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我想使用 Kafka 将 apache 服务器日志加载到 hdfs。
创建主题:

./kafka-topics.sh --create --zookeeper 10.25.3.207:2181 --replication-factor 1 --partitions 1 --topic lognew  

跟踪apache访问日志目录:

tail -f  /var/log/httpd/access_log |./kafka-console-producer.sh --broker-list 10.25.3.207:6667 --topic lognew  

在另一个终端(kafka bin)启动消费者:

./kafka-console-consumer.sh --zookeeper 10.25.3.207:2181 --topic lognew --from-beginning  

camus.properties 文件配置为:

# 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=/user/root/topics
# HDFS location where you want to keep execution files, i.e. offsets, error logs, and count files
etl.execution.base.path=/user/root/exec
# where completed Camus job output directories are kept, usually a sub-dir in the base.path
etl.execution.history.path=/user/root/camus/exec/history

# Kafka-0.8 handles all zookeeper calls
#zookeeper.hosts=
#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.camus.etl.kafka.coders.DummyKafkaMessageEncoder`

# Concrete implementation of the Decoder class to use
  #camus.message.decoder.class=com.linkedin.camus.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.schemaregistry.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=

# 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=lognew
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=10.25.3.207:6667
#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
monitoring.event.class=class.that.generates.record.to.submit.counts.to.kafka

# 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=

执行以下命令时出现错误:

hadoop jar camus-example-0.1.0-SNAPSHOT-shaded.jar com.linkedin.camus.etl.kafka.CamusJob -P camus.properties

以下是错误:

[CamusJob] - Fetching metadata from broker 10.25.3.207:6667 with client id camus for 0 topic(s) []
[CamusJob] - failed to create decoder
com.linkedin.camus.coders.MessageDecoderException:     com.linkedin.camus.coders.MessageDecoderException:     java.lang.NullPointerException
    at     com.linkedin.camus.etl.kafka.coders.MessageDecoderFactory.createMessageDecoder(MessageDecoderFactory.java:28)
    at com.linkedin.camus.etl.kafka.mapred.EtlInputFormat.createMessageDecoder(EtlInputFormat.java:390)
    at com.linkedin.camus.etl.kafka.mapred.EtlInputFormat.getSplits(EtlInputFormat.java:264)
    at org.apache.hadoop.mapreduce.JobSubmitter.writeNewSplits(JobSubmitter.java:301)
    at org.apache.hadoop.mapreduce.JobSubmitter.writeSplits(JobSubmitter.java:318)
    at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:196)
    at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1290)
    at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1287)
    at java.security.AccessController.doPrivileged(Native Method)
    at javax.security.auth.Subject.doAs(Subject.java:415)
    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1657)
    at org.apache.hadoop.mapreduce.Job.submit(Job.java:1287)
    at com.linkedin.camus.etl.kafka.CamusJob.run(CamusJob.java:280)
    at com.linkedin.camus.etl.kafka.CamusJob.run(CamusJob.java:608)
    at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:70)
    at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:84)
    at com.linkedin.camus.etl.kafka.CamusJob.main(CamusJob.java:572)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.hadoop.util.RunJar.run(RunJar.java:221)
    at org.apache.hadoop.util.RunJar.main(RunJar.java:136)
Caused by: com.linkedin.camus.coders.MessageDecoderException: java.lang.NullPointerException
    at com.linkedin.camus.etl.kafka.coders.KafkaAvroMessageDecoder.init(KafkaAvroMessageDecoder.java:40)
    at com.linkedin.camus.etl.kafka.coders.MessageDecoderFactory.createMessageDecoder(MessageDecoderFactory.java:24)
    ... 22 more
Caused by: java.lang.NullPointerException
    at java.lang.Class.forName0(Native Method)
    at java.lang.Class.forName(Class.java:195)
    at     com.linkedin.camus.etl.kafka.coders.KafkaAvroMessageDecoder.init(KafkaAvroMessageDecoder.java:31)
    ... 23 more
[CamusJob] - Discarding topic (Decoder generation failed) : avrotopic
[CamusJob] - failed to create decoder

请建议可以做些什么来解决这个问题。提前致谢

深沉

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1 回答 1

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我从来没有用过加缪。但我相信这是一个与 Kafka 相关的错误,它与您如何编码/解码消息有关。我相信堆栈跟踪中的重要行是

Caused by: com.linkedin.camus.coders.MessageDecoderException: java.lang.NullPointerException
  at com.linkedin.camus.etl.kafka.coders.KafkaAvroMessageDecoder.init(KafkaAvroMessageDecoder.java:40)
  at com.linkedin.camus.etl.kafka.coders.MessageDecoderFactory.createMessageDecoder(MessageDecoderFactory.java:24)

你如何告诉 Kafka 使用你的 Avro 编码?您已在配置中注释掉以下行,

#kafka.message.coder.schema.registry.class=com.linkedin.camus.example.schemaregistry.DummySchemaRegistry

那么你是在代码中的其他地方设置它吗?如果不是,我建议取消注释该配置值并将其设置为您尝试解码/编码的任何 avro 类。

使用正确的类路径等可能需要一些调试,但我相信这是一个容易解决的问题。

编辑 在回复您的评论时,我有一些自己的评论。

  1. 我从来没有用过加缪。所以调试你从加缪那里得到的错误不是我能做得很好或根本不能做的事情。所以你必须花一些时间(可能是几个小时)研究和尝试不同的东西才能让它发挥作用。
  2. 我怀疑DummySchemaRegistry是您需要的正确配置值。任何以 Dummy 开头的东西都可能不是有效的配置选项。
  3. 对 camus 和模式注册表进行简单的谷歌搜索,发现了一些有趣的链接,SchemaRegistry ClassesKafkaAvroMessageEncoder。这些更有可能是您需要的正确配置值。只是我的猜测,因为再一次,我从未使用过加缪。
  4. 也可能对你有用。不知道你有没有看过。但是,如果您还没有,我很确定在您遇到堆栈溢出之前搜索您得到的特定错误可能是您应该做的。
于 2015-11-16T15:55:55.163 回答