写点什么

实时数仓投放主备链路 Diff 测试工具落地实践

作者:得物技术
  • 2024-01-03
    上海
  • 本文字数:8204 字

    阅读完需:约 27 分钟

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"}
复制代码

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"}
复制代码

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 ASSELECT  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_flagFROM    (            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,'$.fields_k')                                END AS fields_k_json_key                                ,ROW_NUMBER() OVER (PARTITION BY fields_as,fields_at,fields_d,fields_i,fields_j,fields_aw ORDER BY offset DESC ) AS rn                        FROM    table_main                        WHERE   pt = 20230628                        -- AND     fields_i = 'realMetric'                    )             WHERE   rn < 6            GROUP BY fields_as                     ,fields_at                     ,fields_d                     ,fields_i                     ,fields_j                     ,fields_aw        ) aLEFT JOIN   (                SELECT  fields_as                        ,fields_at                        ,fields_d                        ,fields_i                        ,fields_j                        ,fields_aw                        ,MAX(CASE WHEN rn = 1 THEN fields_k_json_key END) AS fields_k_json_key                        ,CONCAT_WS(',',COLLECT_SET(fields_k_json_key)) AS fields_k_json_key_list                FROM    (                            SELECT  *                                    ,CASE   WHEN NVL(GET_JSON_OBJECT(message,'$.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            ) bON      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');
复制代码

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);    }}
复制代码



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_cntFROM    table_diff;
复制代码

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sqlString 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);
复制代码

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";    }}
复制代码

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:99.9917fields_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:99.9917fields_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:99.9959fields_k_ratio:99.9969fields_e_ratio:99.9969 fields_bk_ratio:99.9962 fields_bl_ratio:99.8748 fields_al_ratio:99.9958 fields_j_ratio:99.9969fields_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:99.8471total_cnt:714259 执行结果失败明细:fields_bl_ratio:99.8748total_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


本文属得物技术原创,更多精彩文章请看:得物技术官网


未经得物技术许可严禁转载,否则依法追究法律责任!

发布于: 18 分钟前阅读数: 5
用户头像

得物技术

关注

得物APP技术部 2019-11-13 加入

关注微信公众号「得物技术」

评论

发布
暂无评论
实时数仓投放主备链路Diff测试工具落地实践_AI_得物技术_InfoQ写作社区