{"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\/ro\/blog\/administrirovanie\/testirovanie-proizvoditelnosti-analiticheskih-zaprosov-v-postgresql-clickhouse-i-clickhousedb_fdw-postgresql","title":{"rendered":"Configurarea regulatorilor PID: este a\u0219a de \u00eenfrico\u0219\u0103tor cum \u00eel descriu? Partea 1. Sistem monoconductor","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>\u00cen aceast\u0103 cercetare, am dorit s\u0103 investighez ce \u00eembun\u0103t\u0103\u021biri de performan\u021b\u0103 pot fi ob\u021binute folosind sursa de date ClickHouse \u00een loc de PostgreSQL. \u0218tiu ce beneficii de performan\u021b\u0103 primesc utiliz\u00e2nd ClickHouse. Vor fi p\u0103strate aceste avantaje dac\u0103 accesez ClickHouse din PostgreSQL prin intermediul unei interfe\u021be externe de date (FDW)? <\/p>\n<p><noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<p>Mediile de baze de date investigate sunt PostgreSQL v11, clickhousedb_fdw \u0219i baza de date ClickHouse. \u00cen final, din PostgreSQL v11, vom rula diferite interog\u0103ri SQL, redirec\u021bionate prin clickhousedb_fdw c\u0103tre baza de date ClickHouse. Apoi, vom observa cum se compar\u0103 performan\u021ba FDW cu acelea\u0219i interog\u0103ri executate \u00een PostgreSQL nativ \u0219i ClickHouse nativ.<\/p>\n<p><\/p>\n<h3 id=\"baza-dannyh-clickhouse\">Baza de date Clickhouse<\/h3>\n<p><\/p>\n<p>ClickHouse este un sistem de gestionare a bazelor de date de tip coloan\u0103 cu surs\u0103 deschis\u0103, capabil s\u0103 ating\u0103 performan\u021be de 100-1000 de ori mai rapide dec\u00e2t abord\u0103rile tradi\u021bionale de baze de date, fiind capabil s\u0103 proceseze peste un miliard de r\u00e2nduri \u00een mai pu\u021bin de o secund\u0103.<\/p>\n<p><\/p>\n<h3 id=\"clickhousedb_fdw\">Clickhousedb_fdw<\/h3>\n<p><\/p>\n<p>clickhousedb_fdw este o interfa\u021b\u0103 extern\u0103 a bazei de date ClickHouse, sau FDW, un proiect open-source dezvoltat de Percona. <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/Percona-Lab\/clickhousedb_fdw\">Iat\u0103 un link c\u0103tre depozitul proiectului GitHub<\/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\/\">\u00cen martie, am scris un blog care v\u0103 poveste\u0219te mai multe despre FDW-ul nostru<\/a><\/noindex>.<\/p>\n<p><\/p>\n<p>Dup\u0103 cum ve\u021bi observa, acesta ofer\u0103 FDW pentru ClickHouse, care permite SELECT from \u0219i INSERT INTO \u00een baza de date ClickHouse de pe serverul PostgreSQL v11.<\/p>\n<p><\/p>\n<p>FDW suport\u0103 func\u021bii avansate, cum ar fi agregarea \u0219i join-ul. Acest lucru cre\u0219te semnificativ performan\u021ba prin utilizarea resurselor serverului la distan\u021b\u0103 pentru aceste opera\u021bii intensive \u00een resurse.<\/p>\n<p><\/p>\n<h3 id=\"benchmark-environment\">Mediul de benchmark<\/h3>\n<p><\/p>\n<ul>\n<li>Server Supermicro:\n<ul>\n<li>Intel&reg; Xeon&reg; CPU E5-2683 v3 @ 2.00GHz<\/li>\n<li>2 socluri \/ 28 nuclee \/ 56 fire<\/li>\n<li>Memorie: 256GB RAM<\/li>\n<li>Stocare: Samsung SM863 1.9TB SSD Enterprise<\/li>\n<li>Sistem de fi\u0219iere: 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: versiunea 11<\/li>\n<\/ul>\n<p><\/p>\n<h3 id=\"benchmark-tests\">Teste de benchmark<\/h3>\n<p><\/p>\n<p>\u00cen loc s\u0103 folosim un set de date generat de ma\u0219in\u0103 pentru acest test, am folosit datele \u201ePerforman\u021ba pe timp, raportat\u0103 despre timpul de func\u021bionare al operatorului\u201d din 1987 p\u00e2n\u0103 \u00een 2018. Pute\u021bi accesa datele <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/Percona-Lab\/ontime-airline-performance\/blob\/master\/download.sh\">prin intermediul scriptului nostru, disponibil aici<\/a><\/noindex>.<\/p>\n<p><\/p>\n<p>Dimensiunea bazei de date este de 85 GB, av\u00e2nd o tabel\u0103 cu 109 coloane.<\/p>\n<p><\/p>\n<h4 id=\"benchmark-queries\">Interog\u0103ri de benchmark<\/h4>\n<p><\/p>\n<p>Iat\u0103 interog\u0103rile pe care le-am folosit pentru a compara ClickHouse, clickhousedb_fdw \u0219i PostgreSQL.<\/p>\n<p><\/p>\n<p><strong>Q#<\/strong><br \/>\n<strong>Interogare con\u021bine agregate \u0219i 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 \/>\nSELECT DayOfWeek, count(*) AS c FROM ontime WHERE DepDelay&gt;10 AND Year &gt;= 2000 AND Year &lt;= 2008 GROUP BY DayOfWeek ORDER BY c DESC;<\/p>\n<p>Q3<br \/>\nSELECT Origin, count(*) AS c FROM ontime WHERE DepDelay&gt;10 AND Year &gt;= 2000 AND Year &lt;= 2008 GROUP BY Origin ORDER BY c DESC LIMIT 10;<\/p>\n<p>Q4<br \/>\nSELECT Carrier, count(<em>) FROM ontime WHERE DepDelay&gt;10 AND Year = 2007 GROUP BY Carrier ORDER BY count(<\/em>) DESC;<\/p>\n<p>Q5<br \/>\nSELECT a.Carrier, c, c2, c<em>1000\/c2 as c3 FROM ( SELECT Carrier, count(<\/em>) AS c FROM ontime WHERE DepDelay&gt;10 AND Year=2007 GROUP BY Carrier ) a INNER JOIN ( SELECT Carrier,count(*) AS c2 FROM ontime WHERE Year=2007 GROUP BY Carrier)b on a.Carrier=b.Carrier ORDER BY c3 DESC;<\/p>\n<p>Q6<br \/>\nSELECT a.Carrier, c, c2, c<em>1000\/c2 as c3 FROM ( SELECT Carrier, count(<\/em>) AS c FROM ontime WHERE DepDelay&gt;10 AND Year &gt;= 2000 AND Year = 2000 AND Year &lt;= 2008 GROUP BY Carrier ) b on a.Carrier=b.Carrier ORDER BY c3 DESC;<\/p>\n<p>Q7<br \/>\nSELECT Carrier, avg(DepDelay) * 1000 AS c3 FROM ontime WHERE Year &gt;= 2000 AND Year &lt;= 2008 GROUP BY Carrier;<\/p>\n<p>Q8<br \/>\nSELECT Year, avg(DepDelay) FROM ontime GROUP BY Year;<\/p>\n<p>Q9<br \/>\nselect Year, count(*) as c1 from ontime group by Year;<\/p>\n<p>Q10<br \/>\nSELECT avg(cnt) FROM (SELECT Year,Month,count(*) AS cnt FROM ontime WHERE DepDel15=1 GROUP BY Year,Month) a;<\/p>\n<p>Q11<br \/>\nselect avg(c1) from (select Year,Month,count(*) as c1 from ontime group by Year,Month) a;<\/p>\n<p>Q12<br \/>\nSELECT OriginCityName, DestCityName, count(*) AS c FROM ontime GROUP BY OriginCityName, DestCityName ORDER BY c DESC LIMIT 10;<\/p>\n<p>Q13<br \/>\nSELECT OriginCityName, count(*) AS c FROM ontime GROUP BY OriginCityName ORDER BY c DESC LIMIT 10;<\/p>\n<p><strong>Query Contains Joins<\/strong><\/p>\n<p>Q14<br \/>\nSELECT a.Year, c1\/c2 FROM ( select Year, count(<em>)<\/em>1000 as c1 from ontime WHERE DepDelay&gt;10 GROUP BY Year) a INNER JOIN (select Year, count(*) as c2 from ontime GROUP BY Year ) b on a.Year=b.Year ORDER BY a.Year;<\/p>\n<p>Q15<br \/>\nSELECT a.\u201dYear\u201d, c1\/c2 FROM ( select \u201cYear\u201d, count(<em>)<\/em>1000 as c1 FROM fontime WHERE \u201cDepDelay\u201d&gt;10 GROUP BY \u201cYear\u201d) a INNER JOIN (select \u201cYear\u201d, count(*) as c2 FROM fontime GROUP BY \u201cYear\u201d ) b on a.\u201dYear\u201d=b.\u201dYear\u201d;<\/p>\n<p><\/p>\n<p><em>Table-1: Queries used in benchmark<\/em><\/p>\n<p><\/p>\n<h4 id=\"query-executions\">Query executions<\/h4>\n<p><\/p>\n<p>Iat\u0103 rezultatele fiec\u0103rei interog\u0103ri efectuate \u00een diferite configura\u021bii ale bazei de date: PostgreSQL cu indici \u0219i f\u0103r\u0103, ClickHouse propriu \u0219i clickhousedb_fdw. Timpul este exprimat \u00een milisecunde.<\/p>\n<p><\/p>\n<p><strong>Q#<\/strong><br \/>\n<strong>PostgreSQL<\/strong><br \/>\n<strong>PostgreSQL (Indexed)<\/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>Table-1: Time taken to execute the queries used in benchmark<\/em><\/p>\n<p><\/p>\n<p>Vizualizare rezultate<\/p>\n<p><\/p>\n<p>Grafica arat\u0103 timpul de execu\u021bie al interog\u0103rii \u00een milisecunde, axa X arat\u0103 num\u0103rul interog\u0103rii din tabelele de mai sus, iar axa Y arat\u0103 timpul de execu\u021bie \u00een milisecunde. Rezultatele ClickHouse \u0219i datele ob\u021binute din postgres prin clickhousedb_fdw sunt prezentate. Din tabel reiese c\u0103 exist\u0103 o diferen\u021b\u0103 uria\u0219\u0103 \u00eentre PostgreSQL \u0219i ClickHouse, dar o diferen\u021b\u0103 minim\u0103 \u00eentre ClickHouse \u0219i clickhousedb_fdw.<\/p>\n<p><\/p>\n<p><img decoding=\"async\" alt=\"Configurarea regulatorilor PID: este a\u0219a de \u00eenfrico\u0219\u0103tor cum \u00eel descriu? Partea 1. Sistem monoconductor\" src=\"\/wp-content\/uploads\/2020\/07\/e084243ea7b327f30de5cb78339d3d3a.png\" style=\"display:block;margin: 0 auto;\" \/><\/p>\n<p><\/p>\n<p>Aceast\u0103 grafic\u0103 arat\u0103 diferen\u021ba dintre ClickhouseDB \u0219i clickhousedb_fdw. \u00cen majoritatea interog\u0103rilor costurile suplimentare FDW nu sunt at\u00e2t de mari \u0219i abia sunt semnificative, cu excep\u021bia Q12. Aceast\u0103 interogare include uniri \u0219i o clauz\u0103 ORDER BY. Din cauza clauzei ORDER BY GROUP\/BY \u0219i ORDER BY nu pot fi omise p\u00e2n\u0103 la ClickHouse.<\/p>\n<p><\/p>\n<p>\u00cen tabelul 2, observ\u0103m o cre\u0219tere a timpului \u00een cererile Q12 \u0219i Q13. Reiter\u00e2nd, aceasta este cauzat\u0103 de utilizarea propozi\u021biei ORDER BY. Pentru a confirma acest lucru, am executat cererile Q-14 \u0219i Q-15 cu \u0219i f\u0103r\u0103 propozi\u021bia ORDER BY. F\u0103r\u0103 propozi\u021bia ORDER BY, timpul de finalizare este de 259 ms, iar cu propozi\u021bia ORDER BY - 1364212. Pentru a depana aceast\u0103 cerere, explic ambele cereri, iar aici sunt prezentate rezultatele explica\u021biei.<\/p>\n<p><\/p>\n<p>Q15: F\u0103r\u0103 Clauza ORDER BY<\/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: Cerere F\u0103r\u0103 Clauza ORDER BY<\/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: Cerere Cu Clauza ORDER BY<\/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: Planul Cererii cu Clauza ORDER BY<\/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>Ie\u0219ire<\/p>\n<p><\/p>\n<p>Rezultatele acestor experimente arat\u0103 c\u0103 ClickHouse ofer\u0103 cu adev\u0103rat o performan\u021b\u0103 bun\u0103, iar clickhousedb_fdw aduce avantajele performan\u021bei ClickHouse din PostgreSQL. De\u0219i utilizarea clickhousedb_fdw implic\u0103 unele costuri suplimentare, acestea sunt nesemnificative \u0219i comparabile cu performan\u021ba ob\u021binut\u0103 prin utilizarea nativ\u0103 a bazei de date ClickHouse. Aceasta confirm\u0103 de asemenea c\u0103 fdw \u00een PostgreSQL ofer\u0103 rezultate remarcabile.<\/p>\n<p><\/p>\n<p>Chat Telegram pentru Clickhouse <noindex><a rel=\"nofollow\" href=\"https:\/\/t.me\/clickhouse_ru\">https:\/\/t.me\/clickhouse_ru<\/a><\/noindex><br \/>\nChat Telegram pentru PostgreSQL <noindex><a rel=\"nofollow\" href=\"https:\/\/t.me\/pgsql\">https:\/\/t.me\/pgsql<\/a><\/noindex><\/p>\n<p>Sursa: <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 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