clickhouse sql优化，减少查询次数提升效率

技术2022-07-13 75

业务场景：计算sales/count/sku的值、同比、环比用开窗函数neighbor计算yoy同比和chain环比 // 原本计算同比和环比需要使用三条sql聚合后相除 SELECT toStartOfYear(toDate('2020-05-01')) AS date, ROUND(SUM(sales), 2) AS sales, SUM(count) AS count, COUNT(DISTINCT product_id) AS sku FROM mars_rc_roma_wide WHERE (category_id IN (50015380)) AND month >= '2020-01-01' AND month <= '2020-05-01' GROUP BY date SELECT toStartOfYear(toDate('2019-05-01')) AS date, ROUND(SUM(sales), 2) AS sales, SUM(count) AS count, COUNT(DISTINCT product_id) AS sku FROM tmall_wide_idx2 WHERE (category_id IN (50010794)) AND month >= '2019-01-01' AND month <= '2019-05-01' GROUP BY date SELECT toStartOfYear(toDate('2019-03-01')) AS date, ROUND(SUM(sales), 2) AS sales, SUM(count) AS count, COUNT(DISTINCT product_id) AS sku FROM mars_rc_roma_wide WHERE (category_id IN (50015380)) AND month >= '2019-01-01' AND month <= '2019-03-01' GROUP BY date // 使用开窗函数后将其合并成一条查询 SELECT ROUND(SUM(if(month >= '2020-01-01’ AND month <= '2020-05-01', sales, 0)), 2) AS sales_last, SUM(if(month >= '2020-01-01’ AND month <= '2020-05-01', count, 0)) AS count_last, COUNT(DISTINCT(if(month >= '2020-01-01’ AND month <= '2020-05-01', product_id, 0))) AS sku_last, (sales_last / ROUND(SUM(if(month >= '2019-01-01' AND month <= '2019-05-01', sales, 0)), 2) -1) * 100 AS sales_yoy, (count_last / SUM(if(month >= '2019-01-01' AND month <= '2019-05-01', count, 0)) -1) * 100 AS count_yoy, (sku_last / COUNT(DISTINCT(if(month >= '2019-01-01' AND month <= '2019-05-01', product_id, 0))) -1) * 100 AS sku_yoy, (sales_last / ROUND(SUM(if(month >= '2019-01-01' AND month <= '2019-03-01', sales, 0)), 2) -1) * 100 AS sales_ratio, (count_last / SUM(if(month >= '2019-01-01' AND month <= '2019-03-01', count, 0)) -1) * 100 AS count_ratio, (sku_last / COUNT(DISTINCT(if(month >= '2019-01-01' AND month <= '2019-03-01', product_id, 0))) -1) * 100 AS sku_ratio FROM mars_rc_roma_wide WHERE (category_id IN (50015380)) // 注意⚠️：在计算sales销售额和count销量时，使用的是SUM；计算sku时，使用的是count // COUNT(DISTINCT(if(month >= '2019-01-01' AND month <= '2019-05-01', product_id, 0))) // 这里如果对精度要求很高的话，count(if(clause, a, b))中的b不要写0或者任何有意义的数 // 因为不管b是标成0还是-999，count后都会显示成1，这样没办法区分是业务值为1还是0。比较好的方法是写成NULL，这样得到的值会是[NULL]

业务场景：clickhouse中array(T)中某个字符串类型的值，进行数值区间的计算 // 这里有n个区间就会进行 n*2 次查询 SELECT '100+' AS attr_value, SUM(sales) AS sales, SUM(count) AS count FROM (SELECT LOWER(`a.attr_name`) AS attr_name, toUInt64(toFloat64OrZero(a.attr_value)) AS value, ROUND(SUM(sales), 2) AS sales, SUM(count) AS count FROM mars_rc_roma_wide ARRAY JOIN attributes AS a WHERE (category_id IN (50015380)) AND month >= '2019-01-01' AND month <= '2019-05-01' AND a.attr_name = 'mars_rc_roma_unit_price_kg' AND a.attr_value != '' GROUP BY `a.attr_name`, `a.attr_value` ORDER BY sales DESC) WHERE (value >= 100) // 把原本多条 multiprocessing 的多线程函数不同的值，放到一起查询 // 这里原本有n个区间 [duration1, duration2]，将区间前后通过其index进行命名取出 // 改进后将查询缩减成一次，但需要注意的是，if()条件中用到的值(month)需要再子查询中先查出来，以及必须先在子查询进行聚合，才能使用这样的优化 SELECT 'mars_mwc_roma_unit_price_kg' AS attr_value, SUM(if((value > 0 AND value < 20) AND month >= '2020-01-01' AND month <= '2020-05-01' , sales, 0)) AS s0, (s0 / SUM(if((value > 0 AND value < 20) AND month >= '2019-01-01' AND month <= '2019-05-01' , sales, 0)) - 1) * 100 AS s0_ratio, (s0 / SUM(if(month >= '2020-01-01' AND month <= '2020-05-01' , sales, 0))) * 100 AS s0_share FROM (SELECT toUInt64(toFloat64OrZero(a.attr_value)) AS value, SUM(sales) AS sales, month FROM mars_mwc_roma_wide ARRAY JOIN attributes AS a WHERE (category_id IN (1)) AND (price >= 0) AND (size >= 0) AND a.attr_name = 'mars_mwc_roma_unit_price_kg' AND a.attr_value != '' GROUP BY `a.attr_name`, `a.attr_value`, month ORDER BY sales DESC)

业务场景：需要计算各个词的 tgi 的值。，主要是为了观察热词在该品牌的热度，并与其在该品类的热度进行比较。解释 TGI：即Target Group Index（目标群体指数） TGI指数= [目标群体中具有某一特征的群体所占比例/总体中具有相同特征的群体所占比例]*标准数100。例如，在15-24岁的人群中，有8.9%的人过去一年内去过某电影网站看电影，而在总体人群中，去过该电影网站看电影的人数比例为6.6%，则该电影网站在15-24岁人群中的TGI指数是134.9（8.9%/6.6%×100），这说明，该电影网站主要定位在15-24岁的人群中。其数额越大，就表明目标群体吻合度度就越高。 TGI指数表征不同特征用户关注问题的差异情况，其中TGI指数等于100表示平均水平，高于100，代表该类用户对某类问题的关注程度高于整体水平。 // 查看整体的 sales_in_category_brand 和 sales_in_category // 1：sql_sales_in_category_brand SELECT ROUND(SUM(sales), 2) AS sales FROM tmall_wide_idx2 WHERE (category_id IN (50003695, 350407, 350402, 50008555, 50019790, 50012144, 50019649, 50008652, 120611, 121704, 50011867, 50008109, 50016107, 50008739, 50005266, 50024944, 1205, 50005050, 50005174, 50228001, 126412033, 125538002, 350203, 50011883, 1101)) AND LOWER(`brand_name`) = LOWER('HUAWEI\/\华\为') AND (price >= 0) AND (size >= 0) AND `month` >= '2020-01-01' AND `month` <= '2020-05-01’ // 2：sql_sales_in_category SELECT ROUND(SUM(sales), 2) AS sales FROM tmall_wide_idx2 WHERE (category_id IN (50003695, 350407, 350402, 50008555, 50019790, 50012144, 50019649, 50008652, 120611, 121704, 50011867, 50008109, 50016107, 50008739, 50005266, 50024944, 1205, 50005050, 50005174, 50228001, 126412033, 125538002, 350203, 50011883, 1101)) AND (price >= 0) AND (size >= 0) AND `month` >= '2020-01-01' AND `month` <= '2020-05-01' // 上面那两种写法是为了便于理解，实质上对应的是这样一条sql，直接查出两个值 SELECT ROUND(SUM(sales), 2) AS category_sales, ROUND(SUM(if(LOWER(brand_name)=LOWER('HUAWEI\/\华\为'), sales, 0)), 2) AS brand_sales FROM tmall_wide_idx2 WHERE (category_id IN (50003695, 350407, 350402, 50008555, 50019790, 50012144, 50019649, 50008652, 120611, 121704, 50011867, 50008109, 50016107, 50008739, 50005266, 50024944, 1205, 50005050, 50005174, 50228001, 126412033, 125538002, 350203, 50011883, 1101)) AND (price >= 0) AND (size >= 0) AND `month` >= '2020-01-01' AND `month` <= '2020-05-01’ // 优化的重点在于对应的品牌关键词的不同。 // 这些词的来源于我们对商品标题的切词，同样放在Array(T)中，需要时可直接查出） // 原本的写法是带品牌查一次，不带品牌查一次；将两次的结果join；若需要观察40个词，相当于进行了80个查询 SELECT ROUND(SUM(sales), 2) AS sales FROM tmall_wide_idx2 WHERE (category_id IN (50003695, 350407, 350402, 50008555, 50019790, 50012144, 50019649, 50008652, 120611, 121704, 50011867, 50008109, 50016107, 50008739, 50005266, 50024944, 1205, 50005050, 50005174, 50228001, 126412033, 125538002, 350203, 50011883, 1101)) AND LOWER(`brand_name`) = LOWER('HUAWEI\/\华\为') AND LOWER(title) LIKE LOWER('%%\集\显%%') AND (price >= 0) AND (size >= 0) AND `month` >= '2020-01-01' AND `month` <= '2020-05-01' SELECT ROUND(SUM(sales), 2) AS sales FROM tmall_wide_idx2 WHERE (category_id IN (50003695, 350407, 350402, 50008555, 50019790, 50012144, 50019649, 50008652, 120611, 121704, 50011867, 50008109, 50016107, 50008739, 50005266, 50024944, 1205, 50005050, 50005174, 50228001, 126412033, 125538002, 350203, 50011883, 1101)) AND (price >= 0) AND (size >= 0) AND LOWER(title) LIKE LOWER('%%\集\显%%') AND `month` >= '2020-01-01' AND `month` <= '2020-05-01’ // 优化写法是合并，不管查多少个词，就都对应一个查询； // 还有个注意点是需要在条件中加上multisearchany，减小查询范围 SELECT ROUND(SUM(if(LOWER(brand_name)=LOWER('HUAWEI\/\华\为') AND LOWER(title) LIKE LOWER('%%\集\显%%'), sales, 0)), 2) AS sales1_brands, ROUND(SUM(if(LOWER(title) LIKE LOWER('%%\集\显%%'), sales, 0)), 2) AS sales1_no_brands, ROUND(SUM(if(LOWER(brand_name)=LOWER('HUAWEI\/\华\为') AND LOWER(title) LIKE LOWER('%%\声\纹%%'), sales, 0)), 2) AS sales2_brands, ROUND(SUM(if(LOWER(title) LIKE LOWER('%%\声\纹%%'), sales, 0)), 2) AS sales2_no_brands, ROUND(SUM(if(LOWER(brand_name)=LOWER('HUAWEI\/\华\为') AND LOWER(title) LIKE LOWER('%%\摄\像\头%%'), sales, 0)), 2) AS sales3_brands, ROUND(SUM(if(LOWER(title) LIKE LOWER('%%\摄\像\头%%'), sales, 0)), 2) AS sales3_no_brands FROM tmall_wide_idx2 WHERE (category_id IN (50003695, 350407, 350402, 50008555, 50019790, 50012144, 50019649, 50008652, 120611, 121704, 50011867, 50008109, 50016107, 50008739, 50005266, 50024944, 1205, 50005050, 50005174, 50228001, 126412033, 125538002, 350203, 50011883, 1101)) AND (price >= 0) AND (size >= 0) AND (multiSearchAny(LOWER(title), ['集显', '声纹', '摄像头'])) AND `month` >= '2020-01-01' AND `month` <= '2020-05-01'

总结其他的优化思路都大同小异主要就是使用clickhouse内置方法，减少查询次数，提高查询效率

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