{"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\/it\/blog\/administrirovanie\/monitoring-proizvoditelnosti-zaprosov-postgresql-chast-1-reporting","title":{"rendered":"Monitoraggio delle prestazioni delle query PostgreSQL. Parte 1 \u2014 reporting","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Ingegnere \u2014 dal latino \u2014 ispirato.<br \/>\nL'ingegnere pu\u00f2 fare tutto. (c) R.Diesel.<br \/>\n<i>Epigrafi.<\/i><br \/>\n<img decoding=\"async\" alt=\"Monitoraggio delle prestazioni delle query PostgreSQL. Parte 1 \u2014 reporting\" src=\"\/wp-content\/uploads\/2019\/04\/9a2640267ea05006b94e03642b864ddc.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<i>O \u00e8 la storia di perch\u00e9 un amministratore di database dovrebbe ricordare il suo passato da programmatore. <\/i><\/p>\n<h2>Prefazione<\/h2>\n<p>\nTutti i nomi sono cambiati. I coincidenze sono puramente casuali. Il materiale rappresenta esclusivamente l'opinione personale dell'autore.<\/p>\n<blockquote><p><b>Disclaimer di garanzie:<\/b> <i>nella prevista serie di articoli non ci sar\u00e0 una descrizione dettagliata e precisa delle tabelle e degli script utilizzati. I materiali non possono essere utilizzati immediatamente \u00abAS IS\u00bb. <br \/>\nIn primo luogo, a causa del grande volume di materiali, <br \/>\nin secondo luogo a causa della specializzazione con il database di produzione del cliente reale. <br \/>\nPertanto, negli articoli saranno fornite solo idee e descrizioni in termini molto generali. <br \/>\nForse in futuro il sistema evolver\u00e0 al punto da essere pubblicato su GitHub, ma forse no. Il tempo dir\u00e0.<\/i><\/p><\/blockquote>\n<p>\nInizio della storia \u2014 \u00ab<noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/icl_services\/blog\/446314\/\">Ricordi come \u00e8 tutto cominciato?<\/a><\/noindex>\u00bb.<br \/>\nQual \u00e8 il risultato, in termini molto generali \u2014 \u00ab<noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/444988\/\">Il synthesis come uno dei metodi per migliorare le prestazioni di PostgreSQL<\/a><\/noindex>\u00bb<\/p>\n<h2>Perch\u00e9 tutto questo?<\/h2>\n<p>\nBeh, innanzitutto per non dimenticare, ricordando durante la pensione i bei vecchi tempi. <br \/>\nIn secondo luogo, \u00e8 necessario sistematizzare quanto scritto. Infatti, a volte inizio a confondermi e dimentico parti specifiche. <\/p>\n<p>E la cosa pi\u00f9 importante \u00e8 che potrebbe essere utile a qualcuno, evitando di reinventare la ruota e di incappare in problemi gi\u00e0 noti. In altre parole, migliorare la propria karma (non quella di Habr). Infatti, ci\u00f2 che \u00e8 pi\u00f9 prezioso in questo mondo sono le idee. La cosa principale \u00e8 trovare un'idea. Realizzare un'idea nella realt\u00e0 \u00e8 gi\u00e0 una questione puramente tecnica.<\/p>\n<p>Allora, cominciamo, lentamente...<\/p>\n<h2>Definizione del problema.<\/h2>\n<p><\/p>\n<h3>C'\u00e8: <\/h3>\n<p>\nUn database PostgreSQL (10.5), di tipo di carico misto (OLTP+DSS), con carico medio-basso, situato nel cloud AWS. <br \/>\nLa monitorizzazione del database \u00e8 assente, il monitoraggio dell'infrastruttura \u00e8 fornito tramite strumenti standard di AWS in configurazione minima.<\/p>\n<h3>Requisiti:<\/h3>\n<p>\nMonitorare le prestazioni e lo stato del database, raccogliere e avere informazioni iniziali per ottimizzare le query pesanti al DB.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h2>Breve prefazione o analisi delle opzioni di soluzione.<\/h2>\n<p>\nIniziamo a esaminare le opzioni per affrontare il problema dal punto di vista di un'analisi comparativa tra vantaggi e svantaggi per l'ingegnere, mentre i benefici e le perdite per la direzione sono affare di chi di dovere secondo il contratto di lavoro.<\/p>\n<h3>Opzione 1 - \u00abLavorare su richiesta\u00bb<\/h3>\n<p>\nLasciamo tutto com'\u00e8. Se il cliente ha dei problemi con la funzionalit\u00e0, le prestazioni del database o dell'applicazione, informer\u00e0 gli ingegneri DBA tramite e-mail o creando un incidente nella piattaforma di ticketing. <br \/>\nL'ingegnere, ricevuta la notifica, si occupa del problema, propone una soluzione o rimanda la questione, sperando che si risolva da sola, tanto prima o poi ci si dimenticher\u00e0.<br \/>\n<b class=\"spoiler_title\">Frittelle e schiene blu, lividi e bozze<\/b><b>Frittelle e schiene blu:<\/b><br \/>\n1. Non \u00e8 necessario fare nulla di superfluo<br \/>\n2. C'\u00e8 sempre la possibilit\u00e0 di giustificarsi e tirarsela. <br \/>\n3. Una montagna di tempo da spendere a propria discrezione.<br \/>\n<b>Lividi e bozze:<\/b><br \/>\n1. Prima o poi, un cliente rifletter\u00e0 sull'essenza dell'esistenza e sulla giustizia universale in questo mondo e si porr\u00e0 di nuovo la domanda: perch\u00e9 sto pagando per questo? La conseguenza \u00e8 sempre la stessa: l'unica domanda \u00e8 quando il cliente si annoier\u00e0 e abbandoner\u00e0. E il rubinetto si svuoter\u00e0. \u00c8 triste.<br \/>\n2. Lo sviluppo dell'ingegnere \u00e8 nullo.<br \/>\n3. Le difficolt\u00e0 nella pianificazione del lavoro e del carico. <\/p>\n<h3>Opzione 2 - \u00abBalliamo con i tamburi, vendiamo e calziamo\u00bb<\/h3>\n<p>\n<b>Punto 1<\/b>- Perch\u00e9 abbiamo bisogno di un sistema di monitoraggio? Riceveremo tutto tramite richieste. Lanciamo una marea di richieste al dizionario dei dati e alle rappresentazioni dinamiche, accendiamo vari contatori, riuniamo tutto in tabelle, e periodicamente analizziamo a modo nostro liste e tabelle. Il risultato \u00e8 che abbiamo grafici, tabelle e rapporti belli o meno belli. L'importante \u00e8 avere di pi\u00f9, di pi\u00f9.<br \/>\n<b>Punto 2<\/b>- Generiamo attivit\u00e0: avviamo l'analisi di tutto questo.<br \/>\n<b>Punto 3<\/b>- Prepareremo un documento, semplicemente chiamato: \u00abcome organizzare il nostro database\u00bb.<br \/>\n<b>Punto 4<\/b>-Il cliente, vedendo tutta questa meraviglia di grafici e numeri, vive nella naivete di un bambino, convinto che ora tutto funzioner\u00e0, a breve. E si separa facilmente e senza dolori dalle sue risorse finanziarie. Anche il management \u00e8 certo \u2014 i nostri ingegneri fanno un ottimo lavoro. Il carico \u00e8 al massimo. <br \/>\n<b>Punto 5<\/b>-Ripetere regolarmente il Punto 1.<br \/>\n<b class=\"spoiler_title\">Frittelle e schiene blu, lividi e bozze<\/b><b>Frittelle e schiene blu: <\/b><br \/>\n1. La vita dei manager e degli ingegneri \u00e8 semplice, prevedibile e piena di attivit\u00e0. Tutto ronzante, tutti impegnati. <br \/>\n2. Anche la vita del cliente non \u00e8 male \u2014 \u00e8 sempre convinto che basta avere un po' di pazienza e tutto si sistemer\u00e0. Se non si sistema, beh, che ci vuoi fare \u2014 il mondo \u00e8 ingiusto, nella prossima vita andr\u00e0 meglio.<br \/>\n<b>Lividi e bozze:<\/b><br \/>\n1. Prima o poi, trover\u00e0 un fornitore pi\u00f9 veloce di servizi simili, che far\u00e0 le stesse cose a un costo leggermente inferiore. E se il risultato \u00e8 lo stesso, perch\u00e9 pagare di pi\u00f9? Questo porter\u00e0 nuovamente alla scomparsa di opportunit\u00e0.<br \/>\n2. \u00c8 noioso. Come \u00e8 noiosa qualsiasi attivit\u00e0 poco significativa.<br \/>\n3. Come nel caso precedente, non ci sono sviluppi. Tuttavia, per un ingegnere, c\u2019\u00e8 uno svantaggio: a differenza della prima opzione, qui \u00e8 necessario generare costantemente il database. E questo richiede tempo, tempo che potrebbe essere speso per il proprio bene. Perch\u00e9, alla fine dei conti, se non ti prendi cura di te stesso, nessuno lo far\u00e0 per te.<\/p>\n<h3>Opzione 3: Non c'\u00e8 bisogno di inventare la ruota, \u00e8 sufficiente comprarla e andare.<\/h3>\n<p>\nNon \u00e8 un caso che gli ingegneri di altre aziende mangino pizza bevendo birra (ah, i bei tempi di Pietroburgo degli anni '90). Utilizziamo i sistemi di monitoraggio, che sono stati creati, testati e funzionano, e che apportano realmente dei vantaggi (almeno per i loro creatori).<br \/>\n<b class=\"spoiler_title\">Frittelle e schiene blu, lividi e bozze<\/b><b>Frittelle e schiene blu:<\/b><br \/>\n1. Non perdere tempo a reinventare ci\u00f2 che \u00e8 gi\u00e0 stato inventato. Prendi e utilizza.<br \/>\n2. I sistemi di monitoraggio non sono scritti da sciocchi e sono certamente utili.<br \/>\n3. I sistemi di monitoraggio operativi tendono a fornire informazioni utili e filtrate. <br \/>\n<b>Lividi e bozze:<\/b><br \/>\n1. In questo caso, l'ingegnere non \u00e8 un ingegnere, ma semplicemente un utente di un prodotto altrui. O un user.<br \/>\n2. \u00c8 necessario convincere il cliente della necessit\u00e0 di acquistare qualcosa di cui, in generale, non vuole nemmeno sapere, e che, in effetti, il budget annuale \u00e8 approvato e non cambier\u00e0. Poi \u00e8 necessario dedicare una risorsa separata e configurarla per un sistema specifico. Cio\u00e8, prima bisogna pagare, pagare e ancora pagare. Ma il cliente \u00e8 avaro. Questa \u00e8 la norma della vita.<\/p>\n<h2>Cosa fare, \u010cerny\u0161evskij? La tua domanda \u00e8 molto pertinente. (c)<\/h2>\n<p>\nIn questo caso specifico e nella situazione attuale, possiamo comportarci in modo un po' diverso \u2014 <b>e se creassimo il nostro sistema di monitoraggio? <\/b><br \/>\n<img decoding=\"async\" alt=\"Monitoraggio delle prestazioni delle query PostgreSQL. Parte 1 \u2014 reporting\" src=\"\/wp-content\/uploads\/2019\/04\/b59a3ad9e16b68c0fbc962a61d571c52.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\nBeh, non un sistema nel senso completo della parola, sarebbe troppo presuntuoso dirlo, ma in qualche modo alleviare il nostro compito e raccogliere pi\u00f9 informazioni per risolvere gli incidenti di prestazioni. Per non trovarsi nella situazione \u2014 \"vai l\u00e0, non so dove, trova quella cosa, non so che.\"<\/p>\n<h4>Quali sono i vantaggi e gli svantaggi di questa opzione:<\/h4>\n<p><b>Vantaggi:<\/b><br \/>\n1. \u00c8 interessante. Almeno \u00e8 pi\u00f9 interessante rispetto a costanti \"shrink datafile, alter tablespace, ecc.\" <br \/>\n2. Sono nuove competenze e nuova crescita. Che, a lungo termine, prima o poi dar\u00e0 i giusti riconoscimenti e successi.<br \/>\n<b>Contro:<\/b><br \/>\n1. Bisogner\u00e0 lavorare. Lavorare molto. <br \/>\n2. Dovr\u00e0 essere spiegato regolarmente il significato e le prospettive di tutte le attivit\u00e0.<br \/>\n3. Bisogner\u00e0 sacrificare qualcosa, poich\u00e9 l'unica risorsa a disposizione dell'ingegnere, il tempo, \u00e8 limitata dall'Universo. <br \/>\n<b>4. La cosa pi\u00f9 spaventosa e spiacevole<\/b> \u2014 pu\u00f2 risultare qualcosa di simile a 'Non un topolino, non una rana, ma una creatura sconosciuta'.<\/p>\n<p><b>Chi non rischia non beve champagne.<\/b><br \/>\nEcco quindi che inizia la parte pi\u00f9 interessante.<\/p>\n<h2>L'idea generale \u00e8 schematica<\/h2>\n<p>\n<img decoding=\"async\" alt=\"Monitoraggio delle prestazioni delle query PostgreSQL. Parte 1 \u2014 reporting\" src=\"\/wp-content\/uploads\/2019\/04\/d1ae2ddc1d4c43353fe93eebdaa8b81a.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n(<i>L'illustrazione \u00e8 tratta dall'articolo<\/i> \u00ab<noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/444988\/\">Il synthesis come uno dei metodi per migliorare le prestazioni di PostgreSQL<\/a><\/noindex>\u00bb)<\/p>\n<p>Spiegazione:<\/p>\n<ul>\n<li> Nel database di destinazione si installa l'estensione standard di PostgreSQL \u2014 'pg_stat_statements'. <\/li>\n<li>Nel database di monitoraggio creiamo un insieme di tabelle di servizio per memorizzare la cronologia di pg_stat_statements nella fase iniziale e per configurare metriche e monitoraggio successivamente.<\/li>\n<li> Sul host di monitoraggio creiamo un insieme di script bash, inclusi quelli per generare incidenti nel sistema di ticket. <\/li>\n<\/ul>\n<h2>Tabelle di servizio<\/h2>\n<p>\nPer cominciare, una ERD schematicamente semplificata, ecco cosa \u00e8 stato ottenuto alla fine:<br \/>\n<img decoding=\"async\" alt=\"Monitoraggio delle prestazioni delle query PostgreSQL. Parte 1 \u2014 reporting\" src=\"\/wp-content\/uploads\/2019\/04\/03d29f4d6932d470d9d88651cbf0c715.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<b class=\"spoiler_title\">Breve descrizione delle tabelle<\/b><b>endpoint <\/b> \u2014 host, punto di connessione all'istanza<br \/>\n<b>database <\/b> \u2014 parametri del database<br \/>\n<b>pg_stat_history <\/b> \u2014 tabella storica per conservare istantanee temporali della vista pg_stat_statements del database di destinazione<br \/>\n<b>metric_glossary <\/b> \u2014 glossario delle metriche di prestazione<br \/>\n<b>metric_config <\/b> \u2014 configurazione delle metriche specifiche<br \/>\n<b>metric <\/b> \u2014 metrica specifica per la richiesta monitorata<br \/>\n<b>metric_alert_history <\/b> \u2014 cronologia degli avvisi sulle prestazioni<br \/>\n<b>log_query <\/b> \u2014 tabella di sistema per memorizzare i record analizzati dal file di log di PostgreSQL caricato da AWS<br \/>\n<b>baseline <\/b> \u2014 parametri del periodo di tempo utilizzato come base <br \/>\n<b>checkpoint <\/b> \u2014 configurazione delle metriche di controllo dello stato del database<br \/>\n<b>checkpoint_alert_history <\/b> \u2014 cronologia degli avvisi delle metriche di controllo dello stato del database<br \/>\n<b>pg_stat_db_queries <\/b> \u2014 tabella di sistema delle query attive <br \/>\n<b>activity_log <\/b> \u2014 tabella di sistema del registro attivit\u00e0 <br \/>\n<b>trap_oid <\/b> \u2014 tabella di sistema per la configurazione del trap<\/p>\n<p><\/p>\n<h2>Fase 1 \u2014 raccogliamo informazioni statistiche sulle prestazioni e otteniamo report<\/h2>\n<p>\nLa tabella serve per conservare informazioni statistiche <b>pg_stat_history<\/b><br \/>\n<b class=\"spoiler_title\">Struttura della tabella pg_stat_history<\/b><\/p>\n<pre>\n                                          Tabella \"public.pg_stat_history\"\n       Colonna        |            Tipo             |                          Modificatori\n---------------------+-----------------------------+-------------------------------------------\n id                  | intero                      | non nullo, valore di default nextval('pg_stat_history_id_seq'::regclass)\n timestamp_snapshot   | timestamp senza fuso orario |\n database_id         | intero                      |\n dbid                | oid                         |\n userid              | oid                         |\n queryid             | bigint                      |\n query               | testo                       |\n calls               | bigint                      |\n tempo_totale        | doppia precisione           |\n tempo_minimo       | doppia precisione           |\n tempo_massimo      | doppia precisione           |\n tempo_medio        | doppia precisione           |\n stddev_time         | doppia precisione           |\n righe               | 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       | doppia precisione           |\n blk_write_time      | doppia precisione           |\n baseline_id         | intero                      |\nIndici:\n    \"pg_stat_history_pkey\" CHIAVE PRIMARIA, btree (id)\n    \"database_idx\" btree (database_id)\n    \"queryid_idx\" btree (queryid)\n    \"snapshot_timestamp_idx\" btree (snapshot_timestamp)\nVincoli di chiave esterna:\n    \"database_id_fk\" CHIAVE ESTERNA (database_id) RIFERISCE a database(id) ON DELETE CASCADE<\/pre>\n<p>\nCome si pu\u00f2 vedere, la tabella rappresenta solo dati cumulativi di visualizzazione. <b>pg_stat_statements <\/b> nella base dati target.<\/p>\n<h3>L'utilizzo di questa tabella \u00e8 molto semplice.<\/h3>\n<p>\n<b>pg_stat_history<\/b> rappresenter\u00e0 le statistiche accumulate di esecuzione delle query per ogni ora. All'inizio di ogni ora, dopo che la tabella \u00e8 stata riempita, le statistiche <b>pg_stat_statements<\/b> vengono azzerate con <b>pg_stat_statements_reset()<\/b>.<br \/>\nNota: <i>le statistiche vengono raccolte per le query con una durata di esecuzione superiore a 1 secondo.<\/i><br \/>\n<b class=\"spoiler_title\">Il riempimento della tabella 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 'NUOVO SHAPSHOT IN CORSO DI CREAZIONE';\n\t\t\n\t\t--Collegati al DB target\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 % e dbname % ',endpoint_rec.host,database_rec.name;\n\t\tRAISE NOTICE 'Creazione snapshot di pg_stat_statements per il 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 'Creazione snapshot di pg_stat_statements per le query con min_time superiore a 1000ms';\n\t\n        FOR pg_stat_snapshot IN\n          --Tutte le query con max_time maggiore di 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>Di conseguenza, dopo un certo periodo di tempo nella tabella <b>pg_stat_history<\/b> avremo un insieme di istantanee del contenuto della tabella <b>pg_stat_statements <\/b>del database di destinazione. <\/p>\n<h2>Generazione di report<\/h2>\n<p>\nUtilizzando query semplici, \u00e8 possibile ottenere report piuttosto utili e interessanti.<\/p>\n<h2>Dati aggregati per un intervallo di tempo specificato<\/h2>\n<p><b class=\"spoiler_title\">Richiesta<\/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>Tempo DB<\/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>Tempo I\/O<\/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 per total_time<\/h3>\n<p><b class=\"spoiler_title\">Richiesta<\/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 PER TEMPO TOTALE DI ESECUZIONE\n|   #|    queryid|      chiamate|    % chiamate|                tempo_totale (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 per tempo totale di I\/O<\/h4>\n<p><b class=\"spoiler_title\">Richiesta<\/b><\/p>\n<pre><code class=\"plaintext\">SELECT \n  queryid , \n  SUM(calls) AS chiamate ,\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 PER TEMPO TOTALE I\/O\n|   #|    queryid|      chiamate|    % chiamate|                   Tempo I\/O (ms)|% tempo I\/O db\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 per tempo massimo di esecuzione<\/h4>\n<p><b class=\"spoiler_title\">Richiesta<\/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 PER TEMPO DI ESECUZIONE MASSIMO\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 per lettura\/scrittura buffer CONDIVISI<\/h4>\n<p><b class=\"spoiler_title\">Richiesta<\/b><\/p>\n<pre><code class=\"plaintext\">SELEZIONA \n  id AS snapshotid , \n  queryid ,\n  snapshot_timestamp , \n  shared_blks_read , \n  shared_blks_written \nDA \n  pg_stat_history\nDOVE \n  queryid NON \u00c8 NULL E \n  database_id = DATABASE_ID  E\n  snapshot_timestamp TRA BEGIN_TIMEPOINT E END_TIMEPOINT E\n  ( shared_blks_read &gt; 0 OPPURE shared_blks_written &gt; 0 )\nORDINA PER 4 DESC  , 5 DESC \nLIMITA 10<\/code><\/pre>\n<pre>--------------------------------------------------------------------------------------------\n| TOP10 SQL PER LETTURE\/SCRITTURE BUFFERS CONDIVISI\n|   #|          istantanea| snapshotID|    queryid|   blocchi condivisi letti|  blocchi condivisi scritti\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>Istogramma della distribuzione delle query in base al tempo massimo di esecuzione<\/h4>\n<p><b class=\"spoiler_title\">Richieste<\/b><\/p>\n<pre><code class=\"plaintext\">SELEZIONA  \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\nDA  \n  pg_stat_history \nDOVE \n  queryid \u00c8 NON NULL E\n  database_id = DATABASE_ID  E\n  snapshot_timestamp TRA BEGIN_TIMEPOINT E END_TIMEPOINT ;\n\nSELEZIONA \n  SOMMA(calls) AS calls\nDA \n  pg_stat_history \nDOVE \n  queryid \u00c8 NON NULL E\n  database_id = DATABASE_ID  E\n  snapshot_timestamp TRA BEGIN_TIMEPOINT E END_TIMEPOINT E \n  ( max_time &gt;= hist_current_min E  max_time &lt; hist_current_max ) ;\n<\/code><\/pre>\n<pre>|-----------------------------------------------------------------------------------------------\n| ISTOGRAMA DEL TEMPO MASSIMO\n| CHIAMATE TOTALE : 33851920\n| TEMPO MINIMO  : 00:00:01.063\n| TEMPO MASSIMO  : 00:02:01.869\n---------------------------------------------------------------------------------\n|                      durata minima|                      durata massima|     chiamate\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 Istogrammi per Secondo per Query<\/h4>\n<p><b class=\"spoiler_title\">Richieste<\/b><\/p>\n<pre><code class=\"plaintext\">--pg_qps.sql\n--Calcolare le Query al Secondo \nCREATE OR REPLACE FUNCTION pg_qps( pg_stat_history_id integer ) RETURNS double precision AS $$\nDECLARE\n pg_stat_history_rec record;\n prev_pg_stat_history_id integer;\n prev_pg_stat_history_rec record;\n total_seconds double precision;\n result double precision;\nBEGIN \n  result = 0;\n  \n  SELECT *\n  INTO pg_stat_history_rec\n  FROM \n    pg_stat_history\n  WHERE id = pg_stat_history_id;\n\n  IF pg_stat_history_rec.snapshot_timestamp IS NULL \n  THEN\n    RAISE EXCEPTION 'ERRORE - pg_stat_history non trovato per id = %',pg_stat_history_id;\n  END IF;\n  \n --RAISE NOTICE 'pg_stat_history_id = % , snapshot_timestamp = %', pg_stat_history_id , \n pg_stat_history_rec.snapshot_timestamp;\n  \n  SELECT \n    MAX(id)\n  INTO\n    prev_pg_stat_history_id\n  FROM\n    pg_stat_history\n  WHERE \n    database_id = pg_stat_history_rec.database_id AND\n\tqueryid IS NULL AND\n\tid  0 \n  THEN\n    result = pg_stat_history_rec.calls \/ total_seconds;\n  ELSE\n   result = 0;\n  END IF;\n   \n RETURN result;\nEND\n$$ 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 Snapshot ordinati per QueryPerSecond\n-----------------------------------------------------------------------------------------------------------------------------------------------\n|    #|          snapshot| snapshotID|      chiamate|                      tempo totale db|        QPS|                          tempo I\/O| Percentuale tempo I\/O%\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>Cronologia Esecuzioni Orarie con QueryPerSecondi e Tempo I\/O<\/h4>\n<p><b class=\"spoiler_title\">Richiesta<\/b><\/p>\n<pre><code class=\"plaintext\">SELEZIONA \n  id , \n  snapshot_timestamp ,\n  chiamate , \t\n  tempo_totale , \n  ( seleziona pg_qps( id )) COME QPS ,\n  blk_read_time ,\n  blk_write_time\nDA \n  pg_stat_history\nDOVE \n  queryid \u00c8 NULL E \n  database_id = DATABASE_ID  E\n  snapshot_timestamp TRA BEGIN_TIMEPOINT E END_TIMEPOINT\nORDINA PER 2\n<\/code><\/pre>\n<pre>|-----------------------------------------------------------------------------------------------\n| STORIA DI ESECUZIONE ORARIA CON QueryPerSecondo e Tempo I\/O\n-----------------------------------------------------------------------------------------------------------------------------------------------\n| STORIA DELLE QUERY PER SECONDO\n|    #|          istantanea| ID_istantanea|       chiamate|                     tempo db totale|        QPS|                          tempo I\/O| % tempo I\/O\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>Testo di tutte le selezioni SQL<\/h4>\n<p><b class=\"spoiler_title\">Richiesta<\/b><\/p>\n<pre><code class=\"plaintext\">SELECT \n  queryid , \n  query \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 , query\n<\/code><\/pre>\n<h2>Risultato<\/h2>\n<p>\nCome si pu\u00f2 vedere, con strumenti piuttosto semplici, \u00e8 possibile ottenere molte informazioni utili sul carico e sulla stato del database. <\/p>\n<p><b>Nota:<\/b>Se nei query si registra il queryid, si otterr\u00e0 la cronologia per singolo query (per risparmiare spazio, i report per singolo query sono stati omessi).<\/p>\n<p>Quindi, i dati sulle prestazioni delle query sono disponibili e vengono raccolti.<br \/>\nLa prima fase \"raccolta dei dati statistici\" \u00e8 completata.<\/p>\n<p>Si pu\u00f2 passare alla seconda fase \u2013 \"impostazione delle metriche di prestazione\".<br \/>\n<img decoding=\"async\" alt=\"Monitoraggio delle prestazioni delle query PostgreSQL. Parte 1 \u2014 reporting\" src=\"\/wp-content\/uploads\/2019\/04\/a893e49280d4cb425e4667a5c51d2397.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\n<b>Ma questa \u00e8 gi\u00e0 tutta un'altra storia.<\/b><\/p>\n<p><i>Continua...<\/i><br \/>\n<br \/>Fonte: <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 4.9.10 - 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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