kafka中文手册免费版

直接使用磁盘进行存储，线性读写，速度快。kafka中文手册免费版是款具有分布式的软件，kafka中文手册免费版里broker端不维护数据的消费状态，提升了性能，避免了数据在JVM内存和系统内存之间的复制，减少耗性能的创建对象和垃圾回收。kafka下载地址等你哦！ 

kafka中文手册免费版简介

主题和日志

Let’s first dive into the core abstraction Kafka provides for a stream of records—the topic.首先我们考察下kafka提供的核心数据流结构– topic(主题)

A topic is a category or feed name to which records are published. Topics in Kafka are always multi-subscriber; that is, a topic can have zero, one, or many consumers that subscribe to the data written to it. topic是一个分类栏目,由于记录一类数据发布的位置. topic在kafka中通常都有多个订阅者, 也就是说一个topic在写入数据后, 可以零个, 一个, 或多个订阅者进行消费

For each topic, the Kafka cluster maintains a partitioned log that looks like this: 针对每个topic队列, kafka集群构建一组这样的分区日志:

Each partition is an ordered, immutable sequence of records that is continually appended to—a structured commit log. The records in the partitions are each assigned a sequential id number called the offset that uniquely identifies each record within the partition.

每个日志分区都是有序, 不可变, 持续提交的结构化日志, 每条记录提交到日志分区时, 都分配一个有序的位移对象offset, 用以唯一区分记数据在分区的位置

The Kafka cluster retains all published records—whether or not they have been consumed—using a configurable retention period. For example, if the retention policy is set to two days, then for the two days after a record is published, it is available for consumption, after which it will be discarded to free up space. Kafka’s performance is effectively constant with respect to data size so storing data for a long time is not a problem.

无论发布到Kafka的数据是否有被消费, 都会保留所有已经发布的记录, Kafka使用可配置的数据保存周期策略, 例如, 如果保存策略设置为两天, 则两天前发布的数据可以被订阅者消费, 过了两天后, 数据占用的空间就会被删除并回收. 在存储数据上, kafka提供高效的O(1)性能处理算法, 所以保存长期时间不是一个问题

In fact, the only metadata retained on a per-consumer basis is the offset or position of that consumer in the log. This offset is controlled by the consumer: normally a consumer will advance its offset linearly as it reads records, but, in fact, since the position is controlled by the consumer it can consume records in any order it likes. For example a consumer can reset to an older offset to reprocess data from the past or skip ahead to the most recent record and start consuming from “now”.

实际上, 每个消费者唯一保存的元数据信息就是消费者当前消费日志的位移位置. 位移位置是被消费者控制, 正常情况下, 如果消费者读取记录后, 位移位置往前移动. 但是事实上, 由于位移位置是消费者控制的, 所以消费者可以按照任何他喜欢的次序进行消费, 例如, 消费者可以重置位移到之前的位置以便重新处理数据, 或者跳过头部从当前最新的位置进行消费

This combination of features means that Kafka consumers are very cheap—they can come and go without much impact on the cluster or on other consumers. For example, you can use our command line tools to “tail” the contents of any topic without changing what is consumed by any existing consumers.

这些特性表明Kafka消费者消费的代价是十分小的, 消费者可以随时消费或停止, 而对集群或其他消费者没有太多的影响, 例如你可以使用命令行工具, 像”tail”工具那样读取topic的内容, 而对其它消费者没有影响

The partitions in the log serve several purposes. First, they allow the log to scale beyond a size that will fit on a single server. Each individual partition must fit on the servers that host it, but a topic may have many partitions so it can handle an arbitrary amount of data. Second they act as the unit of parallelism—more on that in a bit.

分区在日志中有几个目的, 首先, 它能扩大日志在单个服务器里面的大小, 每个分区大小必须适应它从属的服务器的规定的大小, 但是一个topic可以有任意很多个分区, 这样topic就能存储任意大小的数据量, 另一方面, 分区还和并发有关系, 这个后面会讲到

Distribution 分布式

The partitions of the log are distributed over the servers in the Kafka cluster with each server handling data and requests for a share of the partitions. Each partition is replicated across a configurable number of servers for fault tolerance.

kafka的日志分区机制跨越整个kafka日志集群, 每个服务器使用一组公用的分区进行数据处理, 每个分区可以在集群中配置副本数

Each partition has one server which acts as the “leader” and zero or more servers which act as “followers”. The leader handles all read and write requests for the partition while the followers passively replicate the leader. If the leader fails, one of the followers will automatically become the new leader. Each server acts as a leader for some of its partitions and a follower for others so load is well balanced within the cluster.

每个分区都有一台服务器是主的, 另外零台或多台是从服务器, 主服务器责所有分区的读写请求, 从服务器被动从主分区同步数据. 如果主服务器分区的失败了, 那么备服务器的分区就会自动变成主的. 每台服务器的所有分区中, 只有部分会作为主分区, 另外部分作为从分区, 这样可以在集群中对个个服务器做负载均摊

Producers 生产者

Producers publish data to the topics of their choice. The producer is responsible for choosing which record to assign to which partition within the topic. This can be done in a round-robin fashion simply to balance load or it can be done according to some semantic partition function (say based on some key in the record). More on the use of partitioning in a second!

生产者发布消息到他们选择的topic中, 生产者负责选择记录要发布到topic的那个分区中, 这个可以简单通过轮询的方式进行负载均摊, 或者可以通过特定的分区选择函数(基于记录特定键值), 更多分区的用法后面马上介绍

Consumers 消费者

Consumers label themselves with a consumer group name, and each record published to a topic is delivered to one consumer instance within each subscribing consumer group. Consumer instances can be in separate processes or on separate machines.

消费者使用消费组进行标记, 发布到topic里面的每条记录, 至少会被消费组里面一个消费者实例进行消费. 消费者实例可以是不同的进程, 分布在不同的机器上

If all the consumer instances have the same consumer group, then the records will effectively be load balanced over the consumer instances.

如果所有的消费者属于同一消费组, 则记录会有效地分摊到每一个消费者上, 也就是说每个消费者只会处理部分记录

If all the consumer instances have different consumer groups, then each record will be broadcast to all the consumer processes.

如果所有的消费者都属于不同的消费组, 则记录会被广播到所有的消费者上,  也就说每个消费者会处理所有记录

A two server Kafka cluster hosting four partitions (P0-P3) with two consumer groups. Consumer group A has two consumer instances and group B has four.

图为一个2个服务器的kafka集群, 拥有4个分区, 2个消费组, 消费组A有2个消费者, 消费组B有4个消费者

More commonly, however, we have found that topics have a small number of consumer groups, one for each “logical subscriber”. Each group is composed of many consumer instances for scalability and fault tolerance. This is nothing more than publish-subscribe semantics where the subscriber is a cluster of consumers instead of a single process.

在大多数情况下, 一般一个topic值需要少量的消费者组, 一个消费组对应于一个逻辑上的消费者. 每个消费组一般包含多个实例用于容错和水平扩展. 这仅仅是发布订阅语义，其中订阅者是消费者群集，而不是单个进程.

The way consumption is implemented in Kafka is by dividing up the partitions in the log over the consumer instances so that each instance is the exclusive consumer of a “fair share” of partitions at any point in time. This process of maintaining membership in the group is handled by the Kafka protocol dynamically. If new instances join the group they will take over some partitions from other members of the group; if an instance dies, its partitions will be distributed to the remaining instances.

在kafka中实现日志消费的方式, 是把日志分区后分配到不同的消费者实例上, 所以每个实例在某个时间点都是”公平共享”式独占每个分区. 在这个处理过程中, 维持组内的成员是由kafka协议动态决定的, 如果有新的实例加入组中, 则会从组中的其他成员分配一些分区给新成员, 如果某个实例销毁了, 则它负责的分区也会分配给组内的其它成员

Kafka only provides a total order over records within a partition, not between different partitions in a topic. Per-partition ordering combined with the ability to partition data by key is sufficient for most applications. However, if you require a total order over records this can be achieved with a topic that has only one partition, though this will mean only one consumer process per consumer group.

kafka值提供在一个日志分区里面顺序消费的能力, 在同一topic的不同分区里面是没有保证的. 由于记录可以结合键值做分区, 这样的分区顺序一般可以满足各个应用的需求了, 但是如果你要求topic下的所有记录都要按照次序进行消费, 则可以考虑一个topic值创建一个分区, 这样意味着你这个topic只能让一个消费者消费

软件特别说明
kafka性能测试工具下载专题里等你去使用！