I am trying to ingest data using flume from kafka source to hdfs. Below is my flume conf file.
flume1.sources = kafka-source-1
flume1.channels = hdfs-channel-1
flume1.sinks = hdfs-sink-1
flume1.sources.kafka-source-1.type = org.apache.flume.source.kafka.KafkaSource
flume1.sources.kafka-source-1.bootstrap.servers = localhost:9092
flume1.sources.kafka-source-1.zookeeperConnect = localhost:2181
flume1.sources.kafka-source-1.topic = MYNEWSFEEDS
flume1.sources.kafka-source-1.batchSize = 100
flume1.sources.kafka-source-1.channels = hdfs-channel-1
flume1.channels.hdfs-channel-1.type = memory
flume1.sinks.hdfs-sink-1.channel = hdfs-channel-1
flume1.sinks.hdfs-sink-1.type = hdfs
flume1.sinks.hdfs-sink-1.hdfs.writeFormat = Text
flume1.sinks.hdfs-sink-1.hdfs.fileType = DataStream
flume1.sinks.hdfs-sink-1.hdfs.filePrefix = test-events
flume1.sinks.hdfs-sink-1.hdfs.useLocalTimeStamp = true
flume1.sinks.hdfs-sink-1.hdfs.path = hdfs://quickstart.cloudera:8020/tmp
flume1.sinks.hdfs-sink-1.hdfs.rollCount=100
flume1.sinks.hdfs-sink-1.hdfs.rollSize=0
flume1.channels.hdfs-channel-1.capacity = 10000
flume1.channels.hdfs-channel-1.transactionCapacity = 1000
I am using below command to run flume agent:
sudo flume-ng agent --name flume1 --conf-file '/etc/flume-ng/conf/flafka.conf' Dflume.root.logger=TRACE,console
But I am getting below error:
18/03/12 16:49:18 ERROR node.AbstractConfigurationProvider: Source
kafka-source-1 has been removed due to an error during configuration
org.apache.flume.conf.ConfigurationException: Bootstrap Servers must
be specified at
org.apache.flume.source.kafka.KafkaSource.doConfigure(KafkaSource.java:330)
at
org.apache.flume.source.BasicSourceSemantics.configure(BasicSourceSemantics.java:65)
at
org.apache.flume.source.AbstractPollableSource.configure(AbstractPollableSource.java:63)
at
org.apache.flume.conf.Configurables.configure(Configurables.java:41)
at
org.apache.flume.node.AbstractConfigurationProvider.loadSources(AbstractConfigurationProvider.java:326)
at
org.apache.flume.node.AbstractConfigurationProvider.getConfiguration(AbstractConfigurationProvider.java:97)
at
org.apache.flume.node.PollingPropertiesFileConfigurationProvider$FileWatcherRunnable.run(PollingPropertiesFileConfigurationProvider.java:140)
at
java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:304)
at
java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:178)
at
java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Although, I have specified the Bootstrap Servers in conf file but still it give same error. Have tried many permutations and combinations but no success.
According to the official JavaDoc, you should replace
flume1.sources.kafka-source-1.bootstrap.servers = localhost:9092
with
flume1.sources.kafka-source-1.kafka.bootstrap.servers = localhost:9092
I'm trying to load geojson file with spark and magellan library
My code for loading is:
val polygons = spark.read.format("magellan").option("type", "geojson").load(inJson)
Where inJson is path to my json on s3:
s3n://bucket-name/geojsons/file.json
Error with stack trace:
0.3 in stage 0.0 (TID 3, ip-172-31-19-102.eu-west-1.compute.internal, executor 1): java.lang.IllegalArgumentException: Wrong FS:
s3n://bucket-name/geojsons/file.json, expected:
hdfs://ip-172-31-27-182.eu-west-1.compute.internal:8020 at
org.apache.hadoop.fs.FileSystem.checkPath(FileSystem.java:653) at
org.apache.hadoop.hdfs.DistributedFileSystem.getPathName(DistributedFileSystem.java:194)
at
org.apache.hadoop.hdfs.DistributedFileSystem.access$000(DistributedFileSystem.java:106)
at
org.apache.hadoop.hdfs.DistributedFileSystem$3.doCall(DistributedFileSystem.java:304)
at
org.apache.hadoop.hdfs.DistributedFileSystem$3.doCall(DistributedFileSystem.java:299)
at
org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81)
at
org.apache.hadoop.hdfs.DistributedFileSystem.open(DistributedFileSystem.java:312)
at org.apache.hadoop.fs.FileSystem.open(FileSystem.java:773) at
magellan.mapreduce.WholeFileReader.nextKeyValue(WholeFileReader.scala:45)
at
org.apache.spark.rdd.NewHadoopRDD$$anon$1.hasNext(NewHadoopRDD.scala:199)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439) at
scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408) at
scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408) at
scala.collection.Iterator$class.foreach(Iterator.scala:893) at
scala.collection.AbstractIterator.foreach(Iterator.scala:1336) at
scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:157)
at scala.collection.AbstractIterator.foldLeft(Iterator.scala:1336)
at
scala.collection.TraversableOnce$class.fold(TraversableOnce.scala:212)
at scala.collection.AbstractIterator.fold(Iterator.scala:1336) at
org.apache.spark.rdd.RDD$$anonfun$fold$1$$anonfun$20.apply(RDD.scala:1086)
at
org.apache.spark.rdd.RDD$$anonfun$fold$1$$anonfun$20.apply(RDD.scala:1086)
at
org.apache.spark.SparkContext$$anonfun$33.apply(SparkContext.scala:1980)
at
org.apache.spark.SparkContext$$anonfun$33.apply(SparkContext.scala:1980)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99) at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
The problem occurs only when I run it on more than one machine, so it works fine on EMR cluster with master and 1 instance in core group, but fails like that with 10 instances in core group
This turned out to be problem within Magellan WholeFileReader. It was getting default FileSystem.
It was solved with this pull request
The solutions was like so:
- val fs = FileSystem.get(conf)
+ val fs = path.getFileSystem(conf)
If you are running on EMR, you can just use "s3://bucket/path" instead of "s3n://...."
My Spark application is failing when it has to access numerous CSV files (~1000 # 63MB each) from S3, and pipe them into a Spark RDD. The actual process of splitting up the CSV seems to work, but an extra function call to S3NativeFileSystem seems to be causing an error and the job to crash.
To begin, the following is my PySpark Application:
from pyspark import SparkContext
sc = SparkContext("local", "Simple App")
from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)
import time
startTime = float(time.time())
dataPath = 's3://PATHTODIRECTORY/'
sc._jsc.hadoopConfiguration().set("fs.s3.awsAccessKeyId", "MYKEY")
sc._jsc.hadoopConfiguration().set("fs.s3.awsSecretAccessKey", "MYSECRETKEY")
def buildSchemaDF(tableName, columnList):
currentRDD = sc.textFile(dataPath + tableName).map(lambda line: line.split("|"))
currentDF = currentRDD.toDF(columnList)
return currentDF
loadStartTime = float(time.time())
lineitemDF = buildSchemaDF('lineitem*', ['l_orderkey','l_partkey','l_suppkey','l_linenumber','l_quantity','l_extendedprice','l_discount','l_tax','l_returnflag','l_linestatus','l_shipdate','l_commitdate','l_receiptdate','l_shipinstruct','l_shipmode','l_comment'])
lineitemDF.registerTempTable("lineitem")
loadTimeElapsed = float(time.time()) - loadStartTime
queryStartTime = float(time.time())
qstr = """
SELECT
lineitem.l_returnflag,
lineitem.l_linestatus,
sum(l_quantity) as sum_qty,
sum(l_extendedprice) as sum_base_price,
sum(l_discount) as sum_disc,
sum(l_tax) as sum_tax,
avg(l_quantity) as avg_qty,
avg(l_extendedprice) as avg_price,
avg(l_discount) as avg_disc,
count(l_orderkey) as count_order
FROM
lineitem
WHERE
l_shipdate <= '19981001'
GROUP BY
l_returnflag,
l_linestatus
ORDER BY
l_returnflag,
l_linestatus
"""
tpch1DF = sqlContext.sql(qstr)
queryTimeElapsed = float(time.time()) - queryStartTime
totalTimeElapsed = float(time.time()) - startTime
tpch1DF.show()
queryResults = [qstr, loadTimeElapsed, queryTimeElapsed, totalTimeElapsed]
distData = sc.parallelize(queryResults)
distData.saveAsTextFile(dataPath + 'queryResults.csv')
print 'Load Time: ' + str(loadTimeElapsed)
print 'Query Time: ' + str(queryTimeElapsed)
print 'Total Time: ' + str(totalTimeElapsed)
To take it step by step I start off by spinning up a Spark EMR Cluster with the following AWS CLI command (carriage returns added for readability):
aws emr create-cluster --name "Big TPCH Spark cluster2" --release-label emr-4.6.0
--applications Name=Spark --ec2-attributes KeyName=blazing-test-aws
--log-uri s3://aws-logs-132950491118-us-west-2/elasticmapreduce/j-1WZ39GFS3IX49/
--instance-type m3.2xlarge --instance-count 6 --use-default-roles
After the EMR cluster finishes provisioning I then copy over my Pyspark application onto the master node at '/home/hadoop/pysparkApp.py'. With it copied over I'm able to add the Step for spark-submit.
aws emr add-steps --cluster-id j-1DQJ8BDL1394N --steps
Type=spark,Name=SparkTPCHTests,Args=[--deploy-mode,cluster,-
conf,spark.yarn.submit.waitAppCompletion=true,--num-executors,5,--executor
cores,5,--executor memory,20g,/home/hadoop/tpchSpark.py]
,ActionOnFailure=CONTINUE
Now if I run this step over only a few of the aforementioned CSV files the final results will be generated, but the script will still claim to have failed.
I think it's associated with an extra call to S3NativeFileSystem, but I'm not certain. These are the Yarn log messages I'm getting which lead me to that conclusion. The first call appears to work just fine:
16/05/15 23:18:00 INFO HadoopRDD: Input split: s3://data-set-builder/splitLineItem2/lineitemad:0+64901757
16/05/15 23:18:00 INFO latency: StatusCode=[200], ServiceName=[Amazon S3], AWSRequestID=[ED8011CE4E1F6F18], ServiceEndpoint=[https://data-set-builder.s3-us-west-2.amazonaws.com], HttpClientPoolLeasedCount=0, RetryCapacityConsumed=0, RequestCount=1, HttpClientPoolPendingCount=0, HttpClientPoolAvailableCount=2, ClientExecuteTime=[77.956], HttpRequestTime=[77.183], HttpClientReceiveResponseTime=[20.028], RequestSigningTime=[0.229], CredentialsRequestTime=[0.003], ResponseProcessingTime=[0.128], HttpClientSendRequestTime=[0.35],
While the second one does not seem to execute properly, resulting in "Partial Results" (206 Error):
16/05/15 23:18:00 INFO S3NativeFileSystem: Opening 's3://data-set-builder/splitLineItem2/lineitemad' for reading
16/05/15 23:18:00 INFO latency: StatusCode=[206], ServiceName=[Amazon S3], AWSRequestID=[10BDDE61AE13AFBE], ServiceEndpoint=[https://data-set-builder.s3.amazonaws.com], HttpClientPoolLeasedCount=0, RetryCapacityConsumed=0, RequestCount=1, HttpClientPoolPendingCount=0, HttpClientPoolAvailableCount=2, Client Execute Time=[296.86], HttpRequestTime=[295.801], HttpClientReceiveResponseTime=[293.667], RequestSigningTime=[0.204], CredentialsRequestTime=[0.002], ResponseProcessingTime=[0.34], HttpClientSendRequestTime=[0.337],
16/05/15 23:18:02 INFO ApplicationMaster: Waiting for spark context initialization ...
I'm lost as to why it's even making the second call to S3NativeFileSystem when the first one appears to have responded effectively and even split the file. Is this something that is a product of my EMR configuration? I know S3Native has file limit issues and that a straight S3 call is optimal, which is what I've tried to do, but this call seems to be there no matter what I do. Please help!
Also, to add a few other error messages in my Yarn Log in case they are relevant.
1)
16/05/15 23:19:22 ERROR ApplicationMaster: SparkContext did not initialize after waiting for 100000 ms. Please check earlier log output for errors. Failing the application.
16/05/15 23:19:22 INFO ApplicationMaster: Final app status: FAILED, exitCode: 13, (reason: Timed out waiting for SparkContext.)
2)
16/05/15 23:19:22 ERROR DiskBlockObjectWriter: Uncaught exception while reverting partial writes to file /mnt/yarn/usercache/hadoop/appcache/application_1463354019776_0001/blockmgr-f847744b-c87a-442c-9135-57cae3d1f6f0/2b/temp_shuffle_3fe2e09e-f8e4-4e5d-ac96-1538bdc3b401
java.io.FileNotFoundException: /mnt/yarn/usercache/hadoop/appcache/application_1463354019776_0001/blockmgr-f847744b-c87a-442c-9135-57cae3d1f6f0/2b/temp_shuffle_3fe2e09e-f8e4-4e5d-ac96-1538bdc3b401 (No such file or directory)
at java.io.FileOutputStream.open(Native Method)
at java.io.FileOutputStream.<init>(FileOutputStream.java:221)
at org.apache.spark.storage.DiskBlockObjectWriter.revertPartialWritesAndClose(DiskBlockObjectWriter.scala:162)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.stop(BypassMergeSortShuffleWriter.java:226)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
16/05/15 23:19:22 ERROR BypassMergeSortShuffleWriter: Error while deleting file /mnt/yarn/usercache/hadoop/appcache/application_1463354019776_0001/blockmgr-f847744b-c87a-442c-9135-57cae3d1f6f0/2b/temp_shuffle_3fe2e09e-f8e4-4e5d-ac96-1538bdc3b401
16/05/15 23:19:22 WARN TaskMemoryManager: leak 32.3 MB memory from org.apache.spark.unsafe.map.BytesToBytesMap#762be8fe
16/05/15 23:19:22 ERROR Executor: Managed memory leak detected; size = 33816576 bytes, TID = 14
16/05/15 23:19:22 ERROR Executor: Exception in task 13.0 in stage 1.0 (TID 14)
java.io.FileNotFoundException: /mnt/yarn/usercache/hadoop/appcache/application_1463354019776_0001/blockmgr-f847744b-c87a-442c-9135-57cae3d1f6f0/3a/temp_shuffle_b9001fca-bba9-400d-9bc4-c23c002e0aa9 (No such file or directory)
at java.io.FileOutputStream.open(Native Method)
at java.io.FileOutputStream.<init>(FileOutputStream.java:221)
at org.apache.spark.storage.DiskBlockObjectWriter.open(DiskBlockObjectWriter.scala:88)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:140)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Order of precedence for spark configurations is :
SparkContext (code/application) > Spark-submit > Spark-defaults.conf
So couple of things to point here -
Use YARN cluster as deploy mode and master in your spark submit command -
spark-submit --deploy-mode cluster --master yarn ...
OR
spark-submit --master yarn-cluster ...
Remove "local" string from line sc = SparkContext("local", "Simple App") in your code. Use conf = SparkConf().setAppName(appName)
sc = SparkContext(conf=conf) to initialize Spark context.
Ref - http://spark.apache.org/docs/latest/programming-guide.html
Im using flume to move files to hdfs ... while moving file its showing this error.. please help me to solve this issue.
15/05/20 15:49:26 INFO instrumentation.MonitoredCounterGroup: Component type: SOURCE, name: r1 started
15/05/20 15:49:26 INFO avro.ReliableSpoolingFileEventReader: Preparing to move file /home/crayondata.com/shanmugapriya/apache-flume-1.5.2-bin/staging/HypeVisitorTest.java to /home/crayondata.com/shanmugapriya/apache-flume-1.5.2-bin/staging/HypeVisitorTest.java.COMPLETED
15/05/20 15:49:26 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.
15/05/20 15:49:26 INFO hdfs.HDFSDataStream: Serializer = TEXT, UseRawLocalFileSystem = false
15/05/20 15:49:26 INFO hdfs.BucketWriter: Creating hdfs://localhost:9000/sha/HypeVisitorTest.java.1432117166377.tmp
15/05/20 15:49:26 ERROR hdfs.HDFSEventSink: process failed
java.lang.UnsupportedOperationException: Not implemented by the DistributedFileSystem FileSystem implementation
at org.apache.hadoop.fs.FileSystem.getScheme(FileSystem.java:216)
at org.apache.hadoop.fs.FileSystem.loadFileSystems(FileSystem.java:2564)
at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2574)
at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2591)
at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:91)
at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2630)
at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2612)
at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:370)
at org.apache.hadoop.fs.Path.getFileSystem(Path.java:296)
at org.apache.flume.sink.hdfs.BucketWriter$1.call(BucketWriter.java:270)
at org.apache.flume.sink.hdfs.BucketWriter$1.call(BucketWriter.java:262)
at org.apache.flume.sink.hdfs.BucketWriter$9$1.run(BucketWriter.java:718)
at org.apache.flume.sink.hdfs.BucketWriter.runPrivileged(BucketWriter.java:183)
at org.apache.flume.sink.hdfs.BucketWriter.access$1700(BucketWriter.java:59)
at org.apache.flume.sink.hdfs.BucketWriter$9.call(BucketWriter.java:715)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
15/05/20 15:49:26 ERROR flume.SinkRunner: Unable to deliver event. Exception follows.
org.apache.flume.EventDeliveryException: java.lang.UnsupportedOperationException: Not implemented by the DistributedFileSystem FileSystem implementation
at org.apache.flume.sink.hdfs.HDFSEventSink.process(HDFSEventSink.java:471)
at org.apache.flume.sink.DefaultSinkProcessor.process(DefaultSinkProcessor.java:68)
at org.apache.flume.SinkRunner$PollingRunner.run(SinkRunner.java:147)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.UnsupportedOperationException: Not implemented by the DistributedFileSystem FileSystem implementation
at org.apache.hadoop.fs.FileSystem.getScheme(FileSystem.java:216)
at org.apache.hadoop.fs.FileSystem.loadFileSystems(FileSystem.java:2564)
at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2574)
at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2591)
at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:91)
at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2630)
at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2612)
at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:370)
at org.apache.hadoop.fs.Path.getFileSystem(Path.java:296)
at org.apache.flume.sink.hdfs.BucketWriter$1.call(BucketWriter.java:270)
at org.apache.flume.sink.hdfs.BucketWriter$1.call(BucketWriter.java:262)
at org.apache.flume.sink.hdfs.BucketWriter$9$1.run(BucketWriter.java:718)
at org.apache.flume.sink.hdfs.BucketWriter.runPrivileged(BucketWriter.java:183)
at org.apache.flume.sink.hdfs.BucketWriter.access$1700(BucketWriter.java:59)
at org.apache.flume.sink.hdfs.BucketWriter$9.call(BucketWriter.java:715)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
... 1 more
15/05/20 15:49:26 INFO source.SpoolDirectorySource: Spooling Directory Source runner has shutdown.
Here is my flumeconf.conf file
# example.conf: A single-node Flume configuration
# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# Describe/configure the source
a1.sources.r1.type = spooldir
a1.sources.r1.spoolDir = /home/shanmugapriya/apache-flume-1.5.2-bin/staging
a1.sources.r1.fileHeader = true
a1.sources.r1.maxBackoff = 10000
a1.sources.r1.basenameHeader = true
# Describe the sink
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.path = hdfs://localhost:9000/sha
a1.sinks.k1.hdfs.writeFormat = Text
a1.sinks.k1.hdfs.fileType = DataStream
a1.sinks.k1.hdfs.rollInterval = 0
a1.sinks.k1.hdfs.rollSize = 0
a1.sinks.k1.hdfs.rollCount = 0
a1.sinks.k1.hdfs.idleTimeout = 100
a1.sinks.k1.hdfs.filePrefix = %{basename}
# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 100000
a1.channels.c1.transactionCapacity = 1000
a1.channels.c1.byteCapacity = 0
# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
please help to solve this.. TIA...
#Shan Please confirm you have the relevant Hadoop HDFS jars in your classpath for Apache Flume
Also from your sink to HDFS I see that you have port 9000, however the default port is normally 8020, is this correct?
Flume agent 1 does not connect to Flume agent 2. What could be the reason ?
I am using Flume to stream log file to HDFS using 2 Agents. The first agent is located at the source machine where the log file exists, while the second agent is located in the machine (IP Address is 10.10.201.40) where Hadoop is installed.
The configuration file of the first agent (flume-src-agent.conf) is as follows:
source_agent.sources = weblogic_server
source_agent.sources.weblogic_server.type = exec
source_agent.sources.weblogic_server.command = tail -f AdminServer.log
source_agent.sources.weblogic_server.batchSize = 1
source_agent.sources.weblogic_server.channels = memoryChannel
source_agent.sources.weblogic_server.interceptors = itime ihost itype
source_agent.sources.weblogic_server.interceptors.itime.type = timestamp
source_agent.sources.weblogic_server.interceptors.ihost.type = host
source_agent.sources.weblogic_server.interceptors.ihost.useIP = false
source_agent.sources.weblogic_server.interceptors.ihost.hostHeader = host
source_agent.sources.weblogic_server.interceptors.itype.type = static
source_agent.sources.weblogic_server.interceptors.itype.key = log_type
source_agent.sources.weblogic_server.interceptors.itype.value = apache_access_combined
source_agent.channels = memoryChannel
source_agent.channels.memoryChannel.type = memory
source_agent.channels.memoryChannel.capacity = 100
source_agent.sinks = avro_sink
source_agent.sinks.avro_sink.type = avro
source_agent.sinks.avro_sink.channel = memoryChannel
source_agent.sinks.avro_sink.hostname = 10.10.201.40
source_agent.sinks.avro_sink.port = 4545
The configuration file of the second agent (flume-trg-agent.conf) is as follows:
collector.sources = AvroIn
collector.sources.AvroIn.type = avro
collector.sources.AvroIn.bind = 0.0.0.0
collector.sources.AvroIn.port = 4545
collector.sources.AvroIn.channels = mc1 mc2
collector.channels = mc1 mc2
collector.channels.mc1.type = memory
collector.channels.mc1.capacity = 100
collector.channels.mc2.type = memory
collector.channels.mc2.capacity = 100
collector.sinks = HadoopOut
collector.sinks.HadoopOut.type = hdfs
collector.sinks.HadoopOut.channel = mc2
collector.sinks.HadoopOut.hdfs.path = hdfs://localhost:54310/user/root
collector.sinks.HadoopOut.hdfs.callTimeout = 150000
collector.sinks.HadoopOut.hdfs.fileType = DataStream
collector.sinks.HadoopOut.hdfs.writeFormat = Text
collector.sinks.HadoopOut.hdfs.rollSize = 0
collector.sinks.HadoopOut.hdfs.rollCount = 10000
collector.sinks.HadoopOut.hdfs.rollInterval = 600
When the 1st agent is run, I get the following error:
2015-04-08 15:14:10,251 (SinkRunner-PollingRunner-DefaultSinkProcessor) [ERROR - org.apache.flume.SinkRunner$PollingRunner.run(SinkRunner.java:160)] Unable to deliver event. Exception follows.
org.apache.flume.EventDeliveryException:Failed to send events
at org.apache.flume.sink.AbstractRpcSink.process(AbstractRpcSink.java:382)
at org.apache.flume.sink.DefaultSinkProcessor.process(DefaultSinkProcessor.java:68)
at org.apache.flume.SinkRunner$PollingRunner.run(SinkRunner.java:147)
at java.lang.Thread.run(Thread.java:744)
Caused by: org.apache.flume.FlumeException: NettyAvroRpcClient {host:10.10.201.40, port:4545}: RPC connection error
at org.apache.flume.api.NettyAvroRpcClient.connect(NettyAvroRpcClient.java:161)
at org.apache.flume.api.NettyAvroRpcClient.connect(NettyAvroRpcClient.java:115)
at org.apache.flume.api.NettyAvroRpcClient.configure(NettyAvroRpcClient.java:590)
at org.apache.flume.api.RpcClientFactory.getInstance(RpcClientFactory.java:88)
at org.apache.flume.sink.AvroSink.initializeRpcClient(AvroSink.java:127)
at org.apache.flume.sink.AbstractRpcSink.createConnection(AbstractRpcSink.java:209)
at org.apache.flume.sink.AbstractRpcSink.verifyConnection(AbstractRpcSink.java:269)
at org.apache.flume.sink.AbstractRpcSink.process(AbstractRpcSink.java:339)
... 3 more
Caused by: java.io.IOException: Error connecting to /10.10.201.40:4545
at org.apache.avro.ipc.NettyTransceiver.getChannel(NettyTransceiver.java:261)
at.org.apache.avro.ipc.NettyTransceiver.<init>(NettyTransceiver.java:203)
at.org.apache.avro.ipc.NettyTransceiver.<init>(NettyTransceiver.java:152)
at.org.apache.avro.api.NettyAvroRpcClient.connect(NettyAvroRpcClient.java:147)
When the 2nd Agent is run, I get the following error:
2015-04-08 15:53:31,649 (SinkRunner-PollingRunner-DefaultSinkProcessor) [DEBUG-org.apache.flume.SinkRunner$PollingRunner.run(SinkRunner.java:143)] Pollingsink runner starting
2015-04-08 15:53:31,844 (lifecycleSupervisor-1-3) [ERROR - org.apache.flume.lifecycle.LifecycleSupervisor$MonitorRunnable.run(LifecycleSupervisor.java:253)] Unable to start EventDrivenSourceRunner: { source:Avro source AvroIn: {bindAddress: 0.0.0.0, port: 4545 } } - Exception follows.
org.jboss.netty.channel.ChannelException: Failed to bind to: /0.0.0.0:4545
at org.jboss.netty.bootstrap.ServerBootstrap.bind(ServerBootstrap.java:298)
at org.apache.avro.ipc.NettyServer.<init>(NettyServer.java:106)
at org.apache.flume.source.AvroSource.start(AvroSource.java:225)
at org.apache.flume.source.EventDrivenSourceRunner.start(EventDrivenSourceRunner.java:44)
at org.apache.flume.lifecycle.LifecycleSupervisor$MonitorRunnable.run(LifecycleSupervisor.java:251)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:441)
at java.util.concurrent.FutureTask$Sync.innerRunAndReset(FutureTask.java:317)
at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:150)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$101(ScheduledThreadPoolExecutor.java:98)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.runPeriodic(ScheduledThreadPoolExecutor.java:180)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:204)
at java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908)
at java.lang.Thread.run(Thread.java:662)
Caused by: java.net.BindException: Address already in use
at sun.nio.ch.Net.bind(Native Method)
at sun.nio.ch.ServerSocketChannelImpl.bind(ServerSocketChannelImpl.java:126)
at sun.nio.ch.ServerSocketAdaptor.bind(ServerSocketAdaptor.java:59)
at org.jboss.netty.channel.socket.nio.NioServerSocketPipelineSink.bind(NioServerSocketPipelineSink.java:138)
at org.jboss.netty.channel.socket.nio.NioServerSocketPipelineSink.handleServerSocket(NioServerSocketPipelineSink.java:90)
at org.jboss.netty.channel.socket.nio.NioServerSocketPipelineSink.eventSunk(NioServerSocketPipelineSink.java:64)
at org.jboss.netty.channel.Channels.bind(Channels.java:569)
at org.jboss.netty.channel.AbstractChannel.bind(AbstractChannel.java:187)
at org.jboss.netty.bootstrap.ServerBootstrap$Binder.channelOpen(ServerBootstrap.java:343)
at org.jboss.netty.channel.Channels.fireChannelOpen(Channels.java:170)
at org.jboss.netty.channel.socket.nio.NioServerSocketChannelFactory.newChannel(NioServerSocketChannelFactory.java:158)
at org.jboss.netty.channel.socket.nio.NioServerSocketChannel.<init>(NioServerSocketChannel.java:80)
at org.jboss.netty.channel.socket.nio.NioServerSocketChannelFactory.newChannel(NioServerSocketChannelFactory.java:86)
at org.jboss.netty.bootstrap.ServerBootstrap.bind(ServerBootstrap.java:277)
... 13 more
The answer to your question is in the second log:
Address already in use
The reason for this is that there's another process using port 4545. Just reconfigure both agents to another port, let say 41414, then it should work.
For binding issues type netstat -plten and check for the pid for the process and kill the process. Doing that will solve the binding issues when you run the agent again