{"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\/pt\/blog\/administrirovanie\/testirovanie-proizvoditelnosti-analiticheskih-zaprosov-v-postgresql-clickhouse-i-clickhousedb_fdw-postgresql","title":{"rendered":"Teste de desempenho de consultas anal\u00edticas no PostgreSQL, ClickHouse e clickhousedb_fdw (PostgreSQL)","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Neste estudo, eu quis observar quais melhorias de desempenho podem ser obtidas usando a fonte de dados ClickHouse em vez de PostgreSQL. Eu entendo quais benef\u00edcios de desempenho eu obtenho ao usar ClickHouse. Esses benef\u00edcios ser\u00e3o mantidos se eu acessar o ClickHouse a partir do PostgreSQL atrav\u00e9s de uma camada de dados externa (FDW)? <\/p>\n<p><noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<p>Os ambientes de banco de dados em estudo s\u00e3o PostgreSQL v11, clickhousedb_fdw e o banco de dados ClickHouse. No final, do PostgreSQL v11, vamos executar v\u00e1rias consultas SQL, roteadas atrav\u00e9s do nosso clickhousedb_fdw para o banco de dados ClickHouse. Ent\u00e3o veremos como o desempenho do FDW se compara \u00e0s mesmas consultas executadas no PostgreSQL nativo e no ClickHouse nativo.<\/p>\n<p><\/p>\n<h3 id=\"baza-dannyh-clickhouse\">Banco de dados ClickHouse<\/h3>\n<p><\/p>\n<p>ClickHouse \u00e9 um sistema de gerenciamento de banco de dados baseado em colunas de c\u00f3digo aberto que pode alcan\u00e7ar um desempenho de 100 a 1000 vezes mais r\u00e1pido do que abordagens tradicionais de banco de dados, capaz de processar mais de um bilh\u00e3o de linhas em menos de um segundo.<\/p>\n<p><\/p>\n<h3 id=\"clickhousedb_fdw\">clickhousedb_fdw<\/h3>\n<p><\/p>\n<p>O clickhousedb_fdw \u00e9 uma camada de dados externa para o banco de dados ClickHouse, ou FDW, um projeto de c\u00f3digo aberto da Percona. <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/Percona-Lab\/clickhousedb_fdw\">Aqui est\u00e1 o link para o reposit\u00f3rio do projeto no 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\/\">Em mar\u00e7o, escrevi um blog que conta mais sobre nosso FDW<\/a><\/noindex>.<\/p>\n<p><\/p>\n<p>Como voc\u00ea ver\u00e1, isso fornece um FDW para ClickHouse, que permite SELECT from e INSERT INTO no banco de dados ClickHouse a partir do servidor PostgreSQL v11.<\/p>\n<p><\/p>\n<p>O FDW suporta funcionalidades avan\u00e7adas, como agrega\u00e7\u00f5es e joins. Isso melhora significativamente o desempenho, utilizando os recursos do servidor remoto para essas opera\u00e7\u00f5es que consomem muitos recursos.<\/p>\n<p><\/p>\n<h3 id=\"benchmark-environment\">Ambiente de benchmark<\/h3>\n<p><\/p>\n<ul>\n<li>Servidor Supermicro:\n<ul>\n<li>Intel&reg; Xeon&reg; CPU E5-2683 v3 @ 2.00GHz<\/li>\n<li>2 soquetes \/ 28 n\u00facleos \/ 56 threads<\/li>\n<li>Mem\u00f3ria: 256GB de RAM<\/li>\n<li>Armazenamento: Samsung SM863 1.9TB SSD Enterprise<\/li>\n<li>Sistema de arquivos: 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: vers\u00e3o 11<\/li>\n<\/ul>\n<p><\/p>\n<h3 id=\"benchmark-tests\">Testes de benchmark<\/h3>\n<p><\/p>\n<p>Em vez de usar um conjunto de dados gerado por m\u00e1quinas para este teste, usamos os dados de \"Desempenho ao longo do tempo, reportando o tempo de opera\u00e7\u00e3o\" de 1987 a 2018. Voc\u00ea pode acessar os dados <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/Percona-Lab\/ontime-airline-performance\/blob\/master\/download.sh\">usando nosso script dispon\u00edvel aqui<\/a><\/noindex>.<\/p>\n<p><\/p>\n<p>O tamanho do banco de dados \u00e9 de 85 GB, fornecendo uma tabela com 109 colunas.<\/p>\n<p><\/p>\n<h4 id=\"benchmark-queries\">Consultas de benchmark<\/h4>\n<p><\/p>\n<p>Aqui est\u00e3o as consultas que usei para comparar ClickHouse, clickhousedb_fdw e PostgreSQL.<\/p>\n<p><\/p>\n<p><strong>Q#<\/strong><br \/>\n<strong>Consulta Cont\u00e9m Agrega\u00e7\u00f5es e 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 \/>\nSELECIONE DiaDaSemana, contagem(*) COMO c DE ontime ONDE DepDelay&gt;10 E Ano &gt;= 2000 E Ano &lt;= 2008 AGRUPAR POR DiaDaSemana ORDENAR POR c DESC;<\/p>\n<p>Q3<br \/>\nSELECIONE Origem, contagem(*) COMO c DE ontime ONDE DepDelay&gt;10 E Ano &gt;= 2000 E Ano &lt;= 2008 AGRUPAR POR Origem ORDENAR POR c DESC LIMIT 10;<\/p>\n<p>Q4<br \/>\nSELECIONE Transportadora, contagem(<em>) DE ontime ONDE DepDelay&gt;10 E Ano = 2007 AGRUPAR POR Transportadora ORDENAR POR contagem(<\/em>) DESC;<\/p>\n<p>Q5<br \/>\nSELECIONE a.Transportadora, c, c2, c<em>1000\/c2 COMO c3 DE ( SELECIONE Transportadora, contagem(<\/em>) COMO c DE ontime ONDE DepDelay&gt;10 E Ano=2007 AGRUPAR POR Transportadora ) a INNER JOIN ( SELECIONE Transportadora, contagem(*) COMO c2 DE ontime ONDE Ano=2007 AGRUPAR POR Transportadora)b ON a.Transportadora=b.Transportadora ORDENAR POR c3 DESC;<\/p>\n<p>Q6<br \/>\nSELECIONE a.Transportadora, c, c2, c<em>1000\/c2 COMO c3 DE ( SELECIONE Transportadora, contagem(<\/em>) COMO c DE ontime ONDE DepDelay&gt;10 E Ano &gt;= 2000 E Ano = 2000 E Ano &lt;= 2008 AGRUPAR POR Transportadora ) b ON a.Transportadora=b.Transportadora ORDENAR POR c3 DESC;<\/p>\n<p>Q7<br \/>\nSELECIONE Transportadora, m\u00e9dia(DepDelay) * 1000 COMO c3 DE ontime ONDE Ano &gt;= 2000 E Ano &lt;= 2008 AGRUPAR POR Transportadora;<\/p>\n<p>Q8<br \/>\nSELECIONE Ano, m\u00e9dia(DepDelay) DE ontime AGRUPAR POR Ano;<\/p>\n<p>Q9<br \/>\nselecione Ano, contagem(*) COMO c1 DE ontime AGRUPAR POR Ano;<\/p>\n<p>Q10<br \/>\nSELECIONE m\u00e9dia(cnt) DE (SELECIONE Ano,M\u00eas,contagem(*) COMO cnt DE ontime ONDE DepDel15=1 AGRUPAR POR Ano,M\u00eas) a;<\/p>\n<p>Q11<br \/>\nselecione m\u00e9dia(c1) DE (selecione Ano,M\u00eas,contagem(*) COMO c1 DE ontime AGRUPAR POR Ano,M\u00eas) a;<\/p>\n<p>Q12<br \/>\nSELECIONE NomeCidadeOrigem, NomeCidadeDestino, contagem(*) COMO c DE ontime AGRUPAR POR NomeCidadeOrigem, NomeCidadeDestino ORDENAR POR c DESC LIMIT 10;<\/p>\n<p>Q13<br \/>\nSELECIONE NomeCidadeOrigem, contagem(*) COMO c DE ontime AGRUPAR POR NomeCidadeOrigem ORDENAR POR c DESC LIMIT 10;<\/p>\n<p><strong>Consulta cont\u00e9m jun\u00e7\u00f5es<\/strong><\/p>\n<p>Q14<br \/>\nSELECIONE a.Ano, c1\/c2 DE ( selecione Ano, contagem(<em>)<\/em>1000 COMO c1 DE ontime ONDE DepDelay&gt;10 AGRUPAR POR Ano) a INNER JOIN (selecione Ano, contagem(*) COMO c2 DE ontime AGRUPAR POR Ano ) b ON a.Ano=b.Ano ORDENAR POR a.Ano;<\/p>\n<p>Q15<br \/>\nSELECIONE a.\"Ano\", c1\/c2 DE ( selecione \"Ano\", contagem(<em>)<\/em>1000 COMO c1 DE fontime ONDE \"DepDelay\"&gt;10 AGRUPAR POR \"Ano\") a INNER JOIN (selecione \"Ano\", contagem(*) COMO c2 DE fontime AGRUPAR POR \"Ano\" ) b ON a.\"Ano\"=b.\"Ano\";<\/p>\n<p><\/p>\n<p><em>Tabela-1: Consultas usadas no benchmark<\/em><\/p>\n<p><\/p>\n<h4 id=\"query-executions\">Execu\u00e7\u00f5es de consulta<\/h4>\n<p><\/p>\n<p>Aqui est\u00e3o os resultados de cada uma das consultas executadas com diferentes configura\u00e7\u00f5es de banco de dados: PostgreSQL com e sem \u00edndices, ClickHouse pr\u00f3prio e clickhousedb_fdw. O tempo \u00e9 mostrado em milissegundos.<\/p>\n<p><\/p>\n<p><strong>Q#<\/strong><br \/>\n<strong>PostgreSQL<\/strong><br \/>\n<strong>PostgreSQL (Indexado)<\/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: Tempo levado para executar as consultas usadas no benchmark<\/em><\/p>\n<p><\/p>\n<p>Visualizar resultados<\/p>\n<p><\/p>\n<p>O gr\u00e1fico mostra o tempo de execu\u00e7\u00e3o da consulta em milissegundos, o eixo X mostra o n\u00famero da consulta das tabelas acima, e o eixo Y mostra o tempo de execu\u00e7\u00e3o em milissegundos. Os resultados do ClickHouse e os dados obtidos do postgres atrav\u00e9s do clickhousedb_fdw s\u00e3o mostrados. A partir da tabela, observa-se que h\u00e1 uma enorme diferen\u00e7a entre PostgreSQL e ClickHouse, mas a diferen\u00e7a m\u00ednima entre ClickHouse e clickhousedb_fdw.<\/p>\n<p><\/p>\n<p><img decoding=\"async\" alt=\"Teste de desempenho de consultas anal\u00edticas no PostgreSQL, ClickHouse e clickhousedb_fdw (PostgreSQL)\" src=\"\/wp-content\/uploads\/2020\/07\/e084243ea7b327f30de5cb78339d3d3a.png\" style=\"display:block;margin: 0 auto;\" \/><\/p>\n<p><\/p>\n<p>Este gr\u00e1fico mostra a diferen\u00e7a entre ClickhouseDB e clickhousedb_fdw. Em muitas consultas, os custos operacionais do FDW n\u00e3o s\u00e3o t\u00e3o grandes e raramente significativos, exceto na Q12. Esta consulta envolve jun\u00e7\u00f5es e a cl\u00e1usula ORDER BY. Devido \u00e0 cl\u00e1usula ORDER BY GROUP\/BY, os ORDER BY n\u00e3o s\u00e3o descartados para ClickHouse.<\/p>\n<p><\/p>\n<p>Na Tabela 2, vemos um salto no tempo das consultas Q12 e Q13. Reitero que isso se deve ao uso da cl\u00e1usula ORDER BY. Para confirmar isso, executei as consultas Q-14 e Q-15 com e sem a cl\u00e1usula ORDER BY. Sem a cl\u00e1usula ORDER BY, o tempo de execu\u00e7\u00e3o \u00e9 de 259 ms, enquanto com a cl\u00e1usula ORDER BY \u00e9 de 1364212 ms. Para depurar esta consulta, explico ambas as consultas, e aqui est\u00e3o os resultados da explica\u00e7\u00e3o.<\/p>\n<p><\/p>\n<p>Q15: Sem Cl\u00e1usula 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: Consulta Sem Cl\u00e1usula 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: Consulta Com Cl\u00e1usula 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: Plano de Consulta com Cl\u00e1usula 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>Sa\u00edda<\/p>\n<p><\/p>\n<p>Os resultados desses experimentos mostram que o ClickHouse oferece um desempenho realmente bom, e o clickhousedb_fdw proporciona benef\u00edcios de desempenho do ClickHouse a partir do PostgreSQL. Embora haja algumas sobrecargas ao usar o clickhousedb_fdw, elas s\u00e3o insignificantes e compar\u00e1veis ao desempenho alcan\u00e7ado ao executar diretamente no banco de dados ClickHouse. Isso tamb\u00e9m confirma que o fdw no PostgreSQL oferece resultados excelentes.<\/p>\n<p><\/p>\n<p>Chat do Telegram sobre Clickhouse <noindex><a rel=\"nofollow\" href=\"https:\/\/t.me\/clickhouse_ru\">https:\/\/t.me\/clickhouse_ru<\/a><\/noindex><br \/>\nChat do Telegram sobre PostgreSQL <noindex><a rel=\"nofollow\" href=\"https:\/\/t.me\/pgsql\">https:\/\/t.me\/pgsql<\/a><\/noindex><\/p>\n<p>Fonte: <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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