尚硅谷大数据项目《在线教育之离线数仓》笔记007

# 第12章 报表数据导出
CREATE DATABASE IF NOT EXISTS edu_report DEFAULT CHARSET utf8 COLLATE utf8_general_ci;

# 12.1.2 创建表


# 01）各来源流量统计
DROP TABLE IF EXISTS ads_traffic_stats_by_source;
CREATE TABLE ads_traffic_stats_by_source
(
    `dt`               DATETIME COMMENT '统计日期',
    `recent_days`      BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `source_id`        VARCHAR(255) COMMENT '引流来源id',
    `source_site`      VARCHAR(255) COMMENT '引流来源名称',
    `uv_count`         BIGINT COMMENT '访客人数',
    `avg_duration_sec` BIGINT COMMENT '会话平均停留时长，单位为秒',
    `avg_page_count`   BIGINT COMMENT '会话平均浏览页面数',
    `sv_count`         BIGINT COMMENT '会话数',
    `bounce_rate`      DECIMAL(16, 2) COMMENT '跳出率',
    PRIMARY KEY (`dt`, `recent_days`, `source_id`)
) COMMENT '各引流来源流量统计';


# 02）页面浏览路径分析
DROP TABLE IF EXISTS ads_traffic_page_path;
CREATE TABLE ads_traffic_page_path
(
    `dt`          DATETIME COMMENT '统计日期',
    `recent_days` BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `source`      VARCHAR(255) COMMENT '跳转起始页面id',
    `target`      VARCHAR(255) COMMENT '跳转终到页面id',
    `path_count`  BIGINT COMMENT '跳转次数',
    PRIMARY KEY (`dt`, `recent_days`, `source`, `target`)
) COMMENT '页面浏览路径分析';


# 03）各引流来源销售状况统计
DROP TABLE IF EXISTS ads_traffic_sale_stats_by_source;
CREATE TABLE ads_traffic_sale_stats_by_source
(
    `dt`                 DATETIME COMMENT '统计日期',
    `recent_days`        BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `source_id`          VARCHAR(255) COMMENT '引流来源id',
    `source_site`        VARCHAR(255) COMMENT '引流来源名称',
    `order_total_amount` DECIMAL(16, 2) COMMENT '销售额',
    `order_user_count`   BIGINT COMMENT '下单用户数',
    `pv_visitor_count`   BIGINT COMMENT '引流访客数',
    `convert_rate`       DECIMAL(16, 2) COMMENT '转化率',
    PRIMARY KEY (`dt`, `recent_days`, `source_id`)
) COMMENT '各引流来源销售状况统计';


# 04）用户变动统计
DROP TABLE IF EXISTS ads_user_user_change;
CREATE TABLE ads_user_user_change
(
    `dt`               DATETIME COMMENT '统计日期',
    `user_churn_count` BIGINT COMMENT '流失用户数',
    `user_back_count`  BIGINT COMMENT '回流用户数',
    PRIMARY KEY (`dt`)
) COMMENT '用户变动统计';


# 05）用户留存率
DROP TABLE IF EXISTS ads_user_user_retention;
CREATE TABLE ads_user_user_retention
(
    `dt`              DATETIME COMMENT '统计日期',
    `create_date`     VARCHAR(255) COMMENT '用户新增日期',
    `retention_day`   INT COMMENT '截至当前日期留存天数',
    `retention_count` BIGINT COMMENT '留存用户数量',
    `new_user_count`  BIGINT COMMENT '新增用户数量',
    `retention_rate`  DECIMAL(16, 2) COMMENT '留存率',
    PRIMARY KEY (`dt`, `create_date`, `retention_day`)
) COMMENT '用户留存率';


# 06）用户新增活跃统计
DROP TABLE IF EXISTS ads_user_user_stats;
CREATE TABLE ads_user_user_stats
(
    `dt`                DATETIME COMMENT '统计日期',
    `recent_days`       BIGINT COMMENT '最近n日,1:最近1日,7:最近7日,30:最近30日',
    `new_user_count`    BIGINT COMMENT '新增用户数',
    `active_user_count` BIGINT COMMENT '活跃用户数',
    PRIMARY KEY (`dt`, `recent_days`)
) COMMENT '用户新增活跃统计';


# 07）用户行为漏斗分析
DROP TABLE IF EXISTS ads_user_user_action;
CREATE TABLE ads_user_user_action
(
    `dt`                DATETIME COMMENT '统计日期',
    `recent_days`       BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `home_count`        BIGINT COMMENT '浏览首页人数',
    `good_detail_count` BIGINT COMMENT '浏览商品详情页人数',
    `cart_count`        BIGINT COMMENT '加入购物车人数',
    `order_count`       BIGINT COMMENT '下单人数',
    `payment_count`     BIGINT COMMENT '支付人数',
    PRIMARY KEY (`dt`, `recent_days`)
) COMMENT '用户行为漏斗分析';


# 08）新增交易用户统计
DROP TABLE IF EXISTS ads_user_new_buyer_stats;
CREATE TABLE ads_user_new_buyer_stats
(
    `dt`                     DATETIME COMMENT '统计日期',
    `recent_days`            BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `new_order_user_count`   BIGINT COMMENT '新增下单人数',
    `new_payment_user_count` BIGINT COMMENT '新增支付人数',
    PRIMARY KEY (`dt`, `recent_days`)
) COMMENT '新增交易用户统计';


# 09）各年龄段下单用户数
DROP TABLE IF EXISTS ads_user_order_user_count_by_age_group;
CREATE TABLE ads_user_order_user_count_by_age_group
(
    `dt`               DATETIME COMMENT '统计日期',
    `recent_days`      BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `age_group`        VARCHAR(255) COMMENT '年龄段,18岁及以下、19-24岁、25-29岁、30-34岁、35-39岁、40-49岁、50岁及以上',
    `order_user_count` BIGINT COMMENT '下单人数',
    PRIMARY KEY (`dt`, `recent_days`, `age_group`)
) COMMENT '各年龄段下单用户数统计';


# 10）各类别课程交易统计
DROP TABLE IF EXISTS ads_course_trade_stats_by_category;
CREATE TABLE ads_course_trade_stats_by_category
(
    `dt`               DATETIME COMMENT '统计日期',
    `recent_days`      BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `category_id`      VARCHAR(255) COMMENT '类别id',
    `category_name`    VARCHAR(255) COMMENT '类别名称',
    `order_count`      BIGINT COMMENT '订单数',
    `order_user_count` BIGINT COMMENT '订单人数' ,
    `order_amount`     DECIMAL(16, 2) COMMENT '下单金额',
    PRIMARY KEY (`dt`, `recent_days`, `category_id`)
) COMMENT '各类别课程交易统计';


# 11）各学科课程交易统计
DROP TABLE IF EXISTS ads_course_trade_stats_by_subject;
CREATE TABLE ads_course_trade_stats_by_subject
(
    `dt`               DATETIME COMMENT '统计日期',
    `recent_days`      BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `subject_id`       VARCHAR(255) COMMENT '学科id',
    `subject_name`     VARCHAR(255) COMMENT '学科名称',
    `order_count`      BIGINT COMMENT '订单数',
    `order_user_count` BIGINT COMMENT '订单人数' ,
    `order_amount`     DECIMAL(16, 2) COMMENT '下单金额',
    PRIMARY KEY (`dt`, `recent_days`, `subject_id`)
) COMMENT '各学科课程交易统计';


# 12）各课程交易统计
DROP TABLE IF EXISTS ads_course_trade_stats_by_course;
CREATE TABLE ads_course_trade_stats_by_course
(
    `dt`               DATETIME COMMENT '统计日期',
    `recent_days`      BIGINT COMMENT '最近天数,1:最近 1 天,7:最近 7天,30:最近 30 天',
    `course_id`        VARCHAR(255) COMMENT '课程id',
    `course_name`      VARCHAR(255) COMMENT '课程名称',
    `order_count`      BIGINT COMMENT '下单数',
    `order_user_count` BIGINT COMMENT '下单人数',
    `order_amount`     DECIMAL(16, 2) COMMENT '下单金额',
    PRIMARY KEY (`dt`, `recent_days`, `course_id`)
) COMMENT '各课程交易统计';


# 13）各课程评价统计
DROP TABLE IF EXISTS ads_course_review_stats_by_course;
CREATE TABLE ads_course_review_stats_by_course
(
    `dt`                DATETIME COMMENT '统计日期',
    `recent_days`       BIGINT COMMENT '最近天数,1:最近 1 天,7:最近 7 天,30:最近 30 天',
    `course_id`         VARCHAR(255) COMMENT '课程id',
    `course_name`       VARCHAR(255) COMMENT '课程名称',
    `avg_stars`         BIGINT COMMENT '用户平均评分',
    `review_user_count` BIGINT COMMENT '评价用户数',
    `praise_rate`       DECIMAL(16, 2) COMMENT '好评率',
    PRIMARY KEY (`dt`, `recent_days`, `course_id`)
) COMMENT '各课程评价统计';


# 14）各分类课程试听留存统计
DROP TABLE IF EXISTS ads_sample_retention_stats_by_category;
CREATE TABLE ads_sample_retention_stats_by_category
(
    `dt`                DATETIME COMMENT '统计日期',
    `retention_days`    BIGINT COMMENT '留存天数，1-7 天',
    `category_id`       VARCHAR(255) COMMENT '分类id',
    `category_name`     VARCHAR(255) COMMENT '分类名称',
    `sample_user_count` BIGINT COMMENT '试听人数',
    `retention_rate`    DECIMAL(16, 2) COMMENT '试听留存率',
    PRIMARY KEY (`dt`, `retention_days`, `category_id`)
) COMMENT '各分类课程试听留存统计';


# 15）各学科试听留存统计
DROP TABLE IF EXISTS ads_sample_retention_stats_by_subject;
CREATE TABLE ads_sample_retention_stats_by_subject
(
    `dt`                DATETIME COMMENT '统计日期',
    `retention_days`    BIGINT COMMENT '留存天数，1-7 天',
    `subject_id`        VARCHAR(255) COMMENT '学科id',
    `subject_name`      VARCHAR(255) COMMENT '学科名称',
    `sample_user_count` BIGINT COMMENT '试听人数',
    `retention_rate`    DECIMAL(16, 2) COMMENT '试听留存率',
    PRIMARY KEY (`dt`, `retention_days`, `subject_id`)
) COMMENT '各学科试听留存统计';


# 16）各课程试听留存统计
DROP TABLE IF EXISTS ads_sample_retention_stats_by_course;
CREATE TABLE ads_sample_retention_stats_by_course
(
    `dt`                DATETIME COMMENT '统计日期',
    `retention_days`    BIGINT COMMENT '留存天数，1-7 天',
    `course_id`         VARCHAR(255) COMMENT '课程id',
    `course_name`       VARCHAR(255) COMMENT '课程名称',
    `sample_user_count` BIGINT COMMENT '试听人数',
    `retention_rate`    DECIMAL(16, 2) COMMENT '试听留存率',
    PRIMARY KEY (`dt`, `retention_days`, `course_id`)
) COMMENT '各课程试听留存统计';


# 17）交易综合指标
DROP TABLE IF EXISTS ads_trade_stats;
CREATE TABLE ads_trade_stats
(
    `dt`                 DATETIME COMMENT '统计日期',
    `recent_days`        BIGINT COMMENT '最近天数,1:最近1日,7:最近7天,30:最近30天',
    `order_total_amount` DECIMAL(16, 2) COMMENT '订单总额,GMV',
    `order_count`        BIGINT COMMENT '订单数',
    `order_user_count`   BIGINT COMMENT '下单人数',
    PRIMARY KEY (`dt`, `recent_days`)
) COMMENT '交易综合指标';


# 18）各省份交易统计
DROP TABLE IF EXISTS ads_trade_order_by_province;
CREATE TABLE ads_trade_order_by_province
(
    `dt`                 DATETIME COMMENT '统计日期',
    `recent_days`        BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `province_id`        VARCHAR(10) COMMENT '省份id',
    `province_name`      VARCHAR(30) COMMENT '省份名称',
    `region_id`          VARCHAR(30) COMMENT '大区id',
    `area_code`          VARCHAR(255) COMMENT '地区编码',
    `iso_code`           VARCHAR(255) COMMENT '国际标准地区编码',
    `iso_code_3166_2`    VARCHAR(255) COMMENT '国际标准地区编码',
    `order_count`        BIGINT COMMENT '订单数' ,
    `order_user_count`   BIGINT COMMENT '下单人数',
    `order_total_amount` DECIMAL(16, 2) COMMENT '订单金额',
    PRIMARY KEY (`dt`, `recent_days`, `province_id`, `region_id`, `area_code`, `iso_code`, `iso_code_3166_2`)
) COMMENT '各省份交易统计';


# 19）各试卷平均统计
DROP TABLE IF EXISTS ads_examination_paper_avg_stats;
CREATE TABLE ads_examination_paper_avg_stats
(
    `dt`             DATETIME COMMENT '统计日期',
    `recent_days`    BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `paper_id`       VARCHAR(255) COMMENT '试卷 id',
    `paper_title`    VARCHAR(255) COMMENT '试卷名称',
    `avg_score`      DECIMAL(16, 2) COMMENT '试卷平均分',
    `avg_during_sec` BIGINT COMMENT '试卷平均时长',
    `user_count`     BIGINT COMMENT '试卷用户数',
    PRIMARY KEY (`dt`, `recent_days`, `paper_id`)
) COMMENT '各试卷平均统计';


# 20）最近 1/7/30 日各试卷成绩分布
DROP TABLE IF EXISTS ads_examination_course_avg_stats;
CREATE TABLE ads_examination_course_avg_stats
(
    `dt`             DATETIME COMMENT '统计日期',
    `recent_days`    BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `course_id`      VARCHAR(255) COMMENT '课程id',
    `course_name`    VARCHAR(255) COMMENT '课程名称',
    `avg_score`      DECIMAL(16, 2) COMMENT '平均分',
    `avg_during_sec` BIGINT COMMENT '平均时长',
    `user_count`     BIGINT COMMENT '用户数',
    PRIMARY KEY (`dt`, `recent_days`, `course_id`)
) COMMENT '各课程考试相关指标';


# 21）最近 1/7/30 日各试卷分数分布统计
DROP TABLE IF EXISTS ads_examination_user_count_by_score_duration;
CREATE TABLE ads_examination_user_count_by_score_duration
(
    `dt`             DATETIME COMMENT '统计日期',
    `recent_days`    BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `paper_id`       VARCHAR(255) COMMENT '试卷 id',
    `score_duration` VARCHAR(255) COMMENT '分数区间',
    `user_count`     BIGINT COMMENT '各试卷各分数区间用户数',
    PRIMARY KEY (`dt`, `recent_days`, `paper_id`, `score_duration`)
) COMMENT '各试卷分数分布统计';


# 22）最近 1/7/30 日各题目正确率
DROP TABLE IF EXISTS ads_examination_question_accuracy;
CREATE TABLE ads_examination_question_accuracy
(
    `dt`          DATETIME COMMENT '统计日期',
    `recent_days` BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `question_id` VARCHAR(255) COMMENT '题目 id',
    `accuracy`    DECIMAL(16, 2) COMMENT '题目正确率',
    PRIMARY KEY (`dt`, `recent_days`, `question_id`)
) COMMENT '各题目正确率';


# 23）单章视频播放情况统计
DROP TABLE IF EXISTS ads_learn_play_stats_by_chapter;
CREATE TABLE ads_learn_play_stats_by_chapter
(
    `dt`           DATETIME COMMENT '统计日期',
    `recent_days`  BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `chapter_id`   VARCHAR(30) COMMENT '章节 id',
    `chapter_name` VARCHAR(200) COMMENT '章节名称',
    `video_id`     VARCHAR(255) COMMENT '视频 id',
    `video_name`   VARCHAR(255) COMMENT '视频名称',
    `play_count`   BIGINT COMMENT '各章节视频播放次数',
    `avg_play_sec` BIGINT COMMENT '各章节视频人均观看时长',
    `user_count`   BIGINT COMMENT '各章节观看人数',
    PRIMARY KEY (`dt`, `recent_days`, `chapter_id`, `video_id`)
) COMMENT '单章视频播放情况统计';


# 24）各课程播放情况统计
DROP TABLE IF EXISTS ads_learn_play_stats_by_course;
CREATE TABLE ads_learn_play_stats_by_course
(
    `dt`           DATETIME COMMENT '统计日期',
    `recent_days`  BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `course_id`    VARCHAR(255) COMMENT '课程id',
    `course_name`  VARCHAR(255) COMMENT '课程名称',
    `play_count`   BIGINT COMMENT '各课程视频播放次数',
    `avg_play_sec` BIGINT COMMENT '各课程视频人均观看时长',
    `user_count`   BIGINT COMMENT '各课程观看人数',
    PRIMARY KEY (`dt`, `recent_days`, `course_id`)
) COMMENT '各课程播放情况统计';


# 25）各课程完课人数统计
DROP TABLE IF EXISTS ads_complete_complete_user_count_per_course;
CREATE TABLE ads_complete_complete_user_count_per_course
(
    `dt`          DATETIME COMMENT '统计日期',
    `recent_days` BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `course_id`   VARCHAR(255) COMMENT '课程 id',
    `user_count`  BIGINT COMMENT '各课程完课人数',
    PRIMARY KEY (`dt`, `recent_days`, `course_id`)
) COMMENT '各课程完课人数统计';


# 26）完课综合指标
DROP TABLE IF EXISTS ads_complete_complete_stats;
CREATE TABLE ads_complete_complete_stats
(
    `dt`                         DATETIME COMMENT '统计日期',
    `recent_days`                BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `user_complete_count`        BIGINT COMMENT '完课人数',
    `user_course_complete_count` BIGINT COMMENT '完课人次',
    PRIMARY KEY (`dt`, `recent_days`)
) COMMENT '完课综合指标';


# 27）各课程人均完成章节视频数
DROP TABLE IF EXISTS ads_complete_complete_chapter_count_per_course;
CREATE TABLE ads_complete_complete_chapter_count_per_course
(
    `dt`                     DATETIME COMMENT '统计日期',
    `recent_days`            BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
    `course_id`              VARCHAR(255) COMMENT '课程 id',
    `complete_chapter_count` BIGINT COMMENT '各课程用户平均完成章节数',
    PRIMARY KEY (`dt`, `recent_days`, `course_id`)
) COMMENT '各课程人均完成章节视频数';

02、修改datax的jar包

DataX

GitHub - alibaba/DataX: DataX是阿里云DataWorks数据集成的开源版本。

https://github.com/alibaba/DataX/blob/master/mysqlwriter/doc/mysqlwriter.md

https://github.com/alibaba/DataX/blob/master/hdfsreader/doc/hdfsreader.md

[atguigu@node001 ~]$ cd /opt/module/datax/
[atguigu@node001 datax]$ python bin/datax.py -p"-Dexportdir=/warehouse/edu/ads/ads_traffic_stats_by_source/" job/ads_traffic_stats_by_source.json

2023-09-05 10:59:01.854 [job-0] ERROR RetryUtil - Exception when calling callable, 即将尝试执行第1次重试.本次重试计划等待[1000]ms,实际等待[1001]ms, 异常Msg:[Code:[DBUtilErrorCode-10], Description:[连接数据库失败. 请检查您的账号、密码、数据库名称、IP、Port或者向 DBA 寻求帮助(注意网络环境).]. - 具体错误信息为：com.mysql.jdbc.exceptions.jdbc4.MySQLNonTransientConnectionException: Could not create connection to database server.]
2023-09-05 10:59:03.860 [job-0] ERROR RetryUtil - Exception when calling callable, 即将尝试执行第2次重试.本次重试计划等待[2000]ms,实际等待[2000]ms, 异常Msg:[Code:[DBUtilErrorCode-10], Description:[连接数据库失败. 请检查您的账号、密码、数据库名称、IP、Port或者向 DBA 寻求帮助(注意网络环境).]. - 具体错误信息为：com.mysql.jdbc.exceptions.jdbc4.MySQLNonTransientConnectionException: Could not create connection to database server.]
2023-09-05 10:59:07.865 [job-0] ERROR RetryUtil - Exception when calling callable, 即将尝试执行第3次重试.本次重试计划等待[4000]ms,实际等待[4000]ms, 异常Msg:[Code:[DBUtilErrorCode-10], Description:[连接数据库失败. 请检查您的账号、密码、数据库名称、IP、Port或者向 DBA 寻求帮助(注意网络环境).]. - 具体错误信息为：com.mysql.jdbc.exceptions.jdbc4.MySQLNonTransientConnectionException: Could not create connection to database server.]

解决办法：已检查N遍，账号密码没有问题，将/opt/module/datax/plugin/writer/mysqlwriter/libs与/opt/module/datax/plugin/reader/mysqlreader/libs等两个lib目录下的mysql-connector-java-5.1.34.jar包替换为mysql-connector-java-8.0.29.jar。

03、ads_traffic_stats_by_source.json文件

经DataX智能分析,该任务最可能的错误原因是:
com.alibaba.datax.common.exception.DataXException: Code:[DBUtilErrorCode-01], Description:[获取表字段相关信息失败.]. - 获取表:ads_traffic_stats_by_source 的字段的元信息时失败. 请联系 DBA 核查该库、表信息. - java.sql.SQLSyntaxErrorException: Unknown column 'channel' in 'field list'
at com.mysql.cj.jdbc.exceptions.SQLError.createSQLException(SQLError.java:120)
at com.mysql.cj.jdbc.exceptions.SQLExceptionsMapping.translateException(SQLExceptionsMapping.java:122)
at com.mysql.cj.jdbc.StatementImpl.executeQuery(StatementImpl.java:1201)
at com.alibaba.datax.plugin.rdbms.util.DBUtil.getColumnMetaData(DBUtil.java:563)
at com.alibaba.datax.plugin.rdbms.writer.util.OriginalConfPretreatmentUtil.dealColumnConf(OriginalConfPretreatmentUtil.java:125)
at com.alibaba.datax.plugin.rdbms.writer.util.OriginalConfPretreatmentUtil.dealColumnConf(OriginalConfPretreatmentUtil.java:140)
at com.alibaba.datax.plugin.rdbms.writer.util.OriginalConfPretreatmentUtil.doPretreatment(OriginalConfPretreatmentUtil.java:35)
at com.alibaba.datax.plugin.rdbms.writer.CommonRdbmsWriter$Job.init(CommonRdbmsWriter.java:41)
at com.alibaba.datax.plugin.writer.mysqlwriter.MysqlWriter$Job.init(MysqlWriter.java:31)
at com.alibaba.datax.core.job.JobContainer.initJobWriter(JobContainer.java:704)
at com.alibaba.datax.core.job.JobContainer.init(JobContainer.java:304)
at com.alibaba.datax.core.job.JobContainer.start(JobContainer.java:113)
at com.alibaba.datax.core.Engine.start(Engine.java:92)
at com.alibaba.datax.core.Engine.entry(Engine.java:171)
at com.alibaba.datax.core.Engine.main(Engine.java:204)

at com.alibaba.datax.common.exception.DataXException.asDataXException(DataXException.java:33)
at com.alibaba.datax.plugin.rdbms.util.DBUtil.getColumnMetaData(DBUtil.java:575)
at com.alibaba.datax.plugin.rdbms.writer.util.OriginalConfPretreatmentUtil.dealColumnConf(OriginalConfPretreatmentUtil.java:125)
at com.alibaba.datax.plugin.rdbms.writer.util.OriginalConfPretreatmentUtil.dealColumnConf(OriginalConfPretreatmentUtil.java:140)
at com.alibaba.datax.plugin.rdbms.writer.util.OriginalConfPretreatmentUtil.doPretreatment(OriginalConfPretreatmentUtil.java:35)
at com.alibaba.datax.plugin.rdbms.writer.CommonRdbmsWriter$Job.init(CommonRdbmsWriter.java:41)
at com.alibaba.datax.plugin.writer.mysqlwriter.MysqlWriter$Job.init(MysqlWriter.java:31)
at com.alibaba.datax.core.job.JobContainer.initJobWriter(JobContainer.java:704)
at com.alibaba.datax.core.job.JobContainer.init(JobContainer.java:304)
at com.alibaba.datax.core.job.JobContainer.start(JobContainer.java:113)
at com.alibaba.datax.core.Engine.start(Engine.java:92)
at com.alibaba.datax.core.Engine.entry(Engine.java:171)
at com.alibaba.datax.core.Engine.main(Engine.java:204)

/opt/module/datax/job/ads_traffic_stats_by_source.json

{
    "job": {
        "content": [
            {
                "reader": {
                    "name": "hdfsreader",
                    "parameter": {
                        "column": [
                            "*"
                        ],
                        "defaultFS": "hdfs://node001:8020",
                        "encoding": "UTF-8",
                        "fieldDelimiter": "\t",
                        "fileType": "text",
                        "nullFormat": "\\N",
                        "path": "${exportdir}"
                    }
                },
                "writer": {
                    "name": "mysqlwriter",
                    "parameter": {
                        "column": [
                            "dt",
                            "recent_days",
                            "source_id",
                            "source_site",
                            "uv_count",
                            "avg_duration_sec",
                            "avg_page_count",
                            "sv_count",
                            "bounce_rate"
                        ],
                        "connection": [
                            {
                                "jdbcUrl": "jdbc:mysql://node001:3306/edu_report?useUnicode=true&characterEncoding=utf-8",
                                "table": [
                                    "ads_traffic_stats_by_source"
                                ]
                            }
                        ],
                        "username": "root",
                        "password": "123456",
                        "writeMode": "replace"
                    }
                }
            }
        ],
        "setting": {
            "errorLimit": {
                "percentage": 0.02,
                "record": 0
            },
            "speed": {
                "channel": 3
            }
        }
    }
}

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12.2.2 DataX配置文件生成脚本

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第13章数据仓库工作流调度

Apache DolphinScheduler是一个分布式、易扩展的可视化DAG工作流任务调度平台。致力于解决数据处理流程中错综复杂的依赖关系，使调度系统在数据处理流程中开箱即用。

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第2章 DolphinScheduler部署说明

第3章 DolphinScheduler集群模式部署

3.6 一键部署DolphinScheduler

[atguigu@node001 apache-dolphinscheduler-2.0.3-bin]$ jpsall
================ node001 ================
5360 QuorumPeerMain
2832 NameNode
9296 WorkerServer
3411 JobHistoryServer
5988 RunJar
9668 ApiApplicationServer
6100 RunJar
9414 LoggerServer
3000 DataNode
9545 AlertServer
10540 Jps
7020 NodeManager
================ node002 ================
5296 NodeManager
5984 WorkerServer
6032 LoggerServer
6231 Jps
4745 QuorumPeerMain
5178 ResourceManager
4986 DataNode
================ node003 ================
3985 NodeManager
4658 LoggerServer
4884 Jps
1861 DataNode
3594 QuorumPeerMain
1967 SecondaryNameNode
[atguigu@node001 apache-dolphinscheduler-2.0.3-bin]$

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3.7 DolphinScheduler启停命令

[atguigu@node001 apache-dolphinscheduler-2.0.3-bin]$ cd /opt/module/dolphinScheduler/ds-2.0.3/
[atguigu@node001 ds-2.0.3]$ ll
总用量 60
drwxrwxr-x 2 atguigu atguigu  4096 9月   6 11:21 bin
drwxrwxr-x 5 atguigu atguigu  4096 9月   6 11:21 conf
-rwxrwxr-x 1 atguigu atguigu  5190 9月   6 11:22 install.sh
drwxrwxr-x 2 atguigu atguigu 20480 9月   6 11:22 lib
drwxrwxr-x 2 atguigu atguigu  4096 9月   6 11:23 logs
drwxrwxr-x 2 atguigu atguigu  4096 9月   6 11:22 pid
drwxrwxr-x 2 atguigu atguigu  4096 9月   6 11:22 script
drwxrwxr-x 3 atguigu atguigu  4096 9月   6 11:22 sql
drwxrwxr-x 8 atguigu atguigu  4096 9月   6 11:22 ui
[atguigu@node001 ds-2.0.3]$ cd bin/
[atguigu@node001 bin]$ ll
总用量 20
-rwxrwxr-x 1 atguigu atguigu 6770 9月   6 11:21 dolphinscheduler-daemon.sh
-rwxrwxr-x 1 atguigu atguigu 2427 9月   6 11:21 start-all.sh
-rwxrwxr-x 1 atguigu atguigu 3332 9月   6 11:21 status-all.sh
-rwxrwxr-x 1 atguigu atguigu 2428 9月   6 11:21 stop-all.sh
[atguigu@node001 bin]$

node003没有运行WorkerServer，资源不够，将资源改为8g就能运行了，但没必要。

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启动hadoop、zookeeper、hive、hive-service2、ds。

[atguigu@node001 ~]$ myhadoop.sh start

[atguigu@node001 ~]$ zookeeper.sh start

[atguigu@node001 ~]$ nohup /opt/module/hive/hive-3.1.2/bin/hive &

[atguigu@node001 ~]$ nohup /opt/module/hive/hive-3.1.2/bin/hive --service hiveserver2 &

[atguigu@node001 ~]$ /opt/module/dolphinScheduler/ds-2.0.3/bin/start-all.sh

[atguigu@node001 ~]$ myhadoop.sh start
 ================ 启动 hadoop集群 ================
 ---------------- 启动 hdfs ----------------
Starting namenodes on [node001]
Starting datanodes
Starting secondary namenodes [node003]
 --------------- 启动 yarn ---------------
Starting resourcemanager
Starting nodemanagers
 --------------- 启动 historyserver ---------------
[atguigu@node001 ~]$ zookeeper.sh start
---------- zookeeper node001 启动 ----------
ZooKeeper JMX enabled by default
Using config: /opt/module/zookeeper/zookeeper-3.5.7/bin/../conf/zoo.cfg
Starting zookeeper ... STARTED
---------- zookeeper node002 启动 ----------
ZooKeeper JMX enabled by default
Using config: /opt/module/zookeeper/zookeeper-3.5.7/bin/../conf/zoo.cfg
Starting zookeeper ... STARTED
---------- zookeeper node003 启动 ----------
ZooKeeper JMX enabled by default
Using config: /opt/module/zookeeper/zookeeper-3.5.7/bin/../conf/zoo.cfg
Starting zookeeper ... STARTED
[atguigu@node001 ~]$ nohup /opt/module/hive/hive-3.1.2/bin/hive &
[1] 3741
[atguigu@node001 ~]$ nohup: 忽略输入并把输出追加到"nohup.out"

[atguigu@node001 ~]$ nohup /opt/module/hive/hive-3.1.2/bin/hive --service hiveserver2 &
[2] 3912
[atguigu@node001 ~]$ nohup: 忽略输入并把输出追加到"nohup.out"

[atguigu@node001 ~]$ /opt/module/dolphinScheduler/ds-2.0.3/bin/start-all.sh
node001:default
...

DolphinScheduler工作流运行之后在工作流实例中查不到是什么原因？将node001的运行内存从4G调为8G即可。

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第5章 DolphinScheduler进阶

5.1 工作流传参

DolphinScheduler支持对任务节点进行灵活的传参，任务节点可通过${参数名}引用参数值。

由此可得，优先级由高到低：本地参数 > 全局参数 > 上游任务传递的参数。

5.1.5 参数优先级

3）结论

（1）本地参数 > 全局参数 > 上游任务传递的参数；

（2）多个上游节点均传递同名参数时，下游节点会优先使用值为非空的参数；

（3）如果存在多个值为非空的参数，则按照上游任务的完成时间排序，选择完成时间最早的上游任务对应的参数。

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5.2 引用依赖资源

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13.2 数据准备

启动hadoop、zookeeper、kafka、maxwell、f1、f2、f3。

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13.3 工作流调度实操

[2023-09-06 17:15:26,824] ERROR [Broker id=0] Received LeaderAndIsrRequest with correlation id 1 from controller 1 epoch 33 for partition __consumer_offsets-44 (last update controller epoch 33) but cannot become follower since the new leader -1 is unavailable. (state.change.logger)
[2023-09-06 17:15:26,824] ERROR [Broker id=0] Received LeaderAndIsrRequest with correlation id 1 from controller 1 epoch 33 for partition __consumer_offsets-32 (last update controller epoch 33) but cannot become follower since the new leader -1 is unavailable. (state.change.logger)
[2023-09-06 17:15:26,824] ERROR [Broker id=0] Received LeaderAndIsrRequest with correlation id 1 from controller 1 epoch 33 for partition __consumer_offsets-41 (last update controller epoch 33) but cannot become follower since the new leader -1 is unavailable. (state.change.logger)

[2023-09-06 19:32:27,802] ERROR [Controller id=0 epoch=34] Controller 0 epoch 34 failed to change state for partition __transaction_state-27 from OfflinePartition to OnlinePartition (state.change.logger)
kafka.common.StateChangeFailedException: Failed to elect leader for partition __transaction_state-27 under strategy OfflinePartitionLeaderElectionStrategy(false)
   at kafka.controller.ZkPartitionStateMachine.$anonfun$doElectLeaderForPartitions$7(PartitionStateMachine.scala:424)
   at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
   at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
   at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
   at kafka.controller.ZkPartitionStateMachine.doElectLeaderForPartitions(PartitionStateMachine.scala:421)
   at kafka.controller.ZkPartitionStateMachine.electLeaderForPartitions(PartitionStateMachine.scala:332)
   at kafka.controller.ZkPartitionStateMachine.doHandleStateChanges(PartitionStateMachine.scala:238)
   at kafka.controller.ZkPartitionStateMachine.handleStateChanges(PartitionStateMachine.scala:158)
   at kafka.controller.PartitionStateMachine.triggerOnlineStateChangeForPartitions(PartitionStateMachine.scala:74)
   at kafka.controller.PartitionStateMachine.triggerOnlinePartitionStateChange(PartitionStateMachine.scala:59)
   at kafka.controller.KafkaController.onBrokerStartup(KafkaController.scala:536)
   at kafka.controller.KafkaController.processBrokerChange(KafkaController.scala:1594)
   at kafka.controller.KafkaController.process(KafkaController.scala:2484)
   at kafka.controller.QueuedEvent.process(ControllerEventManager.scala:52)
   at kafka.controller.ControllerEventManager$ControllerEventThread.process$1(ControllerEventManager.scala:130)
   at kafka.controller.ControllerEventManager$ControllerEventThread.$anonfun$doWork$1(ControllerEventManager.scala:133)
   at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
   at kafka.metrics.KafkaTimer.time(KafkaTimer.scala:31)
   at kafka.controller.ControllerEventManager$ControllerEventThread.doWork(ControllerEventManager.scala:133)
   at kafka.utils.ShutdownableThread.run(ShutdownableThread.scala:96)
[2023-09-06 19:32:27,805] INFO [Controller id=0 epoch=34] Changed partition __consumer_offsets-22 from OfflinePartition to OnlinePartition with state LeaderAndIsr(leader=1, leaderEpoch=37, isr=List(1), zkVersion=37) (state.change.logger)

maxwell 报错: java.lang.RuntimeException: error: unhandled character set ‘utf8mb3‘

maxwell 报错: java.lang.RuntimeException: error: unhandled character set ‘utf8mb3‘_你的482的博客-CSDN博客

Maxwell安装使用 - 掘金

这个问题是因为MySQL从 5.5.3 开始，用 utf8mb4 编码来实现完整的 UTF-8，其中 mb4 表示 most bytes 4，最多占用4个字节。而原来的utf8则被utf8mb3则代替。一种解决方案是，将MySQL降级，重新安装5.5.3以下的版本。另一种方法则是修改maxwell源码。解压打开，找到有问题的类：com.zendesk.maxwell.schema.columndef.StringColumnDef，加上能识别utf8mb3的语句,重新打包。打包好的maxwell-1.19.0.jar替换maxwell/lib/maxwell-1.19.0.jar，重启即可。

启动hadoop、zookeeper、kafka、maxwell、f1.sh、f2.sh、f3.sh。

关闭采集的相关组件：kafka、flume(f1、f2、f3)、maxwell；启动hadoop、hive、zookeeper、dolphinscheduler ...

忘记启动zookeeper了...

Error starting ApplicationContext. To display the conditions report re-run your application with 'debug' enabled.
[ERROR] 2023-09-07 14:46:32.033 org.springframework.boot.SpringApplication:[843] - Application run failed
org.springframework.beans.factory.UnsatisfiedDependencyException: Error creating bean with name 'monitorServiceImpl': Unsatisfied dependency expressed through field 'registryClient'; nested exception is org.springframework.beans.factory.BeanCreationException: Error creating bean with name 'registryClient': Invocation of init method failed; nested exception is org.apache.dolphinscheduler.registry.api.RegistryException: zookeeper connect timeout

...

datax将数据同步至hdfs里面，mysql_to_hdfs_full.sh；

数据导入到ods层中，hdfs_to_ods_db.sh、

ods_to_dwd.sh。

export HADOOP_HOME=/opt/module/hadoop/hadoop-3.1.3
export HADOOP_CONF_DIR=/opt/module/hadoop/hadoop-3.1.3/etc/hadoop
export SPARK_HOME=/opt/module/spark/spark-3.0.0-bin-hadoop3.2
export JAVA_HOME=/opt/module/jdk/jdk1.8.0_212
export HIVE_HOME=/opt/module/hive/hive-3.1.2
export DATAX_HOME=/opt/module/datax

export PATH=$HADOOP_HOME/bin:$SPARK_HOME/bin:$JAVA_HOME/bin:$HIVE_HOME/bin:$DATAX_HOME/bin:$PATH

串联

尚硅谷大数据项目《在线教育之离线数仓》笔记007

第12章 报表数据导出

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01、创建数据表

02、修改datax的jar包

03、ads_traffic_stats_by_source.json文件

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P122【122_在线教育数仓开发回顾 04:23】

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第12章报表数据导出