RobotFramework 实战1——数据检查自动化

具体代码如下：

*** Settings ***
Suite Setup date_wsj
Library DatabaseLibrary
Resource ../../../flow.txt

*** Test Cases ***
001-App产品原创稿件阅读量-近1日
#计算出产品变量的个数
${proLen} get Length ${app}
#产品个数就是循环的次数
: FOR ${X} IN RANGE 0 ${proLen} 1
#查询出昨日的数值
${original_read_cnt_yesterday} Query select product_name,original_read_cnt from edm_fact_cs_product_summary_d where product_type='APP'and product_name='${app[${X}]}' and summary_date>='${yesterday}'
#查询出条数（是list的形式）
${qr} Query select product_name,original_read_cnt from edm_fact_cs_product_summary_d where product_type='APP'and product_name='${app[${X}]}' and original_read_cnt > '0'and summary_date>='${monthAgo}'
#将条数换成数值（获取list的长度，将list中的元素个数转换为数值）
${len} Get Length ${qr}
#如果结果为0，继续执行
Continue For Loop If ${len}==0
#计算出平均值
${original_read_cnt_Average} Query select product_name,sum(original_read_cnt) /${len} from edm_fact_cs_product_summary_d where product_type='APP'and product_name='${app[${X}]}' and original_read_cnt > '0'and summary_date>='${monthAgo}'group by product_name
#计算出最大值
${Max_Average} Evaluate ${original_read_cnt_Average[0][1]}*2
#计算出最小值
${Min_Average} Evaluate ${original_read_cnt_Average[0][1]}*0.7
#昨日值与最大值和最小值比较，若在这个范围内，则昨日数据正常
Run Keyword And Continue On Failure Should Be True ${Max_Average}>=${original_read_cnt_yesterday[0][1]}>=${Min_Average}
#打印日志（注意，list[x][y]的用法，此用例中sql查询结果均为list）
log ${qr[0]}:${Max_Average}>=${original_read_cnt_yesterday[0][1]}>=${Min_Average}

001-App产品原创稿件阅读量-近1周
${proLen} get Length ${app}
: FOR ${X} IN RANGE 0 ${proLen} 1
${original_read_cnt_week} Query SELECT product_name,summary_start_date,summary_end_date,original_read_cnt FROM edm_fact_cs_product_summary_w where product_type='APP'and product_name='${app[${X}]}'and summary_end_date>='${weekAgo}'order by summary_start_date desc;
${qr} Query SELECT product_name,summary_start_date,summary_end_date,original_read_cnt FROM edm_fact_cs_product_summary_w where product_type='APP'and product_name='${app[${X}]}'and original_read_cnt > '0'and summary_start_date>='${halfYearAgo}'order by summary_start_date desc;
${len} Get Length ${qr}
Continue For Loop If ${len}==0
${original_read_cnt_Average} Query select product_name,sum(original_read_cnt) /${len} from edm_fact_cs_product_summary_w where product_type='APP'and product_name='${app[${X}]}' and original_read_cnt > '0'and summary_start_date>='${halfYearAgo}'group by product_name
${Max_Average} Evaluate ${original_read_cnt_Average[0][1]}*2
${Min_Average} Evaluate ${original_read_cnt_Average[0][1]}*0.7
Run Keyword And Continue On Failure Should Be True ${Max_Average}>=${original_read_cnt_week[0][3]}>=${Min_Average}
log ${qr[0][0]}:${Max_Average}>=${original_read_cnt_week[0][3]}>=${Min_Average}

001-App产品原创稿件阅读量-近1月
${proLen} get Length ${app}
: FOR ${X} IN RANGE 0 ${proLen} 1
${original_read_cnt_month} Query SELECT product_name,summary_start_date,summary_end_date,original_read_cnt FROM edm_fact_cs_product_summary_m where product_type='APP'and product_name='${app[${X}]}'and summary_end_date>='${monthAgo}'order by summary_start_date desc;
${qr} Query SELECT product_name,summary_start_date,summary_end_date,original_read_cnt FROM edm_fact_cs_product_summary_m where product_type='APP'and product_name='${app[${X}]}'and original_read_cnt > '0'and summary_start_date>='${yearAgo}'order by summary_start_date desc;
${len} Get Length ${qr}
Continue For Loop If ${len}==0
${original_read_cnt_Average} Query select product_name,sum(original_read_cnt) /${len} from edm_fact_cs_product_summary_m where product_type='APP'and product_name='${app[${X}]}' and original_read_cnt > '0'and summary_start_date>='${yearAgo}'group by product_name
${Max_Average} Evaluate ${original_read_cnt_Average[0][1]}*2
${Min_Average} Evaluate ${original_read_cnt_Average[0][1]}*0.7
Run Keyword And Continue On Failure Should Be True ${Max_Average}>=${original_read_cnt_month[0][3]}>=${Min_Average}
log ${qr[0][0]}:${Max_Average}>=${original_read_cnt_month[0][3]}>=${Min_Average}