{"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\/hr\/blog\/administrirovanie\/testirovanie-proizvoditelnosti-analiticheskih-zaprosov-v-postgresql-clickhouse-i-clickhousedb_fdw-postgresql","title":{"rendered":"Testiranje performansi analiti\u010dkih upita u PostgreSQL, ClickHouse i clickhousedb_fdw (PostgreSQL)","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>U ovoj studiji \u017eelio sam istra\u017eiti kakve se pobolj\u0161anja u performansama mogu posti\u0107i kori\u0161tenjem izvora podataka ClickHouse umjesto PostgreSQL. Znam koja su pobolj\u0161anja performansi koja dobivam koriste\u0107i ClickHouse. Ho\u0107e li te prednosti ostati ako pristupim ClickHouse-u iz PostgreSQL-a putem vanjskog omota\u010da podataka (FDW)? <\/p>\n<p><noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<p>Istra\u017eivane baze podataka su PostgreSQL v11, clickhousedb_fdw i baza podataka ClickHouse. Na kraju, iz PostgreSQL v11 pokre\u0107emo razli\u010dite SQL upite, usmjerene kroz na\u0161 clickhousedb_fdw u bazu podataka ClickHouse. Tada \u0107emo vidjeti kako se performanse FDW uspore\u0111uju s istim upitima koji se izvr\u0161avaju u nativnom PostgreSQL-u i nativnom ClickHouse-u.<\/p>\n<p><\/p>\n<h3 id=\"baza-dannyh-clickhouse\">Baza podataka Clickhouse<\/h3>\n<p><\/p>\n<p>ClickHouse je sustav za upravljanje bazama podataka temeljen na kolonama s otvorenim izvorom, koji mo\u017ee posti\u0107i performanse 100-1000 puta br\u017ee od tradicionalnih pristupa bazama podataka, sposoban obraditi vi\u0161e od milijardu redaka u manje od sekunde.<\/p>\n<p><\/p>\n<h3 id=\"clickhousedb_fdw\">Clickhousedb_fdw<\/h3>\n<p><\/p>\n<p>clickhousedb_fdw je vanjski omota\u010d podataka za bazu podataka ClickHouse, ili FDW, projekt s otvorenim izvorom od Percone. <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/Percona-Lab\/clickhousedb_fdw\">Evo linka na GitHub repozitorij projekta<\/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\/\">U o\u017eujku sam napisao blog koji vas detaljnije informira o na\u0161em FDW-u<\/a><\/noindex>.<\/p>\n<p><\/p>\n<p>Kao \u0161to \u0107ete vidjeti, ovo pru\u017ea FDW za ClickHouse koji omogu\u0107uje SELECT iz i INSERT u bazu podataka ClickHouse sa servera PostgreSQL v11.<\/p>\n<p><\/p>\n<p>FDW podr\u017eava napredne funkcije, kao \u0161to su agregati i join. To zna\u010dajno pobolj\u0161ava performanse koriste\u0107i resurse udaljenog servera za ove resursno intenzivne operacije.<\/p>\n<p><\/p>\n<h3 id=\"benchmark-environment\">Benchmark okru\u017eenje<\/h3>\n<p><\/p>\n<ul>\n<li>Supermicro server:\n<ul>\n<li>Intel&reg; Xeon&reg; CPU E5-2683 v3 @ 2.00GHz<\/li>\n<li>2 uti\u010dnice \/ 28 jezgri \/ 56 niti<\/li>\n<li>Memorija: 256GB RAM-a<\/li>\n<li>Pohrana: Samsung SM863 1.9TB Enterprise SSD<\/li>\n<li>Datote\u010dni sustav: 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: verzija 11<\/li>\n<\/ul>\n<p><\/p>\n<h3 id=\"benchmark-tests\">Benchmark testovi<\/h3>\n<p><\/p>\n<p>Umjesto kori\u0161tenja nekog skupa podataka generiranog ra\u010dunalom, za ovaj test koristili smo podatke 'Performanse po vremenu, izvje\u0161tavanje o vremenu rada operatera' od 1987. do 2018. godine. Mo\u017eete pristupiti podacima <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/Percona-Lab\/ontime-airline-performance\/blob\/master\/download.sh\">putem na\u0161eg skripta, dostupnog ovdje<\/a><\/noindex>.<\/p>\n<p><\/p>\n<p>Veli\u010dina baze podataka iznosi 85 GB, osiguravaju\u0107i jednu tablicu s 109 stupaca.<\/p>\n<p><\/p>\n<h4 id=\"benchmark-queries\">Benchmark upiti<\/h4>\n<p><\/p>\n<p>Ovo su upiti koje sam koristio za usporedbu ClickHouse-a, clickhousedb_fdw-a i PostgreSQL-a.<\/p>\n<p><\/p>\n<p><strong>Q#<\/strong><br \/>\n<strong>Upit sadr\u017ei agregate i Group By<\/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 \/>\nODABERITE DanTjedna, COUNT(*) AS c IZ ontime GDJE DepDelay&gt;10 I Godina &gt;= 2000 I Godina &lt;= 2008 GRUPIRAJ PO DanTjedna NAREDI PO c DESC;<\/p>\n<p>Q3<br \/>\nODABERITE Izvor, COUNT(*) AS c IZ ontime GDJE DepDelay&gt;10 I Godina &gt;= 2000 I Godina &lt;= 2008 GRUPIRAJ PO Izvor NAREDI PO c DESC LIMIT 10;<\/p>\n<p>Q4<br \/>\nODABERITE Prijevoznik, COUNT(<em>) IZ ontime GDJE DepDelay&gt;10 I Godina = 2007 GRUPIRAJ PO Prijevoznik NAREDI PO COUNT(<\/em>) DESC;<\/p>\n<p>Q5<br \/>\nODABERITE a.Prijevoznik, c, c2, c<em>1000\/c2 kao c3 IZ ( ODABERITE Prijevoznik, COUNT(<\/em>) AS c IZ ontime GDJE DepDelay&gt;10 I Godina=2007 GRUPIRAJ PO Prijevoznik ) a UNUTAR SPOJA ( ODABERITE Prijevoznik, COUNT(*) AS c2 IZ ontime GDJE Godina=2007 GRUPIRAJ PO Prijevoznik) b ON a.Prijevoznik=b.Prijevoznik NAREDI PO c3 DESC;<\/p>\n<p>Q6<br \/>\nODABERITE a.Prijevoznik, c, c2, c<em>1000\/c2 kao c3 IZ ( ODABERITE Prijevoznik, COUNT(<\/em>) AS c IZ ontime GDJE DepDelay&gt;10 I Godina &gt;= 2000 I Godina = 2000 I Godina &lt;= 2008 GRUPIRAJ PO Prijevoznik ) b ON a.Prijevoznik=b.Prijevoznik NAREDI PO c3 DESC;<\/p>\n<p>Q7<br \/>\nODABERITE Prijevoznik, AVG(DepDelay) * 1000 AS c3 IZ ontime GDJE Godina &gt;= 2000 I Godina &lt;= 2008 GRUPIRAJ PO Prijevoznik;<\/p>\n<p>Q8<br \/>\nODABERITE Godina, AVG(DepDelay) IZ ontime GRUPIRAJ PO Godina;<\/p>\n<p>Q9<br \/>\nODABERITE Godina, COUNT(*) AS c1 IZ ontime GRUPIRAJ PO Godina;<\/p>\n<p>Q10<br \/>\nODABERITE AVG(cnt) IZ (ODABERITE Godina, Mjesec, COUNT(*) AS cnt IZ ontime GDJE DepDel15=1 GRUPIRAJ PO Godina, Mjesec) a;<\/p>\n<p>Q11<br \/>\nODABERITE AVG(c1) IZ (ODABERITE Godina, Mjesec, COUNT(*) AS c1 IZ ontime GRUPIRAJ PO Godina, Mjesec) a;<\/p>\n<p>Q12<br \/>\nODABERITE ImeIzvorCity, ImeDestCity, COUNT(*) AS c IZ ontime GRUPIRAJ PO ImeIzvorCity, ImeDestCity NAREDI PO c DESC LIMIT 10;<\/p>\n<p>Q13<br \/>\nODABERITE ImeIzvorCity, COUNT(*) AS c IZ ontime GRUPIRAJ PO ImeIzvorCity NAREDI PO c DESC LIMIT 10;<\/p>\n<p><strong>Upit sadr\u017ei spoj<\/strong><\/p>\n<p>Q14<br \/>\nODABERITE a.Godina, c1\/c2 IZ ( ODABERITE Godina, COUNT(<em>)<\/em>1000 AS c1 IZ ontime GDJE DepDelay&gt;10 GRUPIRAJ PO Godina) a UNUTAR SPOJA (ODABERITE Godina, COUNT(*) AS c2 IZ ontime GRUPIRAJ PO Godina) b ON a.Godina=b.Godina NAREDI PO a.Godina;<\/p>\n<p>Q15<br \/>\nODABERITE a.\"Godina\", c1\/c2 IZ ( ODABERITE \"Godina\", COUNT(<em>)<\/em>1000 AS c1 IZ fontime GDJE \"DepDelay\"&gt;10 GRUPIRAJ PO \"Godina\") a UNUTAR SPOJA (ODABERITE \"Godina\", COUNT(*) AS c2 IZ fontime GRUPIRAJ PO \"Godina\") b ON a.\"Godina\"=b.\"Godina\";<\/p>\n<p><\/p>\n<p><em>Tabela-1: Upiti kori\u0161teni u benchmarku<\/em><\/p>\n<p><\/p>\n<h4 id=\"query-executions\">Izvr\u0161enja upita<\/h4>\n<p><\/p>\n<p>Evo rezultata svakog od upita prilikom izvr\u0161enja u razli\u010ditim postavkama baze podataka: PostgreSQL s indeksima i bez njih, vlastiti ClickHouse i clickhousedb_fdw. Vrijeme je prikazano u milisekundama.<\/p>\n<p><\/p>\n<p><strong>Q#<\/strong><br \/>\n<strong>PostgreSQL<\/strong><br \/>\n<strong>PostgreSQL (Indeksiran)<\/strong><br \/>\n<strong>ClickHouse<\/strong><br \/>\n<strong>clickhousedb_fdw<\/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>Q5<br \/>\n47220<br \/>\n8976<br \/>\n27<br \/>\n60<\/p>\n<p>Q6<br \/>\n58233<br \/>\n24368<br \/>\n55<br \/>\n153<\/p>\n<p>Q7<br \/>\n30566<br \/>\n13256<br \/>\n52<br \/>\n91<\/p>\n<p>Q8<br \/>\n38309<br \/>\n60511<br \/>\n112<br \/>\n179<\/p>\n<p>Q9<br \/>\n20674<br \/>\n37979<br \/>\n31<br \/>\n81<\/p>\n<p>Q10<br \/>\n34990<br \/>\n20102<br \/>\n56<br \/>\n148<\/p>\n<p>Q11<br \/>\n30489<br \/>\n51658<br \/>\n37<br \/>\n155<\/p>\n<p>Q12<br \/>\n39357<br \/>\n33742<br \/>\n186<br \/>\n1333<\/p>\n<p>Q13<br \/>\n29912<br \/>\n30709<br \/>\n101<br \/>\n384<\/p>\n<p>Q14<br \/>\n54126<br \/>\n39913<br \/>\n124<br \/>\n1364212<\/p>\n<p>Q15<br \/>\n97258<br \/>\n30211<br \/>\n245<br \/>\n259<\/p>\n<p><\/p>\n<p><em>Tabela-1: Vrijeme potrebno za izvr\u0161enje upita kori\u0161tenih u benchmarku<\/em><\/p>\n<p><\/p>\n<p>Pogledajte rezultate<\/p>\n<p><\/p>\n<p>Grafikon prikazuje vrijeme izvr\u0161enja upita u milisekundama, os X prikazuje broj upita iz gornjih tablica, a os Y prikazuje vrijeme izvr\u0161enja u milisekundama. Rezultati ClickHouse-a i podaci dobiveni iz postgres-a putem clickhousedb_fdw su prikazani. Iz tablice se vidi da postoji ogromna razlika izme\u0111u PostgreSQL i ClickHouse, ali minimalna razlika izme\u0111u ClickHouse i clickhousedb_fdw.<\/p>\n<p><\/p>\n<p><img decoding=\"async\" alt=\"Testiranje performansi analiti\u010dkih upita u PostgreSQL, ClickHouse i clickhousedb_fdw (PostgreSQL)\" src=\"\/wp-content\/uploads\/2020\/07\/e084243ea7b327f30de5cb78339d3d3a.png\" style=\"display:block;margin: 0 auto;\" \/><\/p>\n<p><\/p>\n<p>Ovaj grafikon pokazuje razliku izme\u0111u ClickhouseDB i clickhousedb_fdw. U ve\u0107ini upita, tro\u0161kovi FDW nisu zna\u010dajni osim u Q12. Ovaj upit uklju\u010duje spajanja i klauzulu ORDER BY. Zbog klauzule ORDER BY, GROUP BY i ORDER BY ne presko\u010de do ClickHouse.<\/p>\n<p><\/p>\n<p>U tablici 2 vidimo skok vremena u upitima Q12 i Q13. Ponovno \u0107u re\u0107i, to je uzrokovano klauzulom ORDER BY. Da to potvrdim, izvr\u0161io sam upite Q-14 i Q-15 s klauzulom ORDER BY i bez nje. Bez klauzule ORDER BY vrijeme zavr\u0161etka iznosi 259 ms, a s klauzulom ORDER BY \u2014 1364212. Za otklanjanje problema s ovim upitom obja\u0161njavam oba upita, a ovdje su rezultati obja\u0161njenja.<\/p>\n<p><\/p>\n<p>Q15: Bez klauzule ORDER BY<\/p>\n<p><\/p>\n<pre><code class=\"plaintext\">bm=# OBJA\u0160NJENJE VA\u017dNE SELEKCIJE a.&quot;Godina&quot;, c1\/c2 \n     IZ (SELEKTIRAJ &quot;Godina&quot;, count(*)*1000 AS c1 IZ fontime GDJE &quot;DepDelay&quot; &gt; 10 GRUPIRAJ PO &quot;Godina&quot;) a\n     UNUTRA\u0160NJE SPAJANJE(IZABERI &quot;Godina&quot;, count(*) AS c2 IZ fontime GRUPIRAJ PO &quot;Godina&quot;) b NA a.&quot;Godina&quot;=b.&quot;Godina&quot;;<\/code><\/pre>\n<p><\/p>\n<p>Q15: Upit bez klauzule ORDER BY<\/p>\n<p><\/p>\n<pre><code class=\"plaintext\">PLAN UPITA                                                      \nHash Join  (cijena=2250.00..128516.06 redaka=50000000 \u0161irina=12)  \nIzlaz: fontime.&quot;Godina&quot;, (((count(*) * 1000)) \/ b.c2)  \nUnutarnja jedinstvenost: true   Hash Cond: (fontime.&quot;Godina&quot; = b.&quot;Godina&quot;)  \n-&gt;  Strano skeniranje  (cijena=1.00..-1.00 redaka=100000 \u0161irina=12)        \nIzlaz: fontime.&quot;Godina&quot;, ((count(*) * 1000))        \nOdnos: Agregat na (fontime)        \nUdaljeni SQL: SELEKTIRAJ &quot;Godina&quot;, (count(*) * 1000) IZ &quot;default&quot;.ontime GDJE ((&quot;DepDelay&quot; &gt; 10)) GRUPIRAJ PO &quot;Godina&quot;  \n-&gt;  Hash  (cijena=999.00..999.00 redaka=100000 \u0161irina=12)        \nIzlaz: b.c2, b.&quot;Godina&quot;        \n-&gt;  Skener podupita na b  (cijena=1.00..999.00 redaka=100000 \u0161irina=12)              \nIzlaz: b.c2, b.&quot;Godina&quot;              \n-&gt;  Strano skeniranje  (cijena=1.00..-1.00 redaka=100000 \u0161irina=12)                    \nIzlaz: fontime_1.&quot;Godina&quot;, (count(*))                    \nOdnos: Agregat na (fontime)                    \nUdaljeni SQL: SELEKTIRAJ &quot;Godina&quot;, count(*) IZ &quot;default&quot;.ontime GRUPIRAJ PO &quot;Godina&quot;(16 redaka)<\/code><\/pre>\n<p><\/p>\n<p>Q14: Upit s klauzulom ORDER BY<\/p>\n<p><\/p>\n<pre><code class=\"plaintext\">bm=# OBJA\u0160NJENJE VA\u017dNE SELEKCIJE a.&quot;Godina&quot;, c1\/c2 IZ(IZABERI &quot;Godina&quot;, count(*)*1000 AS c1 IZ fontime GDJE &quot;DepDelay&quot; &gt; 10 GRUPIRAJ PO &quot;Godina&quot;) a \n     UNUTRA\u0160NJE SPAJANJE(IZABERI &quot;Godina&quot;, count(*) AS c2 IZ fontime GRUPIRAJ PO &quot;Godina&quot;) b  NA a.&quot;Godina&quot;= b.&quot;Godina&quot; \n     POREDAJ PO a.&quot;Godina&quot;;<\/code><\/pre>\n<p><\/p>\n<p>Q14: Plan upita s klauzulom ORDER BY<\/p>\n<p><\/p>\n<pre><code class=\"plaintext\">PLAN UPITA \nMerge Join\u00a0 (cijena=2.00..628498.02 redaka=50000000 \u0161irina=12)\u00a0\u00a0 \nIzlaz: fontime.&quot;Godina&quot;, (((count(*) * 1000)) \/ (count(*)))\u00a0\u00a0 \nUnutarnja jedinstvenost: true\u00a0\u00a0 Merge Cond: (fontime.&quot;Godina&quot; = fontime_1.&quot;Godina&quot;)\u00a0\u00a0 \n-&gt;\u00a0 GroupAggregate\u00a0 (cijena=1.00..499.01 redaka=1 \u0161irina=12)\u00a0 \u00a0 \u00a0 \u00a0 \nIzlaz: fontime.&quot;Godina&quot;, (count(*) * 1000)\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \nGrupni klju\u010d: fontime.&quot;Godina&quot;\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \n-&gt;\u00a0 Strano skeniranje na public.fontime\u00a0 (cijena=1.00..-1.00 redaka=100000 \u0161irina=4)\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \nUdaljeni SQL: SELEKTIRAJ &quot;Godina&quot; IZ &quot;default&quot;.ontime GDJE ((&quot;DepDelay&quot; &gt; 10)) \n            POREDAJ PO &quot;Godina&quot; ASC\u00a0\u00a0 \n-&gt;\u00a0 GroupAggregate\u00a0 (cijena=1.00..499.01 redaka=1 \u0161irina=12)\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \nIzlaz: fontime_1.&quot;Godina&quot;, count(*)\u00a0\u00a0 \u00a0 \u00a0 \u00a0 Grupni klju\u010d: fontime_1.&quot;Godina&quot;\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \n-&gt;\u00a0 Strano skeniranje na public.fontime fontime_1\u00a0 (cijena=1.00..-1.00 redaka=100000 \u0161irina=4)\u00a0\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \nUdaljeni SQL: SELEKTIRAJ &quot;Godina&quot; IZ &quot;default&quot;.ontime POREDAJ PO &quot;Godina&quot; ASC(16 redaka)<\/code><\/pre>\n<p><\/p>\n<p>Izvje\u0161taj<\/p>\n<p><\/p>\n<p>Rezultati ovih eksperimenata pokazuju da ClickHouse nudi zaista dobru performansu, dok clickhousedb_fdw donosi prednosti performansi ClickHouse-a iz PostgreSQL-a. Iako postoje neki tro\u0161kovi kori\u0161\u0107enja clickhousedb_fdw, oni su mali i uporedivi sa performansom postignutom prilikom izvorne upotrebe ClickHouse baze podataka. Ovo tako\u0111e potvr\u0111uje da fdw u PostgreSQL-u pru\u017ea izvanredne rezultate.<\/p>\n<p><\/p>\n<p>Telegram chat o Clickhouse-u <noindex><a rel=\"nofollow\" href=\"https:\/\/t.me\/clickhouse_ru\">https:\/\/t.me\/clickhouse_ru<\/a><\/noindex><br \/>\nTelegram chat o PostgreSQL-u <noindex><a rel=\"nofollow\" href=\"https:\/\/t.me\/pgsql\">https:\/\/t.me\/pgsql<\/a><\/noindex><\/p>\n<p>Izvor: <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 5.0.4 - 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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