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Get last event in Kafka - JAVA

I'm actually using #KafkaListener to read events in a topic. I want to read 100 events, and after this call Thread.sleep() with a certain time period.
My problem is, when the thread wakes up from sleep, the listener continues on the last event I read, but I want to discard the events when the thread is sleeping and continues with the last events in topic.
Like:
1-100 - Capture
Thread sleeping
101-500
Thread Returns
501 - 601 - Capture
The 101-500 events can be discarded
Code:
#KafkaListener(topics = "topic")
public void consumeBalance(ConsumerRecord<String, String> payload) throws InterruptedException {
this.valorMaximoDeRequest = this.valorMaximoDeRequest + 1;
if (this.valorMaximoDeRequest <= 100) {
log.info("Encontrou evento )");
log.info("Key: " + payload.key() + ", Value:" + payload.value());
log.info("Partition:" + payload.partition() + ",Offset:" + payload.offset());
JsonObject jsonObject = new Gson().fromJson(payload.value(), JsonObject.class);
String accountId = jsonObject.get("accountId").getAsString();
log.info(">>>>>>>>> accountId: " + accountId);
} else {
this.valorMaximoDeRequest = 0;
Thread.sleep(60*1000);
}
}
Kafka config:
#Bean
public Map<String, Object> kafkaFactory() {
Map<String, Object> props = new HashMap<>();
props.put("specific.avro.reader", Boolean.TRUE);
props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "brokers");
props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
props.put(ConsumerConfig.GROUP_ID_CONFIG, "1");
props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "latest");
props.put("security.protocol", "SASL_PLAINTEXT");
return props;
}

First, you shouldn't force the listening thread to sleep. The consumer may be considered as dead and trigger a consumer rebalance. You'd better use pause and resume on the consumer. See https://docs.spring.io/spring-kafka/docs/current/reference/html/#pause-resume
Then, if you want to skip the records published when the consumer was asleep, you'll have to seek (seekToBeginning) when the consumer awakes. See https://docs.spring.io/spring-kafka/docs/current/reference/html/#seek
However it is not simple: The Kafka Consumer doesn't let you seek when the consumer is active nor when it does not own the partition.

The point of having consumer groups is to keep track of the offsets which have been processed so that the subsequent consumption resumes from there and also distribute load across different consumers.
If your use-case doesn't need any of the above, you can use consumer.assign() which doesn't leverage the group management functionality.
#KafkaListener(topicPartitions = #TopicPartition(topic = "so56114299",
partitions = "#{#finder.partitions('so56114299')}"))
public void listen(#Header(KafkaHeaders.RECEIVED_MESSAGE_KEY) String key, String payload) {
System.out.println(key + ":" + payload);
}
Snippet reference: https://stackoverflow.com/a/56114699/2534090
Alternatively, you can write your own KafkaConsumer and manually call the consumer.assign() for assigning the partitions.
To answer your original question, for seeking, you need to call consumer.seekToEnd() method every time your method wakes from Thread.sleep(). The subsequent poll will fetch the records from the end offset. Looks like you can add Consumer as parameter to your #KafkaListener method.

Redis Streams one message per consumer with Java

I'am trying to implement a java application with redis streams where every consomer consumes exactly one message. Like a pipeline/queue where every consumer takes exactly one message, processes it and after finishing the consumer takes the next message which was not processed so far in the stream.
What works is that every message is consumed by exactly one consumer (with xreadgroup).
I started with this tutorial from redislabs
The code:
RedisClient redisClient = RedisClient.create("redis://pw#host:port");
StatefulRedisConnection<String, String> connection = redisClient.connect();
RedisCommands<String, String> syncCommands = connection.sync();
try {
syncCommands.xgroupCreate(XReadArgs.StreamOffset.from(STREAM_KEY, "0-0"), ID_READ_GROUP);
} catch (RedisBusyException redisBusyException) {
System.out.println(String.format("\t Group '%s' already exists", ID_READ_GROUP));
}
System.out.println("Waiting for new messages ");
while (true) {
List<StreamMessage<String, String>> messages = syncCommands.xreadgroup(
Consumer.from(ID_READ_GROUP, ID_WORKER), ReadArgs.StreamOffset.lastConsumed(STREAM_KEY));
if (!messages.isEmpty()) {
System.out.println(messages.size()); //
for (StreamMessage<String, String> message : messages) {
System.out.println(message.getId());
Thread.sleep(5000);
syncCommands.xack(STREAM_KEY, ID_READ_GROUP, message.getId());
}
}
}
My current problem is that a consumer takes more that one message from the queue and in some situations the other consumers are waiting and one consumer is processing 10 messages at once.
Thanks in advance!

Notice that XREADGROUP can get COUNT argument.
See the JavaDoc how to do it in Lettuce xreadgroup, by passing XReadArgs.

Apache kafka producer and consumer at different servres

I have multiple servers from where the messages will be produced, and I need broker and consumer at one server. If I have both producer and consumer running on same server then it works fine, but not sure what changes need to be done to keep producers separate. I don't want any dependency of zookeeper and kafka servers at producer servers as there are many and they will increase. I tried with changing bootstrap server to the broker/consumer server like 192.168.0.1:9092 while setting up KafkaProducer but still not able to generate messages. Not sure what am I missing, please help me out here.
I have followed https://github.com/mapr-demos/kafka-sample-programs for code.
Tried running both producer and consumer on same server, it works fine.
Producer.java
public class Producer {
public static void main(String[] args) throws IOException {
// set up the producer
KafkaProducer<String, String> producer;
try (InputStream props = Resources.getResource("producer.props").openStream()) {
Properties properties = new Properties();
properties.load(props);
producer = new KafkaProducer<>(properties);
}
try {
for (int i = 0; i < 1000000; i++) {
// send lots of messages
producer.send(new ProducerRecord<String, String>(
"fast-messages",
String.format("{\"type\":\"test\", \"t\":%.3f, \"k\":%d}", System.nanoTime() * 1e-9, i)));
// every so often send to a different topic
if (i % 1000 == 0) {
producer.send(new ProducerRecord<String, String>(
"fast-messages",
String.format("{\"type\":\"marker\", \"t\":%.3f, \"k\":%d}", System.nanoTime() * 1e-9, i)));
producer.send(new ProducerRecord<String, String>(
"summary-markers",
String.format("{\"type\":\"other\", \"t\":%.3f, \"k\":%d}", System.nanoTime() * 1e-9, i)));
producer.flush();
System.out.println("Sent msg number " + i);
}
}
} catch (Throwable throwable) {
System.out.printf("%s", throwable.getStackTrace());
} finally {
producer.close();
}
}
prodcuer.props
bootstrap.servers=192.168.0.1:9092
acks=all
retries=0
batch.size=16384
auto.commit.interval.ms=1000
linger.ms=0
key.serializer=org.apache.kafka.common.serialization.StringSerializer
value.serializer=org.apache.kafka.common.serialization.StringSerializer
block.on.buffer.full=true
Consumer.java
public class Consumer {
public static void main(String[] args) throws IOException {
// set up house-keeping
ObjectMapper mapper = new ObjectMapper();
Histogram stats = new Histogram(1, 10000000, 2);
Histogram global = new Histogram(1, 10000000, 2);
// and the consumer
KafkaConsumer<String, String> consumer;
try (InputStream props = Resources.getResource("consumer.props").openStream()) {
Properties properties = new Properties();
properties.load(props);
if (properties.getProperty("group.id") == null) {
properties.setProperty("group.id", "group-" + new Random().nextInt(100000));
}
consumer = new KafkaConsumer<>(properties);
}
consumer.subscribe(Arrays.asList("fast-messages", "summary-markers"));
int timeouts = 0;
//noinspection InfiniteLoopStatement
while (true) {
// read records with a short timeout. If we time out, we don't really care.
ConsumerRecords<String, String> records = consumer.poll(200);
if (records.count() == 0) {
timeouts++;
} else {
System.out.printf("Got %d records after %d timeouts\n", records.count(), timeouts);
timeouts = 0;
}
for (ConsumerRecord<String, String> record : records) {
switch (record.topic()) {
case "fast-messages":
// the send time is encoded inside the message
JsonNode msg = mapper.readTree(record.value());
switch (msg.get("type").asText()) {
case "test":
long latency = (long) ((System.nanoTime() * 1e-9 - msg.get("t").asDouble()) * 1000);
stats.recordValue(latency);
global.recordValue(latency);
break;
case "marker":
// whenever we get a marker message, we should dump out the stats
// note that the number of fast messages won't necessarily be quite constant
System.out.printf("%d messages received in period, latency(min, max, avg, 99%%) = %d, %d, %.1f, %d (ms)\n",
stats.getTotalCount(),
stats.getValueAtPercentile(0), stats.getValueAtPercentile(100),
stats.getMean(), stats.getValueAtPercentile(99));
System.out.printf("%d messages received overall, latency(min, max, avg, 99%%) = %d, %d, %.1f, %d (ms)\n",
global.getTotalCount(),
global.getValueAtPercentile(0), global.getValueAtPercentile(100),
global.getMean(), global.getValueAtPercentile(99));
stats.reset();
break;
default:
throw new IllegalArgumentException("Illegal message type: " + msg.get("type"));
}
break;
case "summary-markers":
break;
default:
throw new IllegalStateException("Shouldn't be possible to get message on topic " + record.topic());
}
}
}
}
}
consumer.props
bootstrap.servers=192.168.0.1:9092
group.id=test
enable.auto.commit=true
key.deserializer=org.apache.kafka.common.serialization.StringDeserializer
value.deserializer=org.apache.kafka.common.serialization.StringDeserializer
# fast session timeout makes it more fun to play with failover
session.timeout.ms=10000
# These buffer sizes seem to be needed to avoid consumer switching to
# a mode where it processes one bufferful every 5 seconds with multiple
# timeouts along the way. No idea why this happens.
fetch.min.bytes=50000
receive.buffer.bytes=262144
max.partition.fetch.bytes=2097152

What exactly happens when the producer is not on the broker machine? Do you see any log- or error-messages? You didn't describe your setup but is the IP 192.168.0.1. (broker machine) reachable from the producer machine and is the port 9092 open to the outside (check iptables)?
Another thing: The above code won't give you meaningful results if the producer and consumer are not on the same machine. You use System.nanoTime() to measure the latency. But according to the official documentation:
This method can only be used to measure elapsed time and is not related to any other notion of system or wall-clock time. The value returned represents nanoseconds since some fixed but arbitrary origin time (perhaps in the future, so values may be negative). The same origin is used by all invocations of this method in an instance of a Java virtual machine; other virtual machine instances are likely to use a different origin.

kafka producers are very slow

I am new in Kafka and I have a question that I'm not able to resolve.
I have installed Kafka and Zookeeper in my own computer in Windows (not in Linux) and I have created a broker with a topic with several partitions (playing between 6 and 12 partitions).
When I create consumers, they works perfectly and read at good speed, but referring producer, I have created the simple producer one can see in many web sites. The producer is inside a loop and is sending many short messages (about 2000 very short messages).
I can see that consumers read the 2000 messages very quicly, but producer sends message to the broker at more or less 140 or 150 messages per second. As I said before, I'm working in my own laptop (only 1 disk), but when I read about millions of messages per second, I think there is something I forgot because I'm light-years far from that.
If I use more producers, the result is worse.
Is a question of more brokers in the same node or something like that? This problem have been imposed to me in my job and I have not the possibility of a better computer.
The code for creating the producer is
public class Producer {
public void publica(String topic, String strKey, String strValue) {
Properties configProperties = new Properties();
configProperties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
configProperties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, LongSerializer.class.getName());
configProperties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());
KafkaProducer<String, String> producer = new KafkaProducer<String, String>(configProperties);
ProducerRecord<String, String> rec = new ProducerRecord<String, String>(topic, strValue);
producer.send(rec);
}
}
and the code for sending messages is (partial):
Producer prod = new Producer();
for (int i = 0; i < 2000; i++)
{
key = String.valueOf(i);
prod.publica("TopicName", key, texto + " - " + key);
// System.out.println(i + " - " + System.currentTimeMillis());
}

You may create your Kafka producer once and use it every time you need to send a message:
public class Producer {
private final KafkaProducer<String, String> producer; // initialize in constructor
public void publica(String topic, String strKey, String strValue) {
ProducerRecord<String, String> rec = new ProducerRecord<String, String>(topic, strValue);
producer.send(rec);
}
}
Also take a look at the producer and broker configurations available here. There are several options with which you can tune for your application's needs.

Why does my Kafka Consumer consume messages quickly on first run, but slows down considerably in future runs?

I am a student researching and playing around with Kafka. After following the examples on the Apache documentation, I'm playing around with the examples portion in the trunk of their current Github repo.
As of right now, the example implements an 'older' version of their Consumer and does not employ the new KafkaConsumer. Following the documentation, I have written my own version of the KafkaConsumer thinking that it would be faster.
This is a vague question, but on runthrough I produce 5000 simple messages such as "Message_CurrentMessageNumber" to a topic "test" and then use my consumer to fetch these messages and print them to stdout. When I run the example code replacing the provided consumer with the newer KafkaConsumer (v 0.8.2 and up) it works pretty quickly and comparably to the example in its first runthrough, but slows down considerably anytime after that.
I notice that my Kafka Server outputs
Rebalancing group group1 generation 3 (kafka.coordinator.ConsumerCoordinator)
or similar messages often which leads me to believe that Kafka has to do some sort of load balancing that slows stuff down but I was wondering if anyone else had insight as to what I am doing wrong.
public class AlternateConsumer extends Thread {
private final KafkaConsumer<Integer, String> consumer;
private final String topic;
private final Boolean isAsync = false;
public AlternateConsumer(String topic) {
Properties properties = new Properties();
properties.put("bootstrap.servers", "localhost:9092");
properties.put("group.id", "newestGroup");
properties.put("partition.assignment.strategy", "roundrobin");
properties.put("enable.auto.commit", "true");
properties.put("auto.commit.interval.ms", "1000");
properties.put("session.timeout.ms", "30000");
properties.put("key.deserializer", "org.apache.kafka.common.serialization.IntegerDeserializer");
properties.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
consumer = new KafkaConsumer<Integer, String>(properties);
consumer.subscribe(topic);
this.topic = topic;
}
public void run() {
while (true) {
ConsumerRecords<Integer, String> records = consumer.poll(100);
for (ConsumerRecord<Integer, String> record : records) {
System.out.println("We received message: " + record.value() + " from topic: " + record.topic());
}
}
// ConsumerRecords<Integer, String> records = consumer.poll(0);
// for (ConsumerRecord<Integer, String> record : records) {
// System.out.println("We received message: " + record.value() + " from topic: " + record.topic());
// }
// consumer.close();
}
}
To start:
package kafka.examples;
public class KafkaConsumerProducerDemo implements KafkaProperties
{
public static void main(String[] args) {
final boolean isAsync = args.length > 0 ? !args[0].trim().toLowerCase().equals("sync") : true;
Producer producerThread = new Producer("test", isAsync);
producerThread.start();
AlternateConsumer consumerThread = new AlternateConsumer("test");
consumerThread.start();
}
}
The producer is the default producer located here: https://github.com/apache/kafka/blob/trunk/examples/src/main/java/kafka/examples/Producer.java

This should not be the case. If the setup is similar between your two consumers you should expect better result with new consumer unless there is issue in the client/consumer implementation, which seems to be the case here.
Can you share your benchmark results and the frequency of reported rebalancing and/or any pattern (i.e. sluggish once at startup, after fixed message consumption, after the queue is drained, etc) you are observing. Also if you can share some details about your consumer implementation.