I am using Confluent 3.0.1 platform on windows. I followed installation guide and developer guide to do all installation and develop my Topology.
I started Zookeeper, then Kafka server and try to run my topology. But getting below error on Kafka server. Even if I create topic manually and run the topology I see same error.
INFO Topic creation {"version":1,"partitions":{"0":[0]}} (kafka.admin.AdminUtils$)
[2016-09-21 17:20:08,807] INFO [KafkaApi-0] Auto creation of topic Text4 with 1 partitions and replication factor 1 is successful (kafka.server.KafkaApis)
[2016-09-21 17:20:09,436] ERROR [KafkaApi-0] Error when handling request {group_id=my-first-streams-application1} (kafka.server.KafkaApis)
kafka.admin.AdminOperationException: replication factor: 3 larger than available brokers: 1
at kafka.admin.AdminUtils$.assignReplicasToBrokers(AdminUtils.scala:117)
at kafka.admin.AdminUtils$.createTopic(AdminUtils.scala:403)
at kafka.server.KafkaApis.kafka$server$KafkaApis$$createTopic(KafkaApis.scala:629)
at kafka.server.KafkaApis.kafka$server$KafkaApis$$createGroupMetadataTopic(KafkaApis.scala:651)
at kafka.server.KafkaApis$$anonfun$getOrCreateGroupMetadataTopic$1.apply(KafkaApis.scala:657)
at kafka.server.KafkaApis$$anonfun$getOrCreateGroupMetadataTopic$1.apply(KafkaApis.scala:657)
at scala.Option.getOrElse(Option.scala:121)
at kafka.server.KafkaApis.getOrCreateGroupMetadataTopic(KafkaApis.scala:657)
at kafka.server.KafkaApis.handleGroupCoordinatorRequest(KafkaApis.scala:818)
at kafka.server.KafkaApis.handle(KafkaApis.scala:86)
at kafka.server.KafkaRequestHandler.run(KafkaRequestHandler.scala:60)
at java.lang.Thread.run(Thread.java:745)
And my topology code is below:
public class MetricTopology implements InitializingBean {
#Autowired
#Qualifier("getStreamsConfig")
private Properties properties;
// Method to build topology.
public void buildTopology() {
System.out.println("MetricTopology.buildTopology()");
TopologyBuilder builder = new TopologyBuilder();
// add the source processor node that takes Kafka topic "Text4" as input
builder.addSource("Source", "Text4")
// add the Metricsprocessor node which takes the source processor as its upstream processor
.addProcessor("Process", () -> new MetricsProcessor(), "Source");
// Building Stream.
KafkaStreams streams = new KafkaStreams(builder, properties);
streams.start();
}
// Called after all properties are set.
public void afterPropertiesSet() throws Exception {
buildTopology();
}
}
Below are the Properties which I am using which is part of different java source file.
Properties settings = new Properties();
// Set a few key parameters. This properties will be picked from property file.
settings.put(StreamsConfig.APPLICATION_ID_CONFIG, "my-first-streams-application1");
settings.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
settings.put(StreamsConfig.ZOOKEEPER_CONNECT_CONFIG, "localhost:2181");
settings.put(StreamsConfig.KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
settings.put(StreamsConfig.VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());
settings.put(StreamsConfig.REPLICATION_FACTOR_CONFIG, "1");
kafka.admin.AdminOperationException: replication factor: 3 larger than available brokers: 1
In order to create a topic with replication factor 3, you need at least 3 running brokers.
Related
I have an application that consumes from kafka topics and produce to kafka topics. I am trying to set up Grafana dashboard to display kafka metrics. I am trying to expose the kafka metrics to prometheus and so Grafana dashboard can fetch these metrics and display them further.
Below are the libraries and properties I am using.
org.apache.kafka:kafka-clients:jar:2.5.1:compile
io.micrometer:micrometer-registry-prometheus:jar:1.5.4:compile
io.micrometer:micrometer-jersey2:jar:1.5.4:compile
application properties:
spring.jmx.enabled=true
management.endpoints.web.base-path=/
management.endpoint.metrics.enabled=true
management.endpoints.web.exposure.include=prometheus
management.endpoint.prometheus.enabled=true
management.metrics.export.prometheus.enabled=true
Using micrometer, the prometheus is displaying jvm, tomcat related metrics and also the custom metrics. But the kafka metrics are not being exposed. I tried to debug and left with no clue. Any suggestions would be a great help.
As a heads-up, I am not using spring kafka annotations. I am running it as standalone multi-threaded where I fetch records using consumer.poll(1000L) method.
kafka consumer is created as: new KafkaConsumer<>(getProps()) and kafka producer is created as new KafkaProducer<>(props)
props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, ...);
props.put(ConsumerConfig.CLIENT_ID_CONFIG, config.getConsumerName());
props.put(ConsumerConfig.GROUP_ID_CONFIG, config.getGroupId());
props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, config.getKeyDeserializer());
props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, config.getValueDeserializer());
props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, config.getEnableAutoCommit());
props.put(ConsumerConfig.RECEIVE_BUFFER_CONFIG, config.getReceiveBufferBytes());
props.put(ConsumerConfig.MAX_PARTITION_FETCH_BYTES_CONFIG, config.getMaxPartitionFetchBytes());
props.put(ConsumerConfig.MAX_POLL_RECORDS_CONFIG, config.getMaxPollRecords());
props.put(ConsumerConfig.PARTITION_ASSIGNMENT_STRATEGY_CONFIG, RoundRobinAssignor.class.getName());
protected KafkaConsumer<byte[], Event> getConsumer(List<String> inputTopics) {
KafkaConsumer<byte[], Event> consumer = new KafkaConsumer<>(props));
consumer.subscribe(inputTopics, new ConsumerRebalanceListener() {
#Override
public void onPartitionsRevoked(Collection<TopicPartition> partitions) {
logger.info("PARTITIONS revoked: " + partitions);
consumer.commitAsync();
}
#Override
public void onPartitionsAssigned(Collection<TopicPartition> partitions) {
logger.info("PARTITIONS assigned: " + partitions);
}
});
return consumer;
}
I already answered you on Gitter - the code above is not using Spring for Apache Kafka, you are creating your own consumer and producer.
If you use Spring for Apache Kafka's DefaultKafkaConsumerFactory, you can add a MicrometerConsumerListener to it and Spring will register metrics for each consumer with the meter registry via the KafkaClientMetrics. If you create your own consumers, you have to do that yourself.
Same thing for producers.
One of Kafka stream application is generating a lot of Unknown Producer Id errors in the Kafka brokers as well as on the consumer side.
Stream Configs are as below:
final Properties streamsConfiguration = new Properties();
streamsConfiguration.put(StreamsConfig.APPLICATION_ID_CONFIG, appName);
streamsConfiguration.put(StreamsConfig.CLIENT_ID_CONFIG,appName + "-Client");
streamsConfiguration.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, this.bootstrapServer);
streamsConfiguration.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.Long().getClass().getName());
streamsConfiguration.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
streamsConfiguration.put(StreamsConfig.PROCESSING_GUARANTEE_CONFIG,StreamsConfig.EXACTLY_ONCE);
streamsConfiguration.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, offset);
streamsConfiguration.put(StreamsConfig.STATE_DIR_CONFIG,state_dir);
streamsConfiguration.put(StreamsConfig.REPLICATION_FACTOR_CONFIG,defaultReplication);
return streamsConfiguration;
Error on the broker side:
Error on the consumer side:
custom configuration for repartition internal topic:
prod.Prod-Job-Summary-v0.4-KTABLE-AGGREGATE-STATE-STORE-0000000049-repartition
What can be the reason behind these?
It's a known issue. See KAFKA-7190
Under low traffic conditions purging repartition topics cause WARN statements about UNKNOWN_PRODUCER_ID and KIP-360: Improve handling of unknown producer.
I am using kafka 0.10.1.1 and storm 1.0.2. In the storm documentation for kafka integration , i can see that offsets are still maintained using zookeeper as we are initializing kafka spout using zookeeper servers.
How can i bootstrap the spout using kafka servers .Is there any example for this .
Example from storm docs
BrokerHosts hosts = new ZkHosts(zkConnString);
SpoutConfig spoutConfig = new SpoutConfig(hosts, topicName, "/" + topicName, UUID.randomUUID().toString());
spoutConfig.scheme = new SchemeAsMultiScheme(new StringScheme());
KafkaSpout kafkaSpout = new KafkaSpout(spoutConfig);
This option using zookeeper is working fine and is consuming the messages . but i was not able to see the consumer group or storm nodes as consumers in kafkamanager ui .
Alternate approach tried is this .
KafkaSpoutConfig<String, String> kafkaSpoutConfig = newKafkaSpoutConfig();
KafkaSpout<String, String> spout = new KafkaSpout<>(kafkaSpoutConfig);
private static KafkaSpoutConfig<String, String> newKafkaSpoutConfig() {
Map<String, Object> props = new HashMap<>();
props.put(KafkaSpoutConfig.Consumer.BOOTSTRAP_SERVERS, bootstrapServers);
props.put(KafkaSpoutConfig.Consumer.GROUP_ID, GROUP_ID);
props.put(KafkaSpoutConfig.Consumer.KEY_DESERIALIZER,
"org.apache.kafka.common.serialization.StringDeserializer");
props.put(KafkaSpoutConfig.Consumer.VALUE_DESERIALIZER,
"org.apache.kafka.common.serialization.StringDeserializer");
props.put(KafkaSpoutConfig.Consumer.ENABLE_AUTO_COMMIT, "true");
String[] topics = new String[1];
topics[0] = topicName;
KafkaSpoutStreams kafkaSpoutStreams =
new KafkaSpoutStreamsNamedTopics.Builder(new Fields("message"), topics).build();
KafkaSpoutTuplesBuilder<String, String> tuplesBuilder =
new KafkaSpoutTuplesBuilderNamedTopics.Builder<>(new TuplesBuilder(topicName)).build();
KafkaSpoutConfig<String, String> spoutConf =
new KafkaSpoutConfig.Builder<>(props, kafkaSpoutStreams, tuplesBuilder).build();
return spoutConf;
}
But this solution is showing CommitFailedException after reading few messages from kafka.
Storm-kafka writes consumer information in a different location and different format in zookeeper with common kafka client. So you can't see it in kafkamanager ui.
You can find some other monitor tools, like
https://github.com/keenlabs/capillary.
On your alternate approach, you're likely getting CommitFailedException due to:
props.put(KafkaSpoutConfig.Consumer.ENABLE_AUTO_COMMIT, "true");
Up to Storm 2.0.0-SNAPSHOT (and since 1.0.6) -
KafkaConsumer autocommit is unsupported
From the docs:
Note that KafkaConsumer autocommit is unsupported. The
KafkaSpoutConfig constructor will throw an exception if the
"enable.auto.commit" property is set, and the consumer used by the
spout will always have that property set to false. You can configure
similar behavior to autocommit through the setProcessingGuarantee
method on the KafkaSpoutConfig builder.
References:
http://storm.apache.org/releases/2.0.0-SNAPSHOT/storm-kafka-client.html
http://storm.apache.org/releases/1.0.6/storm-kafka-client.html
I am using kafka with flink.
In a simple program, I used flinks FlinkKafkaConsumer09, assigned the group id to it.
According to Kafka's behavior, when I run 2 consumers on the same topic with same group.Id, it should work like a message queue. I think it's supposed to work like:
If 2 messages sent to Kafka, each or one of the flink program would process the 2 messages totally twice(let's say 2 lines of output in total).
But the actual result is that, each program would receive 2 pieces of the messages.
I have tried to use consumer client that came with the kafka server download. It worked in the documented way(2 messages processed).
I tried to use 2 kafka consumers in the same Main function of a flink programe. 4 messages processed totally.
I also tried to run 2 instances of flink, and assigned each one of them the same program of kafka consumer. 4 messages.
Any ideas?
This is the output I expect:
1> Kafka and Flink2 says: element-65
2> Kafka and Flink1 says: element-66
Here's the wrong output i always get:
1> Kafka and Flink2 says: element-65
1> Kafka and Flink1 says: element-65
2> Kafka and Flink2 says: element-66
2> Kafka and Flink1 says: element-66
And here is the segment of code:
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
ParameterTool parameterTool = ParameterTool.fromArgs(args);
DataStream<String> messageStream = env.addSource(new FlinkKafkaConsumer09<>(parameterTool.getRequired("topic"), new SimpleStringSchema(), parameterTool.getProperties()));
messageStream.rebalance().map(new MapFunction<String, String>() {
private static final long serialVersionUID = -6867736771747690202L;
#Override
public String map(String value) throws Exception {
return "Kafka and Flink1 says: " + value;
}
}).print();
env.execute();
}
I have tried to run it twice and also in the other way:
create 2 datastreams and env.execute() for each one in the Main function.
There was a quite similar question on the Flink user mailing list today, but I can't find the link to post it here. So here a part of the answer:
"Internally, the Flink Kafka connectors don’t use the consumer group
management functionality because they are using lower-level APIs
(SimpleConsumer in 0.8, and KafkaConsumer#assign(…) in 0.9) on each
parallel instance for more control on individual partition
consumption. So, essentially, the “group.id” setting in the Flink
Kafka connector is only used for committing offsets back to ZK / Kafka
brokers."
Maybe that clarifies things for you.
Also, there is a blog post about working with Flink and Kafka that may help you (https://data-artisans.com/blog/kafka-flink-a-practical-how-to).
Since there is not much use of group.id of flink kafka consumer other than commiting offset to zookeeper. Is there any way of offset monitoring as far as flink kafka consumer is concerned. I could see there is a way [with the help of consumer-groups/consumer-offset-checker] for console consumers but not for flink kafka consumers.
We want to see how our flink kafka consumer is behind/lagging with kafka topic size[total number of messages in topic at given point of time], it is fine to have it at partition level.
In Kafka 0.8beta a topic can be created using a command like below as mentioned here
bin/kafka-create-topic.sh --zookeeper localhost:2181 --replica 2 --partition 3 --topic test
the above command will create a topic named "test" with 3 partitions and 2 replicas per partition.
Can I do the same thing using Java ?
So far what I found is using Java we can create a producer as seen below
Producer<String, String> producer = new Producer<String, String>(config);
producer.send(new KeyedMessage<String, String>("mytopic", msg));
This will create a topic named "mytopic" with the number of partition specified using the "num.partitions" attribute and start producing.
But is there a way to define the partition and replication also ? I couldn't find any such example. If we can't then does that mean we always need to create topic with partitions and replication (as per our requirement) before and then use the producer to produce message within that topic. For example will it be possible if I want to create the "mytopic" the same way but with different number of partition (overriding the num.partitions attribute) ?
Note: My answer covers Kafka 0.8.1+, i.e. the latest stable version available as of April 2014.
Yes, you can create a topic programatically via the Kafka API. And yes, you can specify the desired number of partitions as well as the replication factor for the topic.
Note that the recently released Kafka 0.8.1+ provides a slightly different API than Kafka 0.8.0 (which was used by Biks in his linked reply). I added a code example to create a topic in Kafka 0.8.1+ to my reply to the question How Can we create a topic in Kafka from the IDE using API that Biks was referring to above.
`
import kafka.admin.AdminUtils;
import kafka.cluster.Broker;
import kafka.utils.ZKStringSerializer$;
import kafka.utils.ZkUtils;
String zkConnect = "localhost:2181";
ZkClient zkClient = new ZkClient(zkConnect, 10 * 1000, 8 * 1000, ZKStringSerializer$.MODULE$);
ZkUtils zkUtils = new ZkUtils(zkClient, new ZkConnection(zkConnect), false);
Properties pop = new Properties();
AdminUtils.createTopic(zkUtils, topic.getTopicName(), topic.getPartitionCount(), topic.getReplicationFactor(),
pop);
zkClient.close();`