k8s 上运行我们的 springboot 服务之——flume 同步数据到到 clickHouse

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发布于: 2020 年 07 月 02 日
k8s 上运行我们的 springboot 服务之——flume同步数据到到clickHouse
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clickHouse的简单介绍，详细介绍请查看官网或者百度
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1)clickhouse非hadoop体系
2)使用sql语句，对于熟悉关系数据的人员入门相对简单
3)clickhouse最好用来读，不要用来变更，写用批量的方式
4)各种日志数据我们可以用flume同步到clickhouse来统一管理和做用户行为分析
5)mysql 增量同步到clickhouse，这里有一个思考:系统日志，交易日志，用户行为日志，已生成订单等不变的数据似乎可以同步到clickhouse来做报表、统计、数据分析等。
由于用户经常查询和操作一般都是最近的或者最新的数据，可以把这部分变更的有事务要求的数据放到mysql中。把mysql数据同步到clickhouse，近期最新和变更的数据在mysql中操作，其他大部分数据在clickhouse中操作，这样来减轻关系型数据库的性能瓶颈。
6)面对错综复杂的数据源我们似乎可以使用flink来把数据统一归集到clickhouse 
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以上都需要根据实际情况去测试使用。毕竟实践是检验真理的唯一标准
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flume的简单介绍，详细介绍请查看官网或者百度
1)高可用的，高可靠的，分布式的海量日志采集、聚合和传输的系统
2)支持监听多种方式多种类型的文件或者文件目录数据的变更，以获得变更的数据并把这部分数据推送到不同的数据接收中间件
3)flume提供了多种插件来完成2中的需求，常用的例如：监听TCP的端口做为数据源，监听目录下日志文件的变更等
4）flume可以把变更的数据同步队列中，然后队列把数据分发到我们各种数据仓库中间件中，也可以不通过队列直接把数据同步存储到数据仓库中间件中
5）我们也可以自定义flume的ng来满足我们自己特殊的数据变更同步需求
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本文主要讲解内容如下：
1、自定义实现flume ng
2、测试我们系统生成的日志通过1中实现的flume ng同步到 clickhouse
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下面是核心代码：
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1）在clickhouse中创建表：
CREATE TABLE default.sys_log (
 `id` String,
 `sys_name` String,
 `level` String,
 `msg` String,
 `thread` String,
 `create_date` DateTime,
 `exe_date` DateTime
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(create_date)
ORDER BY exe_date;
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2）自定义flume sink pom.xml
  <dependencies>
        <!-- https://mvnrepository.com/artifact/org.apache.flume/flume-ng-core -->
        <dependency>
            <groupId>org.apache.flume</groupId>
            <artifactId>flume-ng-core</artifactId>
        </dependency>
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
        </dependency>
        <dependency>
            <groupId>com.google.guava</groupId>
            <artifactId>guava</artifactId>
        </dependency>
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        <!-- https://mvnrepository.com/artifact/org.apache.flume/flume-ng-configuration -->
        <dependency>
            <groupId>org.apache.flume</groupId>
            <artifactId>flume-ng-configuration</artifactId>
        </dependency>
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        <!-- https://mvnrepository.com/artifact/org.apache.flume/flume-ng-sdk -->
        <dependency>
            <groupId>org.apache.flume</groupId>
            <artifactId>flume-ng-sdk</artifactId>
        </dependency>
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        <dependency>
            <groupId>com.opencsv</groupId>
            <artifactId>opencsv</artifactId>
            <version>4.2</version>
        </dependency>
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        <dependency>
            <groupId>ru.yandex.clickhouse</groupId>
            <artifactId>clickhouse-jdbc</artifactId>
            <version>0.2.4</version>
        </dependency>
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        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
        </dependency>
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    </dependencies>
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3) flume 自定义sink 核心类
 private static final Logger LOGGER = LoggerFactory.getLogger(ClickHouseSink.class);
    private BalancedClickhouseDataSource dataSource = null;
    private SinkCounter sinkCounter = null;
    private String host = null;
    private String port = null;
    private String user = null;
    private String password = null;
    private String database = null;
    private String table = null;
    private int batchSize;
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    @Override
    public Status process() throws EventDeliveryException {
        Status status = null;
        Channel ch = getChannel();
        Transaction txn = ch.getTransaction();
        txn.begin();
        List<LogLogbackVo> insertData = new ArrayList<>();
        try {
            ClickHouseConnectionImpl conn = (ClickHouseConnectionImpl) dataSource.getConnection();
            int count;
            for (count = 0; count < batchSize; ++count) {
                Event event = ch.take();
                if (event == null) {
                    break;
                }
                insertData.add(StringUtil.buildLog(new String(event.getBody())));
            }
            if (count <= 0) {
                sinkCounter.incrementBatchEmptyCount();
                txn.commit();
                return Status.BACKOFF;
            } else if (count < batchSize) {
                sinkCounter.incrementBatchUnderflowCount();
            } else {
                sinkCounter.incrementBatchCompleteCount();
            }
            sinkCounter.addToEventDrainAttemptCount(count);
            ClickHouseStatement sth = conn.createStatement();
            sth.write().table(String.format(" %s.%s", database, table)).data(new ByteArrayInputStream(JsonUtil.t2JsonString(insertData).getBytes()), ClickHouseFormat.JSONEachRow).addDbParam(ClickHouseQueryParam.MAX_PARALLEL_REPLICAS, MAX_PARALLEL_REPLICAS_VALUE).send();
            sinkCounter.incrementEventDrainSuccessCount();
            status = Status.READY;
            txn.commit();
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        } catch (Throwable t) {
            txn.rollback();
            LOGGER.error(t.getMessage(), t);
            status = Status.BACKOFF;
            // re-throw all Errors
            if (t instanceof Error) {
                throw (Error) t;
            }
        } finally {
            txn.close();
        }
        return status;
    }
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    @Override
    public void configure(Context context) {
        if (sinkCounter == null) {
            sinkCounter = new SinkCounter(getName());
        }
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        Preconditions.checkArgument(context.getString(HOST) != null && context.getString(HOST).length() > 0, "ClickHouse host must be specified!");
        this.host = context.getString(HOST);
        if (!this.host.startsWith(CLICK_HOUSE_PREFIX)) {
            this.host = CLICK_HOUSE_PREFIX + this.host;
        }
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        Preconditions.checkArgument(context.getString(DATABASE) != null && context.getString(DATABASE).length() > 0, "ClickHouse database must be specified!");
        this.database = context.getString(DATABASE);
        Preconditions.checkArgument(context.getString(TABLE) != null && context.getString(TABLE).length() > 0, "ClickHouse table must be specified!");
        this.table = context.getString(TABLE);
        this.port = context.getString(PORT, DEFAULT_PORT);
        this.user = context.getString(USER, DEFAULT_USER);
        this.password = context.getString(PASSWORD, DEFAULT_PASSWORD);
        this.batchSize = context.getInteger(BATCH_SIZE, DEFAULT_BATCH_SIZE);
    }
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    @Override
    public void start() {
        LOGGER.info("clickHouse sink {} starting", getName());
        String jdbcUrl = String.format("%s:%s/%s", this.host, this.port, this.database);
        ClickHouseProperties properties = new ClickHouseProperties().withCredentials(this.user, this.password);
        this.dataSource = new BalancedClickhouseDataSource(jdbcUrl, properties);
        sinkCounter.start();
        super.start();
        LOGGER.info("clickHouse sink {} started", getName());
    }
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    @Override
    public void stop() {
        LOGGER.info("clickHouse sink {} stopping", getName());
        sinkCounter.incrementConnectionClosedCount();
        sinkCounter.stop();
        super.stop();
        LOGGER.info("clickHouse sink {} stopped", getName());
    }
4）flume 对应配置：
# 指定Agent的组件名称  
a1.sources = r1  
a1.sinks = sink1  
a1.channels = c1  
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a1.sources.r1.type=spooldir
a1.sources.r1.spoolDir=/home/spark/flume/data/log
a1.sources.r1.channels=c1
a1.sources.r1.fileHeader = false
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = timestamp
   
# 指定Flume sink  
a1.sinks.sink1.type = com.zhy.frame.newsql.clickhouse.sink.sink.ClickHouseSink
a1.sinks.sink1.host = localhost
a1.sinks.sink1.port = 8123
a1.sinks.sink1.database = default
a1.sinks.sink1.table = sys_log
a1.sinks.sink1.batchSize = 10000
a1.sinks.sink1.user = default
a1.sinks.sink1.password =  
   
# 指定Flume channel  
a1.channels.c1.type = memory  
a1.channels.c1.capacity = 1000  
a1.channels.c1.transactionCapacity = 100  
   
# 绑定source和sink到channel上  
a1.sources.r1.channels = c1  
a1.sinks.sink1.channel = c1
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5)系统logback配置
<?xml version="1.0" encoding="UTF-8"?>
<!-- 日志级别从低到高分为TRACE < DEBUG < INFO < WARN < ERROR < FATAL，如果设置为WARN，则低于WARN的信息都不会输出 -->
<!-- scan:当此属性设置为true时，配置文件如果发生改变，将会被重新加载，默认值为true -->
<!-- scanPeriod:设置监测配置文件是否有修改的时间间隔，如果没有给出时间单位，默认单位是毫秒。当scan为true时，此属性生效。默认的时间间隔为1分钟。 -->
<!-- debug:当此属性设置为true时，将打印出logback内部日志信息，实时查看logback运行状态。默认值为false。 -->
<configuration scan="true" scanPeriod="7 seconds">
    <contextName>logback</contextName>
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    <!--系统名称-->
    <property name="sysName" value="frameSimple"/>
    <!--日志大小 KB MB-->
    <property name="logMaxFileSize" value="1KB"/>
    <!--日志保留天数-->
    <property name="logMaxHistory" value="7"/>
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    <property name="logging.path" value="sysLog"/>
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    <!-- 彩色日志 -->
    <!-- 彩色日志依赖的渲染类 -->
    <conversionRule conversionWord="clr" converterClass="org.springframework.boot.logging.logback.ColorConverter"/>
    <conversionRule conversionWord="wex"
                    converterClass="org.springframework.boot.logging.logback.WhitespaceThrowableProxyConverter"/>
    <conversionRule conversionWord="wEx"
                    converterClass="org.springframework.boot.logging.logback.ExtendedWhitespaceThrowableProxyConverter"/>
    <!-- 彩色日志格式 -->
    <property name="CONSOLE_LOG_PATTERN"
              value="${CONSOLE_LOG_PATTERN:-%clr(%d{yyyy-MM-dd HH:mm:ss.SSS}){faint} %clr(${LOG_LEVEL_PATTERN:-%5p}) %clr(${PID:- }){magenta} %clr(---){faint} %clr([%15.15t]){faint} %clr(%-40.40logger{39}){cyan} %clr(:){faint} %m%n${LOG_EXCEPTION_CONVERSION_WORD:-%wEx}}"/>
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    <!--输出到控制台-->
    <appender name="CONSOLE_APPENDER" class="ch.qos.logback.core.ConsoleAppender">
        <!--此日志appender是为开发使用，只配置最底级别，控制台输出的日志级别是大于或等于此级别的日志信息-->
        <filter class="ch.qos.logback.classic.filter.ThresholdFilter">
            <level>info</level>
        </filter>
        <encoder>
            <Pattern>${CONSOLE_LOG_PATTERN}</Pattern>
            <!-- 设置字符集 -->
            <charset>UTF-8</charset>
        </encoder>
    </appender>
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    <!--输出到文件-->
    <!-- 时间滚动输出 level为 DEBUG 日志 -->
    <appender name="DEBUG_APPENDER" class="ch.qos.logback.core.rolling.RollingFileAppender">
        <!-- 正在记录的日志文件的路径及文件名 -->
        <file>${logging.path}/debug_${sysName}.log</file>
        <!--日志文件输出格式-->
        <encoder class="net.logstash.logback.encoder.LoggingEventCompositeJsonEncoder">
            <jsonGeneratorDecorator
                    class="net.logstash.logback.decorate.FeatureJsonGeneratorDecorator"/>
            <providers>
                <pattern>
                    <pattern>
                        {
                        "sysName":"${sysName}",
                        "thread":"%thread",
                        "exeDate": "%date{yyyy-MM-dd HH:mm:ss.SSS}",
                        "level":"%level",
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                        "msg": "%msg"
                        }
                    </pattern>
                </pattern>
            </providers>
        </encoder>
        <!-- 日志记录器的滚动策略，按日期，按大小记录 -->
        <rollingPolicy class="ch.qos.logback.core.rolling.TimeBasedRollingPolicy">
            <!-- 日志归档 -->
            <fileNamePattern>${logging.path}/debug/debug_${sysName}_%d{yyyy-MM-dd}.%i.log</fileNamePattern>
            <timeBasedFileNamingAndTriggeringPolicy class="ch.qos.logback.core.rolling.SizeAndTimeBasedFNATP">
                <maxFileSize>${logMaxFileSize}</maxFileSize>
            </timeBasedFileNamingAndTriggeringPolicy>
            <!--日志文件保留天数-->
            <maxHistory>${logMaxHistory}</maxHistory>
        </rollingPolicy>
        <!-- 此日志文件只记录debug级别的 -->
        <filter class="ch.qos.logback.classic.filter.LevelFilter">
            <level>DEBUG</level>
            <onMatch>ACCEPT</onMatch>
            <onMismatch>DENY</onMismatch>
        </filter>
    </appender>
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    <!-- 时间滚动输出 level为 INFO 日志 -->
    <appender name="INFO_APPENDER" class="ch.qos.logback.core.rolling.RollingFileAppender">
        <!-- 正在记录的日志文件的路径及文件名 -->
        <file>${logging.path}/info_${sysName}.log</file>
        <encoder class="net.logstash.logback.encoder.LoggingEventCompositeJsonEncoder">
            <jsonGeneratorDecorator
                    class="net.logstash.logback.decorate.FeatureJsonGeneratorDecorator"/>
            <providers>
                <pattern>
                    <pattern>
                        {
                        "sysName":"${sysName}",
                        "thread":"%thread",
                        "exeDate": "%date{yyyy-MM-dd HH:mm:ss.SSS}",
                        "level":"%level",
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                        "msg": "%msg"
                        }
                    </pattern>
                </pattern>
            </providers>
        </encoder>
        <!-- 日志记录器的滚动策略，按日期，按大小记录 -->
        <rollingPolicy class="ch.qos.logback.core.rolling.TimeBasedRollingPolicy">
            <!-- 每天日志归档路径以及格式 -->
            <fileNamePattern>${logging.path}/info/info_${sysName}_%d{yyyy-MM-dd}.%i.log</fileNamePattern>
            <timeBasedFileNamingAndTriggeringPolicy class="ch.qos.logback.core.rolling.SizeAndTimeBasedFNATP">
                <maxFileSize>${logMaxFileSize}</maxFileSize>
            </timeBasedFileNamingAndTriggeringPolicy>
            <!--日志文件保留天数-->
            <maxHistory>${logMaxHistory}</maxHistory>
        </rollingPolicy>
        <!-- 此日志文件只记录info级别的 -->
        <filter class="ch.qos.logback.classic.filter.LevelFilter">
            <level>INFO</level>
            <onMatch>ACCEPT</onMatch>
            <onMismatch>DENY</onMismatch>
        </filter>
    </appender>
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    <!-- 时间滚动输出 level为 WARN 日志 -->
    <appender name="WARN_APPENDER" class="ch.qos.logback.core.rolling.RollingFileAppender">
        <!-- 正在记录的日志文件的路径及文件名 -->
        <file>${logging.path}/warn_${sysName}.log</file>
        <!--日志文件输出格式-->
        <encoder class="net.logstash.logback.encoder.LoggingEventCompositeJsonEncoder">
            <jsonGeneratorDecorator
                    class="net.logstash.logback.decorate.FeatureJsonGeneratorDecorator"/>
            <providers>
                <pattern>
                    <pattern>
                        {
                        "sysName":"${sysName}",
                        "thread":"%thread",
                        "exeDate": "%date{yyyy-MM-dd HH:mm:ss.SSS}",
                        "level":"%level",
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                        "msg": "%msg"
                        }
                    </pattern>
                </pattern>
            </providers>
        </encoder>
        <!-- 日志记录器的滚动策略，按日期，按大小记录 -->
        <rollingPolicy class="ch.qos.logback.core.rolling.TimeBasedRollingPolicy">
            <fileNamePattern>${logging.path}/warn/warn_${sysName}_%d{yyyy-MM-dd}.%i.log</fileNamePattern>
            <timeBasedFileNamingAndTriggeringPolicy class="ch.qos.logback.core.rolling.SizeAndTimeBasedFNATP">
                <maxFileSize>${logMaxFileSize}</maxFileSize>
            </timeBasedFileNamingAndTriggeringPolicy>
            <!--日志文件保留天数-->
            <maxHistory>${logMaxHistory}</maxHistory>
        </rollingPolicy>
        <!-- 此日志文件只记录warn级别的 -->
        <filter class="ch.qos.logback.classic.filter.LevelFilter">
            <level>WARN</level>
            <onMatch>ACCEPT</onMatch>
            <onMismatch>DENY</onMismatch>
        </filter>
    </appender>
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﻿
    <!-- 时间滚动输出 level为 ERROR 日志 -->
    <appender name="ERROR_APPENDER" class="ch.qos.logback.core.rolling.RollingFileAppender">
        <!-- 正在记录的日志文件的路径及文件名 -->
        <file>${logging.path}/error_${sysName}.log</file>
        <!--日志文件输出格式-->
        <encoder class="net.logstash.logback.encoder.LoggingEventCompositeJsonEncoder">
            <jsonGeneratorDecorator
                    class="net.logstash.logback.decorate.FeatureJsonGeneratorDecorator"/>
            <providers>
                <pattern>
                    <pattern>
                        {
                        "sysName":"${sysName}",
                        "thread":"%thread",
                        "exeDate": "%date{yyyy-MM-dd HH:mm:ss.SSS}",
                        "level":"%level",
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                        "msg": "%msg"
                        }
                    </pattern>
                </pattern>
            </providers>
        </encoder>
        <!-- 日志记录器的滚动策略，按日期，按大小记录 -->
        <rollingPolicy class="ch.qos.logback.core.rolling.TimeBasedRollingPolicy">
            <fileNamePattern>${logging.path}/error/error_${sysName}_%d{yyyy-MM-dd}.%i.log</fileNamePattern>
            <timeBasedFileNamingAndTriggeringPolicy class="ch.qos.logback.core.rolling.SizeAndTimeBasedFNATP">
                <maxFileSize>${logMaxFileSize}</maxFileSize>
            </timeBasedFileNamingAndTriggeringPolicy>
            <!--日志文件保留天数-->
            <maxHistory>${logMaxHistory}</maxHistory>
        </rollingPolicy>
        <!-- 此日志文件只记录ERROR级别的 -->
        <filter class="ch.qos.logback.classic.filter.LevelFilter">
            <level>ERROR</level>
            <onMatch>ACCEPT</onMatch>
            <onMismatch>DENY</onMismatch>
        </filter>
    </appender>
    <root level="INFO">
        <appender-ref ref="CONSOLE_APPENDER"/>
        <appender-ref ref="DEBUG_APPENDER"/>
        <appender-ref ref="INFO_APPENDER"/>
        <appender-ref ref="WARN_APPENDER"/>
        <appender-ref ref="ERROR_APPENDER"/>
    </root>
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</configuration>
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综上，系统日志首先会在info.log，error.log，warn.log文件中，会根据文件大小滚动的生成到目录info，error，warn目录下。所以我们只需要监听info，error，warn目录就行
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具体实现，请查看https://gitee.com/lvmoney/zhy-frame-parent
发布于: 2020 年 07 月 02 日阅读数: 75
原文链接:【http://xie.infoq.cn/article/6c71ebe26b990283ef228765b】。
柠檬

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人生尚未成功，朋友仍需努力 2020.05.21 加入
长期从事微服务，中台等后台开发和架构设计。一些见解和实现可查看https://gitee.com/lvmoney/zhy-frame-parent
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