Kafka系列（五）Kafka体系架构英文版

文章目录

• • • 数据缓冲
    • • Kafka：

数据缓冲

Kafka：

high-throughput，distributed，publish-subscribe messaging system

• fast
• scalable
• durable
• real-time

kafka architecture

• kafka maintains feeds of messages in called Topics
• Producers publish messages to a kafka topic
• consumers subscribe to topics and process the feed of published messages
• servers in a kafka cluster are called brokers

kafka topic

• Topics：
• Partitions：
• Logs：
• Retention Period：
• Consumers maintain and track their locations/offsets in each log

#修改脚本
vi .bashrc


# .bashrc

# User specific aliases and functions

alias rm='rm -i'
alias cp='cp -i'
alias mv='mv -i'

# Source global definitions
if [ -f /etc/bashrc ]; then
        . /etc/bashrc
fi
export SPARK_MAJOR_VERSION=2
sed -i s/'history -cw'//g .bash_logout
export PATH=/usr/hdp/current/kafka-broker/bin:$PATH

#启动kafka
kafka-server-start.sh /usr/hdp/2.6.4.0-91/kafka/config/server.properties
#创建topic
kafka-topics.sh --zookeeper sandbox-hdp.hortonworks.com:2181 --create --topic test --partition 3 --replication-factor 1


# 查看topic
kafka-topics.sh --zookeeper sandbox-hdp.hortonworks.com:2181  --list

# 查看topic分区
kafka-topics.sh --zookeeper sandbox-hdp.hortonworks.com:2181 --describe --topic test

# 修改主题数据保留时间
kafka-topics.sh --zookeeper sandbox-hdp.hortonworks.com:2181 --alter --topic test --config retention.ms=10000

kafka-topics.sh --zookeeper sandbox-hdp.hortonworks.com:2181 --alter --topic test -delete-config retention.ms

Kafka Message Flow

producer可以控制指定发送到哪个分区（robin）

consumer可以指定我消费哪个分区

Kafka High-Throughput & Low-Latency

• Batching of individual messages
• Zero copy I/O using sendfile

kafka Broker

• Each partition has one server acting as a leader, and zero or more servers acting as followers / ISRs

  broker数要比分区数大得多, 每个分区的备份最好放在不在leader的其他机器上

• Each server acts as a leader for some partitions and a followers for others, so load is well balanced

  每个broker最好只要有一个leader

kafka Producer

• Producers publish data to the topics of their choice

  通过分区分配策略来控制我发送哪个分区

• Async Publishing (less durable)

  把ack调成0，数据可靠性最差的情况

• All nodes can answer metadata requests about:

  所有机器都可以提供位置信息给你作为传送消息的节点

kafka Consumer

可以定义细粒度：可以去拿topic，也可以去拿partition

• Consumers consume messages through subscriptions

• Multiple Consumers can read from the same topics

• Consumers are organized into Consumer Groups

  一个partition只能被一个group的一个consumer消费，

  partition数量应该大于consumer数量

• Kafka offers messages to Consumer Groups, not Consumer (instance) directly

  kafka的offset消息是在消费组层面上的,在组里每个consumer都知道partition读到什么地方，如果有consumer挂掉，下一个consumer会继续从offset之后拿

• Messages remain on Kafka, which are not removed after they are consumed

• Messaging models

  Queue: a message goes to one of the consumers.

  ​ ⚫ All consumers are in the same Consumer Group

  Publish-Subscribe: a message goes to all consumers.

  ​ ⚫ All consumers are assigned to different Consumer Groups;

  发布订阅，我多个消费组中的一个消费者去消费一个message时，这样可以并行去消费。

kafka ZooKeeper

• Starting from 0.10, Kafka has its own internal topic for the offset storage

kafka API

◆ The Producer API allows an application to publish a stream of records to one or more Kafka topics.

◆ The Consumer API allows an application to subscribe to one or more topics and process the stream of records produced to them.

◆ The Streams API allows an application to act as a stream processor , consuming an input stream from one or more topics and producing an output stream to one or more output topics, effectively transforming the input streams to output streams.

◆ The Connector API allows building and running reusable producers or consumers that connect Kafka topics to existing applications or data systems.

Message Ordering

To ensure global ordering for a topic:

◼ If all message must be ordered within one topic, use one partition

全局有序得话，最好把分区数设为一

◼ If messages can be ordered by certain properties

​ ⚫ Group messages in a partition by Key (defined upon the properties in producer)

​ ⚫ Configure exactly one consumer instance per partition within a consumer group

Message Replication

◼ 0 – the producer never waits for an ack

◼ 1 – the producer gets an ack after the leader replica has received the data

◼ -1 / all – the producer gets an ack after all ISRs (in-sync replication) receives the data

Data Loss at the Producer

◆ Kafka Producer API

​ ◼ Messages are accumulated in buffer in batches

批量发送

​ ◼ Messages are batched by partition, retried at batch level

​ ◼ Expired batches dropped after retries

在重试之后，批处理的消息到期了

◆ Data Loss at Producer

​ ◼ Failed to close / flush producer on termination

没有成功刷新最后得消息

​ ◼ Dropped batches due to communication or other errors when acks = 0 or retry exhaustion

Data Delivered but Loss in the Cluster

Data Duplication

consumer和producer都会发生数据重复消费

Kafka Use Cases

◆ Real-Time Streaming Processing (combined with Spark Streaming)

◆ General purpose Message Bus

◆ Collecting User Activity Data

用户行为数据

◆ Collecting Operational Metrics from Applications, Servers or Devices

◆ Log Aggregation (combined with ELK)

◆ Change Data Capture

◆ Commit Log for distributed System