使用kafka将apache服务器日志加载到hdfs

ghhaqwfi  于 2021-06-08  发布在  Kafka
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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垃圾箱)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

错误如下:

[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

请提出解决这个问题的方法。提前谢谢
深沉的

yzuktlbb

yzuktlbb1#

我从没用过加缪。但我相信这是一个Kafka相关的错误,它与你如何编码/解码信息有关。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)

你是怎么告诉Kafka使用你的avro编码的?您已经在配置中注解掉了下面一行,


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

那么你是不是在代码中的其他地方设置的?如果您没有,我建议您取消对config值的注解,并将其设置为您尝试解码/编码的avro类。
使用正确的类路径可能需要一些调试,但我相信这是一个很容易解决的问题。
在回应你的评论编辑,我有我自己的意见。
我从没用过加缪。所以调试你从加缪那里得到的错误不是我能做得很好或者根本做不到的。因此,你必须花一些时间(也许几个小时)研究和尝试不同的东西,使它工作。
我怀疑dummyschemaregistry是您需要的正确配置值。任何以dummy开头的选项都可能不是有效的配置选项。
在google上搜索camus和schema registry,发现了一些有趣的链接,schemaregistry类,kafkaavromessageencoder。这些更可能是您需要的正确配置值。我猜,因为我从来没用过加缪。
这对你也有帮助。我不知道你是否看过。但如果你没有,我敢肯定,在进入堆栈溢出之前,谷歌搜索你得到的具体错误可能是你应该做的。

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