pgBadger :: PostgreSQL Log Analyzer

Global information

Generated on Fri Jan 9 08:00:20 2026
Log file: /home/postgres/pg_data/data/pg_log/postgresql-2026-01-09_090000.log, ..., /home/postgres/pg_data/data/pg_log/postgresql-2026-01-09_094506.log
Parsed 3,491,837 log entries in 1m18s
Log start from 2026-01-09 09:00:00 to 2026-01-09 10:00:00

Global Stats

304 Number of unique normalized queries
349,385 Number of queries
1h20m34s Total query duration
2026-01-09 09:00:00 First query
2026-01-09 10:00:00 Last query
4,914 queries/s at 2026-01-09 09:45:04 Query peak

1h20m34s Total query duration
9s657ms Prepare/parse total duration
54s178ms Bind total duration
1h19m30s Execute total duration

4 Number of events
2 Number of unique normalized events
2 Max number of times the same event was reported
0 Number of cancellation

41 Total number of automatic vacuums
54 Total number of automatic analyzes

815 Number temporary file
588.84 MiB Max size of temporary file
6.87 MiB Average size of temporary file

4,286 Total number of sessions
11 sessions at 2026-01-09 09:44:03 Session peak
14d13h3m12s Total duration of sessions
4m53s Average duration of sessions
81 Average queries per session
1s128ms Average queries duration per session
4m52s Average idle time per session

4,298 Total number of connections
59 connections/s at 2026-01-09 09:36:34 Connection peak
4 Total number of databases

SQL Traffic

Key values

4,914 queries/s Query Peak
2026-01-09 09:45:04 Date

SELECT Traffic

Key values

2,408 queries/s Query Peak
2026-01-09 09:45:04 Date

INSERT/UPDATE/DELETE Traffic

Key values

170 queries/s Query Peak
2026-01-09 09:53:50 Date

Queries duration

Key values

1h20m34s Total query duration

Prepared queries ratio

Key values

0.00 Ratio of bind vs prepare
0.00 % Ratio between prepared and "usual" statements

General Activity

Day	Hour	Count	Min duration	Max duration	Avg duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Jan 09	09	349,384	0ms	36s782ms	13ms	2m48s	3m4s	3m19s
	10	1	0ms	0ms	0ms	0ms	0ms	0ms

Day	Hour	SELECT	COPY TO	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Jan 09	09	118,056	26	0ms	0ms	0ms	0ms
	10	1	0	0ms	0ms	0ms	0ms

Day	Hour	INSERT	UPDATE	DELETE	COPY FROM	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Jan 09	09	31,523	3,000	16	96	0ms	0ms	0ms	0ms
	10	0	0	0	0	0ms	0ms	0ms	0ms

Day	Hour	Prepare	Bind	Bind/Prepare	Percentage of prepare
Jan 09	09	28,846	142,383	4.94	17.21%
	10	0	1	1.00	0.00%

Day	Hour	Count	Average / Second
Jan 09	09	4,298	1.19/s
	10	0	0.00/s

Day	Hour	Count	Average Duration	Average idle time
Jan 09	09	4,286	4m53s	4m52s
	10	0	0ms	0ms

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Established Connections

Key values

59 connections Connection Peak
2026-01-09 09:36:34 Date

Connections per database

Key values

acaweb_fx Main Database
4,298 connections Total

Chart
Table

Database	User	Count
acaweb_fx		4,190
	pgAdmin 4 - DB:acaweb_fx	1
	postgres	3,802
	psql	387
postgres		1
	pgAdmin 4 - DB:postgres	1
socialmedia		107
	pgAdmin 4 - DB:socialmedia	1
	postgres	106

Connections per user

Key values

postgres Main User
4,298 connections Total

Chart
Table

User	Count
pgAdmin 4 - DB:acaweb_fx	1
pgAdmin 4 - DB:postgres	1
pgAdmin 4 - DB:socialmedia	1
postgres	3,908
psql	387

Connections per host

Key values

192.168.4.142 Main host with 1079 connections
4,298 Total connections

Chart
Table

Host	Count
104.30.164.187	4
127.0.0.1	113
182.165.1.54	2
192.168.0.114	11
192.168.0.216	101
192.168.0.236	3
192.168.0.74	1,052
192.168.0.84	2
192.168.1.127	2
192.168.1.131	2
192.168.1.145	53
192.168.1.15	795
192.168.1.20	100
192.168.1.238	2
192.168.1.239	2
192.168.1.90	73
192.168.1.97	4
192.168.2.126	72
192.168.2.182	12
192.168.2.82	48
192.168.3.199	36
192.168.4.12	4
192.168.4.142	1,079
192.168.4.150	10
192.168.4.222	1
192.168.4.238	12
192.168.4.243	1
192.168.4.249	4
192.168.4.33	94
192.168.4.98	330
[local]	274

Simultaneous sessions

Key values

11 sessions Session Peak
2026-01-09 09:44:03 Date

Histogram of session times

Key values

3,531 0-500ms duration

Chart
Table

Range	Count	Percentage
0ms-500ms	3,531	82.38%
500ms-1s	196	4.57%
1s-30s	367	8.56%
30s-1m	44	1.03%
1m-10m	78	1.82%
10m-30m	40	0.93%
30m-1h	4	0.09%
1h-8h	13	0.30%
> 8h	13	0.30%

Sessions per database

Key values

acaweb_fx Main Database
4,286 sessions Total

Chart
Table

Database	User	Count	Total Duration	Average Duration
acaweb_fx	4,182	14d12h7m39s	4m59s
socialmedia	104	55m32s	32s41ms

Sessions per user

Key values

postgres Main User
4,286 sessions Total

Chart
Table

User	Count	Total Duration	Average Duration
postgres	1	0ms	0ms

Sessions per host

Key values

192.168.4.142 Main Host
4,286 sessions Total

Chart
Table

Host	Count	Total Duration	Average Duration
104.30.164.187	1	7s140ms	7s140ms
127.0.0.1	113	4s284ms	37ms
182.165.1.54	2	23h17m10s	11h38m35s
192.168.0.114	9	47m45s	5m18s
192.168.0.216	101	1m2s	617ms
192.168.0.236	1	6ms	6ms
192.168.0.74	1,052	1d7h30m47s	1m47s
192.168.0.84	2	23h59m17s	11h59m38s
192.168.1.127	2	298ms	149ms
192.168.1.131	4	2d19h12m31s	16h48m7s
192.168.1.145	53	3d3h53m10s	1h25m54s
192.168.1.15	791	2h7m15s	9s653ms
192.168.1.20	100	3d7h32m31s	47m43s
192.168.1.238	2	23h59m16s	11h59m38s
192.168.1.239	2	12ms	6ms
192.168.1.90	73	36s339ms	497ms
192.168.1.97	1	4ms	4ms
192.168.2.126	72	17s767ms	246ms
192.168.2.182	12	1s49ms	87ms
192.168.2.82	48	14s680ms	305ms
192.168.3.199	36	1s267ms	35ms
192.168.4.12	4	20s725ms	5s181ms
192.168.4.142	1,079	8m39s	481ms
192.168.4.150	10	20h8m53s	2h53s
192.168.4.222	1	49s597ms	49s597ms
192.168.4.238	12	15s602ms	1s300ms
192.168.4.243	1	212ms	212ms
192.168.4.249	4	37ms	9ms
192.168.4.33	94	18m41s	11s929ms
192.168.4.98	330	14s964ms	45ms
[local]	274	3m2s	666ms

Sessions per application

Key values

unknown Main Host
4,286 sessions Total

Chart
Table

Application	Count	Total Duration	Average Duration
unknown	4,286	14d13h3m12s	4m53s

Checkpoints Buffers

Key values

9,992 buffers Checkpoint Peak
2026-01-09 09:09:18 Date
209.970 seconds Highest write time
0.020 seconds Sync time

Checkpoints Wal files

Key values

6 files Wal files usage Peak
2026-01-09 09:09:18 Date

Checkpoints distance

Key values

189.93 Mo Distance Peak
2026-01-09 09:09:18 Date

Checkpoints Activity

Day	Hour	Written buffers	Write time	Sync time	Total time
Jan 09	09	53,244	2,012.899s	0.054s	2,013.283s
	10	0	0s	0s	0s

Day	Hour	Added	Removed	Recycled	Synced files	Longest sync	Average sync
Jan 09	09	0	0	26	2,059	0.004s	0s
	10	0	0	0	0	0s	0s

Day	Hour	Count	Avg time (sec)
Jan 09	09	0	0s
	10	0	0s

Day	Hour	Mean distance	Mean estimate
Jan 09	09	35,997.50 kB	71,994.50 kB
	10	0.00 kB	0.00 kB

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Checkpoint causes

Key values

time (12) Main checkpoint cause
12 checkpoints Total

Chart
Table

Cause	Number of checkpoints
time	12
Total	12

Size of temporary files

Key values

397.74 MiB Temp Files size Peak
2026-01-09 09:14:03 Date

Number of temporary files

Key values

62 per second Temp Files Peak
2026-01-09 09:02:09 Date

Temporary Files Activity

Temp Files

Day	Hour	Count	Total size	Average size
Jan 09	09	815	5.47 GiB	6.87 MiB
	10	0	0	0

↑ Back to the top of the Temporary Files Activity table

Queries generating the most temporary files (N)

Rank	Count	Total size	Min size	Max size	Avg size	Query
1	30	1.66 GiB	5.38 MiB	190.92 MiB	56.50 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = ? ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = ? ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = ?) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, ?::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> ? ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = ?) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = ? where (ok.r is null or ok.r = ?) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = ?) and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > ? ? and last.eventtimestamp > current_timestamp - interval ? and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval ?) and last.eventtimestamp > current_timestamp - interval ? and broker.r = ?; with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; Date: 2026-01-09 09:50:07 Duration: 0ms
2	16	617.38 MiB	38.59 MiB	38.59 MiB	38.59 MiB	update solr_relevance_old set new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total from ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total from whatshot_probability where type = ?) sub where result_uid = sub.resultuid; UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type = 'cp') sub WHERE result_uid = sub.resultuid; Date: 2026-01-09 09:46:13 Duration: 0ms
3	16	1.11 GiB	70.81 MiB	70.81 MiB	70.81 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit ?) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then ? else ? end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then ? else ? end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike ? inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike ?) sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike ?; update solr_relevance_old set newrelevant = ? where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike ? and a.resultuid is null); update solr_relevance_old set new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total from ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total from whatshot_probability where type in (...)) sub where result_uid = sub.resultuid; with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; Date: 2026-01-09 09:46:16 Duration: 0ms
4	9	74.16 MiB	8.24 MiB	8.24 MiB	8.24 MiB	jr.resultuid as resultuid, jr.direction as direction, jr.patternendtime as identified, jr.patternlengthbars as length, jr.patternstarttime as patternstarttime, case when jr.trendchangeid = ? then ? else ? end as trendchange, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, jp.patternname as pattern_name, dtt.timezone as timezone, ? as age, cps.pip, g.basegroupname from japsticks_results jr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = jr.symbolid inner join relevance_japsticks_results rar on rar.resultuid = jr.resultuid inner join symbols s on jr.symbolid = s.symbolid and s.nonliquid = ? inner join japsticks_patterns jp on jr.patternid = jp.id inner join downloadersymbolsettings dss on jr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on s.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where jr.gmttimefound > now() - interval ? and s.deleted = ? and (jr.simulation = ? or jr.simulation is null) and (rar.relevant = ?) --and (semicolon_age = ? or rar.age <= semicolon_age) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or jp.patternname in (...)) and (? = ? or jr.patternlengthbars <= ?) ), results as ( select distinct on (symbolid) * from all_results order by symbolid, resultuid ) select * from results order by identified desc, length desc ; jr.resultuid AS resultuid, jr.direction AS direction, jr.patternendtime AS identified, jr.patternlengthbars AS length, jr.patternstarttime AS patternstarttime, case when jr.trendchangeid = 1 then 'Continuation' else 'Reversal' end AS trendchange, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, jp.patternname AS pattern_name, dtt.timezone AS timezone, 0 AS age, cps.pip, g.basegroupname FROM japsticks_results jr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $1 AND bsl.symbolid = jr.symbolid INNER JOIN relevance_japsticks_results rar ON rar.resultuid = jr.resultuid INNER JOIN symbols s ON jr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN japsticks_patterns jp ON jr.patternid = jp.id INNER JOIN downloadersymbolsettings dss ON jr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN symbolgroup sg on s.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' WHERE jr.gmttimefound > now() - INTERVAL '7 DAYS' AND s.deleted = 0 AND (jr.simulation = 0 OR jr.simulation IS NULL) AND (rar.relevant = 1) --AND (semicolon_age = 0 OR rar.age <= semicolon_age) AND ($2 = 0 OR s.timegranularity in ($3)) AND ($4 = 0 OR s.exchange in ($5)) AND ($6 = 0 OR coalesce(bim.code, s.symbol) in ($7)) AND ($8 = 0 OR jp.patternname in ($9)) AND ($10 = 0 OR jr.patternlengthbars <= $11)), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-01-09 09:47:19 Duration: 0ms
5	8	976.91 MiB	122.08 MiB	122.15 MiB	122.11 MiB	select updateresultsmaterializedview (); select updateresultsmaterializedview (); Date: 2026-01-09 09:47:14 Duration: 0ms
6	4	355.99 MiB	88.93 MiB	89.07 MiB	89.00 MiB	select updateageforrelevantresults (); select updateageforrelevantresults (); Date: 2026-01-09 09:47:04 Duration: 0ms
7	2	6.70 MiB	3.35 MiB	3.35 MiB	3.35 MiB	select resultuid from relevance_consecutivecandles_results order by resultuid desc limit ?), all_results as ( select ccr.resultuid as resultuid, ccr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, ccr.patternendtime as identified, dtt.timezone as timezone, ccr.qtyconsecutivecandles as length, g.basegroupname, case when rcr.age is not null then rcr.age when ccr.resultuid <= rm.resultuid then ? else ? end as age, case when rcr.relevant is not null then rcr.relevant when ccr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip, newlevels.filtered from consecutivecandles_results ccr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = ccr.symbolid inner join symbols s on ccr.symbolid = s.symbolid and s.nonliquid = ? inner join downloadersymbolsettings dss on ccr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on ccr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join rar_max rm on ? = ? left outer join relevance_consecutivecandles_results rcr on rcr.resultuid = ccr.resultuid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? left join lateral calc_cc_signal_filter (ccr.resultuid) newlevels on true where ccr.gmttimefound > now() - interval ? and s.deleted = ? and (ccr.simulation = ? or ccr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or ccr.patternlengthbars <= ?)), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc; SELECT resultuid FROM relevance_consecutivecandles_results ORDER BY resultuid DESC LIMIT 1), all_results AS ( SELECT ccr.resultuid AS resultuid, ccr.direction AS direction, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, ccr.patternendtime AS identified, dtt.timezone AS timezone, ccr.qtyconsecutivecandles AS length, g.basegroupname, CASE WHEN rcr.age IS NOT NULL THEN rcr.age WHEN ccr.resultuid <= rm.resultuid THEN 1 ELSE 0 END as age, CASE WHEN rcr.relevant IS NOT NULL THEN rcr.relevant WHEN ccr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip, newLevels.filtered FROM consecutivecandles_results ccr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $1 AND bsl.symbolid = ccr.symbolid INNER JOIN symbols s ON ccr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN downloadersymbolsettings dss ON ccr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN symbolgroup sg on ccr.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid INNER JOIN rar_max rm ON 1 = 1 LEFT OUTER JOIN relevance_consecutivecandles_results rcr ON rcr.resultuid = ccr.resultuid LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' LEFT JOIN LATERAL calc_cc_signal_filter (ccr.resultuid) newLevels on true WHERE ccr.gmttimefound > now() - INTERVAL '7 DAYS' AND s.deleted = 0 AND (ccr.simulation = 0 OR ccr.simulation IS NULL) AND ($2 = 0 OR s.timegranularity in ($3, $4, $5, $6, $7, $8, $9)) AND ($10 = 0 OR s.exchange in ($11)) AND ($12 = 0 OR coalesce(bim.code, s.symbol) in ($13, $14, $15, $16, $17, $18, $19, $20, $21, $22, $23, $24, $25, $26, $27, $28, $29, $30, $31, $32, $33, $34, $35, $36, $37, $38, $39, $40, $41, $42, $43, $44, $45, $46, $47, $48, $49, $50, $51, $52, $53, $54, $55, $56, $57, $58, $59, $60, $61, $62, $63, $64, $65, $66, $67, $68, $69, $70, $71, $72, $73, $74, $75, $76, $77, $78, $79, $80, $81, $82, $83, $84, $85, $86, $87, $88, $89, $90, $91, $92, $93, $94, $95, $96, $97, $98, $99, $100, $101, $102, $103, $104, $105, $106, $107, $108, $109, $110, $111, $112, $113, $114, $115, $116, $117, $118, $119, $120, $121, $122, $123, $124, $125, $126, $127, $128, $129, $130, $131, $132, $133, $134, $135, $136, $137, $138, $139, $140, $141, $142, $143, $144, $145, $146, $147, $148, $149, $150, $151, $152, $153, $154, $155, $156, $157, $158, $159, $160, $161, $162, $163, $164, $165, $166, $167, $168, $169, $170, $171, $172, $173, $174, $175, $176, $177, $178, $179, $180, $181, $182, $183, $184, $185, $186, $187, $188, $189, $190, $191, $192, $193, $194, $195, $196, $197, $198, $199, $200, $201, $202, $203, $204, $205, $206, $207, $208, $209, $210, $211, $212, $213, $214, $215, $216, $217, $218, $219, $220, $221, $222, $223, $224, $225, $226, $227, $228, $229, $230, $231, $232, $233, $234, $235, $236, $237, $238, $239, $240, $241, $242, $243, $244, $245, $246, $247, $248, $249, $250, $251, $252, $253, $254, $255, $256, $257, $258, $259, $260, $261, $262, $263, $264, $265, $266, $267, $268, $269, $270, $271, $272, $273, $274, $275, $276, $277, $278, $279, $280, $281, $282, $283, $284, $285, $286, $287, $288, $289, $290, $291, $292, $293, $294, $295, $296, $297, $298, $299, $300, $301, $302, $303, $304, $305, $306, $307, $308, $309, $310, $311, $312, $313, $314, $315, $316, $317, $318, $319, $320, $321, $322)) AND ($323 = 0 OR ccr.patternlengthbars <= $324)), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results WHERE (FALSE = $325 OR relevant = 1) AND ($326 = 0 OR age <= $327) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-01-09 09:52:26 Duration: 0ms

Queries generating the largest temporary files

Rank	Size	Query
1	190.92 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-09 09:00:04 ]
2	166.44 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-09 09:20:05 ]
3	148.80 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-09 09:10:05 ]
4	126.38 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-09 09:40:04 ]
5	122.15 MiB	select updateresultsmaterializedview (); [ Date: 2026-01-09 09:47:14 ]
6	122.13 MiB	select updateresultsmaterializedview (); [ Date: 2026-01-09 09:32:15 ]
7	122.13 MiB	select updateresultsmaterializedview (); [ Date: 2026-01-09 09:50:32 ]
8	122.11 MiB	select updateresultsmaterializedview (); [ Date: 2026-01-09 09:35:32 ]
9	122.11 MiB	select updateresultsmaterializedview (); [ Date: 2026-01-09 09:02:15 ]
10	122.11 MiB	select updateresultsmaterializedview (); [ Date: 2026-01-09 09:17:14 ]
11	122.09 MiB	select updateresultsmaterializedview (); [ Date: 2026-01-09 09:20:32 ]
12	122.08 MiB	select updateresultsmaterializedview (); [ Date: 2026-01-09 09:05:32 ]
13	96.72 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-09 09:50:05 ]
14	89.07 MiB	select updateageforrelevantresults (); [ Date: 2026-01-09 09:02:05 ]
15	89.02 MiB	select updateageforrelevantresults (); [ Date: 2026-01-09 09:32:06 ]
16	88.98 MiB	select updateageforrelevantresults (); [ Date: 2026-01-09 09:47:04 ]
17	88.93 MiB	select updateageforrelevantresults (); [ Date: 2026-01-09 09:17:04 ]
18	86.40 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-09 09:50:07 ]
19	77.88 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-09 09:30:05 ]
20	72.27 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-01-09 09:40:07 ]

Vacuums / Analyzes Distribution

Key values

0 sec Highest CPU-cost vacuum
Table
Database
Date
0 sec Highest CPU-cost analyze
Table
Database
Date

Analyzes per table

Key values

public.solr_relevance_old (16) Main table analyzed (database acaweb_fx)
54 analyzes Total

Chart
Table

Table	Number of analyzes
acaweb_fx.public.solr_relevance_old	16
acaweb_fx.pg_catalog.pg_attribute	6
acaweb_fx.public.datafeeds_latestrun	4
acaweb_fx.public.relevance_keylevels_results	4
acaweb_fx.pg_catalog.pg_class	4
acaweb_fx.public.relevance_autochartist_results	4
acaweb_fx.public.relevance_fibonacci_results	4
acaweb_fx.pg_catalog.pg_type	3
acaweb_fx.pg_catalog.pg_index	2
acaweb_fx.public.latest_t15_candle_view	2
acaweb_fx.pg_catalog.pg_depend	2
acaweb_fx.public.autochartist_symbolupdates	1
acaweb_fx.public.whatshot_probability	1
acaweb_fx.public.latest_candle_datetime_per_receng	1
Total	54

Vacuums per table

Key values

public.solr_relevance_old (18) Main table vacuumed on database acaweb_fx
41 vacuums Total

Chart
Table

		Index	Buffer usage			Skipped		WAL usage
Table	Vacuums	scans	hits	misses	dirtied	pins	frozen	records	full page	bytes
acaweb_fx.public.solr_relevance_old	18	17	14,277	0	55	0	67	9,289	16	1,673,348
acaweb_fx.public.datafeeds_latestrun	4	0	482	0	12	0	0	60	12	64,330
acaweb_fx.public.relevance_keylevels_results	3	3	12,302	0	307	5	243	2,670	257	927,435
acaweb_fx.public.relevance_autochartist_results	3	3	10,413	0	158	4	741	1,707	130	462,991
acaweb_fx.public.relevance_fibonacci_results	3	3	3,989	0	55	3	127	558	40	143,229
acaweb_fx.pg_toast.pg_toast_2619	2	2	337	0	78	0	0	211	69	277,329
acaweb_fx.pg_catalog.pg_attribute	2	2	1,582	0	308	0	134	699	253	1,544,450
acaweb_fx.pg_catalog.pg_type	1	1	130	0	21	0	0	51	15	106,293
acaweb_fx.public.autochartist_symbolupdates	1	1	24,906	0	1,817	3	37,960	6,873	3,745	1,398,693
acaweb_fx.public.solr_imports	1	1	65	0	3	0	0	6	2	15,937
acaweb_fx.pg_catalog.pg_statistic	1	1	1,020	0	213	0	594	460	192	729,724
acaweb_fx.public.latest_t15_candle_view	1	1	66	0	1	0	0	6	1	9,061
acaweb_fx.pg_catalog.pg_class	1	1	464	0	34	0	0	140	32	199,694
Total	41	36	70,033	51,442	3,062	15	39,866	22,730	4,764	7,552,514

Tuples removed per table

Key values

public.solr_relevance_old (50386) Main table with removed tuples on database acaweb_fx
62467 tuples Total removed

Chart
Table

		Index	Tuples			Pages
Table	Vacuums	scans	removed	remain	not yet removable	removed	remain
acaweb_fx.public.solr_relevance_old	18	17	50,386	114,935	9,052	0	3,655
acaweb_fx.public.autochartist_symbolupdates	1	1	5,360	53,539	8	0	40,691
acaweb_fx.pg_catalog.pg_attribute	2	2	2,462	21,962	252	19	496
acaweb_fx.public.relevance_keylevels_results	3	3	1,643	40,915	2,423	0	837
acaweb_fx.public.relevance_autochartist_results	3	3	970	27,229	1,253	0	1,140
acaweb_fx.pg_catalog.pg_statistic	1	1	522	3,728	0	0	1,194
acaweb_fx.public.relevance_fibonacci_results	3	3	380	4,813	369	0	306
acaweb_fx.public.datafeeds_latestrun	4	0	241	56	0	0	64
acaweb_fx.pg_catalog.pg_class	1	1	148	1,649	0	0	150
acaweb_fx.pg_toast.pg_toast_2619	2	2	130	339	1	6	94
acaweb_fx.pg_catalog.pg_type	1	1	112	1,446	0	0	38
acaweb_fx.public.latest_t15_candle_view	1	1	62	14	0	0	1
acaweb_fx.public.solr_imports	1	1	51	1	0	0	2
Total	41	36	62,467	270,626	13,358	25	48,668

Pages removed per table

Key values

pg_catalog.pg_attribute (19) Main table with removed pages on database acaweb_fx
25 pages Total removed

Chart
Table

Table	Number of vacuums	Index scans	Tuples removed	Pages removed
acaweb_fx.pg_catalog.pg_attribute	2	2	2462	19
acaweb_fx.pg_toast.pg_toast_2619	2	2	130	6
acaweb_fx.pg_catalog.pg_type	1	1	112	0
acaweb_fx.public.datafeeds_latestrun	4	0	241	0
acaweb_fx.public.autochartist_symbolupdates	1	1	5360	0
acaweb_fx.public.solr_imports	1	1	51	0
acaweb_fx.pg_catalog.pg_statistic	1	1	522	0
acaweb_fx.public.latest_t15_candle_view	1	1	62	0
acaweb_fx.public.relevance_keylevels_results	3	3	1643	0
acaweb_fx.pg_catalog.pg_class	1	1	148	0
acaweb_fx.public.solr_relevance_old	18	17	50386	0
acaweb_fx.public.relevance_autochartist_results	3	3	970	0
acaweb_fx.public.relevance_fibonacci_results	3	3	380	0
Total	41	36	62,467	25

Autovacuum Activity

Vacuums / Analyzes

Day	Hour	VACUUMs	ANALYZEs
Jan 09	09	41	54
	10	0	0

↑ Back to the top of the Autovacuum Activity table

Locks by types

Key values

unknown Main Lock Type
0 locks Total

Chart
Table

Type of locks

NO DATASET

Type	Object	Count	Total Duration	Average Duration (s)
NO DATASET

Most frequent waiting queries (N)

Rank	Count	Total time	Min time	Max time	Avg duration	Query
NO DATASET

Queries that waited the most

Rank	Wait time	Query
NO DATASET

Queries by type

Key values

118,057 Total read queries
49,530 Total write queries

Chart
Table

Type	Count	Percentage
SELECT	118,057	33.79%
INSERT	31,523	9.02%
UPDATE	3,000	0.86%
DELETE	16	0.00%
COPY FROM	96	0.03%
COPY TO	26	0.01%
CTE	13,943	3.99%
DDL	16	0.00%
TCL	910	0.26%
CURSOR	0	0.00%
OTHERS	181,798	52.03%

Queries by database

Key values

unknown Main database
348,358 Requests
1h19m30s (unknown)
Main time consuming database

Chart
Table

Database	Request type	Count	Duration
acaweb_fx	Total	919	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	204	0ms
	select	102	0ms
	tcl	332	0ms
	update	39	0ms
postgres	Total	1	0ms
	others	1	0ms
socialmedia	Total	107	0ms
	others	4	0ms
	select	94	0ms
	tcl	9	0ms
unknown	Total	348,358	1h19m30s
	copy from	16	0ms
	cte	13,839	0ms
	insert	31,523	0ms
	others	6,898	0ms
	select	117,861	0ms
	tcl	569	0ms
	update	2,961	0ms

Queries by user

Key values

unknown Main user
348,358 Requests

Queries Chart
Table

User	Request type	Count	Duration
postgres	Total	1,027	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	209	0ms
	select	196	0ms
	tcl	341	0ms
	update	39	0ms
unknown	Total	348,358	1h19m30s
	copy from	16	0ms
	cte	13,839	0ms
	insert	31,523	0ms
	others	6,898	0ms
	select	117,861	0ms
	tcl	569	0ms
	update	2,961	0ms

Duration by user

Key values

1h19m30s (unknown) Main time consuming user

Duration Chart
Table

User	Request type	Count	Duration
postgres	Total	1,027	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	209	0ms
	select	196	0ms
	tcl	341	0ms
	update	39	0ms
unknown	Total	348,358	1h19m30s
	copy from	16	0ms
	cte	13,839	0ms
	insert	31,523	0ms
	others	6,898	0ms
	select	117,861	0ms
	tcl	569	0ms
	update	2,961	0ms

Queries by host

Key values

unknown Main host
349,385 Requests
1h19m30s (unknown)
Main time consuming host

Chart
Table

Host	Request type	Count	Duration
unknown	Total	349,385	1h19m30s
	copy from	96	0ms
	copy to	26	0ms
	cte	13,943	0ms
	ddl	16	0ms
	delete	16	0ms
	insert	31,523	0ms
	others	7,107	0ms
	select	118,057	0ms
	tcl	910	0ms
	update	3,000	0ms

Queries by application

Key values

unknown Main application
348,995 Requests
1h19m30s (unknown)
Main time consuming application

Chart
Table

Application	Request type	Count	Duration
pgAdmin 4 - DB:acaweb_fx	Total	1	0ms
	others	1	0ms
pgAdmin 4 - DB:postgres	Total	1	0ms
	others	1	0ms
pgAdmin 4 - DB:socialmedia	Total	1	0ms
	others	1	0ms
psql	Total	387	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	4	0ms
	select	102	0ms
	update	39	0ms
unknown	Total	348,995	1h19m30s
	copy from	16	0ms
	cte	13,839	0ms
	insert	31,523	0ms
	others	7,100	0ms
	select	117,955	0ms
	tcl	910	0ms
	update	2,961	0ms

Number of cancelled queries

Key values

0 per second Cancelled query Peak
2026-01-09 09:32:47 Date

Number of cancelled queries (5 minutes period)

NO DATASET

Histogram of query times

Key values

131,864 0-1ms duration

Chart
Table

Range	Count	Percentage
0-1ms	131,864	75.48%
1-5ms	38,137	21.83%
5-10ms	2,186	1.25%
10-25ms	962	0.55%
25-50ms	220	0.13%
50-100ms	162	0.09%
100-500ms	322	0.18%
500-1000ms	102	0.06%
1000-10000ms	577	0.33%
> 10000ms	159	0.09%

Slowest individual queries

Rank	Duration	Query
NO DATASET

Time consuming queries

Rank

Total duration

Times executed

Min duration

Max duration

Avg duration

Query

1

0ms

1

Details

0ms

insert into t30 (symbolid, pricedatetime, open, high, low, close, volume, bsf, sastdatetimereceived) values (?, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?, ?, ?, ?::timestamp without time zone) on conflict (symbolid, pricedatetime) do nothing;

Times Reported Time consuming queries #1

Day	Hour	Count	Duration	Avg duration
Jan 09	09	1	0ms	0ms

2

0ms

65

Details

0ms

select key, value from datasources ds inner join datasourceparams dsp on ds.id = dsp.datasourceid where ds.name = ?;

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Jan 09	09	65	0ms	0ms

3

0ms

4

Details

0ms

with rar_max as ( select resultuid from relevance_bigmovement_results order by resultuid desc limit ? ) select bmr.symbolid, patternstarttime, patternendtime, timegranularity, ? as direction, case when bmr.old_resultuid = ? then bmr.old_resultuid else bmr.resultuid end as uid, s.exchange, s.symbol, s.longname, s.shortname, dtt.timezone, bmr.patternmovement, bmr.statisticalmovement, bmr.fromprice, bmr.toprice, bmr.percentile, bmr.patternlengthbars, case when rbr.age is not null then rbr.age when bmr.resultuid <= rm.resultuid then ? else ? end as age, case when rbr.relevant is not null then rbr.relevant when bmr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from bigmovement_results bmr inner join downloadersymbolsettings dss on bmr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on bmr.symbolid = s.symbolid inner join rar_max rm on ? = ? left outer join relevance_bigmovement_results rbr on rbr.resultuid = bmr.resultuid left join currencypips cps on cps.symbol = s.symbol where (bmr.old_resultuid = ? or bmr.resultuid = ?) and dtt.dayofweek = ?;

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Jan 09	09	4	0ms	0ms

4

0ms

2,153

Details

0ms

insert into t60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;

Times Reported Time consuming queries #4

Day	Hour	Count	Duration	Avg duration
Jan 09	09	2,153	0ms	0ms

5

0ms

48

Details

0ms

select count(*) from datafeeds_latestrun where feedname ilike ? and ((latestrxtime > current_timestamp - interval ? and latestdbwritetime > current_timestamp - interval ?) or (latestdbwritetime > current_timestamp - interval ? and lateststartuptime > current_timestamp - interval ?));

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Jan 09	09	48	0ms	0ms

6

0ms

4

Details

0ms

select updaterelevantforrelevantresults ();

Times Reported Time consuming queries #6

Day	Hour	Count	Duration	Avg duration
Jan 09	09	4	0ms	0ms

7

0ms

1

Details

0ms

update executions set isrunning = false, has_results = true, response = ? 's products include cellulose specialties, which are natural polymers that are used as raw materials to manufacture a range of consumer - oriented products, such as liquid crystal displays, impact - resistant plastics, thickeners for food products, pharmaceuticals, cosmetics, cigarette filters, high - tenacity rayon tire cords and industrial hoses, food casings, paints, and lacquers. It also offers commodity products, such as commodity viscose used in woven applications, including rayon textiles for clothing and other fabrics, as well as in non - woven applications comprising baby wipes, cosmetic and personal wipes, industrial wipes, and mattress ticking; and absorbent materials consisting of fluff that are used as an absorbent medium in disposable baby diapers, feminine hygiene products, incontinence pads, convalescent bed pads, industrial towels and wipes, and non - woven fabrics. In addition, the company provides paperboards for packaging, printing documents, brochures, promotional materials, paperback books and catalog covers, file folders, tags, and lottery tickets; and high - yield pulps to produces hardwood aspen, maple, and birch species for paperboard, packaging, printing and writing papers, and various other paper products. Rayonier Advanced Materials Inc. was founded in ? and is headquartered in Jacksonville, Florida. ", " Address ": " ? Riverplace Boulevard, Jacksonville, FL, United States, ? ", " Phone ": " ? ? ? ", " WebURL ": " https: // ryam.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / RYAM.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 01 - 08 ", " ticker ": " RYAM.US ", " code ": " RYAM ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " } }, " Heading ": " Mayores Movimientos ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " Date ": " ? ene ? " }, " text ": { " title ": " Los mayores movimientos de esta semana ", " short_text ": " Los mayores ganadores son: ? D Systems Corporation: + ?.? %, AeroVironment Inc: + ?.? %, Vaxart Inc: + ?.? %, Kratos Defense & Security Solutions: + ?.? %, Bloom Energy Corp: + ?.? %, Revolution Medicines Inc: + ?.? %, Cardlytics Inc: + ?.? %, Vera Bradley Inc: + ?.? %, Luna Innovations Incorporated: + ?.? %, Rayonier Advanced Materials: + ?.? %.Los mayores perdedores son: T ? Biosystems Inc: (?.? %), CorMedix Inc: (?.? %), Gossamer Bio Inc: (?.? %), Akso Health Group ADR: (?.? %), Canadian Solar Inc: (?.? %), Aldeyra The: (?.? %), Viomi Technology ADR: (?.? %), Owens & Minor Inc: (?.? %), Evolus Inc: (?.? %), Regis Corporation Common Stock: (?.? %) ", " long_text ": " Los mayores ganadores son: - ? D Systems Corporation: + ?.? % n - AeroVironment Inc: + ?.? % n - Vaxart Inc: + ?.? % n - Kratos Defense & Security Solutions: + ?.? % n - Bloom Energy Corp: + ?.? % n - Revolution Medicines Inc: + ?.? % n - Cardlytics Inc: + ?.? % n - Vera Bradley Inc: + ?.? % n - Luna Innovations Incorporated: + ?.? % n - Rayonier Advanced Materials: + ?.? % n. Los mayores perdedores son: - T ? Biosystems Inc: (?.? %) n - CorMedix Inc: (?.? %) n - Gossamer Bio Inc: (?.? %) n - Akso Health Group ADR: (?.? %) n - Canadian Solar Inc: (?.? %) n - Aldeyra The: (?.? %) n - Viomi Technology ADR: (?.? %) n - Owens & Minor Inc: (?.? %) n - Evolus Inc: (?.? %) n - Regis Corporation Common Stock: (?.? %) n " }, " warnings ": [], " errors ": [], " has_results ": true, " quantity_results ": ?, " creatomate_response ": { " ? _id ": " ? f123611 - 8239 - 4bd3 - a282 - 968d85ffe333 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? f123611 - 8239 - 4bd3 - a282 - 968d85ffe333.png ", " ? _template_id ": " d547776f - f504 - 4737 - 8de1 - 1a357f411a3c ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (ES) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ? b4fd5 - 5af0 - 415b - 879f - 10fb93a84708 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? b4fd5 - 5af0 - 415b - 879f - 10fb93a84708.png ", " ? _template_id ": " d547776f - f504 - 4737 - 8de1 - 1a357f411a3c ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (ES) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ? afa48 - a137 - 45a2 - 900e-26009 fcb5ae0 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? afa48 - a137 - 45a2 - 900e-26009 fcb5ae0.mp4 ", " ? _template_id ": " d547776f - f504 - 473[...];

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Jan 09	09	1	0ms	0ms

8

0ms

2

Details

0ms

select count(*) as total_records from downloadersymbolsettings_view_retool where classname = ?;

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Jan 09	09	2	0ms	0ms

9

0ms

3

Details

0ms

set datestyle = iso;

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Jan 09	09	3	0ms	0ms

10

0ms

3

Details

0ms

set client_encoding to ?;

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Jan 09	09	3	0ms	0ms

11

0ms

18

Details

0ms

select cast(count(*) / cast(setting as numeric) * ? as int) from pg_stat_activity, pg_settings where name = ? group by setting;

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Jan 09	09	18	0ms	0ms

12

0ms

1

Details

0ms

update executions set isrunning = false, has_results = true, response = ? 's biggest movers ", " short_text ": " The biggest winners are: ? D Systems Corporation: + ?.? %, AeroVironment Inc: + ?.? %, Vaxart Inc: + ?.? %, Kratos Defense & Security Solutions: + ?.? %, Bloom Energy Corp: + ?.? %, Revolution Medicines Inc: + ?.? %, Cardlytics Inc: + ?.? %, Vera Bradley Inc: + ?.? %, Luna Innovations Incorporated: + ?.? %, Rayonier Advanced Materials: + ?.? %.The biggest losers are: T ? Biosystems Inc: (?.? %), CorMedix Inc: (?.? %), Gossamer Bio Inc: (?.? %), Akso Health Group ADR: (?.? %), Canadian Solar Inc: (?.? %), Aldeyra The: (?.? %), Viomi Technology ADR: (?.? %), Owens & Minor Inc: (?.? %), Evolus Inc: (?.? %), Regis Corporation Common Stock: (?.? %) ", " long_text ": " The biggest winners are: - ? D Systems Corporation: + ?.? % n - AeroVironment Inc: + ?.? % n - Vaxart Inc: + ?.? % n - Kratos Defense & Security Solutions: + ?.? % n - Bloom Energy Corp: + ?.? % n - Revolution Medicines Inc: + ?.? % n - Cardlytics Inc: + ?.? % n - Vera Bradley Inc: + ?.? % n - Luna Innovations Incorporated: + ?.? % n - Rayonier Advanced Materials: + ?.? % n. The biggest losers are: - T ? Biosystems Inc: (?.? %) n - CorMedix Inc: (?.? %) n - Gossamer Bio Inc: (?.? %) n - Akso Health Group ADR: (?.? %) n - Canadian Solar Inc: (?.? %) n - Aldeyra The: (?.? %) n - Viomi Technology ADR: (?.? %) n - Owens & Minor Inc: (?.? %) n - Evolus Inc: (?.? %) n - Regis Corporation Common Stock: (?.? %) n " }, " warnings ": [], " errors ": [], " has_results ": true, " quantity_results ": ?, " creatomate_response ": { " ? _id ": " ? a4661 - cdc7 - 4121 - 9d35 - e4ee573dd5bf ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? a4661 - cdc7 - 4121 - 9d35 - e4ee573dd5bf.png ", " ? _template_id ": " ebdfce84 - ee3f - 4a36 - be67 - 2554576d3ff0 ", " ? _template_name ": " Biggest stock gainers and losers MP ? ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " c4bf9525 - 0343 - 42ed - 84ad - c2fecf5ce4a0 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / c4bf9525 - 0343 - 42ed - 84ad - c2fecf5ce4a0.png ", " ? _template_id ": " ebdfce84 - ee3f - 4a36 - be67 - 2554576d3ff0 ", " ? _template_name ": " Biggest stock gainers and losers MP ? ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ? cc6869d - d17c - 4ead - 96a5 - f9bff554f51e ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? cc6869d - d17c - 4ead - 96a5 - f9bff554f51e.mp4 ", " ? _template_id ": " ebdfce84 - ee3f - 4a36 - be67 - 2554576d3ff0 ", " ? _template_name ": " Biggest stock gainers and losers MP ? ", " ? _output_format ": " mp4 ", " ? _frame_rate ": ? }, " image_api ": { " latest ": [ " https: // api.autochartist.com / social_media / image / e9e91047 - c9ea - 4f5b - 9289 - dc8d4069ffab ? broker_id = ?& item = ? ", " https: // api.autochartist.com / social_media / image / e9e91047 - c9ea - 4f5b - 9289 - dc8d4069ffab ? broker_id = ?& item = ? ", " https: // api.autochartist.com / social_media / image / e9e91047 - c9ea - 4f5b - 9289 - dc8d4069ffab ? broker_id = ?& item = ? " ], " snapshot ": [ " https: // api.autochartist.com / social_media / image / e9e91047 - c9ea - 4f5b - 9289 - dc8d4069ffab ? broker_id = ?& item = ?& dt = ? - 01 - 09 % ? % ? A ? % ? A ? ", " https: // api[...];

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Jan 09	09	1	0ms	0ms

13

0ms

453

Details

0ms

commit;

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Jan 09	09	453	0ms	0ms

14

0ms

248

Details

0ms

with rar_max as ( select resultuid from relevance_keylevels_results order by resultuid desc limit ? ), kr as ( select a.*, rr.age, rr.relevant from keylevels_results a left outer join relevance_keylevels_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_keylevels_results) end ), all_results as ( select kr.resultuid as resultuid, kr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, p.patternname as pattern_name, kr.breakout as breakout, kr.atbaridentified as identified, dtt.timezone as timezone, kr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when kr.age is not null then kr.age when kr.resultuid <= rm.resultuid then ? else ? end as age, case when kr.relevant is not null then kr.relevant when kr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from kr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = kr.symbolid inner join symbols s on bsl.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on s.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join hrspatterns p on kr.patternid = p.patternid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left outer join autochartist_symbolupdates au on dss.symbolid = au.symbolid left outer join relevance_keylevels_results rar on rar.resultuid = kr.resultuid left join lateral calc_kl_signal_filter (kr.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where kr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (kr.simulation = ? or kr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or p.patternname in (...)) and (? = ? or kr.patternclassid in (...)) and (? = ? or kr.patternlengthbars <= ?) and kr.patternstarttime::timestamp without time zone >= coalesce(au.earliestpricedatetime, ?::timestamp without time zone) -- to make sure patternstarttime is in our t-tables ), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc limit ?;

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Jan 09	09	248	0ms	0ms

15

0ms

240

Details

0ms

select count(*), sum(size), extract(epoch from now() - min(modification)) from pg_ls_waldir ();

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Jan 09	09	240	0ms	0ms

16

0ms

240

Details

0ms

select system_identifier from pg_control_system ();

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Jan 09	09	240	0ms	0ms

17

0ms

5

Details

0ms

select groupid, exchange, groupname, symbol, longname from prfsymboltree where brokerid = ? order by groupname, symbol;

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Jan 09	09	5	0ms	0ms

18

0ms

5

Details

0ms

insert into t15 (symbolid, pricedatetime, open, high, low, close, volume, bsf, sastdatetimereceived) values (?, ?::timestamp without time zone, ?, ?.?, ?.?, ?.?, ?, ?, ?::timestamp without time zone) on conflict (symbolid, pricedatetime) do nothing;

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Jan 09	09	5	0ms	0ms

19

0ms

12

Details

0ms

select updatedatafeedslatestrun (?);

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Jan 09	09	12	0ms	0ms

20

0ms

1

Details

0ms

insert into t15 (symbolid, pricedatetime, open, high, low, close, volume, bsf, sastdatetimereceived) values (?, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?.?, ?, ?, ?::timestamp without time zone) on conflict (symbolid, pricedatetime) do nothing; insert into t15 (symbolid, pricedatetime, open, high, low, close, volume, bsf, sastdatetimereceived) values (?, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?.?, ?, ?, ?::timestamp without time zone) on conflict (symbolid, pricedatetime) do nothing;

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Jan 09	09	1	0ms	0ms

Most frequent queries (N)

Rank

Times executed

Total duration

Min duration

Max duration

Avg duration

Query

1

56,911

Details

0ms

select ?;

Times Reported Time consuming queries #1

Day	Hour	Count	Duration	Avg duration
Jan 09	09	56,910	0ms	0ms
	10	1	0ms	0ms

2

19,962

Details

0ms

select distinct on (coalesce(bim.code, s.symbol) , s.exchange, s.timegranularity, df.timezone) s.symbolid as id, coalesce(bim.code, s.symbol) as name, s.symbol as symbol, dss.downloadersymbol as ticker, s.exchange as exchange, s.timegranularity as interval, df.timezone as timezone from symbols s inner join downloadersymbolsettings dss on dss.symbolid = s.symbolid inner join datafeedstimetable df on df.classname ilike dss.classname left join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = s.symbolid left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = ? and bim.type = ? where s.symbolid = ?;

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Jan 09	09	19,962	0ms	0ms

3

8,228

Details

0ms

insert into executionlogs (executionid, status, message, details, detailtype) values (null, ?, ?, null, null);

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Jan 09	09	8,228	0ms	0ms

4

7,941

Details

0ms

select category, name, sum(total) as total, sum(correct) as correct, (cast(sum(correct) as float) / cast(sum(total) as float)) * ?.? as percentage, min("from") AS "from", max("to") AS "to" from ( select category, name, total, correct, percentage, "from", "to" from stats_summary where statsid = ? and category = lower(?) union select category, name, total, correct, percentage, "from", "to" from stats_hrs_summary where statsid = ? and category = lower(?) order by correct desc) as summdata group by category, name having sum(total) > ? order by name;

Times Reported Time consuming queries #4

Day	Hour	Count	Duration	Avg duration
Jan 09	09	7,941	0ms	0ms

5

7,380

Details

0ms

select s.symbolid as id, s.symbol as name, s.exchange as exchange, s.timegranularity as interval, dtt.timezone as timezone from symbols s inner join downloadersymbolsettings dss on dss.symbolid = s.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join brokersymbollist bsl on bsl.symbolid = s.symbolid where bsl.brokerid = ? and (? = ? or s.timegranularity = ?) and (s.symbol = ? or dss.downloadersymbol = ?) and dss.enabled = ?;

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Jan 09	09	7,380	0ms	0ms

6

6,980

Details

0ms

with rar_max as ( select resultuid from relevance_autochartist_results order by resultuid desc limit ? ) select a.symbolid, pattern, patternid, resy0, resy1, resx0, resx1, supporty0, supporty1, supportx0, supportx1, predictiontimeto, patternstarttime, timegranularity, patternendtime, direction, trendchange, patternlengthbars, patternquality, case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, s.exchange, s.symbol, s.longname, s.shortname, breakout, dtt.timezone, patternstartprice, patternendprice, qtytp, newlevels.profit, newlevels.stop, newlevels.filtered, case when rar.age is not null then rar.age when a.resultuid <= rm.resultuid then ? else ? end as age, case when rar.relevant is not null then rar.relevant when a.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from autochartist_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern inner join rar_max rm on ? = ? left outer join relevance_autochartist_results rar on rar.resultuid = a.resultuid left join lateral calc_cp_signal (a.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;

Times Reported Time consuming queries #6

Day	Hour	Count	Duration	Avg duration
Jan 09	09	6,980	0ms	0ms

7

5,789

Details

0ms

insert into t15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Jan 09	09	5,789	0ms	0ms

8

5,317

Details

0ms

insert into autochartist_results (resultid, symbolid, bandwidth, pattern, qtytp, gmttimefound, direction, initialtrend, breakout, volumeincrease, noise, symmetry, predictionpricefrom, predictionpriceto, predictiontimefrom, predictiontimeto, patternstarttime, patternendtime, patternstartprice, patternendprice, resx0, resx1, supportx0, supportx1, resy0, resy1, supporty0, supporty1, supportgradient, resgradient, riskreward, patternquality, trendchange, maxmovementafterbreakout, latestbaratbreakouttime, latestbaratbreakoutprice, patternlengthbars, temporarypattern, relevancestartdistance, simulation, writtendatetime) values (?, ?, ?.?, ?, ?, ?::timestamp without time zone, ?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?::timestamp without time zone, ?::timestamp without time zone, ?::timestamp without time zone, ?::timestamp without time zone, ?.?, ?.?, ?::timestamp without time zone, ?::timestamp without time zone, ?::timestamp without time zone, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?, ?.?, ?::timestamp without time zone, ?.?, ?, ?, ?.?, ?, current_timestamp::timestamp without time zone) on conflict do nothing;

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Jan 09	09	5,317	0ms	0ms

9

4,326

Details

0ms

select category, name, sum(total) as total, sum(correct) as correct, (cast(sum(correct) as float) / cast(sum(total) as float)) * ?.? as percentage, min("from") AS "from", max("to") AS "to" from ( select category, name, total, correct, percentage, "from", "to" from stats_hrsapproaches_summary where statsid = ? and category = lower(?) order by correct desc) as summdata group by category, name having sum(total) > ? order by name;

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Jan 09	09	4,326	0ms	0ms

10

3,897

Details

0ms

with rar_max as ( select resultuid from relevance_keylevels_results order by resultuid desc limit ? ) select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as ruid, s.symbolid as sid, s.symbol as sym, longname, shortname, exchange as e, timegranularity as tg, a.patternid as pid, a.direction as d, a.patternprice as pp, atbaridentified as pet, case when (x9 != ?) then x9 when (x8 != ?) then x8 when (x7 != ?) then x7 when (x6 != ?) then x6 when (x5 != ?) then x5 when (x4 != ?) then x4 when (x3 != ?) then x3 when (x2 != ?) then x2 end as pst, patternprice as patp, x0, x1, x2, case when (x3 != ?) then x3 else ? end as x3, case when (x4 != ?) then x4 else ? end as x4, case when (x5 != ?) then x5 else ? end as x5, case when (x6 != ?) then x6 else ? end as x6, case when (x7 != ?) then x7 else ? end as x7, case when (x8 != ?) then x8 else ? end as x8, errormargin as erm, breakoutprice as pe, breakoutbars as be, breakout, atbaridentified as atbar, atpriceidentified as atprice, patternlengthbars as l, bandwidth as bw, qtytp as qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzos, dtt.timezone as timezone, approachingtimestamp as apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb, furthestprice as fp, newlevels.filtered, a.uniquepointsvalue as upv, case when rar.age is not null then rar.age when a.resultuid <= rm.resultuid then ? else ? end as age, case when rar.relevant is not null then rar.relevant when a.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from keylevels_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join hrspatterns p on a.patternid = p.patternid inner join rar_max rm on ? = ? left outer join relevance_keylevels_results rar on a.resultuid = rar.resultuid left join lateral calc_kl_signal_filter (a.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Jan 09	09	3,897	0ms	0ms

11

3,293

Details

0ms

insert into t30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Jan 09	09	3,293	0ms	0ms

12

3,181

Details

0ms

set extra_float_digits = ?;

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Jan 09	09	3,181	0ms	0ms

13

3,139

Details

0ms

set application_name = ?;

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Jan 09	09	3,139	0ms	0ms

14

3,043

Details

0ms

insert into keylevels_results (bandwidth, breakout, patternid, gmttimefound, approachingtimestamp, approachingregion, qtytp, patternlengthbars, patternprice, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, breakoutbars, breakoutprice, patternendtime, atbaridentified, atpriceidentified, errormargin, direction, symbolid, predictionpricefrom, predictionpriceto, predictiontimefrom, predictiontimebars, uniquepointsvalue, furthestprice, relevancestartdistance, patternclassid, patternstarttime, stoplosslevel, simulation, writtendatetime) values (?.?, ?, ?, ?::timestamp without time zone, ?, ?.?, ?, ?, ?.?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?.?, ?::timestamp without time zone, ?, ?.?, ?.?, ?, ?, ?.?, ?.?, ?::timestamp without time zone, ?, ?, ?.?, ?.?, ?, ?, ?.?, ?, current_timestamp::timestamp without time zone) on conflict do nothing;

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Jan 09	09	3,043	0ms	0ms

15

2,562

Details

0ms

insert into fibonacci_results (bandwidth, pattern, gmttimefound, direction, patternstarttime, patternendtime, patternstartprice, patternendprice, qtytp, pricex, timex, pricea, timea, priceb, timeb, pricec, timec, priced, timed, averagequality, timequality, errormargin, patternlengthbars, target10, target06, target16, target07, target12, target05, target03, symbolid, noise, ratiosfound, temporarypattern, uniqueindex, completed, simulation, writtendatetime) values (?.?, ?, ?::timestamp without time zone, ?, ?::timestamp without time zone, ?::timestamp without time zone, ?.?, ?.?, ?, ?.?, ?::timestamp without time zone, ?.?, ?::timestamp without time zone, ?.?, ?::timestamp without time zone, ?.?, ?::timestamp without time zone, ?.?, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?, ?.?, ?, ?, ?, ?, ?, current_timestamp::timestamp without time zone) on conflict do nothing;

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Jan 09	09	2,562	0ms	0ms

16

2,323

Details

0ms

update patternresultsrelevance set relevant = ?, saxo_relevant = ?, notrelevantpricedatetime = ?, reason = ? where uniqueindex = ? and relevant = ?;

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Jan 09	09	2,323	0ms	0ms

17

2,198

Details

0ms

select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as resultuid, s.symbol, pattern as patternname, timegranularity as interval, patternlengthbars as length, patternendtime, direction, breakout, predictiontimeto, predictionpricefrom, predictionpriceto, patternstartprice, resy1, supporty1, dtt.timezone, cps.pip, newlevels.profit from autochartist_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern left join currencypips cps on cps.symbol = s.symbol left join lateral calc_cp_signal (a.resultuid) newlevels on true where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Jan 09	09	2,198	0ms	0ms

18

2,153

Details

0ms

insert into t60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Jan 09	09	2,153	0ms	0ms

19

1,046

Details

0ms

select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as resultuid, s.symbol, a.patternprice, atbaridentified as patternendtime, breakout, p.patternname, dtt.timezone, a.direction, case when a.patternclassid = ? then a.predictionpricefrom else a.patternprice end as predictionpricefrom, case when a.patternclassid = ? then a.predictionpriceto else a.patternprice end as predictionpriceto, s.timegranularity as interval, patternlengthbars as length, cps.pip from keylevels_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join hrspatterns p on a.patternid = p.patternid left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Jan 09	09	1,046	0ms	0ms

20

936

Details

0ms

select symbolid, pricedatetime, classname, downloadfrequency, downloadersymbol, open, high, low, close, volume, bsf, sastdatetimereceived from ( select pricedatetime, dss.classname, dss.downloadfrequency, dss.symbolid, dss.downloadersymbol, t.open, t.high, t.low, t.close, t.volume, t.bsf, t.sastdatetimereceived, row_number() over (partition by t.symbolid order by t.pricedatetime desc) as rn from t15 t, downloadersymbolsettings dss, symbols s where dss.classname = ? and dss.downloadfrequency = ? and dss.symbolid = t.symbolid and s.symbolid = dss.symbolid and dss.enabled = ? and s.deleted = ? and dss.downloadersymbol in (...) and t.pricedatetime > now() - interval ?) as ranked_candles_table where rn = ?;

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Jan 09	09	936	0ms	0ms

Normalized slowest queries (N)

Rank

Min duration

Max duration

Avg duration

Times executed

Total duration

Query

1

0ms

1

Details

0ms

insert into t30 (symbolid, pricedatetime, open, high, low, close, volume, bsf, sastdatetimereceived) values (?, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?, ?, ?, ?::timestamp without time zone) on conflict (symbolid, pricedatetime) do nothing;

Times Reported Time consuming queries #1

Day	Hour	Count	Duration	Avg duration
Jan 09	09	1	0ms	0ms

2

0ms

65

Details

0ms

select key, value from datasources ds inner join datasourceparams dsp on ds.id = dsp.datasourceid where ds.name = ?;

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Jan 09	09	65	0ms	0ms

3

0ms

4

Details

0ms

with rar_max as ( select resultuid from relevance_bigmovement_results order by resultuid desc limit ? ) select bmr.symbolid, patternstarttime, patternendtime, timegranularity, ? as direction, case when bmr.old_resultuid = ? then bmr.old_resultuid else bmr.resultuid end as uid, s.exchange, s.symbol, s.longname, s.shortname, dtt.timezone, bmr.patternmovement, bmr.statisticalmovement, bmr.fromprice, bmr.toprice, bmr.percentile, bmr.patternlengthbars, case when rbr.age is not null then rbr.age when bmr.resultuid <= rm.resultuid then ? else ? end as age, case when rbr.relevant is not null then rbr.relevant when bmr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from bigmovement_results bmr inner join downloadersymbolsettings dss on bmr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on bmr.symbolid = s.symbolid inner join rar_max rm on ? = ? left outer join relevance_bigmovement_results rbr on rbr.resultuid = bmr.resultuid left join currencypips cps on cps.symbol = s.symbol where (bmr.old_resultuid = ? or bmr.resultuid = ?) and dtt.dayofweek = ?;

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Jan 09	09	4	0ms	0ms

4

0ms

2,153

Details

0ms

insert into t60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;

Times Reported Time consuming queries #4

Day	Hour	Count	Duration	Avg duration
Jan 09	09	2,153	0ms	0ms

5

0ms

48

Details

0ms

select count(*) from datafeeds_latestrun where feedname ilike ? and ((latestrxtime > current_timestamp - interval ? and latestdbwritetime > current_timestamp - interval ?) or (latestdbwritetime > current_timestamp - interval ? and lateststartuptime > current_timestamp - interval ?));

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Jan 09	09	48	0ms	0ms

6

0ms

4

Details

0ms

select updaterelevantforrelevantresults ();

Times Reported Time consuming queries #6

Day	Hour	Count	Duration	Avg duration
Jan 09	09	4	0ms	0ms

7

0ms

1

Details

0ms

update executions set isrunning = false, has_results = true, response = ? 's products include cellulose specialties, which are natural polymers that are used as raw materials to manufacture a range of consumer - oriented products, such as liquid crystal displays, impact - resistant plastics, thickeners for food products, pharmaceuticals, cosmetics, cigarette filters, high - tenacity rayon tire cords and industrial hoses, food casings, paints, and lacquers. It also offers commodity products, such as commodity viscose used in woven applications, including rayon textiles for clothing and other fabrics, as well as in non - woven applications comprising baby wipes, cosmetic and personal wipes, industrial wipes, and mattress ticking; and absorbent materials consisting of fluff that are used as an absorbent medium in disposable baby diapers, feminine hygiene products, incontinence pads, convalescent bed pads, industrial towels and wipes, and non - woven fabrics. In addition, the company provides paperboards for packaging, printing documents, brochures, promotional materials, paperback books and catalog covers, file folders, tags, and lottery tickets; and high - yield pulps to produces hardwood aspen, maple, and birch species for paperboard, packaging, printing and writing papers, and various other paper products. Rayonier Advanced Materials Inc. was founded in ? and is headquartered in Jacksonville, Florida. ", " Address ": " ? Riverplace Boulevard, Jacksonville, FL, United States, ? ", " Phone ": " ? ? ? ", " WebURL ": " https: // ryam.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / RYAM.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 01 - 08 ", " ticker ": " RYAM.US ", " code ": " RYAM ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " } }, " Heading ": " Mayores Movimientos ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " Date ": " ? ene ? " }, " text ": { " title ": " Los mayores movimientos de esta semana ", " short_text ": " Los mayores ganadores son: ? D Systems Corporation: + ?.? %, AeroVironment Inc: + ?.? %, Vaxart Inc: + ?.? %, Kratos Defense & Security Solutions: + ?.? %, Bloom Energy Corp: + ?.? %, Revolution Medicines Inc: + ?.? %, Cardlytics Inc: + ?.? %, Vera Bradley Inc: + ?.? %, Luna Innovations Incorporated: + ?.? %, Rayonier Advanced Materials: + ?.? %.Los mayores perdedores son: T ? Biosystems Inc: (?.? %), CorMedix Inc: (?.? %), Gossamer Bio Inc: (?.? %), Akso Health Group ADR: (?.? %), Canadian Solar Inc: (?.? %), Aldeyra The: (?.? %), Viomi Technology ADR: (?.? %), Owens & Minor Inc: (?.? %), Evolus Inc: (?.? %), Regis Corporation Common Stock: (?.? %) ", " long_text ": " Los mayores ganadores son: - ? D Systems Corporation: + ?.? % n - AeroVironment Inc: + ?.? % n - Vaxart Inc: + ?.? % n - Kratos Defense & Security Solutions: + ?.? % n - Bloom Energy Corp: + ?.? % n - Revolution Medicines Inc: + ?.? % n - Cardlytics Inc: + ?.? % n - Vera Bradley Inc: + ?.? % n - Luna Innovations Incorporated: + ?.? % n - Rayonier Advanced Materials: + ?.? % n. Los mayores perdedores son: - T ? Biosystems Inc: (?.? %) n - CorMedix Inc: (?.? %) n - Gossamer Bio Inc: (?.? %) n - Akso Health Group ADR: (?.? %) n - Canadian Solar Inc: (?.? %) n - Aldeyra The: (?.? %) n - Viomi Technology ADR: (?.? %) n - Owens & Minor Inc: (?.? %) n - Evolus Inc: (?.? %) n - Regis Corporation Common Stock: (?.? %) n " }, " warnings ": [], " errors ": [], " has_results ": true, " quantity_results ": ?, " creatomate_response ": { " ? _id ": " ? f123611 - 8239 - 4bd3 - a282 - 968d85ffe333 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? f123611 - 8239 - 4bd3 - a282 - 968d85ffe333.png ", " ? _template_id ": " d547776f - f504 - 4737 - 8de1 - 1a357f411a3c ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (ES) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ? b4fd5 - 5af0 - 415b - 879f - 10fb93a84708 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? b4fd5 - 5af0 - 415b - 879f - 10fb93a84708.png ", " ? _template_id ": " d547776f - f504 - 4737 - 8de1 - 1a357f411a3c ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (ES) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ? afa48 - a137 - 45a2 - 900e-26009 fcb5ae0 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? afa48 - a137 - 45a2 - 900e-26009 fcb5ae0.mp4 ", " ? _template_id ": " d547776f - f504 - 473[...];

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Jan 09	09	1	0ms	0ms

8

0ms

2

Details

0ms

select count(*) as total_records from downloadersymbolsettings_view_retool where classname = ?;

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Jan 09	09	2	0ms	0ms

9

0ms

3

Details

0ms

set datestyle = iso;

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Jan 09	09	3	0ms	0ms

10

0ms

3

Details

0ms

set client_encoding to ?;

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Jan 09	09	3	0ms	0ms

11

0ms

18

Details

0ms

select cast(count(*) / cast(setting as numeric) * ? as int) from pg_stat_activity, pg_settings where name = ? group by setting;

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Jan 09	09	18	0ms	0ms

12

0ms

1

Details

0ms

update executions set isrunning = false, has_results = true, response = ? 's biggest movers ", " short_text ": " The biggest winners are: ? D Systems Corporation: + ?.? %, AeroVironment Inc: + ?.? %, Vaxart Inc: + ?.? %, Kratos Defense & Security Solutions: + ?.? %, Bloom Energy Corp: + ?.? %, Revolution Medicines Inc: + ?.? %, Cardlytics Inc: + ?.? %, Vera Bradley Inc: + ?.? %, Luna Innovations Incorporated: + ?.? %, Rayonier Advanced Materials: + ?.? %.The biggest losers are: T ? Biosystems Inc: (?.? %), CorMedix Inc: (?.? %), Gossamer Bio Inc: (?.? %), Akso Health Group ADR: (?.? %), Canadian Solar Inc: (?.? %), Aldeyra The: (?.? %), Viomi Technology ADR: (?.? %), Owens & Minor Inc: (?.? %), Evolus Inc: (?.? %), Regis Corporation Common Stock: (?.? %) ", " long_text ": " The biggest winners are: - ? D Systems Corporation: + ?.? % n - AeroVironment Inc: + ?.? % n - Vaxart Inc: + ?.? % n - Kratos Defense & Security Solutions: + ?.? % n - Bloom Energy Corp: + ?.? % n - Revolution Medicines Inc: + ?.? % n - Cardlytics Inc: + ?.? % n - Vera Bradley Inc: + ?.? % n - Luna Innovations Incorporated: + ?.? % n - Rayonier Advanced Materials: + ?.? % n. The biggest losers are: - T ? Biosystems Inc: (?.? %) n - CorMedix Inc: (?.? %) n - Gossamer Bio Inc: (?.? %) n - Akso Health Group ADR: (?.? %) n - Canadian Solar Inc: (?.? %) n - Aldeyra The: (?.? %) n - Viomi Technology ADR: (?.? %) n - Owens & Minor Inc: (?.? %) n - Evolus Inc: (?.? %) n - Regis Corporation Common Stock: (?.? %) n " }, " warnings ": [], " errors ": [], " has_results ": true, " quantity_results ": ?, " creatomate_response ": { " ? _id ": " ? a4661 - cdc7 - 4121 - 9d35 - e4ee573dd5bf ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? a4661 - cdc7 - 4121 - 9d35 - e4ee573dd5bf.png ", " ? _template_id ": " ebdfce84 - ee3f - 4a36 - be67 - 2554576d3ff0 ", " ? _template_name ": " Biggest stock gainers and losers MP ? ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " c4bf9525 - 0343 - 42ed - 84ad - c2fecf5ce4a0 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / c4bf9525 - 0343 - 42ed - 84ad - c2fecf5ce4a0.png ", " ? _template_id ": " ebdfce84 - ee3f - 4a36 - be67 - 2554576d3ff0 ", " ? _template_name ": " Biggest stock gainers and losers MP ? ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ? cc6869d - d17c - 4ead - 96a5 - f9bff554f51e ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? cc6869d - d17c - 4ead - 96a5 - f9bff554f51e.mp4 ", " ? _template_id ": " ebdfce84 - ee3f - 4a36 - be67 - 2554576d3ff0 ", " ? _template_name ": " Biggest stock gainers and losers MP ? ", " ? _output_format ": " mp4 ", " ? _frame_rate ": ? }, " image_api ": { " latest ": [ " https: // api.autochartist.com / social_media / image / e9e91047 - c9ea - 4f5b - 9289 - dc8d4069ffab ? broker_id = ?& item = ? ", " https: // api.autochartist.com / social_media / image / e9e91047 - c9ea - 4f5b - 9289 - dc8d4069ffab ? broker_id = ?& item = ? ", " https: // api.autochartist.com / social_media / image / e9e91047 - c9ea - 4f5b - 9289 - dc8d4069ffab ? broker_id = ?& item = ? " ], " snapshot ": [ " https: // api.autochartist.com / social_media / image / e9e91047 - c9ea - 4f5b - 9289 - dc8d4069ffab ? broker_id = ?& item = ?& dt = ? - 01 - 09 % ? % ? A ? % ? A ? ", " https: // api[...];

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Jan 09	09	1	0ms	0ms

13

0ms

453

Details

0ms

commit;

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Jan 09	09	453	0ms	0ms

14

0ms

248

Details

0ms

with rar_max as ( select resultuid from relevance_keylevels_results order by resultuid desc limit ? ), kr as ( select a.*, rr.age, rr.relevant from keylevels_results a left outer join relevance_keylevels_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_keylevels_results) end ), all_results as ( select kr.resultuid as resultuid, kr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, p.patternname as pattern_name, kr.breakout as breakout, kr.atbaridentified as identified, dtt.timezone as timezone, kr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when kr.age is not null then kr.age when kr.resultuid <= rm.resultuid then ? else ? end as age, case when kr.relevant is not null then kr.relevant when kr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from kr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = kr.symbolid inner join symbols s on bsl.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on s.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join hrspatterns p on kr.patternid = p.patternid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left outer join autochartist_symbolupdates au on dss.symbolid = au.symbolid left outer join relevance_keylevels_results rar on rar.resultuid = kr.resultuid left join lateral calc_kl_signal_filter (kr.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where kr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (kr.simulation = ? or kr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or p.patternname in (...)) and (? = ? or kr.patternclassid in (...)) and (? = ? or kr.patternlengthbars <= ?) and kr.patternstarttime::timestamp without time zone >= coalesce(au.earliestpricedatetime, ?::timestamp without time zone) -- to make sure patternstarttime is in our t-tables ), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc limit ?;

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Jan 09	09	248	0ms	0ms

15

0ms

240

Details

0ms

select count(*), sum(size), extract(epoch from now() - min(modification)) from pg_ls_waldir ();

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Jan 09	09	240	0ms	0ms

16

0ms

240

Details

0ms

select system_identifier from pg_control_system ();

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Jan 09	09	240	0ms	0ms

17

0ms

5

Details

0ms

select groupid, exchange, groupname, symbol, longname from prfsymboltree where brokerid = ? order by groupname, symbol;

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Jan 09	09	5	0ms	0ms

18

0ms

5

Details

0ms

insert into t15 (symbolid, pricedatetime, open, high, low, close, volume, bsf, sastdatetimereceived) values (?, ?::timestamp without time zone, ?, ?.?, ?.?, ?.?, ?, ?, ?::timestamp without time zone) on conflict (symbolid, pricedatetime) do nothing;

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Jan 09	09	5	0ms	0ms

19

0ms

12

Details

0ms

select updatedatafeedslatestrun (?);

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Jan 09	09	12	0ms	0ms

20

0ms

1

Details

0ms

insert into t15 (symbolid, pricedatetime, open, high, low, close, volume, bsf, sastdatetimereceived) values (?, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?.?, ?, ?, ?::timestamp without time zone) on conflict (symbolid, pricedatetime) do nothing; insert into t15 (symbolid, pricedatetime, open, high, low, close, volume, bsf, sastdatetimereceived) values (?, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?.?, ?, ?, ?::timestamp without time zone) on conflict (symbolid, pricedatetime) do nothing;

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Jan 09	09	1	0ms	0ms

Time consuming prepare


Date: 2026-01-09 09:35:34
Duration: 16ms
Database: postgres
Date: 2026-01-09 09:36:34
Duration: 13ms
Database: postgres
Date: 2026-01-09 09:16:02
Duration: 2ms
Database: postgres
Date: 2026-01-09 09:01:29
Duration: 1ms
Database: postgres
Date: 2026-01-09 09:36:34
Duration: 4ms
Database: postgres
Date: 2026-01-09 09:30:41
Duration: 0ms
Database: postgres
Date: 2026-01-09 09:11:46
Duration: 0ms
Database: postgres
Date: 2026-01-09 09:50:45
Duration: 4ms
Database: postgres
Date: 2026-01-09 09:06:33
Duration: 0ms
Database: postgres
Date: 2026-01-09 09:56:59
Duration: 0ms
Database: postgres
Date: 2026-01-09 09:06:52
Duration: 6ms
Database: postgres
Date: 2026-01-09 09:06:32
Duration: 3ms
Database: postgres
Date: 2026-01-09 09:10:02
Duration: 3ms
Database: postgres
Date: 2026-01-09 09:50:45
Duration: 0ms
Database: postgres
Date: 2026-01-09 09:10:55
Duration: 1ms
Database: postgres
Date: 2026-01-09 09:13:25
Duration: 0ms
Database: postgres
Date: 2026-01-09 09:06:32
Duration: 4ms
Database: postgres
Date: 2026-01-09 09:33:02
Duration: 1ms
Database: postgres
Date: 2026-01-09 09:27:44
Duration: 1ms
Database: postgres
Date: 2026-01-09 09:13:24
Duration: 2ms
Database: postgres

Time consuming bind

Rank

Total duration

Times executed

Min duration

Max duration

Avg duration

Query

1

31s814ms

12,870

Details

0ms

37ms

2ms

WITH rar_max as ( ;

Times Reported Time consuming bind #1

Day	Hour	Count	Duration	Avg duration
Jan 09	09	12,870	31s814ms	2ms

WITH rar_max as ( ;

Date: 2026-01-09 09:35:34 Duration: 37ms Database: postgres parameters: $1 = '607471466003135303', $2 = '607471466003135303', $3 = '607471466003135303'
WITH rar_max as ( ;

Date: 2026-01-09 09:06:33 Duration: 35ms Database: postgres parameters: $1 = 't', $2 = '558', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '160', $14 = 'AUDSGD', $15 = 'CHFSGD', $16 = 'EURDKK', $17 = 'EURHKD', $18 = 'EURNOK', $19 = 'EURPLN', $20 = 'EURSEK', $21 = 'EURSGD', $22 = 'EURTRY', $23 = 'EURZAR', $24 = 'GBPDKK', $25 = 'GBPNOK', $26 = 'GBPSEK', $27 = 'GBPSGD', $28 = 'NOKJPY', $29 = 'NOKSEK', $30 = 'SEKJPY', $31 = 'SGDJPY', $32 = 'USDCNH', $33 = 'USDCZK', $34 = 'USDDKK', $35 = 'USDHKD', $36 = 'USDHUF', $37 = 'USDMXN', $38 = 'USDNOK', $39 = 'USDPLN', $40 = 'USDRUB', $41 = 'USDSEK', $42 = 'USDTHB', $43 = 'USDTRY', $44 = 'USDZAR', $45 = 'AUDUSD', $46 = 'EURUSD', $47 = 'GBPUSD', $48 = 'USDCAD', $49 = 'USDCHF', $50 = 'USDJPY', $51 = 'AUDCAD', $52 = 'AUDCHF', $53 = 'AUDJPY', $54 = 'AUDNZD', $55 = 'CADCHF', $56 = 'CADJPY', $57 = 'CHFJPY', $58 = 'EURAUD', $59 = 'EURCAD', $60 = 'EURCHF', $61 = 'EURGBP', $62 = 'EURJPY', $63 = 'EURNZD', $64 = 'GBPAUD', $65 = 'GBPCAD', $66 = 'GBPCHF', $67 = 'GBPJPY', $68 = 'GBPNZD', $69 = 'NZDCAD', $70 = 'NZDCHF', $71 = 'NZDJPY', $72 = 'NZDUSD', $73 = 'USDSGD', $74 = 'AUS200', $75 = 'DE30', $76 = 'ES35', $77 = 'F40', $78 = 'HK50', $79 = 'IT40', $80 = 'JP225', $81 = 'STOXX50', $82 = 'UK100', $83 = 'US2000', $84 = 'US30', $85 = 'US500', $86 = 'CHINA50', $87 = 'USTEC', $88 = 'XAGEUR', $89 = 'XAGUSD', $90 = 'XAUUSD', $91 = 'XAUEUR', $92 = 'XPDUSD', $93 = 'XPTUSD', $94 = 'AUDSGD', $95 = 'CHFSGD', $96 = 'EURDKK', $97 = 'EURHKD', $98 = 'EURNOK', $99 = 'EURPLN', $100 = 'EURSEK', $101 = 'EURSGD', $102 = 'EURTRY', $103 = 'EURZAR', $104 = 'GBPDKK', $105 = 'GBPNOK', $106 = 'GBPSEK', $107 = 'GBPSGD', $108 = 'NOKJPY', $109 = 'NOKSEK', $110 = 'SEKJPY', $111 = 'SGDJPY', $112 = 'USDCNH', $113 = 'USDCZK', $114 = 'USDDKK', $115 = 'USDHKD', $116 = 'USDHUF', $117 = 'USDMXN', $118 = 'USDNOK', $119 = 'USDPLN', $120 = 'USDRUB', $121 = 'USDSEK', $122 = 'USDTHB', $123 = 'USDTRY', $124 = 'USDZAR', $125 = 'AUDUSD', $126 = 'EURUSD', $127 = 'GBPUSD', $128 = 'USDCAD', $129 = 'USDCHF', $130 = 'USDJPY', $131 = 'AUDCAD', $132 = 'AUDCHF', $133 = 'AUDJPY', $134 = 'AUDNZD', $135 = 'CADCHF', $136 = 'CADJPY', $137 = 'CHFJPY', $138 = 'EURAUD', $139 = 'EURCAD', $140 = 'EURCHF', $141 = 'EURGBP', $142 = 'EURJPY', $143 = 'EURNZD', $144 = 'GBPAUD', $145 = 'GBPCAD', $146 = 'GBPCHF', $147 = 'GBPJPY', $148 = 'GBPNZD', $149 = 'NZDCAD', $150 = 'NZDCHF', $151 = 'NZDJPY', $152 = 'NZDUSD', $153 = 'USDSGD', $154 = 'AUS200', $155 = 'DE30', $156 = 'ES35', $157 = 'F40', $158 = 'HK50', $159 = 'IT40', $160 = 'JP225', $161 = 'STOXX50', $162 = 'UK100', $163 = 'US2000', $164 = 'US30', $165 = 'US500', $166 = 'CHINA50', $167 = 'USTEC', $168 = 'XAGEUR', $169 = 'XAGUSD', $170 = 'XAUUSD', $171 = 'XAUEUR', $172 = 'XPDUSD', $173 = 'XPTUSD', $174 = '0', $175 = '', $176 = '0', $177 = '0', $178 = '0', $179 = '500', $180 = '500', $181 = 't', $182 = '10', $183 = '10'
WITH rar_max as ( ;

Date: 2026-01-09 09:51:06 Duration: 33ms Database: postgres parameters: $1 = '667', $2 = '7', $3 = '15', $4 = '30', $5 = '60', $6 = '120', $7 = '240', $8 = '480', $9 = '1440', $10 = '0', $11 = '', $12 = '0', $13 = '', $14 = '700', $15 = '700', $16 = 't', $17 = '10', $18 = '10'

2

11s962ms

33,640

Details

0ms

15ms

0ms

SELECT ;

Times Reported Time consuming bind #2

Day	Hour	Count	Duration	Avg duration
	09	33,640	11s962ms	0ms

SELECT ;

Date: 2026-01-09 09:57:11 Duration: 15ms Database: postgres parameters: $1 = '689', $2 = '689', $3 = '515840245859706300'
SELECT ;

Date: 2026-01-09 09:31:32 Duration: 14ms Database: postgres parameters: $1 = '958', $2 = '958', $3 = '515840233383945300'
SELECT ;

Date: 2026-01-09 09:06:41 Duration: 14ms Database: postgres parameters: $1 = '515840233916163300'

3

2s201ms

1,058

Details

1ms

4ms

2ms

SELECT symbolid, ;

Times Reported Time consuming bind #3

Day	Hour	Count	Duration	Avg duration
	09	1,058	2s201ms	2ms

SELECT symbolid, ;

Date: 2026-01-09 09:31:07 Duration: 4ms Database: postgres parameters: $1 = 'MILLENNIUMPF', $2 = '15', $3 = 'EURCAD.FX', $4 = 'EURAUD.ID', $5 = 'EURCAD', $6 = 'EURAUD', $7 = 'EURAUD.FX'
SELECT symbolid, ;

Date: 2026-01-09 09:45:54 Duration: 4ms Database: postgres parameters: $1 = 'PEPPERSTONE', $2 = '15', $3 = 'NZDCAD', $4 = 'NZDUSD', $5 = 'NZDJPY', $6 = 'NZDCHF', $7 = 'NOKSEK', $8 = 'NatGas'
SELECT symbolid, ;

Date: 2026-01-09 09:31:18 Duration: 3ms Database: postgres parameters: $1 = 'MILLENNIUMPF', $2 = '15', $3 = 'GBPUSD.ID', $4 = 'NASDAQ100', $5 = 'GBPUSD.FX', $6 = 'GBPUSD'

4

1s677ms

293

Details

0ms

21ms

5ms

select distinct s.statsid as statsid, sy.exchange as name;

Times Reported Time consuming bind #4

Day	Hour	Count	Duration	Avg duration
	09	293	1s677ms	5ms

select distinct s.statsid as statsid, sy.exchange as name;

Date: 2026-01-09 09:10:55 Duration: 21ms Database: postgres parameters: $1 = '489', $2 = '489'
select distinct s.statsid as statsid, sy.exchange as name;

Date: 2026-01-09 09:10:55 Duration: 21ms Database: postgres parameters: $1 = '489', $2 = '489'
select distinct s.statsid as statsid, sy.exchange as name;

Date: 2026-01-09 09:10:55 Duration: 21ms Database: postgres parameters: $1 = '489', $2 = '489'

5

946ms

13,710

Details

0ms

1ms

0ms

select category, ;

Times Reported Time consuming bind #5

Day	Hour	Count	Duration	Avg duration
	09	13,710	946ms	0ms

select category, ;

Date: 2026-01-09 09:51:42 Duration: 1ms Database: postgres parameters: $1 = '515852059313898307', $2 = 'symbol', $3 = 'BTCUSD', $4 = 'AUDJPY', $5 = 'USDMXN', $6 = 'USDZAR', $7 = 'USDTHB', $8 = 'CADJPY', $9 = 'USDHUF', $10 = 'GBPJPY', $11 = 'USDJPY', $12 = 'NZDJPY', $13 = 'EURZAR', $14 = 'CHFJPY', $15 = 'EURJPY', $16 = 'SEKJPY', $17 = 'SGDJPY', $18 = 'EURTRY', $19 = 'EURHKD', $20 = 'USDSEK', $21 = 'NOKJPY', $22 = 'EURNOK', $23 = 'USDCZK', $24 = 'USDDKK', $25 = 'EURSEK', $26 = 'GBPSEK', $27 = 'USDNOK', $28 = 'USDPLN', $29 = 'GBPDKK', $30 = 'GBPNOK', $31 = 'BTCUSD', $32 = 'GBPAUD', $33 = 'EURPLN', $34 = 'GBPNZD', $35 = 'EURAUD', $36 = 'EURNZD', $37 = 'USDCNH', $38 = 'USDTRY', $39 = 'GBPCAD', $40 = 'EURGBP', $41 = 'SEKJPY', $42 = 'USDHUF', $43 = 'USDZAR', $44 = 'GBPNOK', $45 = 'GBPSGD', $46 = 'EURCAD', $47 = 'GBPCAD', $48 = 'GBPUSD', $49 = 'USDCAD', $50 = 'EURNOK', $51 = 'USDMXN', $52 = 'NOKJPY', $53 = '515852059313898307', $54 = 'symbol', $55 = 'BTCUSD', $56 = 'AUDJPY', $57 = 'USDMXN', $58 = 'USDZAR', $59 = 'USDTHB', $60 = 'CADJPY', $61 = 'USDHUF', $62 = 'GBPJPY', $63 = 'USDJPY', $64 = 'NZDJPY', $65 = 'EURZAR', $66 = 'CHFJPY', $67 = 'EURJPY', $68 = 'SEKJPY', $69 = 'SGDJPY', $70 = 'EURTRY', $71 = 'EURHKD', $72 = 'USDSEK', $73 = 'NOKJPY', $74 = 'EURNOK', $75 = 'USDCZK', $76 = 'USDDKK', $77 = 'EURSEK', $78 = 'GBPSEK', $79 = 'USDNOK', $80 = 'USDPLN', $81 = 'GBPDKK', $82 = 'GBPNOK', $83 = 'BTCUSD', $84 = 'GBPAUD', $85 = 'EURPLN', $86 = 'GBPNZD', $87 = 'EURAUD', $88 = 'EURNZD', $89 = 'USDCNH', $90 = 'USDTRY', $91 = 'GBPCAD', $92 = 'EURGBP', $93 = 'SEKJPY', $94 = 'USDHUF', $95 = 'USDZAR', $96 = 'GBPNOK', $97 = 'GBPSGD', $98 = 'EURCAD', $99 = 'GBPCAD', $100 = 'GBPUSD', $101 = 'USDCAD', $102 = 'EURNOK', $103 = 'USDMXN', $104 = 'NOKJPY'
select category, ;

Date: 2026-01-09 09:45:38 Duration: 1ms Database: postgres parameters: $1 = '515852059312899307', $2 = 'symbol', $3 = 'EXXON', $4 = 'CITI', $5 = 'DISNEY', $6 = 'CVX', $7 = 'JPM', $8 = 'BOEING', $9 = 'WMT', $10 = 'HSBCn', $11 = 'BTI', $12 = 'AT&T', $13 = 'BAC', $14 = 'MERCK', $15 = 'ORCL', $16 = 'IBM', $17 = 'KO', $18 = 'WFC', $19 = 'PFIZER', $20 = 'BUD', $21 = 'JNJ', $22 = 'ALIBABA', $23 = 'VZ', $24 = 'NVS', $25 = 'UNH', $26 = 'VISA', $27 = 'TOYOTA', $28 = 'PG', $29 = 'TSM', $30 = 'MMM', $31 = 'HD', $32 = 'ABBVIE', $33 = 'MA', $34 = 'PM', $35 = 'PFIZER', $36 = 'AT&T', $37 = 'ORCL', $38 = 'KO', $39 = 'BAC', $40 = 'WMT', $41 = 'BOEING', $42 = 'TSM', $43 = 'DISNEY', $44 = 'VZ', $45 = 'UNH', $46 = 'CITI', $47 = 'IBM', $48 = 'JPM', $49 = 'BTI', $50 = 'EXXON', $51 = 'MERCK', $52 = 'JNJ', $53 = '515852059312899307', $54 = 'symbol', $55 = 'EXXON', $56 = 'CITI', $57 = 'DISNEY', $58 = 'CVX', $59 = 'JPM', $60 = 'BOEING', $61 = 'WMT', $62 = 'HSBCn', $63 = 'BTI', $64 = 'AT&T', $65 = 'BAC', $66 = 'MERCK', $67 = 'ORCL', $68 = 'IBM', $69 = 'KO', $70 = 'WFC', $71 = 'PFIZER', $72 = 'BUD', $73 = 'JNJ', $74 = 'ALIBABA', $75 = 'VZ', $76 = 'NVS', $77 = 'UNH', $78 = 'VISA', $79 = 'TOYOTA', $80 = 'PG', $81 = 'TSM', $82 = 'MMM', $83 = 'HD', $84 = 'ABBVIE', $85 = 'MA', $86 = 'PM', $87 = 'PFIZER', $88 = 'AT&T', $89 = 'ORCL', $90 = 'KO', $91 = 'BAC', $92 = 'WMT', $93 = 'BOEING', $94 = 'TSM', $95 = 'DISNEY', $96 = 'VZ', $97 = 'UNH', $98 = 'CITI', $99 = 'IBM', $100 = 'JPM', $101 = 'BTI', $102 = 'EXXON', $103 = 'MERCK', $104 = 'JNJ'
select category, ;

Date: 2026-01-09 09:50:34 Duration: 1ms Database: postgres parameters: $1 = '601729875350308307', $2 = 'symbol', $3 = 'Telefonica', $4 = 'Mapfre', $5 = 'BBVA', $6 = 'Iberdrola', $7 = 'QCOM', $8 = 'INTEL', $9 = 'Tesla', $10 = 'AMEX', $11 = 'MCARD', $12 = 'ALCOA', $13 = 'HLT', $14 = 'Santander', $15 = 'PFIZER', $16 = 'CHEVRON', $17 = 'ALIBABA', $18 = 'EBAY', $19 = 'BOA', $20 = 'GS', $21 = 'TEVA', $22 = 'AMAZON', $23 = 'COKE', $24 = 'BOEING', $25 = 'CISCO', $26 = 'MCDON', $27 = 'APPLE', $28 = 'IBM', $29 = 'Netflix', $30 = 'MSFT', $31 = 'GOOGLE', $32 = 'GE', $33 = 'ILMN', $34 = 'Mapfre', $35 = 'Telefonica', $36 = 'Iberdrola', $37 = 'Santander', $38 = 'BBVA', $39 = 'Netflix', $40 = 'MCDON', $41 = 'GS', $42 = 'ILMN', $43 = 'CISCO', $44 = 'EBAY', $45 = 'MCARD', $46 = 'TEVA', $47 = 'ALCOA', $48 = 'PFIZER', $49 = 'AMEX', $50 = 'GOOGLE', $51 = 'COKE', $52 = 'BOA', $53 = '601729875350308307', $54 = 'symbol', $55 = 'Telefonica', $56 = 'Mapfre', $57 = 'BBVA', $58 = 'Iberdrola', $59 = 'QCOM', $60 = 'INTEL', $61 = 'Tesla', $62 = 'AMEX', $63 = 'MCARD', $64 = 'ALCOA', $65 = 'HLT', $66 = 'Santander', $67 = 'PFIZER', $68 = 'CHEVRON', $69 = 'ALIBABA', $70 = 'EBAY', $71 = 'BOA', $72 = 'GS', $73 = 'TEVA', $74 = 'AMAZON', $75 = 'COKE', $76 = 'BOEING', $77 = 'CISCO', $78 = 'MCDON', $79 = 'APPLE', $80 = 'IBM', $81 = 'Netflix', $82 = 'MSFT', $83 = 'GOOGLE', $84 = 'GE', $85 = 'ILMN', $86 = 'Mapfre', $87 = 'Telefonica', $88 = 'Iberdrola', $89 = 'Santander', $90 = 'BBVA', $91 = 'Netflix', $92 = 'MCDON', $93 = 'GS', $94 = 'ILMN', $95 = 'CISCO', $96 = 'EBAY', $97 = 'MCARD', $98 = 'TEVA', $99 = 'ALCOA', $100 = 'PFIZER', $101 = 'AMEX', $102 = 'GOOGLE', $103 = 'COKE', $104 = 'BOA'

6

867ms

551

Details

1ms

2ms

1ms

SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Times Reported Time consuming bind #6

Day	Hour	Count	Duration	Avg duration
	09	551	867ms	1ms

SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-01-09 09:15:47 Duration: 2ms Database: postgres parameters: $1 = 'BDSWISS'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-01-09 09:15:37 Duration: 2ms Database: postgres parameters: $1 = 'ATFX'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-01-09 09:01:12 Duration: 2ms Database: postgres parameters: $1 = 'MILLENNIUMPF'

7

836ms

56,787

Details

0ms

4ms

0ms

select 1;

Times Reported Time consuming bind #7

Day	Hour	Count	Duration	Avg duration
	09	56,786	836ms	0ms
	10	1	0ms	0ms

select 1;

Date: 2026-01-09 09:57:05 Duration: 4ms Database: postgres
select 1;

Date: 2026-01-09 09:41:32 Duration: 2ms Database: postgres
select 1;

Date: 2026-01-09 09:17:56 Duration: 1ms Database: postgres

8

519ms

23

Details

0ms

35ms

22ms

with wh_patitioned as ( ;

Times Reported Time consuming bind #8

Day	Hour	Count	Duration	Avg duration
	09	23	519ms	22ms

with wh_patitioned as ( ;

Date: 2026-01-09 09:01:22 Duration: 35ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'
with wh_patitioned as ( ;

Date: 2026-01-09 09:01:22 Duration: 35ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'
with wh_patitioned as ( ;

Date: 2026-01-09 09:06:32 Duration: 35ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'

9

494ms

68

Details

4ms

18ms

7ms

WITH last_candle AS ( ;

Times Reported Time consuming bind #9

Day	Hour	Count	Duration	Avg duration
	09	68	494ms	7ms

WITH last_candle AS ( ;

Date: 2026-01-09 09:32:02 Duration: 18ms Database: postgres parameters: $1 = '667', $2 = '667'
WITH last_candle AS ( ;

Date: 2026-01-09 09:16:00 Duration: 12ms Database: postgres parameters: $1 = '558', $2 = '558'
WITH last_candle AS ( ;

Date: 2026-01-09 09:16:00 Duration: 12ms Database: postgres parameters: $1 = '558', $2 = '558'

10

464ms

722

Details

0ms

1ms

0ms

SELECT absolutetimezoneoffset;

Times Reported Time consuming bind #10

Day	Hour	Count	Duration	Avg duration
	09	722	464ms	0ms

SELECT absolutetimezoneoffset;

Date: 2026-01-09 09:10:57 Duration: 1ms Database: postgres parameters: $1 = '538', $2 = 'Shares EU'
SELECT absolutetimezoneoffset;

Date: 2026-01-09 09:10:56 Duration: 1ms Database: postgres parameters: $1 = '538', $2 = 'Shares EU'
SELECT absolutetimezoneoffset;

Date: 2026-01-09 09:10:57 Duration: 1ms Database: postgres parameters: $1 = '538', $2 = 'Shares EU'

11

434ms

12

Details

28ms

45ms

36ms

with sym_info as ( ;

Times Reported Time consuming bind #11

Day	Hour	Count	Duration	Avg duration
	09	12	434ms	36ms

with sym_info as ( ;

Date: 2026-01-09 09:06:52 Duration: 45ms Database: postgres parameters: $1 = '627', $2 = 'Forex', $3 = 'Forex', $4 = '627', $5 = 'Forex', $6 = '627', $7 = '627', $8 = 'Forex', $9 = '627'
with sym_info as ( ;

Date: 2026-01-09 09:06:46 Duration: 45ms Database: postgres parameters: $1 = '617', $2 = 'Forex', $3 = 'Forex', $4 = '617', $5 = 'Forex', $6 = '617', $7 = '617', $8 = 'Forex', $9 = '617'
with sym_info as ( ;

Date: 2026-01-09 09:06:43 Duration: 44ms Database: postgres parameters: $1 = '620', $2 = 'Forex', $3 = 'Forex', $4 = '620', $5 = 'Forex', $6 = '620', $7 = '620', $8 = 'Forex', $9 = '620'

12

364ms

38

Details

0ms

19ms

9ms

WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Times Reported Time consuming bind #12

Day	Hour	Count	Duration	Avg duration
	09	38	364ms	9ms

WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Date: 2026-01-09 09:30:59 Duration: 19ms Database: postgres parameters: $1 = '489', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Date: 2026-01-09 09:35:57 Duration: 17ms Database: postgres parameters: $1 = '489', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Date: 2026-01-09 09:31:01 Duration: 17ms Database: postgres parameters: $1 = '489', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'

13

240ms

5,789

Details

0ms

INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Times Reported Time consuming bind #13

Day	Hour	Count	Duration	Avg duration
	09	5,789	240ms	0ms

INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-09 09:56:59 Duration: 0ms Database: postgres parameters: $1 = '2026-01-09 08:15:00', $2 = '2.89', $3 = '2.9', $4 = '2.89', $5 = '2.9', $6 = '5', $7 = '515840247947986300', $8 = '0', $9 = '2026-01-09 09:56:59.795', $10 = '2026-01-09 09:56:59.721', $11 = '2.89', $12 = '2.9', $13 = '2.89', $14 = '2.9', $15 = '5', $16 = '0', $17 = '2026-01-09 09:56:59.795', $18 = '2026-01-09 09:56:59.721'
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-09 09:56:45 Duration: 0ms Database: postgres parameters: $1 = '2026-01-09 09:30:00', $2 = '8712.4', $3 = '8719.45', $4 = '8711.75', $5 = '8719.45', $6 = '491', $7 = '515840248015086300', $8 = '0', $9 = '2026-01-09 09:56:45.711', $10 = '2026-01-09 09:56:45.629', $11 = '8712.4', $12 = '8719.45', $13 = '8711.75', $14 = '8719.45', $15 = '491', $16 = '0', $17 = '2026-01-09 09:56:45.711', $18 = '2026-01-09 09:56:45.629'
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-09 09:26:45 Duration: 0ms Database: postgres parameters: $1 = '2026-01-09 09:00:00', $2 = '8708.9', $3 = '8710.9', $4 = '8706.4', $5 = '8709.1', $6 = '605', $7 = '515840248015086300', $8 = '0', $9 = '2026-01-09 09:26:45.621', $10 = '2026-01-09 09:26:45.537', $11 = '8708.9', $12 = '8710.9', $13 = '8706.4', $14 = '8709.1', $15 = '605', $16 = '0', $17 = '2026-01-09 09:26:45.621', $18 = '2026-01-09 09:26:45.537'

14

222ms

3,293

Details

0ms

INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Times Reported Time consuming bind #14

Day	Hour	Count	Duration	Avg duration
	09	3,293	222ms	0ms

INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-09 09:12:00 Duration: 0ms Database: postgres parameters: $1 = '2026-01-09 08:30:00', $2 = '25516.65', $3 = '25522.03', $4 = '25504.17', $5 = '25508.17', $6 = '3825', $7 = '515840248039147300', $8 = '0', $9 = '2026-01-09 09:12:00.683', $10 = '2026-01-09 09:12:00.504', $11 = '25516.65', $12 = '25522.03', $13 = '25504.17', $14 = '25508.17', $15 = '3825', $16 = '0', $17 = '2026-01-09 09:12:00.683', $18 = '2026-01-09 09:12:00.504'
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-09 09:46:35 Duration: 0ms Database: postgres parameters: $1 = '2026-01-09 09:00:00', $2 = '45765', $3 = '45771', $4 = '45742', $5 = '45746', $6 = '769', $7 = '515840233917433300', $8 = '0', $9 = '2026-01-09 09:46:35.579', $10 = '2026-01-09 09:46:35.578', $11 = '45765', $12 = '45771', $13 = '45742', $14 = '45746', $15 = '769', $16 = '0', $17 = '2026-01-09 09:46:35.579', $18 = '2026-01-09 09:46:35.578'
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-09 09:31:15 Duration: 0ms Database: postgres parameters: $1 = '2026-01-09 09:00:00', $2 = '453.84', $3 = '453.89', $4 = '453.54', $5 = '453.59', $6 = '55', $7 = '500991628279784200', $8 = '0', $9 = '2026-01-09 09:31:15.647', $10 = '2026-01-09 09:31:15.646', $11 = '453.84', $12 = '453.89', $13 = '453.54', $14 = '453.59', $15 = '55', $16 = '0', $17 = '2026-01-09 09:31:15.647', $18 = '2026-01-09 09:31:15.646'

15

156ms

2,153

Details

0ms

INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Times Reported Time consuming bind #15

Day	Hour	Count	Duration	Avg duration
	09	2,153	156ms	0ms

INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-09 09:15:31 Duration: 0ms Database: postgres parameters: $1 = '2026-01-08 22:00:00', $2 = '17667', $3 = '17717', $4 = '17667', $5 = '17705.8', $6 = '2065', $7 = '515840233914385300', $8 = '0', $9 = '2026-01-09 09:15:31.327', $10 = '2026-01-09 09:15:31.327', $11 = '17667', $12 = '17717', $13 = '17667', $14 = '17705.8', $15 = '2065', $16 = '0', $17 = '2026-01-09 09:15:31.327', $18 = '2026-01-09 09:15:31.327'
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-09 09:00:13 Duration: 0ms Database: postgres parameters: $1 = '2026-01-09 08:00:00', $2 = '3.16206', $3 = '3.17239', $4 = '3.16053', $5 = '3.16957', $6 = '1626', $7 = '515840243974973300', $8 = '0', $9 = '2026-01-09 09:00:13.691', $10 = '2026-01-09 09:00:13.686', $11 = '3.16206', $12 = '3.17239', $13 = '3.16053', $14 = '3.16957', $15 = '1626', $16 = '0', $17 = '2026-01-09 09:00:13.691', $18 = '2026-01-09 09:00:13.686'
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-01-09 09:11:46 Duration: 0ms Database: postgres parameters: $1 = '2026-01-09 08:00:00', $2 = '8704.4', $3 = '8715.4', $4 = '8701.8', $5 = '8708.95', $6 = '2043', $7 = '515840248015562300', $8 = '0', $9 = '2026-01-09 09:11:46.456', $10 = '2026-01-09 09:11:46.362', $11 = '8704.4', $12 = '8715.4', $13 = '8701.8', $14 = '8708.95', $15 = '2043', $16 = '0', $17 = '2026-01-09 09:11:46.456', $18 = '2026-01-09 09:11:46.362'

16

99ms

20

Details

3ms

7ms

4ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Times Reported Time consuming bind #16

Day	Hour	Count	Duration	Avg duration
	09	20	99ms	4ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-01-09 09:10:54 Duration: 7ms Database: postgres parameters: $1 = '627', $2 = '627'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-01-09 09:16:28 Duration: 6ms Database: postgres parameters: $1 = '489', $2 = '489'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-01-09 09:06:31 Duration: 6ms Database: postgres parameters: $1 = '627', $2 = '627'

17

84ms

50

Details

1ms

4ms

1ms

WITH rcr_max as ( ;

Times Reported Time consuming bind #17

Day	Hour	Count	Duration	Avg duration
	09	50	84ms	1ms

WITH rcr_max as ( ;

Date: 2026-01-09 09:33:02 Duration: 4ms Database: postgres parameters: $1 = '607470758196469305', $2 = '607470758196469305', $3 = '607470758196469305'
WITH rcr_max as ( ;

Date: 2026-01-09 09:37:35 Duration: 3ms Database: postgres parameters: $1 = '607470755164874305', $2 = '607470755164874305', $3 = '607470755164874305'
WITH rcr_max as ( ;

Date: 2026-01-09 09:36:17 Duration: 2ms Database: postgres parameters: $1 = '607470755164874305', $2 = '607470755164874305', $3 = '607470755164874305'

18

78ms

201

Details

0ms

SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;

Times Reported Time consuming bind #18

Day	Hour	Count	Duration	Avg duration
	09	201	78ms	0ms

SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;

Date: 2026-01-09 09:13:25 Duration: 0ms Database: postgres
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;

Date: 2026-01-09 09:13:26 Duration: 0ms Database: postgres
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;

Date: 2026-01-09 09:13:25 Duration: 0ms Database: postgres

19

70ms

1,444

Details

0ms

select distinct category;

Times Reported Time consuming bind #19

Day	Hour	Count	Duration	Avg duration
	09	1,444	70ms	0ms

select distinct category;

Date: 2026-01-09 09:51:25 Duration: 0ms Database: postgres parameters: $1 = '515852059305993307', $2 = '515852059305993307'
select distinct category;

Date: 2026-01-09 09:10:57 Duration: 0ms Database: postgres parameters: $1 = '601729875347685307', $2 = '601729875347685307'
select distinct category;

Date: 2026-01-09 09:10:57 Duration: 0ms Database: postgres parameters: $1 = '601729875347685307', $2 = '601729875347685307'

20

68ms

85

Details

0ms

1ms

0ms

SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;

Times Reported Time consuming bind #20

Day	Hour	Count	Duration	Avg duration
	09	85	68ms	0ms

SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;

Date: 2026-01-09 09:00:06 Duration: 1ms Database: postgres parameters: $1 = '558', $2 = 'EURUSD', $3 = '558'
SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;

Date: 2026-01-09 09:10:47 Duration: 1ms Database: postgres parameters: $1 = '689', $2 = 'XAUUSD', $3 = '689'
SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;

Date: 2026-01-09 09:03:14 Duration: 1ms Database: postgres parameters: $1 = '667', $2 = 'USDJPY', $3 = '667'

Log levels

Key values

693,239 Log entries

Chart
Table

Type	Count	Percentage
CONTEXT	638	0.09%
DETAIL	157,362	22.70%
ERROR	2	0.00%
FATAL	2	0.00%
LOG	534,457	77.10%
STATEMENT	778	0.11%

Events distribution

Key values

0 PANIC entries
2 FATAL entries
2 ERROR entries
0 WARNING entries

Most Frequent Errors/Events

Key values

2 Max number of times the same event was reported
4 Total events found

Rank

Times reported

Error

1

2

Details

ERROR: value too long for type character varying(...)

Times Reported Most Frequent Error / Event #1

Day	Hour	Count
Jan 09	09	2

ERROR: value too long for type character varying(50)

2

Details

FATAL: connection to client lost

Times Reported Most Frequent Error / Event #2

Day	Hour	Count
Jan 09	09	2

FATAL: connection to client lost

Global information

Overview

Global Stats

SQL Traffic

Key values

SELECT Traffic

Key values

INSERT/UPDATE/DELETE Traffic

Key values

Queries duration

Key values

Prepared queries ratio

Key values

General Activity

Connections

Established Connections

Key values

Connections per database

Key values

Connections per user

Key values

Connections per host

Key values

Sessions

Simultaneous sessions

Key values

Histogram of session times

Key values

Sessions per database

Key values

Sessions per user

Key values

Sessions per host

Key values

Sessions per application

Key values

Checkpoints / Restartpoints

Checkpoints Buffers

Key values

Checkpoints Wal files

Key values

Checkpoints distance

Key values

Checkpoints Activity

Checkpoint causes

Key values

Temporary Files

Size of temporary files

Key values

Number of temporary files

Key values

Temporary Files Activity

Queries generating the most temporary files (N)

Queries generating the largest temporary files

Vacuums

Vacuums / Analyzes Distribution

Key values

Analyzes per table

Key values

Vacuums per table

Key values

Tuples removed per table

Key values

Pages removed per table

Key values

Autovacuum Activity

Locks

Locks by types

Key values

Type of locks

Most frequent waiting queries (N)

Queries that waited the most

Queries

Queries by type

Key values

Queries by database

Key values

Queries by user

Key values

Duration by user