我是camus的新手，我想尝试使用它我们的kafka 0.8 到目前为止，我下载了源代码创建的2队列，例如期望 配置了作业配置文件（见下文） 并尝试使用此命令在我的机器上运行它（详情如下）
$JAVA_HOME/bin/java -cp camus-example-0.1.0-SNAPSHOT.jar com.linkedin.camus.etl.kafka.CamusJob -P /root/Desktop/camus-workspace/camus-master/camus-example/target/camus.properties
jar包含所有依赖项，如shade文件
[EtlInputFormat] - Discrading topic : TestQueue
[EtlInputFormat] - Discrading topic : test
[EtlInputFormat] - Discrading topic : DummyLog2
[EtlInputFormat] - Discrading topic : test3
[EtlInputFormat] - Discrading topic : TwitterQueue
[EtlInputFormat] - Discrading topic : test2
[EtlInputFormat] - Discarding topic (Decoder generation failed) : DummyLog
[CodecPool] - Got brand-new compressor
[JobClient] - Running job: job_local_0001
[JobClient] - map 0% reduce 0%
[JobClient] - Job complete: job_local_0001
[JobClient] - Counters: 0
[CamusJob] - Job finished
当我尝试用我的intellij-idea编辑器运行它时 我得到了一些错误，但找到了错误的原因
java.lang.RuntimeException: java.lang.ClassNotFoundException: com.linkedin.batch.etl.kafka.coders.LatestSchemaKafkaAvroMessageDecoder
可以向我解释一下我做错了什么吗？
# Needed Camus properties, more cleanup to come
# final top-level data output directory, sub-directory will be dynamically created for each topic pulled
etl.destination.path=/root/Desktop/camus-workspace/camus-master/camus-example/target/1
# HDFS location where you want to keep execution files, i.e. offsets, error logs, and count files
etl.execution.base.path=/root/Desktop/camus-workspace/camus-master/camus-example/target/2
# where completed Camus job output directories are kept, usually a sub-dir in the base.path
etl.execution.history.path=/root/Desktop/camus-workspace/camus-master/camus-example/target3
# Kafka-0.8 handles all zookeeper calls
#zookeeper.hosts=localhost:2181
#zookeeper.broker.topics=/brokers/topics
#zookeeper.broker.nodes=/brokers/ids
# Concrete implementation of the Encoder class to use (used by Kafka Audit, and thus optional for now)
#camus.message.encoder.class=com.linkedin.batch.etl.kafka.coders.DummyKafkaMessageEncoder
# Concrete implementation of the Decoder class to use
camus.message.decoder.class=com.linkedin.batch.etl.kafka.coders.LatestSchemaKafkaAvroMessageDecoder
# Used by avro-based Decoders to use as their Schema Registry
kafka.message.coder.schema.registry.class=com.linkedin.camus.example.DummySchemaRegistry
# Used by the committer to arrange .avro files into a partitioned scheme. This will be the default partitioner for all
# topic that do not have a partitioner specified
#etl.partitioner.class=com.linkedin.camus.etl.kafka.coders.DefaultPartitioner
# Partitioners can also be set on a per-topic basis
#etl.partitioner.class.<topic-name>=com.your.custom.CustomPartitioner
# all files in this dir will be added to the distributed cache and placed on the classpath for hadoop tasks
# hdfs.default.classpath.dir=/root/Desktop/camus-workspace/camus-master/camus-example/target
# max hadoop tasks to use, each task can pull multiple topic partitions
mapred.map.tasks=30
# max historical time that will be pulled from each partition based on event timestamp
kafka.max.pull.hrs=1
# events with a timestamp older than this will be discarded.
kafka.max.historical.days=3
# Max minutes for each mapper to pull messages (-1 means no limit)
kafka.max.pull.minutes.per.task=-1
# if whitelist has values, only whitelisted topic are pulled. nothing on the blacklist is pulled
kafka.blacklist.topics=
kafka.whitelist.topics=DummyLog
log4j.configuration=true
# Name of the client as seen by kafka
kafka.client.name=camus
# Fetch Request Parameters
kafka.fetch.buffer.size=
kafka.fetch.request.correlationid=
kafka.fetch.request.max.wait=
kafka.fetch.request.min.bytes=
# Connection parameters.
kafka.brokers=localhost:9092
kafka.timeout.value=
#Stops the mapper from getting inundated with Decoder exceptions for the same topic
#Default value is set to 10
max.decoder.exceptions.to.print=5
#Controls the submitting of counts to Kafka
#Default value set to true
post.tracking.counts.to.kafka=true
log4j.configuration=true
# everything below this point can be ignored for the time being, will provide more documentation down the road
##########################
etl.run.tracking.post=false
kafka.monitor.tier=
etl.counts.path=
kafka.monitor.time.granularity=10
etl.hourly=hourly
etl.daily=daily
etl.ignore.schema.errors=false
# configure output compression for deflate or snappy. Defaults to deflate
etl.output.codec=deflate
etl.deflate.level=6
#etl.output.codec=snappy
etl.default.timezone=America/Los_Angeles
etl.output.file.time.partition.mins=60
etl.keep.count.files=false
etl.execution.history.max.of.quota=.8
mapred.output.compress=true
mapred.map.max.attempts=1
kafka.client.buffer.size=20971520
kafka.client.so.timeout=60000
#zookeeper.session.timeout=
#zookeeper.connection.timeout=
hortonworks - hdp 2.0.0.6 与kafka 0.8 beta 1
答案 0 :(得分：3)
包名中有错误。
更改
camus.message.decoder.class=com.linkedin.batch.etl.kafka.coders.LatestSchemaKafkaAvroMessageDecoder
到
camus.message.decoder.class=com.linkedin.camus.etl.kafka.coders.LatestSchemaKafkaAvroMessageDecoder
您还需要指定一些与Kafka相关的属性或对其进行注释（这样Camus将使用默认值）：
# Fetch Request Parameters
# kafka.fetch.buffer.size=
# kafka.fetch.request.correlationid=
# kafka.fetch.request.max.wait=
# kafka.fetch.request.min.bytes=
# Connection parameters.
kafka.brokers=localhost:9092
# kafka.timeout.value=