kafka version is 0.7.2，flume version is 1.5.0, flume + kafka plugin: https://github.com/baniuyao/flume-kafka
error info：
2014-08-20 18:55:51,755 (conf-file-poller-0) [ERROR - org.apache.flume.node.PollingPropertiesFileConfigurationProvider$FileWatcherRunnable.run(PollingPropertiesFileConfigurationProvider.java:149)] Unhandled error
java.lang.NoSuchMethodError: scala.math.LowPriorityOrderingImplicits.ordered()Lscala/math/Ordering;
at kafka.producer.ZKBrokerPartitionInfo$$anonfun$kafka$producer$ZKBrokerPartitionInfo$$getZKTopicPartitionInfo$1.apply(ZKBrokerPartitionInfo.scala:172)
flume configuration：
agent_log.sources = r1
agent_log.sinks = kafka
agent_log.channels = c1
agent_log.sources.r1.type = exec
agent_log.sources.r1.channels = c1
agent_log.sources.r1.command = tail -f /var/log/test.log
agent_log.channels.c1.type = memory
agent_log.channels.c1.capacity = 1000
agent_log.channels.c1.trasactionCapacity = 100
agent_log.sinks.kafka.type = com.vipshop.flume.sink.kafka.KafkaSink
agent_log.sinks.kafka.channel = c1
agent_log.sinks.kafka.zk.connect = XXXX:2181
agent_log.sinks.kafka.topic = my-replicated-topic
agent_log.sinks.kafka.batchsize = 200
agent_log.sinks.kafka.producer.type = async
agent_log.sinks.kafka.serializer.class = kafka.serializer.StringEncoder
what could be the error? THX
scala.math.LowPriorityOrderingImplicits.ordered()
Perhaps you need to import the Scala standard libarary and have it in your Flume lib directory.
Related
I have a Kafka Streams application that works fine locally, but when I run it in Docker containers not all data is processed, and I get a lot of repeated errors in the logs about "unable to records bytes produce to topic"
18:58:32.647 [kafka-producer-network-thread | my-app-events-processor.splitPackets-cf462b02-f1e3-4ed5-a1e7-acc1f040495b-StreamThread-1-producer] ERROR o.a.k.s.p.i.RecordCollectorImpl - stream-thread [my-app-events-processor.splitPackets-cf462b02-f1e3-4ed5-a1e7-acc1f040495b-StreamThread-1] task [0_0] Unable to records bytes produced to topic my-app.packet.surface by sink node split-server-log as the node is not recognized.
Known sink nodes are [].
18:58:49.216 [kafka-producer-network-thread | my-app-events-processor.splitPackets-cf462b02-f1e3-4ed5-a1e7-acc1f040495b-StreamThread-1-producer] ERROR o.a.k.s.p.i.RecordCollectorImpl - stream-thread [my-app-events-processor.splitPackets-cf462b02-f1e3-4ed5-a1e7-acc1f040495b-StreamThread-1] task [0_0] Unable to records bytes produced to topic my-app.packet.surface by sink node split-server-log as the node is not recognized.
Known sink nodes are [].
18:59:05.981 [kafka-producer-network-thread | my-app-events-processor.splitPackets-cf462b02-f1e3-4ed5-a1e7-acc1f040495b-StreamThread-1-producer] ERROR o.a.k.s.p.i.RecordCollectorImpl - stream-thread [my-app-events-processor.splitPackets-cf462b02-f1e3-4ed5-a1e7-acc1f040495b-StreamThread-1] task [0_0] Unable to records bytes produced to topic my-app.packet.surface by sink node split-server-log as the node is not recognized.
Known sink nodes are [].
19:00:28.484 [my-app-events-processor.splitPackets-cf462b02-f1e3-4ed5-a1e7-acc1f040495b-StreamThread-1] INFO o.a.k.s.p.internals.StreamThread - stream-thread [my-app-events-processor.splitPackets-cf462b02-f1e3-4ed5-a1e7-acc1f040495b-StreamThread-1] Processed 3 total records, ran 0 punctuators, and committed 3 total tasks since the last update
When I run the application, not all data is processed, only some. Some KafkaStream instances produce data, while others only seem to consume it. I expect it to consume JSON data, and produce images (to be used in a Leaflet web-map). However it will only do this for some of the KafkaStream instances.
I don't get this error when I run locally. What does it mean? How can I fix it?
Application setup
I have a single application, events-processors, written in Kotlin that uses Kafka Streams. The application uses a Kafka Admin instance to create the topics, then launches 4 separate KafkaStream instances using independent Kotlin Coroutines. events-processors runs in a Docker container.
The Kafka instance is using Kafka Kraft, and is running in another Docker container on the same Docker network.
I am using
Kafka 3.3.1
Kotlin 1.7.20
docker-compose version 1.29.2
Docker version 20.10.19
Debian GNU/Linux 11 (bullseye)
Kernel: Linux 5.10.0-18-amd64
Architecture: x86-64
Kafka config
Here is the config of one of the KafkaStreams instances:
18:38:25.138 [DefaultDispatcher-worker-5 #my-app-events-processor.splitPackets#5] INFO o.a.k.s.p.internals.StreamThread - stream-thread [my-app-events-processor.splitPackets-d7b897b3-3a10-48d6-95c7-e291cb1839d8-StreamThread-1] Creating restore consumer client
18:38:25.142 [DefaultDispatcher-worker-5 #my-app-events-processor.splitPackets#5] INFO o.a.k.c.consumer.ConsumerConfig - ConsumerConfig values:
allow.auto.create.topics = true
auto.commit.interval.ms = 5000
auto.offset.reset = none
bootstrap.servers = [http://kafka:29092]
check.crcs = true
client.dns.lookup = use_all_dns_ips
client.id = my-app-events-processor.splitPackets-d7b897b3-3a10-48d6-95c7-e291cb1839d8-StreamThread-1-restore-consumer
client.rack =
connections.max.idle.ms = 540000
default.api.timeout.ms = 60000
enable.auto.commit = false
exclude.internal.topics = true
fetch.max.bytes = 52428800
fetch.max.wait.ms = 500
fetch.min.bytes = 1
group.id = null
group.instance.id = null
heartbeat.interval.ms = 3000
interceptor.classes = []
internal.leave.group.on.close = false
internal.throw.on.fetch.stable.offset.unsupported = true
isolation.level = read_committed
key.deserializer = class org.apache.kafka.common.serialization.ByteArrayDeserializer
max.partition.fetch.bytes = 1048576
max.poll.interval.ms = 300000
max.poll.records = 1000
metadata.max.age.ms = 300000
metric.reporters = []
metrics.num.samples = 2
metrics.recording.level = INFO
metrics.sample.window.ms = 30000
partition.assignment.strategy = [class org.apache.kafka.clients.consumer.RangeAssignor, class org.apache.kafka.clients.consumer.CooperativeStickyAssignor]
receive.buffer.bytes = 65536
reconnect.backoff.max.ms = 1000
reconnect.backoff.ms = 50
request.timeout.ms = 30000
retry.backoff.ms = 100
sasl.client.callback.handler.class = null
sasl.jaas.config = null
sasl.kerberos.kinit.cmd = /usr/bin/kinit
sasl.kerberos.min.time.before.relogin = 60000
sasl.kerberos.service.name = null
sasl.kerberos.ticket.renew.jitter = 0.05
sasl.kerberos.ticket.renew.window.factor = 0.8
sasl.login.callback.handler.class = null
sasl.login.class = null
sasl.login.connect.timeout.ms = null
sasl.login.read.timeout.ms = null
sasl.login.refresh.buffer.seconds = 300
sasl.login.refresh.min.period.seconds = 60
sasl.login.refresh.window.factor = 0.8
sasl.login.refresh.window.jitter = 0.05
sasl.login.retry.backoff.max.ms = 10000
sasl.login.retry.backoff.ms = 100
sasl.mechanism = GSSAPI
sasl.oauthbearer.clock.skew.seconds = 30
sasl.oauthbearer.expected.audience = null
sasl.oauthbearer.expected.issuer = null
sasl.oauthbearer.jwks.endpoint.refresh.ms = 3600000
sasl.oauthbearer.jwks.endpoint.retry.backoff.max.ms = 10000
sasl.oauthbearer.jwks.endpoint.retry.backoff.ms = 100
sasl.oauthbearer.jwks.endpoint.url = null
sasl.oauthbearer.scope.claim.name = scope
sasl.oauthbearer.sub.claim.name = sub
sasl.oauthbearer.token.endpoint.url = null
security.protocol = PLAINTEXT
security.providers = null
send.buffer.bytes = 131072
session.timeout.ms = 45000
socket.connection.setup.timeout.max.ms = 30000
socket.connection.setup.timeout.ms = 10000
ssl.cipher.suites = null
ssl.enabled.protocols = [TLSv1.2, TLSv1.3]
ssl.endpoint.identification.algorithm = https
ssl.engine.factory.class = null
ssl.key.password = null
ssl.keymanager.algorithm = SunX509
ssl.keystore.certificate.chain = null
ssl.keystore.key = null
ssl.keystore.location = null
ssl.keystore.password = null
ssl.keystore.type = JKS
ssl.protocol = TLSv1.3
ssl.provider = null
ssl.secure.random.implementation = null
ssl.trustmanager.algorithm = PKIX
ssl.truststore.certificates = null
ssl.truststore.location = null
ssl.truststore.password = null
ssl.truststore.type = JKS
value.deserializer = class org.apache.kafka.common.serialization.ByteArrayDeserializer
The server config is the Kafka Kraft config, https://github.com/apache/kafka/blob/215d4f93bd16efc8e9b7ccaa9fc99a1433a9bcfa/config/kraft/server.properties, although I have changed the advertised listeners.
advertised.listeners=PLAINTEXT://kafka:29092,PLAINTEXT_HOST://localhost:9092
Docker config
The Docker config is defined in a docker-compose file.
version: "3.9"
services:
events-processors:
image: events-processors
container_name: events-processors
restart: unless-stopped
environment:
KAFKA_BOOTSTRAP_SERVERS: "http://kafka:29092"
networks:
- my-app-infra-nw
depends_on:
- infra-kafka
secrets:
- source: my-app_config
target: /.secret.config.yml
infra-kafka:
image: kafka-kraft
container_name: infra-kafka
restart: unless-stopped
networks:
my-app-infra-nw:
aliases: [ kafka ]
volumes:
- "./config/kafka-server.properties:/kafka/server.properties"
ports:
# note: other Docker containers should use 29092
- "9092:9092"
- "9093:9093"
I have configured my flume agent like below. Somehow, the flume agent doesn't run properly. It keeps hanging without any errors. Is there any problem with the below configuration.
FYI: I have a file named "country" with hard-coded header as state
#Define sources, sink and channels
foo.sources = s1
foo.channels = chn-az chn-oth
foo.sinks = sink-az sink-oth
#
### # # Define a source on agent and connect to channel memory-channel.
foo.sources.s1.type = exec
foo.sources.s1.command = cat /home/hadoop/flume/country.txt
foo.sources.s1.batchSize = 1
foo.sources.s1.channels = chn-ca chn-oth
#selector configuration
foo.sources.s1.selector.type = multiplexing
foo.sources.s1.selector.header = state
foo.sources.s1.selector.mapping.AZ = chn-az
foo.sources.s1.selector.default = chn-oth
#
#
### Define a memory channel on agent called memory-channel.
foo.channels.chn-az.type = memory
foo.channels.chn-oth.type = memory
#
#
##Define sinks that outputs to hdfs.
foo.sinks.sink-az.channel = chn-az
foo.sinks.sink-az.type = hdfs
foo.sinks.sink-az.hdfs.path = hdfs://master:9099/user/hadoop/flume
foo.sinks.sink-az.hdfs.filePrefix = statefilter
foo.sinks.sink-az.hdfs.fileType = DataStream
foo.sinks.sink-az.hdfs.writeFormat = Text
foo.sinks.sink-az.batchSize = 1
foo.sinks.sink-az.rollInterval = 0
#
foo.sinks.sink-oth.channel = chn-oth
foo.sinks.sink-oth.type = hdfs
foo.sinks.sink-oth.hdfs.path = hdfs://master:9099/user/hadoop/flume
foo.sinks.sink-oth.hdfs.filePrefix = statefilter
foo.sinks.sink-oth.hdfs.fileType = DataStream
foo.sinks.sink-oth.batchSize = 1
foo.sinks.sink-oth.rollInterval = 0
Thanks,
Vinoth
Regarding the channels list configured at the source:
foo.sources.s1.channels = chn-ca chn-oth
I think chn-ca should be chn-az.
Nevertheless, I think such a configuration will never work since the "state" header used by the selector is not created by any Flume component. You must introduce an interceptor for that, typically the Regex Extractor Interceptor.
I follow the guid of spark streaming + flume integration. But i can't get any events in the end.
(https://spark.apache.org/docs/latest/streaming-flume-integration.html)
Can any one help me analysis it?
In the fume, I created the file of "avro_flume.conf" as follows:
Describe/configure the source
a1.sources = r1
a1.channels = c1
a1.sources.r1.type = avro
a1.sources.r1.channels = c1
a1.sources.r1.bind = 123.57.54.113
a1.sources.r1.port = 4141
Describe the sink
a1.sinks = k1
a1.sinks.k1.type = avro
Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
a1.sinks.k1.hostname = 123.57.54.113
a1.sinks.k1.port = 6666
a1.sources = r1
a1.sinks = spark
a1.channels = c1
In the file , 123.57.54.113 is the ip of localhost.
I start the programing as follows:
1.Start agent
flume-ng agent -c . -f conf/avro_spark.conf -n a1 Start Spark-streaming
2.Start spark-streaming example
bin/run-example org.apache.spark.examples.streaming.FlumeEventCount 123.57.54.113 6666
3.Then I start the avro-cilent
flume-ng avro-client -c . -H 123.57.54.113 -p 4141 -F test/log.01
4.test/log.01" is a file created by echo which contains some string
In the end ,there is no events at all.
What's the problem?
Thanks !
I see "a1.sinks = spark" under heading "Binding the source and sink to the channel". But the sink with name "spark" is not defined elsewhere in your configuration.
Are you trying approach 1 or approach 2 from "https://spark.apache.org/docs/latest/streaming-flume-integration.html"?
Try removing the line "a1.sinks = spark" if you are trying approach 1.
For approach 2 use the following template:
agent.sinks = spark
agent.sinks.spark.type = org.apache.spark.streaming.flume.sink.SparkSink
agent.sinks.spark.hostname = <hostname of the local machine>
agent.sinks.spark.port = <port to listen on for connection from Spark>
agent.sinks.spark.channel = memoryChannel
I have two graphite setup and I am trying to relay the traffic between the two, but somehow the carbon-relay is not working.
My cache runs on 2003/2004 and relay on 2013/2014
Following are the configurations done :
#carbon file
[cache:b]
LINE_RECEIVER_PORT = 2003
PICKLE_RECEIVER_PORT = 2004
CACHE_QUERY_PORT = 7012
[relay]
LINE_RECEIVER_INTERFACE = 0.0.0.0
LINE_RECEIVER_PORT = 2013
PICKLE_RECEIVER_INTERFACE = 0.0.0.0
PICKLE_RECEIVER_PORT = 2014
RELAY_METHOD = rules
REPLICATION_FACTOR = 1
DESTINATIONS = 127.0.0.1:2003:a, aa.bb.cc.dd:2003:b
#relay-rules file
[default]
default = true
destinations = 127.0.0.1:2003:a, aa.bb.cc.dd:2003:b
Any pointers will be helpful
As part of the recent project at work, I've figured out that carbon demons uses PICKLE protocol when sending data to the destinations.
So the destination of carbon-relay should be carbon-cache's pickle receiver port instead.
#carbon.conf
....
[relay]
LINE_RECEIVER_INTERFACE = 0.0.0.0
LINE_RECEIVER_PORT = 2013
PICKLE_RECEIVER_INTERFACE = 0.0.0.0
PICKLE_RECEIVER_PORT = 2014
RELAY_METHOD = rules
REPLICATION_FACTOR = 1
DESTINATIONS = 127.0.0.1:2004:a, aa.bb.cc.dd:2004:b
Also modify the relay-rules.conf with the same destinations specified in carbon.conf
relay-rules.conf
.....
[default]
default = true
destinations = 127.0.0.1:2004:a, aa.bb.cc.dd:2004:b
I m using apache-flume1.4.0 with hbase0.94.10 and hadoop1.1.2.
flume agent have spool directory as source and hbase as sink and file channel.It is running successfully but very slow.what should I do for improving hbase write performance.
Flume agent conf is as below:
agent1.sources = spool
agent1.channels = fileChannel
agent1.sinks = sink
agent1.sources.spool.type = spooldir
agent1.sources.spool.spoolDir = /opt/spoolTest/
agent1.sources.spool.fileSuffix = .completed
agent1.sources.spool.channels = fileChannel
#agent1.sources.spool.deletePolicy = immediate
agent1.sinks.sink.type = org.apache.flume.sink.hbase.HBaseSink
agent1.sinks.sink.channel = fileChannel
agent1.sinks.sink.table = test
agent1.sinks.sink.columnFamily = log
agent1.sinks.sink.serializer = org.apache.flume.sink.hbase.RegexHbaseEventSerializer
agent1.sinks.sink.serializer.regex = (.*)^C(.*)^C(.*)^C(.*)^C(.*)^C(.*)^C(.*)^C(.*)^C(.*)^C(.*)^C(.*)^C(.*)^C(.*)^C(.*)
agent1.sinks.sink.serializer.colNames = id,no_fill_reason,adInfo,locationInfo,handsetInfo,siteInfo,reportDate,ipaddress,headerContent,userParaContent,reqParaContent,otherPara,others,others1
agent1.sinks.sink1.batchSize = 100
agent1.channels.fileChannel.type = file
agent1.channels.fileChannel.checkpointDir = /usr/flumeFileChannel/chkpointFlume
agent1.channels.fileChannel.dataDirs = /usr/flumeFileChannel/dataFlume
agent1.channels.fileChannel.capacity = 10000000
agent1.channels.fileChannel.transactionCapacity = 100000
What should be capacity,transaction capacity of file channel and batch size of sink.
Please help me.
Thanks in advance.