{"id":38196,"date":"2019-10-31T22:22:13","date_gmt":"2019-10-31T19:22:13","guid":{"rendered":"https:\/\/prohoster.info\/blog\/odin-iz-metodov-polucheniya-profilya-rabochej-nagruzki-i-istorii-ozhidanij-v-postgresql\/"},"modified":"2019-10-31T22:22:13","modified_gmt":"2019-10-31T19:22:13","slug":"odin-iz-metodov-polucheniya-profilya-rabochej-nagruzki-i-istorii-ozhidanij-v-postgresql","status":"publish","type":"post","link":"https:\/\/prohoster.info\/de\/blog\/administrirovanie\/odin-iz-metodov-polucheniya-profilya-rabochej-nagruzki-i-istorii-ozhidanij-v-postgresql","title":{"rendered":"Eine der Methoden zur Analyse von Arbeitslastprofilen und Wartehistorien in PostgreSQL.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Fortsetzung des Artikels &#171;<noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/467181\/\">Versuch, eine Analogie zu ASH f\u00fcr PostgreSQL zu erstellen <\/a><\/noindex>\u201e.<\/p>\n<p>In diesem Artikel wird erl\u00e4utert und anhand konkreter Abfragen und Beispiele gezeigt, welche wertvollen Informationen durch die Historie der Darstellung pg_stat_activity gewonnen werden k\u00f6nnen.<\/p>\n<blockquote><p>Warnung.<br \/>\nWegen der Neuheit des Themas und des unvollendeten Testzeitraums kann der Artikel Fehler enthalten. Kritik und Anmerkungen sind jederzeit willkommen.<\/p><\/blockquote>\n<p><noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h2>Eingabedaten<\/h2>\n<p><\/p>\n<h3>Die Historie der Darstellung pg_stat_statements.<\/h3>\n<p>\n<b class=\"spoiler_title\">pg_stat_history<\/b><\/p>\n<pre><code class=\"pgsql\">CREATE TABLE pg_stat_history ( \nid SERIAL, \nsnapshot_timestamp  timestamp without time zone, \ndatabase_id integer,\ndbid oid,\nuserid oid,\nqueryid bigint,\nquery text,\ncalls bigint, \ntotal_time double precision, \nmin_time double precision, \nmax_time double precision, \nmean_time double precision,\nstddev_time double precision,\nrows bigint,\nshared_blks_hit bigint, \nshared_blks_read bigint, \nshared_blks_dirtied bigint, \nshared_blks_written bigint, \nlocal_blks_hit bigint, \nlocal_blks_read bigint,\nlocal_blks_dirtied bigint, \nlocal_blks_written bigint, \ntemp_blks_read bigint,\ntemp_blks_written bigint,\nblk_read_time double precision, \nblk_write_time double precision, \nbaseline_id integer );<\/code><\/pre>\n<p>\n Die Tabelle wird st\u00fcndlich mit dblink zur Ziel-Datenbank gef\u00fcllt. Die interessanteste und n\u00fctzlichste Spalte in der Tabelle ist selbstverst\u00e4ndlich das. <b>queryid<\/b>.<\/p>\n<h3>Die Historie der Darstellung pg_stat_activity.<\/h3>\n<p>\n<b class=\"spoiler_title\">archive_pg_stat_activity<\/b><\/p>\n<pre><code class=\"pgsql\">ERSTELLEN SIE DIE TABELLE archive_pg_stat_activity\n(\n  zeitpunkt timestamp ohne Zeitzone,\n  datid             oid, \n  datname           name,\n  pid               integer,\n  usesysid          oid,\n  usename           name,\n  anwendungsname    text,\n  client_addr       inet,\n  client_hostname   text,\n  client_port       integer,\n  backend_start     timestamp ohne Zeitzone,\n  xact_start        timestamp ohne Zeitzone,\n  query_start       timestamp ohne Zeitzone,\n  status\u00e4nderung    timestamp ohne Zeitzone,\n  wart_event_typ    text,                     \n  wart_event        text,                   \n  status            text,                  \n  backend_xid       xid,                 \n  backend_xmin      xid,                \n  abfrage           text,               \n  backend_typ       text,\n  queryid           bigint\n);<\/code><\/pre>\n<p>\nDie Tabelle ist eine st\u00fcndlich partitionierte Tabelle history_pg_stat_activity (Weitere Informationen hier \u2013 <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/467277\/\">pg_stat_statements + pg_stat_activity + log_query = pg_ash? <\/a><\/noindex> und hier \u2014 <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/467181\/\">Versuch, ein \u00c4quivalent zu ASH f\u00fcr PostgreSQL zu erstellen.)<\/a><\/noindex><\/p>\n<h2>Ausgabe<\/h2>\n<p><\/p>\n<h3>CLUSTER CPU ZEIT (SYSTEM + CLIENTS)<\/h3>\n<p>\n<b class=\"spoiler_title\">Abfrage<\/b><\/p>\n<pre><code class=\"pgsql\">MIT \n t ALS\n (\n\tSELECT \t\t\n\t\t\tdate_trunc('Sekunde', zeitpunkt)\n\tFROM \tactivity_hist.archive_pg_stat_activity aa\n\tWHERE \tzeitpunkt ZWISCHEN pg_stat_history_begin+(current_hour_diff * intervall '1 Stunde') UND pg_stat_history_end+(current_hour_diff * intervall '1 Stunde')  UND \n\t\t\t( aa.wart_event_typ IST NULL  ) UND\n\t\t\taa.status = 'aktiv'\n )\n SELECT count(*) \n IN cpu_total\n AUS t ;<\/code><\/pre>\n<p>\n<b class=\"spoiler_title\">Beispiel<\/b><\/p>\n<pre><code class=\"plaintext\">CLUSTER CPU ZEIT (SYSTEM + CLIENTS) : 28:37:46<\/code><\/pre>\n<p><\/p>\n<h3>CLUSTER WARTUNGSZEITEN<\/h3>\n<p>\n<b class=\"spoiler_title\">Abfrage<\/b><\/p>\n<pre><code class=\"pgsql\">MIT \n t ALS\n (\n\tSELECT \t\t\n\t\t\tdate_trunc('Sekunde', zeitpunkt)\n\tFROM \tactivity_hist.archive_pg_stat_activity aa\n\tWHERE \tzeitpunkt ZWISCHEN pg_stat_history_begin+(current_hour_diff * intervall '1 Stunde') UND pg_stat_history_end+(current_hour_diff * intervall '1 Stunde')  UND \n\t\t\t( aa.wart_event_typ IST NICHT NULL  ) UND\n\t\t\taa.status = 'aktiv'\n )\n SELECT count(*) \n IN cpu_total\n AUS t ;<\/code><\/pre>\n<p>\n<b class=\"spoiler_title\">Beispiel<\/b><\/p>\n<pre><code class=\"plaintext\">CLUSTER WARTUNGSZEIT : 30:12:49<\/code><\/pre>\n<p><\/p>\n<h3>Gesamtwerte der pg_stat_statements<\/h3>\n<p>\n<b class=\"spoiler_title\">Abfrage<\/b><\/p>\n<pre><code class=\"pgsql\"> --GESAMT pg_stat\n  SELECT \n    SUM(calls) AS anzahl, SUM(total_time) AS gesamtzeit, SUM(rows) AS zeilen ,\n\tSUM(shared_blks_hit) AS geteilte_bl\u00f6cke_getroffen, SUM(shared_blks_read) AS geteilte_bl\u00f6cke_gelesen ,\n\tSUM(shared_blks_dirtied) AS geteilte_bl\u00f6cke_ver\u00e4ndert, SUM(shared_blks_written) AS geteilte_bl\u00f6cke_geschrieben , \n    SUM(local_blks_hit) AS lokale_bl\u00f6cke_getroffen, SUM(local_blks_read) AS lokale_bl\u00f6cke_gelesen , \n\tSUM(local_blks_dirtied) AS lokale_bl\u00f6cke_ver\u00e4ndert, SUM(local_blks_written) AS lokale_bl\u00f6cke_geschrieben,\n\tSUM(temp_blks_read) AS tempor\u00e4re_bl\u00f6cke_gelesen, SUM(temp_blks_written) AS tempor\u00e4re_bl\u00f6cke_geschrieben , \n\tSUM(blk_read_time) AS block_lesezeit, SUM(blk_write_time) AS block_schreibzeit\n  INTO \n    pg_total_stat_history_rec\n  FROM \n    pg_stat_history\n  WHERE \n \tsnapshot_timestamp BETWEEN pg_stat_history_begin AND pg_stat_history_end AND \n\tqueryid IS NULL;<\/code><\/pre>\n<p><\/p>\n<h3>SQL DBTIME \u2014 gesamte Ausf\u00fchrungszeit der Abfragen<\/h3>\n<p>\n<b class=\"spoiler_title\">Abfrage<\/b><\/p>\n<pre><code class=\"pgsql\">dbtime_total = intervall '1 millisekunde' * pg_total_stat_history_rec.gesamtzeit ;<\/code><\/pre>\n<p>\n<b class=\"spoiler_title\">Beispiel<\/b><\/p>\n<pre><code class=\"plaintext\">SQL DBTIME : 136:49:36<\/code><\/pre>\n<p><\/p>\n<h3>SQL CPU TIME \u2014 CPU-Zeit, die zur Ausf\u00fchrung von Abfragen verwendet wird<\/h3>\n<p>\n<b class=\"spoiler_title\">Abfrage<\/b><\/p>\n<pre><code class=\"pgsql\">WITH \n t AS\n (\n\tSELECT \t\t\n\t\t\tdate_trunc('second', timepoint)\n\tFROM \tactivity_hist.archive_pg_stat_activity aa\n\tWHERE \ttimepoint BETWEEN pg_stat_history_begin+(current_hour_diff * intervall '1 stunde') AND pg_stat_history_end+(current_hour_diff * intervall '1 stunde')  AND \n\t\t\t( aa.wait_event_type IS NULL  ) AND\n\t\t\tbackend_type = 'client backend' AND \n\t\t\taa.state = 'aktiv'\n )\n SELECT count(*) \n INTO cpu_total\n FROM t ;<\/code><\/pre>\n<p>\n<b class=\"spoiler_title\">Beispiel<\/b><\/p>\n<pre><code class=\"plaintext\">SQL CPU TIME : 27:40:15<\/code><\/pre>\n<p><\/p>\n<h3>SQL WAITINGS TIME \u2014 gesamte Wartezeit f\u00fcr Abfragen<\/h3>\n<p>\n<b class=\"spoiler_title\">Abfrage<\/b><\/p>\n<pre><code class=\"pgsql\">MIT \n t ALS\n (\n\tSELECT \t\t\n\t\t\tdate_trunc('second', timepoint)\n\tFROM \tactivity_hist.archive_pg_stat_activity aa\n\tWHERE \ttimepoint BETWEEN pg_stat_history_begin+(current_hour_diff * interval '1 hour') AND pg_stat_history_end+(current_hour_diff * interval '1 hour')  AND \n\t\t\t( aa.wait_event_type IS NOT NULL  ) AND\n\t\t\taa.state = 'active' AND \n\t\tbackend_type = 'client backend'\n )\n SELECT count(*) \n INTO waiting_total\n FROM t ;<\/code><\/pre>\n<p>\n<b class=\"spoiler_title\">Beispiel<\/b><\/p>\n<pre><code class=\"plaintext\">SQL WARTUNGSDAUER : 30:04:09<\/code><\/pre>\n<p>\nDie folgenden Abfragen sind trivial und aus Platzgr\u00fcnden werden die Implementierungsdetails weggelassen:<\/p>\n<p><b class=\"spoiler_title\">Beispiel<\/b><\/p>\n<pre><code class=\"plaintext\">| SQL IOTIME : 19:44:50\n| SQL LIESEZEIT : 19:44:32\n| SQL SCHREIBZEIT : 00:00:17\n|\n| SQL AUFRUFE : 12188248\n-------------------------------------------------------------\n| SQL GETEILTE BLOCKLESUNGEN : 7997039120\n| SQL GETEILTE BLOCKTREFFER : 8868286092\n| SQL GETEILTE BLOCKTREFFER\/LESEN % : 110,89\n| SQL GETEILTE BLOCKS M\u00dcLLE : 419945\n| SQL GETEILTE BLOCKS GESCHREIBEN : 19857\n|\n| SQL TEMPOR\u00c4RE BLOCKLESUNGEN : 7836169\n| SQL TEMPOR\u00c4RE BLOCKS GESCHREIBEN : 10683938\n<\/code><\/pre>\n<p>\nKommen wir zum spannendsten Abschnitt<\/p>\n<h2>WARTUNGSSTATISTIKEN<\/h2>\n<p><\/p>\n<h3>TOP 10 WARTUNGEN NACH GESAMTWARTUNGSDAUER F\u00dcR KLIENTENPROZESSE<\/h3>\n<p>\n<b class=\"spoiler_title\">Abfrage<\/b><\/p>\n<pre><code class=\"pgsql\">SELECT \n  wait_event_type , wait_event ,\n  get_system_waiting_duration( wait_event_type , wait_event ,pg_stat_history_begin+(current_hour_diff * interval '1 hour') ,pg_stat_history_end+(current_hour_diff * interval '1 hour') ) as duration \nFROM\n  activity_hist.archive_pg_stat_activity aa\nWHERE \n  timepoint BETWEEN pg_stat_history_begin+(current_hour_diff * interval '1 hour') AND pg_stat_history_end+(current_hour_diff * interval '1 hour') AND backend_type != 'client backend' AND wait_event_type IS NOT NULL \nGROUP BY \n  wait_event_type, wait_event\nORDER BY 3 DESC\nLIMIT 10<\/code><\/pre>\n<p>\n<b class=\"spoiler_title\">Beispiel<\/b><\/p>\n<pre>+------------------------------------------------------------------------------------\n| TOP 10 WARTEZEITEN NACH GESAMTWARTEZEIT F\u00dcR SYSTEMPROZESSE\n+-----+------------------------------+--------------------+--------------------\n|    #|               warte_event_typ|          warte_event|            dauer\n+-----+------------------------------+--------------------+--------------------\n|    1|                      Aktivit\u00e4t| LogicalLauncherMain|            10:43:28\n|    2|                      Aktivit\u00e4t|      AutoVacuumMain|            10:42:49\n|    3|                      Aktivit\u00e4t|       WalWriterMain|            10:28:53\n|    4|                      Aktivit\u00e4t|    CheckpointerMain|            10:23:50\n|    5|                      Aktivit\u00e4t|        BgWriterMain|            09:11:59\n|    6|                      Aktivit\u00e4t|   BgWriterHibernate|            01:37:46\n|    7|                            IO|        BufFileWrite|            00:02:35\n|    8|                        LWLock|      buffer_mapping|            00:01:54\n|    9|                            IO|        DataFileRead|            00:01:23\n|   10|                            IO|            WALWrite|            00:00:59\n+-----+------------------------------+--------------------+--------------------\n<\/pre>\n<p><\/p>\n<h3>TOP 10 WARTUNGEN NACH GESAMTWARTUNGSDAUER F\u00dcR KLIENTENPROZESSE<\/h3>\n<p>\n<b class=\"spoiler_title\">Abfrage<\/b><\/p>\n<pre><code class=\"pgsql\">W\u00c4HLEN SIE \n  warte_event_typ , warte_event ,\n  get_clients_waiting_duration( warte_event_typ , warte_event , pg_stat_history_begin+(current_hour_diff * INTERVAL '1 STUNDE') , pg_stat_history_end+(current_hour_diff * INTERVAL '1 STUNDE') ) AS dauer\nFROM \n  activity_hist.archive_pg_stat_activity aa\nWHERE \n  timepoint BETWEEN pg_stat_history_begin+(current_hour_diff * INTERVAL '1 STUNDE') AND pg_stat_history_end+(current_hour_diff * INTERVAL '1 STUNDE') AND backend_typ = 'client backend' AND warte_event_typ IS NOT NULL \nGROUP BY warte_event_typ, warte_event\nORDER BY 3 DESC\nLIMIT 10<\/code><\/pre>\n<p>\n<b class=\"spoiler_title\">Beispiel<\/b><\/p>\n<pre>+-----+------------------------------+--------------------+--------------------+----------\n|    #|               Warteereignisart|          Warteereignis|            Dauer|  % dbzeit\n+-----+------------------------------+--------------------+--------------------+----------\n|    1|                          Sperre|       Transaktions-ID|            08:16:47|      6.05\n|    2|                            IO|        DatenDateiLesen|            06:13:41|      4.55\n|    3|                       Timeout|             PgSleep|            02:53:21|      2.11\n|    4|                        LWLock|      Puffer-Zuordnung|            00:40:42|       0.5\n|    5|                        LWLock|           Puffer_IO|            00:17:17|      0.21\n|    6|                            IO|        BufDateiSchreiben|            00:01:34|      0.02\n|    7|                          Sperre|               Tupel|            00:01:32|      0.02\n|    8|                        Client|          ClientLesen|            00:01:19|      0.02\n|    9|                            IO|         BufDateiLesen|            00:00:37|      0.01\n|   10|                        LWLock|      Puffer-Inhalt|            00:00:08|         0\n+-----+------------------------------+--------------------+--------------------+----------\n<\/pre>\n<p><\/p>\n<h3>WARTETYPEN NACH GESAMTER WARTEDAUER, F\u00dcR SYSTEMPROZESSE<\/h3>\n<p>\n<b class=\"spoiler_title\">Abfrage<\/b><\/p>\n<pre><code class=\"pgsql\">SELECT \n  warteereignisart,\n  get_system_waiting_type_duration( warteereignisart, pg_stat_history_begin+(current_hour_diff * interval '1 hour') , pg_stat_history_end+(current_hour_diff * interval '1 hour') ) as dauer\nFROM\n  activity_hist.archive_pg_stat_activity aa\nWHERE \n  zeitpunkt BETWEEN pg_stat_history_begin+(current_hour_diff * interval '1 hour') AND pg_stat_history_end+(current_hour_diff * interval '1 hour') AND  backend_typ != 'client backend' AND warteereignisart IS NOT NULL \nGROUP BY warteereignisart\nORDER BY 2 DESC<\/code><\/pre>\n<p>\n<b class=\"spoiler_title\">Beispiel<\/b><\/p>\n<pre>+-----+------------------------------+--------------------\n|    #|               wait_event_type|            duration\n+-----+------------------------------+--------------------\n|    1|                      Aktivit\u00e4t|            53:08:45\n|    2|                            IO|            00:06:24\n|    3|                        LWLock|            00:03:02\n+-----+------------------------------+--------------------\n<\/pre>\n<p><\/p>\n<h3> WARTETYPEN NACH GESAMTEM WARTETEMPO F\u00dcR KLIENTENPROZESSE<\/h3>\n<p>\n<b class=\"spoiler_title\">Abfrage<\/b><\/p>\n<pre><code class=\"pgsql\">W\u00c4HLEN \n  wait_event_type ,\n  get_clients_waiting_type_duration( wait_event_type , pg_stat_history_begin+(current_hour_diff * interval '1 hour') , pg_stat_history_end+(current_hour_diff * interval '1 hour') ) as duration\nVON\n  activity_hist.archive_pg_stat_activity aa\nWO \n  timepoint BETWEEN pg_stat_history_begin+(current_hour_diff * interval '1 hour') UND pg_stat_history_end+(current_hour_diff * interval '1 hour') UND backend_type = 'client backend' UND wait_event_type IS NOT NULL \nGROUP BY wait_event_type\nORDER BY 2 DESC<\/code><\/pre>\n<p>\n<b class=\"spoiler_title\">Beispiel<\/b><\/p>\n<pre>+-----+------------------------------+--------------------+--------------------\n|    #|               wait_event_type|            duration|            % dbtime\n+-----+------------------------------+--------------------+--------------------\n|    1|                          Sperre|            08:18:19|                6.07\n|    2|                            IO|            06:16:01|                4.58\n|    3|                       Timeout|            02:53:21|                2.11\n|    4|                        LWLock|            00:58:12|                0.71\n|    5|                        Client|            00:01:19|                0.02\n|    6|                           IPC|            00:00:04|                   0\n+-----+------------------------------+--------------------+--------------------\n<\/pre>\n<p>\nDauer der Wartezeiten f\u00fcr Systemprozesse und einzelne Anfragen.<\/p>\n<h3>WARTEN F\u00dcR SYSTEMPROZESSE<\/h3>\n<p>\n<b class=\"spoiler_title\">Abfrage<\/b><\/p>\n<pre><code class=\"pgsql\">W\u00c4HLEN \n  backend_type , datname , wait_event_type , wait_event , get_backend_type_waiting_duration( backend_type , wait_event_type , wait_event , pg_stat_history_begin+(current_hour_diff * interval '1 hour') , pg_stat_history_end+(current_hour_diff * interval '1 hour') ) AS dauer \nVON \n  activity_hist.archive_pg_stat_activity aa\nWO \n  timepoint ZWISCHEN pg_stat_history_begin+(current_hour_diff * interval '1 hour') UND pg_stat_history_end+(current_hour_diff * interval '1 hour') UND backend_type != 'client backend' UND wait_event_type IST NICHT NULL \nGROUP BY backend_type , datname , wait_event_type , wait_event\nORDER BY 5 DESC<\/code><\/pre>\n<p>\n<b class=\"spoiler_title\">Beispiel<\/b><\/p>\n<pre>+-----+-----------------------------+----------+--------------------+----------------------+--------------------\n|    #|                 backend_type|    dbname|     wait_event_type|            wait_event|            duration\n+-----+-----------------------------+----------+--------------------+----------------------+--------------------\n|    1| logische Replikation Starter|          |            Aktivit\u00e4t|   LogicalLauncherMain|            10:43:28\n|    2|               Autovacuum-Launcher|          |            Aktivit\u00e4t|        AutoVacuumMain|            10:42:49\n|    3|                        walwriter|          |            Aktivit\u00e4t|         WalWriterMain|            10:28:53\n|    4|                     Checkpointer|          |            Aktivit\u00e4t|      CheckpointerMain|            10:23:50\n|    5|                  Hintergrundschreiber|          |            Aktivit\u00e4t|          BgWriterMain|            09:11:59\n|    6|                  Hintergrundschreiber|          |            Aktivit\u00e4t|     BgWriterHibernate|            01:37:46\n|    7|                    Parallelarbeiter|      tdb1|                  IO|          BufFileWrite|            00:02:35\n|    8|                    Parallelarbeiter|      tdb1|              LWLock|        buffer_mapping|            00:01:41\n|    9|                    Parallelarbeiter|      tdb1|                  IO|          DataFileRead|            00:01:22\n|   10|                    Parallelarbeiter|      tdb1|                  IO|           BufFileRead|            00:00:59\n|   11|                        walwriter|          |                  IO|              WALWrite|            00:00:57\n|   12|                    Parallelarbeiter|      tdb1|              LWLock|             buffer_io|            00:00:47\n|   13|                  Autovacuum-Arbeiter|      tdb1|              LWLock|        buffer_mapping|            00:00:13\n|   14|                  Hintergrundschreiber|          |                  IO|         DataFileWrite|            00:00:12\n|   15|                     Checkpointer|          |                  IO|         DataFileWrite|            00:00:11\n|   16|                        walwriter|          |              LWLock|          WALWriteLock|            00:00:09\n|   17|                     Checkpointer|          |              LWLock|          WALWriteLock|            00:00:06\n|   18|                  Hintergrundschreiber|          |              LWLock|          WALWriteLock|            00:00:06\n|   19|                        walwriter|          |                  IO|          WALInitWrite|            00:00:02\n|   20|                  Autovacuum-Arbeiter|      tdb1|              LWLock|          WALWriteLock|            00:00:02\n|   21|                        walwriter|          |                  IO|           WALInitSync|            00:00:02\n|   22|                  Autovacuum-Arbeiter|      tdb1|                  IO|          DataFileRead|            00:00:01\n|   23|                     Checkpointer|          |                  IO| ControlFileSyncUpdate|            00:00:01\n|   24|                  Hintergrundschreiber|          |                  IO|              WALWrite|            00:00:01\n|   25|                  Hintergrundschreiber|          |                  IO|         DataFileFlush|            00:00:01\n|   26|                     Checkpointer|          |                  IO|         SLRUFlushSync|            00:00:01\n|   27|                  Autovacuum-Arbeiter|      tdb1|                  IO|              WALWrite|            00:00:01\n|   28|                     Checkpointer|          |                  IO|          DataFileSync|            00:00:01\n+-----+-----------------------------+----------+--------------------+----------------------+--------------------<\/pre>\n<p><\/p>\n<h3>WARTEN AUF SQL \u2014 Wartezeiten f\u00fcr einzelne Abfragen nach queryid<\/h3>\n<p>\n<b class=\"spoiler_title\">Abfrage<\/b><\/p>\n<pre><code class=\"pgsql\">SELECT \nqueryid, datname, wait_event_type, wait_event, get_query_waiting_duration(queryid, wait_event_type, wait_event, pg_stat_history_begin + (current_hour_diff * interval '1 hour'), pg_stat_history_end + (current_hour_diff * interval '1 hour')) AS duration \nFROM \n  activity_hist.archive_pg_stat_activity aa\nWHERE \n  timepoint BETWEEN pg_stat_history_begin + (current_hour_diff * interval '1 hour') AND pg_stat_history_end + (current_hour_diff * interval '1 hour') AND backend_type = 'client backend' AND wait_event_type IS NOT NULL AND queryid IS NOT NULL \nGROUP BY queryid, datname, wait_event_type, wait_event\nORDER BY 1, 5 DESC <\/code><\/pre>\n<p>\n<b class=\"spoiler_title\">Beispiel<\/b><\/p>\n<pre>+-----+-------------------------+----------+--------------------+--------------------+--------------------+--------------------\n|    #|                  queryid|    dbname|     wait_event_type|          wait_event|            waitings|               total\n|     |                         |          |                    |                    |            duration|            duration\n+-----+-------------------------+----------+--------------------+--------------------+--------------------+--------------------\n|    1|     -8247416849404883188|      tdb1|              Client|          ClientRead|            00:00:02|\n|    2|     -6572922443698419129|      tdb1|              Client|          ClientRead|            00:00:05|\n|    3|     -6572922443698419129|      tdb1|                  IO|        DataFileRead|            00:00:01|\n|    4|     -5917408132400665328|      tdb1|              Client|          ClientRead|            00:00:04|\n|    5|     -4091009262735781873|      tdb1|              Client|          ClientRead|            00:00:03|\n|    6|     -1473395109729441239|      tdb1|              Client|          ClientRead|            00:00:01|\n|    7|        28942442626229688|      tdb1|                  IO|        BufFileWrite|            00:01:34|            00:46:06\n|    8|        28942442626229688|      tdb1|              LWLock|      buffer_mapping|            00:01:05|            00:46:06\n|    9|        28942442626229688|      tdb1|                  IO|        DataFileRead|            00:00:44|            00:46:06\n|   10|        28942442626229688|      tdb1|                  IO|         BufFileRead|            00:00:37|            00:46:06\n|   11|        28942442626229688|      tdb1|              LWLock|           buffer_io|            00:00:35|            00:46:06\n|   12|        28942442626229688|      tdb1|              Client|          ClientRead|            00:00:05|            00:46:06\n|   13|        28942442626229688|      tdb1|                 IPC| MessageQueueReceive|            00:00:03|            00:46:06\n|   14|        28942442626229688|      tdb1|                 IPC|    BgWorkerShutdown|            00:00:01|            00:46:06\n|   15|       389015618226997618|      tdb1|                Lock|       transactionid|            03:55:09|            04:14:15\n|   16|       389015618226997618|      tdb1|                  IO|        DataFileRead|            03:23:09|            04:14:15\n|   17|       389015618226997618|      tdb1|              LWLock|      buffer_mapping|            00:12:09|            04:14:15\n|   18|       389015618226997618|      tdb1|              LWLock|           buffer_io|            00:10:18|            04:14:15\n|   19|       389015618226997618|      tdb1|                Lock|               tuple|            00:00:35|            04:14:15\n|   20|       389015618226997618|      tdb1|              LWLock|        WALWriteLock|            00:00:02|            04:14:15\n|   21|       389015618226997618|      tdb1|                  IO|       DataFileWrite|            00:00:01|            04:14:15\n|   22|       389015618226997618|      tdb1|              LWLock|        SyncScanLock|            00:00:01|            04:14:15\n|   23|       389015618226997618|      tdb1|              Client|          ClientRead|            00:00:01|            04:14:15\n|   24|       734234407411547467|      tdb1|              Client|          ClientRead|            00:00:11|\n|   25|       734234407411547467|      tdb1|              LWLock|      buffer_mapping|            00:00:05|\n|   26|       734234407411547467|      tdb1|                  IO|        DataFileRead|            00:00:02|\n|   27|      1237430309438971376|      tdb1|              LWLock|      buffer_mapping|            00:02:18|            02:45:40\n|   28|      1237430309438971376|      tdb1|                  IO|        DataFileRead|            00:00:27|            02:45:40\n|   29|      1237430309438971376|      tdb1|              Client|          ClientRead|            00:00:02|            02:45:40\n|   30|      2404820632950544954|      tdb1|              Client|          ClientRead|            00:00:01|\n|   31|      2515308626622579467|      tdb1|              Client|          ClientRead|            00:00:02|\n|   32|      4710212362688288619|      tdb1|              LWLock|      buffer_mapping|            00:03:08|            02:18:21\n|   33|      4710212362688288619|      tdb1|                  IO|        DataFileRead|            00:00:22|            02:18:21\n|   34|      4710212362688288619|      tdb1|              Client|          ClientRead|            00:00:06|            02:18:21\n|   35|      4710212362688288619|      tdb1|              LWLock|           buffer_io|            00:00:02|            02:18:21\n|   36|      9150846928388977274|      tdb1|                  IO|        DataFileRead|            00:01:19|\n|   37|      9150846928388977274|      tdb1|              LWLock|      buffer_mapping|            00:00:34|\n|   38|      9150846928388977274|      tdb1|              Client|          ClientRead|            00:00:10|\n|   39|      9150846928388977274|      tdb1|              LWLock|           buffer_io|            00:00:01|\n+-----+-------------------------+----------+--------------------+--------------------+--------------------+--------------------<\/pre>\n<p><\/p>\n<h3>KUNDEN-SQL-STATISTIKEN \u2013 TOP-Anfragen<\/h3>\n<p>\nDie Abfragen zur Erfassung sind wiederum trivial und werden zur Platzersparnis nicht wiedergegeben. <\/p>\n<p><b class=\"spoiler_title\">Beispiele<\/b><\/p>\n<pre>+------------------------------------------------------------------------------------\n| KLIENT SQL nach verstrichener Zeit sortiert\n+--------------------+----------+----------+----------+----------+----------+--------------------\n|        verstrichene Zeit|     Aufrufe|  % dbzeit|     % CPU|      % IO|    dbname|             queryid\n+--------------------+----------+----------+----------+----------+----------+--------------------\n|            04:14:15|        19|       3.1|     10.83|     11.52|      tdb1|  389015618226997618\n|            02:45:40|       746|      2.02|      4.23|      0.08|      tdb1| 1237430309438971376\n|            02:18:21|       749|      1.69|      3.39|       0.1|      tdb1| 4710212362688288619\n|            00:46:06|       375|      0.56|      0.94|      0.41|      tdb1|   28942442626229688\n+--------------------+----------+----------+----------+----------+----------+--------------------\n| KLIENT SQL nach CPU-Zeit sortiert\n+--------------------+----------+----------+----------+----------+----------+----------+--------------------\n|            cpu zeit|     Aufrufe|  % dbzeit|gesamt_zeit|     % CPU|      % IO|    dbname|             queryid\n+--------------------+----------+----------+----------+----------+----------+----------+--------------------\n|            02:59:49|        19|       3.1|  04:14:15|     10.83|     11.52|      tdb1|  389015618226997618\n|            01:10:12|       746|      2.02|  02:45:40|      4.23|      0.08|      tdb1| 1237430309438971376\n|            00:56:15|       749|      1.69|  02:18:21|      3.39|       0.1|      tdb1| 4710212362688288619\n|            00:15:35|       375|      0.56|  00:46:06|      0.94|      0.41|      tdb1|   28942442626229688\n+--------------------+----------+----------+----------+----------+----------+----------+--------------------\n| KLIENT SQL nach Benutzer-I\/O-Wartezeit sortiert\n+--------------------+----------+----------+----------+----------+----------+----------+--------------------\n|        io_wartezeit|     Aufrufe|  % dbzeit|gesamt_zeit|     % CPU|      % IO|    dbname|             queryid\n+--------------------+----------+----------+----------+----------+----------+----------+--------------------\n|            03:23:10|        19|       3.1|  04:14:15|     10.83|     11.52|      tdb1|  389015618226997618\n|            00:02:54|       375|      0.56|  00:46:06|      0.94|      0.41|      tdb1|   28942442626229688\n|            00:00:27|       746|      2.02|  02:45:40|      4.23|      0.08|      tdb1| 1237430309438971376\n|            00:00:22|       749|      1.69|  02:18:21|      3.39|       0.1|      tdb1| 4710212362688288619\n+--------------------+----------+----------+----------+----------+----------+----------+--------------------\n| KLIENT SQL nach gemeinsam genutzten Pufferlesungen sortiert\n+--------------------+----------+----------+----------+----------+----------+----------+--------------------\n|       pufferlesungen|     Aufrufe|  % dbzeit|gesamt_zeit|     % CPU|      % IO|    dbname|             queryid\n+--------------------+----------+----------+----------+----------+----------+----------+--------------------\n|          1056388566|        19|       3.1|  04:14:15|     10.83|     11.52|      tdb1|  389015618226997618\n|            11709251|       375|      0.56|  00:46:06|      0.94|      0.41|      tdb1|   28942442626229688\n|             3439004|       746|      2.02|  02:45:40|      4.23|      0.08|      tdb1| 1237430309438971376\n|             3373330|       749|      1.69|  02:18:21|      3.39|       0.1|      tdb1| 4710212362688288619\n+--------------------+----------+----------+----------+----------+----------+----------+--------------------\n| KLIENT SQL nach Lesezeiten von Festplatten sortiert\n+--------------------+----------+----------+----------+----------+----------+----------+--------------------\n|           lesezeit|     Aufrufe|  % dbzeit|gesamt_zeit|     % CPU|      % IO|    dbname|             queryid\n+--------------------+----------+----------+----------+----------+----------+----------+--------------------\n|            02:16:30|        19|       3.1|  04:14:15|     10.83|     11.52|      tdb1|  389015618226997618\n|            00:04:50|       375|      0.56|  00:46:06|      0.94|      0.41|      tdb1|   28942442626229688\n|            00:01:10|       749|      1.69|  02:18:21|      3.39|       0.1|      tdb1| 4710212362688288619\n|            00:00:57|       746|      2.02|  02:45:40|      4.23|      0.08|      tdb1| 1237430309438971376\n+--------------------+----------+----------+----------+----------+----------+----------+--------------------\n| KLIENT SQL nach Ausf\u00fchrungen sortiert\n+--------------------+----------+----------+----------+----------+----------+----------+--------------------\n|               Aufrufe|      zeilen|  % dbzeit|gesamt_zeit|     % CPU|      % IO|    dbname|             queryid\n+--------------------+----------+----------+----------+----------+----------+----------+--------------------\n|                 749|       749|      1.69|  02:18:21|      3.39|       0.1|      tdb1| 4710212362688288619\n|                 746|       746|      2.02|  02:45:40|      4.23|      0.08|      tdb1| 1237430309438971376\n|                 375|         0|      0.56|  00:46:06|      0.94|      0.41|      tdb1|   28942442626229688\n|                  19|        19|       3.1|  04:14:15|     10.83|     11.52|      tdb1|  389015618226997618\n+--------------------+----------+----------+----------+----------+----------+----------+--------------------<\/pre>\n<p><\/p>\n<h2>Zusammenfassung<\/h2>\n<p>\nDurch die Nutzung der bereitgestellten Abfragen und der erhaltenen Berichterstattung kann ein umfassenderes Bild zur Analyse und L\u00f6sung von Leistungsdegradationsproblemen f\u00fcr einzelne Abfragen sowie f\u00fcr den gesamten Cluster gewonnen werden.<\/p>\n<h2>Entwicklung<\/h2>\n<p>\nDie zuk\u00fcnftigen Entwicklungspl\u00e4ne sind wie folgt:<\/p>\n<ul>\n<li>Die Berichterstattung um die Historie der Sperren erg\u00e4nzen. Die Abfragen werden getestet und in K\u00fcrze bereitgestellt.<\/li>\n<li>Die Erweiterung TimescaleDB zur Speicherung der Historie von pg_stat_activity und pg_locks nutzen.<\/li>\n<li>Ein Batch-L\u00f6sungspaket auf GitHub vorbereiten, um eine massenhafte Bereitstellung auf Produktionsdatenbanken zu erm\u00f6glichen.<\/li>\n<\/ul>\n<p>\nDie Fortsetzung folgt\u2026<br \/>\n<br \/>Quelle: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/467575\/\">habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041f\u0440\u043e\u0434\u043e\u043b\u0436\u0435\u043d\u0438\u0435 \u0441\u0442\u0430\u0442\u044c\u0438 &#171;\u041f\u043e\u043f\u044b\u0442\u043a\u0430 \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u0430\u043d\u0430\u043b\u043e\u0433 ASH \u0434\u043b\u044f PostgreSQL &#171;. \u0412 \u0441\u0442\u0430\u0442\u044c\u0435 \u0431\u0443\u0434\u0435\u0442 \u0440\u0430\u0441\u0441\u043c\u043e\u0442\u0440\u0435\u043d\u043e \u0438 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e \u043d\u0430 \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u044b\u0445 \u0437\u0430\u043f\u0440\u043e\u0441\u0430\u0445 \u0438 \u043f\u0440\u0438\u043c\u0435\u0440\u0430\u0445 \u2014 \u043a\u0430\u043a\u0443\u044e \u0436\u0435 \u043f\u043e\u043b\u0435\u0437\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043c\u043e\u0436\u043d\u043e \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0438\u0441\u0442\u043e\u0440\u0438\u0438 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u044f pg_stat_activity. \u041f\u0440\u0435\u0434\u0443\u043f\u0440\u0435\u0436\u0434\u0435\u043d\u0438\u0435. \u0412 \u0441\u0432\u044f\u0437\u0438 \u0441 \u043d\u043e\u0432\u0438\u0437\u043d\u043e\u0439 \u0442\u0435\u043c\u044b \u0438 \u043d\u0435\u0437\u0430\u0432\u0435\u0440\u0448\u0435\u043d\u0438\u0435\u043c \u043f\u0435\u0440\u0438\u043e\u0434\u0430 \u0442\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f, \u0441\u0442\u0430\u0442\u044c\u044f \u043c\u043e\u0436\u0435\u0442 \u0441\u043e\u0434\u0435\u0440\u0436\u0430\u0442\u044c \u043e\u0448\u0438\u0431\u043a\u0438. \u041a\u0440\u0438\u0442\u0438\u043a\u0430 \u0438 \u0437\u0430\u043c\u0435\u0447\u0430\u043d\u0438\u044f \u0432\u0441\u044f\u0447\u0435\u0441\u043a\u0438 \u043f\u0440\u0438\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e\u0442\u0441\u044f \u0438 \u043e\u0436\u0438\u0434\u0430\u044e\u0442\u0441\u044f. \u0412\u0445\u043e\u0434\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-38196","post","type-post","status-publish","format-standard","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=\"\u041f\u0440\u043e\u0434\u043e\u043b\u0436\u0435\u043d\u0438\u0435 \u0441\u0442\u0430\u0442\u044c\u0438 &quot;\u041f\u043e\u043f\u044b\u0442\u043a\u0430 \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u0430\u043d\u0430\u043b\u043e\u0433 ASH \u0434\u043b\u044f PostgreSQL &quot;. \u0412 \u0441\u0442\u0430\u0442\u044c\u0435 \u0431\u0443\u0434\u0435\u0442 \u0440\u0430\u0441\u0441\u043c\u043e\u0442\u0440\u0435\u043d\u043e \u0438 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e 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