I'm trying to consolidate the output of each node in a clustered application to an easy, at-a-glance location. I don't need the data to be stored permanently, I just want to see all of the stdout in the same spot. Eventually I'll want to store much less info, probably using log files, but for now, I just want app -> stdOut -> IRC, and flume seems to be a good choice for this.
All of the examples I have seen using the exec source show the command using tail, even though the docs make it seem like you can use any process that outputs to standard out. My config (see below) runs my application as the command, but for troubleshooting, it runs a simple shell script that echoes "test" at set intervals.
I've got everything running, and the IRC sink joins the IRC channel, but it never sends any messages. The last entry in the log is that Exec is starting.
Edit:
flume version flume-ng-1.2.0+24.43-1~squeeze
flume.config:
agent.sources = exec1
agent.channels = mem1
agent.sinks = irc1
agent.sources.exec1.type = exec
agent.sources.exec1.command = sh /var/lib/app/test.sh
agent.sources.exec1.channels = mem1
agent.sinks.irc1.type = irc
agent.sinks.irc1.hostname = 192.168.17.16
agent.sinks.irc1.nick = flume
agent.sinks.irc1.chan = agents
agent.sinks.irc1.channel = mem1
agent.channels.mem1.type = memory
agent.channels.mem1.capacity = 100
log4j.properties:
flume.root.logger=INFO,LOGFILE
flume.log.dir=/var/log/flume-ng
flume.log.file=flume.log
log4j.logger.org.apache.flume.lifecycle = INFO
log4j.logger.org.jboss = WARN
log4j.logger.org.mortbay = INFO
log4j.logger.org.apache.avro.ipc.NettyTransceiver = WARN
log4j.rootLogger=${flume.root.logger}
log4j.appender.LOGFILE=org.apache.log4j.RollingFileAppender
log4j.appender.LOGFILE.MaxFileSize=100MB
log4j.appender.LOGFILE.MaxBackupIndex=10
log4j.appender.LOGFILE.File=${flume.log.dir}/${flume.log.file}
log4j.appender.LOGFILE.layout=org.apache.log4j.PatternLayout
log4j.appender.LOGFILE.layout.ConversionPattern=%d{ISO8601} %p %c{2}: %m%n
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d (%t) [%p - %l] %m%n
test.sh:
#!/bin/bash
x=1
while [ $x -ge 1 ]
do
echo "Test $x"
x=$(( $x + 1 ))
sleep 5
done
flume.log:
2013-01-31 12:45:08,184 INFO nodemanager.DefaultLogicalNodeManager: Node manager starting
2013-01-31 12:45:08,184 INFO properties.PropertiesFileConfigurationProvider: Configuration provider starting
2013-01-31 12:45:08,184 INFO lifecycle.LifecycleSupervisor: Starting lifecycle supervisor 9
2013-01-31 12:45:08,186 INFO properties.PropertiesFileConfigurationProvider: Reloading configuration file:/etc/flume-ng/conf/flume.conf
2013-01-31 12:45:08,194 INFO conf.FlumeConfiguration: Processing:irc1
2013-01-31 12:45:08,194 INFO conf.FlumeConfiguration: Added sinks: irc1 Agent: agent
2013-01-31 12:45:08,194 INFO conf.FlumeConfiguration: Processing:irc1
2013-01-31 12:45:08,194 INFO conf.FlumeConfiguration: Processing:irc1
2013-01-31 12:45:08,194 INFO conf.FlumeConfiguration: Processing:irc1
2013-01-31 12:45:08,194 INFO conf.FlumeConfiguration: Processing:irc1
2013-01-31 12:45:08,207 INFO conf.FlumeConfiguration: Post-validation flume configuration contains configuration for agents: [agent]
2013-01-31 12:45:08,208 INFO properties.PropertiesFileConfigurationProvider: Creating channels
2013-01-31 12:45:08,249 INFO instrumentation.MonitoredCounterGroup: Monitoried counter group for type: CHANNEL, name: mem1, registered successfully.
2013-01-31 12:45:08,249 INFO properties.PropertiesFileConfigurationProvider: created channel mem1
2013-01-31 12:45:08,262 INFO sink.DefaultSinkFactory: Creating instance of sink: irc1, type: irc
2013-01-31 12:45:08,266 INFO nodemanager.DefaultLogicalNodeManager: Starting new configuration:{ sourceRunners:{exec1=EventDrivenSourceRunner: { source:org.apache.flume.source.ExecSource#498665a0 }} sinkRunners:{irc1=SinkRunner: { policy:org.apache.flume.sink.DefaultSinkProcessor#167a1116 counterGroup:{ name:null counters:{} } }} channels:{mem1=org.apache.flume.channel.MemoryChannel#27f7c6e1} }
2013-01-31 12:45:08,266 INFO nodemanager.DefaultLogicalNodeManager: Starting Channel mem1
2013-01-31 12:45:08,266 INFO instrumentation.MonitoredCounterGroup: Component type: CHANNEL, name: mem1 started
2013-01-31 12:45:08,266 INFO nodemanager.DefaultLogicalNodeManager: Starting Sink irc1
2013-01-31 12:45:08,267 INFO irc.IRCSink: IRC sink starting
2013-01-31 12:45:08,267 INFO nodemanager.DefaultLogicalNodeManager: Starting Source exec1
2013-01-31 12:45:08,267 INFO source.ExecSource: Exec source starting with command:sh /var/lib/app/test.sh
Edit batch size seems to have been the issue, since it was waiting until 20 messages (default?), which was 100 seconds until I saw any output. Now with batchsize = 1, a standard logger outputs results, but IRC is complaining about a NullPointerException, probably because Event.body is null somehow?
The docs for IRC sinks (found here: Flume 1.x User Guide) are wrong in saying that splitlines is not required to be configured. It does not have a default value in the code, so you must configure it.
Looking at the source code (found here: IRCSink.java) you must also specify "splitlines" or suffer the NullPointerException. There is code to handle "splitchars" being null, but not splitlines. Reported as FLUME-1892 (Edit: This ticket was resolved in January. This should no longer be an issue.)
Related
I posted about this over on the Isaac forums, but listing it here for visibility as well. I am trying to get the Isaac Realsense examples working on a Jetson Nano with my 435i (firmware downgraded to 5.11.15 per the Isaac documentation), but I've been unable to so far. I've got a Nano flashed with Jetpack4.3 and have installed all dependencies on both the desktop and the Nano. The realsense-viewer works fine, so I know the camera is functioning properly and is being detected by the Nano. However, when I run ./apps/samples/realsense_camera/realsense_camera it throws an error:
ERROR engine/alice/components/Codelet.cpp#229: Component 'camera/realsense' of type 'isaac::RealsenseCamera' reported FAILURE:
No device connected, please connect a RealSense device
ERROR engine/alice/backend/event_manager.cpp#42: Stopping node 'camera' because it reached status 'FAILURE'
I've attached the log of this output as well. I get the same error running locally on my desktop, but that's running through WSL so I was willing to write that off. Any suggestions would be greatly appreciated!
0m2020-06-15 17:18:20.620 INFO engine/alice/tools/websight.cpp#166: Loading websight...0m
33m2020-06-15 17:18:20.621 WARN engine/alice/backend/application_json_loader.cpp#174: This application does not have an explicit scheduler configuration. One will be autogenerated to the best of the system's abilities if possible.0m
0m2020-06-15 17:18:20.622 INFO engine/alice/backend/redis_backend.cpp#40: Successfully connected to Redis server.
0m
33m2020-06-15 17:18:20.623 WARN engine/alice/backend/backend.cpp#201: This application does not have an execution group configuration. One will be autogenerated to the best of the systems abilities if possible.0m
33m2020-06-15 17:18:20.623 WARN engine/gems/scheduler/scheduler.cpp#337: No default execution groups specified. Attempting to create scheduler configuration for 4 remaining cores. This may be non optimal for the system and application.0m
0m2020-06-15 17:18:20.623 INFO engine/gems/scheduler/scheduler.cpp#290: Scheduler execution groups are:0m
0m2020-06-15 17:18:20.623 INFO engine/gems/scheduler/scheduler.cpp#299: __BlockerGroup__: Cores = [3], Workers = No0m
0m2020-06-15 17:18:20.623 INFO engine/gems/scheduler/scheduler.cpp#299: __WorkerGroup__: Cores = [0, 1, 2], Workers = Yes0m
0m2020-06-15 17:18:20.660 INFO engine/alice/backend/modules.cpp#226: Loaded module 'packages/realsense/librealsense_module.so': Now has 45 components total0m
0m2020-06-15 17:18:20.679 INFO engine/alice/backend/modules.cpp#226: Loaded module 'packages/rgbd_processing/librgbd_processing_module.so': Now has 51 components total0m
0m2020-06-15 17:18:20.696 INFO engine/alice/backend/modules.cpp#226: Loaded module 'packages/sight/libsight_module.so': Now has 54 components total0m
0m2020-06-15 17:18:20.720 INFO engine/alice/backend/modules.cpp#226: Loaded module 'packages/viewers/libviewers_module.so': Now has 83 components total0m
90m2020-06-15 17:18:20.720 DEBUG engine/alice/application.cpp#348: Loaded 83 components: isaac::RealsenseCamera, isaac::alice::BufferAllocatorReport, isaac::alice::ChannelMonitor, isaac::alice::CheckJetsonPerformanceModel, isaac::alice::CheckOperatingSystem, isaac::alice::Config, isaac::alice::ConfigBridge, isaac::alice::ConfigLoader, isaac::alice::Failsafe, isaac::alice::FailsafeHeartbeat, isaac::alice::InteractiveMarkersBridge, isaac::alice::JsonToProto, isaac::alice::LifecycleReport, isaac::alice::MessageLedger, isaac::alice::MessagePassingReport, isaac::alice::NodeStatistics, isaac::alice::Pose, isaac::alice::Pose2Comparer, isaac::alice::PoseFromFile, isaac::alice::PoseInitializer, isaac::alice::PoseMessageInjector, isaac::alice::PoseToFile, isaac::alice::PoseToMessage, isaac::alice::PoseTree, isaac::alice::PoseTreeJsonBridge, isaac::alice::PoseTreeRelink, isaac::alice::ProtoToJson, isaac::alice::PyCodelet, isaac::alice::Random, isaac::alice::Recorder, isaac::alice::RecorderBridge, isaac::alice::Replay, isaac::alice::ReplayBridge, isaac::alice::Scheduling, isaac::alice::Sight, isaac::alice::SightChannelStatus, isaac::alice::Subgraph, isaac::alice::Subprocess, isaac::alice::TcpPublisher, isaac::alice::TcpSubscriber, isaac::alice::Throttle, isaac::alice::TimeOffset, isaac::alice::TimeSynchronizer, isaac::alice::UdpPublisher, isaac::alice::UdpSubscriber, isaac::map::Map, isaac::map::ObstacleAtlas, isaac::map::OccupancyGridMapLayer, isaac::map::PolygonMapLayer, isaac::map::WaypointMapLayer, isaac::navigation::DistanceMap, isaac::navigation::NavigationMap, isaac::navigation::RangeScanModelClassic, isaac::navigation::RangeScanModelFlatloc, isaac::rgbd_processing::DepthEdges, isaac::rgbd_processing::DepthImageFlattening, isaac::rgbd_processing::DepthImageToPointCloud, isaac::rgbd_processing::DepthNormals, isaac::rgbd_processing::DepthPoints, isaac::rgbd_processing::FreespaceFromDepth, isaac::sight::AliceSight, isaac::sight::SightWidget, isaac::sight::WebsightServer, isaac::viewers::BinaryMapViewer, isaac::viewers::ColorCameraViewer, isaac::viewers::DepthCameraViewer, isaac::viewers::Detections3Viewer, isaac::viewers::DetectionsViewer, isaac::viewers::FiducialsViewer, isaac::viewers::FlatscanViewer, isaac::viewers::GoalViewer, isaac::viewers::ImageKeypointViewer, isaac::viewers::LidarViewer, isaac::viewers::MosaicViewer, isaac::viewers::ObjectViewer, isaac::viewers::OccupancyMapViewer, isaac::viewers::PointCloudViewer, isaac::viewers::PoseTrailViewer, isaac::viewers::SegmentationCameraViewer, isaac::viewers::SegmentationViewer, isaac::viewers::SkeletonViewer, isaac::viewers::TensorViewer, isaac::viewers::TrajectoryListViewer, 0m
33m2020-06-15 17:18:20.723 WARN engine/alice/application.cpp#164: The function Application::findComponentByName is deprecated. Please use `getNodeComponentOrNull` instead. Note that the new method requires a node name instead of a component name. (argument: 'websight/isaac.sight.AliceSight')0m
0m2020-06-15 17:18:20.723 INFO engine/alice/application.cpp#255: Starting application 'realsense_camera' (instance UUID: 'e24992d0-af66-11ea-8bcf-c957460c567e') ...0m
90m2020-06-15 17:18:20.723 DEBUG engine/gems/scheduler/execution_groups.cpp#476: Launching 0 pre-start job(s)0m
90m2020-06-15 17:18:20.723 DEBUG engine/gems/scheduler/execution_groups.cpp#485: Replaying 0 pre-start event(s)0m
90m2020-06-15 17:18:20.723 DEBUG engine/gems/scheduler/execution_groups.cpp#476: Launching 0 pre-start job(s)0m
90m2020-06-15 17:18:20.723 DEBUG engine/gems/scheduler/execution_groups.cpp#485: Replaying 0 pre-start event(s)0m
0m2020-06-15 17:18:20.723 INFO engine/alice/backend/asio_backend.cpp#33: Starting ASIO service0m
0m2020-06-15 17:18:20.727 INFO packages/sight/WebsightServer.cpp#216: Sight webserver is loaded0m
0m2020-06-15 17:18:20.727 INFO packages/sight/WebsightServer.cpp#217: Please open Chrome Browser and navigate to http://<ip address>:30000m
33m2020-06-15 17:18:20.727 WARN engine/alice/backend/codelet_canister.cpp#225: Codelet 'websight/isaac.sight.AliceSight' was not added to scheduler because no tick method is specified.0m
33m2020-06-15 17:18:20.728 WARN engine/alice/components/Codelet.cpp#53: Function deprecated. Set tick_period to the desired tick paramater0m
33m2020-06-15 17:18:20.728 WARN engine/alice/backend/codelet_canister.cpp#225: Codelet '_check_operating_system/isaac.alice.CheckOperatingSystem' was not added to scheduler because no tick method is specified.0m
33m2020-06-15 17:18:20.728 WARN engine/alice/components/Codelet.cpp#53: Function deprecated. Set tick_period to the desired tick paramater0m
33m2020-06-15 17:18:20.730 WARN engine/alice/components/Codelet.cpp#53: Function deprecated. Set tick_period to the desired tick paramater0m
1;31m2020-06-15 17:18:20.741 ERROR engine/alice/components/Codelet.cpp#229: Component 'camera/realsense' of type 'isaac::RealsenseCamera' reported FAILURE:
No device connected, please connect a RealSense device
0m
1;31m2020-06-15 17:18:20.741 ERROR engine/alice/backend/event_manager.cpp#42: Stopping node 'camera' because it reached status 'FAILURE'0m
33m2020-06-15 17:18:20.743 WARN engine/alice/backend/codelet_canister.cpp#225: Codelet 'camera/realsense' was not added to scheduler because no tick method is specified.0m
0m2020-06-15 17:18:21.278 INFO packages/sight/WebsightServer.cpp#113: Server connected / 10m
0m2020-06-15 17:18:30.723 INFO engine/alice/backend/allocator_backend.cpp#57: Optimized memory CPU allocator.0m
0m2020-06-15 17:18:30.724 INFO engine/alice/backend/allocator_backend.cpp#66: Optimized memory CUDA allocator.0m
I am running locally Kafka using the confluentinc/cp-kafka Docker image and I am setting the following logging container environment variables:
KAFKA_LOG4J_ROOT_LOGLEVEL: ERROR
KAFKA_LOG4J_LOGGERS: >-
org.apache.zookeeper=ERROR,
org.apache.kafka=ERROR,
kafka=ERROR,
kafka.cluster=ERROR,
kafka.controller=ERROR,
kafka.coordinator=ERROR,
kafka.log=ERROR,
kafka.server=ERROR,
kafka.zookeeper=ERROR,
state.change.logger=ERROR
and I see in the Kafka logs that Kafka is starting with the following configuration:
===> ENV Variables ...
ALLOW_UNSIGNED=false
COMPONENT=kafka
CONFLUENT_DEB_VERSION=1
CONFLUENT_PLATFORM_LABEL=
CONFLUENT_VERSION=5.4.1
...
KAFKA_LOG4J_LOGGERS=org.apache.zookeeper=ERROR, org.apache.kafka=ERROR, kafka=ERROR, kafka.cluster=ERROR, kafka.controller=ERROR, kafka.coordinator=ERROR, kafka.log=ERROR, kafka.server=ERROR, kafka.zookeeper=ERROR, state.change.logger=ERROR
KAFKA_LOG4J_ROOT_LOGLEVEL=ERROR
...
Still I see further down in the logs the INFO and TRACE log levels. For example:
[2020-03-26 16:22:12,838] INFO [Controller id=1001] Ready to serve as the new controller with epoch 1 (kafka.controller.KafkaController)
[2020-03-26 16:22:12,848] INFO [Controller id=1001] Partitions undergoing preferred replica election: (kafka.controller.KafkaController)
[2020-03-26 16:22:12,849] INFO [Controller id=1001] Partitions that completed preferred replica election: (kafka.controller.KafkaController)
[2020-03-26 16:22:12,855] INFO [Controller id=1001] Skipping preferred replica election for partitions due to topic deletion: (kafka.controller.KafkaController)
How can I really deactivate the logs below a certain level? In the example above, I really want only ERROR logs.
The approach above is the way described in the Confluent documentation.
And the Apache Kafka source code lists all sorts of loggers that I could not influence using the KAFKA_LOG4J_LOGGERS Docker environment variable.
I went and troubleshot the Dockerfile's and inspected the Kafka container. The cause of this behaviour was the YAML multiline string folding.
Hence the provided environment variable (using a YAML multiline value) is at runtime:
KAFKA_LOG4J_LOGGERS=org.apache.zookeeper=ERROR, org.apache.kafka=ERROR, kafka=ERROR, kafka.cluster=ERROR, kafka.controller=ERROR, kafka.coordinator=ERROR, kafka.log=ERROR, kafka.server=ERROR, kafka.zookeeper=ERROR, state.change.logger=ERROR
instead of (no spaces in between):
KAFKA_LOG4J_LOGGERS=org.apache.zookeeper=ERROR,org.apache.kafka=ERROR, kafka=ERROR, kafka.cluster=ERROR,kafka.controller=ERROR, kafka.coordinator=ERROR,kafka.log=ERROR,kafka.server=ERROR,kafka.zookeeper=ERROR,state.change.logger=ERROR
And this was visible inside the container in the generated /etc/kafka/log4j.properties file:
log4j.rootLogger=ERROR, stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=[%d] %p %m (%c)%n
log4j.logger.kafka.authorizer.logger=WARN
log4j.logger.kafka.cluster=ERROR
log4j.logger.kafka.producer.async.DefaultEventHandler=DEBUG
log4j.logger.kafka.zookeeper=ERROR
log4j.logger.org.apache.kafka=ERROR
log4j.logger.kafka.coordinator=ERROR
log4j.logger.org.apache.zookeeper=ERROR
log4j.logger.kafka.log.LogCleaner=INFO
log4j.logger.kafka.controller=ERROR
log4j.logger.kafka=INFO
log4j.logger.kafka.log=ERROR
log4j.logger.state.change.logger=ERROR
log4j.logger.kafka=ERROR
log4j.logger.kafka.server=ERROR
log4j.logger.kafka.controller=TRACE
log4j.logger.kafka.network.RequestChannel$=WARN
log4j.logger.kafka.request.logger=WARN
log4j.logger.state.change.logger=TRACE
If you really need to split the long line in a YAML multiline value, you would have to use this YAML syntax.
More hints from the code:
here is where the log4j.properties file is generated when a confluent container is run.
these are the default log levels that Kafka will start with.
these should be all the loggers supported by Kafka
[mapr#impetus-i0057 latest_code_deepak]$ dask-worker 172.26.32.37:8786
distributed.nanny - INFO - Start Nanny at: 'tcp://172.26.32.36:50930'
distributed.diskutils - WARNING - Found stale lock file and directory '/home/mapr/latest_code_deepak/dask-worker-space/worker-PwEseH', purging
distributed.worker - INFO - Start worker at: tcp://172.26.32.36:41694
distributed.worker - INFO - Listening to: tcp://172.26.32.36:41694
distributed.worker - INFO - bokeh at: 172.26.32.36:8789
distributed.worker - INFO - nanny at: 172.26.32.36:50930
distributed.worker - INFO - Waiting to connect to: tcp://172.26.32.37:8786
distributed.worker - INFO - -------------------------------------------------
distributed.worker - INFO - Threads: 8
distributed.worker - INFO - Memory: 33.52 GB
distributed.worker - INFO - Local Directory: /home/mapr/latest_code_deepak/dask-worker-spa ce/worker-AkBPtM
distributed.worker - INFO - -------------------------------------------------
distributed.worker - INFO - Registered to: tcp://172.26.32.37:8786
distributed.worker - INFO - -------------------------------------------------
what is the default directory where a dask-worker maintains the temporary files, such as task results, or the downloaded files which was uploaded using upload_file() method from the client.?
for example:-
def my_task_running_on_dask_worker():
//fetch the file from hdfs
// process the file
//store the file back into hdfs
By default a dask worker places a directory in ./dask-worker-space/worker-####### where ###### is some random string for that particular worker.
You can change this location using the --local-directory keyword to the dask-worker executable.
The warning that you're seeing in this line
distributed.diskutils - WARNING - Found stale lock file and directory '/home/mapr/latest_code_deepak/dask-worker-space/worker-PwEseH', purging
says that a Dask worker noticed that the directory for another worker wasn't cleaned up, presumably because it failed in some hard way. This worker is cleaning up the space left behind from the previous worker.
Edit
You can see which worker creates which directory either by looking at the logs of each worker (They print out their local directory)
$ dask-worker localhost:8786
distributed.worker - INFO - Start worker at: tcp://127.0.0.1:36607
...
distributed.worker - INFO - Local Directory: /home/mrocklin/dask-worker-space/worker-ks3mljzt
Or programatically by calling client.scheduler_info()
>>> client.scheduler_info()
{'address': 'tcp://127.0.0.1:34027',
'id': 'Scheduler-bd88dfdf-e3f7-4b39-8814-beae779248f1',
'services': {'bokeh': 8787},
'type': 'Scheduler',
'workers': {'tcp://127.0.0.1:33143': {'cpu': 7.7,
...
'local_directory': '/home/mrocklin/dask-worker-space/worker-8kvk_l81',
},
...
When running a client using `distributed=2022.10.2', I see that the default path is:
/var/folders/v6/.../dask-worker-space/worker-... on Mac M1;
/tmp/dask-worker-space/worker-... on Ubuntu.
I have written my own hadoop program and I can run using pseudo distribute mode in my own laptop, however, when I put the program in the cluster which can run example jar of hadoop, it by default launches the local job though I indicate the hdfs file path, below is the output, give suggestions?
./hadoop -jar MyRandomForest_oob_distance.jar hdfs://montana-01:8020/user/randomforest/input/genotype1.txt hdfs://montana-01:8020/user/randomforest/input/phenotype1.txt hdfs://montana-01:8020/user/randomforest/output1_distance/ hdfs://montana-01:8020/user/randomforest/input/genotype101.txt hdfs://montana-01:8020/user/randomforest/input/phenotype101.txt 33 500 1
12/03/16 16:21:25 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
12/03/16 16:21:25 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
12/03/16 16:21:25 INFO mapred.JobClient: Running job: job_local_0001
12/03/16 16:21:25 INFO mapred.MapTask: io.sort.mb = 100
12/03/16 16:21:25 INFO mapred.MapTask: data buffer = 79691776/99614720
12/03/16 16:21:25 INFO mapred.MapTask: record buffer = 262144/327680
12/03/16 16:21:25 WARN mapred.LocalJobRunner: job_local_0001
java.io.FileNotFoundException: File /user/randomforest/input/genotype1.txt does not exist.
at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:361)
at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:245)
at org.apache.hadoop.fs.ChecksumFileSystem$ChecksumFSInputChecker.<init>(ChecksumFileSystem.java:125)
at org.apache.hadoop.fs.ChecksumFileSystem.open(ChecksumFileSystem.java:283)
at org.apache.hadoop.fs.FileSystem.open(FileSystem.java:356)
at Data.Data.loadData(Data.java:103)
at MapReduce.DearMapper.loadData(DearMapper.java:261)
at MapReduce.DearMapper.setup(DearMapper.java:332)
at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:142)
at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:621)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:305)
at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:177)
12/03/16 16:21:26 INFO mapred.JobClient: map 0% reduce 0%
12/03/16 16:21:26 INFO mapred.JobClient: Job complete: job_local_0001
12/03/16 16:21:26 INFO mapred.JobClient: Counters: 0
Total Running time is: 1 secs
LocalJobRunner has been chosen as your configuration most probably has the mapred.job.tracker property set to local or has not been set at all (in which case the default is local). To check, go to "wherever you extracted/installed hadoop"/etc/hadoop/ and see if the file mapred-site.xml exists (for me it did not, a file called mapped-site.xml.template was there). In that file (or create it if it doesn't exist) make sure it has the following property:
<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
</configuration>
See the source for org.apache.hadoop.mapred.JobClient.init(JobConf)
What is the value of this configuration property in the hadoop configuration on the machine you are submitting this from? Also confirm that the hadoop executable you are running references this configuration (and that you don't have 2+ installations configured differently) - type which hadoop and trace any symlinks you come across.
Alternatively you can override this when you submit your job, if you know the JobTracker host and port number using the -jt option:
hadoop jar MyRandomForest_oob_distance.jar -jt hostname:port hdfs://montana-01:8020/user/randomforest/input/genotype1.txt hdfs://montana-01:8020/user/randomforest/input/phenotype1.txt hdfs://montana-01:8020/user/randomforest/output1_distance/ hdfs://montana-01:8020/user/randomforest/input/genotype101.txt hdfs://montana-01:8020/user/randomforest/input/phenotype101.txt 33 500 1
If you're using Hadoop 2 and your job is running locally instead of on the cluster, ensure that you have setup mapred-site.xml to contain the mapreduce.framework.name property with a value of yarn. You also need to set up an aux-service in yarn-site.xml
Checkout the Cloudera Hadoop 2 operator migration blog for more information.
I had the same problem that every mapreduce v2 (mrv2) or yarn task only ran with the mapred.LocalJobRunner
INFO mapred.LocalJobRunner: Starting task: attempt_local284299729_0001_m_000000_0
The Resourcemanager and Nodemanagers were accessible and the mapreduce.framework.name was set to yarn.
Setting the HADOOP_MAPRED_HOME before executing the job fixed the problem for me.
export HADOOP_MAPRED_HOME=/usr/lib/hadoop-mapreduce
cheers
dan
I'm getting the following error and I have no idea why. If I change the sink to "console", it works fine. I'm just trying to recreate an example from the flume documentation except across two different nodes. This is using CDH3.
2011-10-20 17:41:13,046 [main] WARN text.FormatFactory: Unable to load output format plugin class - Class not found
2011-10-20 17:41:13,065 [main] INFO agent.FlumeNode: Loading spec from command line: 'foo:console|agentSink("somehost",35853);'
2011-10-20 17:41:13,228 [main] WARN agent.FlumeNode: Caught exception loading node:null
I'm trying to run flume as such:
flume node_nowatch -1 -s -n foo -c 'foo:console|agentSink("somehost",35853);'
Thanks in advance.