实时数仓投放主备链路Diff测试工具落地实践

一、背景

目前实时数仓提供的投放实时指标优先级别越来越重要，特别下游为规则引擎提供的数仓数据，直接对投放运营的广告投放产生直接影响，数据延迟或者异常均可能产生直接或者间接的资产损失；从投放管理平台的链路全景图投放全景图来看，目前投放是一个闭环的运行流程，实时数仓处于数据链路中的关键节点，实时数据直接支持规则引擎的自动化操作，以及投放管理平台的手动控盘；实时节点事故，将可能导致整个投放链路无法正常运行；为使投放链路达到99.9%的稳定性，需要对链路任务做相关的稳定性提升，优先级提升。

研发测试综合评估方案对投放实时链路增加一条备链路，投放需求迭代，通过备链路进行迭代修改，完成修改后进行主备链路Diff，确保Diff通过率99.9%，即可上线。

二、实现方案

• 数据准备： 主备链路产出的数据分别实时写入到ODPS中。
• 数据采集： 测试工具服务同时采集主备链路数据切片，保留2份同一个时间周期的数据。
• 数据降噪&Diff： 工具采集数据后将进行第一步的降噪处理；主备数据开始对比&第二步降噪处理。

• 数据Diff结果： 加工数据对比的结果，判断出每个字段的差异量，再最终判断出整体数据的差异量，给出结果。
  

  三、搭建主备链路

  

实时链路解释：源头数据写入Kafka，Flink消费Kafka数据作为数据源（Source），结合属性字段做算子加工处理（Transformatin），处理结果写入Kafka（Sink），做下一步处理。经过一个个Flink任务节点加工分流到应用数据库中。

四、数据准备-数据切片

时间窗口切片

根据测试时间点，进行切片，取当天0点～执行时间段数据进行固定，确保数据不再更新。

业务场景切片

不同业务场景迭代进行切片，下发数据流提供多种下游场景数据，针对发生迭代的业务场景数据进行切片固定。如：fields_a='b'

五、主备链路数据Diff-去噪

数据漂移问题

问题现象：数据流在不断更新，同一条业务数据的数据流更新的最新的一条，主链路可能进入当天分区中，备链路可能进入到第二天分区中。

去噪方案：数据流取末尾1条数据。

数据更新频率问题

问题现象：同一条业务数据在更新过程中，主链路可能发生了10次更新，后面五次数据不发生改动，备链路只发生了5次更新。

去噪方案：同一个业务数据取数据流的N条数据。

数据更新时效问题

问题现象：同一条业务数据更新过程中，主链路更新三个数据为11.68、12.9、13.05；备链路更新三个数据为11.68、12.9、13.1；可以看出后面1次更新的数据并不一样。

去噪方案：同一个业务数据的数据流融合成一个list，主备相互判断末尾数据是否存在于对方截取的数据流list中。

属性字段值不统一问题

问题现象：存在空字符和null、0和0.0的情况，Diff结果为不通过，实际业务含义是OK的。

去噪方案：统一转换后进行Diff。

主备链路message字段解析属性字段不一致问题

问题现象：message字段存储数据JSON格式。同一条业务数据，主备链路解析的JSON对应的属性字段并不是完全一致的，两者之间存在差异。

去噪方案：通过代码解析出全量的属性字段，确保可以完全Diff。

message范本：

{"fields_a":"20230628","fields_b":"2023-06-22 19:48:24","fields_c":"2","fields_d":"plan","fields_e":"3******","fields_f":"0.0","fields_g":"2","fields_h":"4*****","fields_i":"ext","fields_j":"binlog+odps","fields_k":"2","fields_l":"STATUS_*****","fields_m":"1********","fields_n":"孙**","fields_o":"2023-06-28T22:19:43.872"}

转换JSON：

{
   "fields_a": "20230717",
   "fields_d": "plan",
   "fields_e": "3******",
   "fields_aj": "33761.125",
   "fields_p": "37934.0",
   "fields_r": "1250.412",
   "fields_s": "1250.412",
   "fields_t": "33761.125",
   "fields_w": "33761.125",
   "fields_m": "1*********",
   "fields_v": "33761.125",
   "fields_y": "33761.125",
   "fields_n": "孙**",
   "fields_z": "1250.412",
   "fields_ai": "27",
   "fields_ak": "",
   "fields_aa": "33761.125",
   "fields_ab": "33761.125",
   "fields_ac": "33761.0",
   "fields_al": "0.1002",
   "fields_i": "***",
   "fields_j": "***",
   "fields_k": "2",
   "fields_ad": "1.0",
   "fields_ak": "37934.0",
   "fields_x": "1250.412",
   "fields_y": "0.0",
   "fields_ag": "27",
   "fields_af": "27",
   "fields_ah": "0.0",
   "fields_al": "0.0",
   "fields_am": "0.0",
   "fields_ao": "37934.0",
   "fields_ap": "37934.0",
   "fields_an": "33761.125",
   "fields_aq": "1*********",
   "fields_ae": "27",
   "fields_o": "2023-07-17T23:59:00.103",
   "fields_ar": "0.1002"
}

以上五点问题可以通过SQL进行去噪，整体去噪SQL范本如下：

SET odps.sql.mapper.split.size = 64;
SET odps.stage.joiner.num = 4000;
SET odps.stage.reducer.num = 1999;
CREATE TABLE table_diff AS
SELECT  a.fields_as AS fields_as_main
        ,b.fields_as AS fields_as_branch
        ,a.fields_at AS fields_at_main
        ,b.fields_at AS fields_at_branch
        ,a.fields_d AS fields_d_main
        ,b.fields_d AS fields_d_branch
        ,a.fields_i AS fields_i_main
        ,b.fields_i AS fields_i_branch
        ,a.fields_j AS fields_j_main
        ,b.fields_j AS fields_j_branch
        ,a.fields_aw AS fields_aw_main
        ,b.fields_aw AS fields_aw_branch
        ,a.fields_k_json_key AS fields_k_json_key_main
        ,b.fields_k_json_key AS fields_k_json_key_branch
        ,a.fields_k_json_key_list AS fields_k_json_key_list_main
        ,b.fields_k_json_key_list AS fields_k_json_key_list_branch
        ,CASE   WHEN a.fields_k_json_key = b.fields_k_json_key THEN 0
                WHEN b.fields_k_json_key_list RLIKE a.fields_k_json_key THEN 0
                WHEN a.fields_k_json_key_list RLIKE b.fields_k_json_key THEN 0
                ELSE 1
        END AS fields_k_json_key_diff_flag
FROM    (
            SELECT  fields_as
                    ,fields_at
                    ,fields_d
                    ,fields_i
                    ,fields_j
                    ,fields_aw
                    ,MAX(CASE WHEN rn = 1 THEN fields_k_json_key END) AS fields_k_json_key
                    ,CONCAT_WS(',',COLLECT_SET(fields_k_json_key)) AS fields_k_json_key_list
            FROM    (
                        SELECT  *
                                ,CASE   WHEN NVL(GET_JSON_OBJECT(message,'$.fields_k'),'') = '' THEN '---'
                                        WHEN GET_JSON_OBJECT(message,'$.fields_k') IN ('0','0.0') THEN '0-0-0'
                                        ELSE GET_JSON_OBJECT(message,'$.fields_k')
                                END AS fields_k_json_key
                                ,ROW_NUMBER() OVER (PARTITION BY fields_as,fields_at,fields_d,fields_i,fields_j,fields_aw ORDER BY offset DESC ) AS rn
                        FROM    table_main
                        WHERE   pt = 20230628
                        -- AND     fields_i = 'realMetric'
                    ) 
            WHERE   rn < 6
            GROUP BY fields_as
                     ,fields_at
                     ,fields_d
                     ,fields_i
                     ,fields_j
                     ,fields_aw
        ) a
LEFT JOIN   (
                SELECT  fields_as
                        ,fields_at
                        ,fields_d
                        ,fields_i
                        ,fields_j
                        ,fields_aw
                        ,MAX(CASE WHEN rn = 1 THEN fields_k_json_key END) AS fields_k_json_key
                        ,CONCAT_WS(',',COLLECT_SET(fields_k_json_key)) AS fields_k_json_key_list
                FROM    (
                            SELECT  *
                                    ,CASE   WHEN NVL(GET_JSON_OBJECT(message,'$.fields_k'),'') = '' THEN '---'
                                            WHEN GET_JSON_OBJECT(message,'$.fields_k') IN ('0','0.0') THEN '0-0-0'
                                            ELSE GET_JSON_OBJECT(message,'$.fields_k')
                                    END AS fields_k_json_key
                                    ,ROW_NUMBER() OVER (PARTITION BY fields_as,fields_at,fields_d,fields_i,fields_j,fields_aw ORDER BY offset DESC ) AS rn
                            FROM    table_branch
                            WHERE   pt = 20230628
                            -- AND     fields_i = 'realMetric'
                            and fields_d !='group'
                        ) 
                WHERE   rn < 6
                GROUP BY fields_as
                         ,fields_at
                         ,fields_d
                         ,fields_i
                         ,fields_j
                         ,fields_aw
            ) b
ON      NVL(a.fields_as,'-00') = NVL(b.fields_as,'-00')
AND     NVL(a.fields_at,'-00') = NVL(b.fields_at,'-00')
AND     NVL(a.fields_d,'-00') = NVL(b.fields_d,'-00')
AND     NVL(a.fields_i,'-00') = NVL(b.fields_i,'-00')
AND     NVL(a.fields_j,'-00') = NVL(b.fields_j,'-00')
AND     NVL(a.fields_aw,'-00') = NVL(b.fields_aw,'-00')
;

字段去噪问题

问题现象：涉及字段逻辑修改的情况下，Diff结果是不通过的，影响Diff结果。

去噪方案：需要对逻辑修改的字段抛弃，不再判断发生逻辑修改的字段，通过Java灵活控制。

String[] jsonColumnListStrings = jsonColumnList.split(",");
List<String> jsonColumnLists = new ArrayList<String>();
String[] iterationColumnStrings = iterationColumn.split(",");
List<String> iterationColumnLists = Arrays.asList(iterationColumnStrings);
for (String s:jsonColumnListStrings){
    if(!iterationColumnLists.contains(s)){//判断字段是否为去噪字段
        jsonColumnLists.add(s);
    }
}

六、Diff结果分析

根据主备Diff合成的SQL可以产出对比的结果表，对执行结果分析既可以判断本次执行是否通过。

分析逻辑1：判断每一个对比字段通过占比

提供研发分析哪一个解析的字段通过率低.

分析逻辑2：判断所有字段通过占比总记录数

此指标即可判断本次Diff是否通过，如果占比99.9%，表示通过。

分析SQL样本：

SELECT  round(SUM(CASE WHEN fields_k_json_key_diff_flag = 0 THEN 1 ELSE 0 END) / COUNT(1) * 100,4) AS fields_k_ratio
        ,round(SUM(CASE WHEN fields_m_json_key_diff_flag = 0 THEN 1 ELSE 0 END) / COUNT(1) * 100,4) AS fields_m_ratio
        ,round(SUM(CASE WHEN fields_e_json_key_diff_flag = 0 THEN 1 ELSE 0 END) / COUNT(1) * 100,4) AS fields_e_ratio
        ,round(SUM(CASE WHEN fields_a_json_key_diff_flag = 0 THEN 1 ELSE 0 END) / COUNT(1) * 100,4) AS fields_aratio
        ,round(SUM(CASE WHEN fields_n_json_key_diff_flag = 0 THEN 1 ELSE 0 END) / COUNT(1) * 100,4) AS fields_n_ratio
        ,round(SUM(CASE WHEN fields_p_json_key_diff_flag = 0 THEN 1 ELSE 0 END) / COUNT(1) * 100,4) AS fields_p_ratio
        ,round(SUM(CASE WHEN fields_ac_json_key_diff_flag = 0 THEN 1 ELSE 0 END) / COUNT(1) * 100,4) AS fields_ac_ratio
        ,round(SUM(CASE WHEN fields_ar_json_key_diff_flag = 0 THEN 1 ELSE 0 END) / COUNT(1) * 100,4) AS fields_ar_ratio
        ,round(SUM(CASE WHEN fields_k_json_key_diff_flag = 0 AND fields_m_json_key_diff_flag = 0 AND fields_e_json_key_diff_flag = 0 AND fields_a_json_key_diff_flag = 0 AND fields_n_json_key_diff_flag = 0 AND fields_p_json_key_diff_flag = 0 AND fields_ac_json_key_diff_flag = 0 AND fields_ar_json_key_diff_flag = 0 THEN 1 ELSE 0 END) / COUNT(1) * 100,4) AS total_ratio
        ,COUNT(1) AS total_cnt
FROM    table_diff
;

七、工具服务化

后端服务化处理逻辑

主备对比SQL合成

将Diff的SQL植入到代码中，通过代码控制数据切片、去噪等场景，完成测试SQL合成。

for(String s:jsonColumnLists){
    selectSql1 = selectSql1 + " case when NVL(GET_JSON_OBJECT(message,'$." + s + "'),'')='' then '---' when get_json_object(message,'$." + s + "') in ('0','0.0') then '0-0-0' else get_json_object(message,'$." + s + "') end  AS " + s + "_json_key,";
    selectSql2 = selectSql2 + " max(case when rn =1 then " + s + "_json_key end) as " + s + "_json_key,concat_ws(',',collect_set(" + s + "_json_key)) as " + s + "_json_key_list,";
    mergeSql = mergeSql + " a." + s + "_json_key as " + s + "_json_key_main,b." + s + "_json_key as " + s + "_json_key_branch,a." + s + "_json_key_list as " + s + "_json_key_list_main,b." + s + "_json_key_list as " + s + "_json_key_list_branch,case when a." + s + "_json_key = b." + s + "_json_key then 0 when b." + s + "_json_key_list rlike a." + s + "_json_key then 0 when a." + s + "_json_key_list rlike b." + s + "_json_key then 0 else 1 end as " + s + "_json_key_diff_flag,";
}
rowNumberSql ="ROW_NUMBER() OVER (PARTITION BY fields_as,fields_at,fields_d,fields_i,fields_j,fields_aw ORDER BY offset DESC ) AS rn ";
selectSql1 = selectSql1 + rowNumberSql;
whereSql1 = whereSql1 + bizdate + " AND fields_i = 'realMetric' ";
String pretreatmentSqlMain = "";
String pretreatmentSqlBranch = "";
pretreatmentSqlBranch = selectSql2.substring(0,selectSql2.length()-1) + " from(" + selectSql1 + " from " + branchLinkTableName + whereSql1 + ")" + whereSql2 + groupSql.substring(0,groupSql.length()-1);
pretreatmentSqlMain = selectSql2.substring(0,selectSql2.length()-1) + " from(" + selectSql1 + " from " + masterLinkTableName + whereSql1 + ")" + whereSql2 + groupSql.substring(0,groupSql.length()-1);
mergeSql = mergeSql.substring(0,mergeSql.length()-1) + " from (" + pretreatmentSqlMain + ")a left join (" + pretreatmentSqlBranch + ")b " + joinSql.substring(0,joinSql.length()-3) + ";";
SimpleDateFormat simpleDateFormat = new SimpleDateFormat("yyyyMMddHHmmss");
String dateStr = simpleDateFormat.format(new Date());
this.resultDataCreateSql = "set odps.sql.mapper.split.size=64;set odps.stage.joiner.num=4000;set odps.stage.reducer.num=1999; create table du_temp.diff_main_branch_" + dateStr  + "_test as " + mergeSql;
log.info(resultDataCreateSql);
this.resultDataTable = "du_temp.diff_main_branch_" + dateStr  + "_test";
log.info(resultDataTable);
//合成过滤结果数据的sql
String resultSql = " select ";
String totalResultSql = "round(sum(case when ";
for(String s:jsonColumnLists){
    resultSql = resultSql + " round(sum(case when " + s + "_json_key_diff_flag = 0 then 1 else 0 end)/count(1)*100,4) as " + s + "_ratio,";
    totalResultSql = totalResultSql + " " + s + "_json_key_diff_flag = 0 and";
}
this.resultDataFiltrate = resultSql + totalResultSql.substring(0,totalResultSql.length()-3) + " then 1 else 0 end)/count(1)*100,4) as total_ratio , count(1) as total_cnt from " + this.resultDataTable + ";";
log.info(resultDataFiltrate);

Diff结果报告解析

...}
else if(testType.equals("主备diff")) {
    for (Map.Entry entry:testResultRecord.entrySet()) {
        List<String> listValue = (List<String>) entry.getValue();
        this.resultData.put(entry.getKey().toString(),listValue.get(0)) ;
        if(Double.parseDouble(listValue.get(0))< 99.9 & !entry.getKey().toString().equals("total_cnt")){
            this.failDetail.put(entry.getKey().toString(),listValue.get(0)) ;
        }
    }
    if(failDetail.size()>0){
        this.testStatus = "失败";
    }else {
        this.testStatus = "成功";
    }
}

平台可视化

创建任务

执行列表

结果报告-平台展示

如下图：一次执行失败的结果，通过率为99.8471，未达到99.99%。

结果报告-飞书通知

如下样例：
执行需求名称：主备Diff-521 执行者：* 执行类型：主备Diff 执行编号：20230628204636 执行备链路表名：table_main 执行主链路表名：table_branch 执行备链路表分区：20230628 执行结果明细表：table_diff 执行结果明细汇总： fields_am_ratio:99.9958 fields_z_ratio:99.9826 fields_af_ratio:99.9856 fields_ba_ratio:99.9964 fields_al_ratio:99.9915 fields_ad_ratio:99.9873 fields_r_ratio:99.9826 fields_aa_ratio:99.9906 fields_ai_ratio:99.9856 fields_v_ratio:99.9917 fields_ak_ratio:99.9909 fields_m_ratio:99.9969 fields_ak_ratio:99.9945 fields_bb_ratio:99.9964 fields_bc_ratio:99.9957 fields_bd_ratio:99.9954 fields_ae_ratio:99.9856 fields_be_ratio:99.9952 fields_bf_ratio:99.9955 fields_t_ratio:99.9917
fields_ag_ratio:99.9856 fields_p_ratio:99.9909 fields_bg_ratio:99.9948 fields_a_ratio:99.9969 fields_d_ratio:99.9969 fields_x_ratio:99.9826 fields_an_ratio:99.9917
fields_ap_ratio:99.9909 fields_ar_ratio:99.9915 fields_y_ratio:99.9917 fields_bh_ratio:99.9955 fields_aj_ratio:99.9916 fields_bi_ratio:99.987 fields_ac_ratio:99.9908 fields_s_ratio:99.9826 fields_ab_ratio:99.9906 fields_i_ratio:99.9969 fields_bj_ratio:99.9951 fields_ah_ratio:99.9959
fields_k_ratio:99.9969
fields_e_ratio:99.9969 fields_bk_ratio:99.9962 fields_bl_ratio:99.8748 fields_al_ratio:99.9958 fields_j_ratio:99.9969
fields_bm_ratio:99.9951 fields_n_ratio:99.9969 fields_ao_ratio:99.9909 fields_w_ratio:99.9906 fields_bn_ratio:99.9965 fields_bo_ratio:99.9912 fields_bcrate_ratio:99.987 fields_y_ratio:99.9958 主备diff执行结果汇总数据： total_ratio:99.8471
total_cnt:714259 执行结果失败明细：
fields_bl_ratio:99.8748
total_ratio:99.8471% 执行结果状态：失败

八、主备diff工具接入发布流程

投放备链路最终经过主备Diff工具测试通过的情况下，完成上线，目前相当于一条备用生产线。

后续版本迭代，需求上线前通过Diff工具验证通过，即可符合上线要求。

九、总结

实时计算不同于离线数仓，数据的稳定性和准确性很难把控，复杂的链路通过简单的测试无法保障整体数据的质量，双链路Diff的形式可以在迭代中更好保障实时数据的质量。

对于主备Diff的实现中：最大的痛点往往是数据的噪点非常的大，需要通过技术手段进行降噪，确保数据对比结果的准确性和可靠性。

*文/诗雨

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