Hello all, I am using Camus to insert messages into Kafka which then I will upload to HDFS. I'm using CDH5.4 with parcels and JSON file format for data I am able to create a new topic and insert messages into it at one go but if I try to insert a new message or append a new message into the topic it fails giving me an error code: Java.lang.RunTimeException: Null record.
The camus.properties file is as follows # Needed Camus properties, more cleanup to come # # Almost all properties have decent default properties. When in doubt, comment out the property. # # The job name. camus.job.name=Camus Job fs.defaultFS=hdfs://BDA01:8020 etl.record.writer.provider.class=com.linkedin.camus.etl.kafka.common.StringRecordWriterProvider #etl.record.writer.provider.class=com.linkedin.camus.etl.kafka.common.AvroRecordWriterProvider # final top-level data output directory, sub-directory will be dynamically created for each topic pulled etl.destination.path=/user/kafka/ # HDFS location where you want to keep execution files, i.e. offsets, error logs, and count files etl.execution.base.path=/user/kafka/exec # where completed Camus job output directories are kept, usually a sub-dir in the base.path etl.execution.history.path=/user/kafka/camus/exec/history # 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. # Out of the box options are: # com.linkedin.camus.etl.kafka.coders.JsonStringMessageDecoder - Reads JSON events, and tries to extract timestamp. # com.linkedin.camus.etl.kafka.coders.KafkaAvroMessageDecoder - Reads Avro events using a schema from a configured schema repository. # com.linkedin.camus.etl.kafka.coders.LatestSchemaKafkaAvroMessageDecoder - Same, but converts event to latest schema for current topic. camus.message.decoder.class=com.linkedin.camus.etl.kafka.coders.JsonStringMessageDecoder #camus.message.decoder.class=com.linkedin.camus.etl.kafka.coders.LatestSchemaKafkaAvroMessageDecoder # Decoder class can also be set on a per topic basis. #camus.message.decoder.class.<topic-name>=com.your.custom.MessageDecoder # Used by avro-based Decoders (KafkaAvroMessageDecoder and LatestSchemaKafkaAvroMessageDecoder) to use as their schema registry. # Out of the box options are: # com.linkedin.camus.schemaregistry.FileSchemaRegistry # com.linkedin.camus.schemaregistry.MemorySchemaRegistry # com.linkedin.camus.schemaregistry.AvroRestSchemaRegistry # com.linkedin.camus.example.schemaregistry.DummySchemaRegistry kafka.message.coder.schema.registry.class=com.linkedin.camus.example.schemaregistry.DummySchemaRegistry # Used by JsonStringMessageDecoder when extracting the timestamp # Choose the field that holds the time stamp (default "timestamp") camus.message.timestamp.field=time # What format is the timestamp in? Out of the box options are: # "unix" or "unix_seconds": The value will be read as a long containing the seconds since epoc # "unix_milliseconds": The value will be read as a long containing the milliseconds since epoc # "ISO-8601": Timestamps will be fed directly into org.joda.time.DateTime constructor, which reads ISO-8601 # All other values will be fed into the java.text.SimpleDateFormat constructor, which will be used to parse the timestamps # Default is "[dd/MMM/yyyy:HH:mm:ss Z]" #camus.message.timestamp.format=yyyy-MM-dd_HH:mm:ss camus.message.timestamp.format=ISO-8601 # 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. # Out of the box options are (for all options see the source for configuration options): # com.linkedin.camus.etl.kafka.partitioner.HourlyPartitioner, groups files in hourly directories # com.linkedin.camus.etl.kafka.partitioner.DailyPartitioner, groups files in daily directories # com.linkedin.camus.etl.kafka.partitioner.TimeBasedPartitioner, groups files in very configurable directories # com.linkedin.camus.etl.kafka.partitioner.DefaultPartitioner, like HourlyPartitioner but less configurable # com.linkedin.camus.etl.kafka.partitioner.TopicGroupingPartitioner #etl.partitioner.class=com.linkedin.camus.etl.kafka.partitioner.HourlyPartitioner # Partitioners can also be set on a per-topic basis. (Note though that configuration is currently not per-topic.) #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=test log4j.configuration=true # Name of the client as seen by kafka kafka.client.name=camus # The Kafka brokers to connect to, format: kafka.brokers=host1:port,host2:port,host3:port kafka.brokers=BDA01:9092,BDA02:9092, BDA03:9092, BDA04:9092 # Fetch request parameters: #kafka.fetch.buffer.size= #kafka.fetch.request.correlationid= #kafka.fetch.request.max.wait= #kafka.fetch.request.min.bytes= #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=true #kafka.monitor.tier= #etl.counts.path= kafka.monitor.time.granularity=10 #etl.hourly=hourly #etdal.ily=daily # Should we ignore events that cannot be decoded (exception thrown by MessageDecoder)? # `false` will fail the job, `true` will silently drop the event. etl.ignore.schema.errors=true # configure output compression for deflate or snappy. Defaults to deflate mapred.output.compress=false #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 # Configures a customer reporter which extends BaseReporter to send etl data #etl.reporter.class mapred.map.max.attempts=1 kafka.client.buffer.size=20971520 kafka.client.so.timeout=60000 #zookeeper.session.timeout= #zookeeper.connection.timeout= When I execute this command to append a new message to the topic I get this error in attached screenshot hadoop jar /opt/camus/camus-example/target/camus-example-0.1.0-SNAPSHOT-shaded.jar com.linkedin.camus.etl.kafka.CamusJob -P /opt/camus/camus.properties Please can anyone point me to the solution to how to fix it. Thank you, Ashish
