pgBadger :: PostgreSQL Log Analyzer

Global information

Generated on Sun Feb 22 20:59:20 2026
Log file: /home/postgres/pg_data/data/pg_log/postgresql-2026-02-22_220000.log
Parsed 627,457 log entries in 19s
Log start from 2026-02-22 22:00:00 to 2026-02-22 22:59:19

Global Stats

171 Number of unique normalized queries
98,326 Number of queries
1h38m3s Total query duration
2026-02-22 22:00:00 First query
2026-02-22 22:59:19 Last query
2,701 queries/s at 2026-02-22 22:15:04 Query peak

1h38m3s Total query duration
2s615ms Prepare/parse total duration
23s456ms Bind total duration
1h37m37s Execute total duration

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

34 Total number of automatic vacuums
43 Total number of automatic analyzes

438 Number temporary file
137.46 MiB Max size of temporary file
7.07 MiB Average size of temporary file

1,770 Total number of sessions
13 sessions at 2026-02-22 22:53:48 Session peak
1d5h4m28s Total duration of sessions
59s134ms Average duration of sessions
55 Average queries per session
3s323ms Average queries duration per session
55s810ms Average idle time per session

1,768 Total number of connections
28 connections/s at 2026-02-22 22:33:48 Connection peak
3 Total number of databases

SQL Traffic

Key values

2,701 queries/s Query Peak
2026-02-22 22:15:04 Date

SELECT Traffic

Key values

1,347 queries/s Query Peak
2026-02-22 22:15:04 Date

INSERT/UPDATE/DELETE Traffic

Key values

148 queries/s Query Peak
2026-02-22 22:30:51 Date

Queries duration

Key values

1h38m3s 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)
Feb 22	22	98,326	0ms	24s336ms	59ms	4m16s	4m19s	4m48s

Day	Hour	SELECT	COPY TO	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Feb 22	22	31,048	26	0ms	0ms	0ms	0ms

Day	Hour	INSERT	UPDATE	DELETE	COPY FROM	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Feb 22	22	12,192	481	16	96	0ms	0ms	0ms	0ms

Day	Hour	Prepare	Bind	Bind/Prepare	Percentage of prepare
Feb 22	22	8,911	23,991	2.69	18.91%

Day	Hour	Count	Average / Second
Feb 22	22	1,768	0.49/s

Day	Hour	Count	Average Duration	Average idle time
Feb 22	22	1,770	59s134ms	55s825ms

↑ Back to the top of the General Activity table

Established Connections

Key values

28 connections Connection Peak
2026-02-22 22:33:48 Date

Connections per database

Key values

acaweb_fx Main Database
1,768 connections Total

Chart
Table

Database	User	Count
acaweb_fx		1,698
	postgres	1,339
	psql	359
socialmedia		70
	postgres	70

Connections per user

Key values

postgres Main User
1,768 connections Total

Chart
Table

User	Count
postgres	1,409
psql	359

Connections per host

Key values

192.168.4.142 Main host with 587 connections
1,768 Total connections

Chart
Table

Host	Count
127.0.0.1	106
192.168.0.114	2
192.168.0.216	116
192.168.0.74	13
192.168.1.145	61
192.168.1.15	5
192.168.1.20	86
192.168.1.239	56
192.168.1.90	15
192.168.2.126	18
192.168.2.182	12
192.168.3.199	38
192.168.4.142	587
192.168.4.33	70
192.168.4.98	330
[local]	253

Simultaneous sessions

Key values

13 sessions Session Peak
2026-02-22 22:53:48 Date

Histogram of session times

Key values

1,412 0-500ms duration

Chart
Table

Range	Count	Percentage
0ms-500ms	1,412	79.77%
500ms-1s	92	5.20%
1s-30s	218	12.32%
30s-1m	8	0.45%
1m-10m	6	0.34%
10m-30m	25	1.41%
30m-1h	3	0.17%
1h-8h	6	0.34%
> 8h	0	0.00%

Sessions per database

Key values

acaweb_fx Main Database
1,770 sessions Total

Chart
Table

Database	User	Count	Total Duration
acaweb_fx	1,699	1d5m26s	51s45ms
postgres	1	4h58m21s	4h58m21s
socialmedia	70	40s351ms	576ms

Sessions per user

Key values

postgres Main User
1,770 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
1,770 sessions Total

Chart
Table

Host	Count	Total Duration	Average Duration
104.30.164.187	2	9h56m41s	4h58m20s
127.0.0.1	106	3s246ms	30ms
192.168.0.114	2	10m	5m
192.168.0.216	116	10m48s	5s589ms
192.168.0.74	13	3h30m57s	16m13s
192.168.1.145	61	1h45m37s	1m43s
192.168.1.15	5	8m11s	1m38s
192.168.1.20	86	13h10m24s	9m11s
192.168.1.239	56	330ms	5ms
192.168.1.90	15	254ms	16ms
192.168.2.126	18	5s83ms	282ms
192.168.2.182	12	809ms	67ms
192.168.3.199	38	21s480ms	565ms
192.168.4.142	587	6m49s	697ms
192.168.4.33	70	40s351ms	576ms
192.168.4.98	330	15s522ms	47ms
[local]	253	3m30s	833ms

Sessions per application

Key values

unknown Main Host
1,770 sessions Total

Chart
Table

Application	Count	Total Duration	Average Duration
unknown	1,770	1d5h4m28s	59s134ms

Checkpoints Buffers

Key values

2,253 buffers Checkpoint Peak
2026-02-22 22:08:01 Date
209.913 seconds Highest write time
0.005 seconds Sync time

Checkpoints Wal files

Key values

1 files Wal files usage Peak
2026-02-22 22:53:02 Date

Checkpoints distance

Key values

49.42 Mo Distance Peak
2026-02-22 22:08:01 Date

Checkpoints Activity

Day	Hour	Written buffers	Write time	Sync time	Total time
Feb 22	22	13,297	1,447.754s	0.031s	1,448.029s

Day	Hour	Added	Removed	Recycled	Synced files	Longest sync	Average sync
Feb 22	22	0	0	10	1,314	0.004s	0s

Day	Hour	Count	Avg time (sec)
Feb 22	22	0	0s

Day	Hour	Mean distance	Mean estimate
Feb 22	22	13,816.42 kB	22,042.17 kB

↑ Back to the top of the Checkpoint Activity table

Checkpoint causes

Key values

time (11) Main checkpoint cause
11 checkpoints Total

Chart
Table

Cause	Number of checkpoints
time	11
Total	11

Size of temporary files

Key values

107.13 MiB Temp Files size Peak
2026-02-22 22:17:20 Date

Number of temporary files

Key values

30 per second Temp Files Peak
2026-02-22 22:02:10 Date

Temporary Files Activity

Temp Files

Day	Hour	Count	Total size	Average size
Feb 22	22	438	3.02 GiB	7.07 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	46	169.77 MiB	3.48 MiB	3.89 MiB	3.69 MiB	select resultuid from relevance_consecutivecandles_results order by resultuid desc limit ?), all_results as ( select ccr.resultuid as resultuid, ccr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, ccr.patternendtime as identified, dtt.timezone as timezone, ccr.qtyconsecutivecandles as length, g.basegroupname, case when rcr.age is not null then rcr.age when ccr.resultuid <= rm.resultuid then ? else ? end as age, case when rcr.relevant is not null then rcr.relevant when ccr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip, newlevels.filtered from consecutivecandles_results ccr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = ccr.symbolid inner join symbols s on ccr.symbolid = s.symbolid and s.nonliquid = ? inner join downloadersymbolsettings dss on ccr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on ccr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join rar_max rm on ? = ? left outer join relevance_consecutivecandles_results rcr on rcr.resultuid = ccr.resultuid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? left join lateral calc_cc_signal_filter (ccr.resultuid) newlevels on true where ccr.gmttimefound > now() - interval ? and s.deleted = ? and (ccr.simulation = ? or ccr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or ccr.patternlengthbars <= ?)), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc; SELECT resultuid FROM relevance_consecutivecandles_results ORDER BY resultuid DESC LIMIT 1), all_results AS ( SELECT ccr.resultuid AS resultuid, ccr.direction AS direction, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, ccr.patternendtime AS identified, dtt.timezone AS timezone, ccr.qtyconsecutivecandles AS length, g.basegroupname, CASE WHEN rcr.age IS NOT NULL THEN rcr.age WHEN ccr.resultuid <= rm.resultuid THEN 1 ELSE 0 END as age, CASE WHEN rcr.relevant IS NOT NULL THEN rcr.relevant WHEN ccr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip, newLevels.filtered FROM consecutivecandles_results ccr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $1 AND bsl.symbolid = ccr.symbolid INNER JOIN symbols s ON ccr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN downloadersymbolsettings dss ON ccr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN symbolgroup sg on ccr.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid INNER JOIN rar_max rm ON 1 = 1 LEFT OUTER JOIN relevance_consecutivecandles_results rcr ON rcr.resultuid = ccr.resultuid LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' LEFT JOIN LATERAL calc_cc_signal_filter (ccr.resultuid) newLevels on true WHERE ccr.gmttimefound > now() - INTERVAL '7 DAYS' AND s.deleted = 0 AND (ccr.simulation = 0 OR ccr.simulation IS NULL) AND ($2 = 0 OR s.timegranularity in ($3, $4, $5, $6, $7, $8, $9)) AND ($10 = 0 OR s.exchange in ($11)) AND ($12 = 0 OR coalesce(bim.code, s.symbol) in ($13, $14, $15, $16, $17, $18, $19, $20, $21, $22, $23, $24, $25, $26, $27, $28, $29, $30, $31, $32, $33, $34, $35, $36, $37, $38, $39, $40, $41, $42, $43, $44, $45, $46, $47, $48, $49, $50, $51, $52, $53, $54, $55, $56, $57, $58, $59, $60, $61, $62, $63, $64, $65, $66, $67, $68, $69, $70, $71, $72, $73, $74, $75, $76, $77, $78, $79, $80, $81, $82, $83, $84, $85, $86, $87, $88, $89, $90, $91, $92, $93, $94, $95, $96, $97, $98, $99, $100, $101, $102, $103, $104, $105, $106, $107, $108, $109, $110, $111, $112, $113, $114, $115, $116, $117, $118, $119, $120, $121, $122, $123, $124, $125, $126, $127, $128, $129, $130, $131, $132, $133, $134, $135, $136, $137, $138, $139, $140, $141, $142, $143, $144, $145, $146, $147, $148, $149, $150, $151, $152, $153, $154, $155, $156, $157, $158, $159, $160, $161, $162, $163, $164, $165, $166, $167, $168, $169, $170, $171, $172, $173, $174, $175, $176, $177, $178, $179, $180, $181, $182, $183, $184, $185, $186, $187, $188, $189, $190, $191, $192, $193, $194, $195, $196, $197, $198, $199, $200, $201, $202, $203, $204, $205, $206, $207, $208, $209, $210, $211, $212, $213, $214, $215, $216, $217, $218, $219, $220, $221, $222, $223, $224, $225)) AND ($226 = 0 OR ccr.patternlengthbars <= $227)), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results WHERE (FALSE = $228 OR relevant = 1) AND ($229 = 0 OR age <= $230) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-02-22 22:01:55 Duration: 0ms
2	46	189.54 MiB	3.92 MiB	4.34 MiB	4.12 MiB	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; SELECT resultuid FROM relevance_fibonacci_results ORDER BY resultuid DESC LIMIT 1), 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 = $1 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 11 ELSE 0 END as age, CASE WHEN fr.relevant IS NOT NULL THEN fr.relevant WHEN fr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip FROM fr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $2 AND bsl.symbolid = fr.symbolid INNER JOIN symbols s ON fr.symbolid = s.symbolid AND s.nonliquid = 0 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 = 3 INNER JOIN rar_max rm ON 1 = 1 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 = 'OUTBOUND' WHERE fr.gmttimefound > now() - INTERVAL '7 DAYS' AND dss.enabled = 1 AND s.deleted = 0 AND (fr.simulation = 0 OR fr.simulation IS NULL) AND ($3 = 0 OR s.timegranularity in ($4, $5, $6, $7, $8, $9, $10)) AND ($11 = 0 OR s.exchange in ($12)) AND ($13 = 0 OR coalesce(bim.code, s.symbol) in ($14, $15, $16, $17, $18, $19, $20, $21, $22, $23, $24, $25, $26, $27, $28, $29, $30, $31, $32, $33, $34, $35, $36, $37, $38, $39, $40, $41, $42, $43, $44, $45, $46, $47, $48, $49, $50, $51, $52, $53, $54, $55, $56, $57, $58, $59, $60, $61, $62, $63, $64, $65, $66, $67, $68, $69, $70, $71, $72, $73, $74, $75, $76, $77, $78, $79, $80, $81, $82, $83, $84, $85, $86, $87, $88, $89, $90, $91, $92, $93, $94, $95, $96, $97, $98, $99, $100, $101, $102, $103, $104, $105, $106, $107, $108, $109, $110, $111, $112, $113, $114, $115, $116, $117, $118, $119, $120, $121, $122, $123, $124, $125, $126, $127, $128, $129, $130, $131, $132, $133, $134, $135, $136, $137, $138, $139, $140, $141, $142, $143, $144, $145, $146, $147, $148, $149, $150, $151, $152, $153, $154, $155, $156, $157, $158, $159, $160, $161, $162, $163, $164, $165, $166, $167, $168, $169, $170, $171, $172, $173, $174, $175, $176, $177, $178, $179, $180, $181, $182, $183, $184, $185, $186, $187, $188, $189, $190, $191, $192, $193, $194, $195, $196, $197, $198, $199, $200, $201, $202, $203, $204, $205, $206, $207, $208, $209, $210, $211, $212, $213, $214, $215, $216, $217, $218, $219, $220, $221, $222, $223, $224, $225, $226)) AND ($227 = 0 OR fr.pattern in ($228)) AND ($229 = 0 OR fr.patternlengthbars <= $230) AND ($231 = 0 OR ($232 = 1 AND fr.timed > cast('1970-01-01' as timestamp)) OR ($233 = 2 AND fr.timed < cast('1970-01-01' as timestamp)))), results AS ( SELECT DISTINCT ON (symbolid) FROM all_results WHERE (FALSE = $234 OR relevant = 1) AND ($235 = 0 OR age <= $236) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-02-22 22:01:31 Duration: 0ms
3	16	621.12 MiB	38.82 MiB	38.82 MiB	38.82 MiB	update solr_relevance_old set new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total from ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total from whatshot_probability where type = ?) sub where result_uid = sub.resultuid; UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type = 'cp') sub WHERE result_uid = sub.resultuid; Date: 2026-02-22 22:01:13 Duration: 0ms
4	16	1.12 GiB	71.62 MiB	71.62 MiB	71.62 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit ?) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then ? else ? end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then ? else ? end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike ? inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike ?) sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike ?; update solr_relevance_old set newrelevant = ? where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike ? and a.resultuid is null); update solr_relevance_old set new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total from ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total from whatshot_probability where type in (...)) sub where result_uid = sub.resultuid; with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; Date: 2026-02-22 22:01:17 Duration: 0ms
5	13	65.00 MiB	2.25 MiB	7.15 MiB	5.00 MiB	with pet as ( select enddate, t.symbolid, row_number() over (partition by t.symbolid order by enddate desc) r from powerstats_trumpet t inner join brokersymbollist bsl on t.symbolid = bsl.symbolid and bsl.brokerid = ? ) select _xml from ( select * from ( select distinct replace(symbol, ?, ?) \|\| ? \|\| dayofweek \|\| ? \|\| fromtime \|\| ? \|\| round(ave_15, ?) \|\| ? \|\| round(ave_30, ?) \|\| ? \|\| round(ave_60, ?) \|\| ? \|\| round(ave_240, ?) \|\| ? \|\| round(ave_1440, ?) \|\| ? \|\| dayofweek \|\| ? \|\| fromtime \|\| ? as _xml, replace(symbol, ?, ?) as symbol, dayofweek, fromtime, round(ave_15, ?) as ave_15, round(ave_30, ?) as ave_30, round(ave_60, ?) as ave_60, round(ave_240, ?) as ave_240, round(ave_1440, ?) as ave_1440 from symbols s inner join brokersymbollist bsl on s.symbolid = bsl.symbolid inner join pet on pet.r = ? and bsl.symbolid = pet.symbolid and bsl.brokerid = ? inner join powerstats_trumpet pt on bsl.symbolid = pt.symbolid and pet.enddate = pt.enddate where case when ? != ? then ? = ? else ? = ? end and case when ? > ? and ? != ? then s.exchange ilike ? else ? = ? end) b order by symbol, dayofweek, fromtime) a; WITH pet as ( select enddate, t.symbolid, row_number() over (partition by t.symbolid order by enddate desc) r from powerstats_trumpet t inner join brokersymbollist bsl on t.symbolid = bsl.symbolid and bsl.brokerid = 692 ) select _xml FROM ( select * from ( select distinct replace(symbol, ' ', '~') \|\| '%<' \|\| dayofweek \|\| '_' \|\| fromtime \|\| '><15>' \|\| round(ave_15, 5) \|\| '</15><30>' \|\| round(ave_30, 5) \|\| '</30><60>' \|\| round(ave_60, 5) \|\| '</60><240>' \|\| round(ave_240, 5) \|\| '</240><1440>' \|\| round(ave_1440, 5) \|\| '</1440></' \|\| dayofweek \|\| '_' \|\| fromtime \|\| '>' as _xml, replace(symbol, ' ', '~') as symbol, dayofweek, fromtime, round(ave_15, 5) as ave_15, round(ave_30, 5) as ave_30, round(ave_60, 5) as ave_60, round(ave_240, 5) as ave_240, round(ave_1440, 5) as ave_1440 from symbols s inner join brokersymbollist bsl on s.symbolid = bsl.symbolid inner join pet on pet.r = 1 and bsl.symbolid = pet.symbolid and bsl.brokerid = 692 inner join powerstats_trumpet pt on bsl.symbolid = pt.symbolid and pet.enddate = pt.enddate where case when 692 != 285 then 1 = 1 else 1 = 0 end and case when 125 > 500 and 692 != 412 then s.exchange ilike 'FOREX' ELSE 1 = 1 END) b order by symbol, dayofweek, fromtime) a; Date: 2026-02-22 22:09:14 Duration: 0ms
6	4	549.85 MiB	137.46 MiB	137.46 MiB	137.46 MiB	select updateresultsmaterializedview (); select updateresultsmaterializedview (); Date: 2026-02-22 22:02:18 Duration: 0ms
7	4	339.16 MiB	84.79 MiB	84.79 MiB	84.79 MiB	select updateageforrelevantresults (); select updateageforrelevantresults (); Date: 2026-02-22 22:02:05 Duration: 0ms

Queries generating the largest temporary files

Rank	Size	Query
1	137.46 MiB	select updateresultsmaterializedview (); [ Date: 2026-02-22 22:47:16 ]
2	137.46 MiB	select updateresultsmaterializedview (); [ Date: 2026-02-22 22:32:14 ]
3	137.46 MiB	select updateresultsmaterializedview (); [ Date: 2026-02-22 22:17:14 ]
4	137.46 MiB	select updateresultsmaterializedview (); [ Date: 2026-02-22 22:02:18 ]
5	84.79 MiB	select updateageforrelevantresults (); [ Date: 2026-02-22 22:02:05 ]
6	84.79 MiB	select updateageforrelevantresults (); [ Date: 2026-02-22 22:32:04 ]
7	84.79 MiB	select updateageforrelevantresults (); [ Date: 2026-02-22 22:47:06 ]
8	84.79 MiB	select updateageforrelevantresults (); [ Date: 2026-02-22 22:17:04 ]
9	71.62 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: 2026-02-22 22:01:17 ]
10	71.62 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: 2026-02-22 22:03:16 ]
11	71.62 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: 2026-02-22 22:05:16 ]
12	71.62 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: 2026-02-22 22:11:17 ]
13	71.62 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: 2026-02-22 22:16:16 ]
14	71.62 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: 2026-02-22 22:18:15 ]
15	71.62 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: 2026-02-22 22:20:16 ]
16	71.62 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: 2026-02-22 22:26:17 ]
17	71.62 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: 2026-02-22 22:31:17 ]
18	71.62 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: 2026-02-22 22:33:16 ]
19	71.62 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: 2026-02-22 22:35:15 ]
20	71.62 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: 2026-02-22 22:41:16 ]

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)
43 analyzes Total

Chart
Table

Table	Number of analyzes
acaweb_fx.public.solr_relevance_old	16
acaweb_fx.pg_catalog.pg_attribute	8
acaweb_fx.pg_catalog.pg_type	4
acaweb_fx.pg_catalog.pg_class	4
acaweb_fx.public.datafeeds_latestrun	3
acaweb_fx.public.latest_t15_candle_view	2
acaweb_fx.pg_catalog.pg_depend	2
acaweb_fx.pg_catalog.pg_index	1
acaweb_fx.public.autochartist_symbolupdates	1
acaweb_fx.public.latest_candle_datetime_per_receng	1
acaweb_fx.public.relevance_fibonacci_results	1
Total	43

Vacuums per table

Key values

public.solr_relevance_old (19) Main table vacuumed on database acaweb_fx
34 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	19	16	13,726	0	52	0	0	8,330	16	1,498,297
acaweb_fx.public.datafeeds_latestrun	3	0	367	0	5	0	0	39	11	40,618
acaweb_fx.pg_catalog.pg_attribute	3	3	2,952	0	354	0	201	1,082	347	2,010,295
acaweb_fx.pg_catalog.pg_type	2	2	387	0	51	0	0	154	49	248,819
acaweb_fx.pg_catalog.pg_class	2	2	959	0	72	0	0	257	73	457,785
acaweb_fx.pg_toast.pg_toast_2619	1	1	139	0	24	0	0	99	24	112,134
acaweb_fx.public.solr_imports	1	1	65	0	1	0	0	6	1	8,937
acaweb_fx.pg_catalog.pg_statistic	1	1	929	0	110	0	628	361	99	377,199
acaweb_fx.public.latest_t15_candle_view	1	1	66	0	4	0	0	6	1	8,178
acaweb_fx.pg_catalog.pg_depend	1	1	462	0	76	0	59	180	71	358,296
Total	34	28	20,052	1,868	749	0	888	10,514	692	5,120,558

Tuples removed per table

Key values

public.solr_relevance_old (48238) Main table with removed tuples on database acaweb_fx
56257 tuples Total removed

Chart
Table

		Index	Tuples			Pages
Table	Vacuums	scans	removed	remain	not yet removable	removed	remain
acaweb_fx.public.solr_relevance_old	19	16	48,238	126,591	18,076	0	3,466
acaweb_fx.pg_catalog.pg_attribute	3	3	5,141	32,146	0	18	782
acaweb_fx.pg_catalog.pg_depend	1	1	886	14,386	0	0	143
acaweb_fx.pg_catalog.pg_type	2	2	761	2,896	0	0	88
acaweb_fx.pg_catalog.pg_statistic	1	1	704	3,955	0	0	1,194
acaweb_fx.pg_catalog.pg_class	2	2	185	3,354	54	0	300
acaweb_fx.public.datafeeds_latestrun	3	0	178	42	0	0	48
acaweb_fx.pg_toast.pg_toast_2619	1	1	63	168	3	0	50
acaweb_fx.public.solr_imports	1	1	51	1	0	0	2
acaweb_fx.public.latest_t15_candle_view	1	1	50	22	8	0	1
Total	34	28	56,257	183,561	18,141	18	6,074

Pages removed per table

Key values

pg_catalog.pg_attribute (18) Main table with removed pages on database acaweb_fx
18 pages Total removed

Chart
Table

Table	Number of vacuums	Index scans	Tuples removed	Pages removed
acaweb_fx.pg_catalog.pg_attribute	3	3	5141	18
acaweb_fx.pg_toast.pg_toast_2619	1	1	63	0
acaweb_fx.pg_catalog.pg_type	2	2	761	0
acaweb_fx.public.datafeeds_latestrun	3	0	178	0
acaweb_fx.public.solr_imports	1	1	51	0
acaweb_fx.pg_catalog.pg_statistic	1	1	704	0
acaweb_fx.public.latest_t15_candle_view	1	1	50	0
acaweb_fx.pg_catalog.pg_depend	1	1	886	0
acaweb_fx.pg_catalog.pg_class	2	2	185	0
acaweb_fx.public.solr_relevance_old	19	16	48238	0
Total	34	28	56,257	18

Autovacuum Activity

Vacuums / Analyzes

Day	Hour	VACUUMs	ANALYZEs
Feb 22	22	34	43

↑ 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

31,048 Total read queries
16,084 Total write queries

Chart
Table

Type	Count	Percentage
SELECT	31,048	31.58%
INSERT	12,192	12.40%
UPDATE	481	0.49%
DELETE	16	0.02%
COPY FROM	96	0.10%
COPY TO	26	0.03%
CTE	2,593	2.64%
DDL	16	0.02%
TCL	664	0.68%
CURSOR	0	0.00%
OTHERS	51,194	52.07%

Queries by database

Key values

unknown Main database
97,343 Requests
1h37m37s (unknown)
Main time consuming database

Chart
Table

Database	Request type	Count	Duration
acaweb_fx	Total	913	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	108	0ms
	ddl	16	0ms
	delete	16	0ms
	others	226	0ms
	select	73	0ms
	tcl	332	0ms
	update	36	0ms
socialmedia	Total	70	0ms
	select	70	0ms
unknown	Total	97,343	1h37m37s
	copy from	16	0ms
	cte	2,485	0ms
	insert	12,192	0ms
	others	2,001	0ms
	select	30,905	0ms
	tcl	332	0ms
	update	445	0ms

Queries by user

Key values

unknown Main user
97,343 Requests

Queries Chart
Table

User	Request type	Count	Duration
postgres	Total	983	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	108	0ms
	ddl	16	0ms
	delete	16	0ms
	others	226	0ms
	select	143	0ms
	tcl	332	0ms
	update	36	0ms
unknown	Total	97,343	1h37m37s
	copy from	16	0ms
	cte	2,485	0ms
	insert	12,192	0ms
	others	2,001	0ms
	select	30,905	0ms
	tcl	332	0ms
	update	445	0ms

Duration by user

Key values

1h37m37s (unknown) Main time consuming user

Duration Chart
Table

User	Request type	Count	Duration
postgres	Total	983	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	108	0ms
	ddl	16	0ms
	delete	16	0ms
	others	226	0ms
	select	143	0ms
	tcl	332	0ms
	update	36	0ms
unknown	Total	97,343	1h37m37s
	copy from	16	0ms
	cte	2,485	0ms
	insert	12,192	0ms
	others	2,001	0ms
	select	30,905	0ms
	tcl	332	0ms
	update	445	0ms

Queries by host

Key values

unknown Main host
98,326 Requests
1h37m37s (unknown)
Main time consuming host

Chart
Table

Host	Request type	Count	Duration
unknown	Total	98,326	1h37m37s
	copy from	96	0ms
	copy to	26	0ms
	cte	2,593	0ms
	ddl	16	0ms
	delete	16	0ms
	insert	12,192	0ms
	others	2,227	0ms
	select	31,048	0ms
	tcl	664	0ms
	update	481	0ms

Queries by application

Key values

unknown Main application
97,967 Requests
1h37m37s (unknown)
Main time consuming application

Chart
Table

Application	Request type	Count	Duration
psql	Total	359	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	108	0ms
	ddl	16	0ms
	delete	16	0ms
	others	4	0ms
	select	73	0ms
	update	36	0ms
unknown	Total	97,967	1h37m37s
	copy from	16	0ms
	cte	2,485	0ms
	insert	12,192	0ms
	others	2,223	0ms
	select	30,975	0ms
	tcl	664	0ms
	update	445	0ms

Number of cancelled queries

Key values

0 per second Cancelled query Peak
2026-02-22 22:51:12 Date

Number of cancelled queries (5 minutes period)

NO DATASET

Histogram of query times

Key values

27,990 0-1ms duration

Chart
Table

Range	Count	Percentage
0-1ms	27,990	57.16%
1-5ms	17,032	34.78%
5-10ms	1,163	2.38%
10-25ms	1,375	2.81%
25-50ms	147	0.30%
50-100ms	80	0.16%
100-500ms	253	0.52%
500-1000ms	77	0.16%
1000-10000ms	731	1.49%
> 10000ms	119	0.24%

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

11

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 #1

Day	Hour	Count	Duration	Avg duration
Feb 22	22	11	0ms	0ms

2

0ms

193

Details

0ms

with rar_max as ( select resultuid from relevance_consecutivecandles_results order by resultuid desc limit ? ), all_results as ( select ccr.resultuid as resultuid, ccr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, ccr.patternendtime as identified, dtt.timezone as timezone, ccr.qtyconsecutivecandles as length, g.basegroupname, case when rcr.age is not null then rcr.age when ccr.resultuid <= rm.resultuid then ? else ? end as age, case when rcr.relevant is not null then rcr.relevant when ccr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip, newlevels.filtered from consecutivecandles_results ccr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = ccr.symbolid inner join symbols s on ccr.symbolid = s.symbolid and s.nonliquid = ? inner join downloadersymbolsettings dss on ccr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on ccr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join rar_max rm on ? = ? left outer join relevance_consecutivecandles_results rcr on rcr.resultuid = ccr.resultuid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? left join lateral calc_cc_signal_filter (ccr.resultuid) newlevels on true where ccr.gmttimefound > now() - interval ? and s.deleted = ? and (ccr.simulation = ? or ccr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or ccr.patternlengthbars <= ?) ), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc;

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Feb 22	22	193	0ms	0ms

3

0ms

2,319

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
Feb 22	22	2,319	0ms	0ms

4

0ms

21

Details

0ms

select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as resultuid, s.symbol, timegranularity as interval, direction as direction, patternendtime as patternendtime, patternstartprice as psp, patternendprice as pep, target03 as t03, target16 as t16, patternlengthbars as length, p.patternname as patternname, dtt.timezone, cps.pip from fibonacci_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 fibonaccipatterns p on a.pattern = p.patternname left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;

Times Reported Time consuming queries #4

Day	Hour	Count	Duration	Avg duration
Feb 22	22	21	0ms	0ms

5

0ms

34

Details

0ms

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

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Feb 22	22	34	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
Feb 22	22	18	0ms	0ms

7

0ms

237

Details

0ms

select v.datname, c.relname, v.phase, v.heap_blks_total, v.heap_blks_scanned, v.heap_blks_vacuumed, v.index_vacuum_count, v.max_dead_tuples, v.num_dead_tuples from pg_stat_progress_vacuum as v join pg_class c on c.oid = v.relid;

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Feb 22	22	237	0ms	0ms

8

0ms

23

Details

0ms

select * from ( select pricedatetime, open, high, low, close, volume, bsf from t60 where symbolid = ? and (bsf = ? or bsf is null) order by pricedatetime desc limit ?) a order by pricedatetime asc;

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Feb 22	22	23	0ms	0ms

9

0ms

332

Details

0ms

commit;

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Feb 22	22	332	0ms	0ms

10

0ms

6

Details

0ms

select psd.symbolid, psd.hourlysymbolid as intervalsymbolid, s.symbol, dtt.timezone, dtt.absolutetimezoneoffset, psh.hour as index, psh.ave, psh.stddev, ((psh.ave - psh.stddev) / ?.?) as low, ((psh.ave + psh.stddev) / ?.?) as high, ee.timezone as exchangetimezone, ee.mon_t1start as exchangestart, ee.mon_t1end as exchangeend from powerstats_symboldata psd inner join powerstats_hourly psh on psd.hourlysymbolid = psh.symbolid inner join symbols s on psh.symbolid = s.symbolid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join mat_ps_hourly_symbolid_max_enddate e on psh.enddate = e.enddate and psh.symbolid = e.symbolid inner join exchanges ee on ee.exchange = s.exchange where psd.symbolid = ? and dtt.dayofweek = ? order by hour asc;

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Feb 22	22	6	0ms	0ms

11

0ms

273

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 #11

Day	Hour	Count	Duration	Avg duration
Feb 22	22	273	0ms	0ms

12

0ms

237

Details

0ms

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

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Feb 22	22	237	0ms	0ms

13

0ms

2

Details

0ms

select (cast(substring(tz.gmoffset from ? for ?) as float) * ? + cast(substring(tz.gmoffset from ? for ?) as float)) / ? as offset from timezones tz where tz.timezone = ?;

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Feb 22	22	2	0ms	0ms

14

0ms

21

Details

0ms

select * from ( select pricedatetime, open, high, low, close, volume, bsf from t30 where symbolid = ? and (bsf = ? or bsf is null) order by pricedatetime desc limit ?) a order by pricedatetime asc;

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Feb 22	22	21	0ms	0ms

15

0ms

6

Details

0ms

select pid, datname, client_addr from pg_stat_activity where query ilike ? and pid <> pg_backend_pid() and state = ? and datname = ?;

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Feb 22	22	6	0ms	0ms

16

0ms

9

Details

0ms

select distinct replace(s.symbol, ?, ?) || ? || replace(bmi.code, ?, ?) from symbols s inner join brokersymbollist bsl on s.symbolid = bsl.symbolid inner join downloadersymbolsettings dss on bsl.symbolid = dss.symbolid inner join brokerinstrumentmapping bmi on dss.datafeedinstrumentid = bmi.datafeedinstrumentid and bsl.brokerid = bmi.brokerid where bsl.brokerid = ? and case when ? != ? then ? = ? else ? = ? end and case when ? > ? and ? != ? then s.exchange ilike ? else ? = ? end;

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Feb 22	22	9	0ms	0ms

17

0ms

11

Details

0ms

update executions set isrunning = false, has_results = false, response = ? where id = ?;

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Feb 22	22	11	0ms	0ms

18

0ms

237

Details

0ms

select system_identifier from pg_control_system ();

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Feb 22	22	237	0ms	0ms

19

0ms

3

Details

0ms

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

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Feb 22	22	3	0ms	0ms

20

0ms

8

Details

0ms

with pet as ( select enddate, t.symbolid, row_number() over (partition by t.symbolid order by enddate desc) r from powerstats_trumpet t inner join brokersymbollist bsl on t.symbolid = bsl.symbolid and bsl.brokerid = ? ) select _xml from ( select * from ( select distinct replace(symbol, ?, ?) || ? || dayofweek || ? || fromtime || ? || round(ave_15, ?) || ? || round(ave_30, ?) || ? || round(ave_60, ?) || ? || round(ave_240, ?) || ? || round(ave_1440, ?) || ? || dayofweek || ? || fromtime || ? as _xml, replace(symbol, ?, ?) as symbol, dayofweek, fromtime, round(ave_15, ?) as ave_15, round(ave_30, ?) as ave_30, round(ave_60, ?) as ave_60, round(ave_240, ?) as ave_240, round(ave_1440, ?) as ave_1440 from symbols s inner join brokersymbollist bsl on s.symbolid = bsl.symbolid inner join pet on pet.r = ? and bsl.symbolid = pet.symbolid and bsl.brokerid = ? inner join powerstats_trumpet pt on bsl.symbolid = pt.symbolid and pet.enddate = pt.enddate where case when ? != ? then ? = ? else ? = ? end and case when ? > ? and ? != ? then s.exchange ilike ? else ? = ? end) b order by symbol, dayofweek, fromtime) a;

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Feb 22	22	8	0ms	0ms

WITH pet as ( select enddate, t.symbolid, row_number() over (partition by t.symbolid order by enddate desc) r from powerstats_trumpet t inner join brokersymbollist bsl on t.symbolid = bsl.symbolid and bsl.brokerid = 692 ) select _xml FROM ( select * from ( select distinct replace(symbol, ' ', '~') || '%<' || dayofweek || '_' || fromtime || '><15>' || round(ave_15, 5) || '</15><30>' || round(ave_30, 5) || '</30><60>' || round(ave_60, 5) || '</60><240>' || round(ave_240, 5) || '</240><1440>' || round(ave_1440, 5) || '</1440></' || dayofweek || '_' || fromtime || '>' as _xml, replace(symbol, ' ', '~') as symbol, dayofweek, fromtime, round(ave_15, 5) as ave_15, round(ave_30, 5) as ave_30, round(ave_60, 5) as ave_60, round(ave_240, 5) as ave_240, round(ave_1440, 5) as ave_1440 from symbols s inner join brokersymbollist bsl on s.symbolid = bsl.symbolid inner join pet on pet.r = 1 and bsl.symbolid = pet.symbolid and bsl.brokerid = 692 inner join powerstats_trumpet pt on bsl.symbolid = pt.symbolid and pet.enddate = pt.enddate where case when 692 != 285 then 1 = 1 else 1 = 0 end and case when 125 > 500 and 692 != 412 then s.exchange ilike 'FOREX' ELSE 1 = 1 END) b order by symbol, dayofweek, fromtime) a;

Date: 2026-02-22 22:09:14 Duration: 0ms

Most frequent queries (N)

Rank

Times executed

Total duration

Min duration

Max duration

Avg duration

Query

1

5,837

Details

0ms

select ?;

Times Reported Time consuming queries #1

Day	Hour	Count	Duration	Avg duration
Feb 22	22	5,837	0ms	0ms

2

4,211

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
Feb 22	22	4,211	0ms	0ms

3

3,272

Details

0ms

select datid, datname, pid, usesysid, usename, application_name, client_addr, client_hostname, client_port, backend_start, xact_start, query_start, state_change, wait_event_type, wait_event, state, backend_xid, backend_xmin, query, backend_type from pg_stat_activity where backend_type != ? or (coalesce(trim(query), ?) != ? and pid != pg_backend_pid() and query_start is not null and datname not ilike ? and datname not ilike ? and datname not ilike ? and datname not ilike ? and not (query_start < ?::timestamptz and state = ?));

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Feb 22	22	3,272	0ms	0ms

4

3,053

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 #4

Day	Hour	Count	Duration	Avg duration
Feb 22	22	3,053	0ms	0ms

5

2,716

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
Feb 22	22	2,716	0ms	0ms

6

2,584

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 #6

Day	Hour	Count	Duration	Avg duration
Feb 22	22	2,584	0ms	0ms

7

2,346

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 #7

Day	Hour	Count	Duration	Avg duration
Feb 22	22	2,346	0ms	0ms

8

2,319

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 #8

Day	Hour	Count	Duration	Avg duration
Feb 22	22	2,319	0ms	0ms

9

1,185

Details

0ms

select relname, schemaname, heap_blks_read, heap_blks_hit, idx_blks_read, idx_blks_hit, toast_blks_read, toast_blks_hit, tidx_blks_read, tidx_blks_hit from pg_statio_user_tables where ((relname ~ ?));

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Feb 22	22	1,185	0ms	0ms

10

1,185

Details

0ms

select relname, schemaname, indexrelname, idx_scan, idx_tup_read, idx_tup_fetch, pg_relation_size(indexrelid) as index_size from pg_stat_user_indexes where ((relname ~ ?));

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Feb 22	22	1,185	0ms	0ms

11

1,185

Details

0ms

select n.nspname as schemaname, count(*) from ( select c.relnamespace from pg_class c where c.relkind in (...)) as subquery left join pg_namespace n on (n.oid = relnamespace) where n.nspname not in (...) group by n.nspname;

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Feb 22	22	1,185	0ms	0ms

12

1,185

Details

0ms

select current_database(), s.schemaname, s.table, s.partition_of, s.relpages, s.reltuples, s.relallvisible, s.relation_size + s.toast_size, s.relation_size, s.index_size, s.toast_size, s.relation_size + s.index_size + s.toast_size from ( select n.nspname as schemaname, relname as table, i.inhparent::regclass as partition_of, c.relpages, c.reltuples, c.relallvisible, pg_relation_size(c.oid) as relation_size, case when c.relhasindex then pg_indexes_size(c.oid) else ? end as index_size, case when c.reltoastrelid > ? then pg_relation_size(c.reltoastrelid) else ? end as toast_size from pg_class c left join pg_namespace n on (n.oid = c.relnamespace) left join pg_inherits i on (i.inhrelid = c.oid) left join pg_locks l on c.oid = l.relation and l.locktype = ? where not (nspname = any (?)) and (l.relation is null or l.mode <> ? or not l.granted) and relkind = ? and ((relname ~ ?)) limit ?) as s;

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Feb 22	22	1,185	0ms	0ms

13

1,185

Details

0ms

select mode, locktype, pn.nspname, pd.datname, pc.relname, granted, fastpath, count(*) as lock_count from pg_locks l join pg_database pd on (l.database = pd.oid) join pg_class pc on (l.relation = pc.oid) left join pg_namespace pn on (pn.oid = pc.relnamespace) where ((relname ~ ?)) and l.mode is not null and pc.relname not like ? escape ? group by pd.datname, pc.relname, pn.nspname, locktype, mode, granted, fastpath;

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Feb 22	22	1,185	0ms	0ms

14

1,185

Details

0ms

select relname, schemaname, seq_scan, seq_tup_read, idx_scan, idx_tup_fetch, n_tup_ins, n_tup_upd, n_tup_del, n_tup_hot_upd, n_live_tup, n_dead_tup, vacuum_count, autovacuum_count, analyze_count, autoanalyze_count, extract(epoch from age(current_timestamp, last_vacuum)), extract(epoch from age(current_timestamp, last_autovacuum)), extract(epoch from age(current_timestamp, last_analyze)), extract(epoch from age(current_timestamp, last_autoanalyze)) from pg_stat_user_tables where ((relname ~ ?));

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Feb 22	22	1,185	0ms	0ms

15

978

Details

0ms

select datetimeupdate from latest_candle_datetime_per_receng where recognitionengine ilike ?;

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Feb 22	22	978	0ms	0ms

16

773

Details

0ms

set extra_float_digits = ?;

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Feb 22	22	773	0ms	0ms

17

761

Details

0ms

set application_name = ?;

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Feb 22	22	761	0ms	0ms

18

440

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 #18

Day	Hour	Count	Duration	Avg duration
Feb 22	22	440	0ms	0ms

19

356

Details

0ms

select count(*) from pg_stat_statements (false);

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Feb 22	22	356	0ms	0ms

20

332

Details

0ms

commit;

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Feb 22	22	332	0ms	0ms

Normalized slowest queries (N)

Rank

Min duration

Max duration

Avg duration

Times executed

Total duration

Query

1

0ms

11

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 #1

Day	Hour	Count	Duration	Avg duration
Feb 22	22	11	0ms	0ms

2

0ms

193

Details

0ms

with rar_max as ( select resultuid from relevance_consecutivecandles_results order by resultuid desc limit ? ), all_results as ( select ccr.resultuid as resultuid, ccr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, ccr.patternendtime as identified, dtt.timezone as timezone, ccr.qtyconsecutivecandles as length, g.basegroupname, case when rcr.age is not null then rcr.age when ccr.resultuid <= rm.resultuid then ? else ? end as age, case when rcr.relevant is not null then rcr.relevant when ccr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip, newlevels.filtered from consecutivecandles_results ccr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = ccr.symbolid inner join symbols s on ccr.symbolid = s.symbolid and s.nonliquid = ? inner join downloadersymbolsettings dss on ccr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on ccr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join rar_max rm on ? = ? left outer join relevance_consecutivecandles_results rcr on rcr.resultuid = ccr.resultuid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? left join lateral calc_cc_signal_filter (ccr.resultuid) newlevels on true where ccr.gmttimefound > now() - interval ? and s.deleted = ? and (ccr.simulation = ? or ccr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or ccr.patternlengthbars <= ?) ), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc;

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Feb 22	22	193	0ms	0ms

3

0ms

2,319

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
Feb 22	22	2,319	0ms	0ms

4

0ms

21

Details

0ms

select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as resultuid, s.symbol, timegranularity as interval, direction as direction, patternendtime as patternendtime, patternstartprice as psp, patternendprice as pep, target03 as t03, target16 as t16, patternlengthbars as length, p.patternname as patternname, dtt.timezone, cps.pip from fibonacci_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 fibonaccipatterns p on a.pattern = p.patternname left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;

Times Reported Time consuming queries #4

Day	Hour	Count	Duration	Avg duration
Feb 22	22	21	0ms	0ms

5

0ms

34

Details

0ms

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

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Feb 22	22	34	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
Feb 22	22	18	0ms	0ms

7

0ms

237

Details

0ms

select v.datname, c.relname, v.phase, v.heap_blks_total, v.heap_blks_scanned, v.heap_blks_vacuumed, v.index_vacuum_count, v.max_dead_tuples, v.num_dead_tuples from pg_stat_progress_vacuum as v join pg_class c on c.oid = v.relid;

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Feb 22	22	237	0ms	0ms

8

0ms

23

Details

0ms

select * from ( select pricedatetime, open, high, low, close, volume, bsf from t60 where symbolid = ? and (bsf = ? or bsf is null) order by pricedatetime desc limit ?) a order by pricedatetime asc;

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Feb 22	22	23	0ms	0ms

9

0ms

332

Details

0ms

commit;

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Feb 22	22	332	0ms	0ms

10

0ms

6

Details

0ms

select psd.symbolid, psd.hourlysymbolid as intervalsymbolid, s.symbol, dtt.timezone, dtt.absolutetimezoneoffset, psh.hour as index, psh.ave, psh.stddev, ((psh.ave - psh.stddev) / ?.?) as low, ((psh.ave + psh.stddev) / ?.?) as high, ee.timezone as exchangetimezone, ee.mon_t1start as exchangestart, ee.mon_t1end as exchangeend from powerstats_symboldata psd inner join powerstats_hourly psh on psd.hourlysymbolid = psh.symbolid inner join symbols s on psh.symbolid = s.symbolid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join mat_ps_hourly_symbolid_max_enddate e on psh.enddate = e.enddate and psh.symbolid = e.symbolid inner join exchanges ee on ee.exchange = s.exchange where psd.symbolid = ? and dtt.dayofweek = ? order by hour asc;

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Feb 22	22	6	0ms	0ms

11

0ms

273

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 #11

Day	Hour	Count	Duration	Avg duration
Feb 22	22	273	0ms	0ms

12

0ms

237

Details

0ms

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

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Feb 22	22	237	0ms	0ms

13

0ms

2

Details

0ms

select (cast(substring(tz.gmoffset from ? for ?) as float) * ? + cast(substring(tz.gmoffset from ? for ?) as float)) / ? as offset from timezones tz where tz.timezone = ?;

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Feb 22	22	2	0ms	0ms

14

0ms

21

Details

0ms

select * from ( select pricedatetime, open, high, low, close, volume, bsf from t30 where symbolid = ? and (bsf = ? or bsf is null) order by pricedatetime desc limit ?) a order by pricedatetime asc;

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Feb 22	22	21	0ms	0ms

15

0ms

6

Details

0ms

select pid, datname, client_addr from pg_stat_activity where query ilike ? and pid <> pg_backend_pid() and state = ? and datname = ?;

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Feb 22	22	6	0ms	0ms

16

0ms

9

Details

0ms

select distinct replace(s.symbol, ?, ?) || ? || replace(bmi.code, ?, ?) from symbols s inner join brokersymbollist bsl on s.symbolid = bsl.symbolid inner join downloadersymbolsettings dss on bsl.symbolid = dss.symbolid inner join brokerinstrumentmapping bmi on dss.datafeedinstrumentid = bmi.datafeedinstrumentid and bsl.brokerid = bmi.brokerid where bsl.brokerid = ? and case when ? != ? then ? = ? else ? = ? end and case when ? > ? and ? != ? then s.exchange ilike ? else ? = ? end;

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Feb 22	22	9	0ms	0ms

17

0ms

11

Details

0ms

update executions set isrunning = false, has_results = false, response = ? where id = ?;

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Feb 22	22	11	0ms	0ms

18

0ms

237

Details

0ms

select system_identifier from pg_control_system ();

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Feb 22	22	237	0ms	0ms

19

0ms

3

Details

0ms

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

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Feb 22	22	3	0ms	0ms

20

0ms

8

Details

0ms

with pet as ( select enddate, t.symbolid, row_number() over (partition by t.symbolid order by enddate desc) r from powerstats_trumpet t inner join brokersymbollist bsl on t.symbolid = bsl.symbolid and bsl.brokerid = ? ) select _xml from ( select * from ( select distinct replace(symbol, ?, ?) || ? || dayofweek || ? || fromtime || ? || round(ave_15, ?) || ? || round(ave_30, ?) || ? || round(ave_60, ?) || ? || round(ave_240, ?) || ? || round(ave_1440, ?) || ? || dayofweek || ? || fromtime || ? as _xml, replace(symbol, ?, ?) as symbol, dayofweek, fromtime, round(ave_15, ?) as ave_15, round(ave_30, ?) as ave_30, round(ave_60, ?) as ave_60, round(ave_240, ?) as ave_240, round(ave_1440, ?) as ave_1440 from symbols s inner join brokersymbollist bsl on s.symbolid = bsl.symbolid inner join pet on pet.r = ? and bsl.symbolid = pet.symbolid and bsl.brokerid = ? inner join powerstats_trumpet pt on bsl.symbolid = pt.symbolid and pet.enddate = pt.enddate where case when ? != ? then ? = ? else ? = ? end and case when ? > ? and ? != ? then s.exchange ilike ? else ? = ? end) b order by symbol, dayofweek, fromtime) a;

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Feb 22	22	8	0ms	0ms

WITH pet as ( select enddate, t.symbolid, row_number() over (partition by t.symbolid order by enddate desc) r from powerstats_trumpet t inner join brokersymbollist bsl on t.symbolid = bsl.symbolid and bsl.brokerid = 692 ) select _xml FROM ( select * from ( select distinct replace(symbol, ' ', '~') || '%<' || dayofweek || '_' || fromtime || '><15>' || round(ave_15, 5) || '</15><30>' || round(ave_30, 5) || '</30><60>' || round(ave_60, 5) || '</60><240>' || round(ave_240, 5) || '</240><1440>' || round(ave_1440, 5) || '</1440></' || dayofweek || '_' || fromtime || '>' as _xml, replace(symbol, ' ', '~') as symbol, dayofweek, fromtime, round(ave_15, 5) as ave_15, round(ave_30, 5) as ave_30, round(ave_60, 5) as ave_60, round(ave_240, 5) as ave_240, round(ave_1440, 5) as ave_1440 from symbols s inner join brokersymbollist bsl on s.symbolid = bsl.symbolid inner join pet on pet.r = 1 and bsl.symbolid = pet.symbolid and bsl.brokerid = 692 inner join powerstats_trumpet pt on bsl.symbolid = pt.symbolid and pet.enddate = pt.enddate where case when 692 != 285 then 1 = 1 else 1 = 0 end and case when 125 > 500 and 692 != 412 then s.exchange ilike 'FOREX' ELSE 1 = 1 END) b order by symbol, dayofweek, fromtime) a;

Date: 2026-02-22 22:09:14 Duration: 0ms

Time consuming prepare


Date: 2026-02-22 22:06:34
Duration: 8ms
Database: postgres
Date: 2026-02-22 22:02:19
Duration: 2ms
Database: postgres
Date: 2026-02-22 22:11:51
Duration: 0ms
Database: postgres
Date: 2026-02-22 22:11:41
Duration: 0ms
Database: postgres
Date: 2026-02-22 22:01:26
Duration: 1ms
Database: postgres
Date: 2026-02-22 22:00:48
Duration: 2ms
Database: postgres
Date: 2026-02-22 22:00:48
Duration: 2ms
Database: postgres
Date: 2026-02-22 22:26:53
Duration: 0ms
Database: postgres
Date: 2026-02-22 22:31:09
Duration: 3ms
Database: postgres
Date: 2026-02-22 22:00:48
Duration: 3ms
Database: postgres
Date: 2026-02-22 22:00:05
Duration: 2ms
Database: postgres
Date: 2026-02-22 22:16:35
Duration: 1ms
Database: postgres
Date: 2026-02-22 22:00:48
Duration: 0ms
Database: postgres
Date: 2026-02-22 22:02:11
Duration: 0ms
Database: postgres
Date: 2026-02-22 22:56:43
Duration: 1ms
Database: postgres
Date: 2026-02-22 22:32:00
Duration: 3ms
Database: postgres
Date: 2026-02-22 22:30:27
Duration: 0ms
Database: postgres
Date: 2026-02-22 22:50:27
Duration: 1ms
Database: postgres
Date: 2026-02-22 22:26:37
Duration: 0ms
Database: postgres
Date: 2026-02-22 22:08:55
Duration: 1ms
Database: postgres

Time consuming bind

Rank

Total duration

Times executed

Min duration

Max duration

Avg duration

Query

1

18s251ms

1,646

Details

0ms

55ms

11ms

WITH rar_max as ( ;

Times Reported Time consuming bind #1

Day	Hour	Count	Duration	Avg duration
Feb 22	22	1,646	18s251ms	11ms

WITH rar_max as ( ;

Date: 2026-02-22 22:46:26 Duration: 55ms Database: postgres parameters: $1 = '667', $2 = '7', $3 = '15', $4 = '30', $5 = '60', $6 = '120', $7 = '240', $8 = '480', $9 = '1440', $10 = '0', $11 = '', $12 = '84', $13 = 'AUDCAD', $14 = 'AUDCHF', $15 = 'AUDJPY', $16 = 'AUDNZD', $17 = 'AUDSGD', $18 = 'CADCHF', $19 = 'CADJPY', $20 = 'CHFJPY', $21 = 'EURAUD', $22 = 'EURCAD', $23 = 'EURCHF', $24 = 'EURCZK', $25 = 'EURGBP', $26 = 'EURHUF', $27 = 'EURJPY', $28 = 'EURNOK', $29 = 'EURNZD', $30 = 'EURPLN', $31 = 'EURSEK', $32 = 'EURSGD', $33 = 'EURTRY', $34 = 'EURZAR', $35 = 'GBPAUD', $36 = 'GBPCAD', $37 = 'GBPCHF', $38 = 'GBPJPY', $39 = 'GBPNZD', $40 = 'GBPPLN', $41 = 'GBPSEK', $42 = 'GBPSGD', $43 = 'NZDCAD', $44 = 'NZDCHF', $45 = 'NZDJPY', $46 = 'NZDSGD', $47 = 'USDCNH', $48 = 'USDCZK', $49 = 'USDHUF', $50 = 'USDNOK', $51 = 'USDPLN', $52 = 'USDSEK', $53 = 'USDSGD', $54 = 'USDTRY', $55 = 'USDZAR', $56 = 'WTI', $57 = 'XBRUSD', $58 = 'XTIUSD', $59 = 'BTCUSD', $60 = 'XAGAUD', $61 = 'XAGUSD', $62 = 'XAUAUD', $63 = 'XAUUSD', $64 = 'XPTUSD', $65 = 'XPDUSD', $66 = 'AUDUSD', $67 = 'EURUSD', $68 = 'GBPUSD', $69 = 'NZDUSD', $70 = 'USDCAD', $71 = 'USDCHF', $72 = 'USDHKD', $73 = 'USDJPY', $74 = 'AUS200', $75 = 'CHINA300', $76 = 'CHINA50', $77 = 'DJ30', $78 = 'ESP35t', $79 = 'EUR50', $80 = 'EURO50', $81 = 'FRA40', $82 = 'GDAXI', $83 = 'GDAXIm', $84 = 'HK50', $85 = 'ITA40', $86 = 'J225', $87 = 'JP225', $88 = 'NAS100', $89 = 'SING30', $90 = 'SPA35', $91 = 'STOXX50', $92 = 'SUI20', $93 = 'UK100', $94 = 'US100', $95 = 'US30', $96 = 'US500', $97 = '700', $98 = '700', $99 = 't', $100 = '10', $101 = '10'
WITH rar_max as ( ;

Date: 2026-02-22 22:46:26 Duration: 38ms Database: postgres parameters: $1 = '667', $2 = '7', $3 = '15', $4 = '30', $5 = '60', $6 = '120', $7 = '240', $8 = '480', $9 = '1440', $10 = '0', $11 = '', $12 = '84', $13 = 'AUDCAD', $14 = 'AUDCHF', $15 = 'AUDJPY', $16 = 'AUDNZD', $17 = 'AUDSGD', $18 = 'CADCHF', $19 = 'CADJPY', $20 = 'CHFJPY', $21 = 'EURAUD', $22 = 'EURCAD', $23 = 'EURCHF', $24 = 'EURCZK', $25 = 'EURGBP', $26 = 'EURHUF', $27 = 'EURJPY', $28 = 'EURNOK', $29 = 'EURNZD', $30 = 'EURPLN', $31 = 'EURSEK', $32 = 'EURSGD', $33 = 'EURTRY', $34 = 'EURZAR', $35 = 'GBPAUD', $36 = 'GBPCAD', $37 = 'GBPCHF', $38 = 'GBPJPY', $39 = 'GBPNZD', $40 = 'GBPPLN', $41 = 'GBPSEK', $42 = 'GBPSGD', $43 = 'NZDCAD', $44 = 'NZDCHF', $45 = 'NZDJPY', $46 = 'NZDSGD', $47 = 'USDCNH', $48 = 'USDCZK', $49 = 'USDHUF', $50 = 'USDNOK', $51 = 'USDPLN', $52 = 'USDSEK', $53 = 'USDSGD', $54 = 'USDTRY', $55 = 'USDZAR', $56 = 'WTI', $57 = 'XBRUSD', $58 = 'XTIUSD', $59 = 'BTCUSD', $60 = 'XAGAUD', $61 = 'XAGUSD', $62 = 'XAUAUD', $63 = 'XAUUSD', $64 = 'XPTUSD', $65 = 'XPDUSD', $66 = 'AUDUSD', $67 = 'EURUSD', $68 = 'GBPUSD', $69 = 'NZDUSD', $70 = 'USDCAD', $71 = 'USDCHF', $72 = 'USDHKD', $73 = 'USDJPY', $74 = 'AUS200', $75 = 'CHINA300', $76 = 'CHINA50', $77 = 'DJ30', $78 = 'ESP35t', $79 = 'EUR50', $80 = 'EURO50', $81 = 'FRA40', $82 = 'GDAXI', $83 = 'GDAXIm', $84 = 'HK50', $85 = 'ITA40', $86 = 'J225', $87 = 'JP225', $88 = 'NAS100', $89 = 'SING30', $90 = 'SPA35', $91 = 'STOXX50', $92 = 'SUI20', $93 = 'UK100', $94 = 'US100', $95 = 'US30', $96 = 'US500', $97 = '700', $98 = '700', $99 = 't', $100 = '10', $101 = '10'
WITH rar_max as ( ;

Date: 2026-02-22 22:36:32 Duration: 34ms Database: postgres parameters: $1 = '667', $2 = '7', $3 = '15', $4 = '30', $5 = '60', $6 = '120', $7 = '240', $8 = '480', $9 = '1440', $10 = '0', $11 = '', $12 = '84', $13 = 'AUDCAD', $14 = 'AUDCHF', $15 = 'AUDJPY', $16 = 'AUDNZD', $17 = 'AUDSGD', $18 = 'CADCHF', $19 = 'CADJPY', $20 = 'CHFJPY', $21 = 'EURAUD', $22 = 'EURCAD', $23 = 'EURCHF', $24 = 'EURCZK', $25 = 'EURGBP', $26 = 'EURHUF', $27 = 'EURJPY', $28 = 'EURNOK', $29 = 'EURNZD', $30 = 'EURPLN', $31 = 'EURSEK', $32 = 'EURSGD', $33 = 'EURTRY', $34 = 'EURZAR', $35 = 'GBPAUD', $36 = 'GBPCAD', $37 = 'GBPCHF', $38 = 'GBPJPY', $39 = 'GBPNZD', $40 = 'GBPPLN', $41 = 'GBPSEK', $42 = 'GBPSGD', $43 = 'NZDCAD', $44 = 'NZDCHF', $45 = 'NZDJPY', $46 = 'NZDSGD', $47 = 'USDCNH', $48 = 'USDCZK', $49 = 'USDHUF', $50 = 'USDNOK', $51 = 'USDPLN', $52 = 'USDSEK', $53 = 'USDSGD', $54 = 'USDTRY', $55 = 'USDZAR', $56 = 'WTI', $57 = 'XBRUSD', $58 = 'XTIUSD', $59 = 'BTCUSD', $60 = 'XAGAUD', $61 = 'XAGUSD', $62 = 'XAUAUD', $63 = 'XAUUSD', $64 = 'XPTUSD', $65 = 'XPDUSD', $66 = 'AUDUSD', $67 = 'EURUSD', $68 = 'GBPUSD', $69 = 'NZDUSD', $70 = 'USDCAD', $71 = 'USDCHF', $72 = 'USDHKD', $73 = 'USDJPY', $74 = 'AUS200', $75 = 'CHINA300', $76 = 'CHINA50', $77 = 'DJ30', $78 = 'ESP35t', $79 = 'EUR50', $80 = 'EURO50', $81 = 'FRA40', $82 = 'GDAXI', $83 = 'GDAXIm', $84 = 'HK50', $85 = 'ITA40', $86 = 'J225', $87 = 'JP225', $88 = 'NAS100', $89 = 'SING30', $90 = 'SPA35', $91 = 'STOXX50', $92 = 'SUI20', $93 = 'UK100', $94 = 'US100', $95 = 'US30', $96 = 'US500', $97 = '700', $98 = '700', $99 = 't', $100 = '10', $101 = '10'

2

2s94ms

6,207

Details

0ms

5ms

0ms

SELECT ;

Times Reported Time consuming bind #2

Day	Hour	Count	Duration	Avg duration
	22	6,207	2s94ms	0ms

SELECT ;

Date: 2026-02-22 22:00:09 Duration: 5ms Database: postgres parameters: $1 = '515840243198780300'
SELECT ;

Date: 2026-02-22 22:50:27 Duration: 4ms Database: postgres parameters: $1 = '515840243935135300'
SELECT ;

Date: 2026-02-22 22:04:06 Duration: 4ms Database: postgres parameters: $1 = '515840243198780300'

3

1s382ms

514

Details

1ms

5ms

2ms

SELECT symbolid, ;

Times Reported Time consuming bind #3

Day	Hour	Count	Duration	Avg duration
	22	514	1s382ms	2ms

SELECT symbolid, ;

Date: 2026-02-22 22:02:10 Duration: 5ms Database: postgres parameters: $1 = 'MILLENNIUMPF', $2 = '15', $3 = 'USDJPY', $4 = 'R_SSI', $5 = 'USDCAD.ID', $6 = 'USDCHF.ID', $7 = 'USDCHF', $8 = 'USDCHF.FX', $9 = 'USDCAD.FX', $10 = 'USDCAD', $11 = 'R_HSI', $12 = 'SP500'
SELECT symbolid, ;

Date: 2026-02-22 22:01:51 Duration: 4ms Database: postgres parameters: $1 = 'PEPPERSTONEMT5', $2 = '15', $3 = 'AAPL.US'
SELECT symbolid, ;

Date: 2026-02-22 22:02:21 Duration: 4ms Database: postgres parameters: $1 = 'ATFX', $2 = '15', $3 = 'NAS100', $4 = 'SPX500'

4

342ms

48

Details

4ms

14ms

7ms

WITH last_candle AS ( ;

Times Reported Time consuming bind #4

Day	Hour	Count	Duration	Avg duration
	22	48	342ms	7ms

WITH last_candle AS ( ;

Date: 2026-02-22 22:52:00 Duration: 14ms Database: postgres parameters: $1 = '558', $2 = '558'
WITH last_candle AS ( ;

Date: 2026-02-22 22:52:00 Duration: 13ms Database: postgres parameters: $1 = '558', $2 = '558'
WITH last_candle AS ( ;

Date: 2026-02-22 22:32:00 Duration: 13ms Database: postgres parameters: $1 = '558', $2 = '558'

5

229ms

16

Details

0ms

41ms

14ms

with wh_patitioned as ( ;

Times Reported Time consuming bind #5

Day	Hour	Count	Duration	Avg duration
	22	16	229ms	14ms

with wh_patitioned as ( ;

Date: 2026-02-22 22:15:09 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: 2026-02-22 22:50:24 Duration: 30ms Database: postgres parameters: $1 = '621', $2 = '621', $3 = '621', $4 = '621', $5 = '621', $6 = '621', $7 = '621', $8 = '621', $9 = '621'
with wh_patitioned as ( ;

Date: 2026-02-22 22:30:02 Duration: 28ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'

6

214ms

125

Details

1ms

2ms

1ms

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

Times Reported Time consuming bind #6

Day	Hour	Count	Duration	Avg duration
	22	125	214ms	1ms

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

Date: 2026-02-22 22:01:26 Duration: 2ms Database: postgres parameters: $1 = 'GLOBALGTMT5'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-02-22 22:00:35 Duration: 2ms Database: postgres parameters: $1 = 'ICMARKETS'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-02-22 22:46:36 Duration: 2ms Database: postgres parameters: $1 = 'ICMARKETS'

7

193ms

2,319

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 #7

Day	Hour	Count	Duration	Avg duration
	22	2,319	193ms	0ms

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

Date: 2026-02-22 22:11:30 Duration: 0ms Database: postgres parameters: $1 = '2026-02-20 21:00:00', $2 = '257.23', $3 = '258.04', $4 = '255.95', $5 = '256.97', $6 = '1528', $7 = '515840247899857300', $8 = '0', $9 = '2026-02-22 22:11:30.973', $10 = '2026-02-22 22:11:30.888', $11 = '257.23', $12 = '258.04', $13 = '255.95', $14 = '256.97', $15 = '1528', $16 = '0', $17 = '2026-02-22 22:11:30.973', $18 = '2026-02-22 22:11:30.888'
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-02-22 22:02:23 Duration: 0ms Database: postgres parameters: $1 = '2026-02-22 22:00:00', $2 = '1944.24', $3 = '1946.88', $4 = '1941.58', $5 = '1945.1', $6 = '1404', $7 = '515840249394513300', $8 = '0', $9 = '2026-02-22 22:02:23.327', $10 = '2026-02-22 22:02:23.326', $11 = '1944.24', $12 = '1946.88', $13 = '1941.58', $14 = '1945.1', $15 = '1404', $16 = '0', $17 = '2026-02-22 22:02:23.327', $18 = '2026-02-22 22:02:23.326'
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-02-22 22:11:51 Duration: 0ms Database: postgres parameters: $1 = '2026-02-20 17:00:00', $2 = '26655.3', $3 = '26803.8', $4 = '26601.8', $5 = '26772.1', $6 = '24823', $7 = '515840247933961300', $8 = '0', $9 = '2026-02-22 22:11:51.158', $10 = '2026-02-22 22:11:51.093', $11 = '26655.3', $12 = '26803.8', $13 = '26601.8', $14 = '26772.1', $15 = '24823', $16 = '0', $17 = '2026-02-22 22:11:51.158', $18 = '2026-02-22 22:11:51.093'

8

187ms

5,727

Details

0ms

5ms

0ms

select 1;

Times Reported Time consuming bind #8

Day	Hour	Count	Duration	Avg duration
	22	5,727	187ms	0ms

select 1;

Date: 2026-02-22 22:20:54 Duration: 5ms Database: postgres
select 1;

Date: 2026-02-22 22:21:26 Duration: 5ms Database: postgres
select 1;

Date: 2026-02-22 22:05:57 Duration: 4ms Database: postgres

9

170ms

2,346

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 #9

Day	Hour	Count	Duration	Avg duration
	22	2,346	170ms	0ms

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

Date: 2026-02-22 22:11:41 Duration: 0ms Database: postgres parameters: $1 = '2026-02-20 21:00:00', $2 = '9088.4', $3 = '9088.75', $4 = '9081.4', $5 = '9083.9', $6 = '4215', $7 = '515840248015340300', $8 = '0', $9 = '2026-02-22 22:11:41.127', $10 = '2026-02-22 22:11:41.006', $11 = '9088.4', $12 = '9088.75', $13 = '9081.4', $14 = '9083.9', $15 = '4215', $16 = '0', $17 = '2026-02-22 22:11:41.127', $18 = '2026-02-22 22:11:41.006'
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-02-22 22:10:40 Duration: 0ms Database: postgres parameters: $1 = '2026-02-20 21:00:00', $2 = '9088.4', $3 = '9088.75', $4 = '9081.4', $5 = '9083.9', $6 = '4215', $7 = '515840248015340300', $8 = '0', $9 = '2026-02-22 22:10:40.576', $10 = '2026-02-22 22:10:40.495', $11 = '9088.4', $12 = '9088.75', $13 = '9081.4', $14 = '9083.9', $15 = '4215', $16 = '0', $17 = '2026-02-22 22:10:40.576', $18 = '2026-02-22 22:10:40.495'
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-02-22 22:41:39 Duration: 0ms Database: postgres parameters: $1 = '2026-02-20 21:00:00', $2 = '9088.4', $3 = '9088.75', $4 = '9081.4', $5 = '9083.9', $6 = '4215', $7 = '515840248015340300', $8 = '0', $9 = '2026-02-22 22:41:39.84', $10 = '2026-02-22 22:41:39.746', $11 = '9088.4', $12 = '9088.75', $13 = '9081.4', $14 = '9083.9', $15 = '4215', $16 = '0', $17 = '2026-02-22 22:41:39.84', $18 = '2026-02-22 22:41:39.746'

10

126ms

25

Details

0ms

18ms

5ms

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

Times Reported Time consuming bind #10

Day	Hour	Count	Duration	Avg duration
	22	25	126ms	5ms

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

Date: 2026-02-22 22:51:53 Duration: 18ms Database: postgres parameters: $1 = '489', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Date: 2026-02-22 22:17:59 Duration: 12ms Database: postgres parameters: $1 = '489', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Date: 2026-02-22 22:08:09 Duration: 12ms Database: postgres parameters: $1 = '489', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'

11

116ms

2,716

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 #11

Day	Hour	Count	Duration	Avg duration
	22	2,716	116ms	0ms

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

Date: 2026-02-22 22:02:25 Duration: 0ms Database: postgres parameters: $1 = '2026-02-21 00:45:00', $2 = '0.77538', $3 = '0.77619', $4 = '0.775035', $5 = '0.775875', $6 = '488', $7 = '515840249377809300', $8 = '0', $9 = '2026-02-22 22:02:25.14', $10 = '2026-02-22 22:02:25.08', $11 = '0.77538', $12 = '0.77619', $13 = '0.775035', $14 = '0.775875', $15 = '488', $16 = '0', $17 = '2026-02-22 22:02:25.14', $18 = '2026-02-22 22:02:25.08'
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-02-22 22:25:40 Duration: 0ms Database: postgres parameters: $1 = '2026-02-20 21:30:00', $2 = '9083.4', $3 = '9085.6', $4 = '9075.4', $5 = '9077.4', $6 = '2038', $7 = '515840248015086300', $8 = '0', $9 = '2026-02-22 22:25:40.938', $10 = '2026-02-22 22:25:40.85', $11 = '9083.4', $12 = '9085.6', $13 = '9075.4', $14 = '9077.4', $15 = '2038', $16 = '0', $17 = '2026-02-22 22:25:40.938', $18 = '2026-02-22 22:25:40.85'
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-02-22 22:15:24 Duration: 0ms Database: postgres parameters: $1 = '2026-02-20 22:30:00', $2 = '9080.31', $3 = '9087.31', $4 = '9075.31', $5 = '9086.31', $6 = '203', $7 = '515840238058964300', $8 = '0', $9 = '2026-02-22 22:15:24.198', $10 = '2026-02-22 22:15:24.157', $11 = '9080.31', $12 = '9087.31', $13 = '9075.31', $14 = '9086.31', $15 = '203', $16 = '0', $17 = '2026-02-22 22:15:24.198', $18 = '2026-02-22 22:15:24.157'

12

23ms

84

Details

0ms

4ms

0ms

/*server.CPResult*/ SELECT patternid, resy0, resy1, supporty0, supporty1, predictiontimeto, patternstarttime, s.symbolid, resx0, resx1, supportx0, supportx1, symbol, longname, shortname, timegranularity, patternendtime, pattern, a.direction, trendchange, patternlengthbars, patternquality, resultuid as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, exchange, breakout, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz 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 where resultuid = $1;

Times Reported Time consuming bind #12

Day	Hour	Count	Duration	Avg duration
	22	84	23ms	0ms

/*server.CPResult*/ SELECT patternid, resy0, resy1, supporty0, supporty1, predictiontimeto, patternstarttime, s.symbolid, resx0, resx1, supportx0, supportx1, symbol, longname, shortname, timegranularity, patternendtime, pattern, a.direction, trendchange, patternlengthbars, patternquality, resultuid as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, exchange, breakout, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz 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 where resultuid = $1;

Date: 2026-02-22 22:26:21 Duration: 4ms Database: postgres parameters: $1 = '607713699366867301'
/*server.CPResult*/ SELECT patternid, resy0, resy1, supporty0, supporty1, predictiontimeto, patternstarttime, s.symbolid, resx0, resx1, supportx0, supportx1, symbol, longname, shortname, timegranularity, patternendtime, pattern, a.direction, trendchange, patternlengthbars, patternquality, resultuid as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, exchange, breakout, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz 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 where resultuid = $1;

Date: 2026-02-22 22:35:39 Duration: 3ms Database: postgres parameters: $1 = '607713701032720301'
/*server.CPResult*/ SELECT patternid, resy0, resy1, supporty0, supporty1, predictiontimeto, patternstarttime, s.symbolid, resx0, resx1, supportx0, supportx1, symbol, longname, shortname, timegranularity, patternendtime, pattern, a.direction, trendchange, patternlengthbars, patternquality, resultuid as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, exchange, breakout, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz 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 where resultuid = $1;

Date: 2026-02-22 22:35:36 Duration: 3ms Database: postgres parameters: $1 = '607712815500372301'

13

13ms

18

Details

0ms

select cast(count(*) / cast(setting as numeric) * 100 as int) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by setting;

Times Reported Time consuming bind #13

Day	Hour	Count	Duration	Avg duration
	22	18	13ms	0ms

select cast(count(*) / cast(setting as numeric) * 100 as int) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by setting;

Date: 2026-02-22 22:21:01 Duration: 0ms Database: postgres
select cast(count(*) / cast(setting as numeric) * 100 as int) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by setting;

Date: 2026-02-22 22:51:14 Duration: 0ms Database: postgres
select cast(count(*) / cast(setting as numeric) * 100 as int) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by setting;

Date: 2026-02-22 22:50:02 Duration: 0ms Database: postgres

14

12ms

2

Details

6ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Times Reported Time consuming bind #14

Day	Hour	Count	Duration	Avg duration
	22	2	12ms	6ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-02-22 22:31:24 Duration: 6ms Database: postgres parameters: $1 = '627', $2 = '627'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-02-22 22:36:00 Duration: 6ms Database: postgres parameters: $1 = '627', $2 = '627'

15

10ms

22

Details

0ms

3ms

0ms

/*server.FibonacciResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, Exchange AS e, longname as lo, shortname as sho, timegranularity AS tg, p.PatternID AS pid, Direction AS d, PatternStartTime AS pst, PatternEndTime AS pet, PatternStartPrice AS psp, PatternEndPrice AS pep, priceX as px, timeX as tx, priceA as pa, timeA as ta, priceB as pb, timeB as tb, priceC as pc, timeC as tc, priceD as pd, timeD as td, averagequality as aq, timequality as tq, 1 - errormargin as rq, 1 - noise as c, target10 as t10, target06 as t06, target16 as t16, target07 as t07, target12 as t12, target03 as t03, target05 as t05, PatternLengthBars AS l, temporarypattern as tp, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM Fibonacci_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 fibonaccipatterns p on a.pattern = p.patternname where resultuid = $1 and dtt.dayofweek = 3;

Times Reported Time consuming bind #15

Day	Hour	Count	Duration	Avg duration
	22	22	10ms	0ms

/*server.FibonacciResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, Exchange AS e, longname as lo, shortname as sho, timegranularity AS tg, p.PatternID AS pid, Direction AS d, PatternStartTime AS pst, PatternEndTime AS pet, PatternStartPrice AS psp, PatternEndPrice AS pep, priceX as px, timeX as tx, priceA as pa, timeA as ta, priceB as pb, timeB as tb, priceC as pc, timeC as tc, priceD as pd, timeD as td, averagequality as aq, timequality as tq, 1 - errormargin as rq, 1 - noise as c, target10 as t10, target06 as t06, target16 as t16, target07 as t07, target12 as t12, target03 as t03, target05 as t05, PatternLengthBars AS l, temporarypattern as tp, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM Fibonacci_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 fibonaccipatterns p on a.pattern = p.patternname where resultuid = $1 and dtt.dayofweek = 3;

Date: 2026-02-22 22:27:04 Duration: 3ms Database: postgres parameters: $1 = '607713935342476302'
/*server.FibonacciResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, Exchange AS e, longname as lo, shortname as sho, timegranularity AS tg, p.PatternID AS pid, Direction AS d, PatternStartTime AS pst, PatternEndTime AS pet, PatternStartPrice AS psp, PatternEndPrice AS pep, priceX as px, timeX as tx, priceA as pa, timeA as ta, priceB as pb, timeB as tb, priceC as pc, timeC as tc, priceD as pd, timeD as td, averagequality as aq, timequality as tq, 1 - errormargin as rq, 1 - noise as c, target10 as t10, target06 as t06, target16 as t16, target07 as t07, target12 as t12, target03 as t03, target05 as t05, PatternLengthBars AS l, temporarypattern as tp, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM Fibonacci_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 fibonaccipatterns p on a.pattern = p.patternname where resultuid = $1 and dtt.dayofweek = 3;

Date: 2026-02-22 22:05:22 Duration: 2ms Database: postgres parameters: $1 = '607713936437116302'
/*server.FibonacciResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, Exchange AS e, longname as lo, shortname as sho, timegranularity AS tg, p.PatternID AS pid, Direction AS d, PatternStartTime AS pst, PatternEndTime AS pet, PatternStartPrice AS psp, PatternEndPrice AS pep, priceX as px, timeX as tx, priceA as pa, timeA as ta, priceB as pb, timeB as tb, priceC as pc, timeC as tc, priceD as pd, timeD as td, averagequality as aq, timequality as tq, 1 - errormargin as rq, 1 - noise as c, target10 as t10, target06 as t06, target16 as t16, target07 as t07, target12 as t12, target03 as t03, target05 as t05, PatternLengthBars AS l, temporarypattern as tp, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM Fibonacci_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 fibonaccipatterns p on a.pattern = p.patternname where resultuid = $1 and dtt.dayofweek = 3;

Date: 2026-02-22 22:38:32 Duration: 1ms Database: postgres parameters: $1 = '607713936806956302'

16

8ms

66

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 #16

Day	Hour	Count	Duration	Avg duration
	22	66	8ms	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: 2026-02-22 22:02:11 Duration: 0ms Database: postgres parameters: $1 = '2026-02-20 16:00:00', $2 = '56752.5', $3 = '57267.5', $4 = '56657.5', $5 = '57057.5', $6 = '5989', $7 = '515840230556263300', $8 = '0', $9 = '2026-02-22 22:02:11.955', $10 = '2026-02-22 22:02:11.955', $11 = '56752.5', $12 = '57267.5', $13 = '56657.5', $14 = '57057.5', $15 = '5989', $16 = '0', $17 = '2026-02-22 22:02:11.955', $18 = '2026-02-22 22:02:11.955'
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: 2026-02-22 22:16:26 Duration: 0ms Database: postgres parameters: $1 = '2026-02-20 16:00:00', $2 = '56755', $3 = '57270', $4 = '56660', $5 = '57060', $6 = '5989', $7 = '515840230561612300', $8 = '0', $9 = '2026-02-22 22:16:26.175', $10 = '2026-02-22 22:16:26.174', $11 = '56755', $12 = '57270', $13 = '56660', $14 = '57060', $15 = '5989', $16 = '0', $17 = '2026-02-22 22:16:26.175', $18 = '2026-02-22 22:16:26.174'
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: 2026-02-22 22:07:08 Duration: 0ms Database: postgres parameters: $1 = '2026-02-20 16:00:00', $2 = '56755', $3 = '57270', $4 = '56660', $5 = '57060', $6 = '5989', $7 = '515840230561612300', $8 = '0', $9 = '2026-02-22 22:07:08.895', $10 = '2026-02-22 22:07:08.895', $11 = '56755', $12 = '57270', $13 = '56660', $14 = '57060', $15 = '5989', $16 = '0', $17 = '2026-02-22 22:07:08.895', $18 = '2026-02-22 22:07:08.895'

17

8ms

108

Details

0ms

1ms

0ms

SELECT df.absolutetimezoneoffset FROM datafeedstimetable df INNER JOIN downloadersymbolsettings dss ON df.classname = dss.classname WHERE dss.symbolid = $1 GROUP BY df.absolutetimezoneoffset LIMIT 1;

Times Reported Time consuming bind #17

Day	Hour	Count	Duration	Avg duration
	22	108	8ms	0ms

SELECT df.absolutetimezoneoffset FROM datafeedstimetable df INNER JOIN downloadersymbolsettings dss ON df.classname = dss.classname WHERE dss.symbolid = $1 GROUP BY df.absolutetimezoneoffset LIMIT 1;

Date: 2026-02-22 22:17:42 Duration: 1ms Database: postgres parameters: $1 = '515840243870885300'
SELECT df.absolutetimezoneoffset FROM datafeedstimetable df INNER JOIN downloadersymbolsettings dss ON df.classname = dss.classname WHERE dss.symbolid = $1 GROUP BY df.absolutetimezoneoffset LIMIT 1;

Date: 2026-02-22 22:18:11 Duration: 0ms Database: postgres parameters: $1 = '515840243881788300'
SELECT df.absolutetimezoneoffset FROM datafeedstimetable df INNER JOIN downloadersymbolsettings dss ON df.classname = dss.classname WHERE dss.symbolid = $1 GROUP BY df.absolutetimezoneoffset LIMIT 1;

Date: 2026-02-22 22:26:21 Duration: 0ms Database: postgres parameters: $1 = '500991628280661200'

18

7ms

9

Details

0ms

1ms

0ms

select feedname, to_char(latestrxtime, 'yyyy-mm-dd HH24:MI'), to_char(LatestDBWriteTime, 'yyyy-mm-dd HH24:MI'), to_char(LatestStartupTime, 'yyyy-mm-dd HH24:MI'), StartupTimeInMinutes, dm.source_type, dm.transport_type, case when latestrxtime < (CURRENT_TIMESTAMP - 5 * interval '1 minute') then 'X' else 'OK' end, case when (feedname ilike '%_EOD' OR feedname ilike 'IQFEED_DAILIES' or feedname ilike 'YAHOO%' or feedname ilike 'QUANDL_FUTURES%' or feedname ilike 'BAR_CHART') then case when LatestDBWriteTime < (CURRENT_TIMESTAMP - 24 * interval '1 hour') then 'X' else 'OK' end else case when (LatestDBWriteTime < (CURRENT_TIMESTAMP - 15 * interval '1 minute') and LatestStartupTime < (CURRENT_TIMESTAMP - 30 * interval '1 minute')) OR latestrxtime < CURRENT_TIMESTAMP - interval '2 hour' then 'X' else 'OK' end end as statusDB, comment from datafeeds_latestrun dlr left outer join datafeeds df on dlr.feedname ilike df.name inner join datafeeds_metadata dm on df.metadata_id = dm.id order by feedname;

Times Reported Time consuming bind #18

Day	Hour	Count	Duration	Avg duration
	22	9	7ms	0ms

select feedname, to_char(latestrxtime, 'yyyy-mm-dd HH24:MI'), to_char(LatestDBWriteTime, 'yyyy-mm-dd HH24:MI'), to_char(LatestStartupTime, 'yyyy-mm-dd HH24:MI'), StartupTimeInMinutes, dm.source_type, dm.transport_type, case when latestrxtime < (CURRENT_TIMESTAMP - 5 * interval '1 minute') then 'X' else 'OK' end, case when (feedname ilike '%_EOD' OR feedname ilike 'IQFEED_DAILIES' or feedname ilike 'YAHOO%' or feedname ilike 'QUANDL_FUTURES%' or feedname ilike 'BAR_CHART') then case when LatestDBWriteTime < (CURRENT_TIMESTAMP - 24 * interval '1 hour') then 'X' else 'OK' end else case when (LatestDBWriteTime < (CURRENT_TIMESTAMP - 15 * interval '1 minute') and LatestStartupTime < (CURRENT_TIMESTAMP - 30 * interval '1 minute')) OR latestrxtime < CURRENT_TIMESTAMP - interval '2 hour' then 'X' else 'OK' end end as statusDB, comment from datafeeds_latestrun dlr left outer join datafeeds df on dlr.feedname ilike df.name inner join datafeeds_metadata dm on df.metadata_id = dm.id order by feedname;

Date: 2026-02-22 22:16:35 Duration: 1ms Database: postgres
select feedname, to_char(latestrxtime, 'yyyy-mm-dd HH24:MI'), to_char(LatestDBWriteTime, 'yyyy-mm-dd HH24:MI'), to_char(LatestStartupTime, 'yyyy-mm-dd HH24:MI'), StartupTimeInMinutes, dm.source_type, dm.transport_type, case when latestrxtime < (CURRENT_TIMESTAMP - 5 * interval '1 minute') then 'X' else 'OK' end, case when (feedname ilike '%_EOD' OR feedname ilike 'IQFEED_DAILIES' or feedname ilike 'YAHOO%' or feedname ilike 'QUANDL_FUTURES%' or feedname ilike 'BAR_CHART') then case when LatestDBWriteTime < (CURRENT_TIMESTAMP - 24 * interval '1 hour') then 'X' else 'OK' end else case when (LatestDBWriteTime < (CURRENT_TIMESTAMP - 15 * interval '1 minute') and LatestStartupTime < (CURRENT_TIMESTAMP - 30 * interval '1 minute')) OR latestrxtime < CURRENT_TIMESTAMP - interval '2 hour' then 'X' else 'OK' end end as statusDB, comment from datafeeds_latestrun dlr left outer join datafeeds df on dlr.feedname ilike df.name inner join datafeeds_metadata dm on df.metadata_id = dm.id order by feedname;

Date: 2026-02-22 22:26:37 Duration: 0ms Database: postgres
select feedname, to_char(latestrxtime, 'yyyy-mm-dd HH24:MI'), to_char(LatestDBWriteTime, 'yyyy-mm-dd HH24:MI'), to_char(LatestStartupTime, 'yyyy-mm-dd HH24:MI'), StartupTimeInMinutes, dm.source_type, dm.transport_type, case when latestrxtime < (CURRENT_TIMESTAMP - 5 * interval '1 minute') then 'X' else 'OK' end, case when (feedname ilike '%_EOD' OR feedname ilike 'IQFEED_DAILIES' or feedname ilike 'YAHOO%' or feedname ilike 'QUANDL_FUTURES%' or feedname ilike 'BAR_CHART') then case when LatestDBWriteTime < (CURRENT_TIMESTAMP - 24 * interval '1 hour') then 'X' else 'OK' end else case when (LatestDBWriteTime < (CURRENT_TIMESTAMP - 15 * interval '1 minute') and LatestStartupTime < (CURRENT_TIMESTAMP - 30 * interval '1 minute')) OR latestrxtime < CURRENT_TIMESTAMP - interval '2 hour' then 'X' else 'OK' end end as statusDB, comment from datafeeds_latestrun dlr left outer join datafeeds df on dlr.feedname ilike df.name inner join datafeeds_metadata dm on df.metadata_id = dm.id order by feedname;

Date: 2026-02-22 22:46:41 Duration: 0ms Database: postgres

19

7ms

26

Details

0ms

2ms

0ms

/*server.KeyLevelResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, 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 '1900-01-01' END as x3, CASE WHEN (x4 != '') THEN x4 ELSE '1900-01-01' END as x4, CASE WHEN (x5 != '') THEN x5 ELSE '1900-01-01' END as x5, CASE WHEN (x6 != '') THEN x6 ELSE '1900-01-01' END as x6, CASE WHEN (x7 != '') THEN x7 ELSE '1900-01-01' END as x7, CASE WHEN (x8 != '') THEN x8 ELSE '1900-01-01' END as x8, CASE WHEN (x9 != '') THEN x9 ELSE '1900-01-01' END as x9, errorMargin as erm, breakoutprice as pE, breakoutbars as be, breakout, atbaridentified as atBar, PatternLengthBars AS l, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz, approachingtimestamp AS apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb 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 where resultuid = $1 and dtt.dayofweek = 3;

Times Reported Time consuming bind #19

Day	Hour	Count	Duration	Avg duration
	22	26	7ms	0ms

/*server.KeyLevelResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, 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 '1900-01-01' END as x3, CASE WHEN (x4 != '') THEN x4 ELSE '1900-01-01' END as x4, CASE WHEN (x5 != '') THEN x5 ELSE '1900-01-01' END as x5, CASE WHEN (x6 != '') THEN x6 ELSE '1900-01-01' END as x6, CASE WHEN (x7 != '') THEN x7 ELSE '1900-01-01' END as x7, CASE WHEN (x8 != '') THEN x8 ELSE '1900-01-01' END as x8, CASE WHEN (x9 != '') THEN x9 ELSE '1900-01-01' END as x9, errorMargin as erm, breakoutprice as pE, breakoutbars as be, breakout, atbaridentified as atBar, PatternLengthBars AS l, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz, approachingtimestamp AS apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb 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 where resultuid = $1 and dtt.dayofweek = 3;

Date: 2026-02-22 22:40:54 Duration: 2ms Database: postgres parameters: $1 = '607713819188870303'
/*server.KeyLevelResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, 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 '1900-01-01' END as x3, CASE WHEN (x4 != '') THEN x4 ELSE '1900-01-01' END as x4, CASE WHEN (x5 != '') THEN x5 ELSE '1900-01-01' END as x5, CASE WHEN (x6 != '') THEN x6 ELSE '1900-01-01' END as x6, CASE WHEN (x7 != '') THEN x7 ELSE '1900-01-01' END as x7, CASE WHEN (x8 != '') THEN x8 ELSE '1900-01-01' END as x8, CASE WHEN (x9 != '') THEN x9 ELSE '1900-01-01' END as x9, errorMargin as erm, breakoutprice as pE, breakoutbars as be, breakout, atbaridentified as atBar, PatternLengthBars AS l, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz, approachingtimestamp AS apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb 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 where resultuid = $1 and dtt.dayofweek = 3;

Date: 2026-02-22 22:09:17 Duration: 2ms Database: postgres parameters: $1 = '607713819188870303'
/*server.KeyLevelResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, 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 '1900-01-01' END as x3, CASE WHEN (x4 != '') THEN x4 ELSE '1900-01-01' END as x4, CASE WHEN (x5 != '') THEN x5 ELSE '1900-01-01' END as x5, CASE WHEN (x6 != '') THEN x6 ELSE '1900-01-01' END as x6, CASE WHEN (x7 != '') THEN x7 ELSE '1900-01-01' END as x7, CASE WHEN (x8 != '') THEN x8 ELSE '1900-01-01' END as x8, CASE WHEN (x9 != '') THEN x9 ELSE '1900-01-01' END as x9, errorMargin as erm, breakoutprice as pE, breakoutbars as be, breakout, atbaridentified as atBar, PatternLengthBars AS l, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz, approachingtimestamp AS apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb 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 where resultuid = $1 and dtt.dayofweek = 3;

Date: 2026-02-22 22:33:14 Duration: 0ms Database: postgres parameters: $1 = '607713293945407303'

20

5ms

9

Details

0ms

select distinct classname, to_char(created_datetime, 'yyyy-mm-dd HH24:MI'), to_char(cleared_datetime, 'yyyy-mm-dd HH24:MI'), action_to_take, description, created_datetime from datafeed_restarter_events where (is_current_entry = 1 OR cleared_datetime > current_timestamp - interval '17 hour') order by created_datetime desc;

Times Reported Time consuming bind #20

Day	Hour	Count	Duration	Avg duration
	22	9	5ms	0ms

select distinct classname, to_char(created_datetime, 'yyyy-mm-dd HH24:MI'), to_char(cleared_datetime, 'yyyy-mm-dd HH24:MI'), action_to_take, description, created_datetime from datafeed_restarter_events where (is_current_entry = 1 OR cleared_datetime > current_timestamp - interval '17 hour') order by created_datetime desc;

Date: 2026-02-22 22:36:39 Duration: 0ms Database: postgres
select distinct classname, to_char(created_datetime, 'yyyy-mm-dd HH24:MI'), to_char(cleared_datetime, 'yyyy-mm-dd HH24:MI'), action_to_take, description, created_datetime from datafeed_restarter_events where (is_current_entry = 1 OR cleared_datetime > current_timestamp - interval '17 hour') order by created_datetime desc;

Date: 2026-02-22 22:31:38 Duration: 0ms Database: postgres
select distinct classname, to_char(created_datetime, 'yyyy-mm-dd HH24:MI'), to_char(cleared_datetime, 'yyyy-mm-dd HH24:MI'), action_to_take, description, created_datetime from datafeed_restarter_events where (is_current_entry = 1 OR cleared_datetime > current_timestamp - interval '17 hour') order by created_datetime desc;

Date: 2026-02-22 22:46:41 Duration: 0ms Database: postgres

Log levels

Key values

171,863 Log entries

Chart
Table

Type	Count	Percentage
CONTEXT	297	0.17%
DETAIL	33,354	19.41%
ERROR	393	0.23%
LOG	137,084	79.76%
STATEMENT	735	0.43%

Events distribution

Key values

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

Most Frequent Errors/Events

Key values

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

Rank

Times reported

Error

1

356

Details

ERROR: pg_stat_statements must be loaded via shared_preload_libraries

Times Reported Most Frequent Error / Event #1

Day	Hour	Count
Feb 22	22	356

ERROR: pg_stat_statements must be loaded via shared_preload_libraries

2

37

Details

ERROR: schema "..." does not exist

Times Reported Most Frequent Error / Event #2

Day	Hour	Count
Feb 22	22	37

ERROR: schema "datadog" does not exist at character 38

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