{"id":32353,"date":"2019-10-31T21:46:32","date_gmt":"2019-10-31T18:46:32","guid":{"rendered":"https:\/\/prohoster.info\/blog\/monitoring-proizvoditelnosti-zaprosov-postgresql-chast-1-reporting\/"},"modified":"2019-10-31T21:46:32","modified_gmt":"2019-10-31T18:46:32","slug":"monitoring-proizvoditelnosti-zaprosov-postgresql-chast-1-reporting","status":"publish","type":"post","link":"https:\/\/prohoster.info\/da\/blog\/administrirovanie\/monitoring-proizvoditelnosti-zaprosov-postgresql-chast-1-reporting","title":{"rendered":"Overv\u00e5gning af PostgreSQL foresp\u00f8rgselsydelse. Del 1 - rapportering","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Ingeni\u00f8r - oversat fra latin - inspireret.<br \/>\nIngeni\u00f8ren kan alt. (c) R. Diesel.<br \/>\n<i>Epigrafen.<\/i><br \/>\n<img decoding=\"async\" alt=\"Overv\u00e5gning af PostgreSQL foresp\u00f8rgselsydelse. Del 1 - rapportering\" src=\"\/wp-content\/uploads\/2019\/04\/9a2640267ea05006b94e03642b864ddc.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<i>Eller historien om, hvorfor databaseadministratoren skal huske sin programm\u00f8rfortid. <\/i><\/p>\n<h2>Forord<\/h2>\n<p>\nAlle navne er \u00e6ndret. Sammenfald er tilf\u00e6ldige. Materialet pr\u00e6senterer udelukkende forfatterens personlige mening.<\/p>\n<blockquote><p><b>Fraskrivelse af garantier:<\/b> <i>i den planlagte artikelcyklus vil der ikke v\u00e6re detaljerede og n\u00f8jagtige beskrivelser af de anvendte tabeller og scripts. Materialerne kan ikke umiddelbart bruges \"AS IS\". <br \/>\nFor det f\u00f8rste p\u00e5 grund af det store materialeomfang, <br \/>\nfor det andet p\u00e5 grund af tilpasningen til den virkelige kundes produktionsdatabase. <br \/>\nDerfor vil artiklerne kun indeholde ideer og beskrivelser i meget generelle termer. <br \/>\nDet kan v\u00e6re, at systemet i fremtiden vokser til niveauet for at blive lagt op p\u00e5 GitHub, eller m\u00e5ske ikke. Tiden vil vise.<\/i><\/p><\/blockquote>\n<p>\nBegyndelsen p\u00e5 historien - \"<noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/icl_services\/blog\/446314\/\">Husker du, hvordan det hele begyndte<\/a><\/noindex>\u00bb.<br \/>\nHvad der kom ud af det, i meget generelle tr\u00e6k - \"<noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/444988\/\">Syntese som en metode til at forbedre ydeevnen af PostgreSQL.<\/a><\/noindex>\u00bb<\/p>\n<h2>Hvorfor skal jeg alt dette?<\/h2>\n<p>\nFor det f\u00f8rste for at huske det selv, mens jeg holder op med at arbejde og mindes de gode gamle dage. <br \/>\nFor det andet for at systematisere det skrevne. For jeg begynder selv nogle gange at blive forvirret og glemme enkelte dele. <\/p>\n<p>Og vigtigst af alt - m\u00e5ske kan det v\u00e6re til hj\u00e6lp for nogen og forhindre dem i at opfinde hjulet forfra og samle skrald. Med andre ord, forbedre sin karma (ikke den, der findes p\u00e5 Habr). For det mest v\u00e6rdifulde i denne verden er id\u00e9er. Det vigtigste er at finde id\u00e9en. At realisere id\u00e9en i virkeligheden er allerede et rent teknisk sp\u00f8rgsm\u00e5l.<\/p>\n<p>S\u00e5 lad os begynde, stille og roligt...<\/p>\n<h2>Problemstilling.<\/h2>\n<p><\/p>\n<h3>Der er: <\/h3>\n<p>\nPostgreSQL database (10.5), hybrid belastningstype (OLTP + DSS), median-lav belastning, placeret i AWS cloud. <br \/>\nDatabaseoverv\u00e5gning mangler, infrastruktur overv\u00e5gning er repr\u00e6senteret i form af standard AWS-v\u00e6rkt\u00f8jer i minimal konfiguration.<\/p>\n<h3>P\u00e5kr\u00e6vet:<\/h3>\n<p>\nAt overv\u00e5ge ydeevnen og tilstanden af databasen, finde og have grundl\u00e6ggende oplysninger til optimering af tunge foresp\u00f8rgsler til databasen.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h2>Kort forord eller analyse af l\u00f8sningsmuligheder<\/h2>\n<p>\nLad os f\u00f8rst fors\u00f8ge at analysere l\u00f8sningsmulighederne ud fra et komparativt synspunkt af fordele og ulemper for ingeni\u00f8ren, mens fordelene og tabene for ledelsen m\u00e5 h\u00e5ndteres af dem, der er ansvarlige if\u00f8lge organisationens skema.<\/p>\n<h3>Mulighed 1 - \"Working on demand\"<\/h3>\n<p>\nVi lader alt v\u00e6re som det er. Hvis kunden er utilfreds med noget i funktionaliteten eller ydeevnen af databasen eller applikationen, informerer han DBAs ingeni\u00f8rer via e-mail eller opretter en h\u00e6ndelse i ticket-systemet. <br \/>\nIngeni\u00f8ren, der modtager notifikationen, unders\u00f8ger problemet, foresl\u00e5r en l\u00f8sning eller l\u00e6gger problemet p\u00e5 hylden, i h\u00e5bet om at det l\u00f8ser sig selv, og om lidt bliver alt glemt.<br \/>\n<b class=\"spoiler_title\">Sm\u00e5kager og donuts, bl\u00e5 m\u00e6rker og buler<\/b><b>Sm\u00e5kager og donuts:<\/b><br \/>\n1. Man skal ikke g\u00f8re noget overfl\u00f8digt.<br \/>\n2. Der er altid mulighed for at slippe af sted med det. <br \/>\n3. En masse tid, som man kan bruge, som man vil.<br \/>\n<b>Bl\u00e5 m\u00e6rker og buler:<\/b><br \/>\n1. F\u00f8r eller siden vil kunden t\u00e6nke over livets essens og universets retf\u00e6rdighed i denne verden og igen stille sig selv sp\u00f8rgsm\u00e5let - hvorfor betaler jeg dem mine penge? Konsekvenserne er altid de samme - sp\u00f8rgsm\u00e5let er kun, hvorn\u00e5r kunden bliver tr\u00e6t og vinker farvel. Og foderbr\u00f8nden vil v\u00e6re tom. Det er trist.<br \/>\n2. Ingeni\u00f8rens udvikling - nul.<br \/>\n3. Problemer med planl\u00e6gning af arbejde og belastning <\/p>\n<h3>Variant 2 - 'Danser med trommerne, s\u00e6lger og skuffer.'<\/h3>\n<p>\n<b>Punkt 1<\/b>-Hvorfor har vi et overv\u00e5gningssystem, vi vil modtage alting via foresp\u00f8rgsler. Vi starter en masse forskellige foresp\u00f8rgsler til datakataloget og dynamiske visninger, t\u00e6nder for forskellige t\u00e6llere, samler alt i tabeller, analyserer periodisk lister og tabeller. Resultatet er smukke eller mindre smukke grafer, tabeller, rapporter. Det vigtigste er, at der er s\u00e5 meget som muligt.<br \/>\n<b>Punkt 2<\/b>-Vi genererer aktivitet - starter analysen af det hele.<br \/>\n<b>Punkt 3<\/b>-Vi forbereder et eller andet dokument, kalder dette dokument blot - 'hvordan vi organiserer databasen.'<br \/>\n<b>Punkt 4<\/b>-Kunden, som ser dette pragt af grafer og tal, er i en barnlig naiv tro - nu vil alt fungerer, snart. Og han skiller sig let og smertefrit af med sine penge. Ledelsen er ogs\u00e5 overbevist - ingeni\u00f8rerne arbejder fantastisk. Belastningen er p\u00e5 maksimum. <br \/>\n<b>Punkt 5<\/b>-Gentag regelm\u00e6ssigt Punkt 1.<br \/>\n<b class=\"spoiler_title\">Sm\u00e5kager og donuts, bl\u00e5 m\u00e6rker og buler<\/b><b>Sm\u00e5kager og donuts: <\/b><br \/>\n1. Livet for ledere og ingeni\u00f8rer er enkelt, forudsigeligt og fyldt med aktivitet. Alt suser, alle er optaget. <br \/>\n2. Livet for kunden er ogs\u00e5 rimeligt - han er altid sikker p\u00e5, at han bare skal have lidt t\u00e5lmodighed, s\u00e5 l\u00f8ser alt sig. Det l\u00f8ser sig ikke, n\u00e5, hvad skal man g\u00f8re - det er en uretf\u00e6rdig verden, n\u00e6ste liv - mon ikke han er heldigere.<br \/>\n<b>Bl\u00e5 m\u00e6rker og buler:<\/b><br \/>\n1. F\u00f8r eller siden vil der blive fundet en mere effektiv leverand\u00f8r af tilsvarende tjenester, som tilbyder det samme, men til en lavere pris. Og hvis resultatet er det samme, hvorfor betale mere? Dette vil igen f\u00f8re til, at f\u00f8dekilden forsvinder.<br \/>\n2. Det er kedeligt. Ligesom enhver meningsl\u00f8s aktivitet.<br \/>\n3. Som i den tidligere mulighed \u2014 der er ingen udvikling. Men for ingeni\u00f8ren er ulempe, at man i mods\u00e6tning til den f\u00f8rste mulighed konstant skal generere dokumentation. Og det tager tid. Tid, der kunne bruges til noget nyttigt for en selv. For hvis man ikke tager sig af sig selv, er alle ligeglade.<\/p>\n<h3>Mulighed 3 - Man skal ikke opfinde cyklen p\u00e5 ny, man skal k\u00f8be den og k\u00f8re p\u00e5 den.<\/h3>\n<p>\nIngeni\u00f8rer fra andre selskaber spiser ikke for ingenting pizza med \u00f8l (ah, de gode tider i Sankt Petersborg i 90'erne). Lad os bruge overv\u00e5gningssystemer, der er lavet, testet og fungerer, og som faktisk bringer brugbarhed (i det mindste for dem, der har lavet dem).<br \/>\n<b class=\"spoiler_title\">Sm\u00e5kager og donuts, bl\u00e5 m\u00e6rker og buler<\/b><b>Sm\u00e5kager og donuts:<\/b><br \/>\n1. Man skal ikke spilde tid p\u00e5 at finde p\u00e5 det, der allerede er opfundet. Tag det og brug det.<br \/>\n2. Overv\u00e5gningssystemer skrives ikke af idioter, og de er selvf\u00f8lgelig nyttige.<br \/>\n3. Funktionelle overv\u00e5gningssystemer giver som regel brugbar filtreret information. <br \/>\n<b>Bl\u00e5 m\u00e6rker og buler:<\/b><br \/>\n1. Ingeni\u00f8ren er i dette tilf\u00e6lde ikke en ingeni\u00f8r, men blot en bruger af et andres produkt. Eller en bruger.<br \/>\n2. Kunden skal overtales til at k\u00f8be noget, som han i grunden ikke \u00f8nsker at forst\u00e5, og som han heller ikke burde og overhovedet er budgettet godkendt for \u00e5ret, og det vil ikke \u00e6ndre sig. S\u00e5 skal man afs\u00e6tte separate ressourcer og konfigurere til et specifikt system. Dvs. f\u00f8rst skal man betale, betale og betale igen. Men kunden er n\u00e6rig. Det er normen i dette liv.<\/p>\n<h2>Hvad skal man s\u00e5 g\u00f8re - Tjernysjov? Dit sp\u00f8rgsm\u00e5l er yderst relevant. (c)<\/h2>\n<p>\nI dette konkrete tilf\u00e6lde og i den aktuelle situation kan man g\u00f8re det lidt anderledes \u2014 <b>lad os lave vores eget overv\u00e5gningssystem. <\/b><br \/>\n<img decoding=\"async\" alt=\"Overv\u00e5gning af PostgreSQL foresp\u00f8rgselsydelse. Del 1 - rapportering\" src=\"\/wp-content\/uploads\/2019\/04\/b59a3ad9e16b68c0fbc962a61d571c52.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\nN\u00e5, ikke et system i ordets fulde forstand, det ville v\u00e6re for dramatisk og selvbevidst, men vi kan i det mindste lette opgaven og indsamle flere oplysninger til at l\u00f8se problemer med ydeevnen. S\u00e5 man ikke havner i situationen - \"g\u00e5 derhen, jeg ved ikke hvor, find det, jeg ved ikke hvad\".<\/p>\n<h4>Hvad er fordele og ulemper ved denne mulighed:<\/h4>\n<p><b>Fordele:<\/b><br \/>\n1. Det er interessant. I det mindste mere interessant end konstant at \"shrink datafile, alter tablespace, etc.\" <br \/>\n2. Dette er nye f\u00e6rdigheder og ny udvikling. Hvad der p\u00e5 sigt, f\u00f8r eller senere, vil give fortjente bel\u00f8nninger og muligheder.<br \/>\n<b>Ulemper:<\/b><br \/>\n1. Du m\u00e5 arbejde. Arbejde meget. <br \/>\n2. Du skal regelm\u00e6ssigt forklare meningen og perspektiverne for hele aktiviteten.<br \/>\n3. Du m\u00e5 give afkald p\u00e5 noget, da den eneste ressource, som ingeni\u00f8ren har adgang til - tid - er begr\u00e6nset af universet. <br \/>\n<b>4. Det mest skr\u00e6mmende og ubehagelige<\/b> \u2014 kan i sidste ende resultere i noget som \"Ikke en mus, ikke en fr\u00f8, men et ukendt v\u00e6sen.\"<\/p>\n<p><b>Den, der ikke risikerer, f\u00e5r ikke champagne.<\/b><br \/>\nS\u00e5 \u2014 det mest interessante begynder.<\/p>\n<h2>Den overordnede id\u00e9 \u2014 skematisk<\/h2>\n<p>\n<img decoding=\"async\" alt=\"Overv\u00e5gning af PostgreSQL foresp\u00f8rgselsydelse. Del 1 - rapportering\" src=\"\/wp-content\/uploads\/2019\/04\/d1ae2ddc1d4c43353fe93eebdaa8b81a.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n(<i>Illustrationen er taget fra artiklen<\/i> \u00ab<noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/444988\/\">Syntese som en metode til at forbedre ydeevnen af PostgreSQL.<\/a><\/noindex>\u00bb)<\/p>\n<p>Forklaring:<\/p>\n<ul>\n<li> I den m\u00e5lrettede database installeres standardudvidelsen PostgreSQL \u2014 \u201cpg_stat_statements\u201d. <\/li>\n<li>I overv\u00e5gningsdatabasen opretter vi et s\u00e6t service tabeller til at gemme historikken for pg_stat_statements i starten og til indstilling af metrikker og overv\u00e5gning senere.<\/li>\n<li> P\u00e5 overv\u00e5gningsv\u00e6rten opretter vi et s\u00e6t bash-scripts, ogs\u00e5 til at generere h\u00e6ndelser i ticketsystemet. <\/li>\n<\/ul>\n<h2>Servicetabeller<\/h2>\n<p>\nFor at starte med en skematisk og forenklet ERD, hvad kom der ud af det:<br \/>\n<img decoding=\"async\" alt=\"Overv\u00e5gning af PostgreSQL foresp\u00f8rgselsydelse. Del 1 - rapportering\" src=\"\/wp-content\/uploads\/2019\/04\/03d29f4d6932d470d9d88651cbf0c715.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<b class=\"spoiler_title\">Kort beskrivelse af tabellerne<\/b><b>endpoint <\/b> \u2014 v\u00e6rten, forbindingspunktet til instansen<br \/>\n<b>database <\/b> \u2014 parametre for databasen<br \/>\n<b>pg_stat_history <\/b> \u2014 historisk tabel til opbevaring af tidsbilleder af visningen pg_stat_statements i den m\u00e5lrettede database<br \/>\n<b>metric_glossary <\/b> \u2014 ordbog over ydeevnemetrikker<br \/>\n<b>metric_config <\/b> \u2014 konfiguration af enkelte metrikker<br \/>\n<b>metric <\/b> \u2014 en specifik metrisk for den overv\u00e5gede anmodning<br \/>\n<b>metric_alert_history <\/b> \u2014 historik over ydeevneadvarsler<br \/>\n<b>log_query <\/b> \u2014 systemtabel til opbevaring af analyserede poster fra logfilen PostgreSQL, der uploades fra AWS<br \/>\n<b>baseline <\/b> \u2014 parametre for den tidsperiode, der anvendes som baseline <br \/>\n<b>checkpoint <\/b> \u2014 konfiguration af metrikker for database-tilstandskontrol<br \/>\n<b>checkpoint_alert_history <\/b> \u2014 historik over advarsler for metrikker for database-tilstandskontrol<br \/>\n<b>pg_stat_db_queries <\/b> \u2014 systemtabel for aktive foresp\u00f8rgsler <br \/>\n<b>activity_log <\/b> \u2014 systemtabel for aktivitetslog <br \/>\n<b>trap_oid <\/b> \u2014 systemtabel for trap-konfiguration<\/p>\n<p><\/p>\n<h2>Trin 1 \u2014 indsamle statistisk information om ydeevne og f\u00e5 rapporter<\/h2>\n<p>\nTil opbevaring af statistisk information anvendes tabellen <b>pg_stat_history<\/b><br \/>\n<b class=\"spoiler_title\">Strukturen af tabellen pg_stat_history<\/b><\/p>\n<pre>\n                                          Tabel \"public.pg_stat_history\"\n       Kolonne        |            Type             |                          Modifikatorer\n---------------------+-----------------------------+-------------------------------------------\n id                  | integer                     | not null default nextval('pg_stat_history_id_seq'::regclass)\n snapshot_timestamp  | timestamp uden tidszone |\n database_id         | integer                     |\n dbid                | oid                         |\n userid              | oid                         |\n queryid             | bigint                      |\n query               | text                        |\n calls               | bigint                      |\n total_time          | double precision            |\n min_time            | double precision            |\n max_time            | double precision            |\n mean_time           | double precision            |\n stddev_time         | double precision            |\n rows                | bigint                      |\n shared_blks_hit     | bigint                      |\n shared_blks_read    | bigint                      |\n shared_blks_dirtied | bigint                      |\n shared_blks_written | bigint                      |\n local_blks_hit      | bigint                      |\n local_blks_read     | bigint                      |\n local_blks_dirtied  | bigint                      |\n local_blks_written  | bigint                      |\n temp_blks_read      | bigint                      |\n temp_blks_written   | bigint                      |\n blk_read_time       | double precision            |\n blk_write_time      | double precision            |\n baseline_id         | integer                     |\nIndekser:\n    \"pg_stat_history_pkey\" PRIMARY KEY, btree (id)\n    \"database_idx\" btree (database_id)\n    \"queryid_idx\" btree (queryid)\n    \"snapshot_timestamp_idx\" btree (snapshot_timestamp)\nUdenlandske n\u00f8glebegr\u00e6nsninger:\n    \"database_id_fk\" FOREIGN KEY (database_id) REFERENCES database(id) ON DELETE CASCADE<\/pre>\n<p>\nSom det fremg\u00e5r, repr\u00e6senterer tabellen blot kumulative data for visningen <b>pg_stat_statements <\/b> i den m\u00e5lrettede database.<\/p>\n<h3>Brugen af denne tabel er meget enkel<\/h3>\n<p>\n<b>pg_stat_history<\/b> vil repr\u00e6sentere den samlede statistik for udf\u00f8relsen af foresp\u00f8rgsler hver time. I starten af hver time, efter at tabellen er udfyldt, nulstilles statistikken <b>pg_stat_statements<\/b> ved hj\u00e6lp af <b>pg_stat_statements_reset()<\/b>.<br \/>\nBem\u00e6rk: <i>statistikken indsamles for foresp\u00f8rgsler, der har en udf\u00f8relsestid p\u00e5 mere end 1 sekund.<\/i><br \/>\n<b class=\"spoiler_title\">Udfyldningen af tabellen pg_stat_history<\/b><\/p>\n<pre><code class=\"plaintext\">--pg_stat_history.sql\nCREATE OR REPLACE FUNCTION pg_stat_history( ) RETURNS boolean AS $$\nDECLARE\n  endpoint_rec record ;\n  database_rec record ;\n  pg_stat_snapshot record ;\n  current_snapshot_timestamp timestamp without time zone;\nBEGIN\n  current_snapshot_timestamp = date_trunc('minute',now());  \n  \n  FOR endpoint_rec IN SELECT * FROM endpoint \n  LOOP\n    FOR database_rec IN SELECT * FROM database WHERE endpoint_id = endpoint_rec.id \n\t  LOOP\n\t    \n\t\tRAISE NOTICE 'NY SHAPSHOT SKABES';\n\t\t\n\t\t--Forbind til m\u00e5ldatabasen\t  \n\t    EXECUTE 'SELECT dblink_connect(''LINK1'',''host='||endpoint_rec.host||' dbname='||database_rec.name||' user=USER password=PASSWORD '')';\n \n        RAISE NOTICE 'host % and dbname % ',endpoint_rec.host,database_rec.name;\n\t\tRAISE NOTICE 'Oprettelse af snapshot af pg_stat_statements for database %',database_rec.name;\n\t\t\n\t\tSELECT \n\t      *\n\t\tINTO \n\t\t  pg_stat_snapshot\n\t    FROM dblink('LINK1',\n\t      'SELECT \n\t       dbid , SUM(calls),SUM(total_time),SUM(rows) ,SUM(shared_blks_hit) ,SUM(shared_blks_read) ,SUM(shared_blks_dirtied) ,SUM(shared_blks_written) , \n           SUM(local_blks_hit) , SUM(local_blks_read) , SUM(local_blks_dirtied) , SUM(local_blks_written) , SUM(temp_blks_read) , SUM(temp_blks_written) , SUM(blk_read_time) , SUM(blk_write_time)\n\t       FROM pg_stat_statements WHERE dbid=(SELECT oid from pg_database where datname=current_database() ) \n\t\t   GROUP BY dbid\n  \t      '\n\t               )\n\t      AS t\n\t       ( dbid oid , calls bigint , \n  \t         total_time double precision , \n\t         rows bigint , shared_blks_hit bigint , shared_blks_read bigint ,shared_blks_dirtied bigint ,shared_blks_written\t bigint ,\n             local_blks_hit\t bigint ,local_blks_read bigint , local_blks_dirtied bigint ,local_blks_written bigint ,\n             temp_blks_read\t bigint ,temp_blks_written bigint ,\n             blk_read_time double precision , blk_write_time double precision\t  \n\t       );\n\t\t \n\t\tINSERT INTO pg_stat_history\n          ( \n\t\t    snapshot_timestamp  ,database_id  ,\n\t\t\tdbid , calls  ,total_time ,\n            rows ,shared_blks_hit  ,shared_blks_read  ,shared_blks_dirtied  ,shared_blks_written ,local_blks_hit , \t \t\n            local_blks_read,local_blks_dirtied,local_blks_written,temp_blks_read,temp_blks_written, \t\n            blk_read_time, blk_write_time \n\t\t  )\t\t  \n\t    VALUES\n\t      (\n\t       current_snapshot_timestamp ,\n\t\t   database_rec.id ,\n\t       pg_stat_snapshot.dbid ,pg_stat_snapshot.calls,\n\t       pg_stat_snapshot.total_time,\n\t       pg_stat_snapshot.rows ,pg_stat_snapshot.shared_blks_hit ,pg_stat_snapshot.shared_blks_read ,pg_stat_snapshot.shared_blks_dirtied ,pg_stat_snapshot.shared_blks_written , \n           pg_stat_snapshot.local_blks_hit , pg_stat_snapshot.local_blks_read , pg_stat_snapshot.local_blks_dirtied , pg_stat_snapshot.local_blks_written , \n\t       pg_stat_snapshot.temp_blks_read , pg_stat_snapshot.temp_blks_written , pg_stat_snapshot.blk_read_time , pg_stat_snapshot.blk_write_time \t   \n\t      );\t\t   \n\t\t  \n        RAISE NOTICE 'Oprettelse af snapshot af pg_stat_statements for foresp\u00f8rgsler med min_tid st\u00f8rre end 1000ms';\n\t\n        FOR pg_stat_snapshot IN\n          --Alle foresp\u00f8rgsler med max_tid st\u00f8rre end 1000 ms\n\t      SELECT \n\t        *\n\t      FROM dblink('LINK1',\n\t        'SELECT \n\t         dbid , userid ,queryid,query,calls,total_time,min_time ,max_time,mean_time, stddev_time ,rows ,shared_blks_hit ,\n\t\t\t shared_blks_read ,shared_blks_dirtied ,shared_blks_written , \n             local_blks_hit , local_blks_read , local_blks_dirtied , \n\t\t\t local_blks_written , temp_blks_read , temp_blks_written , blk_read_time , \n\t\t\t blk_write_time\n\t         FROM pg_stat_statements \n\t\t\t WHERE dbid=(SELECT oid from pg_database where datname=current_database() AND min_time &gt;= 1000 ) \n  \t        '\n\n\t                  )\n\t        AS t\n\t         ( dbid oid , userid oid , queryid bigint ,query text , calls bigint , \n  \t           total_time double precision ,min_time double precision\t ,max_time double precision\t , mean_time double precision\t ,  stddev_time double precision\t , \n\t           rows bigint , shared_blks_hit bigint , shared_blks_read bigint ,shared_blks_dirtied bigint ,shared_blks_written\t bigint ,\n               local_blks_hit\t bigint ,local_blks_read bigint , local_blks_dirtied bigint ,local_blks_written bigint ,\n               temp_blks_read\t bigint ,temp_blks_written bigint ,\n               blk_read_time double precision , blk_write_time double precision\t  \n\t         )\n\t    LOOP\n\t\t  INSERT INTO pg_stat_history\n          ( \n\t\t    snapshot_timestamp  ,database_id  ,\n\t\t\tdbid ,userid  , queryid  , query  , calls  ,total_time ,min_time ,max_time ,mean_time ,stddev_time ,\n            rows ,shared_blks_hit  ,shared_blks_read  ,shared_blks_dirtied  ,shared_blks_written ,local_blks_hit , \t \t\n            local_blks_read,local_blks_dirtied,local_blks_written,temp_blks_read,temp_blks_written, \t\n            blk_read_time, blk_write_time \n\t\t  )\t\t  \n\t      VALUES\n\t      (\n\t       current_snapshot_timestamp ,\n\t\t   database_rec.id ,\n\t       pg_stat_snapshot.dbid ,pg_stat_snapshot.userid ,pg_stat_snapshot.queryid,pg_stat_snapshot.query,pg_stat_snapshot.calls,\n\t       pg_stat_snapshot.total_time,pg_stat_snapshot.min_time ,pg_stat_snapshot.max_time,pg_stat_snapshot.mean_time, pg_stat_snapshot.stddev_time ,\n\t       pg_stat_snapshot.rows ,pg_stat_snapshot.shared_blks_hit ,pg_stat_snapshot.shared_blks_read ,pg_stat_snapshot.shared_blks_dirtied ,pg_stat_snapshot.shared_blks_written , \n           pg_stat_snapshot.local_blks_hit , pg_stat_snapshot.local_blks_read , pg_stat_snapshot.local_blks_dirtied , pg_stat_snapshot.local_blks_written , \n\t       pg_stat_snapshot.temp_blks_read , pg_stat_snapshot.temp_blks_written , pg_stat_snapshot.blk_read_time , pg_stat_snapshot.blk_write_time \t   \n\t      );\n\t\t  \n        END LOOP;\n\n        PERFORM dblink_disconnect('LINK1');  \n\t\t\t\t\n\t  END LOOP ;--FOR database_rec IN SELECT * FROM database WHERE endpoint_id = endpoint_rec.id \n    \n  END LOOP;\n\nRETURN TRUE;  \nEND\n$$ LANGUAGE plpgsql;<\/code><\/pre>\n<p>Som f\u00f8lge af en periode vil vi i tabellen <b>pg_stat_history<\/b> have et s\u00e6t snapshots af tabelindholdet <b>pg_stat_statements <\/b>i den m\u00e5lrettede database. <\/p>\n<h2>Egentlig rapportering<\/h2>\n<p>\nVed at bruge enkle foresp\u00f8rgsler kan der opn\u00e5s nyttige og interessante rapporter.<\/p>\n<h2>Aggerede data for den angivne periode<\/h2>\n<p><b class=\"spoiler_title\">Foresp\u00f8rgsel<\/b><\/p>\n<pre><code class=\"plaintext\">SELECT \n  database_id , \n  SUM(calls) AS calls ,SUM(total_time)  AS total_time ,\n  SUM(rows) AS rows , SUM(shared_blks_hit)  AS shared_blks_hit,\n  SUM(shared_blks_read) AS shared_blks_read ,\n  SUM(shared_blks_dirtied) AS shared_blks_dirtied,\n  SUM(shared_blks_written) AS shared_blks_written , \n  SUM(local_blks_hit) AS local_blks_hit , \n  SUM(local_blks_read) AS local_blks_read , \n  SUM(local_blks_dirtied) AS local_blks_dirtied , \n  SUM(local_blks_written)  AS local_blks_written,\n  SUM(temp_blks_read) AS temp_blks_read, \n  SUM(temp_blks_written) AS temp_blks_written , \n  SUM(blk_read_time) AS blk_read_time , \n  SUM(blk_write_time) AS blk_write_time\nFROM \n  pg_stat_history\nWHERE \n  queryid IS NULL AND\n  database_id = DATABASE_ID  AND\n  snapshot_timestamp BETWEEN BEGIN_TIMEPOINT AND END_TIMEPOINT\nGROUP BY database_id ;<\/code><\/pre>\n<p><\/p>\n<h4>DB Tid<\/h4>\n<blockquote><p>to_char(interval '1 millisecond' * pg_total_stat_history_rec.total_time, 'HH24:MI:SS.MS')<\/p><\/blockquote>\n<p><\/p>\n<h4>I\/O Tid<\/h4>\n<blockquote><p>to_char(interval '1 millisecond' * (pg_total_stat_history_rec.blk_read_time + pg_total_stat_history_rec.blk_write_time), 'HH24:MI:SS.MS')<\/p><\/blockquote>\n<h3>TOP10 SQL efter total_tid<\/h3>\n<p><b class=\"spoiler_title\">Foresp\u00f8rgsel<\/b><\/p>\n<pre><code class=\"plaintext\">SELECT \n  queryid , \n  SUM(calls) AS calls ,\n  SUM(total_time)  AS total_time  \t\nFROM \n  pg_stat_history\nWHERE \n  queryid IS NOT NULL AND \n  database_id = DATABASE_ID AND\n  snapshot_timestamp BETWEEN BEGIN_TIMEPOINT AND END_TIMEPOINT \nGROUP BY queryid \nORDER BY 3 DESC \nLIMIT 10<\/code><\/pre>\n<pre>-------------------------------------------------------------------------------------\n| TOP10 SQL EFTER TOTAL UDF\u00d8RELSESTID\n|   #|    queryid|      calls|    calls %|                total_time (ms) |  dbtime %\n+----+-----------+-----------+-----------+--------------------------------+----------\n|   1|  821760255|          2|     .00001|00:03:23.141(    203141.681 ms.)|      5.42\n|   2| 4152624390|          2|     .00001|00:03:13.929(    193929.215 ms.)|      5.17\n|   3| 1484454471|          4|     .00001|00:02:09.129(    129129.057 ms.)|      3.44\n|   4|  655729273|          1|     .00000|00:02:01.869(    121869.981 ms.)|      3.25\n|   5| 2460318461|          1|     .00000|00:01:33.113(     93113.835 ms.)|      2.48\n|   6| 2194493487|          4|     .00001|00:00:17.377(     17377.868 ms.)|       .46\n|   7| 1053044345|          1|     .00000|00:00:06.156(      6156.352 ms.)|       .16\n|   8| 3644780286|          1|     .00000|00:00:01.063(      1063.830 ms.)|       .03\n<\/pre>\n<h4>TOP10 SQL efter total I\/O tid<\/h4>\n<p><b class=\"spoiler_title\">Foresp\u00f8rgsel<\/b><\/p>\n<pre><code class=\"plaintext\">SELECT \n  queryid , \n  SUM(calls) AS calls ,\n  SUM(blk_read_time + blk_write_time)  AS io_time\nFROM \n  pg_stat_history\nWHERE \n  queryid IS NOT NULL AND \n  database_id = DATABASE_ID  AND\n  snapshot_timestamp BETWEEN BEGIN_TIMEPOINT AND END_TIMEPOINT\nGROUP BY  queryid \nORDER BY 3 DESC \nLIMIT 10<\/code><\/pre>\n<pre>----------------------------------------------------------------------------------------\n| TOP10 SQL EFTER TOTAL I\/O TID\n|   #|    foresp\u00f8rgsels-id|      opkald|    opkald %|                   I\/O tid (ms)|db I\/O tid %\n+----+-----------+-----------+-----------+--------------------------------+-------------\n|   1| 4152624390|          2|     .00001|00:08:31.616(    511616.592 ms.)|        31.06\n|   2|  821760255|          2|     .00001|00:08:27.099(    507099.036 ms.)|        30.78\n|   3|  655729273|          1|     .00000|00:05:02.209(    302209.137 ms.)|        18.35\n|   4| 2460318461|          1|     .00000|00:04:05.981(    245981.117 ms.)|        14.93\n|   5| 1484454471|          4|     .00001|00:00:39.144(     39144.221 ms.)|         2.38\n|   6| 2194493487|          4|     .00001|00:00:18.182(     18182.816 ms.)|         1.10\n|   7| 1053044345|          1|     .00000|00:00:16.611(     16611.722 ms.)|         1.01\n|   8| 3644780286|          1|     .00000|00:00:00.436(       436.205 ms.)|          .03\n<\/pre>\n<h4>TOP10 SQL efter maksimal udf\u00f8relsestid<\/h4>\n<p><b class=\"spoiler_title\">Foresp\u00f8rgsel<\/b><\/p>\n<pre><code class=\"plaintext\">SELECT \n  id AS snapshotid , \n  foresp\u00f8rgsels-id , \n  snapshot_timestamp ,  \n  max_time \nFROM \n  pg_stat_history \nWHERE \n  foresp\u00f8rgsels-id IS NOT NULL AND \n  database_id = DATABASE_ID  AND\n  snapshot_timestamp BETWEEN BEGIN_TIMEPOINT AND END_TIMEPOINT\nORDER BY 4 DESC \nLIMIT 10<\/code><\/pre>\n<p><\/p>\n<pre>-----------------------------------------------------------------------------------------\n| TOP10 SQL EFTER MAKSIMAL UDF\u00d8RELSISTID\n|   #|          snapshot| snapshotID|    foresp\u00f8rgsels-id|                           max_time (ms)\n+----+------------------+-----------+-----------+----------------------------------------\n|   1|  05.04.2019 01:03|       4169|  655729273|        00:02:01.869(    121869.981 ms.)\n|   2|  04.04.2019 17:00|       4153|  821760255|        00:01:41.570(    101570.841 ms.)\n|   3|  04.04.2019 16:00|       4146|  821760255|        00:01:41.570(    101570.841 ms.)\n|   4|  04.04.2019 16:00|       4144| 4152624390|        00:01:36.964(     96964.607 ms.)\n|   5|  04.04.2019 17:00|       4151| 4152624390|        00:01:36.964(     96964.607 ms.)\n|   6|  05.04.2019 10:00|       4188| 1484454471|        00:01:33.452(     93452.150 ms.)\n|   7|  04.04.2019 17:00|       4150| 2460318461|        00:01:33.113(     93113.835 ms.)\n|   8|  04.04.2019 15:00|       4140| 1484454471|        00:00:11.892(     11892.302 ms.)\n|   9|  04.04.2019 16:00|       4145| 1484454471|        00:00:11.892(     11892.302 ms.)\n|  10|  04.04.2019 17:00|       4152| 1484454471|        00:00:11.892(     11892.302 ms.)\n<\/pre>\n<h4>TOP10 SQL efter DELT buffer l\u00e6sning\/skrivning<\/h4>\n<p><b class=\"spoiler_title\">Foresp\u00f8rgsel<\/b><\/p>\n<pre><code class=\"plaintext\">SELECT \n  id AS snapshotid , \n  foresp\u00f8rgsels-id ,\n  snapshot_timestamp , \n  shared_blks_read , \n  shared_blks_written \nFROM \n  pg_stat_history\nWHERE \n  foresp\u00f8rgsels-id IS NOT NULL AND \n  database_id = DATABASE_ID  AND\n  snapshot_timestamp BETWEEN BEGIN_TIMEPOINT AND END_TIMEPOINT AND\n  ( shared_blks_read &gt; 0 OR shared_blks_written &gt; 0 )\nORDER BY 4 DESC  , 5 DESC \nLIMIT 10<\/code><\/pre>\n<pre>--------------------------------------------------------------------------------------------\n| TOP10 SQL EFTER DELT BUFFER L\u00c6SNING\/SKRIVNING\n|   #|          snapshot| snapshotID|    queryid|   delte blokke l\u00e6st|  delte blokke skrevet\n+----+------------------+-----------+-----------+---------------------+---------------------\n|   1|  04.04.2019 17:00|       4153|  821760255|               797308|                    0\n|   2|  04.04.2019 16:00|       4146|  821760255|               797308|                    0\n|   3|  05.04.2019 01:03|       4169|  655729273|               797158|                    0\n|   4|  04.04.2019 16:00|       4144| 4152624390|               756514|                    0\n|   5|  04.04.2019 17:00|       4151| 4152624390|               756514|                    0\n|   6|  04.04.2019 17:00|       4150| 2460318461|               734117|                    0\n|   7|  04.04.2019 17:00|       4155| 3644780286|                52973|                    0\n|   8|  05.04.2019 01:03|       4168| 1053044345|                52818|                    0\n|   9|  04.04.2019 15:00|       4141| 2194493487|                52813|                    0\n|  10|  04.04.2019 16:00|       4147| 2194493487|                52813|                    0\n--------------------------------------------------------------------------------------------\n<\/pre>\n<h4>Histogram over foresp\u00f8rgsler efter maksimal udf\u00f8relsestid<\/h4>\n<p><b class=\"spoiler_title\">Anmodninger<\/b><\/p>\n<pre><code class=\"plaintext\">SELECT  \n  MIN(max_time) AS hist_min  , \n  MAX(max_time) AS hist_max , \n  (( MAX(max_time) - MIN(min_time) ) \/ hist_columns ) as hist_width\nFROM \n  pg_stat_history \nWHERE \n  queryid IS NOT NULL AND\n  database_id = DATABASE_ID  AND\n  snapshot_timestamp BETWEEN BEGIN_TIMEPOINT AND END_TIMEPOINT ;\n\nSELECT \n  SUM(calls) AS calls\nFROM \n  pg_stat_history \nWHERE \n  queryid IS NOT NULL AND\n  database_id =DATABASE_ID  AND\n  snapshot_timestamp BETWEEN BEGIN_TIMEPOINT AND END_TIMEPOINT AND \n  ( max_time &gt;= hist_current_min AND  max_time &lt; hist_current_max ) ;\n<\/code><\/pre>\n<pre>|-----------------------------------------------------------------------------------------------\n| MAX_TIME HISTOGRAM\n| TOTAL CALLS : 33851920\n| MIN TIME  : 00:00:01.063\n| MAX TIME  : 00:02:01.869\n---------------------------------------------------------------------------------\n|                      min varighed|                      max varighed|     opkald\n+----------------------------------+----------------------------------+----------\n| 00:00:01.063(      1063.830 ms.) | 00:00:13.144(     13144.445 ms.) | 9\n| 00:00:13.144(     13144.445 ms.) | 00:00:25.225(     25225.060 ms.) | 0\n| 00:00:25.225(     25225.060 ms.) | 00:00:37.305(     37305.675 ms.) | 0\n| 00:00:37.305(     37305.675 ms.) | 00:00:49.386(     49386.290 ms.) | 0\n| 00:00:49.386(     49386.290 ms.) | 00:01:01.466(     61466.906 ms.) | 0\n| 00:01:01.466(     61466.906 ms.) | 00:01:13.547(     73547.521 ms.) | 0\n| 00:01:13.547(     73547.521 ms.) | 00:01:25.628(     85628.136 ms.) | 0\n| 00:01:25.628(     85628.136 ms.) | 00:01:37.708(     97708.751 ms.) | 4\n| 00:01:37.708(     97708.751 ms.) | 00:01:49.789(    109789.366 ms.) | 2\n| 00:01:49.789(    109789.366 ms.) | 00:02:01.869(    121869.981 ms.) | 0\n<\/pre>\n<h4>TOP10 Snapshots efter foresp\u00f8rgsler pr. sekund<\/h4>\n<p><b class=\"spoiler_title\">Anmodninger<\/b><\/p>\n<pre><code class=\"plaintext\">--pg_qps.sql\n--Beregn Sp\u00f8rgsm\u00e5l Pr. Sekund \nCREATE OR REPLACE FUNCTION pg_qps( pg_stat_history_id integer ) RETURNS double precision AS $$\nDECLARE\n pg_stat_history_rec record ;\n prev_pg_stat_history_id integer ;\n prev_pg_stat_history_rec record;\n total_seconds double precision ;\n result double precision;\nBEGIN \n  result = 0 ;\n  \n  SELECT *\n  INTO pg_stat_history_rec\n  FROM \n    pg_stat_history\n  WHERE id = pg_stat_history_id ;\n\n  IF pg_stat_history_rec.snapshot_timestamp IS NULL \n  THEN\n    RAISE EXCEPTION 'ERROR - Ikke fundet pg_stat_history for id = %',pg_stat_history_id;\n  END IF ;  \n  \n --RAISE NOTICE 'pg_stat_history_id = % , snapshot_timestamp = %', pg_stat_history_id , \n pg_stat_history_rec.snapshot_timestamp ;\n  \n  SELECT \n    MAX(id)   \n  INTO\n    prev_pg_stat_history_id\n  FROM\n    pg_stat_history\n  WHERE \n    database_id = pg_stat_history_rec.database_id AND\n\tqueryid IS NULL AND\n\tid  0 \n  THEN\n    result = pg_stat_history_rec.calls \/ total_seconds ;\n  ELSE\n   result = 0 ; \n  END IF;\n   \n RETURN result ;\nEND\n$$ LANGUAGE plpgsql;\n\n\nSELECT \n  id , \n  snapshot_timestamp ,\n  calls , \t\n  total_time , \n  ( select pg_qps( id )) AS QPS ,\n  blk_read_time ,\n  blk_write_time\nFROM \n  pg_stat_history\nWHERE \n  queryid IS NULL AND \n  database_id = DATABASE_ID  AND\n  snapshot_timestamp BETWEEN BEGIN_TIMEPOINT AND END_TIMEPOINT AND\n  ( select pg_qps( id )) IS NOT NULL \nORDER BY 5 DESC \nLIMIT 10\n<\/code><\/pre>\n<pre>|-----------------------------------------------------------------------------------------------\n| TOP10 Snapshots sorteret efter QueryPerSeconds-nummer\n-----------------------------------------------------------------------------------------------------------------------------------------------\n|    #|          snapshot| snapshotID|      opkald|                      samlet dbtid|        QPS|                          I\/O tid| I\/O tid %\n+-----+------------------+-----------+-----------+----------------------------------+-----------+----------------------------------+-----------\n|    1|  04.04.2019 20:04|       4161|    5758631|  00:06:30.513(    390513.926 ms.)|   1573.396|  00:00:01.470(      1470.110 ms.)|       .376\n|    2|  04.04.2019 17:00|       4149|    3529197|  00:11:48.830(    708830.618 ms.)|    980.332|  00:12:47.834(    767834.052 ms.)|    108.324\n|    3|  04.04.2019 16:00|       4143|    3525360|  00:10:13.492(    613492.351 ms.)|    979.267|  00:08:41.396(    521396.555 ms.)|     84.988\n|    4|  04.04.2019 21:03|       4163|    2781536|  00:03:06.470(    186470.979 ms.)|    785.745|  00:00:00.249(       249.865 ms.)|       .134\n|    5|  04.04.2019 19:03|       4159|    2890362|  00:03:16.784(    196784.755 ms.)|    776.979|  00:00:01.441(      1441.386 ms.)|       .732\n|    6|  04.04.2019 14:00|       4137|    2397326|  00:04:43.033(    283033.854 ms.)|    665.924|  00:00:00.024(        24.505 ms.)|       .009\n|    7|  04.04.2019 15:00|       4139|    2394416|  00:04:51.435(    291435.010 ms.)|    665.116|  00:00:12.025(     12025.895 ms.)|      4.126\n|    8|  04.04.2019 13:00|       4135|    2373043|  00:04:26.791(    266791.988 ms.)|    659.179|  00:00:00.064(        64.261 ms.)|       .024\n|    9|  05.04.2019 01:03|       4167|    4387191|  00:06:51.380(    411380.293 ms.)|    609.332|  00:05:18.847(    318847.407 ms.)|     77.507\n|   10|  04.04.2019 18:01|       4157|    1145596|  00:01:19.217(     79217.372 ms.)|    313.004|  00:00:01.319(      1319.676 ms.)|      1.666\n<\/pre>\n<h4>Time-for-time udf\u00f8relseshistorik med QueryPerSeconds og I\/O tid<\/h4>\n<p><b class=\"spoiler_title\">Foresp\u00f8rgsel<\/b><\/p>\n<pre><code class=\"plaintext\">SELECT \n  id , \n  snapshot_timestamp ,\n  opkald , \t\n  total_time , \n  ( select pg_qps( id )) AS QPS ,\n  blk_read_time ,\n  blk_write_time\nFROM \n  pg_stat_history\nWHERE \n  queryid IS NULL AND \n  database_id = DATABASE_ID  AND\n  snapshot_timestamp BETWEEN BEGIN_TIMEPOINT AND END_TIMEPOINT\nORDER BY 2\n<\/code><\/pre>\n<pre>|-----------------------------------------------------------------------------------------------\n| TIMERETNING HISTORIK MED QueryPerSeconds og I\/O Tid\n-----------------------------------------------------------------------------------------------------------------------------------------------\n| SP\u00d8RGSM\u00c5L PER SEKUND HISTORIK\n|    #|          snapshot| snapshotID|      opkald|                      total dbtime|        QPS|                          I\/O tid| I\/O tid %\n+-----+------------------+-----------+-----------+----------------------------------+-----------+----------------------------------+-----------\n|    1|  04.04.2019 11:00|       4131|       3747|  00:00:00.835(       835.374 ms.)|      1.041|  00:00:00.000(          .000 ms.)|       .000\n|    2|  04.04.2019 12:00|       4133|    1002722|  00:01:52.419(    112419.376 ms.)|    278.534|  00:00:00.149(       149.105 ms.)|       .133\n|    3|  04.04.2019 13:00|       4135|    2373043|  00:04:26.791(    266791.988 ms.)|    659.179|  00:00:00.064(        64.261 ms.)|       .024\n|    4|  04.04.2019 14:00|       4137|    2397326|  00:04:43.033(    283033.854 ms.)|    665.924|  00:00:00.024(        24.505 ms.)|       .009\n|    5|  04.04.2019 15:00|       4139|    2394416|  00:04:51.435(    291435.010 ms.)|    665.116|  00:00:12.025(     12025.895 ms.)|      4.126\n|    6|  04.04.2019 16:00|       4143|    3525360|  00:10:13.492(    613492.351 ms.)|    979.267|  00:08:41.396(    521396.555 ms.)|     84.988\n|    7|  04.04.2019 17:00|       4149|    3529197|  00:11:48.830(    708830.618 ms.)|    980.332|  00:12:47.834(    767834.052 ms.)|    108.324\n|    8|  04.04.2019 18:01|       4157|    1145596|  00:01:19.217(     79217.372 ms.)|    313.004|  00:00:01.319(      1319.676 ms.)|      1.666\n|    9|  04.04.2019 19:03|       4159|    2890362|  00:03:16.784(    196784.755 ms.)|    776.979|  00:00:01.441(      1441.386 ms.)|       .732\n|   10|  04.04.2019 20:04|       4161|    5758631|  00:06:30.513(    390513.926 ms.)|   1573.396|  00:00:01.470(      1470.110 ms.)|       .376\n|   11|  04.04.2019 21:03|       4163|    2781536|  00:03:06.470(    186470.979 ms.)|    785.745|  00:00:00.249(       249.865 ms.)|       .134\n|   12|  04.04.2019 23:03|       4165|    1443155|  00:01:34.467(     94467.539 ms.)|    200.438|  00:00:00.015(        15.287 ms.)|       .016\n|   13|  05.04.2019 01:03|       4167|    4387191|  00:06:51.380(    411380.293 ms.)|    609.332|  00:05:18.847(    318847.407 ms.)|     77.507\n|   14|  05.04.2019 02:03|       4171|     189852|  00:00:10.989(     10989.899 ms.)|     52.737|  00:00:00.539(       539.110 ms.)|      4.906\n|   15|  05.04.2019 03:01|       4173|       3627|  00:00:00.103(       103.000 ms.)|      1.042|  00:00:00.004(         4.131 ms.)|      4.010\n|   16|  05.04.2019 04:00|       4175|       3627|  00:00:00.085(        85.235 ms.)|      1.025|  00:00:00.003(         3.811 ms.)|      4.471\n|   17|  05.04.2019 05:00|       4177|       3747|  00:00:00.849(       849.454 ms.)|      1.041|  00:00:00.006(         6.124 ms.)|       .721\n|   18|  05.04.2019 06:00|       4179|       3747|  00:00:00.849(       849.561 ms.)|      1.041|  00:00:00.000(          .051 ms.)|       .006\n|   19|  05.04.2019 07:00|       4181|       3747|  00:00:00.839(       839.416 ms.)|      1.041|  00:00:00.000(          .062 ms.)|       .007\n|   20|  05.04.2019 08:00|       4183|       3747|  00:00:00.846(       846.382 ms.)|      1.041|  00:00:00.000(          .007 ms.)|       .001\n|   21|  05.04.2019 09:00|       4185|       3747|  00:00:00.855(       855.426 ms.)|      1.041|  00:00:00.000(          .065 ms.)|       .008\n|   22|  05.04.2019 10:00|       4187|       3797|  00:01:40.150(    100150.165 ms.)|      1.055|  00:00:21.845(     21845.217 ms.)|     21.812\n<\/pre>\n<h4>Tekst af alle SQL-selects<\/h4>\n<p><b class=\"spoiler_title\">Foresp\u00f8rgsel<\/b><\/p>\n<pre><code class=\"plaintext\">V\u00c6LG \n  queryid , \n  foresp\u00f8rgsel \nFRA \n  pg_stat_history\nHVOR \n  queryid IKKE ER NULL OG \n  database_id = DATABASE_ID  OG\n  snapshot_timestamp MELLEM BEGIN_TIMEPOINT OG END_TIMEPOINT\nGROUP BY queryid , foresp\u00f8rgsel\n<\/code><\/pre>\n<h2>Resultat<\/h2>\n<p>\nSom det fremg\u00e5r, kan man med ganske enkle midler f\u00e5 en betydelig m\u00e6ngde nyttig information om belastningen og tilstanden p\u00e5 databasen. <\/p>\n<p><b>Bem\u00e6rkning:<\/b>Hvis vi registrerer queryid i foresp\u00f8rgslerne, f\u00e5r vi historik for den enkelte foresp\u00f8rgsel (for at spare plads er rapporter for den enkelte foresp\u00f8rgsel udeladt).<\/p>\n<p>S\u00e5ledes findes der statistiske data om foresp\u00f8rgselsydelse \u2014 de indsamles.<br \/>\nF\u00f8rste trin \"indsamling af statistiske data\" \u2014 afsluttet.<\/p>\n<p>Vi kan g\u00e5 videre til anden fase - \"ops\u00e6tning af ydelsesm\u00e5l\".<br \/>\n<img decoding=\"async\" alt=\"Overv\u00e5gning af PostgreSQL foresp\u00f8rgselsydelse. Del 1 - rapportering\" src=\"\/wp-content\/uploads\/2019\/04\/a893e49280d4cb425e4667a5c51d2397.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\n<b>Men det er en helt anden historie.<\/b><\/p>\n<p><i>Forts\u00e6tter\u2026<\/i><br \/>\n<br \/>Kilde: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/icl_services\/blog\/446734\/\">habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u0418\u043d\u0436\u0435\u043d\u0435\u0440 \u2014 \u0432 \u043f\u0435\u0440\u0435\u0432\u043e\u0434\u0435 \u0441 \u043b\u0430\u0442\u044b\u043d\u0438 \u2014 \u0432\u0434\u043e\u0445\u043d\u043e\u0432\u0435\u043d\u043d\u044b\u0439. \u0418\u043d\u0436\u0435\u043d\u0435\u0440 \u043c\u043e\u0436\u0435\u0442 \u0432\u0441\u0451. (\u0441) \u0420.\u0414\u0438\u0437\u0435\u043b\u044c. \u042d\u043f\u0438\u0433\u0440\u0430\u0444\u044b. \u0418\u043b\u0438 \u0438\u0441\u0442\u043e\u0440\u0438\u044f \u043e \u0442\u043e\u043c, \u0437\u0430\u0447\u0435\u043c \u0430\u0434\u043c\u0438\u043d\u0438\u0441\u0442\u0440\u0430\u0442\u043e\u0440\u0443 \u0431\u0430\u0437 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432\u0441\u043f\u043e\u043c\u0438\u043d\u0430\u0442\u044c \u0441\u0432\u043e\u0435 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0441\u0442\u0441\u043a\u043e\u0435 \u043f\u0440\u043e\u0448\u043b\u043e\u0435. \u041f\u0440\u0435\u0434\u0438\u0441\u043b\u043e\u0432\u0438\u0435 \u0412\u0441\u0435 \u0438\u043c\u0435\u043d\u0430 \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u044b. \u0421\u043e\u0432\u043f\u0430\u0434\u0435\u043d\u0438\u044f \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u044b. \u041c\u0430\u0442\u0435\u0440\u0438\u0430\u043b \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 \u0441\u043e\u0431\u043e\u0439 \u0438\u0441\u043a\u043b\u044e\u0447\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u043b\u0438\u0447\u043d\u043e\u0435 \u043c\u043d\u0435\u043d\u0438\u0435 \u0430\u0432\u0442\u043e\u0440\u0430. Disclaimer of warranties: \u0432 \u043f\u043b\u0430\u043d\u0438\u0440\u0443\u0435\u043c\u043e\u043c \u0446\u0438\u043a\u043b\u0435 \u0441\u0442\u0430\u0442\u0435\u0439 \u043d\u0435 \u0431\u0443\u0434\u0435\u0442 \u043f\u043e\u0434\u0440\u043e\u0431\u043d\u043e\u0433\u043e \u0438 \u0442\u043e\u0447\u043d\u043e\u0433\u043e \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c\u044b\u0445 \u0442\u0430\u0431\u043b\u0438\u0446 \u0438 [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":24168,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-32353","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=\"\u0418\u043d\u0436\u0435\u043d\u0435\u0440 \u2014 \u0432 \u043f\u0435\u0440\u0435\u0432\u043e\u0434\u0435 \u0441 \u043b\u0430\u0442\u044b\u043d\u0438 \u2014 \u0432\u0434\u043e\u0445\u043d\u043e\u0432\u0435\u043d\u043d\u044b\u0439. \u0418\u043d\u0436\u0435\u043d\u0435\u0440 \u043c\u043e\u0436\u0435\u0442 \u0432\u0441\u0451. 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