Question

我有一个Kafka Streams应用程序，它从少数主题中获取数据并加入数据并将其放入另一个主题中。

Kafka配置：

5 kafka brokers
Kafka Topics - 15 partitions and 3 replication factor.

注意：我在运行Kafka Brokers的机器上运行Kafka Streams应用程序。

每小时消耗/生产的记录数量达数百万。每当我关闭任何一个kafka经纪人时，它都会进入重新平衡状态并且大约需要。重新平衡需要30分钟甚至更长时间。

任何人都知道如何解决卡夫卡消费者的再平衡问题？此外，很多时候它会在重新平衡时抛出异常。

这使我们无法通过此设置在生产环境中上线。任何帮助将不胜感激。

Caused by: org.apache.kafka.clients.consumer.CommitFailedException: ?
Commit cannot be completed since the group has already rebalanced and assigned the partitions to another member. This means that the time between subsequent calls to poll() was longer than the configured max.poll.interval.ms, which typically implies that the poll loop is spending too much time message processing. You can address this either by increasing the session timeout or by reducing the maximum size of batches returned in poll() with max.poll.records.
at org.apache.kafka.clients.consumer.internals.ConsumerCoordinator.sendOffsetCommitRequest(ConsumerCoordinator.java:725)

at org.apache.kafka.clients.consumer.internals.ConsumerCoordinator.commitOffsetsSync(ConsumerCoordinator.java:604)
at org.apache.kafka.clients.consumer.KafkaConsumer.commitSync(KafkaConsumer.java:1173)
at org.apache.kafka.streams.processor.internals.StreamTask.commitOffsets(StreamTask.java:307)
at org.apache.kafka.streams.processor.internals.StreamTask.access$000(StreamTask.java:49)
at org.apache.kafka.streams.processor.internals.StreamTask$1.run(StreamTask.java:268)
at org.apache.kafka.streams.processor.internals.StreamsMetricsImpl.measureLatencyNs(StreamsMetricsImpl.java:187)
at org.apache.kafka.streams.processor.internals.StreamTask.commitImpl(StreamTask.java:259)
at org.apache.kafka.streams.processor.internals.StreamTask.suspend(StreamTask.java:362)
at org.apache.kafka.streams.processor.internals.StreamTask.suspend(StreamTask.java:346)
at org.apache.kafka.streams.processor.internals.StreamThread$3.apply(StreamThread.java:1118)
at org.apache.kafka.streams.processor.internals.StreamThread.performOnStreamTasks(StreamThread.java:1448)
at org.apache.kafka.streams.processor.internals.StreamThread.suspendTasksAndState(StreamThread.java:1110)

Kafka Streams配置：

bootstrap.servers=kafka-1:9092,kafka-2:9092,kafka-3:9092,kafka-4:9092,kafka-5:9092
max.poll.records = 100
request.timeout.ms=40000

它内部创建的ConsumerConfig是：

    auto.commit.interval.ms = 5000
    auto.offset.reset = earliest
    bootstrap.servers = [kafka-1:9092, kafka-2:9092, kafka-3:9092, kafka-4:9092, kafka-5:9092]
    check.crcs = true
    client.id = conversion-live-StreamThread-1-restore-consumer
    connections.max.idle.ms = 540000
    enable.auto.commit = false
    exclude.internal.topics = true
    fetch.max.bytes = 52428800
    fetch.max.wait.ms = 500
    fetch.min.bytes = 1
    group.id = 
    heartbeat.interval.ms = 3000
    interceptor.classes = null
    internal.leave.group.on.close = false
    isolation.level = read_uncommitted
    key.deserializer = class org.apache.kafka.common.serialization.ByteArrayDeserializer
    max.partition.fetch.bytes = 1048576
    max.poll.interval.ms = 2147483647
    max.poll.records = 100
    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]
    receive.buffer.bytes = 65536
    reconnect.backoff.max.ms = 1000
    reconnect.backoff.ms = 50
    request.timeout.ms = 40000
    retry.backoff.ms = 100
    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.mechanism = GSSAPI
    security.protocol = PLAINTEXT
    send.buffer.bytes = 131072
    session.timeout.ms = 10000
    ssl.cipher.suites = null
    ssl.enabled.protocols = [TLSv1.2, TLSv1.1, TLSv1]
    ssl.endpoint.identification.algorithm = null
    ssl.key.password = null
    ssl.keymanager.algorithm = SunX509
    ssl.keystore.location = null
    ssl.keystore.password = null
    ssl.keystore.type = JKS
    ssl.protocol = TLS
    ssl.provider = null
    ssl.secure.random.implementation = null
    ssl.trustmanager.algorithm = PKIX
    ssl.truststore.location = null
    ssl.truststore.password = null
    ssl.truststore.type = JKS
    value.deserializer = class org.apache.kafka.common.serialization.ByteArrayDeserializer

Answer 1

我建议通过参数StandbyTasks配置num.standby.replicas=1（默认为0）。这应该有助于显着缩短重新平衡时间。

此外，我建议将您的应用程序升级到Kafka 0.11。注意，Streams API 0.11向后兼容0.10.1和0.10.2代理，因此，您无需为此升级代理。重新平衡行为在0.11中得到了很大改善，并且在即将发布的1.0版本中将进一步改进（参见https://cwiki.apache.org/confluence/display/KAFKA/KIP-167%3A+Add+interface+for+the+state+store+restoration+process），因此，将应用程序升级到最新版本始终是重新平衡的改进。

Answer 2

根据我的经验，第一考虑到你的工作量，你的max.poll.records太小了：每小时消耗/生产的记录数量很少。

因此，如果max.poll.records太小，则说1，那么重新平衡需要很长时间。我不知道原因。

第二，确保输入主题的输入主题的分区数量是一致的。例如如果APP-1有两个输入主题A和B.如果A有4个分区，B有2个，那么重新平衡需要很长时间。但是，如果A和B都有4个分区事件，则某些分区处于空闲状态，那么重新平衡时间就很好。希望它有所帮助

卡夫卡消费者再平衡需要太长时间

2 个答案: