I am using Kafka producer 0.8.2 and I am trying to send a single message to the topic, in a way that the message is sent immediately. I have a console consumer to observe if the message arrives. I notice that the message is not sent immediately, unless of course I run producer.close(), immediately after sending, which isn't what I would like to do.
What is the correct producer configuration setting to target this? I'm using the following (I'm aware that it looks like a mess of different configurations/versions, but I simply cannot find something that's working as I would expect in the documentation):
Properties props = new Properties();
props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, brokersStr);
props.put(ProducerConfig.RETRIES_CONFIG, "3");
props.put("producer.type", "sync");
props.put("batch.num.messages", "1");
props.put(ProducerConfig.ACKS_CONFIG, "all");
props.put(ProducerConfig.COMPRESSION_TYPE_CONFIG, "none");
props.put(ProducerConfig.BATCH_SIZE_CONFIG, 1);
props.put(ProducerConfig.BLOCK_ON_BUFFER_FULL_CONFIG, true);
props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
I found a solution, which seems reasonable, and involves running get() on the Future returned by the Producer's send() command. I changed the send command from:
producer.send(record);
to the following:
producer.send(record).get();
It would be nice to hear from the more experienced Kafka users if there are any issues with that approach? Also, I would be interested to learn if there is a configuration setting for the Producer to achieve the same thing (that is, send a single message immediately without running get() of the Future).
Old post but I have struggled way to much to miss a post here.
I stumbled upon the same behavior trying to run the Kafka examples and this .get() was the only thing that got the messages to Kafka. The Javadoc for KafkaProducer.send(…) states this method is asynchronous. On my test code, the message was thus sent to Kafka while my code continued to run and actually just got to the end of the run and terminated before the message was actually sent inside the Future.
So this .get() just blocks on the Future until it is realized. This actually removes the benefits of the Future. A cleaner way to do it could be to wait a bit with a Thread.sleep(…) right after the .send(…) (depends on your use case).
Related
I'm writing an application with Spring Boot so to write to Kafka I do:
#Autowired
private KafkaTemplate<String, String> kafkaTemplate;
and then inside my method:
kafkaTemplate.send(topic, data)
But I feel like I'm just relying on this to work, how can I know if this has worked? If it's asynchronous, is it a good practice to return a 200 code and hoped it did work? I'm confused. If Kafka isn't available, won't this fail? Shouldn't I be prompted to catch an exception?
Along with what #mjuarez has mentioned you can try playing with two Kafka producer properties. One is ProducerConfig.ACKS_CONFIG, which lets you set the level of acknowledgement that you think is safe for your use case. This knob has three possible values. From Kafka doc
acks=0: Producer doesn't care about acknowledgement from server, and considers it as sent.
acks=1: This will mean the leader will write the record to its local log but will respond without awaiting full acknowledgement from all followers.
acks=all: This means the leader will wait for the full set of in-sync replicas to acknowledge the record.
The other property is ProducerConfig.RETRIES_CONFIG. Setting a value greater than zero will cause the client to resend any record whose send fails with a potentially transient error.
Yes, if Kafka is not available, that .send() call will fail, but if you send it async, no one will be notified. You can specify a callback that you want to be executed when the future finally finishes. Full interface spec here: https://kafka.apache.org/20/javadoc/org/apache/kafka/clients/producer/Callback.html
From the official Kafka javadoc here: https://kafka.apache.org/20/javadoc/index.html?org/apache/kafka/clients/producer/KafkaProducer.html
Fully non-blocking usage can make use of the Callback parameter to
provide a callback that will be invoked when the request is complete.
ProducerRecord<byte[],byte[]> record = new ProducerRecord<byte[],byte[]>("the-topic", key, value);
producer.send(myRecord,
new Callback() {
public void onCompletion(RecordMetadata metadata, Exception e) {
if(e != null) {
e.printStackTrace();
} else {
System.out.println("The offset of the record we just sent is: " + metadata.offset());
}
}
});
you can use below command while sending messages to kafka:
bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic topic-name
while above command is running you should run your code and if sending messages being successful then the message must be printed on the console.
Furthermore, likewise any other connection to any resources if the connection could not be established, then doing any kinds of operations would result some exception raises.
I have a single instance java application that uses KTable from Kafka Streams. Until recently I could retrieve all data using KTable when suddenly some of the messages seemed to vanish. There should be ~33k messages with unique keys there.
When I want to retrieve messages by key I don't get some of the messages. I use ReadOnlyKeyValueStore to retrieve messages:
final ReadOnlyKeyValueStore<GenericRecord, GenericRecord> store = ((KafkaStreams)streams).store(storeName, QueryableStoreTypes.keyValueStore());
store.get(key);
These are the configuration settings I set to the KafkaStreams.
final Properties config = new Properties();
config.put(StreamsConfig.APPLICATION_SERVER_CONFIG, serverId);
config.put(StreamsConfig.APPLICATION_ID_CONFIG, applicationId);
config.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
config.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
config.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, schemaRegistryUrl);
config.put(StreamsConfig.KEY_SERDE_CLASS_CONFIG, GenericAvroSerde.class);
config.put(StreamsConfig.VALUE_SERDE_CLASS_CONFIG, GenericAvroSerde.class);
config.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 0);
Kafka: 0.10.2.0-cp1
Confluent: 3.2.0
Investigations brought me to some very worrying insights. Using REST Proxy I manually read partitions and found out that some offsets return error.
Request:
/topics/{topic}/partitions/{partition}/messages?offset={offset}
{
"error_code": 50002,
"message": "Kafka error: Fetch response contains an error code: 1"
}
No client, neither java nor command line however return any error. They just skip over the faulty missing messages resulting in missing data in KTables. Everything was fine and without notice it seems that somehow some of the messages got corrupt.
I have two brokers and all the topics have the replication factor of 2 and are fully replicated. Both brokers separately return the same. Restarting brokers makes no difference.
What could possibly be the cause?
How to detect this case in a client?
By default Kafka Broker config key cleanup.policy is set to delete. Set it to compact to keep the latest message for each key. See compaction.
Deletion of old messages does not change the minimum offset so trying to retrieve message below it causes an error. The error is very vague. The Kafka Streams client will start reading messages from minimum offset so there is no error. The only visible effect is missing data in KTables.
While the application is running thanks to the caches all data might still be available even after messages are deleted from Kafka itself. They will vanish after cleanup.
I'm completely new to Kafka and i have some troubles using the KafkaProducer.
The send Method of the producer blocks exactly 1min and then the application proceeds without an exception. This is obviously some timeout but no Exception is thrown.
I can also see nothing really in the logs.
The servers seam to be setup correctly. If i use the bin/kafka-console-consumer and producer applications i can send and receive messages correctly. Also the code seams to work to some extend.
If i want to write to a topic which does not exist yet i can see in the /tmp/kafka-logs folder the new entry and also in the console output of the KafkaServer.
Here is the Code i use:
Properties props = ResourceUtils.loadProperties("kafka.properties");
Producer<String, String> producer = new KafkaProducer<>(props);
for (String line : lines)
{
producer.send(new ProducerRecord<>("topic", Id, line));
producer.flush();
}
producer.close();
The properties in the kafka.properties file:
bootstrap.servers=localhost:9092
key.serializer=org.apache.kafka.common.serialization.StringSerializer
value.serializer=org.apache.kafka.common.serialization.StringSerializer
acks=all
retries=0
batch.size=16384
linger.ms=1
buffer.memory=33554432
So, producer.send blocks for 1minute and then it continues. At the end nothing is stored in Kafka, but the new topic is created.
Thank you for any help!
Try set the bootstrap.servers to 127.0.0.1:9092
I have an implementation of a KafkaConsumer in java, and currently it is never exiting the .poll method. When I drill down into the source code in debug mode I've found that it is getting stuck in the while loop in AbstractCoordinator.ensureCoordinatorKnown(), as the coordinator is never found.
The future returned from sendGroupMetadataRequest() in the loop fails the first time with org.apache.kafka.clients.consumer.internals.SendFailedException, and then will fail every subsequent time with org.apache.kafka.common.errors.GroupCoordinatorNotAvailableException: The group coordinator is not available.. Does anyone know why this might happen?
If I use the console producer/consumer I am able to successfully send and receive messages, it is only when I use my implementation of the KafkaConsumer. Additionally, the consumer does work on two of my servers so I know it is not the implementation of the consumer.
Here are the properties my consumer is created with:
Properties props = new Properties();
props.put("bootstrap.servers", "myserver:9000);
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
props.put("group.id", groupId);
props.put("enable.auto.commit", "true");
props.put("auto.commit.interval.ms", "1000");
props.put("session.timeout.ms", "30000");
Edit:
The topic is definitely created before the consumer starts.
Edit 2:
I deleted all of the brokers in my cluster and recreated them, and now I'm failing at a different point. In AbstractCoordinator.ensureActiveGroup() while trying to rejoin, the future returned from performGroupJoin() repeatedly fails with org.apache.kafka.common.errors.NotCoordinatorForGroupException: This is not the correct coordinator for this group.. Still not sure what is going on.
Edit 3:
I deleted the brokers and recreated them with a different id and now the .poll() method is returning and it's successfully consuming messages. I'd still like to know why it failed in the first place though so I can make sure it doesn't happen again.
Deleting the brokers and creating new ones fixed the problem. Still not sure went wrong with the brokers though.
I'm using Kafka and we have a use case to build a fault tolerant system where not even a single message should be missed. So here's the problem:
If publishing to Kafka fails due to any reason (ZooKeeper down, Kafka broker down etc) how can we robustly handle those messages and replay them once things are back up again. Again as I say we cannot afford even a single message failure.
Another use case is we also need to know at any given point in time how many messages were failed to publish to Kafka due to any reason i.e. something like counter functionality and now those messages needs to be re-published again.
One of the solution is to push those messages to some database (like Cassandra where writes are very fast but we also need counter functionality and I guess Cassandra counter functionality is not that great and we don't want to use that.) which can handle that kind of load and also provide us with the counter facility which is very accurate.
This question is more from architecture perspective and then which technology to use to make that happen.
PS: We handle some where like 3000TPS. So when system start failing those failed messages can grow very fast in very short time. We're using java based frameworks.
Thanks for your help!
The reason Kafka was built in a distributed, fault-tolerant way is to handle problems exactly like yours, multiple failures of core components should avoid service interruptions. To avoid a down Zookeeper, deploy at least 3 instances of Zookeepers (if this is in AWS, deploy them across availability zones). To avoid broker failures, deploy multiple brokers, and ensure you're specifying multiple brokers in your producer bootstrap.servers property. To ensure that the Kafka cluster has written your message in a durable manor, ensure that the acks=all property is set in the producer. This will acknowledge a client write when all in-sync replicas acknowledge reception of the message (at the expense of throughput). You can also set queuing limits to ensure that if writes to the broker start backing up you can catch an exception and handle it and possibly retry.
Using Cassandra (another well thought out distributed, fault tolerant system) to "stage" your writes doesn't seem like it adds any reliability to your architecture, but does increase the complexity, plus Cassandra wasn't written to be a message queue for a message queue, I would avoid this.
Properly configured, Kafka should be available to handle all your message writes and provide suitable guarantees.
I am super late to the party. But I see something missing in above answers :)
The strategy of choosing some distributed system like Cassandra is a decent idea. Once the Kafka is up and normal, you can retry all the messages that were written into this.
I would like to answer on the part of "knowing how many messages failed to publish at a given time"
From the tags, I see that you are using apache-kafka and kafka-consumer-api.You can write a custom call back for your producer and this call back can tell you if the message has failed or successfully published. On failure, log the meta data for the message.
Now, you can use log analyzing tools to analyze your failures. One such decent tool is Splunk.
Below is a small code snippet than can explain better about the call back I was talking about:
public class ProduceToKafka {
private ProducerRecord<String, String> message = null;
// TracerBulletProducer class has producer properties
private KafkaProducer<String, String> myProducer = TracerBulletProducer
.createProducer();
public void publishMessage(String string) {
ProducerRecord<String, String> message = new ProducerRecord<>(
"topicName", string);
myProducer.send(message, new MyCallback(message.key(), message.value()));
}
class MyCallback implements Callback {
private final String key;
private final String value;
public MyCallback(String key, String value) {
this.key = key;
this.value = value;
}
#Override
public void onCompletion(RecordMetadata metadata, Exception exception) {
if (exception == null) {
log.info("--------> All good !!");
} else {
log.info("--------> not so good !!");
log.info(metadata.toString());
log.info("" + metadata.serializedValueSize());
log.info(exception.getMessage());
}
}
}
}
If you analyze the number of "--------> not so good !!" logs per time unit, you can get the required insights.
God speed !
Chris already told about how to keep the system fault tolerant.
Kafka by default supports at-least once message delivery semantics, it means when it try to send a message something happens, it will try to resend it.
When you create a Kafka Producer properties, you can configure this by setting retries option more than 0.
Properties props = new Properties();
props.put("bootstrap.servers", "localhost:4242");
props.put("acks", "all");
props.put("retries", 0);
props.put("batch.size", 16384);
props.put("linger.ms", 1);
props.put("buffer.memory", 33554432);
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
Producer<String, String> producer = new KafkaProducer<>(props);
For more info check this.