memsqlの80Mレコードから選択あたり5秒

Question

ウェブ解析プロジェクトにはmemsql 5.1が使用されています。 1日に約80Mのレコードと0.5Mのレコードがあります。簡単なリクエストは約5秒間に実行されます。ドメイン、地理、langごとに受信したデータの数です。私はその時間を減らすことは可能だと思うが、私は方法を見つけることができません。方法を教えてください。このような1つの

CREATE TABLE `domains` (
    `date` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP, 
    `geo` varchar(100) CHARACTER SET utf8 COLLATE utf8_general_ci DEFAULT NULL, 
    `lang` char(5) CHARACTER SET utf8 COLLATE utf8_general_ci DEFAULT NULL, 
    `browser` varchar(20) CHARACTER SET utf8 COLLATE utf8_general_ci DEFAULT NULL, 
    `os` varchar(20) CHARACTER SET utf8 COLLATE utf8_general_ci DEFAULT NULL, 
    `device` varchar(20) CHARACTER SET utf8 COLLATE utf8_general_ci DEFAULT NULL, 
    `domain` varchar(200) CHARACTER SET utf8 COLLATE utf8_general_ci DEFAULT NULL, 
    `ref` varchar(200) CHARACTER SET utf8 COLLATE utf8_general_ci DEFAULT NULL, 
    `blk_cnt` int(11) DEFAULT NULL, 
    KEY `date` (`date`,`geo`,`lang`,`domain`) /*!90619 USING CLUSTERED COLUMNSTORE */ 
    /*!90618 , SHARD KEY() */ 
    ) 

要求等

テーブル：推奨

• 元のクエリの時間の適用後

  memsql> explain SELECT domain, geo, lang, avg(blk_cnt) as blk_cnt, count(*) as cnt FROM domains WHERE date BETWEEN '2016-07-31 0:00' AND '2016-08-01 0:00' GROUP BY domain, geo, lang ORDER BY blk_cnt ASC limit 40; 
      +-------------------------------------------------------------------------------------------------------------------------------------------------------+ 
      | EXPLAIN                                      | 
      +-------------------------------------------------------------------------------------------------------------------------------------------------------+ 
      | Project [r0.domain, r0.geo, r0.lang, $0/CAST(COALESCE($1,0) AS SIGNED) AS blk_cnt, CAST(COALESCE($2,0) AS SIGNED) AS cnt]         | 
      | Top limit:40                                     | 
      | GatherMerge [SUM(r0.s)/CAST(COALESCE(SUM(r0.c),0) AS SIGNED)] partitions:all est_rows:40                 | 
      | Project [r0.domain, r0.geo, r0.lang, s/CAST(COALESCE(c,0) AS SIGNED) AS blk_cnt, CAST(COALESCE(cnt_1,0) AS SIGNED) AS cnt, s, c, cnt_1] est_rows:40 | 
      | TopSort limit:40 [SUM(r0.s)/CAST(COALESCE(SUM(r0.c),0) AS SIGNED)]                       | 
      | HashGroupBy [SUM(r0.s) AS s, SUM(r0.c) AS c, SUM(r0.cnt) AS cnt_1] groups:[r0.domain, r0.geo, r0.lang]              | 
      | TableScan r0 storage:list stream:no                               | 
      | Repartition [domains.domain, domains.geo, domains.lang, cnt, s, c] AS r0 shard_key:[domain, geo, lang] est_rows:40 est_select_cost:144350216   | 
      | HashGroupBy [COUNT(*) AS cnt, SUM(domains.blk_cnt) AS s, COUNT(domains.blk_cnt) AS c] groups:[domains.domain, domains.geo, domains.lang]    | 
      | Filter [domains.date >= '2016-07-31 0:00' AND domains.date <= '2016-08-01 0:00']                    | 
      | ColumnStoreScan scan_js_data.domains, KEY date (date, geo, lang, domain) USING CLUSTERED COLUMNSTORE est_table_rows:72175108 est_filtered:18043777 | 
      +-------------------------------------------------------------------------------------------------------------------------------------------------------+ 
  

  - 5Sタイムスタンプ最適化

• - 3.7秒
タイムスタンプ+ shardkeyと
• - 2.6s

はあなたに非常にマッチをありがとうございました！

Answer 1

おそらくグループを実行することは、このクエリの中で最も高価な部分です。グループに一致するシャードキー、つまりSHARD KEY (domain, geo, lang)を使用すると、グループをより速く実行できます。