{"id":89498,"date":"2020-07-23T13:42:07","date_gmt":"2020-07-23T11:42:07","guid":{"rendered":"https:\/\/prohoster.info\/blog\/administrirovanie\/testirovanie-proizvoditelnosti-analiticheskih-zaprosov-v-postgresql-clickhouse-i-clickhousedb_fdw-postgresql"},"modified":"2020-07-23T13:42:07","modified_gmt":"2020-07-23T11:42:07","slug":"testirovanie-proizvoditelnosti-analiticheskih-zaprosov-v-postgresql-clickhouse-i-clickhousedb_fdw-postgresql","status":"publish","type":"post","link":"https:\/\/prohoster.info\/hu\/blog\/administrirovanie\/testirovanie-proizvoditelnosti-analiticheskih-zaprosov-v-postgresql-clickhouse-i-clickhousedb_fdw-postgresql","title":{"rendered":"Teljes\u00edtm\u00e9nytesztel\u00e9s analitikai lek\u00e9rdez\u00e9sekben PostgreSQL, ClickHouse \u00e9s clickhousedb_fdw (PostgreSQL)","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Ebben a tanulm\u00e1nyban azt szerettem volna megvizsg\u00e1lni, hogy milyen teljes\u00edtm\u00e9nyjavul\u00e1s \u00e9rhet\u0151 el, ha a ClickHouse adatforr\u00e1st haszn\u00e1lom a PostgreSQL helyett. Tudom, hogy milyen teljes\u00edtm\u00e9nyel\u0151ny\u00f6ket biztos\u00edt a ClickHouse haszn\u00e1lata. Ezek az el\u0151ny\u00f6k megmaradnak, ha a PostgreSQL-b\u0151l k\u00fcls\u0151 adatburok (FDW) seg\u00edts\u00e9g\u00e9vel \u00e9rek el ClickHouse-t? <\/p>\n<p><noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<p>Az egyszer megvizsg\u00e1lt adatb\u00e1zisok a PostgreSQL v11, a clickhousedb_fdw \u00e9s a ClickHouse adatb\u00e1zis. V\u00e9g\u00fcl a PostgreSQL v11-r\u0151l k\u00fcl\u00f6nb\u00f6z\u0151 SQL-lek\u00e9rdez\u00e9seket fogunk futtatni, amelyeket a clickhousedb_fdw-n kereszt\u00fcl ir\u00e1ny\u00edtunk a ClickHouse adatb\u00e1zisba. Ezut\u00e1n megn\u00e9zz\u00fck, hogyan hasonl\u00edt a FDW teljes\u00edtm\u00e9nye ugyanazokkal a lek\u00e9rdez\u00e9sekkel, amelyek nat\u00edvan a PostgreSQL-ben \u00e9s a ClickHouse-ban futnak.<\/p>\n<p><\/p>\n<h3 id=\"baza-dannyh-clickhouse\">Clickhouse adatb\u00e1zis<\/h3>\n<p><\/p>\n<p>A ClickHouse egy ny\u00edlt forr\u00e1sk\u00f3d\u00fa oszlop alap\u00fa adatb\u00e1zis-kezel\u0151 rendszer, amely 100-1000-szer gyorsabb teljes\u00edtm\u00e9nyt k\u00e9pes el\u00e9rni, mint a hagyom\u00e1nyos adatb\u00e1zis-megk\u00f6zel\u00edt\u00e9sek, \u00e9s k\u00e9pes t\u00f6bb mint egymilli\u00e1rd sort feldolgozni kevesebb, mint egy m\u00e1sodperc alatt.<\/p>\n<p><\/p>\n<h3 id=\"clickhousedb_fdw\">Clickhousedb_fdw<\/h3>\n<p><\/p>\n<p>A clickhousedb_fdw a ClickHouse adatb\u00e1zis k\u00fcls\u0151 adatburokja (FDW), amely a Percona ny\u00edlt forr\u00e1sk\u00f3d\u00fa projektje. <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/Percona-Lab\/clickhousedb_fdw\">Itt van egy link a projekt GitHub repositoryj\u00e1hoz<\/a><\/noindex>.<\/p>\n<p><\/p>\n<p><noindex><a rel=\"nofollow\" href=\"https:\/\/www.percona.com\/blog\/2019\/03\/29\/postgresql-access-clickhouse-one-of-the-fastest-column-dbmss-with-clickhousedb_fdw\/\">M\u00e1rciusban \u00edrtam egy blogbejegyz\u00e9st, amely t\u00f6bbet mes\u00e9l a FDW-nkr\u0151l<\/a><\/noindex>.<\/p>\n<p><\/p>\n<p>Ahogy l\u00e1thatja, ez egy FDW-t biztos\u00edt a ClickHouse sz\u00e1m\u00e1ra, amely lehet\u0151v\u00e9 teszi a SELECT from \u00e9s az INSERT INTO parancsok haszn\u00e1lat\u00e1t a ClickHouse adatb\u00e1zisban a PostgreSQL v11-r\u0151l.<\/p>\n<p><\/p>\n<p>A FDW fejlett funkci\u00f3kat is t\u00e1mogat, mint az aggregate \u00e9s a join. Ez jelent\u0151sen n\u00f6veli a teljes\u00edtm\u00e9nyt az\u00e1ltal, hogy a t\u00e1voli szerver er\u0151forr\u00e1sait haszn\u00e1lja ezekhez a munkaig\u00e9nyes m\u0171veletekhez.<\/p>\n<p><\/p>\n<h3 id=\"benchmark-environment\">Benchmark k\u00f6rnyezet<\/h3>\n<p><\/p>\n<ul>\n<li>Supermicro szerver:\n<ul>\n<li>Intel&reg; Xeon&reg; CPU E5-2683 v3 @ 2.00GHz<\/li>\n<li>2 socket \/ 28 mag \/ 56 sz\u00e1l<\/li>\n<li>Mem\u00f3ria: 256GB RAM<\/li>\n<li>T\u00e1rhely: Samsung SM863 1.9TB Enterprise SSD<\/li>\n<li>F\u00e1jlrendszer: ext4\/xfs<\/li>\n<\/ul>\n<\/li>\n<li>OS: Linux smblade01 4.15.0-42-generic #45~16.04.1-Ubuntu<\/li>\n<li>PostgreSQL: 11-es verzi\u00f3<\/li>\n<\/ul>\n<p><\/p>\n<h3 id=\"benchmark-tests\">Benchmark tesztek<\/h3>\n<p><\/p>\n<p>A teszthez nem haszn\u00e1ltunk valamilyen g\u00e9p \u00e1ltal gener\u00e1lt adatokat, hanem a \"Teljes\u00edtm\u00e9ny id\u0151ben, amely az \u00fczemeltet\u0151 munkaid\u0151j\u00e1r\u00f3l sz\u00e1mol be\" 1987 \u00e9s 2018 k\u00f6z\u00f6tt. Hozz\u00e1f\u00e9rhet az adatokhoz <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/Percona-Lab\/ontime-airline-performance\/blob\/master\/download.sh\">a mi skript\u00fcnkkel, amely itt el\u00e9rhet\u0151<\/a><\/noindex>.<\/p>\n<p><\/p>\n<p>Az adatb\u00e1zis m\u00e9rete 85 GB, amelyben egy t\u00e1bl\u00e1zat tal\u00e1lhat\u00f3 109 oszlopb\u00f3l.<\/p>\n<p><\/p>\n<h4 id=\"benchmark-queries\">Benchmark lek\u00e9rdez\u00e9sek<\/h4>\n<p><\/p>\n<p>Itt vannak a lek\u00e9rdez\u00e9sek, amelyeket a ClickHouse, a clickhousedb_fdw \u00e9s a PostgreSQL \u00f6sszehasonl\u00edt\u00e1s\u00e1hoz haszn\u00e1ltam.<\/p>\n<p><\/p>\n<p><strong>Q#<\/strong><br \/>\n<strong>A lek\u00e9rdez\u00e9s aggreg\u00e1tumokat \u00e9s csoportos\u00edt\u00e1st tartalmaz<\/strong><\/p>\n<p>Q1<br \/>\nSELECT DayOfWeek, count(*) AS c FROM ontime WHERE Year &gt;= 2000 AND Year &lt;= 2008 GROUP BY DayOfWeek ORDER BY c DESC;<\/p>\n<p>Q2<br \/>\nV\u00e1lassza ki a DayOfWeek-t, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (c) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, ahol a DepDelay &gt; 10, \u00e9s az \u00c9v &gt;= 2000 \u00e9s \u00c9v &lt;= 2008, csoportos\u00edtva a DayOfWeek szerint, a sz\u00e1m szerint cs\u00f6kken\u0151 sorrendben;<\/p>\n<p>Q3<br \/>\nV\u00e1lassza ki az Origin-t, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (c) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, ahol a DepDelay &gt; 10, \u00e9s az \u00c9v &gt;= 2000 \u00e9s \u00c9v &lt;= 2008, csoportos\u00edtva az Origin szerint, a sz\u00e1m szerint cs\u00f6kken\u0151 sorrendben, maximum 10;<\/p>\n<p>Q4<br \/>\nV\u00e1lassza ki a Carrier-t, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (<em>) az ontime t\u00e1bl\u00e1b\u00f3l, ahol a DepDelay &gt; 10, \u00e9s az \u00c9v = 2007, csoportos\u00edtva a Carrier szerint, a sz\u00e1m szerint cs\u00f6kken\u0151 sorrendben;<\/em>Q5<\/p>\n<p>V\u00e1lassza ki a a.Carrier-t, c, c2, c<br \/>\n1000\/c2 mint c3 az (V\u00e1lassza ki a Carrier-t, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (<em>) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, ahol a DepDelay &gt; 10, \u00e9s az \u00c9v = 2007, csoportos\u00edtva a Carrier szerint) a INNER JOIN (V\u00e1lassza ki a Carrier-t, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (c2) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, ahol az \u00c9v = 2007, csoportos\u00edtva a Carrier szerint)b on a.Carrier=b.Carrier, cs\u00f6kken\u0151 c3 sorrendben;<\/em>Q6<\/p>\n<p>) mint c az ontime t\u00e1bl\u00e1b\u00f3l, ahol a DepDelay &gt; 10, \u00e9s az \u00c9v &gt;= 2000, \u00e9s \u00c9v = 2000, \u00e9s \u00c9v &lt;= 2008, csoportos\u00edtva a Carrier szerint) b on a.Carrier=b.Carrier, cs\u00f6kken\u0151 c3 sorrendben;<br \/>\n1000\/c2 mint c3 az (V\u00e1lassza ki a Carrier-t, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (<em>) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, ahol a DepDelay &gt; 10, \u00e9s az \u00c9v = 2007, csoportos\u00edtva a Carrier szerint) a INNER JOIN (V\u00e1lassza ki a Carrier-t, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (c2) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, ahol az \u00c9v = 2007, csoportos\u00edtva a Carrier szerint)b on a.Carrier=b.Carrier, cs\u00f6kken\u0151 c3 sorrendben;<\/em>Q7<\/p>\n<p>V\u00e1lassza ki a Carrier-t, az \u00e1tlagos DepDelay * 1000 \u00e9rt\u00e9k\u00e9t mint c3 az ontime t\u00e1bl\u00e1b\u00f3l, ahol az \u00c9v &gt;= 2000, \u00e9s \u00c9v &lt;= 2008, csoportos\u00edtva a Carrier szerint;<br \/>\nQ8<\/p>\n<p>V\u00e1lassza ki az \u00c9vet, az \u00e1tlagos DepDelay-t az ontime t\u00e1bl\u00e1b\u00f3l, csoportos\u00edtva az \u00c9v szerint;<br \/>\nQ9<\/p>\n<p>V\u00e1lassza ki az \u00c9vet, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (c1) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, csoportos\u00edtva az \u00c9v szerint;<br \/>\nQ10<\/p>\n<p>V\u00e1lassza ki az \u00e1tlagos cnt \u00e9rt\u00e9k\u00e9t az (V\u00e1lassza ki az \u00c9vet, H\u00f3napot, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (cnt) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, ahol a DepDel15 = 1, csoportos\u00edtva az \u00c9v, H\u00f3nap szerint) a;<br \/>\nQ11<\/p>\n<p>V\u00e1lassza ki az \u00e1tlagos c1 \u00e9rt\u00e9k\u00e9t az (V\u00e1lassza ki az \u00c9vet, H\u00f3napot, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (c1) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, csoportos\u00edtva az \u00c9v, H\u00f3nap szerint) a;<br \/>\nQ12<\/p>\n<p>V\u00e1lassza ki az OriginCityName-t, a DestCityName-t, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (c) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, csoportos\u00edtva az OriginCityName, DestCityName szerint, a sz\u00e1m szerint cs\u00f6kken\u0151 sorrendben, maximum 10;<br \/>\nQ13<\/p>\n<p>V\u00e1lassza ki az OriginCityName-t, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (c) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, csoportos\u00edtva az OriginCityName szerint, a sz\u00e1m szerint cs\u00f6kken\u0151 sorrendben, maximum 10;<br \/>\nQuery tartalmaz csatlakoz\u00e1sokat<\/p>\n<p><strong>Q14<\/strong><\/p>\n<p>V\u00e1lassza ki az a.Year-t, c1\/c2 \u00e9rt\u00e9k\u00e9t az (V\u00e1lassza ki az \u00c9vet, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (<br \/>\n1000 mint c1 az ontime t\u00e1bl\u00e1b\u00f3l, ahol a DepDelay &gt; 10, csoportos\u00edtva az \u00c9v szerint) a INNER JOIN (V\u00e1lassza ki az \u00c9vet, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (c2) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, csoportos\u00edtva az \u00c9v szerint) b on a.Year=b.Year, a.Year szerint cs\u00f6kken\u0151 sorrendben;<em>)<\/em>Q15<\/p>\n<p>V\u00e1lassza ki az a.Year-t, c1\/c2 \u00e9rt\u00e9k\u00e9t az (V\u00e1lassza ki az \u00c9vet, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (<br \/>\n1000 mint c1 az ontime t\u00e1bl\u00e1b\u00f3l, ahol a DepDelay &gt; 10, csoportos\u00edtva az \u00c9v szerint) a INNER JOIN (V\u00e1lassza ki az \u00c9vet, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (c2) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, csoportos\u00edtva az \u00c9v szerint) b on a.Year=b.Year;<em>)<\/em>Table-1: A benchmarkban haszn\u00e1lt lek\u00e9rdez\u00e9sek<\/p>\n<p><\/p>\n<p><em>Lek\u00e9rdez\u00e9sek v\u00e9grehajt\u00e1sa<\/em><\/p>\n<p><\/p>\n<h4 id=\"query-executions\">Itt l\u00e1that\u00f3k a lek\u00e9rdez\u00e9sek eredm\u00e9nyei k\u00fcl\u00f6nb\u00f6z\u0151 adatb\u00e1zis-be\u00e1ll\u00edt\u00e1sokkal: PostgreSQL indexekkel \u00e9s an\u00e9lk\u00fcl, saj\u00e1t ClickHouse \u00e9s clickhousedb_fdw. Az id\u0151 millikondratumban van megadva.<\/h4>\n<p><\/p>\n<p>PostgreSQL (Rendezett)<\/p>\n<p><\/p>\n<p><strong>Q#<\/strong><br \/>\n<strong>PostgreSQL<\/strong><br \/>\n<strong>ClickHouse<\/strong><br \/>\n<strong>clickhousedb_fdw<\/strong><br \/>\n<strong>Table-1: Az elk\u00e9sz\u00edtett lek\u00e9rdez\u00e9sek v\u00e9grehajt\u00e1s\u00e1hoz sz\u00fcks\u00e9ges id\u0151<\/strong><\/p>\n<p>Q1<br \/>\n27920<br \/>\n19634<br \/>\n23<br \/>\n57<\/p>\n<p>Q2<br \/>\n35124<br \/>\n17301<br \/>\n50<br \/>\n80<\/p>\n<p>Q3<br \/>\n34046<br \/>\n15618<br \/>\n67<br \/>\n115<\/p>\n<p>Q4<br \/>\n31632<br \/>\n7667<br \/>\n25<br \/>\n37<\/p>\n<p>V\u00e1lassza ki a a.Carrier-t, c, c2, c<br \/>\n47220<br \/>\n8976<br \/>\n27<br \/>\n60<\/p>\n<p>) mint c az ontime t\u00e1bl\u00e1b\u00f3l, ahol a DepDelay &gt; 10, \u00e9s az \u00c9v &gt;= 2000, \u00e9s \u00c9v = 2000, \u00e9s \u00c9v &lt;= 2008, csoportos\u00edtva a Carrier szerint) b on a.Carrier=b.Carrier, cs\u00f6kken\u0151 c3 sorrendben;<br \/>\n58233<br \/>\n24368<br \/>\n55<br \/>\n153<\/p>\n<p>V\u00e1lassza ki a Carrier-t, az \u00e1tlagos DepDelay * 1000 \u00e9rt\u00e9k\u00e9t mint c3 az ontime t\u00e1bl\u00e1b\u00f3l, ahol az \u00c9v &gt;= 2000, \u00e9s \u00c9v &lt;= 2008, csoportos\u00edtva a Carrier szerint;<br \/>\n30566<br \/>\n13256<br \/>\n52<br \/>\n91<\/p>\n<p>V\u00e1lassza ki az \u00c9vet, az \u00e1tlagos DepDelay-t az ontime t\u00e1bl\u00e1b\u00f3l, csoportos\u00edtva az \u00c9v szerint;<br \/>\n38309<br \/>\n60511<br \/>\n112<br \/>\n179<\/p>\n<p>V\u00e1lassza ki az \u00c9vet, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (c1) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, csoportos\u00edtva az \u00c9v szerint;<br \/>\n20674<br \/>\n37979<br \/>\n31<br \/>\n81<\/p>\n<p>V\u00e1lassza ki az \u00e1tlagos cnt \u00e9rt\u00e9k\u00e9t az (V\u00e1lassza ki az \u00c9vet, H\u00f3napot, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (cnt) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, ahol a DepDel15 = 1, csoportos\u00edtva az \u00c9v, H\u00f3nap szerint) a;<br \/>\n34990<br \/>\n20102<br \/>\n56<br \/>\n148<\/p>\n<p>V\u00e1lassza ki az \u00e1tlagos c1 \u00e9rt\u00e9k\u00e9t az (V\u00e1lassza ki az \u00c9vet, H\u00f3napot, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (c1) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, csoportos\u00edtva az \u00c9v, H\u00f3nap szerint) a;<br \/>\n30489<br \/>\n51658<br \/>\n37<br \/>\n155<\/p>\n<p>V\u00e1lassza ki az OriginCityName-t, a DestCityName-t, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (c) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, csoportos\u00edtva az OriginCityName, DestCityName szerint, a sz\u00e1m szerint cs\u00f6kken\u0151 sorrendben, maximum 10;<br \/>\n39357<br \/>\n33742<br \/>\n186<br \/>\n1333<\/p>\n<p>V\u00e1lassza ki az OriginCityName-t, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (c) \u00e9rt\u00e9k\u00e9t az ontime t\u00e1bl\u00e1b\u00f3l, csoportos\u00edtva az OriginCityName szerint, a sz\u00e1m szerint cs\u00f6kken\u0151 sorrendben, maximum 10;<br \/>\n29912<br \/>\n30709<br \/>\n101<br \/>\n384<\/p>\n<p>V\u00e1lassza ki az a.Year-t, c1\/c2 \u00e9rt\u00e9k\u00e9t az (V\u00e1lassza ki az \u00c9vet, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (<br \/>\n54126<br \/>\n39913<br \/>\n124<br \/>\n1364212<\/p>\n<p>V\u00e1lassza ki az a.Year-t, c1\/c2 \u00e9rt\u00e9k\u00e9t az (V\u00e1lassza ki az \u00c9vet, az \u00f6sszes\u00edtett sz\u00e1ml\u00e1l\u00f3 (<br \/>\n97258<br \/>\n30211<br \/>\n245<br \/>\n259<\/p>\n<p><\/p>\n<p><em>Eredm\u00e9nyek megtekint\u00e9se<\/em><\/p>\n<p><\/p>\n<p>A grafikon bemutatja a lek\u00e9rdez\u00e9s v\u00e9grehajt\u00e1s\u00e1nak idej\u00e9t millikondratumban, az X tengely a fenti t\u00e1bl\u00e1zatok lek\u00e9rdez\u00e9ssz\u00e1m\u00e1t, m\u00edg az Y tengely a v\u00e9grehajt\u00e1sig eltelt id\u0151t mutatja. Az eredm\u00e9nyek k\u00f6z\u00f6tt a ClickHouse \u00e9s a PostgreSQL-b\u0151l nyert adatok a clickhousedb_fdw mint\u00e1t is tartalmazz\u00e1k. A t\u00e1bl\u00e1zatb\u00f3l l\u00e1that\u00f3, hogy hatalmas k\u00fcl\u00f6nbs\u00e9g van a PostgreSQL \u00e9s a ClickHouse k\u00f6z\u00f6tt, de minim\u00e1lis elt\u00e9r\u00e9s a ClickHouse \u00e9s a clickhousedb_fdw k\u00f6z\u00f6tt.<\/p>\n<p><\/p>\n<p>Ez a grafikon a ClickhouseDB \u00e9s a clickhousedb_fdw k\u00f6z\u00f6tti elt\u00e9r\u00e9seket mutatja. A legt\u00f6bb lek\u00e9rdez\u00e9s eset\u00e9ben az FDW k\u00f6lts\u00e9gek nem t\u00fals\u00e1gosan magasak, \u00e9s alig \u00e9szlelhet\u0151k, kiv\u00e9ve a Q12-t. Ez a k\u00e9rd\u00e9s csatlakoz\u00e1sokat \u00e9s ORDER BY kifejez\u00e9st foglal mag\u00e1ban. Az ORDER BY GROUP\/BY \u00e9s az ORDER BY nem s\u00fcllyed le a ClickHouse-hoz.<\/p>\n<p><\/p>\n<p><img decoding=\"async\" alt=\"Teljes\u00edtm\u00e9nytesztel\u00e9s analitikai lek\u00e9rdez\u00e9sekben PostgreSQL, ClickHouse \u00e9s clickhousedb_fdw (PostgreSQL)\" src=\"\/wp-content\/uploads\/2020\/07\/e084243ea7b327f30de5cb78339d3d3a.png\" style=\"display:block;margin: 0 auto;\" \/><\/p>\n<p><\/p>\n<p>Az ORDER BY GROUP\/BY \u00e9s az ORDER BY nem s\u00fcllyed le a ClickHouse-hoz.<\/p>\n<p><\/p>\n<p>A 2. t\u00e1bl\u00e1zatban l\u00e1that\u00f3 a Q12 \u00e9s Q13 lek\u00e9rdez\u00e9sek v\u00e9grehajt\u00e1si idej\u00e9nek ugr\u00e1sa. Ism\u00e9tlem, ezt az ORDER BY javaslat okozza. Ennek meger\u0151s\u00edt\u00e9s\u00e9re v\u00e9grehajtottam a Q-14 \u00e9s Q-15 lek\u00e9rdez\u00e9seket ORDER BY javaslattal \u00e9s an\u00e9lk\u00fcl. ORDER BY javaslat n\u00e9lk\u00fcl a befejez\u00e9si id\u0151 259 ms, m\u00edg ORDER BY javaslattal 1364212 ms. A lek\u00e9rdez\u00e9s hibakeres\u00e9se \u00e9rdek\u00e9ben mindk\u00e9t lek\u00e9rdez\u00e9st elmagyar\u00e1zom, \u00e9s itt olvashat\u00f3 az eredm\u00e9ny.<\/p>\n<p><\/p>\n<p>Q15: ORDER BY z\u00e1rad\u00e9k n\u00e9lk\u00fcl<\/p>\n<p><\/p>\n<pre><code class=\"plaintext\">bm=# EXPLAIN VERBOSE SELECT a.&quot;Year&quot;, c1\/c2 \n     FROM (SELECT &quot;Year&quot;, count(*)*1000 AS c1 FROM fontime WHERE &quot;DepDelay&quot; &gt; 10 GROUP BY &quot;Year&quot;) a\n     INNER JOIN(SELECT &quot;Year&quot;, count(*) AS c2 FROM fontime GROUP BY &quot;Year&quot;) b ON a.&quot;Year&quot;=b.&quot;Year&quot;;<\/code><\/pre>\n<p><\/p>\n<p>Q15: Lek\u00e9rdez\u00e9s ORDER BY z\u00e1rad\u00e9k n\u00e9lk\u00fcl<\/p>\n<p><\/p>\n<pre><code class=\"plaintext\">QUERY PLAN                                                      \nHash Join  (cost=2250.00..128516.06 rows=50000000 width=12)  \nOutput: fontime.&quot;Year&quot;, (((count(*) * 1000)) \/ b.c2)  \nInner Unique: true   Hash Cond: (fontime.&quot;Year&quot; = b.&quot;Year&quot;)  \n-&gt;  Foreign Scan  (cost=1.00..-1.00 rows=100000 width=12)        \nOutput: fontime.&quot;Year&quot;, ((count(*) * 1000))        \nRelations: Aggregate on (fontime)        \nRemote SQL: SELECT &quot;Year&quot;, (count(*) * 1000) FROM &quot;default&quot;.ontime WHERE ((&quot;DepDelay&quot; &gt; 10)) GROUP BY &quot;Year&quot;  \n-&gt;  Hash  (cost=999.00..999.00 rows=100000 width=12)        \nOutput: b.c2, b.&quot;Year&quot;        \n-&gt;  Subquery Scan on b  (cost=1.00..999.00 rows=100000 width=12)              \nOutput: b.c2, b.&quot;Year&quot;              \n-&gt;  Foreign Scan  (cost=1.00..-1.00 rows=100000 width=12)                    \nOutput: fontime_1.&quot;Year&quot;, (count(*))                    \nRelations: Aggregate on (fontime)                    \nRemote SQL: SELECT &quot;Year&quot;, count(*) FROM &quot;default&quot;.ontime GROUP BY &quot;Year&quot;(16 rows)<\/code><\/pre>\n<p><\/p>\n<p>Q14: Lek\u00e9rdez\u00e9s ORDER BY z\u00e1rad\u00e9kkal<\/p>\n<p><\/p>\n<pre><code class=\"plaintext\">bm=# EXPLAIN VERBOSE SELECT a.&quot;Year&quot;, c1\/c2 FROM(SELECT &quot;Year&quot;, count(*)*1000 AS c1 FROM fontime WHERE &quot;DepDelay&quot; &gt; 10 GROUP BY &quot;Year&quot;) a \n     INNER JOIN(SELECT &quot;Year&quot;, count(*) as c2 FROM fontime GROUP BY &quot;Year&quot;) b  ON a.&quot;Year&quot;= b.&quot;Year&quot; \n     ORDER BY a.&quot;Year&quot;;<\/code><\/pre>\n<p><\/p>\n<p>Q14: Lek\u00e9rdez\u00e9si terv ORDER BY z\u00e1rad\u00e9kkal<\/p>\n<p><\/p>\n<pre><code class=\"plaintext\">QUERY PLAN \nMerge Join\u00a0 (cost=2.00..628498.02 rows=50000000 width=12)\u00a0\u00a0 \nOutput: fontime.&quot;Year&quot;, (((count(*) * 1000)) \/ (count(*)))\u00a0\u00a0 \nInner Unique: true\u00a0\u00a0 Merge Cond: (fontime.&quot;Year&quot; = fontime_1.&quot;Year&quot;)\u00a0\u00a0 \n-&gt;\u00a0 GroupAggregate\u00a0 (cost=1.00..499.01 rows=1 width=12)\u00a0 \u00a0 \u00a0 \u00a0 \nOutput: fontime.&quot;Year&quot;, (count(*) * 1000)\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \nGroup Key: fontime.&quot;Year&quot;\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \n-&gt;\u00a0 Foreign Scan on public.fontime\u00a0 (cost=1.00..-1.00 rows=100000 width=4)\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \nRemote SQL: SELECT &quot;Year&quot; FROM &quot;default&quot;.ontime WHERE ((&quot;DepDelay&quot; &gt; 10)) \n            ORDER BY &quot;Year&quot; ASC\u00a0\u00a0 \n-&gt;\u00a0 GroupAggregate\u00a0 (cost=1.00..499.01 rows=1 width=12)\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \nOutput: fontime_1.&quot;Year&quot;, count(*)\u00a0\u00a0 \u00a0 \u00a0 \u00a0 Group Key: fontime_1.&quot;Year&quot;\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \n-&gt;\u00a0 Foreign Scan on public.fontime fontime_1\u00a0 (cost=1.00..-1.00 rows=100000 width=4)\u00a0\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \nRemote SQL: SELECT &quot;Year&quot; FROM &quot;default&quot;.ontime ORDER BY &quot;Year&quot; ASC(16 rows)<\/code><\/pre>\n<p><\/p>\n<p>K\u00f6vetkeztet\u00e9s<\/p>\n<p><\/p>\n<p>Ezek a k\u00eds\u00e9rletek azt mutatj\u00e1k, hogy a ClickHouse val\u00f3ban j\u00f3 teljes\u00edtm\u00e9nyt ny\u00fajt, a clickhousedb_fdw pedig a ClickHouse teljes\u00edtm\u00e9ny\u00e9nek el\u0151nyeit k\u00edn\u00e1lja a PostgreSQL-b\u0151l. B\u00e1r a clickhousedb_fdw haszn\u00e1latakor vannak bizonyos k\u00f6lts\u00e9gek, ezek eleny\u00e9sz\u0151ek \u00e9s \u00f6sszehasonl\u00edthat\u00f3k a ClickHouse adatb\u00e1zisban el\u00e9rt nat\u00edv teljes\u00edtm\u00e9nnyel. Ez azt is meger\u0151s\u00edti, hogy a PostgreSQL fdw figyelemre m\u00e9lt\u00f3 eredm\u00e9nyeket biztos\u00edt.<\/p>\n<p><\/p>\n<p>ClickHouse Telegram cseveg\u00e9s <noindex><a rel=\"nofollow\" href=\"https:\/\/t.me\/clickhouse_ru\">https:\/\/t.me\/clickhouse_ru<\/a><\/noindex><br \/>\nPostgreSQL Telegram cseveg\u00e9s <noindex><a rel=\"nofollow\" href=\"https:\/\/t.me\/pgsql\">https:\/\/t.me\/pgsql<\/a><\/noindex><\/p>\n<p>Forr\u00e1s: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/511992\/\">habr.com<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u0412 \u044d\u0442\u043e\u043c \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u0438 \u044f \u0445\u043e\u0442\u0435\u043b \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0435\u0442\u044c, \u043a\u0430\u043a\u0438\u0435 \u0443\u043b\u0443\u0447\u0448\u0435\u043d\u0438\u044f \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u043c\u043e\u0436\u043d\u043e \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a \u0434\u0430\u043d\u043d\u044b\u0445 ClickHouse, \u0430 \u043d\u0435 PostgreSQL. \u042f \u0437\u043d\u0430\u044e, \u043a\u0430\u043a\u0438\u0435 \u043f\u0440\u0435\u0438\u043c\u0443\u0449\u0435\u0441\u0442\u0432\u0430 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u043f\u0440\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 ClickHouse \u044f \u043f\u043e\u043b\u0443\u0447\u0430\u044e. \u0411\u0443\u0434\u0443\u0442 \u043b\u0438 \u044d\u0442\u0438 \u043f\u0440\u0435\u0438\u043c\u0443\u0449\u0435\u0441\u0442\u0432\u0430 \u0441\u043e\u0445\u0440\u0430\u043d\u0435\u043d\u044b, \u0435\u0441\u043b\u0438 \u044f \u043f\u043e\u043b\u0443\u0447\u0443 \u0434\u043e\u0441\u0442\u0443\u043f \u043a ClickHouse \u0438\u0437 PostgreSQL \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0432\u043d\u0435\u0448\u043d\u0435\u0439 \u043e\u0431\u043e\u043b\u043e\u0447\u043a\u0438 \u0434\u0430\u043d\u043d\u044b\u0445 (FDW)? \u0418\u0441\u0441\u043b\u0435\u0434\u0443\u0435\u043c\u044b\u043c\u0438 \u0441\u0440\u0435\u0434\u0430\u043c\u0438 \u0431\u0430\u0437 \u0434\u0430\u043d\u043d\u044b\u0445 \u044f\u0432\u043b\u044f\u044e\u0442\u0441\u044f PostgreSQL v11, clickhousedb_fdw [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":89499,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-89498","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-administrirovanie"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.3 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u0412 \u044d\u0442\u043e\u043c \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u0438 \u044f \u0445\u043e\u0442\u0435\u043b \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0435\u0442\u044c, \u043a\u0430\u043a\u0438\u0435 \u0443\u043b\u0443\u0447\u0448\u0435\u043d\u0438\u044f \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u043c\u043e\u0436\u043d\u043e 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