bs(JobGenerator.scala:246)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.scheduler.JobGenerator.org$apache
>>>> $spark$streaming$scheduler$JobGenerator$$processEvent(JobGen
>>>> erator.scala:182)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.on
>>>> Receive(JobGenerator.scala:88)
>>>> >> > at
>>>> >> >
>>>> >> > org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.on
>>>> Receive(JobGenerator.scala:87)
>>>> >> > at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
>>>> >> >
>>>> >> > But it seems like the commit is not the actual problem. The job
>>>> also
>>>> >> > falls
>>>> >> > behind if I do not commit the offsets. The delay would be ok if the
>>>> >> > processing time was bigger than the batch size, but thats not the
>>>> case
>>>> >> > in
>>>> >> > any of the microbatches. Imho for some reason one of the
>>>> microbatches
>>>> >> > falls
>>>> >> > behind more than session.timeout.ms. Then the consumer we regroup
>>>> which
>>>> >> > takes about 1 minute (see timestamps below). Know begins a circle
>>>> of
>>>> >> > slow
>>>> >> > batches each triggering a consumer regroup. Would this be possible?
>>>> >> >
>>>> >> >
>>>> >> > 16/09/28 08:15:55 INFO JobScheduler: Total delay: 141.580 s for
>>>> time
>>>> >> > 1475050414000 ms (execution: 0.360 s) --> the job for 08:13:34
>>>> >> > 16/09/28 08:16:48 INFO AbstractCoordinator: Successfully joined
>>>> group
>>>> >> > spark_aggregation_job-kafka010 with generation 6
>>>> >> > 16/09/28 08:16:48 INFO ConsumerCoordinator: Setting newly assigned
>>>> >> > partitions [sapxm.adserving.log.ad_request-0,
>>>> >> > sapxm.adserving.log.ad_request-2, sapxm.adserving.log.ad_request
>>>> -1,
>>>> >> > sapxm.adserving.log.ad_request-4, sapxm.adserving.log.ad_request
>>>> -3,
>>>> >> > sapxm.adserving.log.ad_request-6, sapxm.adserving.log.ad_request
>>>> -5,
>>>> >> > sapxm.adserving.log.ad_request-8, sapxm.adserving.log.ad_request
>>>> -7,
>>>> >> > sapxm.adserving.log.ad_request-9] for group
>>>> >> > spark_aggregation_job-kafka010
>>>> >> > 16/09/28 08:16:48 INFO ConsumerCoordinator: Revoking previously
>>>> assigned
>>>> >> > partitions [sapxm.adserving.log.view-3, sapxm.adserving.log.view-4,
>>>> >> > sapxm.adserving.log.view-1, sapxm.adserving.log.view-2,
>>>> >> > sapxm.adserving.log.view-0, sapxm.adserving.log.view-9,
>>>> >> > sapxm.adserving.log.view-7, sapxm.adserving.log.view-8,
>>>> >> > sapxm.adserving.log.view-5, sapxm.adserving.log.view-6] for group
>>>> >> > spark_aggregation_job-kafka010
>>>> >> > 16/09/28 08:16:48 INFO AbstractCoordinator: (Re-)joining group
>>>> >> > spark_aggregation_job-kafka010
>>>> >> >
>>>> >> > 2016-09-27 18:55 GMT+02:00 Cody Koeninger <c...@koeninger.org>:
>>>> >> >>
>>>> >> >> What's the actual stacktrace / exception you're getting related to
>>>> >> >> commit failure?
>>>> >> >>
>>>> >> >> On Tue, Sep 27, 2016 at 9:37 AM, Matthias Niehoff
>>>> >> >> <matthias.nieh...@codecentric.de> wrote:
>>>> >> >> > Hi everybody,
>>>> >> >> >
>>>> >> >> > i am using the new Kafka Receiver for Spark Streaming for my
>>>> Job.
>>>> >> >> > When
>>>> >> >> > running with old consumer it runs fine.
>>>> >> >> >
>>>> >> >> > The Job consumes 3 Topics, saves the data to Cassandra,
>>>> cogroups the
>>>> >> >> > topic,
>>>> >> >> > calls mapWithState and stores the results in cassandra. After
>>>> that I
>>>> >> >> > manually commit the Kafka offsets using the commitAsync method
>>>> of the
>>>> >> >> > KafkaDStream.
>>>> >> >> >
>>>> >> >> > With the new consumer I experience the following problem:
>>>> >> >> >
>>>> >> >> > After a certain amount of time (about 4-5 minutes, might be
>>>> more or
>>>> >> >> > less)
>>>> >> >> > there are exceptions that the offset commit failed. The
>>>> processing
>>>> >> >> > takes
>>>> >> >> > less than the batch interval. I also adjusted the
>>>> session.timeout and
>>>> >> >> > request.timeout as well as the max.poll.records setting which
>>>> did not
>>>> >> >> > help.
>>>> >> >> >
>>>> >> >> > After the first offset commit failed the time it takes from
>>>> kafka
>>>> >> >> > until
>>>> >> >> > the
>>>> >> >> > microbatch is started increases, the processing time is
>>>> constantly
>>>> >> >> > below
>>>> >> >> > the
>>>> >> >> > batch interval. Moreover further offset commits also fail and as
>>>> >> >> > result
>>>> >> >> > the
>>>> >> >> > delay time builds up.
>>>> >> >> >
>>>> >> >> > Has anybody made this experience as well?
>>>> >> >> >
>>>> >> >> > Thank you
>>>> >> >> >
>>>> >> >> > Relevant Kafka Parameters:
>>>> >> >> >
>>>> >> >> > "session.timeout.ms" -> s"${1 * 60 * 1000}",
>>>> >> >> > "request.timeout.ms" -> s"${2 * 60 * 1000}",
>>>> >> >> > "auto.offset.reset" -> "largest",
>>>> >> >> > "enable.auto.commit" -> "false",
>>>> >> >> > "max.poll.records" -> "1000"
>>>> >> >> >
>>>> >> >> >
>>>> >> >> >
>>>> >> >> > --
>>>> >> >> > Matthias Niehoff | IT-Consultant | Agile Software Factory |
>>>> >> >> > Consulting
>>>> >> >> > codecentric AG | Zeppelinstr 2 | 76185 Karlsruhe | Deutschland
>>>> >> >> > tel: +49 (0) 721.9595-681 | fax: +49 (0) 721.9595-666 | mobil:
>>>> +49
>>>> >> >> > (0)
>>>> >> >> > 172.1702676
>>>> >> >> > www.codecentric.de | blog.codecentric.de |
>>>> www.meettheexperts.de |
>>>> >> >> > www.more4fi.de
>>>> >> >> >
>>>> >> >> > Sitz der Gesellschaft: Solingen | HRB 25917| Amtsgericht
>>>> Wuppertal
>>>> >> >> > Vorstand: Michael Hochgürtel . Mirko Novakovic . Rainer Vehns
>>>> >> >> > Aufsichtsrat: Patric Fedlmeier (Vorsitzender) . Klaus Jäger .
>>>> Jürgen
>>>> >> >> > Schütz
>>>> >> >> >
>>>> >> >> > Diese E-Mail einschließlich evtl. beigefügter Dateien enthält
>>>> >> >> > vertrauliche
>>>> >> >> > und/oder rechtlich geschützte Informationen. Wenn Sie nicht der
>>>> >> >> > richtige
>>>> >> >> > Adressat sind oder diese E-Mail irrtümlich erhalten haben,
>>>> >> >> > informieren
>>>> >> >> > Sie
>>>> >> >> > bitte sofort den Absender und löschen Sie diese E-Mail und evtl.
>>>> >> >> > beigefügter
>>>> >> >> > Dateien umgehend. Das unerlaubte Kopieren, Nutzen oder Öffnen
>>>> evtl.
>>>> >> >> > beigefügter Dateien sowie die unbefugte Weitergabe dieser
>>>> E-Mail ist
>>>> >> >> > nicht
>>>> >> >> > gestattet
>>>> >> >
>>>> >> >
>>>> >> >
>>>> >> >
>>>> >> > --
>>>> >> > Matthias Niehoff | IT-Consultant | Agile Software Factory |
>>>> Consulting
>>>> >> > codecentric AG | Zeppelinstr 2 | 76185 Karlsruhe | Deutschland
>>>> >> > tel: +49 (0) 721.9595-681 | fax: +49 (0) 721.9595-666 | mobil:
>>>> +49 (0)
>>>> >> > 172.1702676
>>>> >> > www.codecentric.de | blog.codecentric.de | www.meettheexperts.de |
>>>> >> > www.more4fi.de
>>>> >> >
>>>> >> > Sitz der Gesellschaft: Solingen | HRB 25917| Amtsgericht Wuppertal
>>>> >> > Vorstand: Michael Hochgürtel . Mirko Novakovic . Rainer Vehns
>>>> >> > Aufsichtsrat: Patric Fedlmeier (Vorsitzender) . Klaus Jäger .
>>>> Jürgen
>>>> >> > Schütz
>>>> >> >
>>>> >> > Diese E-Mail einschließlich evtl. beigefügter Dateien enthält
>>>> >> > vertrauliche
>>>> >> > und/oder rechtlich geschützte Informationen. Wenn Sie nicht der
>>>> richtige
>>>> >> > Adressat sind oder diese E-Mail irrtümlich erhalten haben,
>>>> informieren
>>>> >> > Sie
>>>> >> > bitte sofort den Absender und löschen Sie diese E-Mail und evtl.
>>>> >> > beigefügter
>>>> >> > Dateien umgehend. Das unerlaubte Kopieren, Nutzen oder Öffnen evtl.
>>>> >> > beigefügter Dateien sowie die unbefugte Weitergabe dieser E-Mail
>>>> ist
>>>> >> > nicht
>>>> >> > gestattet
>>>> >
>>>> >
>>>> >
>>>> >
>>>> > --
>>>> > Matthias Niehoff | IT-Consultant | Agile Software Factory |
>>>> Consulting
>>>> > codecentric AG | Zeppelinstr 2 | 76185 Karlsruhe | Deutschland
>>>> > tel: +49 (0) 721.9595-681 | fax: +49 (0) 721.9595-666 | mobil: +49
>>>> (0)
>>>> > 172.1702676
>>>> > www.codecentric.de | blog.codecentric.de | www.meettheexperts.de |
>>>> > www.more4fi.de
>>>> >
>>>> > Sitz der Gesellschaft: Solingen | HRB 25917| Amtsgericht Wuppertal
>>>> > Vorstand: Michael Hochgürtel . Mirko Novakovic . Rainer Vehns
>>>> > Aufsichtsrat: Patric Fedlmeier (Vorsitzender) . Klaus Jäger . Jürgen
>>>> Schütz
>>>> >
>>>> > Diese E-Mail einschließlich evtl. beigefügter Dateien enthält
>>>> vertrauliche
>>>> > und/oder rechtlich geschützte Informationen. Wenn Sie nicht der
>>>> richtige
>>>> > Adressat sind oder diese E-Mail irrtümlich erhalten haben,
>>>> informieren Sie
>>>> > bitte sofort den Absender und löschen Sie diese E-Mail und evtl.
>>>> beigefügter
>>>> > Dateien umgehend. Das unerlaubte Kopieren, Nutzen oder Öffnen evtl.
>>>> > beigefügter Dateien sowie die unbefugte Weitergabe dieser E-Mail ist
>>>> nicht
>>>> > gestattet
>>>>
>>>
>>>
>>>
>>> --
>>> Matthias Niehoff | IT-Consultant | Agile Software Factory | Consulting
>>> codecentric AG | Zeppelinstr 2 | 76185 Karlsruhe | Deutschland
>>> tel: +49 (0) 721.9595-681 | fax: +49 (0) 721.9595-666 | mobil: +49 (0)
>>> 172.1702676
>>> www.codecentric.de | blog.codecentric.de | www.meettheexperts.de |
>>> www.more4fi.de
>>>
>>> Sitz der Gesellschaft: Solingen | HRB 25917| Amtsgericht Wuppertal
>>> Vorstand: Michael Hochgürtel . Mirko Novakovic . Rainer Vehns
>>> Aufsichtsrat: Patric Fedlmeier (Vorsitzender) . Klaus Jäger . Jürgen
>>> Schütz
>>>
>>> Diese E-Mail einschließlich evtl. beigefügter Dateien enthält
>>> vertrauliche und/oder rechtlich geschützte Informationen. Wenn Sie nicht
>>> der richtige Adressat sind oder diese E-Mail irrtümlich erhalten haben,
>>> informieren Sie bitte sofort den Absender und löschen Sie diese E-Mail und
>>> evtl. beigefügter Dateien umgehend. Das unerlaubte Kopieren, Nutzen oder
>>> Öffnen evtl. beigefügter Dateien sowie die unbefugte Weitergabe dieser
>>> E-Mail ist nicht gestattet
>>>
>>
>>
>>
>> --
>> Matthias Niehoff | IT-Consultant | Agile Software Factory | Consulting
>> codecentric AG | Zeppelinstr 2 | 76185 Karlsruhe | Deutschland
>> tel: +49 (0) 721.9595-681 | fax: +49 (0) 721.9595-666 | mobil: +49 (0)
>> 172.1702676
>> www.codecentric.de | blog.codecentric.de | www.meettheexperts.de |
>> www.more4fi.de
>>
>> Sitz der Gesellschaft: Solingen | HRB 25917| Amtsgericht Wuppertal
>> Vorstand: Michael Hochgürtel . Mirko Novakovic . Rainer Vehns
>> Aufsichtsrat: Patric Fedlmeier (Vorsitzender) . Klaus Jäger . Jürgen
>> Schütz
>>
>> Diese E-Mail einschließlich evtl. beigefügter Dateien enthält
>> vertrauliche und/oder rechtlich geschützte Informationen. Wenn Sie nicht
>> der richtige Adressat sind oder diese E-Mail irrtümlich erhalten haben,
>> informieren Sie bitte sofort den Absender und löschen Sie diese E-Mail und
>> evtl. beigefügter Dateien umgehend. Das unerlaubte Kopieren, Nutzen oder
>> Öffnen evtl. beigefügter Dateien sowie die unbefugte Weitergabe dieser
>> E-Mail ist nicht gestattet
>>
>
>
>
> --
> Matthias Niehoff | IT-Consultant | Agile Software Factory | Consulting
> codecentric AG | Zeppelinstr 2 | 76185 Karlsruhe | Deutschland
> tel: +49 (0) 721.9595-681 | fax: +49 (0) 721.9595-666 | mobil: +49 (0)
> 172.1702676
> www.codecentric.de | blog.codecentric.de | www.meettheexperts.de | www.
> more4fi.de
>
> Sitz der Gesellschaft: Solingen | HRB 25917| Amtsgericht Wuppertal
> Vorstand: Michael Hochgürtel . Mirko Novakovic . Rainer Vehns
> Aufsichtsrat: Patric Fedlmeier (Vorsitzender) . Klaus Jäger . Jürgen Schütz
>
> Diese E-Mail einschließlich evtl. beigefügter Dateien enthält vertrauliche
> und/oder rechtlich geschützte Informationen. Wenn Sie nicht der richtige
> Adressat sind oder diese E-Mail irrtümlich erhalten haben, informieren Sie
> bitte sofort den Absender und löschen Sie diese E-Mail und evtl.
> beigefügter Dateien umgehend. Das unerlaubte Kopieren, Nutzen oder Öffnen
> evtl. beigefügter Dateien sowie die unbefugte Weitergabe dieser E-Mail ist
> nicht gestattet
>
