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tar -zxvf 压缩包目录 -C 解压目录

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listeners=PLAINTEXT://localhost:9092

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# 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 ZK-based mode, where Apache ZooKeeper is required.
# See kafka.server.KafkaConfig for additional details and defaults
#

############################# Server Basics #############################

# The id of the broker. This must be set to a unique integer for each broker.
broker.id=0

############################# Socket Server Settings #############################

# The address the socket server listens on. If not configured, the host name will be 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
listeners=PLAINTEXT://localhost:9092

# Listener name, hostname and port the broker will advertise to clients.
# If not set, it uses the value for "listeners".
#advertised.listeners=PLAINTEXT://your.host.name:9092

# 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=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=D:\\softwareLocate\\kafka\\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=168

# 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

############################# Zookeeper #############################

# Zookeeper connection string (see zookeeper docs for details).
# This is a comma separated host:port pairs, each corresponding to a zk
# server. e.g. "127.0.0.1:3000,127.0.0.1:3001,127.0.0.1:3002".
# You can also append an optional chroot string to the urls to specify the
# root directory for all kafka znodes.
zookeeper.connect=localhost:2181

# Timeout in ms for connecting to zookeeper
zookeeper.connection.timeout.ms=18000


############################# Group Coordinator Settings #############################

# The following configuration specifies the time, in milliseconds, that the GroupCoordinator will delay the initial consumer rebalance.
# The rebalance will be further delayed by the value of group.initial.rebalance.delay.ms as new members join the group, up to a maximum of max.poll.interval.ms.
# The default value for this is 3 seconds.
# We override this to 0 here as it makes for a better out-of-the-box experience for development and testing.
# However, in production environments the default value of 3 seconds is more suitable as this will help to avoid unnecessary, and potentially expensive, rebalances during application startup.
group.initial.rebalance.delay.ms=0

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# The number of milliseconds of each tick
tickTime=2000
# The number of ticks that the initial 
# synchronization phase can take
initLimit=10
# The number of ticks that can pass between 
# sending a request and getting an acknowledgement
syncLimit=5
# the directory where the snapshot is stored.
# do not use /tmp for storage, /tmp here is just 
# example sakes.
dataDir=D:\\softwareLocate\\zookeeper\\data
dataLogDir=D:\\softwareLocate\\zookeeper\\log
# the port at which the clients will connect
clientPort=2181
# the maximum number of client connections.
# increase this if you need to handle more clients
#maxClientCnxns=60
#
# Be sure to read the maintenance section of the 
# administrator guide before turning on autopurge.
#
# https://zookeeper.apache.org/doc/current/zookeeperAdmin.html#sc_maintenance
#
# The number of snapshots to retain in dataDir
#autopurge.snapRetainCount=3
# Purge task interval in hours
# Set to "0" to disable auto purge feature
#autopurge.purgeInterval=1

## Metrics Providers
#
# https://prometheus.io Metrics Exporter
#metricsProvider.className=org.apache.zookeeper.metrics.prometheus.PrometheusMetricsProvider
#metricsProvider.httpHost=0.0.0.0
#metricsProvider.httpPort=7000
#metricsProvider.exportJvmInfo=true

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zkServer.cmd

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bin\windows\kafka-server-start.bat config\server.properties

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        <dependency>
            <groupId>org.springframework.kafka</groupId>
            <artifactId>spring-kafka</artifactId>
        </dependency>

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spring:
  kafka:
    bootstrap-servers: "kafka服务器地址"
    producer:
    # 消息生产者key和value序列化器，这里是字符串序列化器
      key-serializer: org.apache.kafka.common.serialization.StringSerializer
      value-serializer: org.apache.kafka.common.serialization.StringSerializer
    # 类似地，这里是消费者key和value序列化器
    consumer:
      key-deserializer: org.apache.kafka.common.serialization.StringDeserializer
      value-deserializer: org.apache.kafka.common.serialization.StringDeserializer
      group-id: "使用者组名称"

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    @Resource
    private KafkaTemplate<String, String> kafkaTemplate;

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        kafkaTemplate.send("smartCanvas_genChartByAI", JSONUtil.toJsonStr(chart));

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    @Resource
    private ChartService chartService; //接口依赖的其他服务

    @Resource
    private UserService userService; //接口依赖的其他服务

    @Resource
    private AiService aiService; //接口依赖的其他服务

    @Resource
    private RedissonUtils redissonUtils; //接口依赖的其他服务

    @Resource
    private KafkaTemplate<String, String> kafkaTemplate;

    /**
     * 智能分析（异步）
     *
     * @param file       上传的文件
     * @param requestDTO 智能分析请求
     * @param request    请求
     * @return 智能分析结果
     */
    @PostMapping("/gen/async")
    public BaseResponse<GenResultVO> genChartAsyncByAi(@RequestPart("file") MultipartFile file,
                                                       GenChartByAiRequest requestDTO, HttpServletRequest request) {
        chartService.validGenChartParams(file, requestDTO);

        User user = userService.getLoginUser(request);
        try {
            redissonUtils.limitRate("smartCanvas_genChartByAI_async_" + user.getId(), 2L);
        } catch (BusinessException e) {
            throw new BusinessException(ErrorCode.TOO_MANY_REQUESTS_ERROR, "请求过于频繁，请稍后再试");
        }
        String data = ExcelUtils.excelToCsv(file);

        Chart chart = Chart.builder()
                .chartData(data)
                .chartName(requestDTO.getChartName())
                .chartType(requestDTO.getChartType())
                .goal(requestDTO.getGoal())
                .createrId(user.getId())
                .status(ChartStatusEnums.PROCESSING.getValue())
                .execmsg(ChartStatusEnums.PROCESSING.getDesc())
                .build();

        chartService.save(chart);

        //提交给kafka消息队列
        kafkaTemplate.send("smartCanvas_genChartByAI", JSONUtil.toJsonStr(chart));
        return ResultUtils.success(new GenResultVO(null, "", "{}", ChartStatusEnums.PROCESSING.getValue(), ChartStatusEnums.PROCESSING.getDesc()));
    }

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package cn.cola.smartcanvas.service.impl;

import cn.cola.smartcanvas.model.enums.ChartStatusEnums;
import cn.cola.smartcanvas.model.po.Chart;
import cn.cola.smartcanvas.model.vo.GenResultVO;
import cn.cola.smartcanvas.service.AiService;
import cn.cola.smartcanvas.service.ChartService;
import cn.cola.smartcanvas.service.KafkaService;
import cn.hutool.json.JSONUtil;
import lombok.extern.slf4j.Slf4j;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.springframework.kafka.annotation.KafkaListener;
import org.springframework.stereotype.Service;

import javax.annotation.Resource;

/**
 * 卡夫卡服务实现类
 *
 * @author ColaBlack
 */
@Service
@Slf4j
public class KafkaServiceImpl implements KafkaService {

    @Resource
    private AiService aiService;

    @Resource
    private ChartService chartService;

    /**
     * 智能分析任务
     *
     * @param record 消息记录
     */
    @Override
    @KafkaListener(topics = {"smartCanvas_genChartByAI"})
    public void genResultTask(ConsumerRecord<String, String> record) {
        if (record == null || record.value() == null) {
            log.error("kafka 中消息记录为空");
            return;
        }
        GenResultVO resultVO;
        Chart chart = JSONUtil.toBean(record.value(), Chart.class);
        String chartData = chart.getChartData();
        String goal = chart.getGoal();
        String chartType = chart.getChartType();
        if (chartType == null) {
            chartType = "任意统计图";
        }
        try {
            resultVO = aiService.genResult(goal, chartType, chartData);
            resultVO.setId(chart.getId());
        } catch (Exception e) {
            log.error("智能分析异常", e);
            chart.setStatus(ChartStatusEnums.FAILED.getValue());
            chart.setExecmsg(ChartStatusEnums.FAILED.getDesc());
            chartService.updateById(chart);
            return;
        }

        chart.setGeneratedChart(resultVO.getOption());
        chart.setAnalyzedResult(resultVO.getResult());

        chart.setStatus(ChartStatusEnums.SUCCESS.getValue());
        chart.setExecmsg(ChartStatusEnums.SUCCESS.getDesc());
        chartService.updateById(chart);
    }
}

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        Chart chart = JSONUtil.toBean(record.value(), Chart.class);