Kafka消费者再平衡耗时太长

Question

我有一个Kafka流应用程序，它从几个主题中获取数据，并将数据加入另一个主题。

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/产生数百万条记录。每当我让Kafka经纪人倒下时，都会进入再平衡阶段，再平衡大约需要30分钟，有时甚至更长时间。

有人知道如何解决Kafka消费者的再平衡问题吗？而且，很多时候，它在重新平衡时抛出异常。

这将阻止我们使用此设置在生产环境中使用。任何帮助都将不胜感激。

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流配置：

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

它在内部创建的用户配置是：

    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

根据我的经验，首先是你的最高投票。考虑到您的工作量，记录太小：每小时消耗/生成的记录数不多。

所以如果max.poll.records太小，比如说1，那么再平衡需要很长时间。我不知道原因。

第二，确保输入主题到你的流应用程序的分区数是一致的。例如，如果APP-1有两个输入主题A和B。如果A有4个分区，而B有2个，那么重新平衡需要很长时间。然而，如果A和B都有4个分区，如果一些分区空闲，那么重新平衡时间是好的。希望有所帮助

Answer 2

我建议通过参数num.standby.replicas=1（默认为0）配置备用任务。这将有助于显著减少重新平衡时间。

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，您不需要为此升级代理。再平衡行为在0.11中得到了极大改进，并将在即将发布的1.0版本中得到进一步改进（参见。https://cwiki.apache.org/confluence/display/KAFKA/KIP-167:因此，将应用程序升级到最新版本始终是重新平衡的改进。