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zookeeper中怎么存储Kafka，相信很多没有经验的人对此束手无策，为此本文总结了问题出现的原因和解决方法，通过这篇文章希望你能解决这个问题。
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/brokers/topics/[topic] :
存储某个topic的partitions所有分配信息
[zk: localhost:2181(CONNECTED) 1] get /brokers/topics/topic2
Schema:
{
"version": "版本编号目前固定为数字1",
"partitions": {
"partitionId编号": [
同步副本组brokerId列表
],
"partitionId编号": [
同步副本组brokerId列表
],
.......
}
}
Example:
{
"version": 1,
"partitions": {
"2": [1, 2, 3],
"1": [0, 1, 2],
"0": [3, 0, 1],
}
}
Schema:
{
"controller_epoch": 表示kafka集群中的中央控制器选举次数,
"leader": 表示该partition选举leader的brokerId,
"version": 版本编号默认为1,
"leader_epoch": 该partition leader选举次数,
"isr": [同步副本组brokerId列表]
}
Example:
{
"controller_epoch": 1,
"leader": 3,
"version": 1,
"leader_epoch": 0,
"isr": [3, 0, 1]
}
Schema:
{
"jmx_port": jmx端口号,
"timestamp": kafka broker初始启动时的时间戳,
"host": 主机名或ip地址,
"version": 版本编号默认为1,
"port": kafka broker的服务端端口号,由server.properties中参数port确定
}
Example:
{
"jmx_port": -1,
"timestamp":"1525741823119"
"version": 1,
"host": "hadoop1",
"port": 9092
}
/controller -> int (broker id of the controller) 存储center controller中央控制器所在kafka broker的信息
Schema:
{
"version": 版本编号默认为1,
"brokerid": kafka集群中broker唯一编号,
"timestamp": kafka broker中央控制器变更时的时间戳
}
Example:
{
"version": 1,
"brokerid": 0,
"timestamp": "1525741822769"
}
a.每个consumer客户端被创建时,会向zookeeper注册自己的信息;
b.此作用主要是为了"负载均衡".
c.同一个Consumer Group中的Consumers，Kafka将相应Topic中的每个消息只发送给其中一个Consumer。
d.Consumer Group中的每个Consumer读取Topic的一个或多个Partitions，并且是唯一的Consumer；
e.一个Consumer group的多个consumer的所有线程依次有序地消费一个topic的所有partitions,如果Consumer group中所有consumer总线程大于partitions数量，则会出现空闲情况;
举例说明：
kafka集群中创建一个topic为report-log 4 partitions 索引编号为0,1,2,3
假如有目前有三个消费者node：注意-->一个consumer中一个消费线程可以消费一个或多个partition.
如果每个consumer创建一个consumer thread线程,各个node消费情况如下，node1消费索引编号为0,1分区，node2费索引编号为2,node3费索引编号为3
如果每个consumer创建2个consumer thread线程，各个node消费情况如下(是从consumer node先后启动状态来确定的)，node1消费索引编号为0,1分区；node2费索引编号为2,3；node3为空闲状态
总结：
从以上可知，Consumer Group中各个consumer是根据先后启动的顺序有序消费一个topic的所有partitions的。
如果Consumer Group中所有consumer的总线程数大于partitions数量，则可能consumer thread或consumer会出现空闲状态。
当一个group中,有consumer加入或者离开时,会触发partitions均衡.均衡的最终目的,是提升topic的并发消费能力.
1) 假如topic1,具有如下partitions: P0,P1,P2,P3
2) 加入group中,有如下consumer: C0,C1
3) 首先根据partition索引号对partitions排序: P0,P1,P2,P3
4) 根据(consumer.id + '-'+ thread序号)排序: C0,C1
5) 计算倍数: M = [P0,P1,P2,P3].size / [C0,C1].size,本例值M=2(向上取整)
6) 然后依次分配partitions: C0 = [P0,P1],C1=[P2,P3],即Ci = [P(i * M),P((i + 1) * M -1)]
每个consumer都有一个唯一的ID(consumerId可以通过配置文件指定,也可以由系统生成),此id用来标记消费者信息.
/consumers/[groupId]/ids/[consumerIdString]
是一个临时的znode,此节点的值为请看consumerIdString产生规则,即表示此consumer目前所消费的topic + partitions列表.
consumerId产生规则：
StringconsumerUuid = null;
if(config.consumerId!=null && config.consumerId)
consumerUuid = consumerId;
else {
String uuid = UUID.randomUUID()
consumerUuid = "%s-%d-%s".format(
InetAddress.getLocalHost.getHostName, System.currentTimeMillis,
uuid.getMostSignificantBits().toHexString.substring(0,8));}
String consumerIdString = config.groupId + "_" + consumerUuid;
[zk: localhost:2181(CONNECTED) 11] get /consumers/console-consumer-2304/ids/console-consumer-2304_hadoop2-1525747915241-6b48ff32
Schema:
{
"version": 版本编号默认为1,
"subscription": { //订阅topic列表
"topic名称": consumer中topic消费者线程数
},
"pattern": "static",
"timestamp": "consumer启动时的时间戳"
}
Example:
{
"version": 1,
"subscription": {
"topic2": 1
},
"pattern": "white_list",
"timestamp": "1525747915336"
}
/consumers/[groupId]/offsets/[topic]/[partitionId] -> long (offset)
用来跟踪每个consumer目前所消费的partition中最大的offset
此znode为持久节点,可以看出offset跟group_id有关,以表明当消费者组(consumer group)中一个消费者失效,
重新触发balance,其他consumer可以继续消费.
/admin/reassign_partitions
{
"fields":[
{
"name":"version",
"type":"int",
"doc":"version id"
},
{
"name":"partitions",
"type":{
"type":"array",
"items":{
"fields":[
{
"name":"topic",
"type":"string",
"doc":"topic of the partition to be reassigned"
},
{
"name":"partition",
"type":"int",
"doc":"the partition to be reassigned"
},
{
"name":"replicas",
"type":"array",
"items":"int",
"doc":"a list of replica ids"
}
],
}
"doc":"an array of partitions to be reassigned to new replicas"
}
}
]
}
Example:
{
"version": 1,
"partitions":
[
{
"topic": "Foo",
"partition": 1,
"replicas": [0, 1, 3]
}
]
}
{
"fields":[
{
"name":"version",
"type":"int",
"doc":"version id"
},
{
"name":"partitions",
"type":{
"type":"array",
"items":{
"fields":[
{
"name":"topic",
"type":"string",
"doc":"topic of the partition for which preferred replica election should be triggered"
},
{
"name":"partition",
"type":"int",
"doc":"the partition for which preferred replica election should be triggered"
}
],
}
"doc":"an array of partitions for which preferred replica election should be triggered"
}
}
]
}
例子:
{
"version": 1,
"partitions":
[
{
"topic": "Foo",
"partition": 1
},
{
"topic": "Bar",
"partition": 0
}
]
}
Schema:
{ "fields":
[ {"name": "version", "type": "int", "doc": "version id"},
{"name": "topics",
"type": { "type": "array", "items": "string", "doc": "an array of topics to be deleted"}
} ]
}
例子:
{
"version": 1,
"topics": ["foo", "bar"]
}/config/topics/[topic_name]
看完上述内容，你们掌握zookeeper中怎么存储Kafka的方法了吗？如果还想学到更多技能或想了解更多相关内容，欢迎关注创新互联行业资讯频道，感谢各位的阅读！
