使用 Flink 将 Kafka 数据写入 MatrixOne
本章节将介绍如何使用 Flink 将 Kafka 数据写入到 MatrixOne。
前期准备
本次实践需要安装部署以下软件环境：
- 完成单机部署 MatrixOne。
- 下载安装 lntelliJ IDEA(2022.2.1 or later version)。
- 根据你的系统环境选择 JDK 8+ version 版本进行下载安装。
- 下载并安装 Kafka。
- 下载并安装 Flink，最低支持版本为 1.11。
- 下载并安装 MySQL Client。
操作步骤
步骤一：启动 Kafka 服务
Kafka 集群协调和元数据管理可以通过 KRaft 或 ZooKeeper 来实现。在这里，我们将使用 Kafka 3.5.0 版本，无需依赖独立的 ZooKeeper 软件，而是使用 Kafka 自带的 KRaft 来进行元数据管理。请按照以下步骤配置配置文件，该文件位于 Kafka 软件根目录下的 config/kraft/server.properties。
配置文件内容如下：
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# This configuration file is intended for use in KRaft mode, where
# Apache ZooKeeper is not present. See config/kraft/README.md for details.
#
############################# Server Basics #############################
# The role of this server. Setting this puts us in KRaft mode
process.roles=broker,controller
# The node id associated with this instance's roles
node.id=1
# The connect string for the controller quorum
controller.quorum.voters=1@xx.xx.xx.xx:9093
############################# Socket Server Settings #############################
# The address the socket server listens on.
# Combined nodes (i.e. those with `process.roles=broker,controller`) must list the controller listener here at a minimum.
# If the broker listener is not defined, the default listener will use a host name that is equal to the value of java.net.InetAddress.getCanonicalHostName(),
# with PLAINTEXT listener name, and port 9092.
# FORMAT:
# listeners = listener_name://host_name:port
# EXAMPLE:
# listeners = PLAINTEXT://your.host.name:9092
#listeners=PLAINTEXT://:9092,CONTROLLER://:9093
listeners=PLAINTEXT://xx.xx.xx.xx:9092,CONTROLLER://xx.xx.xx.xx:9093
# Name of listener used for communication between brokers.
inter.broker.listener.name=PLAINTEXT
# Listener name, hostname and port the broker will advertise to clients.
# If not set, it uses the value for "listeners".
#advertised.listeners=PLAINTEXT://localhost:9092
# A comma-separated list of the names of the listeners used by the controller.
# If no explicit mapping set in `listener.security.protocol.map`, default will be using PLAINTEXT protocol
# This is required if running in KRaft mode.
controller.listener.names=CONTROLLER
# Maps listener names to security protocols, the default is for them to be the same. See the config documentation for more details
listener.security.protocol.map=CONTROLLER:PLAINTEXT,PLAINTEXT:PLAINTEXT,SSL:SSL,SASL_PLAINTEXT:SASL_PLAINTEXT,SASL_SSL:SASL_SSL
# The number of threads that the server uses for receiving requests from the network and sending responses to the network
num.network.threads=3
# The number of threads that the server uses for processing requests, which may include disk I/O
num.io.threads=8
# The send buffer (SO_SNDBUF) used by the socket server
socket.send.buffer.bytes=102400
# The receive buffer (SO_RCVBUF) used by the socket server
socket.receive.buffer.bytes=102400
# The maximum size of a request that the socket server will accept (protection against OOM)
socket.request.max.bytes=104857600
############################# Log Basics #############################
# A comma separated list of directories under which to store log files
log.dirs=/home/software/kafka_2.13-3.5.0/kraft-combined-logs
# The default number of log partitions per topic. More partitions allow greater
# parallelism for consumption, but this will also result in more files across
# the brokers.
num.partitions=1
# The number of threads per data directory to be used for log recovery at startup and flushing at shutdown.
# This value is recommended to be increased for installations with data dirs located in RAID array.
num.recovery.threads.per.data.dir=1
############################# Internal Topic Settings #############################
# The replication factor for the group metadata internal topics "__consumer_offsets" and "__transaction_state"
# For anything other than development testing, a value greater than 1 is recommended to ensure availability such as 3.
offsets.topic.replication.factor=1
transaction.state.log.replication.factor=1
transaction.state.log.min.isr=1
############################# Log Flush Policy #############################
# Messages are immediately written to the filesystem but by default we only fsync() to sync
# the OS cache lazily. The following configurations control the flush of data to disk.
# There are a few important trade-offs here:
# 1. Durability: Unflushed data may be lost if you are not using replication.
# 2. Latency: Very large flush intervals may lead to latency spikes when the flush does occur as there will be a lot of data to flush.
# 3. Throughput: The flush is generally the most expensive operation, and a small flush interval may lead to excessive seeks.
# The settings below allow one to configure the flush policy to flush data after a period of time or
# every N messages (or both). This can be done globally and overridden on a per-topic basis.
# The number of messages to accept before forcing a flush of data to disk
#log.flush.interval.messages=10000
# The maximum amount of time a message can sit in a log before we force a flush
#log.flush.interval.ms=1000
############################# Log Retention Policy #############################
# The following configurations control the disposal of log segments. The policy can
# be set to delete segments after a period of time, or after a given size has accumulated.
# A segment will be deleted whenever *either* of these criteria are met. Deletion always happens
# from the end of the log.
# The minimum age of a log file to be eligible for deletion due to age
log.retention.hours=72
# A size-based retention policy for logs. Segments are pruned from the log unless the remaining
# segments drop below log.retention.bytes. Functions independently of log.retention.hours.
#log.retention.bytes=1073741824
# The maximum size of a log segment file. When this size is reached a new log segment will be created.
log.segment.bytes=1073741824
# The interval at which log segments are checked to see if they can be deleted according
# to the retention policies
log.retention.check.interval.ms=300000
文件配置完成后，执行如下命令，启动 Kafka 服务：
#生成集群 ID
$ KAFKA_CLUSTER_ID="$(bin/kafka-storage.sh random-uuid)"
#设置日志目录格式
$ bin/kafka-storage.sh format -t $KAFKA_CLUSTER_ID -c config/kraft/server.properties
#启动 Kafka 服务
$ bin/kafka-server-start.sh config/kraft/server.properties
步骤二：创建 Kafka 主题
为了使 Flink 能够从中读取数据并写入到 MatrixOne，我们需要首先创建一个名为 "matrixone" 的 Kafka 主题。在下面的命令中，使用 --bootstrap-server 参数指定 Kafka 服务的监听地址为 xx.xx.xx.xx:9092：
$ bin/kafka-topics.sh --create --topic matrixone --bootstrap-server xx.xx.xx.xx:9092
步骤三：读取 MatrixOne 数据
在连接到 MatrixOne 数据库之后，需要执行以下操作以创建所需的数据库和数据表：
-
在 MatrixOne 中创建数据库和数据表，并导入数据：
CREATE TABLE `users` ( `id` INT DEFAULT NULL, `name` VARCHAR(255) DEFAULT NULL, `age` INT DEFAULT NULL ) -
在 IDEA 集成开发环境中编写代码：
在 IDEA 中，创建两个类：
User.java和Kafka2Mo.java。这些类用于使用 Flink 从 Kafka 读取数据，并将数据写入 MatrixOne 数据库中。
package com.matrixone.flink.demo.entity;
public class User {
private int id;
private String name;
private int age;
public int getId() {
return id;
}
public void setId(int id) {
this.id = id;
}
public String getName() {
return name;
}
public void setName(String name) {
this.name = name;
}
public int getAge() {
return age;
}
public void setAge(int age) {
this.age = age;
}
}
package com.matrixone.flink.demo;
import com.alibaba.fastjson2.JSON;
import com.matrixone.flink.demo.entity.User;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.serialization.AbstractDeserializationSchema;
import org.apache.flink.connector.jdbc.JdbcExecutionOptions;
import org.apache.flink.connector.jdbc.JdbcSink;
import org.apache.flink.connector.jdbc.JdbcStatementBuilder;
import org.apache.flink.connector.jdbc.internal.options.JdbcConnectorOptions;
import org.apache.flink.connector.kafka.source.KafkaSource;
import org.apache.flink.connector.kafka.source.enumerator.initializer.OffsetsInitializer;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.kafka.clients.consumer.OffsetResetStrategy;
import java.nio.charset.StandardCharsets;
/**
* @author MatrixOne
* @desc
*/
public class Kafka2Mo {
private static String srcServer = "xx.xx.xx.xx:9092";
private static String srcTopic = "matrixone";
private static String consumerGroup = "matrixone_group";
private static String destHost = "xx.xx.xx.xx";
private static Integer destPort = 6001;
private static String destUserName = "root";
private static String destPassword = "111";
private static String destDataBase = "test";
private static String destTable = "person";
public static void main(String[] args) throws Exception {
//初始化环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
//设置并行度
env.setParallelism(1);
//设置 kafka source 信息
KafkaSource<User> source = KafkaSource.<User>builder()
//Kafka 服务
.setBootstrapServers(srcServer)
//消息主题
.setTopics(srcTopic)
//消费组
.setGroupId(consumerGroup)
//偏移量 当没有提交偏移量则从最开始开始消费
.setStartingOffsets(OffsetsInitializer.committedOffsets(OffsetResetStrategy.LATEST))
//自定义解析消息内容
.setValueOnlyDeserializer(new AbstractDeserializationSchema<User>() {
@Override
public User deserialize(byte[] message) {
return JSON.parseObject(new String(message, StandardCharsets.UTF_8), User.class);
}
})
.build();
DataStreamSource<User> kafkaSource = env.fromSource(source, WatermarkStrategy.noWatermarks(), "kafka_maxtixone");
//kafkaSource.print();
//设置 matrixone sink 信息
kafkaSource.addSink(JdbcSink.sink(
"insert into users (id,name,age) values(?,?,?)",
(JdbcStatementBuilder<User>) (preparedStatement, user) -> {
preparedStatement.setInt(1, user.getId());
preparedStatement.setString(2, user.getName());
preparedStatement.setInt(3, user.getAge());
},
JdbcExecutionOptions.builder()
//默认值 5000
.withBatchSize(1000)
//默认值为 0
.withBatchIntervalMs(200)
//最大尝试次数
.withMaxRetries(5)
.build(),
JdbcConnectorOptions.builder()
.setDBUrl("jdbc:mysql://"+destHost+":"+destPort+"/"+destDataBase)
.setUsername(destUserName)
.setPassword(destPassword)
.setDriverName("com.mysql.cj.jdbc.Driver")
.setTableName(destTable)
.build()
));
env.execute();
}
}
代码编写完成后，你可以运行 Flink 任务，即在 IDEA 中选择 Kafka2Mo.java 文件，然后执行 Kafka2Mo.Main()。
步骤四：生成数据
使用 Kafka 提供的命令行生产者工具，您可以向 Kafka 的 "matrixone" 主题中添加数据。在下面的命令中，使用 --topic 参数指定要添加到的主题，而 --bootstrap-server 参数指定了 Kafka 服务的监听地址。
bin/kafka-console-producer.sh --topic matrixone --bootstrap-server xx.xx.xx.xx:9092
执行上述命令后，您将在控制台上等待输入消息内容。只需直接输入消息值 (value)，每行表示一条消息（以换行符分隔），如下所示：
{"id": 10, "name": "xiaowang", "age": 22}
{"id": 20, "name": "xiaozhang", "age": 24}
{"id": 30, "name": "xiaogao", "age": 18}
{"id": 40, "name": "xiaowu", "age": 20}
{"id": 50, "name": "xiaoli", "age": 42}
步骤五：查看执行结果
在 MatrixOne 中执行如下 SQL 查询结果：
mysql> select * from test.users;
+------+-----------+------+
| id | name | age |
+------+-----------+------+
| 10 | xiaowang | 22 |
| 20 | xiaozhang | 24 |
| 30 | xiaogao | 18 |
| 40 | xiaowu | 20 |
| 50 | xiaoli | 42 |
+------+-----------+------+
5 rows in set (0.01 sec)