我让一个生产者编写一个有100个分区的kafka主题,它根据用户id选择分区,因此用户的消息必须按照提交到队列的顺序进行处理。
负责消费的服务有2-10个示例,每个示例在其配置中都有:
spring.cloud.stream.bindings.input.consumer.concurrency=10
spring.cloud.stream.bindings.input.consumer.partitioned=true
我最近注意到,尽管使用者开始按顺序处理分区消息,但有时一条消息在它之后的一条消息之前处理,因为它比下一条消息更容易处理。
保持服务当前的处理速度对我来说很重要,因为我不熟悉spring cloud stream的线程模型,所以我想请教一下其他人。确保一个用户的消息只在前一个消息完成后才被处理的最佳方法是什么?
--编辑--
根据要求,提供更多相关参数。
Kafka版本:0.10.2.1
spring cloud stream版本:1.1.0.release
活页夹参数:
spring.cloud.stream.kafka.bindings.input.consumer.autoCommitOffset=false
spring.cloud.stream.kafka.bindings.input.consumer.autoCommitOnError=true
spring.cloud.stream.kafka.bindings.input.consumer.enableDlq=true
打印到控制台的使用者配置:
2018-12-11 09:56:51,975 [RMI TCP Connection(6)-127.0.0.1] INFO [AbstractConfig::logAll] - ConsumerConfig values:
metric.reporters = []
metadata.max.age.ms = 300000
partition.assignment.strategy = [org.apache.kafka.clients.consumer.RangeAssignor]
reconnect.backoff.ms = 50
sasl.kerberos.ticket.renew.window.factor = 0.8
max.partition.fetch.bytes = 1048576
bootstrap.servers = [localhost:9092]
ssl.keystore.type = JKS
enable.auto.commit = true
sasl.mechanism = GSSAPI
interceptor.classes = null
exclude.internal.topics = true
ssl.truststore.password = null
client.id = consumer-11
ssl.endpoint.identification.algorithm = null
max.poll.records = 2147483647
check.crcs = true
request.timeout.ms = 40000
heartbeat.interval.ms = 3000
auto.commit.interval.ms = 5000
receive.buffer.bytes = 65536
ssl.truststore.type = JKS
ssl.truststore.location = null
ssl.keystore.password = null
fetch.min.bytes = 1
send.buffer.bytes = 131072
value.deserializer = class org.apache.kafka.common.serialization.ByteArrayDeserializer
group.id =
retry.backoff.ms = 100
sasl.kerberos.kinit.cmd = /usr/bin/kinit
sasl.kerberos.service.name = null
sasl.kerberos.ticket.renew.jitter = 0.05
ssl.trustmanager.algorithm = PKIX
ssl.key.password = null
fetch.max.wait.ms = 500
sasl.kerberos.min.time.before.relogin = 60000
connections.max.idle.ms = 540000
session.timeout.ms = 30000
metrics.num.samples = 2
key.deserializer = class org.apache.kafka.common.serialization.ByteArrayDeserializer
ssl.protocol = TLS
ssl.provider = null
ssl.enabled.protocols = [TLSv1.2, TLSv1.1, TLSv1]
ssl.keystore.location = null
ssl.cipher.suites = null
security.protocol = PLAINTEXT
ssl.keymanager.algorithm = SunX509
metrics.sample.window.ms = 30000
auto.offset.reset = latest
打印到控制台的生产者配置:
2018-12-11 09:56:52,439 [-kafka-listener-1] INFO [AbstractConfig::logAll] - ProducerConfig values:
metric.reporters = []
metadata.max.age.ms = 300000
reconnect.backoff.ms = 50
sasl.kerberos.ticket.renew.window.factor = 0.8
bootstrap.servers = [localhost:9092]
ssl.keystore.type = JKS
sasl.mechanism = GSSAPI
max.block.ms = 60000
interceptor.classes = null
ssl.truststore.password = null
client.id = producer-5
ssl.endpoint.identification.algorithm = null
request.timeout.ms = 30000
acks = 1
receive.buffer.bytes = 32768
ssl.truststore.type = JKS
retries = 0
ssl.truststore.location = null
ssl.keystore.password = null
send.buffer.bytes = 131072
compression.type = none
metadata.fetch.timeout.ms = 60000
retry.backoff.ms = 100
sasl.kerberos.kinit.cmd = /usr/bin/kinit
buffer.memory = 33554432
timeout.ms = 30000
key.serializer = class org.apache.kafka.common.serialization.ByteArraySerializer
sasl.kerberos.service.name = null
sasl.kerberos.ticket.renew.jitter = 0.05
ssl.trustmanager.algorithm = PKIX
block.on.buffer.full = false
ssl.key.password = null
sasl.kerberos.min.time.before.relogin = 60000
connections.max.idle.ms = 540000
max.in.flight.requests.per.connection = 5
metrics.num.samples = 2
ssl.protocol = TLS
ssl.provider = null
ssl.enabled.protocols = [TLSv1.2, TLSv1.1, TLSv1]
batch.size = 16384
ssl.keystore.location = null
ssl.cipher.suites = null
security.protocol = PLAINTEXT
max.request.size = 1048576
value.serializer = class org.apache.kafka.common.serialization.ByteArraySerializer
ssl.keymanager.algorithm = SunX509
metrics.sample.window.ms = 30000
partitioner.class = class org.apache.kafka.clients.producer.internals.DefaultPartitioner
linger.ms = 0
1条答案
按热度按时间8wigbo561#
分区分布在容器线程中。
如果容器并发性为10,并且您有20个分区,那么通常会为每个使用者(线程)分配2个分区。
这保证了分区内的交货顺序。