使用kafka 0.8运行camus示例

Question

我是加缪的新手，我想试着使用它，到目前为止，我的kafka 0.8下载了源代码，创建了2个队列，如例expect配置了作业配置文件(见下文)，并尝试使用以下命令在我的计算机上运行它(详细信息如下)

$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，如阴影文件。

我得到了一个错误：

[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

当我试图使用intellij-idea编辑器运行它时，我得到了一些错误，但发现了错误的原因。

java.lang.RuntimeException: java.lang.ClassNotFoundException: com.linkedin.batch.etl.kafka.coders.LatestSchemaKafkaAvroMessageDecoder

有人能向我解释我做错了什么吗？

camus配置文件

# 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=

机器详细信息：

hortonworks - hdp 2.0.0.6和kafka 0.8 beta 1

Answer 1

包名上有错误。

变化

camus.message.decoder.class=com.linkedin.batch.etl.kafka.coders.LatestSchemaKafkaAvroMessageDecoder

至

camus.message.decoder.class=com.linkedin.camus.etl.kafka.coders.LatestSchemaKafkaAvroMessageDecoder

此外，您还需要指定一些与Kafka相关的属性或注释它(通过这种方式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=