pgBadger :: PostgreSQL Log Analyzer

Global information

Generated on Sun Feb 8 08:59:17 2026
Log file: /home/postgres/pg_data/data/pg_log/postgresql-2026-02-08_100000.log
Parsed 538,892 log entries in 16s
Log start from 2026-02-08 10:00:00 to 2026-02-08 10:59:15

Global Stats

178 Number of unique normalized queries
80,848 Number of queries
1h9m22s Total query duration
2026-02-08 10:00:00 First query
2026-02-08 10:59:15 Last query
1,534 queries/s at 2026-02-08 10:45:04 Query peak

1h9m22s Total query duration
2s421ms Prepare/parse total duration
19s603ms Bind total duration
1h9m Execute total duration

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

30 Total number of automatic vacuums
42 Total number of automatic analyzes

456 Number temporary file
142.18 MiB Max size of temporary file
6.37 MiB Average size of temporary file

1,726 Total number of sessions
13 sessions at 2026-02-08 10:51:34 Session peak
15h12m44s Total duration of sessions
31s728ms Average duration of sessions
46 Average queries per session
2s411ms Average queries duration per session
29s317ms Average idle time per session

1,727 Total number of connections
27 connections/s at 2026-02-08 10:03:48 Connection peak
3 Total number of databases

SQL Traffic

Key values

1,534 queries/s Query Peak
2026-02-08 10:45:04 Date

SELECT Traffic

Key values

723 queries/s Query Peak
2026-02-08 10:45:04 Date

INSERT/UPDATE/DELETE Traffic

Key values

142 queries/s Query Peak
2026-02-08 10:36:51 Date

Queries duration

Key values

1h9m22s 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 08	10	80,848	0ms	19s478ms	51ms	3m34s	3m56s	4m16s

Day	Hour	SELECT	COPY TO	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Feb 08	10	19,229	26	0ms	0ms	0ms	0ms

Day	Hour	INSERT	UPDATE	DELETE	COPY FROM	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Feb 08	10	15,468	701	16	96	0ms	0ms	0ms	0ms

Day	Hour	Prepare	Bind	Bind/Prepare	Percentage of prepare
Feb 08	10	9,141	22,645	2.48	23.88%

Day	Hour	Count	Average / Second
Feb 08	10	1,727	0.48/s

Day	Hour	Count	Average Duration	Average idle time
Feb 08	10	1,726	31s728ms	29s330ms

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Established Connections

Key values

27 connections Connection Peak
2026-02-08 10:03:48 Date

Connections per database

Key values

acaweb_fx Main Database
1,727 connections Total

Chart
Table

Database	User	Count
acaweb_fx		1,642
	postgres	1,300
	psql	342
socialmedia		85
	postgres	85

Connections per user

Key values

postgres Main User
1,727 connections Total

Chart
Table

User	Count
postgres	1,385
psql	342

Connections per host

Key values

192.168.4.142 Main host with 649 connections
1,727 Total connections

Chart
Table

Host	Count
127.0.0.1	106
192.168.0.114	3
192.168.0.216	116
192.168.0.74	8
192.168.1.145	4
192.168.1.15	48
192.168.1.20	23
192.168.1.239	56
192.168.1.90	6
192.168.2.126	20
192.168.3.199	38
192.168.4.142	649
192.168.4.33	84
192.168.4.98	330
[local]	236

Simultaneous sessions

Key values

13 sessions Session Peak
2026-02-08 10:51:34 Date

Histogram of session times

Key values

1,313 0-500ms duration

Chart
Table

Range	Count	Percentage
0ms-500ms	1,313	76.07%
500ms-1s	147	8.52%
1s-30s	222	12.86%
30s-1m	9	0.52%
1m-10m	5	0.29%
10m-30m	27	1.56%
30m-1h	1	0.06%
1h-8h	2	0.12%
> 8h	0	0.00%

Sessions per database

Key values

acaweb_fx Main Database
1,726 sessions Total

Chart
Table

Database	User	Count	Total Duration	Average Duration
acaweb_fx	1,641	15h6m37s	33s149ms
socialmedia	85	6m6s	4s309ms

Sessions per user

Key values

postgres Main User
1,726 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
1,726 sessions Total

Chart
Table

Host	Count	Total Duration	Average Duration
127.0.0.1	106	5s673ms	53ms
192.168.0.114	3	15m	5m
192.168.0.216	116	7m25s	3s840ms
192.168.0.74	7	1h33s	8m39s
192.168.1.145	4	20m3s	5m
192.168.1.15	48	3h1m32s	3m46s
192.168.1.20	23	10h15m44s	26m46s
192.168.1.239	56	345ms	6ms
192.168.1.90	6	58ms	9ms
192.168.2.126	20	5s705ms	285ms
192.168.3.199	38	21s614ms	568ms
192.168.4.142	649	7m21s	679ms
192.168.4.33	84	1m6s	787ms
192.168.4.98	330	13s533ms	41ms
[local]	236	3m9s	801ms

Sessions per application

Key values

unknown Main Host
1,726 sessions Total

Chart
Table

Application	Count	Total Duration	Average Duration
unknown	1,726	15h12m44s	31s728ms

Checkpoints Buffers

Key values

2,875 buffers Checkpoint Peak
2026-02-08 10:35:15 Date
209.383 seconds Highest write time
0.184 seconds Sync time

Checkpoints Wal files

Key values

2 files Wal files usage Peak
2026-02-08 10:39:42 Date

Checkpoints distance

Key values

44.45 Mo Distance Peak
2026-02-08 10:09:55 Date

Checkpoints Activity

Day	Hour	Written buffers	Write time	Sync time	Total time
Feb 08	10	18,096	1,758.845s	0.388s	1,759.497s

Day	Hour	Added	Removed	Recycled	Synced files	Longest sync	Average sync
Feb 08	10	0	0	12	1,392	0.180s	0.002s

Day	Hour	Count	Avg time (sec)
Feb 08	10	0	0s

Day	Hour	Mean distance	Mean estimate
Feb 08	10	15,290.33 kB	20,814.17 kB

↑ Back to the top of the Checkpoint Activity table

Checkpoint causes

Key values

time (12) Main checkpoint cause
12 checkpoints Total

Chart
Table

Cause	Number of checkpoints
time	12
Total	12

Size of temporary files

Key values

100.45 MiB Temp Files size Peak
2026-02-08 10:47:19 Date

Number of temporary files

Key values

30 per second Temp Files Peak
2026-02-08 10:32:09 Date

Temporary Files Activity

Temp Files

Day	Hour	Count	Total size	Average size
Feb 08	10	456	2.84 GiB	6.37 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	38	173.27 MiB	4.52 MiB	4.60 MiB	4.56 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, $227, $228, $229, $230, $231, $232, $233, $234, $235, $236, $237, $238, $239, $240, $241, $242, $243, $244, $245, $246, $247, $248, $249, $250, $251, $252, $253, $254, $255, $256, $257, $258, $259, $260, $261, $262, $263, $264, $265, $266, $267, $268, $269, $270, $271, $272, $273, $274, $275, $276, $277, $278, $279, $280, $281, $282, $283, $284, $285, $286, $287, $288, $289, $290, $291, $292, $293, $294, $295, $296, $297, $298, $299, $300, $301, $302, $303, $304, $305, $306, $307, $308, $309, $310, $311, $312, $313, $314, $315, $316, $317, $318, $319, $320, $321, $322, $323)) AND ($324 = 0 OR fr.pattern in ($325)) AND ($326 = 0 OR fr.patternlengthbars <= $327) AND ($328 = 0 OR ($329 = 1 AND fr.timed > cast('1970-01-01' as timestamp)) OR ($330 = 2 AND fr.timed < cast('1970-01-01' as timestamp)))), results AS ( SELECT DISTINCT ON (symbolid) FROM all_results WHERE (FALSE = $331 OR relevant = 1) AND ($332 = 0 OR age <= $333) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-02-08 10:00:47 Duration: 0ms
2	16	619.88 MiB	38.74 MiB	38.74 MiB	38.74 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: 2026-02-08 10:01:13 Duration: 0ms
3	16	49.75 MiB	3.05 MiB	3.28 MiB	3.11 MiB	select resultuid from relevance_consecutivecandles_results order by resultuid desc limit ?), all_results as ( select ccr.resultuid as resultuid, ccr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, ccr.patternendtime as identified, dtt.timezone as timezone, ccr.qtyconsecutivecandles as length, g.basegroupname, case when rcr.age is not null then rcr.age when ccr.resultuid <= rm.resultuid then ? else ? end as age, case when rcr.relevant is not null then rcr.relevant when ccr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip, newlevels.filtered from consecutivecandles_results ccr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = ccr.symbolid inner join symbols s on ccr.symbolid = s.symbolid and s.nonliquid = ? inner join downloadersymbolsettings dss on ccr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on ccr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join rar_max rm on ? = ? left outer join relevance_consecutivecandles_results rcr on rcr.resultuid = ccr.resultuid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? left join lateral calc_cc_signal_filter (ccr.resultuid) newlevels on true where ccr.gmttimefound > now() - interval ? and s.deleted = ? and (ccr.simulation = ? or ccr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or ccr.patternlengthbars <= ?)), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc; SELECT resultuid FROM relevance_consecutivecandles_results ORDER BY resultuid DESC LIMIT 1), all_results AS ( SELECT ccr.resultuid AS resultuid, ccr.direction AS direction, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, ccr.patternendtime AS identified, dtt.timezone AS timezone, ccr.qtyconsecutivecandles AS length, g.basegroupname, CASE WHEN rcr.age IS NOT NULL THEN rcr.age WHEN ccr.resultuid <= rm.resultuid THEN 1 ELSE 0 END as age, CASE WHEN rcr.relevant IS NOT NULL THEN rcr.relevant WHEN ccr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip, newLevels.filtered FROM consecutivecandles_results ccr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $1 AND bsl.symbolid = ccr.symbolid INNER JOIN symbols s ON ccr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN downloadersymbolsettings dss ON ccr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN symbolgroup sg on ccr.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid INNER JOIN rar_max rm ON 1 = 1 LEFT OUTER JOIN relevance_consecutivecandles_results rcr ON rcr.resultuid = ccr.resultuid LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' LEFT JOIN LATERAL calc_cc_signal_filter (ccr.resultuid) newLevels on true WHERE ccr.gmttimefound > now() - INTERVAL '7 DAYS' AND s.deleted = 0 AND (ccr.simulation = 0 OR ccr.simulation IS NULL) AND ($2 = 0 OR s.timegranularity in ($3, $4, $5, $6, $7, $8, $9)) AND ($10 = 0 OR s.exchange in ($11)) AND ($12 = 0 OR coalesce(bim.code, s.symbol) in ($13, $14, $15, $16, $17, $18, $19, $20, $21, $22, $23, $24, $25, $26, $27, $28, $29, $30, $31, $32, $33, $34, $35, $36, $37, $38, $39, $40, $41, $42, $43, $44, $45, $46, $47, $48, $49, $50, $51, $52, $53, $54, $55, $56, $57, $58, $59, $60, $61, $62, $63, $64, $65, $66, $67, $68, $69, $70, $71, $72, $73, $74, $75, $76, $77, $78, $79, $80, $81, $82, $83, $84, $85, $86, $87, $88, $89, $90, $91, $92, $93, $94, $95, $96, $97, $98, $99, $100, $101, $102, $103, $104, $105, $106, $107, $108, $109, $110, $111, $112, $113, $114, $115, $116, $117, $118, $119, $120, $121, $122, $123, $124, $125, $126, $127, $128, $129, $130, $131, $132, $133, $134, $135, $136, $137, $138, $139, $140, $141, $142, $143, $144, $145, $146, $147, $148, $149, $150, $151, $152, $153, $154, $155, $156, $157, $158, $159, $160, $161, $162, $163, $164, $165, $166, $167, $168, $169, $170, $171, $172, $173, $174, $175, $176, $177, $178, $179, $180, $181, $182, $183, $184, $185, $186, $187, $188, $189, $190, $191, $192, $193, $194, $195, $196, $197, $198, $199, $200, $201, $202, $203, $204, $205, $206, $207, $208, $209, $210, $211, $212, $213, $214, $215, $216, $217, $218, $219, $220, $221, $222, $223, $224, $225, $226, $227, $228, $229, $230, $231, $232, $233, $234, $235, $236, $237, $238, $239, $240, $241, $242, $243, $244, $245, $246, $247, $248, $249, $250, $251, $252, $253, $254, $255, $256, $257, $258, $259, $260, $261, $262, $263, $264, $265, $266, $267, $268, $269, $270, $271, $272, $273, $274, $275, $276, $277, $278, $279, $280, $281, $282, $283, $284, $285, $286, $287, $288, $289, $290, $291, $292, $293, $294, $295, $296, $297, $298, $299, $300, $301, $302, $303, $304, $305, $306, $307, $308, $309, $310, $311, $312, $313, $314, $315, $316, $317, $318, $319, $320, $321, $322)) AND ($323 = 0 OR ccr.patternlengthbars <= $324)), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results WHERE (FALSE = $325 OR relevant = 1) AND ($326 = 0 OR age <= $327) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-02-08 10:01:15 Duration: 0ms
4	16	1.11 GiB	71.12 MiB	71.12 MiB	71.12 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: 2026-02-08 10:01:17 Duration: 0ms
5	4	568.71 MiB	142.17 MiB	142.18 MiB	142.18 MiB	select updateresultsmaterializedview (); select updateresultsmaterializedview (); Date: 2026-02-08 10:02:14 Duration: 0ms
6	4	347.48 MiB	86.86 MiB	86.87 MiB	86.87 MiB	select updateageforrelevantresults (); select updateageforrelevantresults (); Date: 2026-02-08 10:02:04 Duration: 0ms

Queries generating the largest temporary files

Rank	Size	Query
1	142.18 MiB	select updateresultsmaterializedview (); [ Date: 2026-02-08 10:32:15 ]
2	142.18 MiB	select updateresultsmaterializedview (); [ Date: 2026-02-08 10:47:15 ]
3	142.17 MiB	select updateresultsmaterializedview (); [ Date: 2026-02-08 10:02:14 ]
4	142.17 MiB	select updateresultsmaterializedview (); [ Date: 2026-02-08 10:17:15 ]
5	86.87 MiB	select updateageforrelevantresults (); [ Date: 2026-02-08 10:02:04 ]
6	86.87 MiB	select updateageforrelevantresults (); [ Date: 2026-02-08 10:32:05 ]
7	86.87 MiB	select updateageforrelevantresults (); [ Date: 2026-02-08 10:47:04 ]
8	86.86 MiB	select updateageforrelevantresults (); [ Date: 2026-02-08 10:17:05 ]
9	71.12 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: 2026-02-08 10:01:17 ]
10	71.12 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: 2026-02-08 10:03:15 ]
11	71.12 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: 2026-02-08 10:05:15 ]
12	71.12 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: 2026-02-08 10:11:15 ]
13	71.12 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: 2026-02-08 10:16:15 ]
14	71.12 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: 2026-02-08 10:18:15 ]
15	71.12 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: 2026-02-08 10:20:15 ]
16	71.12 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: 2026-02-08 10:26:15 ]
17	71.12 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: 2026-02-08 10:31:15 ]
18	71.12 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: 2026-02-08 10:33:15 ]
19	71.12 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: 2026-02-08 10:35:15 ]
20	71.12 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: 2026-02-08 10:41:15 ]

Vacuums / Analyzes Distribution

Key values

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

Analyzes per table

Key values

public.solr_relevance_old (16) Main table analyzed (database acaweb_fx)
42 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.pg_catalog.pg_depend	2
acaweb_fx.pg_catalog.pg_index	1
acaweb_fx.public.latest_t15_candle_view	1
acaweb_fx.public.relevance_keylevels_results	1
acaweb_fx.public.relevance_autochartist_results	1
acaweb_fx.public.latest_candle_datetime_per_receng	1
Total	42

Vacuums per table

Key values

public.solr_relevance_old (16) Main table vacuumed on database acaweb_fx
30 vacuums Total

Chart
Table

		Index	Buffer usage			Skipped		WAL usage
Table	Vacuums	scans	hits	misses	dirtied	pins	frozen	records	full page	bytes
acaweb_fx.public.solr_relevance_old	16	16	11,181	0	48	0	0	7,000	16	1,351,822
acaweb_fx.pg_catalog.pg_attribute	3	3	3,834	0	405	0	201	1,185	403	2,448,779
acaweb_fx.pg_catalog.pg_type	2	2	475	0	34	0	0	147	32	190,254
acaweb_fx.public.datafeeds_latestrun	2	0	246	0	3	0	0	26	3	22,858
acaweb_fx.pg_catalog.pg_class	2	2	1,113	0	85	0	0	249	87	483,924
acaweb_fx.pg_toast.pg_toast_2619	1	1	213	0	34	0	0	103	32	109,035
acaweb_fx.pg_catalog.pg_index	1	1	115	0	13	0	0	28	12	86,955
acaweb_fx.public.solr_imports	1	1	69	0	2	0	0	6	2	14,499
acaweb_fx.pg_catalog.pg_statistic	1	1	1,101	0	172	0	594	395	158	589,708
acaweb_fx.public.latest_t15_candle_view	1	1	67	0	1	0	0	6	1	9,061
Total	30	28	18,414	282	797	0	795	9,145	746	5,306,895

Tuples removed per table

Key values

public.solr_relevance_old (20459) Main table with removed tuples on database acaweb_fx
27950 tuples Total removed

Chart
Table

		Index	Tuples			Pages
Table	Vacuums	scans	removed	remain	not yet removable	removed	remain
acaweb_fx.public.solr_relevance_old	16	16	20,459	85,081	0	0	2,682
acaweb_fx.pg_catalog.pg_attribute	3	3	5,579	32,398	0	25	770
acaweb_fx.pg_catalog.pg_type	2	2	732	2,892	0	8	78
acaweb_fx.pg_catalog.pg_statistic	1	1	682	3,808	0	0	1,194
acaweb_fx.pg_catalog.pg_class	2	2	167	3,298	0	0	300
acaweb_fx.public.datafeeds_latestrun	2	0	121	28	0	0	32
acaweb_fx.pg_toast.pg_toast_2619	1	1	72	167	0	0	53
acaweb_fx.public.latest_t15_candle_view	1	1	60	14	0	0	1
acaweb_fx.public.solr_imports	1	1	51	1	0	0	2
acaweb_fx.pg_catalog.pg_index	1	1	27	813	0	0	22
Total	30	28	27,950	128,500	0	33	5,134

Pages removed per table

Key values

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

Chart
Table

Table	Number of vacuums	Index scans	Tuples removed	Pages removed
acaweb_fx.pg_catalog.pg_attribute	3	3	5579	25
acaweb_fx.pg_catalog.pg_type	2	2	732	8
acaweb_fx.pg_toast.pg_toast_2619	1	1	72	0
acaweb_fx.pg_catalog.pg_index	1	1	27	0
acaweb_fx.public.datafeeds_latestrun	2	0	121	0
acaweb_fx.public.solr_imports	1	1	51	0
acaweb_fx.pg_catalog.pg_statistic	1	1	682	0
acaweb_fx.public.latest_t15_candle_view	1	1	60	0
acaweb_fx.pg_catalog.pg_class	2	2	167	0
acaweb_fx.public.solr_relevance_old	16	16	20459	0
Total	30	28	27,950	33

Autovacuum Activity

Vacuums / Analyzes

Day	Hour	VACUUMs	ANALYZEs
Feb 08	10	30	42

↑ 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

19,229 Total read queries
19,042 Total write queries

Chart
Table

Type	Count	Percentage
SELECT	19,229	23.78%
INSERT	15,468	19.13%
UPDATE	701	0.87%
DELETE	16	0.02%
COPY FROM	96	0.12%
COPY TO	26	0.03%
CTE	2,035	2.52%
DDL	16	0.02%
TCL	684	0.85%
CURSOR	0	0.00%
OTHERS	42,577	52.66%

Queries by database

Key values

unknown Main database
79,877 Requests
1h9m (unknown)
Main time consuming database

Chart
Table

Database	Request type	Count	Duration
acaweb_fx	Total	886	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	100	0ms
	ddl	16	0ms
	delete	16	0ms
	others	214	0ms
	select	64	0ms
	tcl	334	0ms
	update	36	0ms
socialmedia	Total	85	0ms
	select	84	0ms
	tcl	1	0ms
unknown	Total	79,877	1h9m
	copy from	16	0ms
	cte	1,935	0ms
	insert	15,468	0ms
	others	1,939	0ms
	select	19,081	0ms
	tcl	349	0ms
	update	665	0ms

Queries by user

Key values

unknown Main user
79,877 Requests

Queries Chart
Table

User	Request type	Count	Duration
postgres	Total	971	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	100	0ms
	ddl	16	0ms
	delete	16	0ms
	others	214	0ms
	select	148	0ms
	tcl	335	0ms
	update	36	0ms
unknown	Total	79,877	1h9m
	copy from	16	0ms
	cte	1,935	0ms
	insert	15,468	0ms
	others	1,939	0ms
	select	19,081	0ms
	tcl	349	0ms
	update	665	0ms

Duration by user

Key values

1h9m (unknown) Main time consuming user

Duration Chart
Table

User	Request type	Count	Duration
postgres	Total	971	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	100	0ms
	ddl	16	0ms
	delete	16	0ms
	others	214	0ms
	select	148	0ms
	tcl	335	0ms
	update	36	0ms
unknown	Total	79,877	1h9m
	copy from	16	0ms
	cte	1,935	0ms
	insert	15,468	0ms
	others	1,939	0ms
	select	19,081	0ms
	tcl	349	0ms
	update	665	0ms

Queries by host

Key values

unknown Main host
80,848 Requests
1h9m (unknown)
Main time consuming host

Chart
Table

Host	Request type	Count	Duration
unknown	Total	80,848	1h9m
	copy from	96	0ms
	copy to	26	0ms
	cte	2,035	0ms
	ddl	16	0ms
	delete	16	0ms
	insert	15,468	0ms
	others	2,153	0ms
	select	19,229	0ms
	tcl	684	0ms
	update	701	0ms

Queries by application

Key values

unknown Main application
80,506 Requests
1h9m (unknown)
Main time consuming application

Chart
Table

Application	Request type	Count	Duration
psql	Total	342	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	100	0ms
	ddl	16	0ms
	delete	16	0ms
	others	4	0ms
	select	64	0ms
	update	36	0ms
unknown	Total	80,506	1h9m
	copy from	16	0ms
	cte	1,935	0ms
	insert	15,468	0ms
	others	2,149	0ms
	select	19,165	0ms
	tcl	684	0ms
	update	665	0ms

Number of cancelled queries

Key values

0 per second Cancelled query Peak
2026-02-08 10:30:31 Date

Number of cancelled queries (5 minutes period)

NO DATASET

Histogram of query times

Key values

27,057 0-1ms duration

Chart
Table

Range	Count	Percentage
0-1ms	27,057	66.93%
1-5ms	11,544	28.56%
5-10ms	227	0.56%
10-25ms	354	0.88%
25-50ms	167	0.41%
50-100ms	113	0.28%
100-500ms	265	0.66%
500-1000ms	45	0.11%
1000-10000ms	540	1.34%
> 10000ms	112	0.28%

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

25

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 08	10	25	0ms	0ms

2

0ms

138

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 08	10	138	0ms	0ms

3

0ms

2

Details

0ms

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

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Feb 08	10	2	0ms	0ms

4

0ms

2,411

Details

0ms

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

Times Reported Time consuming queries #4

Day	Hour	Count	Duration	Avg duration
Feb 08	10	2,411	0ms	0ms

5

0ms

95

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 #5

Day	Hour	Count	Duration	Avg duration
Feb 08	10	95	0ms	0ms

6

0ms

239

Details

0ms

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

Times Reported Time consuming queries #6

Day	Hour	Count	Duration	Avg duration
Feb 08	10	239	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 08	10	237	0ms	0ms

8

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 #8

Day	Hour	Count	Duration	Avg duration
Feb 08	10	18	0ms	0ms

9

0ms

14

Details

0ms

select distinct s.statsid as statsid, sy.exchange as name from stats s inner join broker b on s.brokerid = b.brokerid inner join brokerconfig bc on b.brokerid = bc.brokerid inner join stats_symbols ss on s.statsid = ss.statsid inner join downloadersymbolsettings dss on ss.symbolid = dss.symbolid inner join symbols sy on dss.symbolid = sy.symbolid where dss.enabled = ? and s.brokerid is not null and b.brokerid = ? and s.groupingtype ilike ? and s.description ilike ? || b.name || ? and s.description ilike ? || sy.exchange || ? and s.description not ilike ? || ? || ? union all select distinct s.statsid as statsid, basegroupname as name from stats s inner join broker b on s.brokerid = b.brokerid inner join brokerconfig bc on b.brokerid = bc.brokerid inner join stats_symbols ss on s.statsid = ss.statsid inner join downloadersymbolsettings dss on ss.symbolid = dss.symbolid inner join symbolgroup sg on dss.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bg.brokerid = b.brokerid where dss.enabled = ? and s.brokerid is not null and b.brokerid = ? and s.groupingtype not ilike ? and s.description ilike ? || b.name || ? and s.description ilike ? || g.basegroupname || ? and s.description not ilike ? || ? || ?;

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Feb 08	10	14	0ms	0ms

10

0ms

23

Details

0ms

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

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Feb 08	10	23	0ms	0ms

11

0ms

8

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 #11

Day	Hour	Count	Duration	Avg duration
Feb 08	10	8	0ms	0ms

12

0ms

342

Details

0ms

commit;

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Feb 08	10	342	0ms	0ms

13

0ms

5

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 #13

Day	Hour	Count	Duration	Avg duration
Feb 08	10	5	0ms	0ms

14

0ms

222

Details

0ms

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

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Feb 08	10	222	0ms	0ms

15

0ms

237

Details

0ms

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

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Feb 08	10	237	0ms	0ms

16

0ms

12

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 #16

Day	Hour	Count	Duration	Avg duration
Feb 08	10	12	0ms	0ms

17

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 #17

Day	Hour	Count	Duration	Avg duration
Feb 08	10	6	0ms	0ms

18

0ms

1

Details

0ms

select name, value from systemsettings limit ? offset ?;

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Feb 08	10	1	0ms	0ms

19

0ms

25

Details

0ms

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

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Feb 08	10	25	0ms	0ms

20

0ms

237

Details

0ms

select system_identifier from pg_control_system ();

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Feb 08	10	237	0ms	0ms

Most frequent queries (N)

Rank

Times executed

Total duration

Min duration

Max duration

Avg duration

Query

1

6,540

Details

0ms

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

Times Reported Time consuming queries #1

Day	Hour	Count	Duration	Avg duration
Feb 08	10	6,540	0ms	0ms

2

5,295

Details

0ms

select ?;

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Feb 08	10	5,295	0ms	0ms

3

2,819

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 #3

Day	Hour	Count	Duration	Avg duration
Feb 08	10	2,819	0ms	0ms

4

2,443

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 #4

Day	Hour	Count	Duration	Avg duration
Feb 08	10	2,443	0ms	0ms

5

2,411

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 #5

Day	Hour	Count	Duration	Avg duration
Feb 08	10	2,411	0ms	0ms

6

2,178

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 #6

Day	Hour	Count	Duration	Avg duration
Feb 08	10	2,178	0ms	0ms

7

1,968

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 #7

Day	Hour	Count	Duration	Avg duration
Feb 08	10	1,968	0ms	0ms

8

971

Details

0ms

select datetimeupdate from latest_candle_datetime_per_receng where recognitionengine ilike ?;

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Feb 08	10	971	0ms	0ms

9

744

Details

0ms

set extra_float_digits = ?;

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Feb 08	10	744	0ms	0ms

10

732

Details

0ms

set application_name = ?;

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Feb 08	10	732	0ms	0ms

11

476

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 #11

Day	Hour	Count	Duration	Avg duration
Feb 08	10	476	0ms	0ms

12

462

Details

0ms

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

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Feb 08	10	462	0ms	0ms

13

342

Details

0ms

commit;

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Feb 08	10	342	0ms	0ms

14

342

Details

0ms

begin;

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Feb 08	10	342	0ms	0ms

15

332

Details

0ms

update datafeeds_latestrun set latestrxtime = subquery.latestrxtime, latestdbwritetime = subquery.latestdbwritetime from ( select latestrxtime, latestdbwritetime from latest_t15_candle_view where classname ilike ?) as subquery where datafeeds_latestrun.feedname ilike ?;

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Feb 08	10	332	0ms	0ms

16

276

Details

0ms

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

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Feb 08	10	276	0ms	0ms

17

253

Details

0ms

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

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Feb 08	10	253	0ms	0ms

18

249

Details

0ms

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

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Feb 08	10	249	0ms	0ms

19

239

Details

0ms

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

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Feb 08	10	239	0ms	0ms

20

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 #20

Day	Hour	Count	Duration	Avg duration
Feb 08	10	237	0ms	0ms

Normalized slowest queries (N)

Rank

Min duration

Max duration

Avg duration

Times executed

Total duration

Query

1

0ms

25

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 08	10	25	0ms	0ms

2

0ms

138

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 08	10	138	0ms	0ms

3

0ms

2

Details

0ms

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

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Feb 08	10	2	0ms	0ms

4

0ms

2,411

Details

0ms

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

Times Reported Time consuming queries #4

Day	Hour	Count	Duration	Avg duration
Feb 08	10	2,411	0ms	0ms

5

0ms

95

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 #5

Day	Hour	Count	Duration	Avg duration
Feb 08	10	95	0ms	0ms

6

0ms

239

Details

0ms

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

Times Reported Time consuming queries #6

Day	Hour	Count	Duration	Avg duration
Feb 08	10	239	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 08	10	237	0ms	0ms

8

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 #8

Day	Hour	Count	Duration	Avg duration
Feb 08	10	18	0ms	0ms

9

0ms

14

Details

0ms

select distinct s.statsid as statsid, sy.exchange as name from stats s inner join broker b on s.brokerid = b.brokerid inner join brokerconfig bc on b.brokerid = bc.brokerid inner join stats_symbols ss on s.statsid = ss.statsid inner join downloadersymbolsettings dss on ss.symbolid = dss.symbolid inner join symbols sy on dss.symbolid = sy.symbolid where dss.enabled = ? and s.brokerid is not null and b.brokerid = ? and s.groupingtype ilike ? and s.description ilike ? || b.name || ? and s.description ilike ? || sy.exchange || ? and s.description not ilike ? || ? || ? union all select distinct s.statsid as statsid, basegroupname as name from stats s inner join broker b on s.brokerid = b.brokerid inner join brokerconfig bc on b.brokerid = bc.brokerid inner join stats_symbols ss on s.statsid = ss.statsid inner join downloadersymbolsettings dss on ss.symbolid = dss.symbolid inner join symbolgroup sg on dss.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bg.brokerid = b.brokerid where dss.enabled = ? and s.brokerid is not null and b.brokerid = ? and s.groupingtype not ilike ? and s.description ilike ? || b.name || ? and s.description ilike ? || g.basegroupname || ? and s.description not ilike ? || ? || ?;

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Feb 08	10	14	0ms	0ms

10

0ms

23

Details

0ms

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

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Feb 08	10	23	0ms	0ms

11

0ms

8

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 #11

Day	Hour	Count	Duration	Avg duration
Feb 08	10	8	0ms	0ms

12

0ms

342

Details

0ms

commit;

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Feb 08	10	342	0ms	0ms

13

0ms

5

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 #13

Day	Hour	Count	Duration	Avg duration
Feb 08	10	5	0ms	0ms

14

0ms

222

Details

0ms

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

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Feb 08	10	222	0ms	0ms

15

0ms

237

Details

0ms

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

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Feb 08	10	237	0ms	0ms

16

0ms

12

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 #16

Day	Hour	Count	Duration	Avg duration
Feb 08	10	12	0ms	0ms

17

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 #17

Day	Hour	Count	Duration	Avg duration
Feb 08	10	6	0ms	0ms

18

0ms

1

Details

0ms

select name, value from systemsettings limit ? offset ?;

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Feb 08	10	1	0ms	0ms

19

0ms

25

Details

0ms

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

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Feb 08	10	25	0ms	0ms

20

0ms

237

Details

0ms

select system_identifier from pg_control_system ();

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Feb 08	10	237	0ms	0ms

Time consuming prepare


Date: 2026-02-08 10:16:03
Duration: 3ms
Database: postgres
Date: 2026-02-08 10:36:55
Duration: 6ms
Database: postgres
Date: 2026-02-08 10:11:44
Duration: 0ms
Database: postgres
Date: 2026-02-08 10:41:02
Duration: 0ms
Database: postgres
Date: 2026-02-08 10:25:52
Duration: 7ms
Database: postgres
Date: 2026-02-08 10:45:55
Duration: 1ms
Database: postgres
Date: 2026-02-08 10:25:52
Duration: 9ms
Database: postgres
Date: 2026-02-08 10:32:00
Duration: 0ms
Database: postgres
Date: 2026-02-08 10:11:01
Duration: 3ms
Database: postgres
Date: 2026-02-08 10:30:04
Duration: 2ms
Database: postgres
Date: 2026-02-08 10:36:55
Duration: 1ms
Database: postgres
Date: 2026-02-08 10:16:04
Duration: 0ms
Database: postgres
Date: 2026-02-08 10:45:52
Duration: 0ms
Database: postgres
Date: 2026-02-08 10:00:57
Duration: 0ms
Database: postgres
Date: 2026-02-08 10:05:57
Duration: 0ms
Database: postgres
Date: 2026-02-08 10:32:04
Duration: 0ms
Database: postgres
Date: 2026-02-08 10:06:00
Duration: 1ms
Database: postgres
Date: 2026-02-08 10:37:19
Duration: 1ms
Database: postgres
Date: 2026-02-08 10:11:21
Duration: 0ms
Database: postgres
Date: 2026-02-08 10:14:30
Duration: 1ms
Database: postgres

Time consuming bind

Rank

Total duration

Times executed

Min duration

Max duration

Avg duration

Query

1

14s293ms

1,131

Details

0ms

56ms

12ms

WITH rar_max as ( ;

Times Reported Time consuming bind #1

Day	Hour	Count	Duration	Avg duration
Feb 08	10	1,131	14s293ms	12ms

WITH rar_max as ( ;

Date: 2026-02-08 10:00:57 Duration: 56ms 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: 2026-02-08 10:00:41 Duration: 53ms Database: postgres parameters: $1 = 't', $2 = '667', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '84', $14 = 'AUDCAD', $15 = 'AUDCHF', $16 = 'AUDJPY', $17 = 'AUDNZD', $18 = 'AUDSGD', $19 = 'CADCHF', $20 = 'CADJPY', $21 = 'CHFJPY', $22 = 'EURAUD', $23 = 'EURCAD', $24 = 'EURCHF', $25 = 'EURCZK', $26 = 'EURGBP', $27 = 'EURHUF', $28 = 'EURJPY', $29 = 'EURNOK', $30 = 'EURNZD', $31 = 'EURPLN', $32 = 'EURSEK', $33 = 'EURSGD', $34 = 'EURTRY', $35 = 'EURZAR', $36 = 'GBPAUD', $37 = 'GBPCAD', $38 = 'GBPCHF', $39 = 'GBPJPY', $40 = 'GBPNZD', $41 = 'GBPPLN', $42 = 'GBPSEK', $43 = 'GBPSGD', $44 = 'NZDCAD', $45 = 'NZDCHF', $46 = 'NZDJPY', $47 = 'NZDSGD', $48 = 'USDCNH', $49 = 'USDCZK', $50 = 'USDHUF', $51 = 'USDNOK', $52 = 'USDPLN', $53 = 'USDSEK', $54 = 'USDSGD', $55 = 'USDTRY', $56 = 'USDZAR', $57 = 'WTI', $58 = 'XBRUSD', $59 = 'XTIUSD', $60 = 'BTCUSD', $61 = 'XAGAUD', $62 = 'XAGUSD', $63 = 'XAUAUD', $64 = 'XAUUSD', $65 = 'XPTUSD', $66 = 'XPDUSD', $67 = 'AUDUSD', $68 = 'EURUSD', $69 = 'GBPUSD', $70 = 'NZDUSD', $71 = 'USDCAD', $72 = 'USDCHF', $73 = 'USDHKD', $74 = 'USDJPY', $75 = 'AUS200', $76 = 'CHINA300', $77 = 'CHINA50', $78 = 'DJ30', $79 = 'ESP35t', $80 = 'EUR50', $81 = 'EURO50', $82 = 'FRA40', $83 = 'GDAXI', $84 = 'GDAXIm', $85 = 'HK50', $86 = 'ITA40', $87 = 'J225', $88 = 'JP225', $89 = 'NAS100', $90 = 'SING30', $91 = 'SPA35', $92 = 'STOXX50', $93 = 'SUI20', $94 = 'UK100', $95 = 'US100', $96 = 'US30', $97 = 'US500', $98 = '0', $99 = '', $100 = '0', $101 = '0', $102 = '0', $103 = '700', $104 = '700', $105 = 't', $106 = '10', $107 = '10'
WITH rar_max as ( ;

Date: 2026-02-08 10:00:57 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'

2

1s795ms

5,137

Details

0ms

9ms

0ms

SELECT ;

Times Reported Time consuming bind #2

Day	Hour	Count	Duration	Avg duration
	10	5,137	1s795ms	0ms

SELECT ;

Date: 2026-02-08 10:26:29 Duration: 9ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '515840233921283300'
SELECT ;

Date: 2026-02-08 10:25:52 Duration: 6ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '515840243237349300'
SELECT ;

Date: 2026-02-08 10:00:41 Duration: 6ms Database: postgres parameters: $1 = '515840243190162300'

3

1s401ms

589

Details

0ms

4ms

2ms

SELECT symbolid, ;

Times Reported Time consuming bind #3

Day	Hour	Count	Duration	Avg duration
	10	589	1s401ms	2ms

SELECT symbolid, ;

Date: 2026-02-08 10:01:04 Duration: 4ms Database: postgres parameters: $1 = 'BDSWISS', $2 = '30', $3 = 'LTCUSD', $4 = 'NAS100'
SELECT symbolid, ;

Date: 2026-02-08 10:01:08 Duration: 3ms Database: postgres parameters: $1 = 'BDSWISS', $2 = '30', $3 = 'SPX500', $4 = 'US30', $5 = 'TRXUSD'
SELECT symbolid, ;

Date: 2026-02-08 10:01:06 Duration: 3ms Database: postgres parameters: $1 = 'GLOBALGTMT5', $2 = '15', $3 = 'LTCUSD', $4 = 'MATUSD'

4

426ms

18

Details

0ms

40ms

23ms

with wh_patitioned as ( ;

Times Reported Time consuming bind #4

Day	Hour	Count	Duration	Avg duration
	10	18	426ms	23ms

with wh_patitioned as ( ;

Date: 2026-02-08 10:20:02 Duration: 40ms 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: 2026-02-08 10:50:02 Duration: 40ms 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: 2026-02-08 10:15:02 Duration: 31ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'

5

303ms

45

Details

4ms

15ms

6ms

WITH last_candle AS ( ;

Times Reported Time consuming bind #5

Day	Hour	Count	Duration	Avg duration
	10	45	303ms	6ms

WITH last_candle AS ( ;

Date: 2026-02-08 10:08:00 Duration: 15ms Database: postgres parameters: $1 = '558', $2 = '558'
WITH last_candle AS ( ;

Date: 2026-02-08 10:56:00 Duration: 9ms Database: postgres parameters: $1 = '558', $2 = '558'
WITH last_candle AS ( ;

Date: 2026-02-08 10:08:00 Duration: 8ms Database: postgres parameters: $1 = '558', $2 = '558'

6

254ms

156

Details

1ms

2ms

1ms

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

Times Reported Time consuming bind #6

Day	Hour	Count	Duration	Avg duration
	10	156	254ms	1ms

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

Date: 2026-02-08 10:01:00 Duration: 2ms Database: postgres parameters: $1 = 'GLOBALGTMT5'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-02-08 10:46:01 Duration: 2ms Database: postgres parameters: $1 = 'GLOBALGTMT5'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-02-08 10:30:20 Duration: 2ms Database: postgres parameters: $1 = 'ATFX'

7

187ms

5,191

Details

0ms

7ms

0ms

select 1;

Times Reported Time consuming bind #7

Day	Hour	Count	Duration	Avg duration
	10	5,191	187ms	0ms

select 1;

Date: 2026-02-08 10:26:29 Duration: 7ms Database: postgres
select 1;

Date: 2026-02-08 10:05:51 Duration: 7ms Database: postgres
select 1;

Date: 2026-02-08 10:20:51 Duration: 6ms Database: postgres

8

175ms

2,411

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 #8

Day	Hour	Count	Duration	Avg duration
	10	2,411	175ms	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: 2026-02-08 10:11:44 Duration: 0ms Database: postgres parameters: $1 = '2026-02-06 20:00:00', $2 = '8767.4', $3 = '8777.9', $4 = '8765.2', $5 = '8774.95', $6 = '4787', $7 = '515840248015562300', $8 = '0', $9 = '2026-02-08 10:11:44.898', $10 = '2026-02-08 10:11:44.786', $11 = '8767.4', $12 = '8777.9', $13 = '8765.2', $14 = '8774.95', $15 = '4787', $16 = '0', $17 = '2026-02-08 10:11:44.898', $18 = '2026-02-08 10:11:44.786'
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: 2026-02-08 10:17:11 Duration: 0ms Database: postgres parameters: $1 = '2026-02-07 00:00:00', $2 = '50109.65', $3 = '50127.15', $4 = '50081.15', $5 = '50117.1', $6 = '4415', $7 = '515840249388246300', $8 = '0', $9 = '2026-02-08 10:17:11.875', $10 = '2026-02-08 10:17:11.874', $11 = '50109.65', $12 = '50127.15', $13 = '50081.15', $14 = '50117.1', $15 = '4415', $16 = '0', $17 = '2026-02-08 10:17:11.875', $18 = '2026-02-08 10:17:11.874'
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: 2026-02-08 10:46:55 Duration: 0ms Database: postgres parameters: $1 = '2026-02-07 00:00:00', $2 = '110.269', $3 = '110.3825', $4 = '110.251', $5 = '110.2635', $6 = '3417', $7 = '515840249384093300', $8 = '0', $9 = '2026-02-08 10:46:55.053', $10 = '2026-02-08 10:46:55.053', $11 = '110.269', $12 = '110.3825', $13 = '110.251', $14 = '110.2635', $15 = '3417', $16 = '0', $17 = '2026-02-08 10:46:55.053', $18 = '2026-02-08 10:46:55.053'

9

167ms

14

Details

9ms

18ms

11ms

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

Times Reported Time consuming bind #9

Day	Hour	Count	Duration	Avg duration
	10	14	167ms	11ms

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

Date: 2026-02-08 10:32:04 Duration: 18ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Date: 2026-02-08 10:36:31 Duration: 12ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Date: 2026-02-08 10:47:03 Duration: 12ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'

10

153ms

2,443

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 #10

Day	Hour	Count	Duration	Avg duration
	10	2,443	153ms	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: 2026-02-08 10:41:02 Duration: 0ms Database: postgres parameters: $1 = '2026-02-06 18:00:00', $2 = '26935.7', $3 = '26975.5', $4 = '26930.7', $5 = '26963.8', $6 = '7047', $7 = '515840247933633300', $8 = '0', $9 = '2026-02-08 10:41:02.197', $10 = '2026-02-08 10:41:02.13', $11 = '26935.7', $12 = '26975.5', $13 = '26930.7', $14 = '26963.8', $15 = '7047', $16 = '0', $17 = '2026-02-08 10:41:02.197', $18 = '2026-02-08 10:41:02.13'
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: 2026-02-08 10:41:04 Duration: 0ms Database: postgres parameters: $1 = '2026-02-06 23:00:00', $2 = '25040.4', $3 = '25050.78', $4 = '24999.65', $5 = '25006.15', $6 = '8557', $7 = '515840248039147300', $8 = '0', $9 = '2026-02-08 10:41:04.245', $10 = '2026-02-08 10:41:04.168', $11 = '25040.4', $12 = '25050.78', $13 = '24999.65', $14 = '25006.15', $15 = '8557', $16 = '0', $17 = '2026-02-08 10:41:04.245', $18 = '2026-02-08 10:41:04.168'
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: 2026-02-08 10:41:44 Duration: 0ms Database: postgres parameters: $1 = '2026-02-06 21:00:00', $2 = '8774.9', $3 = '8779.45', $4 = '8771.85', $5 = '8775', $6 = '2091', $7 = '515840248015340300', $8 = '0', $9 = '2026-02-08 10:41:44.524', $10 = '2026-02-08 10:41:44.425', $11 = '8774.9', $12 = '8779.45', $13 = '8771.85', $14 = '8775', $15 = '2091', $16 = '0', $17 = '2026-02-08 10:41:44.524', $18 = '2026-02-08 10:41:44.425'

11

116ms

2,819

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
	10	2,819	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: 2026-02-08 10:46:38 Duration: 0ms Database: postgres parameters: $1 = '2026-02-06 22:45:00', $2 = '0.958425', $3 = '0.958745', $4 = '0.958365', $5 = '0.958365', $6 = '732', $7 = '515840230419121300', $8 = '0', $9 = '2026-02-08 10:46:38.801', $10 = '2026-02-08 10:46:38.729', $11 = '0.958425', $12 = '0.958745', $13 = '0.958365', $14 = '0.958365', $15 = '732', $16 = '0', $17 = '2026-02-08 10:46:38.801', $18 = '2026-02-08 10:46:38.729'
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: 2026-02-08 10:46:31 Duration: 0ms Database: postgres parameters: $1 = '2026-02-06 21:00:00', $2 = '46057.51', $3 = '46057.51', $4 = '46027.49', $5 = '46037.51', $6 = '82', $7 = '500991628285199200', $8 = '0', $9 = '2026-02-08 10:46:31.196', $10 = '2026-02-08 10:46:31.134', $11 = '46057.51', $12 = '46057.51', $13 = '46027.49', $14 = '46037.51', $15 = '82', $16 = '0', $17 = '2026-02-08 10:46:31.196', $18 = '2026-02-08 10:46:31.134'
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: 2026-02-08 10:32:00 Duration: 0ms Database: postgres parameters: $1 = '2026-02-07 00:45:00', $2 = '14.1735', $3 = '14.207', $4 = '14.163', $5 = '14.1705', $6 = '1222', $7 = '515840249401951300', $8 = '0', $9 = '2026-02-08 10:32:00.299', $10 = '2026-02-08 10:32:00.235', $11 = '14.1735', $12 = '14.207', $13 = '14.163', $14 = '14.1705', $15 = '1222', $16 = '0', $17 = '2026-02-08 10:32:00.299', $18 = '2026-02-08 10:32:00.235'

12

69ms

14

Details

0ms

18ms

4ms

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

Times Reported Time consuming bind #12

Day	Hour	Count	Duration	Avg duration
	10	14	69ms	4ms

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

Date: 2026-02-08 10:05:50 Duration: 18ms Database: postgres parameters: $1 = '667', $2 = '667'
select distinct s.statsid as statsid, sy.exchange as name;

Date: 2026-02-08 10:01:09 Duration: 18ms Database: postgres parameters: $1 = '621', $2 = '621'
select distinct s.statsid as statsid, sy.exchange as name;

Date: 2026-02-08 10:00:43 Duration: 18ms Database: postgres parameters: $1 = '619', $2 = '619'

13

51ms

437

Details

0ms

1ms

0ms

select category, ;

Times Reported Time consuming bind #13

Day	Hour	Count	Duration	Avg duration
	10	437	51ms	0ms

select category, ;

Date: 2026-02-08 10:00:50 Duration: 1ms Database: postgres parameters: $1 = '515852059324253307', $2 = 'symbol', $3 = 'USDMXN', $4 = 'CHFZAR', $5 = 'AUDJPY', $6 = 'CHFJPY', $7 = 'USDZAR', $8 = 'USDJPY', $9 = 'CADJPY', $10 = 'ZARJPY', $11 = 'USDHUF', $12 = 'EURCNH', $13 = 'AUDZAR', $14 = 'GBPJPY', $15 = 'TRYJPY', $16 = 'NZDJPY', $17 = 'GBPZAR', $18 = 'EURMXN', $19 = 'EURZAR', $20 = 'EURNOK', $21 = 'EURHKD', $22 = 'SGDJPY', $23 = 'USDNOK', $24 = 'USDCZK', $25 = 'USDDKK', $26 = 'EURSEK', $27 = 'CHFHUF', $28 = 'EURTRY', $29 = 'USDSEK', $30 = 'NZDSEK', $31 = 'EURJPY', $32 = 'EURHUF', $33 = 'USDPLN', $34 = 'GBPNZD', $35 = 'USDCNH', $36 = 'EURCZK', $37 = 'EURPLN', $38 = 'USDILS', $39 = 'GBPAUD', $40 = 'EURGBP', $41 = 'TRYJPY', $42 = 'EURNZD', $43 = 'EURCZK', $44 = 'EURAUD', $45 = 'EURHUF', $46 = 'ZARJPY', $47 = 'CHFHUF', $48 = 'USDZAR', $49 = 'GBPCAD', $50 = 'USDCAD', $51 = 'EURDKK', $52 = 'GBPCAD', $53 = '515852059324253307', $54 = 'symbol', $55 = 'USDMXN', $56 = 'CHFZAR', $57 = 'AUDJPY', $58 = 'CHFJPY', $59 = 'USDZAR', $60 = 'USDJPY', $61 = 'CADJPY', $62 = 'ZARJPY', $63 = 'USDHUF', $64 = 'EURCNH', $65 = 'AUDZAR', $66 = 'GBPJPY', $67 = 'TRYJPY', $68 = 'NZDJPY', $69 = 'GBPZAR', $70 = 'EURMXN', $71 = 'EURZAR', $72 = 'EURNOK', $73 = 'EURHKD', $74 = 'SGDJPY', $75 = 'USDNOK', $76 = 'USDCZK', $77 = 'USDDKK', $78 = 'EURSEK', $79 = 'CHFHUF', $80 = 'EURTRY', $81 = 'USDSEK', $82 = 'NZDSEK', $83 = 'EURJPY', $84 = 'EURHUF', $85 = 'USDPLN', $86 = 'GBPNZD', $87 = 'USDCNH', $88 = 'EURCZK', $89 = 'EURPLN', $90 = 'USDILS', $91 = 'GBPAUD', $92 = 'EURGBP', $93 = 'TRYJPY', $94 = 'EURNZD', $95 = 'EURCZK', $96 = 'EURAUD', $97 = 'EURHUF', $98 = 'ZARJPY', $99 = 'CHFHUF', $100 = 'USDZAR', $101 = 'GBPCAD', $102 = 'USDCAD', $103 = 'EURDKK', $104 = 'GBPCAD'
select category, ;

Date: 2026-02-08 10:05:57 Duration: 1ms Database: postgres parameters: $1 = '500996399109302207', $2 = 'symbol', $3 = 'XAUUSD', $4 = 'BTCUSD', $5 = 'XTIUSD', $6 = 'WTI', $7 = 'XAUAUD', $8 = 'FRA40', $9 = 'US500', $10 = 'US30', $11 = 'UK100', $12 = 'EURO50', $13 = 'XAGUSD', $14 = 'US100', $15 = 'XBRUSD', $16 = 'JP225', $17 = 'CHINA50', $18 = 'XPTUSD', $19 = 'XAGAUD', $20 = 'SING30', $21 = 'AUS200', $22 = 'XPDUSD', $23 = 'HK50', $24 = 'SING30', $25 = 'CHINA50', $26 = 'EURO50', $27 = 'UK100', $28 = 'WTI', $29 = 'XAUAUD', $30 = 'FRA40', $31 = 'XTIUSD', $32 = 'XAGUSD', $33 = 'XPDUSD', $34 = 'AUS200', $35 = 'HK50', $36 = 'US500', $37 = 'US30', $38 = 'JP225', $39 = 'BTCUSD', $40 = 'XBRUSD', $41 = 'XAUUSD', $42 = 'XAGAUD', $43 = 'US100', $44 = 'XPTUSD', $45 = 'ITA40', $46 = 'SPA35', $47 = 'SPA35', $48 = 'ITA40', $49 = '500996399109302207', $50 = 'symbol', $51 = 'XAUUSD', $52 = 'BTCUSD', $53 = 'XTIUSD', $54 = 'WTI', $55 = 'XAUAUD', $56 = 'FRA40', $57 = 'US500', $58 = 'US30', $59 = 'UK100', $60 = 'EURO50', $61 = 'XAGUSD', $62 = 'US100', $63 = 'XBRUSD', $64 = 'JP225', $65 = 'CHINA50', $66 = 'XPTUSD', $67 = 'XAGAUD', $68 = 'SING30', $69 = 'AUS200', $70 = 'XPDUSD', $71 = 'HK50', $72 = 'SING30', $73 = 'CHINA50', $74 = 'EURO50', $75 = 'UK100', $76 = 'WTI', $77 = 'XAUAUD', $78 = 'FRA40', $79 = 'XTIUSD', $80 = 'XAGUSD', $81 = 'XPDUSD', $82 = 'AUS200', $83 = 'HK50', $84 = 'US500', $85 = 'US30', $86 = 'JP225', $87 = 'BTCUSD', $88 = 'XBRUSD', $89 = 'XAUUSD', $90 = 'XAGAUD', $91 = 'US100', $92 = 'XPTUSD', $93 = 'ITA40', $94 = 'SPA35', $95 = 'SPA35', $96 = 'ITA40'
select category, ;

Date: 2026-02-08 10:01:27 Duration: 1ms Database: postgres parameters: $1 = '515852059324736307', $2 = 'symbol', $3 = 'USDMXN', $4 = 'CHFZAR', $5 = 'AUDJPY', $6 = 'CHFJPY', $7 = 'USDZAR', $8 = 'USDJPY', $9 = 'CADJPY', $10 = 'ZARJPY', $11 = 'USDHUF', $12 = 'EURCNH', $13 = 'AUDZAR', $14 = 'GBPJPY', $15 = 'TRYJPY', $16 = 'NZDJPY', $17 = 'GBPZAR', $18 = 'EURMXN', $19 = 'EURZAR', $20 = 'EURNOK', $21 = 'EURHKD', $22 = 'SGDJPY', $23 = 'USDNOK', $24 = 'USDCZK', $25 = 'USDDKK', $26 = 'EURSEK', $27 = 'CHFHUF', $28 = 'EURTRY', $29 = 'USDSEK', $30 = 'NZDSEK', $31 = 'EURJPY', $32 = 'EURHUF', $33 = 'USDPLN', $34 = 'GBPNZD', $35 = 'USDCNH', $36 = 'EURCZK', $37 = 'EURPLN', $38 = 'USDILS', $39 = 'GBPAUD', $40 = 'EURGBP', $41 = 'TRYJPY', $42 = 'EURNZD', $43 = 'EURCZK', $44 = 'EURAUD', $45 = 'EURHUF', $46 = 'ZARJPY', $47 = 'CHFHUF', $48 = 'USDZAR', $49 = 'GBPCAD', $50 = 'USDCAD', $51 = 'EURDKK', $52 = 'GBPCAD', $53 = '515852059324736307', $54 = 'symbol', $55 = 'USDMXN', $56 = 'CHFZAR', $57 = 'AUDJPY', $58 = 'CHFJPY', $59 = 'USDZAR', $60 = 'USDJPY', $61 = 'CADJPY', $62 = 'ZARJPY', $63 = 'USDHUF', $64 = 'EURCNH', $65 = 'AUDZAR', $66 = 'GBPJPY', $67 = 'TRYJPY', $68 = 'NZDJPY', $69 = 'GBPZAR', $70 = 'EURMXN', $71 = 'EURZAR', $72 = 'EURNOK', $73 = 'EURHKD', $74 = 'SGDJPY', $75 = 'USDNOK', $76 = 'USDCZK', $77 = 'USDDKK', $78 = 'EURSEK', $79 = 'CHFHUF', $80 = 'EURTRY', $81 = 'USDSEK', $82 = 'NZDSEK', $83 = 'EURJPY', $84 = 'EURHUF', $85 = 'USDPLN', $86 = 'GBPNZD', $87 = 'USDCNH', $88 = 'EURCZK', $89 = 'EURPLN', $90 = 'USDILS', $91 = 'GBPAUD', $92 = 'EURGBP', $93 = 'TRYJPY', $94 = 'EURNZD', $95 = 'EURCZK', $96 = 'EURAUD', $97 = 'EURHUF', $98 = 'ZARJPY', $99 = 'CHFHUF', $100 = 'USDZAR', $101 = 'GBPCAD', $102 = 'USDCAD', $103 = 'EURDKK', $104 = 'GBPCAD'

14

40ms

9

Details

3ms

7ms

4ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Times Reported Time consuming bind #14

Day	Hour	Count	Duration	Avg duration
	10	9	40ms	4ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-02-08 10:06:00 Duration: 7ms Database: postgres parameters: $1 = '627', $2 = '627'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-02-08 10:10:52 Duration: 5ms Database: postgres parameters: $1 = '958', $2 = '958'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-02-08 10:06:05 Duration: 5ms Database: postgres parameters: $1 = '627', $2 = '627'

15

23ms

79

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 #15

Day	Hour	Count	Duration	Avg duration
	10	79	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: 2026-02-08 10:21:26 Duration: 4ms Database: postgres parameters: $1 = '607634601773067301'
/*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: 2026-02-08 10:08:25 Duration: 2ms Database: postgres parameters: $1 = '607634603224561301'
/*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: 2026-02-08 10:21:25 Duration: 2ms Database: postgres parameters: $1 = '607634662039188301'

16

17ms

23

Details

0ms

1ms

0ms

SELECT absolutetimezoneoffset;

Times Reported Time consuming bind #16

Day	Hour	Count	Duration	Avg duration
	10	23	17ms	0ms

SELECT absolutetimezoneoffset;

Date: 2026-02-08 10:05:51 Duration: 1ms Database: postgres parameters: $1 = '667', $2 = 'BROKER'
SELECT absolutetimezoneoffset;

Date: 2026-02-08 10:00:56 Duration: 0ms Database: postgres parameters: $1 = '619', $2 = 'BROKER'
SELECT absolutetimezoneoffset;

Date: 2026-02-08 10:00:44 Duration: 0ms Database: postgres parameters: $1 = '619', $2 = 'FOREX'

17

15ms

32

Details

0ms

4ms

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 #17

Day	Hour	Count	Duration	Avg duration
	10	32	15ms	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: 2026-02-08 10:06:05 Duration: 4ms Database: postgres parameters: $1 = '607633789328409303'
/*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: 2026-02-08 10:21:01 Duration: 4ms Database: postgres parameters: $1 = '607635262448197303'
/*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: 2026-02-08 10:22:07 Duration: 1ms Database: postgres parameters: $1 = '607634661738230303'

18

12ms

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 #18

Day	Hour	Count	Duration	Avg duration
	10	18	12ms	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: 2026-02-08 10:21:02 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: 2026-02-08 10:51:01 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: 2026-02-08 10:51:01 Duration: 0ms Database: postgres

19

10ms

10

Details

0ms

1ms

/*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 #19

Day	Hour	Count	Duration	Avg duration
	10	10	10ms	1ms

/*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: 2026-02-08 10:33:05 Duration: 1ms Database: postgres parameters: $1 = '607641975980136302'
/*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: 2026-02-08 10:13:52 Duration: 1ms Database: postgres parameters: $1 = '607630949872549302'
/*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: 2026-02-08 10:18:35 Duration: 1ms Database: postgres parameters: $1 = '607641975980136302'

20

9ms

68

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 #20

Day	Hour	Count	Duration	Avg duration
	10	68	9ms	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: 2026-02-08 10:00:57 Duration: 0ms Database: postgres parameters: $1 = '2026-02-06 20:00:00', $2 = '250.689', $3 = '256.764', $4 = '249.1905', $5 = '251.73', $6 = '78426', $7 = '515840249404204300', $8 = '0', $9 = '2026-02-08 10:00:57.273', $10 = '2026-02-08 10:00:57.057', $11 = '250.689', $12 = '256.764', $13 = '249.1905', $14 = '251.73', $15 = '78426', $16 = '0', $17 = '2026-02-08 10:00:57.273', $18 = '2026-02-08 10:00:57.057'
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: 2026-02-08 10:45:53 Duration: 0ms Database: postgres parameters: $1 = '2026-02-06 16:00:00', $2 = '55512.5', $3 = '56287.5', $4 = '55452.5', $5 = '56262.5', $6 = '5788', $7 = '515840230556263300', $8 = '0', $9 = '2026-02-08 10:45:53.74', $10 = '2026-02-08 10:45:53.74', $11 = '55512.5', $12 = '56287.5', $13 = '55452.5', $14 = '56262.5', $15 = '5788', $16 = '0', $17 = '2026-02-08 10:45:53.74', $18 = '2026-02-08 10:45:53.74'
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: 2026-02-08 10:45:51 Duration: 0ms Database: postgres parameters: $1 = '2026-02-06 16:00:00', $2 = '55515', $3 = '56290', $4 = '55455', $5 = '56265', $6 = '5788', $7 = '515840230561612300', $8 = '0', $9 = '2026-02-08 10:45:51.14', $10 = '2026-02-08 10:45:51.139', $11 = '55515', $12 = '56290', $13 = '55455', $14 = '56265', $15 = '5788', $16 = '0', $17 = '2026-02-08 10:45:51.14', $18 = '2026-02-08 10:45:51.139'

Log levels

Key values

151,034 Log entries

Chart
Table

Type	Count	Percentage
CONTEXT	368	0.24%
DETAIL	31,888	21.11%
LOG	118,378	78.38%
STATEMENT	400	0.26%

Events distribution

Key values

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

Errors per 5 minutes

NO DATASET

Most Frequent Errors/Events

Key values

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

Rank	Times reported	Error
NO DATASET

Global information

Overview

Global Stats

SQL Traffic

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SELECT Traffic

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INSERT/UPDATE/DELETE Traffic

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Queries duration

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Prepared queries ratio

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General Activity

Connections

Established Connections

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Connections per database

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Connections per user

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Connections per host

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Sessions

Simultaneous sessions

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Histogram of session times

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Sessions per database

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Sessions per user

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Sessions per host

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Sessions per application

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Checkpoints / Restartpoints

Checkpoints Buffers

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Checkpoints Wal files

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Checkpoints distance

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Checkpoints Activity

Checkpoint causes

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Temporary Files

Size of temporary files

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Number of temporary files

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Temporary Files Activity

Queries generating the most temporary files (N)

Queries generating the largest temporary files

Vacuums

Vacuums / Analyzes Distribution

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Analyzes per table

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Vacuums per table

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Tuples removed per table

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Pages removed per table

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Autovacuum Activity

Locks

Locks by types

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Type of locks

Most frequent waiting queries (N)

Queries that waited the most

Queries

Queries by type

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Queries by database

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Queries by user

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Duration by user