Kafka学习笔记--快速开始
前言
书接前文，这一篇笔记记录一下Kafka如何配置（总体而言）。本篇文章主要是结合Kafka的quickstart的文章来理解，打算中英文混排--尽管这样做，是很多如何学好英语的建议里面所极力反对的--这样做，是为了简化书写，抓住重点进行记录。
正文
Download the 2.1.0 release and un-tar it.
> tar -xzf kafka_2.11-2.1.0.tgz
> cd kafka_2.11-2.1.0Kafka uses ZooKeeper so you need to first start a ZooKeeper server if you don't already have one. You can use the convenience script packaged with kafka to get a quick-and-dirty single-node ZooKeeper instance.
> bin/zookeeper-server-start.sh config/zookeeper.properties
[2013-04-22 15:01:37,495] INFO Reading configuration from: config/zookeeper.properties (org.apache.zookeeper.server.quorum.QuorumPeerConfig)
...这里要使用到ZooKeeper，这是一个单节点的ZooKeeper启动实例。刚好借这个机会，研究一下这个脚本，把以前有些一直没有搞明白的问题搞明白。
if [ $# -lt 1 ];
then
echo "USAGE: $0 [-daemon] zookeeper.properties"
exit 1
fi
# $#的意思是所有参数的数目，参考https://unix.stackexchange.com/questions/122343/what-does-mean-in-shell
base_dir=$(dirname $0)
# 获取运行命令的目录作为base
if [ "x$KAFKA_LOG4J_OPTS" = "x" ]; then
export KAFKA_LOG4J_OPTS="-Dlog4j.configuration=file:$base_dir/../config/log4j.properties"
fi
# 配置KAFKA_LOG4J_OPTS参数。这种前面加“x”的用法是shell的一种技巧，判断KAFKA_LOG4J_OPTS参数是否为空。
if [ "x$KAFKA_HEAP_OPTS" = "x" ]; then
export KAFKA_HEAP_OPTS="-Xmx512M -Xms512M"
fi
# 配置KAFKA_HEAP_OPTS参数。
EXTRA_ARGS=${EXTRA_ARGS-'-name zookeeper -loggc'}
# 这里有点不太理解
COMMAND=$1
case $COMMAND in
-daemon)
|EXTRA_ARGS="-daemon "$EXTRA_ARGS
|shift
|;;
*)
|;;
esac
# 判断第一个参数是不是“daemon”，如果是的话，编入EXTRA_ARGS变量，下面要送给新的命令，然后使用shift从入参栈中移除掉这个参数，后面会使用“$@”来使用剩余的入参
exec $base_dir/kafka-run-class.sh $EXTRA_ARGS org.apache.zookeeper.server.quorum.QuorumPeerMain "$@"
# 启动kafka-run-class.sh脚本，把上述参数送进去Now start the Kafka server:
> bin/kafka-server-start.sh config/server.properties
[2013-04-22 15:01:47,028] INFO Verifying properties (kafka.utils.VerifiableProperties)
[2013-04-22 15:01:47,051] INFO Property socket.send.buffer.bytes is overridden to 1048576 (kafka.utils.VerifiableProperties)
...Let's create a topic named "test" with a single partition and only one replica:
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We can now see that topic if we run the list topic command:
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Alternatively, instead of manually creating topics you can also configure your brokers to auto-create topics when a non-existent topic is published to.
Kafka comes with a command line client that will take input from a file or from standard input and send it out as messages to the Kafka cluster. By default, each line will be sent as a separate message.
Run the producer and then type a few messages into the console to send to the server.
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Kafka also has a command line consumer that will dump out messages to standard output.
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If you have each of the above commands running in a different terminal then you should now be able to type messages into the producer terminal and see them appear in the consumer terminal.
All of the command line tools have additional options; running the command with no arguments will display usage information documenting them in more detail.
上面是一个简单的启动流程，没有做太多的配置，语言也比较简单。下面要进行多broker的集群配置。Kafka的server被称为broker，经纪人。
Step 6: Setting up a multi-broker cluster
So far we have been running against a single broker, but that's no fun. For Kafka, a single broker is just a cluster of size one, so nothing much changes other than starting a few more broker instances. But just to get feel for it, let's expand our cluster to three nodes (still all on our local machine).
First we make a config file for each of the brokers (on Windows use the copy command instead):
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Now edit these new files and set the following properties:
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The broker.id property is the unique and permanent name of each node in the cluster. We have to override the port and log directory only because we are running these all on the same machine and we want to keep the brokers from all trying to register on the same port or overwrite each other's data.
broker.id这个属性，在这个集群中必须是唯一并且持久的名字，来标识这个节点。
We already have Zookeeper and our single node started, so we just need to start the two new nodes:
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Now create a new topic with a replication factor of three:
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Okay but now that we have a cluster how can we know which broker is doing what? To see that run the "describe topics" command:
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Here is an explanation of output. The first line gives a summary of all the partitions, each additional line gives information about one partition. Since we have only one partition for this topic there is only one line.
对上面的输出做一下解释。第一行给出了所有分区的概要，后面增加的行给出了关于每一个分区的信息，我们只有一个分区，所以只有一行。
- "leader" is the node responsible for all reads and writes for the given partition. Each node will be the leader for a randomly selected portion of the partitions.
- “leader”是负责对于给定的分区来进行所有的读写操作的。每一个节点都被分区的挑选出来的部分来随机地挑选成为leader。
- "replicas" is the list of nodes that the log for this partition regardless of whether they are the leader or even if they are currently alive.
- “replicas”是来记录这个分区的节点列表（备份节点），无论他们是否是leader甚至它们当前是否存活。
- "isr" is the set of "in-sync" replicas. This is the subset of the replicas list that is currently alive and caught-up to the leader.
- “isr”是"in-sync"的replica的集合。这是replicas的子集，是那些当前或者的，并且赶上leader的replica。
Note that in my example node 1 is the leader for the only partition of the topic.
We can run the same command on the original topic we created to see where it is:
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So there is no surprise there—the original topic has no replicas and is on server 0, the only server in our cluster when we created it.
Let's publish a few messages to our new topic:
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Now let's consume these messages:
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Now let's test out fault-tolerance. Broker 1 was acting as the leader so let's kill it:
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On Windows use:
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Leadership has switched to one of the slaves and node 1 is no longer in the in-sync replica set:
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But the messages are still available for consumption even though the leader that took the writes originally is down:
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Step 7: Use Kafka Connect to import/export data
Writing data from the console and writing it back to the console is a convenient place to start, but you'll probably want to use data from other sources or export data from Kafka to other systems. For many systems, instead of writing custom integration code you can use Kafka Connect to import or export data.
Kafka Connect is a tool included with Kafka that imports and exports data to Kafka. It is an extensible tool that runs connectors, which implement the custom logic for interacting with an external system. In this quickstart we'll see how to run Kafka Connect with simple connectors that import data from a file to a Kafka topic and export data from a Kafka topic to a file.
First, we'll start by creating some seed data to test with:
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Or on Windows:
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Next, we'll start two connectors running in standalone mode, which means they run in a single, local, dedicated process. We provide three configuration files as parameters. The first is always the configuration for the Kafka Connect process, containing common configuration such as the Kafka brokers to connect to and the serialization format for data. The remaining configuration files each specify a connector to create. These files include a unique connector name, the connector class to instantiate, and any other configuration required by the connector.
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These sample configuration files, included with Kafka, use the default local cluster configuration you started earlier and create two connectors: the first is a source connector that reads lines from an input file and produces each to a Kafka topic and the second is a sink connector that reads messages from a Kafka topic and produces each as a line in an output file.
During startup you'll see a number of log messages, including some indicating that the connectors are being instantiated. Once the Kafka Connect process has started, the source connector should start reading lines from test.txt and producing them to the topic connect-test, and the sink connector should start reading messages from the topic connect-test and write them to the file test.sink.txt. We can verify the data has been delivered through the entire pipeline by examining the contents of the output file:
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Note that the data is being stored in the Kafka topic connect-test, so we can also run a console consumer to see the data in the topic (or use custom consumer code to process it):
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The connectors continue to process data, so we can add data to the file and see it move through the pipeline:
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You should see the line appear in the console consumer output and in the sink file.
这里讲了Kafka的连接功能，可以从别的源导入数据，或者导出数据到别的地方。
Step 8: Use Kafka Streams to process data
Kafka Streams is a client library for building mission-critical real-time applications and microservices, where the input and/or output data is stored in Kafka clusters. Kafka Streams combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology to make these applications highly scalable, elastic, fault-tolerant, distributed, and much more. This quickstart example will demonstrate how to run a streaming application coded in this library.
这里很简单的讲了Kafka的流化处理能力。
总结
这里主要是介绍了如何快速搭建Kafka的方法，比较简单，同时总结了一下Shell的一些用法，关于这些用法，一直用的模模糊糊，这次好好总结一下，不要让它一直像夹生饭一样存在。
参考
https://kafka.apache.org/quickstart
https://unix.stackexchange.com/questions/254494/how-does-bash-differentiate-between-brace-expansion-and-command-grouping 介绍了Shell下大括号的使用
https://unix.stackexchange.com/questions/174566/what-is-the-purpose-of-using-shift-in-shell-scripts 介绍了shift的用法
https://unix.stackexchange.com/questions/122343/what-does-mean-in-shell 介绍了$#的用法
posted on 2019-12-15 17:06 chaiyu2002 阅读(112) 评论(0) 编辑 收藏 举报
