问题
I am running into a strange behaviour with my Kafka producer and consumer. Below is my setup on my local machine
- 1 zookeper node
- 2 kafka broker nodes
- 1 producer (doing async writes) and 1 subscriber written in go using this library
I am creating a topic using kafka's command line tool as below
./kafka-topics.sh --zookeeper localhost:2181 --create --topic foo --partitions 1 --replication-factor 2 --config min.insync.replicas=2
The issue is that whenever i kill leader node of the partition, the producer and consumer still keep on pushing and pulling messages from the kafka cluster even though the min.insync.replicas setting for my topic is 2. I expect producer to throw exceptions and partition should not be allowed for writing as per the docs.
I found one more thread similar to mine wherein it was suggested to set min.insync.replicas per topic which i have done but still there are no errors on producer
Am i doing something wrong somewhere ?
Producer code (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)
}
}
}()
}
My broker config as as below
############################# 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
Update - filed a issue in github repo link
来源：https://stackoverflow.com/questions/52664195/kafka-producer-unexpected-behaviour