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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!