我与我的Kafka生产者和消费者碰到一种奇怪的行为。 以下是我在本地计算机上的设置
我正在使用如下所示的kafka命令行工具创建主题
./kafka-topics.sh --zookeeper localhost:2181 --create --topic foo --partitions 1 --replication-factor 2 --config min.insync.replicas=2
问题在于，每当我杀死分区的领导节点时，即使我的主题的min.insync.replicas设置为2，生产者和消费者仍会继续从kafka群集中推送和提取消息。我希望生产者能够根据文档，不应抛出异常和分区。
我发现了另外一个thread类似于我的，建议我为每个主题设置min.insync.replicas，但是生产者仍然没有错误
我在某处做错了吗？
生产者代码（golang）
func main() {
kafkaProducer, kafkaConErr = kafka.NewProducer( & kafka.ConfigMap {
"bootstrap.servers": "localhost:9092",
"acks": "-1"
})
if kafkaConErr != nil {
fmt.Println("Error creating InfluxDB Client: ", kafkaConErr.Error())
}
defer kafkaProducer.Close()
topic: = "foo"
/* for range []string{"Welcome", "to", "the", "Confluent", "Kafka", "Golang", "client"} { */
perr: = kafkaProducer.Produce( & kafka.Message {
TopicPartition: kafka.TopicPartition {
Topic: & topic,
Partition: kafka.PartitionAny
},
Value: empData,
}, nil)
if perr != nil {
fmt.Println(err.Error())
return
}
deliveryReportHandler()
}
func deliveryReportHandler() {
// Delivery report handler for produced messages
go func() {
for e: = range kafkaProducer.Events() {
switch ev: = e.(type) {
case *kafka.Message:
if ev.TopicPartition.Error != nil {
fmt.Printf("Delivery failed: %v\n", ev.TopicPartition)
} else {
fmt.Printf("Delivered message to topic %s [%d] at offset %v\n", * ev.TopicPartition.Topic, ev.TopicPartition.Partition, ev.TopicPartition.Offset)
}
default:
fmt.Printf("Ignored event: %s\n", ev)
}
}
}()
}
我的经纪人配置如下
############################# Server Basics #############################
# The id of the broker. This must be set to a unique integer for each broker.
broker.id=0
############################# Socket Server Settings #############################
# The address the socket server listens on. It will get the value returned from
# java.net.InetAddress.getCanonicalHostName() if not configured.
# FORMAT:
# listeners = listener_name://host_name:port
# EXAMPLE:
# listeners = PLAINTEXT://your.host.name:9092
listeners=PLAINTEXT://:9092
# Hostname and port the broker will advertise to producers and consumers. If not set,
# it uses the value for "listeners" if configured. Otherwise, it will use the value
# returned from java.net.InetAddress.getCanonicalHostName().
#advertised.listeners=PLAINTEXT://your.host.name:9092
# Maps listener names to security protocols, the default is for them to be the same. See the config documentation for more details
#listener.security.protocol.map=PLAINTEXT:PLAINTEXT,SSL:SSL,SASL_PLAINTEXT:SASL_PLAINTEXT,SASL_SSL:SASL_SSL
# The number of threads that the server uses for receiving requests from the network and sending responses to the network
num.network.threads=3
# The number of threads that the server uses for processing requests, which may include disk I/O
num.io.threads=8
# The send buffer (SO_SNDBUF) used by the socket server
socket.send.buffer.bytes=102400
# The receive buffer (SO_RCVBUF) used by the socket server
socket.receive.buffer.bytes=102400
# The maximum size of a request that the socket server will accept (protection against OOM)
socket.request.max.bytes=104857600
############################# Log Basics #############################
# A comma separated list of directories under which to store log files
log.dirs=/tmp/kafka-logs
# The default number of log partitions per topic. More partitions allow greater
# parallelism for consumption, but this will also result in more files across
# the brokers.
num.partitions=1
# The number of threads per data directory to be used for log recovery at startup and flushing at shutdown.
# This value is recommended to be increased for installations with data dirs located in RAID array.
num.recovery.threads.per.data.dir=1
############################# Internal Topic Settings #############################
# The replication factor for the group metadata internal topics "__consumer_offsets" and "__transaction_state"
# For anything other than development testing, a value greater than 1 is recommended for to ensure availability such as 3.
#offsets.topic.replication.factor=1
transaction.state.log.replication.factor=1
transaction.state.log.min.isr=1
############################# Log Flush Policy #############################
# Messages are immediately written to the filesystem but by default we only fsync() to sync
# the OS cache lazily. The following configurations control the flush of data to disk.
# There are a few important trade-offs here:
# 1. Durability: Unflushed data may be lost if you are not using replication.
# 2. Latency: Very large flush intervals may lead to latency spikes when the flush does occur as there will be a lot of data to flush.
# 3. Throughput: The flush is generally the most expensive operation, and a small flush interval may lead to excessive seeks.
# The settings below allow one to configure the flush policy to flush data after a period of time or
# every N messages (or both). This can be done globally and overridden on a per-topic basis.
# The number of messages to accept before forcing a flush of data to disk
#log.flush.interval.messages=10000
# The maximum amount of time a message can sit in a log before we force a flush
#log.flush.interval.ms=1000
############################# Log Retention Policy #############################
# The following configurations control the disposal of log segments. The policy can
# be set to delete segments after a period of time, or after a given size has accumulated.
# A segment will be deleted whenever *either* of these criteria are met. Deletion always happens
# from the end of the log.
# The minimum age of a log file to be eligible for deletion due to age
log.retention.hours=168
# A size-based retention policy for logs. Segments are pruned from the log unless the remaining
# segments drop below log.retention.bytes. Functions independently of log.retention.hours.
#log.retention.bytes=1073741824
# The maximum size of a log segment file. When this size is reached a new log segment will be created.
log.segment.bytes=1073741824
# The interval at which log segments are checked to see if they can be deleted according
# to the retention policies
log.retention.check.interval.ms=300000
############################# Zookeeper #############################
# Zookeeper connection string (see zookeeper docs for details).
# This is a comma separated host:port pairs, each corresponding to a zk
# server. e.g. "127.0.0.1:3000,127.0.0.1:3001,127.0.0.1:3002".
# You can also append an optional chroot string to the urls to specify the
# root directory for all kafka znodes.
zookeeper.connect=localhost:2181
# Timeout in ms for connecting to zookeeper
zookeeper.connection.timeout.ms=6000
############################# Group Coordinator Settings #############################
# The following configuration specifies the time, in milliseconds, that the GroupCoordinator will delay the initial consumer rebalance.
# The rebalance will be further delayed by the value of group.initial.rebalance.delay.ms as new members join the group, up to a maximum of max.poll.interval.ms.
# The default value for this is 3 seconds.
# We override this to 0 here as it makes for a better out-of-the-box experience for development and testing.
# However, in production environments the default value of 3 seconds is more suitable as this will help to avoid unnecessary, and potentially expensive, rebalances during application startup.
group.initial.rebalance.delay.ms=0
auto.leader.rebalance.enable=true
leader.imbalance.check.interval.seconds=5
leader.imbalance.per.broker.percentage=0
min.insync.replicas=2
offsets.topic.replication.factor=2
replica.lag.time.max.ms=1000
更新-在github repo link
中提出了一个问题