group-offsets

Group offsets refer to the difference or distance between two or more groups in a dataset. It is a measure of how far apart the groups are from each other. Group offsets are commonly used in data analysis and statistics to compare and contrast different groups in a study, such as comparing the average salaries of different professions or the test scores of different schools. By calculating group offsets, researchers can determine the significance of the differences between the groups and draw meaningful conclusions from their data.

相关问题

flink specific-offsets'

"specific-offsets" 是 Apache Flink 中用于指定 Kafka 数据源消费特定分区的一种方式。默认情况下，Flink 会按照 Kafka 中的 offset 顺序消费所有分区的数据，但如果需要只消费特定的分区，可以使用 "specific-offsets"。

具体而言，可以通过 `FlinkKafkaConsumer` 构造函数的参数来指定 "specific-offsets"。例如，以下代码片段展示了如何指定消费 Kafka 中 topic 为 "myTopic" 的分区 0 和 1 的数据：

Properties props = new Properties();
props.setProperty("bootstrap.servers", "localhost:9092");
props.setProperty("group.id", "myGroup");

FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>("myTopic", new SimpleStringSchema(), props);

Map<KafkaTopicPartition, Long> specificOffsets = new HashMap<>();
specificOffsets.put(new KafkaTopicPartition("myTopic", 0), 23L);
specificOffsets.put(new KafkaTopicPartition("myTopic", 1), 42L);

consumer.setStartFromSpecificOffsets(specificOffsets);

上述代码中，`specificOffsets` 指定了分区 0 的 offset 为 23，分区 1 的 offset 为 42。因此，Flink 会从指定的 offset 开始消费对应分区的数据。

bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --describe --group test 用java实现

你可以使用 Kafka 的 Java API 来实现这个命令，具体实现如下：

import org.apache.kafka.clients.admin.AdminClient;
import org.apache.kafka.clients.admin.AdminClientConfig;
import org.apache.kafka.clients.admin.ConsumerGroupDescription;
import org.apache.kafka.clients.admin.ConsumerGroupListing;
import org.apache.kafka.clients.admin.ListConsumerGroupOffsetsResult;
import org.apache.kafka.clients.admin.ListConsumerGroupsResult;
import org.apache.kafka.clients.admin.ListOffsetsResult;
import org.apache.kafka.clients.admin.OffsetSpec;
import org.apache.kafka.clients.admin.TopicDescription;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.common.TopicPartition;
import org.apache.kafka.common.serialization.StringDeserializer;

import java.time.Duration;
import java.util.Collections;
import java.util.Map;
import java.util.Properties;
import java.util.Set;
import java.util.concurrent.ExecutionException;

public class KafkaConsumerGroups {

    public static void main(String[] args) throws ExecutionException, InterruptedException {
        Properties adminProps = new Properties();
        adminProps.put(AdminClientConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        AdminClient adminClient = AdminClient.create(adminProps);

        // List Consumer Groups
        ListConsumerGroupsResult consumerGroupsResult = adminClient.listConsumerGroups();
        Set<ConsumerGroupListing> consumerGroups = consumerGroupsResult.all().get();
        for (ConsumerGroupListing group : consumerGroups) {
            System.out.println("Consumer Group: " + group.groupId());
            // Describe Consumer Group
            ConsumerGroupDescription consumerGroupDescription = adminClient.describeConsumerGroups(Collections.singleton(group.groupId())).all().get().get(group.groupId());
            System.out.println("State: " + consumerGroupDescription.state());
            System.out.println("Coordinator: " + consumerGroupDescription.coordinator().toString());
            System.out.println("Members: " + consumerGroupDescription.members().size());
            System.out.println("Topic Partitions:");
            Map<String, TopicDescription> topicDescriptions = adminClient.describeTopics(consumerGroupDescription.members().stream().map(member -> member.assignment().topicPartitions().iterator().next().topic()).distinct().toArray(String[]::new)).all().get();
            for (TopicDescription topicDescription : topicDescriptions.values()) {
                for (TopicPartition partition : topicDescription.partitions()) {
                    System.out.println("\t" + topicDescription.name() + "-" + partition.partition() + ": " + consumerGroupDescription.assignment().partitionsForTopic(topicDescription.name()).contains(partition));
                }
            }

            // List Consumer Group Offsets
            ListConsumerGroupOffsetsResult consumerGroupOffsetsResult = adminClient.listConsumerGroupOffsets(group.groupId());
            Map<TopicPartition, Long> consumerGroupOffsets = consumerGroupOffsetsResult.partitionsToOffsetAndMetadata().get();
            System.out.println("Consumer Group Offsets:");
            for (Map.Entry<TopicPartition, Long> entry : consumerGroupOffsets.entrySet()) {
                System.out.println("\t" + entry.getKey().topic() + "-" + entry.getKey().partition() + ": " + entry.getValue());
            }

            // List Latest Offsets
            Properties consumerProps = new Properties();
            consumerProps.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
            consumerProps.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "false");
            consumerProps.put(ConsumerConfig.GROUP_ID_CONFIG, group.groupId());
            consumerProps.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
            consumerProps.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
            KafkaConsumer<String, String> consumer = new KafkaConsumer<>(consumerProps);
            Map<TopicPartition, Long> latestOffsets = consumer.endOffsets(consumerGroupOffsets.keySet(), Duration.ofSeconds(5));
            System.out.println("Latest Offsets:");
            for (Map.Entry<TopicPartition, Long> entry : latestOffsets.entrySet()) {
                System.out.println("\t" + entry.getKey().topic() + "-" + entry.getKey().partition() + ": " + entry.getValue());
            }

            // List Earliest Offsets
            Map<TopicPartition, Long> earliestOffsets = consumer.beginningOffsets(consumerGroupOffsets.keySet(), Duration.ofSeconds(5));
            System.out.println("Earliest Offsets:");
            for (Map.Entry<TopicPartition, Long> entry : earliestOffsets.entrySet()) {
                System.out.println("\t" + entry.getKey().topic() + "-" + entry.getKey().partition() + ": " + entry.getValue());
            }
        }
    }
}

这个程序使用 Kafka 的 AdminClient 来获取消费者组信息、消费者组偏移量以及主题分区的最早和最新偏移量。你可以根据自己的需求修改程序，例如只获取特定的消费者组信息等。