WITH TAB_B AS ( select DISTINCT A.calling_imsi as IMSI, coalesce(B.ANTIGUA,C.ANTIGUA) GJ, coalesce(B.APUA,C.APUA) YYS from ( SELECT A.calling_imsi, substr(a.calling_imsi,1,6) calling_imsi_6, substr(a.calling_imsi,1,5) calling_imsi_5, substr(a.calling_imsi,1,4) calling_imsi_4 FROM spark_odc_dwd.D_ENS_GBSSAP_CC a WHERE p_hour='#{time yyyyMMddHH}' ) a left join ( select SUBSTR(regexp_replace(IMSI,'[^0-9]+',''),1,6) IMSI_6, SUBSTR(regexp_replace(IMSI,'[^0-9]+',''),1,5) IMSI_5, ANTIGUA, APUA from spark_odc_data.A_DM_CO_BA_VR_OPT_IMSI )b on a.calling_imsi_6=b.IMSI_6 left join ( select SUBSTR(regexp_replace(IMSI,'[^0-9]+',''),1,6) IMSI_6, SUBSTR(regexp_replace(IMSI,'[^0-9]+',''),1,5) IMSI_5, ANTIGUA, APUA from spark_odc_data.A_DM_CO_BA_VR_OPT_IMSI )c on a.calling_imsi_5=c.IMSI_5)

基于Hadoop_hbase的Distinct实现.zip

标题 "基于Hadoop_HBase的Distinct实现.zip" 暗示了这个压缩包可能包含一个关于如何在Hadoop和HBase环境下实现唯一值（Distinct）计算的教程或项目。Hadoop是一个开源框架，用于处理和存储大量数据，而HBase是建立在...

CBST.rar_The Keys_bst

A Binary Search Tree (BST) is recursively defined as a binary tree which has the following properties: The left subtree of a node contains only nodes with keys less than the node's key. The right ...

mismatched input 'WITH' expecting {'(', 'SELECT', 'FROM', 'VALUES', 'TABLE', 'INSERT', 'MAP', 'REDUCE'}(line 1, pos 1191) == SQL == WITH TAB_A AS ( select DISTINCT A.IMSI, coalesce(B.COUNTRY,C.COUNTRY) GJ, coalesce(B.OPERATOR,C.OPERATOR) YYS from ( SELECT A.START_TIME, A.END_TIME, A.MSISDN, A.IMSI, A.CALLING_GT, A.OPERATE_CODE, A.RESULT, A.ERROR_CODE, substr(a.CALLING_GT,1,6) CALLING_GT_6, substr(a.CALLING_GT,1,5) CALLING_GT_5, substr(a.CALLING_GT,1,4) CALLING_GT_4 FROM spark_odc_dwd.D_ENS_GMAP_MM a WHERE p_hour='2023060600' ) a left join ( SELECT DISTINCT regexp_replace(E164_IR21,'[^0-9]+','') E164_IR21, SUBSTR(regexp_replace(E164_IR21,'[^0-9]+',''),1,6) E164_IR21_6, SUBSTR(regexp_replace(E164_IR21,'[^0-9]+',''),1,5) E164_IR21_5, SUBSTR(regexp_replace(E164_IR21,'[^0-9]+',''),1,4) E164_IR21_4, COUNTRY, VENDOR_NAME AS OPERATOR FROM spark_odc_data.A_DM_CO_BA_VR_OPT_GT A) b on A.CALLING_GT_5=b.E164_IR21_5 left join ( SELECT DISTINCT regexp_replace(E164_IR21,'[^0-9]+','') E164_IR21, SUBSTR(regexp_replace(E164_IR21,'[^0-9]+',''),1,6) E164_IR21_6, SUBSTR(regexp_replace(E164_IR21,'[^0-9]+',''),1,5) E164_IR21_5, SUBSTR(regexp_replace(E164_IR21,'[^0-9]+',''),1,4) E164_IR21_4, COUNTRY, VENDOR_NAME AS OPERATOR FROM spark_odc_data.A_DM_CO_BA_VR_OPT_GT A) c on A.CALLING_GT_4=c.E164_IR21_4)

SELECT DISTINCT A.IMSI, coalesce(B.COUNTRY,C.COUNTRY) AS GJ, coalesce(B.OPERATOR,C.OPERATOR) AS YYS FROM ( SELECT START_TIME, END_TIME, MSISDN, IMSI, CALLING_GT, OPERATE_CODE, RESULT, ERROR_CODE, ...

SELECT DISTINCT t.FAULT_CODE AS "dataIndex", t.FAULT_NAME AS "title" FROM FAULT_TYPE t WHERE t.PAR_FAULT_CODE = t.FAULT_CODE UNION ALL SELECT COALESCE('reportCount', 'N/A') AS "dataIndex", '合计' AS "title" FROM DUAL怎么根据FAULT_CODE排序

SELECT COALESCE('reportCount', 'N/A') AS "dataIndex", '合计' AS "title" FROM DUAL ORDER BY "dataIndex" ASC; 其中，ORDER BY子句中的"dataIndex"是要排序的列名，ASC表示升序排序（也可以使用DESC表示...

SELECT t1.supplier_id,IFNULL(t1.receiptcount,0) as receipt_count,IFNULL(t1.receiptamount,0) as receipt_amount,IFNULL(t2.refundcount,0) as refund_count,IFNULL(t2.refundamount,0) as refund_amount from ( SELECT a.supplier_id, count(DISTINCT a.order_id) AS receiptcount, sum(IFNULL(b.receipt_amount,0))/100 AS receiptamount FROM order_info a left JOIN order_receipt b ON a.order_id = b.order_id WHERE a.order_type IN (70, 75) AND a.is_del = 0 AND a.order_state IN (40, 50, 60) AND a.create_time >= '2023-05-28 00:00:00' AND a.create_time < '2023-05-29 00:00:00' AND a.supplier_id in ( 78,63,58,57,64,72,71,74,83,77,70,69,67,82,65,87,73,59,66,60,86,85,79,80,84,90 ) GROUP BY a.supplier_id order by a.supplier_id ASC ) t1 LEFT JOIN ( select c.supplier_id,count(DISTINCT c.order_id) as refundcount,sum(IFNULL(c.refund_amount,0)) / 100 as refundamount from order_refund c LEFT JOIN order_info d on c.order_id = d.order_id WHERE d.order_type in (70,75) AND d.order_state IN (40, 50, 60) and c.refund_state =13 AND c.is_del = 0 and c.create_time >= '2023-05-28 00:00:00' AND c.create_time < '2023-05-29 00:00:00' AND c.supplier_id in ( 78,63,58,57,64,72,71,74,83,77,70,69,67,82,65,87,73,59,66,60,86,85,79,80,84,90 ) GROUP BY c.supplier_id ) t2 on t1.supplier_id = t2.supplier_id order by t1.supplier_id asc

AND EXISTS (SELECT 1 FROM order_receipt b WHERE a.order_id = b.order_id) GROUP BY a.supplier_id ORDER BY a.supplier_id ASC) t1 LEFT JOIN (SELECT c.supplier_id, count(DISTINCT c.order_id) AS ...

new_df_cols = new_df.columns old_df_cols = older_df.columns total = set(new_df_cols + old_df_cols) new_df = new_df.select(self.fill_empty_colums(new_df_cols, total)).withColumn("row_priority",F.lit(0)) older_df = older_df.select(self.fill_empty_colums(old_df_cols, total)).withColumn("row_priority",F.lit(1)) key_column = [F.col(column_name) for column_name in key_columns] merge_spec = Window.partitionBy(key_column).orderBy("row_priority") ranked_df=new_df.unionByName(older_df).withColumn("rank", F.rank().over(merge_spec)) return self.update_audit_created_column(ranked_df,key_column).where(F.col("rank") == 1).drop("rank", "row_priority")

SELECT COALESCE(col1, '') AS col1, COALESCE(col2, '') AS col2, ..., COALESCE(colN, '') AS colN, 0 AS row_priority FROM new_df ), new_df_selected AS ( SELECT *, ROW_NUMBER() OVER (PARTITION BY key_...

SELECT un., sta., CASE WHEN COALESCE(un.lot_time, 0) > COALESCE(sta.dock_time_limit, 0) THEN COALESCE(un.lot_time, 0) - COALESCE(sta.dock_time_limit, 0) ELSE 0 END AS lotDuration FROM unload_over_time un LEFT JOIN ( SELECT parking_lot_code, dock_time_limit FROM stall_infor WHERE data_state = 0 AND delete_state = 1 AND industry_park_code='YQWQML' ) sta ON un.parking_lot_code = sta.parking_lot_code INNER JOIN ( SELECT DISTINCT plate_number FROM supplier_info WHERE data_state = 0 AND delete_state = 1 AND arrived_atetime IS NOT NULL AND left_datetime IS NULL AND industry_park_code='YQWQML' ) sup ON un.plate_number = sup.plate_number WHERE un.data_state = 0 AND un.delete_state = 1 AND un.is_out_of_time = 1 AND un.industry_park_code='YQWQML' AND un.parking_lot_code IN ( SELECT DISTINCT parking_lot_code FROM unload_over_time WHERE data_state = 0 AND delete_state = 1 AND is_out_of_time = 1 AND industry_park_code='YQWQML' ) ；SQL优化

3. 避免使用SELECT *：在实际应用中，尽量避免使用SELECT *，而是明确列出需要查询的列。这样可以减少不必要的数据传输和查询时间。 4. 考虑数据量和表结构：如果表中数据量较大，可以考虑进行分区操作或者进行数据...

看下这段sql为何报错了 SELECT d.year,d.month,d.门店属性,sum(d.target_days) as target_days FROM (SELECT DISTINCT c.year,c.month,c.餐厅名称,c.门店编码,c.餐厅原始编号,c.品牌,c.门店属性,coalesce(c.target_days,0) as target_days FROM(SELECT year(a.日期) as year,month(a.日期) as month,a.*,case when a.品牌 like'大王早餐%' then b.operating_days else null end as target_days from input1 a left JOIN input2 b ON a.餐厅原始编号=b.storeid AND year(a.日期)=b.Year AND month(a.日期)=b.Month WHERE a.品牌 like'大王早餐%')c WHERE c.门店属性='KBF直营店')d GROUP BY 1,2,3 UNION SELECT input1.Year as year,input1.Month as month,input1.属性 as 门店属性,sum(input1.operating_days) as target_days from input1 GROUP BY 1,2,3 ORDER BY 3,1,2

SELECT DISTINCT c.year, c.month, c.餐厅名称, c.门店编码, c.餐厅原始编号, c.品牌, c.门店属性, coalesce(c.target_days, 0) as target_days FROM ( SELECT year(a.日期) as year, ...

SELECT CUST_ID agentId, AGE_NAME agentName, countReleased, dayAmountReleased, CASE o.agelimitType WHEN '1' THEN TO_CHAR(o.agelimitNum) ELSE TO_CHAR(o.issueamtlimit) END countLimit, CASE o.agelimitType WHEN '1' THEN '不限' ELSE TO_CHAR(o.daylimitamount) END dayMaxCount FROM (SELECT m.CUST_ID, m.AGE_NAME, ad.countReleased, t.dayAmountReleased, d.daylimitamount, d.issueamtlimit, (SELECT AGE_LIMIT_NUM FROM P_ACT_ALLOW_MANAGER WHERE ACTIVITY_ID = #{activityId} AND rownum = 1 ) AS agelimitNum, (SELECT AGE_LIMIT_NUM_TYPE FROM P_ACT_MANAGER_ACTIVITY WHERE ACTIVITY_ID = #{activityId} ) AS agelimitType FROM (SELECT c1.CUST_ID, c1.AGE_NAME FROM (SELECT CUST_ID, AGE_NAME FROM PM_AGET_INFO START WITH CUST_ID = #{agetId} CONNECT BY PRIOR CUST_ID = FATH_CUST_ID ) c1 INNER JOIN (SELECT DISTINCT CUST_ID, AGE_NAME FROM PM_AGET_INFO START WITH CUST_ID in (SELECT CUST_ID FROM P_ACT_ALLOW_MANAGER WHERE ACTIVITY_ID = #{activityId} ) CONNECT BY PRIOR CUST_ID = FATH_CUST_ID ) c2 ON c1.CUST_ID = c2.CUST_ID ) m LEFT JOIN (SELECT CUST_ID, count(RECORD_ID) countReleased FROM P_ACT_MANAGER_RECORD WHERE ACTIVITY_ID = #{activityId} AND RECORD_STATUS NOT IN (2, 5) AND to_date(CREATE_TIME, 'yyyy-MM-dd hh24:mi:ss') BETWEEN to_date(#{beginTime}, 'yyyyMMddhh24miss') AND to_date(#{endTime}, 'yyyyMMddhh24miss') GROUP BY CUST_ID ) ad ON m.CUST_ID = ad.CUST_ID LEFT JOIN (SELECT AGE_ID cust_id, DAY_LIMIT_AMOUNT daylimitAmount, ISSUE_AMT_LIMIT issueAmtLimit FROM P_ACT_MANAGER_LIMIT_ALLOT WHERE ACT_ID =#{activityId} ) d ON m.CUST_ID = d.CUST_ID LEFT JOIN (SELECT CUST_ID, count(RECORD_ID) dayAmountReleased FROM P_ACT_MANAGER_RECORD WHERE ACTIVITY_ID =#{activityId} AND RECORD_STATUS NOT IN (2, 5) AND to_date(CREATE_TIME, 'yyyy-MM-dd hh24:mi:ss') BETWEEN to_date(#{todayBegin}, 'yyyyMMddhh24miss') AND to_date(#{todayend}, 'yyyyMMddhh24miss') GROUP BY CUST_ID ) t ON m.CUST_ID = t.CUST_ID) o WHERE 1 = 1 AND o.CUST_ID = #{agentId} AND o.AGE_NAME = #{agentName} ORDER BY o.countReleased DESC NULLS LAST 如何优化此段sql代码,使得更加简洁？

INNER JOIN cte2 c ON m.CUST_ID = c.CUST_ID AND m.AGE_NAME = c.AGE_NAME LEFT JOIN cte3 ad ON m.CUST_ID = ad.CUST_ID LEFT JOIN cte4 t ON m.CUST_ID = t.CUST_ID LEFT JOIN cte5 ON 1 = 1 LEFT JOIN ...

SQL 错误 [47]: ClickHouse exception, code: 47, host: 192.168.2.101, port: 8123; Code: 47, e.displayText() = DB::Exception: Missing columns: 'DISTINCT' while processing query: 'SELECT DAY, AREA_TYPE, AREA_ID, DISTINCT AS MSISDN, AGE FROM TV_D_AREA_VISITOR_IMSI_ALL', required columns: 'DAY' 'AREA_TYPE' 'AGE' 'AREA_ID' 'DISTINCT', source columns: 'PHONE_BILL' 'AGE' 'OWNER_CITY' 'OWNER_PROV' 'RESID_DURA' 'NATIONALITY' 'MSISDN' 'IS_OVERNIGHT' 'DAY' 'SEX' 'AREA_CITY' 'AREA_PROV' 'AREA_ID' 'SOURCE_COUNTRY' 'MOBILE_DATA' 'APP' 'AREA_TYPE' (version 20.8.3.18)

这个错误提示是因为在你的 SQL 查询语句中，使用了关键字 DISTINCT 作为...SELECT DAY, AREA_TYPE, AREA_ID, MSISDN AS DISTINCT_MSISDN, AGE FROM TV_D_AREA_VISITOR_IMSI_ALL 这样就可以避免关键字冲突导致的错误。

INSERT OVERWRITE TABLE dw_rpt_report60010105049 SELECT COALESCE(n.char3, dw.tran_country) AS tran_country, dw.col2, dw.col3, ... FROM dw_rpt_report60010105049 dw LEFT JOIN ( SELECT char2, MAX(char3) AS char3 FROM orsuser_kara.para_nationality GROUP BY char2 ) n ON dw.tran_country = n.char2 WHERE n.char2 IS NOT NULL 此段代码中的主表的tran_country不是唯一的，可能会导致发散，怎么优化

SELECT COALESCE(n.char3, t.tran_country) AS tran_country, t.col2, t.col3, ... FROM ( SELECT DISTINCT tran_country, col2, col3, ... FROM dw_rpt_report60010105049 ) t LEFT JOIN ( SELECT char2, MAX...

YOLO算法-城市电杆数据集-496张图像带标签-电杆.zip

YOLO系列算法目标检测数据集，包含标签，可以直接训练模型和验证测试，数据集已经划分好，包含数据集配置文件data.yaml，适用yolov5,yolov8,yolov9,yolov7,yolov10,yolo11算法；包含两种标签格:yolo格式（txt文件）和voc格式（xml文件），分别保存在两个文件夹中，文件名末尾是部分类别名称; yolo格式：<class> <x_center> <y_center> <width> <height>，其中： <class> 是目标的类别索引（从0开始）。 <x_center> 和 <y_center> 是目标框中心点的x和y坐标，这些坐标是相对于图像宽度和高度的比例值，范围在0到1之间。 <width> 和 <height> 是目标框的宽度和高度，也是相对于图像宽度和高度的比例值；【注】可以下拉页面，在资源详情处查看标签具体内容；

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