pgBadger :: PostgreSQL Log Analyzer

Global information

Generated on Fri Dec 26 07:59:49 2025
Log file: /home/postgres/pg_data/data/pg_log/postgresql-2025-12-26_090000.log
Parsed 1,995,712 log entries in 48s
Log start from 2025-12-26 09:00:00 to 2025-12-26 09:59:47

Global Stats

264 Number of unique normalized queries
226,753 Number of queries
1h3m44s Total query duration
2025-12-26 09:00:00 First query
2025-12-26 09:59:47 Last query
4,545 queries/s at 2025-12-26 09:10:54 Query peak

1h3m44s Total query duration
4s887ms Prepare/parse total duration
35s990ms Bind total duration
1h3m3s Execute total duration

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

36 Total number of automatic vacuums
52 Total number of automatic analyzes

570 Number temporary file
582.09 MiB Max size of temporary file
9.15 MiB Average size of temporary file

2,687 Total number of sessions
13 sessions at 2025-12-26 09:56:07 Session peak
14d9h31m57s Total duration of sessions
7m42s Average duration of sessions
84 Average queries per session
1s423ms Average queries duration per session
7m41s Average idle time per session

2,688 Total number of connections
31 connections/s at 2025-12-26 09:40:02 Connection peak
3 Total number of databases

SQL Traffic

Key values

4,545 queries/s Query Peak
2025-12-26 09:10:54 Date

SELECT Traffic

Key values

2,259 queries/s Query Peak
2025-12-26 09:10:54 Date

INSERT/UPDATE/DELETE Traffic

Key values

209 queries/s Query Peak
2025-12-26 09:00:56 Date

Queries duration

Key values

1h3m44s 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)
Dec 26	09	226,753	0ms	25s37ms	16ms	2m22s	2m39s	2m57s

Day	Hour	SELECT	COPY TO	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Dec 26	09	68,208	26	0ms	0ms	0ms	0ms

Day	Hour	INSERT	UPDATE	DELETE	COPY FROM	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Dec 26	09	30,066	2,839	16	96	0ms	0ms	0ms	0ms

Day	Hour	Prepare	Bind	Bind/Prepare	Percentage of prepare
Dec 26	09	17,669	83,296	4.71	16.13%

Day	Hour	Count	Average / Second
Dec 26	09	2,688	0.75/s

Day	Hour	Count	Average Duration	Average idle time
Dec 26	09	2,687	7m42s	7m41s

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Established Connections

Key values

31 connections Connection Peak
2025-12-26 09:40:02 Date

Connections per database

Key values

acaweb_fx Main Database
2,688 connections Total

Chart
Table

Database	User	Count
acaweb_fx		2,597
	postgres	2,211
	psql	386
socialmedia		91
	postgres	91

Connections per user

Key values

postgres Main User
2,688 connections Total

Chart
Table

User	Count
postgres	2,302
psql	386

Connections per host

Key values

192.168.4.142 Main host with 1139 connections
2,688 Total connections

Chart
Table

Host	Count
127.0.0.1	112
182.165.1.54	2
192.168.0.114	2
192.168.0.216	101
192.168.0.74	188
192.168.0.84	2
192.168.1.131	2
192.168.1.145	29
192.168.1.15	88
192.168.1.20	54
192.168.1.238	2
192.168.1.239	2
192.168.1.90	65
192.168.1.97	3
192.168.2.126	72
192.168.2.182	12
192.168.2.82	48
192.168.3.199	36
192.168.4.142	1,139
192.168.4.150	10
192.168.4.222	1
192.168.4.238	12
192.168.4.32	1
192.168.4.33	90
192.168.4.34	4
192.168.4.52	7
192.168.4.98	330
[local]	274

Simultaneous sessions

Key values

13 sessions Session Peak
2025-12-26 09:56:07 Date

Histogram of session times

Key values

2,145 0-500ms duration

Chart
Table

Range	Count	Percentage
0ms-500ms	2,145	79.83%
500ms-1s	134	4.99%
1s-30s	306	11.39%
30s-1m	9	0.33%
1m-10m	18	0.67%
10m-30m	31	1.15%
30m-1h	12	0.45%
1h-8h	20	0.74%
> 8h	12	0.45%

Sessions per database

Key values

acaweb_fx Main Database
2,687 sessions Total

Chart
Table

Database	User	Count	Total Duration	Average Duration
acaweb_fx	2,596	14d9h13m52s	7m58s
socialmedia	91	18m5s	11s925ms

Sessions per user

Key values

postgres Main User
2,687 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
2,687 sessions Total

Chart
Table

Host	Count	Total Duration	Average Duration
127.0.0.1	112	5s963ms	53ms
182.165.1.54	2	23h18m49s	11h39m24s
192.168.0.114	2	10m1s	5m
192.168.0.216	101	1m22s	818ms
192.168.0.74	188	1d8h58m34s	10m31s
192.168.0.84	2	23h59m23s	11h59m41s
192.168.1.131	2	23h59m22s	11h59m41s
192.168.1.145	29	3d5h41m47s	2h40m45s
192.168.1.15	89	2d7m58s	32m26s
192.168.1.20	54	2d22h24m38s	1h18m14s
192.168.1.238	2	23h59m27s	11h59m43s
192.168.1.239	2	12ms	6ms
192.168.1.90	65	33s941ms	522ms
192.168.1.97	1	4ms	4ms
192.168.2.126	72	17s24ms	236ms
192.168.2.182	12	778ms	64ms
192.168.2.82	48	12s7ms	250ms
192.168.3.199	36	1s405ms	39ms
192.168.4.142	1,139	10m57s	576ms
192.168.4.150	10	20h20m36s	2h2m3s
192.168.4.222	1	45s199ms	45s199ms
192.168.4.238	12	14s978ms	1s248ms
192.168.4.32	1	164ms	164ms
192.168.4.33	90	13m3s	8s704ms
192.168.4.34	4	41ms	10ms
192.168.4.52	7	36s831ms	5s261ms
192.168.4.98	330	13s800ms	41ms
[local]	274	2m50s	622ms

Sessions per application

Key values

unknown Main Host
2,687 sessions Total

Chart
Table

Application	Count	Total Duration	Average Duration
unknown	2,687	14d9h31m57s	7m42s

Checkpoints Buffers

Key values

15,402 buffers Checkpoint Peak
2025-12-26 09:06:57 Date
209.980 seconds Highest write time
0.012 seconds Sync time

Checkpoints Wal files

Key values

6 files Wal files usage Peak
2025-12-26 09:06:57 Date

Checkpoints distance

Key values

199.34 Mo Distance Peak
2025-12-26 09:06:57 Date

Checkpoints Activity

Day	Hour	Written buffers	Write time	Sync time	Total time
Dec 26	09	49,353	1,773.533s	0.055s	1,773.882s

Day	Hour	Added	Removed	Recycled	Synced files	Longest sync	Average sync
Dec 26	09	0	0	24	1,839	0.004s	0s

Day	Hour	Count	Avg time (sec)
Dec 26	09	0	0s

Day	Hour	Mean distance	Mean estimate
Dec 26	09	32,265.58 kB	197,107.50 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

582.09 MiB Temp Files size Peak
2025-12-26 09:13:34 Date

Number of temporary files

Key values

30 per second Temp Files Peak
2025-12-26 09:47:07 Date

Temporary Files Activity

Temp Files

Day	Hour	Count	Total size	Average size
Dec 26	09	570	5.09 GiB	9.15 MiB

↑ 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	1.65 GiB	2.34 MiB	162.50 MiB	56.31 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: 2025-12-26 09:00:06 Duration: 0ms
2	16	504.12 MiB	31.51 MiB	31.51 MiB	31.51 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: 2025-12-26 09:01:12 Duration: 0ms
3	16	1.10 GiB	70.66 MiB	70.66 MiB	70.66 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: 2025-12-26 09:01:15 Duration: 0ms
4	8	956.02 MiB	119.48 MiB	119.52 MiB	119.50 MiB	select updateresultsmaterializedview (); select updateresultsmaterializedview (); Date: 2025-12-26 09:02:11 Duration: 0ms
5	5	32.20 MiB	6.44 MiB	6.44 MiB	6.44 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: 2025-12-26 09:00:03 Duration: 0ms
6	4	220.83 MiB	55.14 MiB	55.30 MiB	55.21 MiB	select updateageforrelevantresults (); select updateageforrelevantresults (); Date: 2025-12-26 09:02:06 Duration: 0ms

Queries generating the largest temporary files

Rank	Size	Query
1	162.50 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 = 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: 2025-12-26 09:00:06 ]
2	157.54 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: 2025-12-26 09:10:07 ]
3	119.52 MiB	select updateresultsmaterializedview (); [ Date: 2025-12-26 09:32:11 ]
4	119.52 MiB	select updateresultsmaterializedview (); [ Date: 2025-12-26 09:02:11 ]
5	119.52 MiB	select updateresultsmaterializedview (); [ Date: 2025-12-26 09:47:09 ]
6	119.50 MiB	select updateresultsmaterializedview (); [ Date: 2025-12-26 09:35:33 ]
7	119.50 MiB	select updateresultsmaterializedview (); [ Date: 2025-12-26 09:50:33 ]
8	119.50 MiB	select updateresultsmaterializedview (); [ Date: 2025-12-26 09:17:10 ]
9	119.49 MiB	select updateresultsmaterializedview (); [ Date: 2025-12-26 09:20:33 ]
10	119.48 MiB	select updateresultsmaterializedview (); [ Date: 2025-12-26 09:05:33 ]
11	115.28 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: 2025-12-26 09:40:04 ]
12	111.39 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: 2025-12-26 09:20:05 ]
13	98.78 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: 2025-12-26 09:40:05 ]
14	94.32 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: 2025-12-26 09:50:04 ]
15	91.66 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: 2025-12-26 09:30:05 ]
16	88.31 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: 2025-12-26 09:30:04 ]
17	85.25 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: 2025-12-26 09:20:05 ]
18	72.65 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: 2025-12-26 09:50:05 ]
19	72.31 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: 2025-12-26 09:50:05 ]
20	70.66 MiB	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: 2025-12-26 09:01:15 ]

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)
52 analyzes Total

Chart
Table

Table	Number of analyzes
acaweb_fx.public.solr_relevance_old	16
acaweb_fx.pg_catalog.pg_attribute	5
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.public.latest_t15_candle_view	2
acaweb_fx.pg_catalog.pg_index	1
acaweb_fx.public.autochartist_symbolupdates	1
acaweb_fx.public.relevance_consecutivecandles_results	1
acaweb_fx.public.symbollatestupdatetime	1
acaweb_fx.pg_catalog.pg_depend	1
acaweb_fx.public.latest_candle_datetime_per_receng	1
Total	52

Vacuums per table

Key values

public.solr_relevance_old (16) Main table vacuumed on database acaweb_fx
36 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	16	16	13,165	0	54	0	0	8,126	1,183	5,389,290
acaweb_fx.public.datafeeds_latestrun	4	0	482	0	19	0	0	60	12	64,010
acaweb_fx.pg_toast.pg_toast_2619	2	2	318	0	60	0	0	236	76	313,761
acaweb_fx.pg_catalog.pg_attribute	2	2	1,704	0	308	0	134	727	269	1,615,713
acaweb_fx.public.relevance_keylevels_results	2	2	7,751	0	249	0	164	2,149	233	755,820
acaweb_fx.public.relevance_autochartist_results	2	2	6,886	0	139	0	466	1,382	125	424,114
acaweb_fx.pg_catalog.pg_class	2	2	929	0	105	0	0	294	103	547,594
acaweb_fx.public.relevance_fibonacci_results	2	2	2,650	0	43	2	76	504	28	121,586
acaweb_fx.pg_catalog.pg_type	1	1	128	0	25	0	0	48	16	108,670
acaweb_fx.public.autochartist_symbolupdates	1	1	27,429	0	487	2	37,071	9,205	407	788,696
acaweb_fx.pg_catalog.pg_statistic	1	1	984	0	180	0	582	444	164	584,631
acaweb_fx.public.latest_t15_candle_view	1	1	66	0	1	0	0	6	1	9,049
Total	36	32	62,492	44,015	1,670	4	38,493	23,181	2,617	10,722,934

Tuples removed per table

Key values

public.solr_relevance_old (13394) Main table with removed tuples on database acaweb_fx
29266 tuples Total removed

Chart
Table

		Index	Tuples			Pages
Table	Vacuums	scans	removed	remain	not yet removable	removed	remain
acaweb_fx.public.solr_relevance_old	16	16	13,394	92,676	0	0	3,207
acaweb_fx.public.autochartist_symbolupdates	1	1	6,298	61,090	29	0	40,691
acaweb_fx.public.relevance_keylevels_results	2	2	3,407	25,437	0	0	558
acaweb_fx.pg_catalog.pg_attribute	2	2	2,826	21,809	0	39	480
acaweb_fx.public.relevance_autochartist_results	2	2	1,321	17,511	0	0	760
acaweb_fx.public.relevance_fibonacci_results	2	2	584	3,416	0	0	204
acaweb_fx.pg_catalog.pg_statistic	1	1	578	3,726	0	0	1,194
acaweb_fx.pg_catalog.pg_class	2	2	326	3,298	0	0	300
acaweb_fx.public.datafeeds_latestrun	4	0	225	56	0	0	64
acaweb_fx.pg_toast.pg_toast_2619	2	2	147	340	0	0	106
acaweb_fx.pg_catalog.pg_type	1	1	100	1,446	0	0	38
acaweb_fx.public.latest_t15_candle_view	1	1	60	14	0	0	1
Total	36	32	29,266	230,819	29	39	47,603

Pages removed per table

Key values

pg_catalog.pg_attribute (39) Main table with removed pages on database acaweb_fx
39 pages Total removed

Chart
Table

Table	Number of vacuums	Index scans	Tuples removed	Pages removed
acaweb_fx.pg_catalog.pg_attribute	2	2	2826	39
acaweb_fx.pg_toast.pg_toast_2619	2	2	147	0
acaweb_fx.pg_catalog.pg_type	1	1	100	0
acaweb_fx.public.datafeeds_latestrun	4	0	225	0
acaweb_fx.public.autochartist_symbolupdates	1	1	6298	0
acaweb_fx.pg_catalog.pg_statistic	1	1	578	0
acaweb_fx.public.latest_t15_candle_view	1	1	60	0
acaweb_fx.public.relevance_keylevels_results	2	2	3407	0
acaweb_fx.public.solr_relevance_old	16	16	13394	0
acaweb_fx.public.relevance_autochartist_results	2	2	1321	0
acaweb_fx.pg_catalog.pg_class	2	2	326	0
acaweb_fx.public.relevance_fibonacci_results	2	2	584	0
Total	36	32	29,266	39

Autovacuum Activity

Vacuums / Analyzes

Day	Hour	VACUUMs	ANALYZEs
Dec 26	09	36	52

↑ 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

68,208 Total read queries
41,355 Total write queries

Chart
Table

Type	Count	Percentage
SELECT	68,208	30.08%
INSERT	30,066	13.26%
UPDATE	2,839	1.25%
DELETE	16	0.01%
COPY FROM	96	0.04%
COPY TO	26	0.01%
CTE	7,610	3.36%
DDL	16	0.01%
TCL	686	0.30%
CURSOR	0	0.00%
OTHERS	117,190	51.68%

Queries by database

Key values

unknown Main database
225,746 Requests
1h3m3s (unknown)
Main time consuming database

Chart
Table

Database	Request type	Count	Duration
acaweb_fx	Total	916	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	203	0ms
	select	102	0ms
	tcl	331	0ms
	update	38	0ms
socialmedia	Total	91	0ms
	select	90	0ms
	tcl	1	0ms
unknown	Total	225,746	1h3m3s
	copy from	16	0ms
	cte	7,506	0ms
	insert	30,066	0ms
	others	3,609	0ms
	select	68,016	0ms
	tcl	354	0ms
	update	2,801	0ms

Queries by user

Key values

unknown Main user
225,746 Requests

Queries Chart
Table

User	Request type	Count	Duration
postgres	Total	1,007	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	203	0ms
	select	192	0ms
	tcl	332	0ms
	update	38	0ms
unknown	Total	225,746	1h3m3s
	copy from	16	0ms
	cte	7,506	0ms
	insert	30,066	0ms
	others	3,609	0ms
	select	68,016	0ms
	tcl	354	0ms
	update	2,801	0ms

Duration by user

Key values

1h3m3s (unknown) Main time consuming user

Duration Chart
Table

User	Request type	Count	Duration
postgres	Total	1,007	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	203	0ms
	select	192	0ms
	tcl	332	0ms
	update	38	0ms
unknown	Total	225,746	1h3m3s
	copy from	16	0ms
	cte	7,506	0ms
	insert	30,066	0ms
	others	3,609	0ms
	select	68,016	0ms
	tcl	354	0ms
	update	2,801	0ms

Queries by host

Key values

unknown Main host
226,753 Requests
1h3m3s (unknown)
Main time consuming host

Chart
Table

Host	Request type	Count	Duration
unknown	Total	226,753	1h3m3s
	copy from	96	0ms
	copy to	26	0ms
	cte	7,610	0ms
	ddl	16	0ms
	delete	16	0ms
	insert	30,066	0ms
	others	3,812	0ms
	select	68,208	0ms
	tcl	686	0ms
	update	2,839	0ms

Queries by application

Key values

unknown Main application
226,367 Requests
1h3m3s (unknown)
Main time consuming application

Chart
Table

Application	Request type	Count	Duration
psql	Total	386	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	38	0ms
unknown	Total	226,367	1h3m3s
	copy from	16	0ms
	cte	7,506	0ms
	insert	30,066	0ms
	others	3,808	0ms
	select	68,106	0ms
	tcl	686	0ms
	update	2,801	0ms

Number of cancelled queries

Key values

0 per second Cancelled query Peak
2025-12-26 09:37:19 Date

Number of cancelled queries (5 minutes period)

NO DATASET

Histogram of query times

Key values

84,748 0-1ms duration

Chart
Table

Range	Count	Percentage
0-1ms	84,748	74.75%
1-5ms	24,888	21.95%
5-10ms	1,489	1.31%
10-25ms	723	0.64%
25-50ms	229	0.20%
50-100ms	118	0.10%
100-500ms	333	0.29%
500-1000ms	84	0.07%
1000-10000ms	699	0.62%
> 10000ms	67	0.06%

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
Dec 26	09	1	0ms	0ms

2

0ms

41

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
Dec 26	09	41	0ms	0ms

3

0ms

2,050

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 #3

Day	Hour	Count	Duration	Avg duration
Dec 26	09	2,050	0ms	0ms

4

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 #4

Day	Hour	Count	Duration	Avg duration
Dec 26	09	48	0ms	0ms

5

0ms

4

Details

0ms

select updaterelevantforrelevantresults ();

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Dec 26	09	4	0ms	0ms

6

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 #6

Day	Hour	Count	Duration	Avg duration
Dec 26	09	18	0ms	0ms

7

0ms

343

Details

0ms

commit;

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Dec 26	09	343	0ms	0ms

8

0ms

260

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 #8

Day	Hour	Count	Duration	Avg duration
Dec 26	09	260	0ms	0ms

9

0ms

239

Details

0ms

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

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Dec 26	09	239	0ms	0ms

10

0ms

239

Details

0ms

select system_identifier from pg_control_system ();

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Dec 26	09	239	0ms	0ms

11

0ms

2

Details

0ms

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

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Dec 26	09	2	0ms	0ms

12

0ms

2

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 #12

Day	Hour	Count	Duration	Avg duration
Dec 26	09	2	0ms	0ms

13

0ms

7

Details

0ms

select updatedatafeedslatestrun (?);

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Dec 26	09	7	0ms	0ms

14

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;

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Dec 26	09	1	0ms	0ms

15

0ms

2

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 #15

Day	Hour	Count	Duration	Avg duration
Dec 26	09	2	0ms	0ms

16

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;

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Dec 26	09	1	0ms	0ms

17

0ms

236

Details

0ms

with rar_max as ( select resultuid from relevance_fibonacci_results order by resultuid desc limit ? ), fr as ( select a.*, rr.age, rr.relevant from fibonacci_results a left outer join relevance_fibonacci_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) end ), all_results as ( select fr.resultuid as resultuid, fr.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, fr.pattern as pattern_name, fr.timed as timed, fr.patternendtime as identified, dtt.timezone as timezone, fr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when fr.age is not null then fr.age when fr.resultuid <= rm.resultuid then ? else ? end as age, case when fr.relevant is not null then fr.relevant when fr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from fr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = fr.symbolid inner join symbols s on fr.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on fr.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 downloadersymbolsettings dss on fr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left join lateral calc_fib_signal_filter (fr.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 fr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (fr.simulation = ? or fr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or fr.pattern in (...)) and (? = ? or fr.patternlengthbars <= ?) and (? = ? or (? = ? and fr.timed > cast(? as timestamp)) or (? = ? and fr.timed < cast(? as timestamp))) ), 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;

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Dec 26	09	236	0ms	0ms

18

0ms

2

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
Dec 26	09	2	0ms	0ms

19

0ms

12

Details

0ms

select distinct classname, to_char(created_datetime, ?), to_char(cleared_datetime, ?), action_to_take, description, created_datetime from datafeed_restarter_events where (is_current_entry = ? or cleared_datetime > current_timestamp - interval ?) order by created_datetime desc;

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Dec 26	09	12	0ms	0ms

20

0ms

16

Details

0ms

with max_ra as ( select resultuid from relevance_fibonacci_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 left outer join relevance_fibonacci_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 fibonacci_results a on a.resultuid = s.result_uid where s.uuid ilike ? and a.resultuid is null);

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Dec 26	09	16	0ms	0ms

Most frequent queries (N)

Rank

Times executed

Total duration

Min duration

Max duration

Avg duration

Query

1

29,747

Details

0ms

select ?;

Times Reported Time consuming queries #1

Day	Hour	Count	Duration	Avg duration
Dec 26	09	29,747	0ms	0ms

2

8,263

Details

0ms

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

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Dec 26	09	8,263	0ms	0ms

3

7,510

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 #3

Day	Hour	Count	Duration	Avg duration
Dec 26	09	7,510	0ms	0ms

4

5,502

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 #4

Day	Hour	Count	Duration	Avg duration
Dec 26	09	5,502	0ms	0ms

5

5,423

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 #5

Day	Hour	Count	Duration	Avg duration
Dec 26	09	5,423	0ms	0ms

6

5,192

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 #6

Day	Hour	Count	Duration	Avg duration
Dec 26	09	5,192	0ms	0ms

7

4,715

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 #7

Day	Hour	Count	Duration	Avg duration
Dec 26	09	4,715	0ms	0ms

8

3,308

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 #8

Day	Hour	Count	Duration	Avg duration
Dec 26	09	3,308	0ms	0ms

9

3,048

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 #9

Day	Hour	Count	Duration	Avg duration
Dec 26	09	3,048	0ms	0ms

10

2,826

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 #10

Day	Hour	Count	Duration	Avg duration
Dec 26	09	2,826	0ms	0ms

11

2,776

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 #11

Day	Hour	Count	Duration	Avg duration
Dec 26	09	2,776	0ms	0ms

12

2,255

Details

0ms

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

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Dec 26	09	2,255	0ms	0ms

13

2,128

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 #13

Day	Hour	Count	Duration	Avg duration
Dec 26	09	2,128	0ms	0ms

14

2,050

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 #14

Day	Hour	Count	Duration	Avg duration
Dec 26	09	2,050	0ms	0ms

15

1,591

Details

0ms

set extra_float_digits = ?;

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Dec 26	09	1,591	0ms	0ms

16

1,553

Details

0ms

set application_name = ?;

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Dec 26	09	1,553	0ms	0ms

17

1,407

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
Dec 26	09	1,407	0ms	0ms

18

1,301

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 #18

Day	Hour	Count	Duration	Avg duration
Dec 26	09	1,301	0ms	0ms

19

1,003

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 #19

Day	Hour	Count	Duration	Avg duration
Dec 26	09	1,003	0ms	0ms

20

837

Details

0ms

select t.pricedatetime, t.open, t.high, t.low, t.close, t.volume, t.symbolid, dss.downloadersymbol as symbol, dss.classname as datafeed, t.bsf, t.sastdatetimewritten, t.sastdatetimereceived from t15 t inner join downloadersymbolsettings dss on t.symbolid = dss.symbolid where dss.classname = ? and dss.downloadersymbol in (...) and dss.downloadfrequency = ? and t.pricedatetime between ?::timestamp and ?::timestamp order by t.pricedatetime;

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Dec 26	09	837	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
Dec 26	09	1	0ms	0ms

2

0ms

41

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
Dec 26	09	41	0ms	0ms

3

0ms

2,050

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 #3

Day	Hour	Count	Duration	Avg duration
Dec 26	09	2,050	0ms	0ms

4

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 #4

Day	Hour	Count	Duration	Avg duration
Dec 26	09	48	0ms	0ms

5

0ms

4

Details

0ms

select updaterelevantforrelevantresults ();

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Dec 26	09	4	0ms	0ms

6

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 #6

Day	Hour	Count	Duration	Avg duration
Dec 26	09	18	0ms	0ms

7

0ms

343

Details

0ms

commit;

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Dec 26	09	343	0ms	0ms

8

0ms

260

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 #8

Day	Hour	Count	Duration	Avg duration
Dec 26	09	260	0ms	0ms

9

0ms

239

Details

0ms

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

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Dec 26	09	239	0ms	0ms

10

0ms

239

Details

0ms

select system_identifier from pg_control_system ();

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Dec 26	09	239	0ms	0ms

11

0ms

2

Details

0ms

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

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Dec 26	09	2	0ms	0ms

12

0ms

2

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 #12

Day	Hour	Count	Duration	Avg duration
Dec 26	09	2	0ms	0ms

13

0ms

7

Details

0ms

select updatedatafeedslatestrun (?);

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Dec 26	09	7	0ms	0ms

14

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;

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Dec 26	09	1	0ms	0ms

15

0ms

2

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 #15

Day	Hour	Count	Duration	Avg duration
Dec 26	09	2	0ms	0ms

16

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;

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Dec 26	09	1	0ms	0ms

17

0ms

236

Details

0ms

with rar_max as ( select resultuid from relevance_fibonacci_results order by resultuid desc limit ? ), fr as ( select a.*, rr.age, rr.relevant from fibonacci_results a left outer join relevance_fibonacci_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) end ), all_results as ( select fr.resultuid as resultuid, fr.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, fr.pattern as pattern_name, fr.timed as timed, fr.patternendtime as identified, dtt.timezone as timezone, fr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when fr.age is not null then fr.age when fr.resultuid <= rm.resultuid then ? else ? end as age, case when fr.relevant is not null then fr.relevant when fr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from fr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = fr.symbolid inner join symbols s on fr.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on fr.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 downloadersymbolsettings dss on fr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left join lateral calc_fib_signal_filter (fr.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 fr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (fr.simulation = ? or fr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or fr.pattern in (...)) and (? = ? or fr.patternlengthbars <= ?) and (? = ? or (? = ? and fr.timed > cast(? as timestamp)) or (? = ? and fr.timed < cast(? as timestamp))) ), 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;

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Dec 26	09	236	0ms	0ms

18

0ms

2

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
Dec 26	09	2	0ms	0ms

19

0ms

12

Details

0ms

select distinct classname, to_char(created_datetime, ?), to_char(cleared_datetime, ?), action_to_take, description, created_datetime from datafeed_restarter_events where (is_current_entry = ? or cleared_datetime > current_timestamp - interval ?) order by created_datetime desc;

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Dec 26	09	12	0ms	0ms

20

0ms

16

Details

0ms

with max_ra as ( select resultuid from relevance_fibonacci_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 left outer join relevance_fibonacci_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 fibonacci_results a on a.resultuid = s.result_uid where s.uuid ilike ? and a.resultuid is null);

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Dec 26	09	16	0ms	0ms

Time consuming prepare


Date: 2025-12-26 09:16:11
Duration: 13ms
Database: postgres
Date: 2025-12-26 09:50:33
Duration: 8ms
Database: postgres
Date: 2025-12-26 09:20:25
Duration: 10ms
Database: postgres
Date: 2025-12-26 09:15:53
Duration: 1ms
Database: postgres
Date: 2025-12-26 09:48:02
Duration: 0ms
Database: postgres
Date: 2025-12-26 09:57:56
Duration: 0ms
Database: postgres
Date: 2025-12-26 09:01:02
Duration: 1ms
Database: postgres
Date: 2025-12-26 09:02:33
Duration: 0ms
Database: postgres
Date: 2025-12-26 09:10:49
Duration: 0ms
Database: postgres
Date: 2025-12-26 09:16:01
Duration: 0ms
Database: postgres
Date: 2025-12-26 09:21:45
Duration: 6ms
Database: postgres
Date: 2025-12-26 09:50:33
Duration: 4ms
Database: postgres
Date: 2025-12-26 09:41:07
Duration: 3ms
Database: postgres
Date: 2025-12-26 09:00:44
Duration: 4ms
Database: postgres
Date: 2025-12-26 09:10:49
Duration: 1ms
Database: postgres
Date: 2025-12-26 09:04:51
Duration: 4ms
Database: postgres
Date: 2025-12-26 09:13:17
Duration: 0ms
Database: postgres
Date: 2025-12-26 09:31:54
Duration: 0ms
Database: postgres
Date: 2025-12-26 09:00:04
Duration: 3ms
Database: postgres
Date: 2025-12-26 09:00:50
Duration: 3ms
Database: postgres

Time consuming bind

Rank

Total duration

Times executed

Min duration

Max duration

Avg duration

Query

1

21s557ms

6,578

Details

0ms

35ms

3ms

WITH rar_max as ( ;

Times Reported Time consuming bind #1

Day	Hour	Count	Duration	Avg duration
Dec 26	09	6,578	21s557ms	3ms

WITH rar_max as ( ;

Date: 2025-12-26 09:11:11 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 = '80', $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 = '0', $95 = '', $96 = '0', $97 = '0', $98 = '0', $99 = '0', $100 = '0', $101 = 't', $102 = '10', $103 = '10'
WITH rar_max as ( ;

Date: 2025-12-26 09:56:34 Duration: 34ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '0', $10 = 't', $11 = '0', $12 = '0'
WITH rar_max as ( ;

Date: 2025-12-26 09:50:33 Duration: 34ms Database: postgres parameters: $1 = '607393135187658302', $2 = '607393135187658302', $3 = '607393135187658302'

2

5s788ms

17,698

Details

0ms

8ms

0ms

SELECT ;

Times Reported Time consuming bind #2

Day	Hour	Count	Duration	Avg duration
	09	17,698	5s788ms	0ms

SELECT ;

Date: 2025-12-26 09:15:04 Duration: 8ms Database: postgres parameters: $1 = '515840243268101300'
SELECT ;

Date: 2025-12-26 09:58:59 Duration: 7ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '515840243280578300'
SELECT ;

Date: 2025-12-26 09:54:48 Duration: 7ms Database: postgres parameters: $1 = '958', $2 = '958', $3 = '515840249413895300'

3

2s332ms

1,113

Details

0ms

4ms

2ms

SELECT symbolid, ;

Times Reported Time consuming bind #3

Day	Hour	Count	Duration	Avg duration
	09	1,113	2s332ms	2ms

SELECT symbolid, ;

Date: 2025-12-26 09:16:01 Duration: 4ms Database: postgres parameters: $1 = 'PEPPERSTONE', $2 = '15', $3 = 'USDJPY', $4 = 'USDMXN', $5 = 'USDX', $6 = 'USDNOK', $7 = 'USDHUF'
SELECT symbolid, ;

Date: 2025-12-26 09:45:39 Duration: 3ms Database: postgres parameters: $1 = 'ICMARKETS', $2 = '15', $3 = 'USDJPY', $4 = 'USDMXN', $5 = 'US500', $6 = 'USDCZK', $7 = 'USDDKK', $8 = 'USDCHF', $9 = 'USDHKD', $10 = 'US2000', $11 = 'USDCNH', $12 = 'US30', $13 = 'USDCAD', $14 = 'USDHUF'
SELECT symbolid, ;

Date: 2025-12-26 09:10:40 Duration: 3ms Database: postgres parameters: $1 = 'BDSWISS', $2 = '60', $3 = 'AUS_200'

4

1s257ms

192

Details

0ms

27ms

6ms

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

Times Reported Time consuming bind #4

Day	Hour	Count	Duration	Avg duration
	09	192	1s257ms	6ms

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

Date: 2025-12-26 09:00:42 Duration: 27ms Database: postgres parameters: $1 = '489', $2 = '489'
select distinct s.statsid as statsid, sy.exchange as name;

Date: 2025-12-26 09:10:49 Duration: 21ms Database: postgres parameters: $1 = '489', $2 = '489'
select distinct s.statsid as statsid, sy.exchange as name;

Date: 2025-12-26 09:10:49 Duration: 21ms Database: postgres parameters: $1 = '489', $2 = '489'

5

723ms

443

Details

1ms

17ms

1ms

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

Times Reported Time consuming bind #5

Day	Hour	Count	Duration	Avg duration
	09	443	723ms	1ms

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

Date: 2025-12-26 09:15:59 Duration: 17ms Database: postgres parameters: $1 = 'FPMARKETS'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2025-12-26 09:15:57 Duration: 2ms Database: postgres parameters: $1 = 'GLOBALGTMT5'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2025-12-26 09:45:55 Duration: 2ms Database: postgres parameters: $1 = 'PEPPERSTONEMT5'

6

663ms

8,961

Details

0ms

1ms

0ms

select category, ;

Times Reported Time consuming bind #6

Day	Hour	Count	Duration	Avg duration
	09	8,961	663ms	0ms

select category, ;

Date: 2025-12-26 09:10:54 Duration: 1ms Database: postgres parameters: $1 = '515852059319772307', $2 = 'symbol', $3 = 'BTCUSD', $4 = 'WTI', $5 = 'GER30', $6 = 'NAS100', $7 = 'US30', $8 = 'SPX500', $9 = 'CHI50', $10 = 'HK50', $11 = 'WTI', $12 = 'SPX500', $13 = 'US30', $14 = 'NAS100', $15 = 'BTCUSD', $16 = 'CHI50', $17 = 'GER30', $18 = 'HK50', $19 = '#TSLA', $20 = '#AAPL', $21 = '#AAPL', $22 = '#TSLA', $23 = '515852059319772307', $24 = 'symbol', $25 = 'BTCUSD', $26 = 'WTI', $27 = 'GER30', $28 = 'NAS100', $29 = 'US30', $30 = 'SPX500', $31 = 'CHI50', $32 = 'HK50', $33 = 'WTI', $34 = 'SPX500', $35 = 'US30', $36 = 'NAS100', $37 = 'BTCUSD', $38 = 'CHI50', $39 = 'GER30', $40 = 'HK50', $41 = '#TSLA', $42 = '#AAPL', $43 = '#AAPL', $44 = '#TSLA'
select category, ;

Date: 2025-12-26 09:10:49 Duration: 1ms Database: postgres parameters: $1 = '515852059317765307', $2 = 'symbol', $3 = 'AUDJPY', $4 = 'GBPJPY', $5 = 'XAUUSD', $6 = 'OIL', $7 = 'DOW30', $8 = 'USDJPY', $9 = 'XAGUSD', $10 = 'CHFJPY', $11 = 'NASDAQ100', $12 = 'EURJPY', $13 = 'SP500', $14 = 'NZDJPY', $15 = 'GBPAUD', $16 = 'EURAUD', $17 = 'EURCAD', $18 = 'GBPCHF', $19 = 'EURGBP', $20 = 'EURCHF', $21 = 'XAGUSD', $22 = 'RK_SSI', $23 = 'R_SSI', $24 = 'GBPUSD', $25 = 'OIL', $26 = 'DOW30', $27 = 'USDCAD', $28 = 'EURCAD', $29 = 'EURUSD', $30 = 'NZDUSD', $31 = 'EURUSD', $32 = 'USDCAD', $33 = 'SP500', $34 = 'GBPAUD', $35 = 'GBPJPY', $36 = 'AUDUSD', $37 = 'EURJPY', $38 = 'USDCHF', $39 = 'GBPUSD', $40 = 'AUDJPY', $41 = 'CHFJPY', $42 = 'EURAUD', $43 = 'AUDNZD', $44 = 'XAUUSD', $45 = 'USDJPY', $46 = 'NZDUSD', $47 = 'NZDJPY', $48 = 'NASDAQ100', $49 = 'AUDUSD', $50 = 'USDCHF', $51 = 'GBPCHF', $52 = 'EURCHF', $53 = '515852059317765307', $54 = 'symbol', $55 = 'AUDJPY', $56 = 'GBPJPY', $57 = 'XAUUSD', $58 = 'OIL', $59 = 'DOW30', $60 = 'USDJPY', $61 = 'XAGUSD', $62 = 'CHFJPY', $63 = 'NASDAQ100', $64 = 'EURJPY', $65 = 'SP500', $66 = 'NZDJPY', $67 = 'GBPAUD', $68 = 'EURAUD', $69 = 'EURCAD', $70 = 'GBPCHF', $71 = 'EURGBP', $72 = 'EURCHF', $73 = 'XAGUSD', $74 = 'RK_SSI', $75 = 'R_SSI', $76 = 'GBPUSD', $77 = 'OIL', $78 = 'DOW30', $79 = 'USDCAD', $80 = 'EURCAD', $81 = 'EURUSD', $82 = 'NZDUSD', $83 = 'EURUSD', $84 = 'USDCAD', $85 = 'SP500', $86 = 'GBPAUD', $87 = 'GBPJPY', $88 = 'AUDUSD', $89 = 'EURJPY', $90 = 'USDCHF', $91 = 'GBPUSD', $92 = 'AUDJPY', $93 = 'CHFJPY', $94 = 'EURAUD', $95 = 'AUDNZD', $96 = 'XAUUSD', $97 = 'USDJPY', $98 = 'NZDUSD', $99 = 'NZDJPY', $100 = 'NASDAQ100', $101 = 'AUDUSD', $102 = 'USDCHF', $103 = 'GBPCHF', $104 = 'EURCHF'
select category, ;

Date: 2025-12-26 09:10:49 Duration: 1ms Database: postgres parameters: $1 = '515852059317765307', $2 = 'symbol', $3 = 'AUDJPY', $4 = 'GBPJPY', $5 = 'XAUUSD', $6 = 'OIL', $7 = 'DOW30', $8 = 'USDJPY', $9 = 'XAGUSD', $10 = 'CHFJPY', $11 = 'NASDAQ100', $12 = 'EURJPY', $13 = 'SP500', $14 = 'NZDJPY', $15 = 'GBPAUD', $16 = 'EURAUD', $17 = 'EURCAD', $18 = 'GBPCHF', $19 = 'EURGBP', $20 = 'EURCHF', $21 = 'XAGUSD', $22 = 'RK_SSI', $23 = 'R_SSI', $24 = 'GBPUSD', $25 = 'OIL', $26 = 'DOW30', $27 = 'USDCAD', $28 = 'EURCAD', $29 = 'EURUSD', $30 = 'NZDUSD', $31 = 'EURUSD', $32 = 'USDCAD', $33 = 'SP500', $34 = 'GBPAUD', $35 = 'GBPJPY', $36 = 'AUDUSD', $37 = 'EURJPY', $38 = 'USDCHF', $39 = 'GBPUSD', $40 = 'AUDJPY', $41 = 'CHFJPY', $42 = 'EURAUD', $43 = 'AUDNZD', $44 = 'XAUUSD', $45 = 'USDJPY', $46 = 'NZDUSD', $47 = 'NZDJPY', $48 = 'NASDAQ100', $49 = 'AUDUSD', $50 = 'USDCHF', $51 = 'GBPCHF', $52 = 'EURCHF', $53 = '515852059317765307', $54 = 'symbol', $55 = 'AUDJPY', $56 = 'GBPJPY', $57 = 'XAUUSD', $58 = 'OIL', $59 = 'DOW30', $60 = 'USDJPY', $61 = 'XAGUSD', $62 = 'CHFJPY', $63 = 'NASDAQ100', $64 = 'EURJPY', $65 = 'SP500', $66 = 'NZDJPY', $67 = 'GBPAUD', $68 = 'EURAUD', $69 = 'EURCAD', $70 = 'GBPCHF', $71 = 'EURGBP', $72 = 'EURCHF', $73 = 'XAGUSD', $74 = 'RK_SSI', $75 = 'R_SSI', $76 = 'GBPUSD', $77 = 'OIL', $78 = 'DOW30', $79 = 'USDCAD', $80 = 'EURCAD', $81 = 'EURUSD', $82 = 'NZDUSD', $83 = 'EURUSD', $84 = 'USDCAD', $85 = 'SP500', $86 = 'GBPAUD', $87 = 'GBPJPY', $88 = 'AUDUSD', $89 = 'EURJPY', $90 = 'USDCHF', $91 = 'GBPUSD', $92 = 'AUDJPY', $93 = 'CHFJPY', $94 = 'EURAUD', $95 = 'AUDNZD', $96 = 'XAUUSD', $97 = 'USDJPY', $98 = 'NZDUSD', $99 = 'NZDJPY', $100 = 'NASDAQ100', $101 = 'AUDUSD', $102 = 'USDCHF', $103 = 'GBPCHF', $104 = 'EURCHF'

7

516ms

21

Details

0ms

41ms

24ms

with wh_patitioned as ( ;

Times Reported Time consuming bind #7

Day	Hour	Count	Duration	Avg duration
	09	21	516ms	24ms

with wh_patitioned as ( ;

Date: 2025-12-26 09:35:40 Duration: 41ms 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: 2025-12-26 09:04:51 Duration: 34ms 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: 2025-12-26 09:06:19 Duration: 30ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'

8

443ms

29,627

Details

0ms

1ms

0ms

select 1;

Times Reported Time consuming bind #8

Day	Hour	Count	Duration	Avg duration
	09	29,627	443ms	0ms

select 1;

Date: 2025-12-26 09:32:44 Duration: 1ms Database: postgres
select 1;

Date: 2025-12-26 09:50:44 Duration: 1ms Database: postgres
select 1;

Date: 2025-12-26 09:02:43 Duration: 0ms Database: postgres

9

387ms

12

Details

28ms

44ms

32ms

with sym_info as ( ;

Times Reported Time consuming bind #9

Day	Hour	Count	Duration	Avg duration
	09	12	387ms	32ms

with sym_info as ( ;

Date: 2025-12-26 09:21:45 Duration: 44ms Database: postgres parameters: $1 = '620', $2 = 'Forex', $3 = 'Forex', $4 = '620', $5 = 'Forex', $6 = '620', $7 = '620', $8 = 'Forex', $9 = '620'
with sym_info as ( ;

Date: 2025-12-26 09:21:47 Duration: 43ms 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: 2025-12-26 09:36:43 Duration: 41ms Database: postgres parameters: $1 = '620', $2 = 'Forex', $3 = 'Forex', $4 = '620', $5 = 'Forex', $6 = '620', $7 = '620', $8 = 'Forex', $9 = '620'

10

307ms

472

Details

0ms

1ms

0ms

SELECT absolutetimezoneoffset;

Times Reported Time consuming bind #10

Day	Hour	Count	Duration	Avg duration
	09	472	307ms	0ms

SELECT absolutetimezoneoffset;

Date: 2025-12-26 09:10:50 Duration: 1ms Database: postgres parameters: $1 = '538', $2 = 'Shares EU'
SELECT absolutetimezoneoffset;

Date: 2025-12-26 09:10:50 Duration: 1ms Database: postgres parameters: $1 = '538', $2 = 'Shares EU'
SELECT absolutetimezoneoffset;

Date: 2025-12-26 09:10:50 Duration: 1ms Database: postgres parameters: $1 = '538', $2 = 'Shares EU'

11

304ms

34

Details

0ms

21ms

8ms

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

Times Reported Time consuming bind #11

Day	Hour	Count	Duration	Avg duration
	09	34	304ms	8ms

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

Date: 2025-12-26 09:11:50 Duration: 21ms Database: postgres parameters: $1 = '667', $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: 2025-12-26 09:46:08 Duration: 19ms Database: postgres parameters: $1 = '667', $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: 2025-12-26 09:06:13 Duration: 18ms Database: postgres parameters: $1 = '489', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'

12

303ms

47

Details

4ms

14ms

6ms

WITH last_candle AS ( ;

Times Reported Time consuming bind #12

Day	Hour	Count	Duration	Avg duration
	09	47	303ms	6ms

WITH last_candle AS ( ;

Date: 2025-12-26 09:15:05 Duration: 14ms Database: postgres parameters: $1 = '667', $2 = '667'
WITH last_candle AS ( ;

Date: 2025-12-26 09:16:00 Duration: 13ms Database: postgres parameters: $1 = '558', $2 = '558'
WITH last_candle AS ( ;

Date: 2025-12-26 09:00:44 Duration: 11ms Database: postgres parameters: $1 = '558', $2 = '558'

13

234ms

5,423

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	5,423	234ms	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: 2025-12-26 09:02:08 Duration: 0ms Database: postgres parameters: $1 = '2025-12-26 08:45:00', $2 = '0.78881', $3 = '0.78888', $4 = '0.78861', $5 = '0.7887', $6 = '220', $7 = '515840230585517300', $8 = '0', $9 = '2025-12-26 09:02:08.735', $10 = '2025-12-26 09:02:08.414', $11 = '0.78881', $12 = '0.78888', $13 = '0.78861', $14 = '0.7887', $15 = '220', $16 = '0', $17 = '2025-12-26 09:02:08.735', $18 = '2025-12-26 09:02:08.414'
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: 2025-12-26 09:02:33 Duration: 0ms Database: postgres parameters: $1 = '2025-12-26 08:30:00', $2 = '7.46947', $3 = '7.46951', $4 = '7.46942', $5 = '7.4695', $6 = '29', $7 = '515840243190162300', $8 = '0', $9 = '2025-12-26 09:02:33.934', $10 = '2025-12-26 09:02:33.692', $11 = '7.46947', $12 = '7.46951', $13 = '7.46942', $14 = '7.4695', $15 = '29', $16 = '0', $17 = '2025-12-26 09:02:33.934', $18 = '2025-12-26 09:02:33.692'
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: 2025-12-26 09:10:40 Duration: 0ms Database: postgres parameters: $1 = '2025-12-24 05:00:00', $2 = '8750.45', $3 = '8757.95', $4 = '8749.05', $5 = '8752.05', $6 = '1552', $7 = '515840248015086300', $8 = '0', $9 = '2025-12-26 09:10:40.04', $10 = '2025-12-26 09:10:39.998', $11 = '8750.45', $12 = '8757.95', $13 = '8749.05', $14 = '8752.05', $15 = '1552', $16 = '0', $17 = '2025-12-26 09:10:40.04', $18 = '2025-12-26 09:10:39.998'

14

202ms

3,048

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	3,048	202ms	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: 2025-12-26 09:01:23 Duration: 0ms Database: postgres parameters: $1 = '2025-12-24 19:30:00', $2 = '87.56', $3 = '87.955', $4 = '87.445', $5 = '87.795', $6 = '383', $7 = '515840249458206300', $8 = '0', $9 = '2025-12-26 09:01:23.683', $10 = '2025-12-26 09:01:23.35', $11 = '87.56', $12 = '87.955', $13 = '87.445', $14 = '87.795', $15 = '383', $16 = '0', $17 = '2025-12-26 09:01:23.683', $18 = '2025-12-26 09:01:23.35'
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: 2025-12-26 09:31:01 Duration: 0ms Database: postgres parameters: $1 = '2025-12-26 10:00:00', $2 = '210.8205', $3 = '210.8525', $4 = '210.6605', $5 = '210.661', $6 = '5609', $7 = '515840249381487300', $8 = '0', $9 = '2025-12-26 09:31:01.35', $10 = '2025-12-26 09:31:01.349', $11 = '210.8205', $12 = '210.8525', $13 = '210.6605', $14 = '210.661', $15 = '5609', $16 = '0', $17 = '2025-12-26 09:31:01.35', $18 = '2025-12-26 09:31:01.349'
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: 2025-12-26 09:41:51 Duration: 0ms Database: postgres parameters: $1 = '2025-12-24 05:00:00', $2 = '25838.4', $3 = '25850.2', $4 = '25761.6', $5 = '25780.7', $6 = '7513', $7 = '515840247933633300', $8 = '0', $9 = '2025-12-26 09:41:51.929', $10 = '2025-12-26 09:41:51.887', $11 = '25838.4', $12 = '25850.2', $13 = '25761.6', $14 = '25780.7', $15 = '7513', $16 = '0', $17 = '2025-12-26 09:41:51.929', $18 = '2025-12-26 09:41:51.887'

15

135ms

2,050

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	2,050	135ms	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: 2025-12-26 09:15:51 Duration: 0ms Database: postgres parameters: $1 = '2025-12-26 09:00:00', $2 = '0.35427', $3 = '0.3572925', $4 = '0.3536725', $5 = '0.35537', $6 = '2854', $7 = '515840249401411300', $8 = '0', $9 = '2025-12-26 09:15:51.924', $10 = '2025-12-26 09:15:51.923', $11 = '0.35427', $12 = '0.3572925', $13 = '0.3536725', $14 = '0.35537', $15 = '2854', $16 = '0', $17 = '2025-12-26 09:15:51.924', $18 = '2025-12-26 09:15:51.923'
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: 2025-12-26 09:15:36 Duration: 0ms Database: postgres parameters: $1 = '2025-12-26 08:00:00', $2 = '1.61178', $3 = '1.6118', $4 = '1.609645', $5 = '1.61018', $6 = '4671', $7 = '515840230464521300', $8 = '0', $9 = '2025-12-26 09:15:36.808', $10 = '2025-12-26 09:15:36.808', $11 = '1.61178', $12 = '1.6118', $13 = '1.609645', $14 = '1.61018', $15 = '4671', $16 = '0', $17 = '2025-12-26 09:15:36.809', $18 = '2025-12-26 09:15:36.808'
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: 2025-12-26 09:45:33 Duration: 0ms Database: postgres parameters: $1 = '2025-12-26 08:00:00', $2 = '7.46949', $3 = '7.46958', $4 = '7.46942', $5 = '7.46954', $6 = '103', $7 = '515840243190752300', $8 = '0', $9 = '2025-12-26 09:45:33.53', $10 = '2025-12-26 09:45:33.484', $11 = '7.46949', $12 = '7.46958', $13 = '7.46942', $14 = '7.46954', $15 = '103', $16 = '0', $17 = '2025-12-26 09:45:33.531', $18 = '2025-12-26 09:45:33.484'

16

65ms

12

Details

3ms

9ms

5ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Times Reported Time consuming bind #16

Day	Hour	Count	Duration	Avg duration
	09	12	65ms	5ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2025-12-26 09:10:49 Duration: 9ms Database: postgres parameters: $1 = '958', $2 = '958'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2025-12-26 09:10:48 Duration: 9ms Database: postgres parameters: $1 = '627', $2 = '627'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2025-12-26 09:01:22 Duration: 9ms Database: postgres parameters: $1 = '627', $2 = '627'

17

65ms

808

Details

0ms

INSERT INTO T240 (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 #17

Day	Hour	Count	Duration	Avg duration
	09	808	65ms	0ms

INSERT INTO T240 (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: 2025-12-26 09:16:01 Duration: 0ms Database: postgres parameters: $1 = '2025-12-24 16:00:00', $2 = '50407.5', $3 = '50497.5', $4 = '50282.5', $5 = '50437.5', $6 = '1454', $7 = '515840230556263300', $8 = '0', $9 = '2025-12-26 09:16:01.29', $10 = '2025-12-26 09:16:01.29', $11 = '50407.5', $12 = '50497.5', $13 = '50282.5', $14 = '50437.5', $15 = '1454', $16 = '0', $17 = '2025-12-26 09:16:01.29', $18 = '2025-12-26 09:16:01.29'
INSERT INTO T240 (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: 2025-12-26 09:46:02 Duration: 0ms Database: postgres parameters: $1 = '2025-12-24 16:00:00', $2 = '6962.125', $3 = '6988.125', $4 = '6957.25', $5 = '6982.25', $6 = '4690', $7 = '515840230575424300', $8 = '0', $9 = '2025-12-26 09:46:02.685', $10 = '2025-12-26 09:46:02.685', $11 = '6962.125', $12 = '6988.125', $13 = '6957.25', $14 = '6982.25', $15 = '4690', $16 = '0', $17 = '2025-12-26 09:46:02.685', $18 = '2025-12-26 09:46:02.685'
INSERT INTO T240 (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: 2025-12-26 09:15:53 Duration: 0ms Database: postgres parameters: $1 = '2025-12-24 16:00:00', $2 = '25823.25', $3 = '25891.75', $4 = '25784.5', $5 = '25878.5', $6 = '40354', $7 = '515840230516312300', $8 = '0', $9 = '2025-12-26 09:15:53.234', $10 = '2025-12-26 09:15:53.234', $11 = '25823.25', $12 = '25891.75', $13 = '25784.5', $14 = '25878.5', $15 = '40354', $16 = '0', $17 = '2025-12-26 09:15:53.234', $18 = '2025-12-26 09:15:53.234'

18

63ms

75

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 #18

Day	Hour	Count	Duration	Avg duration
	09	75	63ms	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: 2025-12-26 09:16:22 Duration: 1ms Database: postgres parameters: $1 = '558', $2 = 'NOKSEK', $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: 2025-12-26 09:18:05 Duration: 1ms Database: postgres parameters: $1 = '558', $2 = 'USDCAD', $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: 2025-12-26 09:46:19 Duration: 1ms Database: postgres parameters: $1 = '689', $2 = 'XAUUSD', $3 = '689'

19

48ms

12

Details

1ms

8ms

4ms

WITH pre_symbols AS ( /* find relevant symbols */ ;

Times Reported Time consuming bind #19

Day	Hour	Count	Duration	Avg duration
	09	12	48ms	4ms

WITH pre_symbols AS ( /* find relevant symbols */ ;

Date: 2025-12-26 09:00:50 Duration: 8ms Database: postgres parameters: $1 = '619', $2 = 'AXIORY', $3 = 'EURUSD', $4 = 'USDJPY', $5 = 'GBPUSD', $6 = 'AUDUSD', $7 = 'USDCHF', $8 = 'USDCAD', $9 = 'NZDUSD', $10 = 'GBPJPY', $11 = 'EURJPY', $12 = 'EURCHF', $13 = 'GBPCHF', $14 = 'EURCAD', $15 = 'GBPCAD', $16 = 'EURNZD', $17 = 'GBPNZD', $18 = 'CADJPY', $19 = 'CADCHF', $20 = 'CHFJPY', $21 = 'NZDJPY', $22 = 'XAUUSD', $23 = 'XAGUSD', $24 = 'EURUSD', $25 = 'USDJPY', $26 = 'GBPUSD', $27 = 'AUDUSD', $28 = 'USDCHF', $29 = 'USDCAD', $30 = 'NZDUSD', $31 = 'GBPJPY', $32 = 'EURJPY', $33 = 'EURCHF', $34 = 'GBPCHF', $35 = 'EURCAD', $36 = 'GBPCAD', $37 = 'EURNZD', $38 = 'GBPNZD', $39 = 'CADJPY', $40 = 'CADCHF', $41 = 'CHFJPY', $42 = 'NZDJPY', $43 = 'XAUUSD', $44 = 'XAGUSD', $45 = '5'
WITH pre_symbols AS ( /* find relevant symbols */ ;

Date: 2025-12-26 09:13:16 Duration: 6ms Database: postgres parameters: $1 = '1018', $2 = 'ICMARKETS-AU-MT5', $3 = 'AAPL.NAS', $4 = 'ABBV.NYSE', $5 = 'AMCR.NYSE', $6 = 'AMP.NYSE', $7 = 'AMZN.NAS', $8 = 'ANZ.ASX', $9 = 'AUDJPY', $10 = 'AUDUSD', $11 = 'AUS200', $12 = 'BABA.NYSE', $13 = 'BIIB.NAS', $14 = 'BXB.ASX', $15 = 'CBA.ASX', $16 = 'CHINA50', $17 = 'CSL.ASX', $18 = 'DE30', $19 = 'ES35', $20 = 'EURCHF', $21 = 'EURGBP', $22 = 'EURUSD', $23 = 'F40', $24 = 'FMG.ASX', $25 = 'GBPJPY', $26 = 'GBPUSD', $27 = 'GOOG.NAS', $28 = 'HK50', $29 = 'IT40', $30 = 'JP225', $31 = 'KO.NYSE', $32 = 'MQG.ASX', $33 = 'MSFT.NAS', $34 = 'NAB.ASX', $35 = 'NFLX.NAS', $36 = 'PYPL.NAS', $37 = 'QBE.ASX', $38 = 'STOXX50', $39 = 'SUN.ASX', $40 = 'TCL.ASX', $41 = 'TLS.ASX', $42 = 'TSLA.NAS', $43 = 'UK100', $44 = 'UNH.NYSE', $45 = 'US2000', $46 = 'US30', $47 = 'US500', $48 = 'USDCAD', $49 = 'USDCHF', $50 = 'USDCNH', $51 = 'USDJPY', $52 = 'USTEC', $53 = 'WBC.ASX', $54 = 'WES.ASX', $55 = 'WOW.ASX', $56 = 'WPL.ASX', $57 = 'XAUEUR', $58 = 'XAUUSD', $59 = 'XBRUSD', $60 = 'XTIUSD', $61 = 'AAPL.NAS', $62 = 'ABBV.NYSE', $63 = 'AMCR.NYSE', $64 = 'AMP.NYSE', $65 = 'AMZN.NAS', $66 = 'ANZ.ASX', $67 = 'AUDJPY', $68 = 'AUDUSD', $69 = 'AUS200', $70 = 'BABA.NYSE', $71 = 'BIIB.NAS', $72 = 'BXB.ASX', $73 = 'CBA.ASX', $74 = 'CHINA50', $75 = 'CSL.ASX', $76 = 'DE30', $77 = 'ES35', $78 = 'EURCHF', $79 = 'EURGBP', $80 = 'EURUSD', $81 = 'F40', $82 = 'FMG.ASX', $83 = 'GBPJPY', $84 = 'GBPUSD', $85 = 'GOOG.NAS', $86 = 'HK50', $87 = 'IT40', $88 = 'JP225', $89 = 'KO.NYSE', $90 = 'MQG.ASX', $91 = 'MSFT.NAS', $92 = 'NAB.ASX', $93 = 'NFLX.NAS', $94 = 'PYPL.NAS', $95 = 'QBE.ASX', $96 = 'STOXX50', $97 = 'SUN.ASX', $98 = 'TCL.ASX', $99 = 'TLS.ASX', $100 = 'TSLA.NAS', $101 = 'UK100', $102 = 'UNH.NYSE', $103 = 'US2000', $104 = 'US30', $105 = 'US500', $106 = 'USDCAD', $107 = 'USDCHF', $108 = 'USDCNH', $109 = 'USDJPY', $110 = 'USTEC', $111 = 'WBC.ASX', $112 = 'WES.ASX', $113 = 'WOW.ASX', $114 = 'WPL.ASX', $115 = 'XAUEUR', $116 = 'XAUUSD', $117 = 'XBRUSD', $118 = 'XTIUSD', $119 = '5'
WITH pre_symbols AS ( /* find relevant symbols */ ;

Date: 2025-12-26 09:13:16 Duration: 6ms Database: postgres parameters: $1 = '1018', $2 = 'ICMARKETS-AU-MT5', $3 = 'AAPL.NAS', $4 = 'ABBV.NYSE', $5 = 'AMCR.NYSE', $6 = 'AMP.NYSE', $7 = 'AMZN.NAS', $8 = 'ANZ.ASX', $9 = 'AUDJPY', $10 = 'AUDUSD', $11 = 'AUS200', $12 = 'BABA.NYSE', $13 = 'BIIB.NAS', $14 = 'BXB.ASX', $15 = 'CBA.ASX', $16 = 'CHINA50', $17 = 'CSL.ASX', $18 = 'DE30', $19 = 'ES35', $20 = 'EURCHF', $21 = 'EURGBP', $22 = 'EURUSD', $23 = 'F40', $24 = 'FMG.ASX', $25 = 'GBPJPY', $26 = 'GBPUSD', $27 = 'GOOG.NAS', $28 = 'HK50', $29 = 'IT40', $30 = 'JP225', $31 = 'KO.NYSE', $32 = 'MQG.ASX', $33 = 'MSFT.NAS', $34 = 'NAB.ASX', $35 = 'NFLX.NAS', $36 = 'PYPL.NAS', $37 = 'QBE.ASX', $38 = 'STOXX50', $39 = 'SUN.ASX', $40 = 'TCL.ASX', $41 = 'TLS.ASX', $42 = 'TSLA.NAS', $43 = 'UK100', $44 = 'UNH.NYSE', $45 = 'US2000', $46 = 'US30', $47 = 'US500', $48 = 'USDCAD', $49 = 'USDCHF', $50 = 'USDCNH', $51 = 'USDJPY', $52 = 'USTEC', $53 = 'WBC.ASX', $54 = 'WES.ASX', $55 = 'WOW.ASX', $56 = 'WPL.ASX', $57 = 'XAUEUR', $58 = 'XAUUSD', $59 = 'XBRUSD', $60 = 'XTIUSD', $61 = 'AAPL.NAS', $62 = 'ABBV.NYSE', $63 = 'AMCR.NYSE', $64 = 'AMP.NYSE', $65 = 'AMZN.NAS', $66 = 'ANZ.ASX', $67 = 'AUDJPY', $68 = 'AUDUSD', $69 = 'AUS200', $70 = 'BABA.NYSE', $71 = 'BIIB.NAS', $72 = 'BXB.ASX', $73 = 'CBA.ASX', $74 = 'CHINA50', $75 = 'CSL.ASX', $76 = 'DE30', $77 = 'ES35', $78 = 'EURCHF', $79 = 'EURGBP', $80 = 'EURUSD', $81 = 'F40', $82 = 'FMG.ASX', $83 = 'GBPJPY', $84 = 'GBPUSD', $85 = 'GOOG.NAS', $86 = 'HK50', $87 = 'IT40', $88 = 'JP225', $89 = 'KO.NYSE', $90 = 'MQG.ASX', $91 = 'MSFT.NAS', $92 = 'NAB.ASX', $93 = 'NFLX.NAS', $94 = 'PYPL.NAS', $95 = 'QBE.ASX', $96 = 'STOXX50', $97 = 'SUN.ASX', $98 = 'TCL.ASX', $99 = 'TLS.ASX', $100 = 'TSLA.NAS', $101 = 'UK100', $102 = 'UNH.NYSE', $103 = 'US2000', $104 = 'US30', $105 = 'US500', $106 = 'USDCAD', $107 = 'USDCHF', $108 = 'USDCNH', $109 = 'USDJPY', $110 = 'USTEC', $111 = 'WBC.ASX', $112 = 'WES.ASX', $113 = 'WOW.ASX', $114 = 'WPL.ASX', $115 = 'XAUEUR', $116 = 'XAUUSD', $117 = 'XBRUSD', $118 = 'XTIUSD', $119 = '5'

20

48ms

34

Details

1ms

2ms

1ms

WITH rcr_max as ( ;

Times Reported Time consuming bind #20

Day	Hour	Count	Duration	Avg duration
	09	34	48ms	1ms

WITH rcr_max as ( ;

Date: 2025-12-26 09:45:52 Duration: 2ms Database: postgres parameters: $1 = '607391482554958305', $2 = '607391482554958305', $3 = '607391482554958305'
WITH rcr_max as ( ;

Date: 2025-12-26 09:15:49 Duration: 1ms Database: postgres parameters: $1 = '607391482554958305', $2 = '607391482554958305', $3 = '607391482554958305'
WITH rcr_max as ( ;

Date: 2025-12-26 09:07:50 Duration: 1ms Database: postgres parameters: $1 = '607391482554958305', $2 = '607391482554958305', $3 = '607391482554958305'

Log levels

Key values

437,904 Log entries

Chart
Table

Type	Count	Percentage
CONTEXT	413	0.09%
DETAIL	100,456	22.94%
LOG	336,486	76.84%
STATEMENT	549	0.13%

Events distribution

Key values

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

Errors per 5 minutes

NO DATASET

Most Frequent Errors/Events

Key values

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

Rank	Times reported	Error
NO DATASET

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