{"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\/et\/blog\/administrirovanie\/monitoring-proizvoditelnosti-zaprosov-postgresql-chast-1-reporting","title":{"rendered":"PostgreSQL p\u00e4ringute j\u00f5udluse j\u00e4lgimine. Osa 1 - raportite koostamine","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Insener \u2014 t\u00f5lgituna ladina keelest \u2014 inspireeritud.<br \/>\nInsener suudab k\u00f5ike. (c) R.Dizel.<br \/>\n<i>Epigraafid.<\/i><br \/>\n<img decoding=\"async\" alt=\"PostgreSQL p\u00e4ringute j\u00f5udluse j\u00e4lgimine. Osa 1 - raportite koostamine\" src=\"\/wp-content\/uploads\/2019\/04\/9a2640267ea05006b94e03642b864ddc.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<i>V\u00f5i lugu sellest, miks andmebaasi administraator peab oma programmeerimise minevikku m\u00e4letama. <\/i><\/p>\n<h2>Eess\u00f5na<\/h2>\n<p>\nK\u00f5ik nimed on muudetud. Kokkusattumised on juhuslikud. Materjal kajastab autori isiklikku arvamust.<\/p>\n<blockquote><p><b>Garantiide loobumise klausel:<\/b> <i>planeeritavas artiklite ts\u00fcklis ei ole tabelite ja skriptide kasutamiseks detailselt ja t\u00e4pselt kirjeldatud. Materjale ei saa kohe kasutada 'NII NAGU ON'. <br \/>\nEsiteks, suure materjalihulga t\u00f5ttu, <br \/>\nteiseks, reaalse kliendi tootmisbaasi v\u00e4hese kohandatuse t\u00f5ttu. <br \/>\nSeega sisaldavad artiklid ainult ideid ja kirjeldusi k\u00f5ige \u00fcldises vormis. <br \/>\nV\u00f5ib-olla tulevikus j\u00f5uab s\u00fcsteem tasemele, kus see avaldatakse GitHubis, aga v\u00f5ib-olla ka mitte. Aeg n\u00e4itab.<\/i><\/p><\/blockquote>\n<p>\nLoo algus \u2014 \"<noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/icl_services\/blog\/446314\/\">Kas sa m\u00e4letad, kuidas k\u00f5ik algas<\/a><\/noindex>\u00bb.<br \/>\nMis tulemusena sai, k\u00f5ige \u00fcldisemalt \u2014 \"<noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/444988\/\">S\u00fcntees kui \u00fcks meetod PostgreSQL t\u00f6\u00f6kindluse parandamiseks<\/a><\/noindex>\u00bb<\/p>\n<h2>Miks ma seda k\u00f5ike teen?<\/h2>\n<p>\nEsiteks, et mitte unustada, meenutades pensionip\u00f5lves toredaid aegu. <br \/>\nTeiseks, et s\u00fcsteemida kirjutatut. Sest ma ise hakkan m\u00f5nikord segadusse minema ja unustama \u00fcksikuid osi. <\/p>\n<p>Ja k\u00f5ige t\u00e4htsam \u2014 ehk on kellelegi kasu ja aitab v\u00e4ltida ratta leiutamist ja reha kogumist. Teisis\u00f5nu, parandada oma karma (mitte Habr-aadi). Sest k\u00f5ige v\u00e4\u00e4rtuslikum asi selles maailmas on ideed. Peamine on leida idee. Idee ellu viimine on juba puhtalt tehniline k\u00fcsimus.<\/p>\n<p>Nii et alustame, vaikselt...<\/p>\n<h2>Probleemi seadmine.<\/h2>\n<p><\/p>\n<h3>On olemas: <\/h3>\n<p>\nAndmebaas PostgreSQL (10.5), segat\u00fc\u00fcpi koormusega (OLTP+DSS), keskmise v\u00e4ikese koormusega, asub AWS-is. <br \/>\nAndmebaasi j\u00e4lgimine puudub, infrastruktuuri j\u00e4lgimine toimub AWS-i vaikimisi seadmete kaudu minimaalses konfiguratsioonis.<\/p>\n<h3>N\u00f5utav:<\/h3>\n<p>\nJ\u00e4lgida andmebaasi j\u00f5udlust ja seisundit, leida ja omada p\u00f5hiteavet objektiivne, et optimeerida andmebaasis raskeid p\u00e4ringuid.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h2>L\u00fchi\u00fclevaade v\u00f5i lahenduste anal\u00fc\u00fcs<\/h2>\n<p>\nAlustuseks proovime anal\u00fc\u00fcsida lahenduste variante v\u00f5rreldes kasude ja probleemidega insenerile, haldamise kasu ja kadusid las teevad need, kellel on vastavad ametikohad.<\/p>\n<h3>Variant 1 \u2014 \"T\u00f6\u00f6tamine n\u00f5udmisel\"<\/h3>\n<p>\nJ\u00e4tame k\u00f5ik nii nagu on. Kui klient ei ole rahul andmebaasi v\u00f5i rakenduse t\u00f6\u00f6kindluse ja j\u00f5udlusega, teavitab ta DBA insenere e-posti teel v\u00f5i loob probleemipileti. <br \/>\nInsener, saades teate, uurib probleemi, pakub lahendust v\u00f5i l\u00fckkab probleemi edasi, lootes, et k\u00f5ik laheneb iseenesest ning varsti unustatakse k\u00f5ik.<br \/>\n<b class=\"spoiler_title\">Igal keedul on oma hind ja allak\u00e4inta.<\/b><b>Igal keedul on oma hind ja allak\u00e4inta:<\/b><br \/>\n1. Midagi \u00fcleliigset tegema ei pea.<br \/>\n2. Alati on v\u00f5imalus lohutada ja lahti libiseda. <br \/>\n3. Palju aega, mida saab kulutada isikliku meelelahutuse heaks.<br \/>\n<b>Allak\u00e4inta ja keedu k\u00f5rvalm\u00f5jud:<\/b><br \/>\n1. \u00dchel v\u00f5i teisel hetkel hakkab klient m\u00f5tlema eksistentsi ja universumi \u00f5igluse olemusele ning k\u00fcsib endalt j\u00e4lle, milleks ta neile raha maksab? Tulemuseks on alati sama \u2014 k\u00fcsimus on ainult selles, millal klient igavleb ja loobub. Siis j\u00e4\u00e4b s\u00f6\u00f6gikoht t\u00fchjaks. See on kurb.<br \/>\n2. Inseneri areng on null.<br \/>\n3. T\u00f6\u00f6 ja koormuse planeerimise raskused. <\/p>\n<h3>Variant 2 - \"Tantsime rummidega, m\u00fc\u00fcme ja paneme jalga\".<\/h3>\n<p>\n<b>Punkt 1<\/b>- Miks meil on monitooringus\u00fcsteem, k\u00f5ik, mida me saame, tulevad p\u00e4ringutega. K\u00fcllap k\u00e4ivitame mitmesuguseid p\u00e4ringuid andmes\u00f5nastikele ja d\u00fcnaamilistele esitlusele, aktiveerime igasuguseid loendureid, koondame k\u00f5ik tabelitesse, anal\u00fc\u00fcsime perioodiliselt nimekirju ja tabeleid. Tulemuseks on ilusad v\u00f5i mitte nii ilusad graafikud, tabelid, aruanded. Peamine \u2014 et neid oleks rohkem, rohkem.<br \/>\n<b>Punkt 2<\/b>- Loome aktiivsust - k\u00e4ivitame selle k\u00f5ikse anal\u00fc\u00fcsimiseks.<br \/>\n<b>Punkt 3<\/b>- Koostame mingisuguse dokumendi, nimetame selle lihtsalt - \"kuidas korraldada andmebaasi\".<br \/>\n<b>Punkt 4<\/b>- Klient, n\u00e4hes k\u00f5iki neid graafikute ja numbrite ilu, on lastesarnases naiivsuses kindel - n\u00fc\u00fcd hakkab meil k\u00f5ik varsti t\u00f6\u00f6le. Ta laseb h\u00f5lpsalt ja valutult lahti oma rahalistest vahenditest. Juhtkond on samuti kindel - meie insenerid t\u00f6\u00f6tavad t\u00f5husalt. Koormus on maksimaalne. <br \/>\n<b>Punkt 5<\/b>- Korda Punkti 1 regulaarselt.<br \/>\n<b class=\"spoiler_title\">Igal keedul on oma hind ja allak\u00e4inta.<\/b><b>Igal keedul on oma hind ja allak\u00e4inta: <\/b><br \/>\n1. Juhtide ja inseneride elu on lihtne, ettearvatav ja t\u00e4is tegevust. K\u00f5ik sumiseb, k\u00f5ik on h\u00f5ivatud. <br \/>\n2. Kliendi elu pole samuti halb - ta on alati kindel, et peab vaid veidi ootama ja k\u00f5ik laheneb. Kui ei lahe, no mis siis - see elu on ebaaus, j\u00e4rgmises elus \u2014 \u00f5nnestub.<br \/>\n<b>Allak\u00e4inta ja keedu k\u00f5rvalm\u00f5jud:<\/b><br \/>\n1. Varem v\u00f5i hiljem leiab leidub kiiremini tegutsev teenusepakkuja, kes pakub sama teenust veidi odavamalt. Kui tulemus on sama, siis miks maksta rohkem? See viib omakorda toitmiss\u00fcsteemi kadumiseni.<br \/>\n2. See on igav. Nagu igas m\u00f5ttetuks tegevuses.<br \/>\n3. Nagu eelnevas variandis \u2014 areng puudub. Kuid inseneri jaoks on miinus see, et erinevalt esimesest variandist tuleb siin pidevalt genereerida andmebaasi. Ja see v\u00f5tab aega. Aega, mida v\u00f5iks enda kasuks kasutada. Sest kui ise enda eest ei hoolitse, ei hooli keegi sinu p\u00e4rast.<\/p>\n<h3>Variant 3 - Ei pea ratast leiutama, vaid tuleb see osta ja s\u00f5ita.<\/h3>\n<p>\nTeiste ettev\u00f5tete insenerid ei s\u00f6\u00f6 pitsa ja joo \u00f5lut ilma p\u00f5hjuseta (ah, \u00fclevad ajad Peterburis 90ndatel). Kasutame j\u00e4lgimisse s\u00fcsteeme, mis on loodud, testitud ja t\u00f6\u00f6tavad ning toovad tegelikult kasu ( v\u00e4hemalt nende loojatele).<br \/>\n<b class=\"spoiler_title\">Igal keedul on oma hind ja allak\u00e4inta.<\/b><b>Igal keedul on oma hind ja allak\u00e4inta:<\/b><br \/>\n1. Ei pea aega kulutama selle m\u00f5tletemise peale, mis on juba v\u00e4lja m\u00f5eldud. Vota ja kasuta.<br \/>\n2. J\u00e4lgimiss\u00fcsteemid ei ole lollide loodud ja need on kindlasti kasulikud.<br \/>\n3. Tootvad j\u00e4lgimiss\u00fcsteemid annavad tavaliselt kasulikku filtreeritud teavet. <br \/>\n<b>Allak\u00e4inta ja keedu k\u00f5rvalm\u00f5jud:<\/b><br \/>\n1. Insener ei ole antud juhul insener, vaid lihtsalt kellegi toote kasutaja. V\u00f5i kasutaja.<br \/>\n2. Tellijat peab veenma vajaduses osta midagi, milles ta tegelikult ei tahaks ja ei peakski aru saama, ning aastab\u00fc\u00fct on kinnitatud ja ei muutu. Siis peab eraldi ressurssi eraldama, mis tuleb kohandada konkreetse s\u00fcsteemi j\u00e4rgi. St. Esiteks tuleb maksta, maksta ja veel kord maksta. Ja tellija on kitsi. See on elu norm.<\/p>\n<h2>Mida siis teha - T\u0161ern\u00f5\u0161evski? Sinu k\u00fcsimus on t\u00e4iesti asjakohane. (c)<\/h2>\n<p>\nAntud juhul ja tekkinud olukorras on v\u00f5imalik teha veidi teistmoodi \u2014 <b>aga teeme oma j\u00e4lgimiss\u00fcsteemi. <\/b><br \/>\n<img decoding=\"async\" alt=\"PostgreSQL p\u00e4ringute j\u00f5udluse j\u00e4lgimine. Osa 1 - raportite koostamine\" src=\"\/wp-content\/uploads\/2019\/04\/b59a3ad9e16b68c0fbc962a61d571c52.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\nNo ei ole s\u00fcsteem, kindlasti mitte t\u00e4ies m\u00f5ttes, see on liiga suurejooneline ja ennast t\u00e4is\u00f5hustatud, aga kuidagi leevendada \u00fclesannet ja koguda rohkem teavet j\u00f5udlusprobleemide lahendamiseks. Et mitte sattuda olukorda - \"mine sinna, ei tea kuhu, leia see, ei tea mida\".<\/p>\n<h4>Millised on selle variandi plussid ja miinused:<\/h4>\n<p><b>Plussid:<\/b><br \/>\n1. See on huvitav. No v\u00e4hemalt on see huvitavam kui pidevad \"shrink datafile, alter tablespace, jne.\" <br \/>\n2. Need to acquire new skills and develop further. This will eventually yield well-deserved rewards and treats.<br \/>\n<b>Miinused:<\/b><br \/>\n1. You will have to work. Work a lot. <br \/>\n2. You will have to regularly explain the meaning and prospects of all activities.<br \/>\n3. You will have to sacrifice something, as the only resource available to an engineer\u2014time\u2014is limited by the Universe. <br \/>\n<b>4. The most terrifying and unpleasant part<\/b> \u2014 as a result, it may turn into something like 'Neither a mouse, nor a frog, but an unknown creature.'<\/p>\n<p><b>He who risks nothing, drinks no champagne.<\/b><br \/>\nSo \u2014 the most interesting begins now.<\/p>\n<h2>The general idea \u2014 schematically<\/h2>\n<p>\n<img decoding=\"async\" alt=\"PostgreSQL p\u00e4ringute j\u00f5udluse j\u00e4lgimine. Osa 1 - raportite koostamine\" src=\"\/wp-content\/uploads\/2019\/04\/d1ae2ddc1d4c43353fe93eebdaa8b81a.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n(<i>The illustration is taken from the article<\/i> \u00ab<noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/444988\/\">S\u00fcntees kui \u00fcks meetod PostgreSQL t\u00f6\u00f6kindluse parandamiseks<\/a><\/noindex>\u00bb)<\/p>\n<p>Explanation:<\/p>\n<ul>\n<li> In the target database, the standard PostgreSQL extension\u2014'pg_stat_statements'\u2014is installed. <\/li>\n<li>In the monitoring database, we create a set of service tables to store the history of 'pg_stat_statements' in the initial phase and for setting up metrics and monitoring later on.<\/li>\n<li> On the monitoring host, we create a set of bash scripts, including those for generating incidents in the ticketing system. <\/li>\n<\/ul>\n<h2>Service tables<\/h2>\n<p>\nTo start, here's a schematic simplified ERD of what we ended up with:<br \/>\n<img decoding=\"async\" alt=\"PostgreSQL p\u00e4ringute j\u00f5udluse j\u00e4lgimine. Osa 1 - raportite koostamine\" src=\"\/wp-content\/uploads\/2019\/04\/03d29f4d6932d470d9d88651cbf0c715.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<b class=\"spoiler_title\">Brief description of the tables<\/b><b>endpoint <\/b> \u2014 host, connection point to the instance<br \/>\n<b>database <\/b> \u2014 database parameters<br \/>\n<b>pg_stat_history <\/b> \u2014 historical table for storing snapshots of the 'pg_stat_statements' view of the target database<br \/>\n<b>metric_glossary <\/b> \u2014 performance metrics glossary<br \/>\n<b>metric_config <\/b> \u2014 configuration of individual metrics<br \/>\n<b>metric <\/b> \u2014 specific metric for the query being monitored<br \/>\n<b>metric_alert_history <\/b> \u2014 history of performance alerts<br \/>\n<b>log_query <\/b> \u2014 service table for storing parsed entries from the PostgreSQL log file loaded from AWS<br \/>\n<b>baseline <\/b> \u2014 parameters of the time period used as a baseline <br \/>\n<b>checkpoint <\/b> \u2014 configuration of database state check metrics<br \/>\n<b>checkpoint_alert_history <\/b> \u2014 history of state check metrics alerts<br \/>\n<b>pg_stat_db_queries <\/b> \u2014 service table of active queries <br \/>\n<b>activity_log <\/b> \u2014 service activity log table <br \/>\n<b>trap_oid <\/b> \u2014 service configuration table for trap<\/p>\n<p><\/p>\n<h2>Stage 1 \u2014 collecting statistical performance information and generating reports<\/h2>\n<p>\nThe table serves to store statistical information <b>pg_stat_history<\/b><br \/>\n<b class=\"spoiler_title\">Structure of the table 'pg_stat_history'<\/b><\/p>\n<pre>\n                                          Tabel \"public.pg_stat_history\"\n       Veerg        |            T\u00fc\u00fcp              |                          Modifikaatorid\n---------------------+-----------------------------+-------------------------------------------\n id                  | integer                     | ei tohi olla t\u00fchi, vaikimisi j\u00e4rgmine v\u00e4\u00e4rtus ('pg_stat_history_id_seq'::regclass)\n snapshot_timestamp  | timestamp ilma ajatsoonita |\n database_id         | integer                     |\n dbid                | oid                         |\n userid              | oid                         |\n queryid             | bigint                      |\n query               | text                        |\n calls               | bigint                      |\n total_time          | topelt t\u00e4psus              |\n min_time            | topelt t\u00e4psus              |\n max_time            | topelt t\u00e4psus              |\n mean_time           | topelt t\u00e4psus              |\n stddev_time         | topelt t\u00e4psus              |\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       | topelt t\u00e4psus              |\n blk_write_time      | topelt t\u00e4psus              |\n baseline_id         | integer                     |\nIndexid:\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)\nV\u00e4lisv\u00f5tme piirangud:\n    \"database_id_fk\" V\u00c4LISV\u00d5TI (database_id) VIITAB andmebaasile (id) KUSTUTAMISE K\u00c4TTE<\/pre>\n<p>\nNagu n\u00e4ha, on tabel lihtsalt kumulatiivne teave vaate kohta <b>pg_stat_statements <\/b> sihtandmebaasis.<\/p>\n<h3>Selle tabeli kasutamine on v\u00e4ga lihtne<\/h3>\n<p>\n<b>pg_stat_history<\/b> esindab kumulatiivset statistikat p\u00e4ringute t\u00e4itmise kohta iga tunni jaoks. Iga tunni alguses, p\u00e4rast tabeli t\u00e4itmist, statistika <b>pg_stat_statements<\/b> nullitakse kasutades <b>pg_stat_statements_reset()<\/b>.<br \/>\nM\u00e4rkus: <i>statistika kogutakse p\u00e4ringute jaoks, mille t\u00e4itmise kestus on \u00fcle 1 sekundi.<\/i><br \/>\n<b class=\"spoiler_title\">Tabeli t\u00e4itmine 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 'UUE SHAPSHOT LOODATAVAKS';\n\t\t\n\t\t--Connect to the target DB\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 % ja dbname % ',endpoint_rec.host,database_rec.name;\n\t\tRAISE NOTICE 'Loome pg_stat_statements jaoks andmebaasi %',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 'Loome pg_stat_statements jaoks p\u00e4ringute jaoks, mille min_time on suurem kui 1000ms';\n\t\n        FOR pg_stat_snapshot IN\n          --K\u00f5ik p\u00e4ringud, mille max_time on suurem kui 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>Seega, p\u00e4rast teatud aja m\u00f6\u00f6dumist tabelis <b>pg_stat_history<\/b> on meil komplekt tabeli sisu pilte <b>pg_stat_statements <\/b>sihtandmebaasist. <\/p>\n<h2>Tegelikult raportimine<\/h2>\n<p>\nLihtsaid p\u00e4ringuid kasutades on v\u00f5imalik saada \u00fcsna kasulikke ja huvitavaid raporteid.<\/p>\n<h2>Kogutud andmed m\u00e4\u00e4ratud ajavahemiku kohta<\/h2>\n<p><b class=\"spoiler_title\">P\u00e4ring<\/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) 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 Aeg<\/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 Aeg<\/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 kokku aja j\u00e4rgi<\/h3>\n<p><b class=\"spoiler_title\">P\u00e4ring<\/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 T\u00c4ITMISE KOGUAEG\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 kokku I\/O aja j\u00e4rgi<\/h4>\n<p><b class=\"spoiler_title\">P\u00e4ring<\/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 KOGU T\u00c4IELIK I\/O AEG\n|   #|    queryid|      k\u00f5ned|    k\u00f5nede %|                   I\/O aeg (ms)|db I\/O aege %\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 maksimaalse t\u00e4itmise aja j\u00e4rgi<\/h4>\n<p><b class=\"spoiler_title\">P\u00e4ring<\/b><\/p>\n<pre><code class=\"plaintext\">SELECT \n  id AS snapshotid , \n  queryid , \n  snapshot_timestamp ,  \n  max_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\nORDER BY 4 DESC \nLIMIT 10<\/code><\/pre>\n<p><\/p>\n<pre>-----------------------------------------------------------------------------------------\n| TOP10 SQL MAXIMAALNE T\u00c4ITMISAEG\n|   #|          snapshot| snapshotID|    queryid|                           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 jagatud puhver lugemise\/kirjutamise j\u00e4rgi<\/h4>\n<p><b class=\"spoiler_title\">P\u00e4ring<\/b><\/p>\n<pre><code class=\"plaintext\">SELECT \n  id AS snapshotid , \n  queryid ,\n  snapshot_timestamp , \n  shared_blks_read , \n  shared_blks_written \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  ( 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 BY SHARED BUFFER READ\/WRITE\n|   #|          snapshot| snapshotID|    queryid|   shared blocks read|  shared blocks write\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>P\u00e4ringute jaotuse histogram maksimaalse t\u00e4itmise aja j\u00e4rgi<\/h4>\n<p><b class=\"spoiler_title\">P\u00e4ringud<\/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 duration|                      max duration|     calls\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 L\u00e4bipaistvused p\u00e4ringu sekundis<\/h4>\n<p><b class=\"spoiler_title\">P\u00e4ringud<\/b><\/p>\n<pre><code class=\"plaintext\">--pg_qps.sql\n--K\u00fcsimuste arvu sekundis arvutamine \nCREATE OR REPLACE FUNCTION pg_qps( pg_stat_history_id integer ) RETURNS double precision AS $$\nDECLARE\n pg_stat_history_rec record ;\n prev_pg_stat_history_id integer ;\n prev_pg_stat_history_rec record;\n total_seconds double precision ;\n result double precision;\nBEGIN \n  result = 0 ;\n  \n  SELECT *\n  INTO pg_stat_history_rec\n  FROM \n    pg_stat_history\n  WHERE id = pg_stat_history_id ;\n\n  IF pg_stat_history_rec.snapshot_timestamp IS NULL \n  THEN\n    RAISE EXCEPTION 'VIGA - pg_stat_history ei leitud id = %',pg_stat_history_id;\n  END IF ;  \n  \n --RAISE NOTICE 'pg_stat_history_id = % , snapshot_timestamp = %', pg_stat_history_id , \n pg_stat_history_rec.snapshot_timestamp ;\n  \n  SELECT \n    MAX(id)   \n  INTO\n    prev_pg_stat_history_id\n  FROM\n    pg_stat_history\n  WHERE \n    database_id = pg_stat_history_rec.database_id AND\n\tqueryid IS NULL AND\n\tid  0 \n  THEN\n    result = pg_stat_history_rec.calls \/ total_seconds ;\n  ELSE\n   result = 0 ; \n  END IF;\n   \n RETURN result ;\nEND\n$$ 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 Snapshotid p\u00e4ringu sekundite j\u00e4rgi\n-----------------------------------------------------------------------------------------------------------------------------------------------\n|    #|          snapshot| snapshotID|      k\u00f5ned|                      tot. db aeg|        QPS|                          I\/O aeg| I\/O aeg %\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>Tunnine t\u00e4itmise ajalugu p\u00e4ringu sekundite ja I\/O aja j\u00e4rgi<\/h4>\n<p><b class=\"spoiler_title\">P\u00e4ring<\/b><\/p>\n<pre><code class=\"plaintext\">SELECT \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\nORDER BY 2\n<\/code><\/pre>\n<pre>|-----------------------------------------------------------------------------------------------\n| TUNNIT\u00c4ITSE K\u00c4IVITAMISE AJALUGU QUERY PER SECOND JA I\/O AEG\n-----------------------------------------------------------------------------------------------------------------------------------------------\n| K\u00dcSIMUSI PER SEKUND AJA AJALUGU\n|    #|          hetkepilt| hetkepildi ID|      k\u00f5ned|                      kogu DB aeg|        KPS|                          I\/O aeg| I\/O aeg %\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>K\u00f5igi SQL-selectide tekst<\/h4>\n<p><b class=\"spoiler_title\">P\u00e4ring<\/b><\/p>\n<pre><code class=\"plaintext\">VALI \n  k\u00fcsitlusid , \n  k\u00fcsitlus \nFROM \n  pg_stat_history\nWHERE \n  k\u00fcsitlusid ON KINNITATUD JA \n  andmebaasi_id = DATABASE_ID  JA\n  hetkeseis BETWEEN BEGIN_TIMEPOINT JA END_TIMEPOINT\nGROUP BY k\u00fcsitlusid , k\u00fcsitlus\n<\/code><\/pre>\n<h2>Kokkuv\u00f5te<\/h2>\n<p>\nKuidas n\u00e4ha, on \u00fcsna lihtsate vahenditega v\u00f5imalik saada piisavalt palju kasulikku teavet andmebaasi koormuse ja seisundi kohta. <\/p>\n<p><b>M\u00e4rkus:<\/b>Kui p\u00e4ringutes fikseerida k\u00fcsitlusid, saame ajaloo eraldi p\u00e4ringu kohta (ruumi s\u00e4\u00e4stmiseks on eraldi p\u00e4ringute aruanne v\u00e4lja j\u00e4etud).<\/p>\n<p>Seega on olemas ja kogutakse p\u00e4ringute j\u00f5udluse statistikat.<br \/>\nEsimene etapp \"statistika kogumine\" on l\u00f5petatud.<\/p>\n<p>V\u00f5ime minna teisele etapile - \u201ej\u00f5udluse m\u00f5\u00f5dikute seadistamine\u201c.<br \/>\n<img decoding=\"async\" alt=\"PostgreSQL p\u00e4ringute j\u00f5udluse j\u00e4lgimine. Osa 1 - raportite koostamine\" src=\"\/wp-content\/uploads\/2019\/04\/a893e49280d4cb425e4667a5c51d2397.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\n<b>Aga see on juba hoopis teine lugu.<\/b><\/p>\n<p><i>J\u00e4tkub\u2026<\/i><br \/>\n<br \/>Allikas: <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 5.0.1.1 - 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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