pgBadger :: PostgreSQL Log Analyzer

Global information

Generated on Tue Feb 24 08:00:00 2026
Log file: /home/postgres/pg_data/data/pg_log/postgresql-2026-02-24_090000.log
Parsed 2,562,390 log entries in 59s
Log start from 2026-02-24 09:00:00 to 2026-02-24 09:59:59

Global Stats

269 Number of unique normalized queries
283,974 Number of queries
2h19m13s Total query duration
2026-02-24 09:00:00 First query
2026-02-24 09:59:59 Last query
4,488 queries/s at 2026-02-24 09:12:11 Query peak

2h19m13s Total query duration
8s223ms Prepare/parse total duration
58s675ms Bind total duration
2h18m6s Execute total duration

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

49 Total number of automatic vacuums
59 Total number of automatic analyzes

985 Number temporary file
611.06 MiB Max size of temporary file
6.57 MiB Average size of temporary file

2,959 Total number of sessions
13 sessions at 2026-02-24 09:53:48 Session peak
20d7h59m30s Total duration of sessions
9m53s Average duration of sessions
95 Average queries per session
2s823ms Average queries duration per session
9m50s Average idle time per session

2,949 Total number of connections
27 connections/s at 2026-02-24 09:33:48 Connection peak
3 Total number of databases

SQL Traffic

Key values

4,488 queries/s Query Peak
2026-02-24 09:12:11 Date

SELECT Traffic

Key values

2,243 queries/s Query Peak
2026-02-24 09:12:11 Date

INSERT/UPDATE/DELETE Traffic

Key values

172 queries/s Query Peak
2026-02-24 09:30:40 Date

Queries duration

Key values

2h19m13s 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 24	09	283,974	0ms	1m5s	29ms	4m28s	5m6s	5m30s

Day	Hour	SELECT	COPY TO	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Feb 24	09	99,081	26	0ms	0ms	0ms	0ms

Day	Hour	INSERT	UPDATE	DELETE	COPY FROM	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Feb 24	09	25,549	3,381	16	96	0ms	0ms	0ms	0ms

Day	Hour	Prepare	Bind	Bind/Prepare	Percentage of prepare
Feb 24	09	21,783	104,041	4.78	15.82%

Day	Hour	Count	Average / Second
Feb 24	09	2,949	0.82/s

Day	Hour	Count	Average Duration	Average idle time
Feb 24	09	2,959	9m53s	9m50s

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Established Connections

Key values

27 connections Connection Peak
2026-02-24 09:33:48 Date

Connections per database

Key values

acaweb_fx Main Database
2,949 connections Total

Chart
Table

Database	User	Count
acaweb_fx		2,858
	postgres	2,501
	psql	357
socialmedia		91
	postgres	91

Connections per user

Key values

postgres Main User
2,949 connections Total

Chart
Table

User	Count
postgres	2,592
psql	357

Connections per host

Key values

192.168.4.142 Main host with 1167 connections
2,949 Total connections

Chart
Table

Host	Count
127.0.0.1	113
182.165.1.54	2
192.168.0.114	7
192.168.0.171	9
192.168.0.216	103
192.168.0.74	342
192.168.0.84	2
192.168.1.127	6
192.168.1.131	2
192.168.1.145	128
192.168.1.15	51
192.168.1.154	9
192.168.1.20	158
192.168.1.201	1
192.168.1.238	2
192.168.1.239	14
192.168.1.90	72
192.168.2.126	48
192.168.2.182	12
192.168.3.199	12
192.168.4.100	1
192.168.4.125	4
192.168.4.142	1,167
192.168.4.150	10
192.168.4.222	1
192.168.4.227	4
192.168.4.238	12
192.168.4.33	79
192.168.4.92	4
192.168.4.98	330
[local]	244

Simultaneous sessions

Key values

13 sessions Session Peak
2026-02-24 09:53:48 Date

Histogram of session times

Key values

2,350 0-500ms duration

Chart
Table

Range	Count	Percentage
0ms-500ms	2,350	79.42%
500ms-1s	183	6.18%
1s-30s	259	8.75%
30s-1m	16	0.54%
1m-10m	55	1.86%
10m-30m	50	1.69%
30m-1h	8	0.27%
1h-8h	18	0.61%
> 8h	20	0.68%

Sessions per database

Key values

acaweb_fx Main Database
2,959 sessions Total

Chart
Table

Database	User	Count	Total Duration	Average Duration
acaweb_fx	2,869	20d7h32m38s	10m11s
socialmedia	90	26m52s	17s915ms

Sessions per user

Key values

postgres Main User
2,959 sessions Total

Chart
Table

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

Sessions per host

Key values

192.168.4.142 Main Host
2,959 sessions Total

Chart
Table

Host	Count	Total Duration	Average Duration
127.0.0.1	113	10s884ms	96ms
182.165.1.54	2	16h38m38s	8h19m19s
192.168.0.114	6	30m24s	5m4s
192.168.0.171	15	5d18h24m38s	9h13m38s
192.168.0.216	103	1m44s	1s10ms
192.168.0.74	342	1d5m3s	4m13s
192.168.0.84	2	23h58m56s	11h59m28s
192.168.1.127	6	433ms	72ms
192.168.1.131	2	23h58m54s	11h59m27s
192.168.1.145	128	1d13h32m2s	17m35s
192.168.1.15	52	1d4h48m	33m13s
192.168.1.154	13	3d22h17m59s	7h15m13s
192.168.1.20	158	2d7h22m41s	21m1s
192.168.1.201	1	11ms	11ms
192.168.1.238	2	23h58m37s	11h59m18s
192.168.1.239	14	118ms	8ms
192.168.1.90	72	42s119ms	584ms
192.168.2.126	48	17s466ms	363ms
192.168.2.182	12	13s11ms	1s84ms
192.168.3.199	12	941ms	78ms
192.168.4.100	1	236ms	236ms
192.168.4.125	4	27s172ms	6s793ms
192.168.4.142	1,167	8m7s	417ms
192.168.4.150	10	20h3m4s	2h18s
192.168.4.222	1	2m54s	2m54s
192.168.4.227	4	53ms	13ms
192.168.4.238	12	15s517ms	1s293ms
192.168.4.33	79	1m27s	1s105ms
192.168.4.92	4	36ms	9ms
192.168.4.98	330	13s637ms	41ms
[local]	244	3m52s	952ms

Sessions per application

Key values

unknown Main Host
2,959 sessions Total

Chart
Table

Application	Count	Total Duration	Average Duration
unknown	2,959	20d7h59m30s	9m53s

Checkpoints Buffers

Key values

10,864 buffers Checkpoint Peak
2026-02-24 09:08:15 Date
210.054 seconds Highest write time
0.006 seconds Sync time

Checkpoints Wal files

Key values

5 files Wal files usage Peak
2026-02-24 09:08:15 Date

Checkpoints distance

Key values

179.60 Mo Distance Peak
2026-02-24 09:08:15 Date

Checkpoints Activity

Day	Hour	Written buffers	Write time	Sync time	Total time
Feb 24	09	33,887	1,751.897s	0.025s	1,752.25s

Day	Hour	Added	Removed	Recycled	Synced files	Longest sync	Average sync
Feb 24	09	0	0	22	1,884	0.005s	0s

Day	Hour	Count	Avg time (sec)
Feb 24	09	0	0s

Day	Hour	Mean distance	Mean estimate
Feb 24	09	30,218.75 kB	181,785.08 kB

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Checkpoint causes

Key values

time (12) Main checkpoint cause
12 checkpoints Total

Chart
Table

Cause	Number of checkpoints
time	12
Total	12

Size of temporary files

Key values

611.06 MiB Temp Files size Peak
2026-02-24 09:38:34 Date

Number of temporary files

Key values

62 per second Temp Files Peak
2026-02-24 09:47:09 Date

Temporary Files Activity

Temp Files

Day	Hour	Count	Total size	Average size
Feb 24	09	985	6.32 GiB	6.57 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	68	292.58 MiB	4.16 MiB	4.42 MiB	4.30 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)) AND ($5 = 0 OR s.exchange in ($6)) AND ($7 = 0 OR coalesce(bim.code, s.symbol) in ($8)) AND ($9 = 0 OR fr.pattern in ($10)) AND ($11 = 0 OR fr.patternlengthbars <= $12) AND ($13 = 0 OR ($14 = 1 AND fr.timed > cast('1970-01-01' as timestamp)) OR ($15 = 2 AND fr.timed < cast('1970-01-01' as timestamp)))), results AS ( SELECT DISTINCT ON (symbolid) FROM all_results WHERE (FALSE = $16 OR relevant = 1) AND ($17 = 0 OR age <= $18) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-02-24 09:02:09 Duration: 0ms
2	59	212.84 MiB	3.47 MiB	3.96 MiB	3.61 MiB	select resultuid from relevance_consecutivecandles_results order by resultuid desc limit ?), all_results as ( select ccr.resultuid as resultuid, ccr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, ccr.patternendtime as identified, dtt.timezone as timezone, ccr.qtyconsecutivecandles as length, g.basegroupname, case when rcr.age is not null then rcr.age when ccr.resultuid <= rm.resultuid then ? else ? end as age, case when rcr.relevant is not null then rcr.relevant when ccr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip, newlevels.filtered from consecutivecandles_results ccr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = ccr.symbolid inner join symbols s on ccr.symbolid = s.symbolid and s.nonliquid = ? inner join downloadersymbolsettings dss on ccr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on ccr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join rar_max rm on ? = ? left outer join relevance_consecutivecandles_results rcr on rcr.resultuid = ccr.resultuid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? left join lateral calc_cc_signal_filter (ccr.resultuid) newlevels on true where ccr.gmttimefound > now() - interval ? and s.deleted = ? and (ccr.simulation = ? or ccr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or ccr.patternlengthbars <= ?)), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc; SELECT resultuid FROM relevance_consecutivecandles_results ORDER BY resultuid DESC LIMIT 1), all_results AS ( SELECT ccr.resultuid AS resultuid, ccr.direction AS direction, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, ccr.patternendtime AS identified, dtt.timezone AS timezone, ccr.qtyconsecutivecandles AS length, g.basegroupname, CASE WHEN rcr.age IS NOT NULL THEN rcr.age WHEN ccr.resultuid <= rm.resultuid THEN 1 ELSE 0 END as age, CASE WHEN rcr.relevant IS NOT NULL THEN rcr.relevant WHEN ccr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip, newLevels.filtered FROM consecutivecandles_results ccr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $1 AND bsl.symbolid = ccr.symbolid INNER JOIN symbols s ON ccr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN downloadersymbolsettings dss ON ccr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN symbolgroup sg on ccr.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid INNER JOIN rar_max rm ON 1 = 1 LEFT OUTER JOIN relevance_consecutivecandles_results rcr ON rcr.resultuid = ccr.resultuid LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' LEFT JOIN LATERAL calc_cc_signal_filter (ccr.resultuid) newLevels on true WHERE ccr.gmttimefound > now() - INTERVAL '7 DAYS' AND s.deleted = 0 AND (ccr.simulation = 0 OR ccr.simulation IS NULL) AND ($2 = 0 OR s.timegranularity in ($3, $4, $5, $6, $7, $8, $9)) AND ($10 = 0 OR s.exchange in ($11)) AND ($12 = 0 OR coalesce(bim.code, s.symbol) in ($13, $14, $15, $16, $17, $18, $19, $20, $21, $22, $23, $24, $25, $26, $27, $28, $29, $30, $31, $32, $33, $34, $35, $36, $37, $38, $39, $40, $41, $42, $43, $44, $45, $46, $47, $48, $49, $50, $51, $52, $53, $54, $55, $56, $57, $58, $59, $60, $61, $62, $63, $64, $65, $66, $67, $68, $69, $70, $71, $72, $73, $74, $75, $76, $77, $78, $79, $80, $81, $82, $83, $84, $85, $86, $87, $88, $89, $90, $91, $92, $93, $94, $95, $96, $97, $98, $99, $100, $101, $102, $103, $104, $105, $106, $107, $108, $109, $110, $111, $112, $113, $114, $115, $116, $117, $118, $119, $120, $121, $122, $123, $124, $125, $126, $127, $128, $129, $130, $131, $132, $133, $134, $135, $136, $137, $138, $139, $140, $141, $142, $143, $144, $145, $146, $147, $148, $149, $150, $151, $152, $153, $154, $155, $156, $157, $158, $159, $160, $161, $162, $163, $164, $165, $166, $167, $168, $169, $170, $171, $172, $173, $174, $175, $176, $177, $178, $179, $180, $181, $182, $183, $184, $185, $186, $187, $188, $189, $190, $191, $192, $193, $194, $195, $196, $197, $198, $199, $200, $201, $202, $203, $204, $205, $206, $207, $208, $209, $210, $211, $212, $213, $214, $215, $216, $217, $218, $219, $220, $221, $222, $223, $224, $225)) AND ($226 = 0 OR ccr.patternlengthbars <= $227)), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results WHERE (FALSE = $228 OR relevant = 1) AND ($229 = 0 OR age <= $230) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-02-24 09:02:07 Duration: 0ms
3	41	251.66 MiB	3.29 MiB	9.44 MiB	6.14 MiB	jr.resultuid as resultuid, jr.direction as direction, jr.patternendtime as identified, jr.patternlengthbars as length, jr.patternstarttime as patternstarttime, case when jr.trendchangeid = ? then ? else ? end as trendchange, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, jp.patternname as pattern_name, dtt.timezone as timezone, ? as age, cps.pip, g.basegroupname from japsticks_results jr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = jr.symbolid inner join relevance_japsticks_results rar on rar.resultuid = jr.resultuid inner join symbols s on jr.symbolid = s.symbolid and s.nonliquid = ? inner join japsticks_patterns jp on jr.patternid = jp.id inner join downloadersymbolsettings dss on jr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on s.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where jr.gmttimefound > now() - interval ? and s.deleted = ? and (jr.simulation = ? or jr.simulation is null) and (rar.relevant = ?) --and (semicolon_age = ? or rar.age <= semicolon_age) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or jp.patternname in (...)) and (? = ? or jr.patternlengthbars <= ?) ), results as ( select distinct on (symbolid) * from all_results order by symbolid, resultuid ) select * from results order by identified desc, length desc ; jr.resultuid AS resultuid, jr.direction AS direction, jr.patternendtime AS identified, jr.patternlengthbars AS length, jr.patternstarttime AS patternstarttime, case when jr.trendchangeid = 1 then 'Continuation' else 'Reversal' end AS trendchange, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, jp.patternname AS pattern_name, dtt.timezone AS timezone, 0 AS age, cps.pip, g.basegroupname FROM japsticks_results jr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $1 AND bsl.symbolid = jr.symbolid INNER JOIN relevance_japsticks_results rar ON rar.resultuid = jr.resultuid INNER JOIN symbols s ON jr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN japsticks_patterns jp ON jr.patternid = jp.id INNER JOIN downloadersymbolsettings dss ON jr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN symbolgroup sg on s.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' WHERE jr.gmttimefound > now() - INTERVAL '7 DAYS' AND s.deleted = 0 AND (jr.simulation = 0 OR jr.simulation IS NULL) AND (rar.relevant = 1) --AND (semicolon_age = 0 OR rar.age <= semicolon_age) AND ($2 = 0 OR s.timegranularity in ($3)) AND ($4 = 0 OR s.exchange in ($5)) AND ($6 = 0 OR coalesce(bim.code, s.symbol) in ($7)) AND ($8 = 0 OR jp.patternname in ($9)) AND ($10 = 0 OR jr.patternlengthbars <= $11)), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-02-24 09:01:03 Duration: 0ms
4	30	1.65 GiB	2.34 MiB	186.60 MiB	56.48 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = ? ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = ? ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = ?) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, ?::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> ? ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = ?) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = ? where (ok.r is null or ok.r = ?) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = ?) and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > ? ? and last.eventtimestamp > current_timestamp - interval ? and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval ?) and last.eventtimestamp > current_timestamp - interval ? and broker.r = ?; with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; Date: 2026-02-24 09:00:08 Duration: 0ms
5	16	621.62 MiB	38.85 MiB	38.85 MiB	38.85 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: 2026-02-24 09:01:13 Duration: 0ms
6	16	1.15 GiB	73.55 MiB	73.56 MiB	73.56 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: 2026-02-24 09:01:16 Duration: 0ms
7	8	1.06 GiB	135.48 MiB	135.54 MiB	135.52 MiB	select updateresultsmaterializedview (); select updateresultsmaterializedview (); Date: 2026-02-24 09:02:16 Duration: 0ms
8	4	343.77 MiB	85.88 MiB	86.01 MiB	85.94 MiB	select updateageforrelevantresults (); select updateageforrelevantresults (); Date: 2026-02-24 09:02:07 Duration: 0ms
9	1	3.29 MiB	3.29 MiB	3.29 MiB	3.29 MiB	from downloadersymbolsettings dss inner join symbols s on dss.symbolid = s.symbolid where dss.classname = ? and s.deleted = ? and dss.enabled = ?; FROM downloadersymbolsettings dss INNER JOIN symbols s ON dss.symbolid = s.symbolid WHERE dss.classname = $1 AND s.deleted = 0 AND dss.enabled = 1; Date: 2026-02-24 09:10:21 Duration: 0ms

Queries generating the largest temporary files

Rank	Size	Query
1	186.60 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-02-24 09:40:04 ]
2	168.45 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-02-24 09:30:06 ]
3	135.54 MiB	select updateresultsmaterializedview (); [ Date: 2026-02-24 09:47:14 ]
4	135.54 MiB	select updateresultsmaterializedview (); [ Date: 2026-02-24 09:32:17 ]
5	135.53 MiB	select updateresultsmaterializedview (); [ Date: 2026-02-24 09:50:32 ]
6	135.52 MiB	select updateresultsmaterializedview (); [ Date: 2026-02-24 09:35:33 ]
7	135.52 MiB	select updateresultsmaterializedview (); [ Date: 2026-02-24 09:02:16 ]
8	135.51 MiB	select updateresultsmaterializedview (); [ Date: 2026-02-24 09:17:12 ]
9	135.50 MiB	select updateresultsmaterializedview (); [ Date: 2026-02-24 09:20:33 ]
10	135.48 MiB	select updateresultsmaterializedview (); [ Date: 2026-02-24 09:05:33 ]
11	113.81 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-02-24 09:00:06 ]
12	90.24 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-02-24 09:20:04 ]
13	89.42 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-02-24 09:50:06 ]
14	86.34 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-02-24 09:10:07 ]
15	86.17 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-02-24 09:10:04 ]
16	86.01 MiB	select updateageforrelevantresults (); [ Date: 2026-02-24 09:02:07 ]
17	85.97 MiB	select updateageforrelevantresults (); [ Date: 2026-02-24 09:32:07 ]
18	85.92 MiB	select updateageforrelevantresults (); [ Date: 2026-02-24 09:47:05 ]
19	85.88 MiB	select updateageforrelevantresults (); [ Date: 2026-02-24 09:17:04 ]
20	74.62 MiB	with rankedmt4 as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select from rankedmt4 where r = 1 ), ok_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select , row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select , row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1; [ Date: 2026-02-24 09:50:06 ]

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)
59 analyzes Total

Chart
Table

Table	Number of analyzes
acaweb_fx.public.solr_relevance_old	16
acaweb_fx.pg_catalog.pg_attribute	8
acaweb_fx.public.relevance_keylevels_results	4
acaweb_fx.pg_catalog.pg_class	4
acaweb_fx.public.relevance_fibonacci_results	4
acaweb_fx.pg_catalog.pg_type	4
acaweb_fx.public.relevance_autochartist_results	4
acaweb_fx.public.datafeeds_latestrun	3
acaweb_fx.pg_catalog.pg_index	2
acaweb_fx.public.latest_t15_candle_view	2
acaweb_fx.pg_catalog.pg_depend	2
acaweb_fx.public.t60	1
acaweb_fx.public.autochartist_symbolupdates	1
acaweb_fx.public.solr_imports	1
socialmedia.public.processes	1
acaweb_fx.public.latest_candle_datetime_per_receng	1
acaweb_fx.public.symbollatestupdatetime	1
Total	59

Vacuums per table

Key values

public.solr_relevance_old (17) Main table vacuumed on database acaweb_fx
49 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	17	16	13,178	0	50	0	0	9,199	16	1,637,649
acaweb_fx.pg_catalog.pg_attribute	4	4	3,123	0	618	0	268	1,347	522	2,957,227
acaweb_fx.public.relevance_keylevels_results	4	4	14,551	0	426	7	362	2,738	898	2,751,672
acaweb_fx.public.relevance_fibonacci_results	4	4	4,824	0	105	3	164	664	70	272,359
acaweb_fx.pg_catalog.pg_type	3	3	458	0	101	0	0	224	60	323,312
acaweb_fx.public.datafeeds_latestrun	3	0	360	0	5	0	0	39	24	50,658
acaweb_fx.pg_catalog.pg_class	3	3	1,378	0	131	0	0	387	113	628,997
acaweb_fx.public.relevance_autochartist_results	3	3	9,884	0	181	4	745	1,263	510	1,431,208
acaweb_fx.pg_toast.pg_toast_2619	2	2	285	0	58	0	0	193	54	204,601
acaweb_fx.public.latest_t15_candle_view	2	2	137	0	6	0	0	13	5	20,318
acaweb_fx.pg_catalog.pg_index	1	1	105	0	11	0	0	28	10	75,218
acaweb_fx.public.autochartist_symbolupdates	1	1	23,104	0	394	2	38,435	5,799	366	765,107
acaweb_fx.pg_catalog.pg_statistic	1	1	967	0	197	0	594	462	186	747,900
acaweb_fx.public.solr_imports	1	1	65	0	1	0	0	6	1	8,923
Total	49	45	72,419	59,733	2,284	16	40,568	22,362	2,835	11,875,149

Tuples removed per table

Key values

public.solr_relevance_old (68072) Main table with removed tuples on database acaweb_fx
83745 tuples Total removed

Chart
Table

		Index	Tuples			Pages
Table	Vacuums	scans	removed	remain	not yet removable	removed	remain
acaweb_fx.public.solr_relevance_old	17	16	68,072	102,476	6,107	0	3,278
acaweb_fx.pg_catalog.pg_attribute	4	4	5,872	43,044	252	0	1,048
acaweb_fx.public.autochartist_symbolupdates	1	1	5,386	51,347	2	0	40,691
acaweb_fx.public.relevance_keylevels_results	4	4	1,549	53,303	3,359	0	1,116
acaweb_fx.pg_catalog.pg_type	3	3	851	4,416	72	2	132
acaweb_fx.pg_catalog.pg_statistic	1	1	536	3,805	36	0	1,194
acaweb_fx.pg_catalog.pg_class	3	3	336	5,153	203	0	450
acaweb_fx.public.relevance_fibonacci_results	4	4	332	7,315	553	0	408
acaweb_fx.public.relevance_autochartist_results	3	3	329	27,127	1,959	0	1,140
acaweb_fx.public.datafeeds_latestrun	3	0	163	42	0	0	48
acaweb_fx.pg_toast.pg_toast_2619	2	2	141	341	13	0	106
acaweb_fx.public.latest_t15_candle_view	2	2	109	26	0	0	2
acaweb_fx.public.solr_imports	1	1	51	1	0	0	2
acaweb_fx.pg_catalog.pg_index	1	1	18	813	0	0	22
Total	49	45	83,745	299,209	12,556	2	49,637

Pages removed per table

Key values

pg_catalog.pg_type (2) Main table with removed pages on database acaweb_fx
2 pages Total removed

Chart
Table

Table	Number of vacuums	Index scans	Tuples removed	Pages removed
acaweb_fx.pg_catalog.pg_type	3	3	851	2
acaweb_fx.pg_toast.pg_toast_2619	2	2	141	0
acaweb_fx.pg_catalog.pg_index	1	1	18	0
acaweb_fx.public.datafeeds_latestrun	3	0	163	0
acaweb_fx.public.autochartist_symbolupdates	1	1	5386	0
acaweb_fx.pg_catalog.pg_statistic	1	1	536	0
acaweb_fx.public.solr_imports	1	1	51	0
acaweb_fx.pg_catalog.pg_attribute	4	4	5872	0
acaweb_fx.public.latest_t15_candle_view	2	2	109	0
acaweb_fx.public.relevance_keylevels_results	4	4	1549	0
acaweb_fx.pg_catalog.pg_class	3	3	336	0
acaweb_fx.public.solr_relevance_old	17	16	68072	0
acaweb_fx.public.relevance_autochartist_results	3	3	329	0
acaweb_fx.public.relevance_fibonacci_results	4	4	332	0
Total	49	45	83,745	2

Autovacuum Activity

Vacuums / Analyzes

Day	Hour	VACUUMs	ANALYZEs
Feb 24	09	49	59

↑ 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

99,081 Total read queries
38,602 Total write queries

Chart
Table

Type	Count	Percentage
SELECT	99,081	34.89%
INSERT	25,549	9.00%
UPDATE	3,381	1.19%
DELETE	16	0.01%
COPY FROM	96	0.03%
COPY TO	26	0.01%
CTE	8,784	3.09%
DDL	16	0.01%
TCL	734	0.26%
CURSOR	0	0.00%
OTHERS	146,291	51.52%

Queries by database

Key values

unknown Main database
283,054 Requests
2h18m6s (unknown)
Main time consuming database

Chart
Table

Database	Request type	Count	Duration
acaweb_fx	Total	829	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	145	0ms
	select	72	0ms
	tcl	331	0ms
	update	39	0ms
socialmedia	Total	91	0ms
	others	6	0ms
	select	79	0ms
	tcl	6	0ms
unknown	Total	283,054	2h18m6s
	copy from	16	0ms
	cte	8,680	0ms
	insert	25,549	0ms
	others	4,356	0ms
	select	98,930	0ms
	tcl	397	0ms
	update	3,342	0ms

Queries by user

Key values

unknown Main user
283,054 Requests

Queries Chart
Table

User	Request type	Count	Duration
postgres	Total	920	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	151	0ms
	select	151	0ms
	tcl	337	0ms
	update	39	0ms
unknown	Total	283,054	2h18m6s
	copy from	16	0ms
	cte	8,680	0ms
	insert	25,549	0ms
	others	4,356	0ms
	select	98,930	0ms
	tcl	397	0ms
	update	3,342	0ms

Duration by user

Key values

2h18m6s (unknown) Main time consuming user

Duration Chart
Table

User	Request type	Count	Duration
postgres	Total	920	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	151	0ms
	select	151	0ms
	tcl	337	0ms
	update	39	0ms
unknown	Total	283,054	2h18m6s
	copy from	16	0ms
	cte	8,680	0ms
	insert	25,549	0ms
	others	4,356	0ms
	select	98,930	0ms
	tcl	397	0ms
	update	3,342	0ms

Queries by host

Key values

unknown Main host
283,974 Requests
2h18m6s (unknown)
Main time consuming host

Chart
Table

Host	Request type	Count	Duration
unknown	Total	283,974	2h18m6s
	copy from	96	0ms
	copy to	26	0ms
	cte	8,784	0ms
	ddl	16	0ms
	delete	16	0ms
	insert	25,549	0ms
	others	4,507	0ms
	select	99,081	0ms
	tcl	734	0ms
	update	3,381	0ms

Queries by application

Key values

unknown Main application
283,617 Requests
2h18m6s (unknown)
Main time consuming application

Chart
Table

Application	Request type	Count	Duration
psql	Total	357	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	4	0ms
	select	72	0ms
	update	39	0ms
unknown	Total	283,617	2h18m6s
	copy from	16	0ms
	cte	8,680	0ms
	insert	25,549	0ms
	others	4,503	0ms
	select	99,009	0ms
	tcl	734	0ms
	update	3,342	0ms

Number of cancelled queries

Key values

0 per second Cancelled query Peak
2026-02-24 09:51:40 Date

Number of cancelled queries (5 minutes period)

NO DATASET

Histogram of query times

Key values

94,817 0-1ms duration

Chart
Table

Range	Count	Percentage
0-1ms	94,817	66.87%
1-5ms	39,820	28.08%
5-10ms	3,342	2.36%
10-25ms	1,808	1.28%
25-50ms	253	0.18%
50-100ms	161	0.11%
100-500ms	397	0.28%
500-1000ms	132	0.09%
1000-10000ms	843	0.59%
> 10000ms	211	0.15%

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

26

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 24	09	26	0ms	0ms

2

0ms

1

Details

0ms

select distinct "public"."processes"."live" AS "live" from "public"."processes" left outer join "public"."brokers" "LT?" on "LT?"."id" = "public"."processes"."brokerid" left outer join "public"."contenttypes" "LT?" on "LT?"."id" = "public"."processes"."contenttypeid" where "public"."processes"."id" = ? and "public"."processes"."id" = ? order by ? asc;

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Feb 24	09	1	0ms	0ms

3

0ms

2,006

Details

0ms

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

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Feb 24	09	2,006	0ms	0ms

4

0ms

33

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 #4

Day	Hour	Count	Duration	Avg duration
Feb 24	09	33	0ms	0ms

5

0ms

4

Details

0ms

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

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Feb 24	09	4	0ms	0ms

6

0ms

4

Details

0ms

select updaterelevantforrelevantresults ();

Times Reported Time consuming queries #6

Day	Hour	Count	Duration	Avg duration
Feb 24	09	4	0ms	0ms

7

0ms

6

Details

0ms

set datestyle = iso;

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Feb 24	09	6	0ms	0ms

8

0ms

1

Details

0ms

update "public"."processes" set "locale" = ?, "region" = ?, "schedule" = ? where "id" = ?;

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Feb 24	09	1	0ms	0ms

9

0ms

6

Details

0ms

set client_encoding to ?;

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Feb 24	09	6	0ms	0ms

10

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 #10

Day	Hour	Count	Duration	Avg duration
Feb 24	09	18	0ms	0ms

11

0ms

1

Details

0ms

select distinct "public"."processes"."enabled" AS "enabled" from "public"."processes" left outer join "public"."brokers" "LT?" on "LT?"."id" = "public"."processes"."brokerid" left outer join "public"."contenttypes" "LT?" on "LT?"."id" = "public"."processes"."contenttypeid" where "public"."processes"."id" = ? and "public"."processes"."id" = ? order by ? asc;

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Feb 24	09	1	0ms	0ms

12

0ms

367

Details

0ms

commit;

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Feb 24	09	367	0ms	0ms

13

0ms

1

Details

0ms

select "public"."processes"."id" AS "id", "public"."processes"."locale" AS "locale", "public"."processes"."region" AS "region", "public"."processes"."schedule" AS "schedule", "public"."processes"."enabled" AS "enabled", "public"."processes"."live" AS "live", "public"."processes"."lastmodified" AS "lastmodified", "public"."processes"."lastrun" AS "lastrun", "public"."processes"."contenttypeid" AS "contenttypeid", "public"."processes"."brokerid" AS "brokerid", "public"."processes"."uuid" AS "uuid", "LT?"."name" AS "LA?", "LT?"."name" AS "LA?" from "public"."processes" left outer join "public"."brokers" "LT?" on "LT?"."id" = "public"."processes"."brokerid" left outer join "public"."contenttypes" "LT?" on "LT?"."id" = "public"."processes"."contenttypeid" where "public"."processes"."id" = ? and "public"."processes"."id" = ? and (brokerid = ?) order by "public"."processes"."id" asc limit ? offset ?;

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Feb 24	09	1	0ms	0ms

14

0ms

324

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 24	09	324	0ms	0ms

15

0ms

240

Details

0ms

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

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Feb 24	09	240	0ms	0ms

16

0ms

240

Details

0ms

select system_identifier from pg_control_system ();

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Feb 24	09	240	0ms	0ms

17

0ms

9

Details

0ms

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

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Feb 24	09	9	0ms	0ms

18

0ms

7

Details

0ms

select "public"."processes"."id" AS "id", "public"."processes"."locale" AS "locale", "public"."processes"."region" AS "region", "public"."processes"."schedule" AS "schedule", "public"."processes"."enabled" AS "enabled", "public"."processes"."live" AS "live", "public"."processes"."lastmodified" AS "lastmodified", "public"."processes"."lastrun" AS "lastrun", "public"."processes"."contenttypeid" AS "contenttypeid", "public"."processes"."brokerid" AS "brokerid", "public"."processes"."uuid" AS "uuid", "LT?"."name" AS "LA?", "LT?"."name" AS "LA?" from "public"."processes" left outer join "public"."brokers" "LT?" on "LT?"."id" = "public"."processes"."brokerid" left outer join "public"."contenttypes" "LT?" on "LT?"."id" = "public"."processes"."contenttypeid" where "public"."processes"."id" = ?;

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Feb 24	09	7	0ms	0ms

19

0ms

324

Details

0ms

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

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Feb 24	09	324	0ms	0ms

20

0ms

43

Details

0ms

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

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Feb 24	09	43	0ms	0ms

Most frequent queries (N)

Rank

Times executed

Total duration

Min duration

Max duration

Avg duration

Query

1

37,458

Details

0ms

select ?;

Times Reported Time consuming queries #1

Day	Hour	Count	Duration	Avg duration
Feb 24	09	37,458	0ms	0ms

2

12,882

Details

0ms

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

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Feb 24	09	12,882	0ms	0ms

3

11,048

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 #3

Day	Hour	Count	Duration	Avg duration
Feb 24	09	11,048	0ms	0ms

4

5,203

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 #4

Day	Hour	Count	Duration	Avg duration
Feb 24	09	5,203	0ms	0ms

5

4,741

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 #5

Day	Hour	Count	Duration	Avg duration
Feb 24	09	4,741	0ms	0ms

6

4,692

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 #6

Day	Hour	Count	Duration	Avg duration
Feb 24	09	4,692	0ms	0ms

7

4,358

Details

0ms

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

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Feb 24	09	4,358	0ms	0ms

8

3,512

Details

0ms

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

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Feb 24	09	3,512	0ms	0ms

9

3,357

Details

0ms

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

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Feb 24	09	3,357	0ms	0ms

10

3,148

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 #10

Day	Hour	Count	Duration	Avg duration
Feb 24	09	3,148	0ms	0ms

11

2,996

Details

0ms

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

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Feb 24	09	2,996	0ms	0ms

12

2,917

Details

0ms

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

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Feb 24	09	2,917	0ms	0ms

13

2,586

Details

0ms

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

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Feb 24	09	2,586	0ms	0ms

14

2,287

Details

0ms

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

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Feb 24	09	2,287	0ms	0ms

15

2,006

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 #15

Day	Hour	Count	Duration	Avg duration
Feb 24	09	2,006	0ms	0ms

16

1,987

Details

0ms

set extra_float_digits = ?;

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Feb 24	09	1,987	0ms	0ms

17

1,934

Details

0ms

set application_name = ?;

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Feb 24	09	1,934	0ms	0ms

18

1,740

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 #18

Day	Hour	Count	Duration	Avg duration
Feb 24	09	1,740	0ms	0ms

19

1,700

Details

0ms

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

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Feb 24	09	1,700	0ms	0ms

20

1,200

Details

0ms

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

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Feb 24	09	1,200	0ms	0ms

Normalized slowest queries (N)

Rank

Min duration

Max duration

Avg duration

Times executed

Total duration

Query

1

0ms

26

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 24	09	26	0ms	0ms

2

0ms

1

Details

0ms

select distinct "public"."processes"."live" AS "live" from "public"."processes" left outer join "public"."brokers" "LT?" on "LT?"."id" = "public"."processes"."brokerid" left outer join "public"."contenttypes" "LT?" on "LT?"."id" = "public"."processes"."contenttypeid" where "public"."processes"."id" = ? and "public"."processes"."id" = ? order by ? asc;

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Feb 24	09	1	0ms	0ms

3

0ms

2,006

Details

0ms

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

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Feb 24	09	2,006	0ms	0ms

4

0ms

33

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 #4

Day	Hour	Count	Duration	Avg duration
Feb 24	09	33	0ms	0ms

5

0ms

4

Details

0ms

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

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Feb 24	09	4	0ms	0ms

6

0ms

4

Details

0ms

select updaterelevantforrelevantresults ();

Times Reported Time consuming queries #6

Day	Hour	Count	Duration	Avg duration
Feb 24	09	4	0ms	0ms

7

0ms

6

Details

0ms

set datestyle = iso;

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Feb 24	09	6	0ms	0ms

8

0ms

1

Details

0ms

update "public"."processes" set "locale" = ?, "region" = ?, "schedule" = ? where "id" = ?;

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Feb 24	09	1	0ms	0ms

9

0ms

6

Details

0ms

set client_encoding to ?;

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Feb 24	09	6	0ms	0ms

10

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 #10

Day	Hour	Count	Duration	Avg duration
Feb 24	09	18	0ms	0ms

11

0ms

1

Details

0ms

select distinct "public"."processes"."enabled" AS "enabled" from "public"."processes" left outer join "public"."brokers" "LT?" on "LT?"."id" = "public"."processes"."brokerid" left outer join "public"."contenttypes" "LT?" on "LT?"."id" = "public"."processes"."contenttypeid" where "public"."processes"."id" = ? and "public"."processes"."id" = ? order by ? asc;

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Feb 24	09	1	0ms	0ms

12

0ms

367

Details

0ms

commit;

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Feb 24	09	367	0ms	0ms

13

0ms

1

Details

0ms

select "public"."processes"."id" AS "id", "public"."processes"."locale" AS "locale", "public"."processes"."region" AS "region", "public"."processes"."schedule" AS "schedule", "public"."processes"."enabled" AS "enabled", "public"."processes"."live" AS "live", "public"."processes"."lastmodified" AS "lastmodified", "public"."processes"."lastrun" AS "lastrun", "public"."processes"."contenttypeid" AS "contenttypeid", "public"."processes"."brokerid" AS "brokerid", "public"."processes"."uuid" AS "uuid", "LT?"."name" AS "LA?", "LT?"."name" AS "LA?" from "public"."processes" left outer join "public"."brokers" "LT?" on "LT?"."id" = "public"."processes"."brokerid" left outer join "public"."contenttypes" "LT?" on "LT?"."id" = "public"."processes"."contenttypeid" where "public"."processes"."id" = ? and "public"."processes"."id" = ? and (brokerid = ?) order by "public"."processes"."id" asc limit ? offset ?;

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Feb 24	09	1	0ms	0ms

14

0ms

324

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 24	09	324	0ms	0ms

15

0ms

240

Details

0ms

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

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Feb 24	09	240	0ms	0ms

16

0ms

240

Details

0ms

select system_identifier from pg_control_system ();

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Feb 24	09	240	0ms	0ms

17

0ms

9

Details

0ms

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

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Feb 24	09	9	0ms	0ms

18

0ms

7

Details

0ms

select "public"."processes"."id" AS "id", "public"."processes"."locale" AS "locale", "public"."processes"."region" AS "region", "public"."processes"."schedule" AS "schedule", "public"."processes"."enabled" AS "enabled", "public"."processes"."live" AS "live", "public"."processes"."lastmodified" AS "lastmodified", "public"."processes"."lastrun" AS "lastrun", "public"."processes"."contenttypeid" AS "contenttypeid", "public"."processes"."brokerid" AS "brokerid", "public"."processes"."uuid" AS "uuid", "LT?"."name" AS "LA?", "LT?"."name" AS "LA?" from "public"."processes" left outer join "public"."brokers" "LT?" on "LT?"."id" = "public"."processes"."brokerid" left outer join "public"."contenttypes" "LT?" on "LT?"."id" = "public"."processes"."contenttypeid" where "public"."processes"."id" = ?;

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Feb 24	09	7	0ms	0ms

19

0ms

324

Details

0ms

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

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Feb 24	09	324	0ms	0ms

20

0ms

43

Details

0ms

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

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Feb 24	09	43	0ms	0ms

Time consuming prepare


Date: 2026-02-24 09:15:37
Duration: 17ms
Database: postgres
Date: 2026-02-24 09:02:55
Duration: 9ms
Database: postgres
Date: 2026-02-24 09:15:14
Duration: 6ms
Database: postgres
Date: 2026-02-24 09:30:33
Duration: 2ms
Database: postgres
Date: 2026-02-24 09:07:15
Duration: 8ms
Database: postgres
Date: 2026-02-24 09:30:46
Duration: 6ms
Database: postgres
Date: 2026-02-24 09:11:46
Duration: 0ms
Database: postgres
Date: 2026-02-24 09:56:53
Duration: 0ms
Database: postgres
Date: 2026-02-24 09:02:32
Duration: 0ms
Database: postgres
Date: 2026-02-24 09:45:31
Duration: 2ms
Database: postgres
Date: 2026-02-24 09:20:30
Duration: 5ms
Database: postgres
Date: 2026-02-24 09:51:47
Duration: 7ms
Database: postgres
Date: 2026-02-24 09:25:50
Duration: 1ms
Database: postgres
Date: 2026-02-24 09:03:24
Duration: 2ms
Database: postgres
Date: 2026-02-24 09:56:14
Duration: 1ms
Database: postgres
Date: 2026-02-24 09:45:41
Duration: 1ms
Database: postgres
Date: 2026-02-24 09:41:03
Duration: 2ms
Database: postgres
Date: 2026-02-24 09:00:40
Duration: 8ms
Database: postgres
Date: 2026-02-24 09:10:36
Duration: 3ms
Database: postgres
Date: 2026-02-24 09:00:04
Duration: 4ms
Database: postgres

Time consuming bind

Rank

Total duration

Times executed

Min duration

Max duration

Avg duration

Query

1

35s365ms

7,646

Details

0ms

52ms

4ms

WITH rar_max as ( ;

Times Reported Time consuming bind #1

Day	Hour	Count	Duration	Avg duration
Feb 24	09	7,646	35s365ms	4ms

WITH rar_max as ( ;

Date: 2026-02-24 09:02:35 Duration: 52ms Database: postgres parameters: $1 = 't', $2 = '627', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '213', $14 = '#ADBE', $15 = '#ALVG', $16 = '#AMZN', $17 = '#APPL', $18 = '#BA', $19 = '#BABA', $20 = '#BAYGn', $21 = '#BMWG', $22 = '#BNPP', $23 = '#CAT', $24 = '#CBKG', $25 = '#DAIGn', $26 = '#DIS', $27 = '#EA', $28 = '#FB', $29 = '#FDX', $30 = '#GE', $31 = '#GM', $32 = '#GOOGL', $33 = '#GS', $34 = '#INTC', $35 = '#JPM', $36 = '#KO', $37 = '#META', $38 = '#MSFT', $39 = '#NFLX', $40 = '#TSLA', $41 = '#VOWG', $42 = '#WMT', $43 = '#XOM', $44 = 'AUDCAD', $45 = 'AUDCHF', $46 = 'AUDJPY', $47 = 'AUDNZD', $48 = 'AUDUSD', $49 = 'AUS_200', $50 = 'BTCEUR', $51 = 'BTCGBP', $52 = 'BTCUSD', $53 = 'CADCHF', $54 = 'CADJPY', $55 = 'CHFJPY', $56 = 'CL_BRENT', $57 = 'DASHUSD', $58 = 'EOSUSD', $59 = 'ESP_35', $60 = 'ETHEUR', $61 = 'ETHGBP', $62 = 'ETHUSD', $63 = 'EURAUD', $64 = 'EURCAD', $65 = 'EURCHF', $66 = 'EURGBP', $67 = 'EURJPY', $68 = 'EURMXN', $69 = 'EURNOK', $70 = 'EURNZD', $71 = 'EURPLN', $72 = 'EURSEK', $73 = 'EURTRY', $74 = 'EURUSD', $75 = 'EUR_50', $76 = 'FRA_40', $77 = 'GBPAUD', $78 = 'GBPCAD', $79 = 'GBPCHF', $80 = 'GBPJPY', $81 = 'GBPNZD', $82 = 'GBPUSD', $83 = 'GBPZAR', $84 = 'GBR_100', $85 = 'HKDJPY', $86 = 'HKG_50', $87 = 'IOTAUSD', $88 = 'LTCEUR', $89 = 'LTCUSD', $90 = 'NAS100', $91 = 'NEOUSD', $92 = 'NOKJPY', $93 = 'NZDCAD', $94 = 'NZDCHF', $95 = 'NZDJPY', $96 = 'NZDUSD', $97 = 'OMGUSD', $98 = 'SPX500', $99 = 'TRXUSD', $100 = 'US30', $101 = 'USDCAD', $102 = 'USDCHF', $103 = 'USDCNH', $104 = 'USDDKK', $105 = 'USDJPY', $106 = 'USDMXN', $107 = 'USDNOK', $108 = 'USDPLN', $109 = 'USDSEK', $110 = 'USDSGD', $111 = 'USDZAR', $112 = 'USOIL', $113 = 'XAGUSD', $114 = 'XAUEUR', $115 = 'XAUUSD', $116 = 'XMRUSD', $117 = 'XPTUSD', $118 = 'XRPUSD', $119 = 'ZARJPY', $120 = 'ZECUSD', $121 = 'AUDCAD', $122 = 'AUDCHF', $123 = 'AUDJPY', $124 = 'AUDNZD', $125 = 'AUDUSD', $126 = 'CADCHF', $127 = 'CADJPY', $128 = 'CHFJPY', $129 = 'EURAUD', $130 = 'EURCAD', $131 = 'EURCHF', $132 = 'EURGBP', $133 = 'EURJPY', $134 = 'EURMXN', $135 = 'EURNOK', $136 = 'EURNZD', $137 = 'EURPLN', $138 = 'EURSEK', $139 = 'EURTRY', $140 = 'EURUSD', $141 = 'GBPAUD', $142 = 'GBPCAD', $143 = 'GBPCHF', $144 = 'GBPJPY', $145 = 'GBPNZD', $146 = 'GBPUSD', $147 = 'GBPZAR', $148 = 'HKDJPY', $149 = 'NOKJPY', $150 = 'NZDCAD', $151 = 'NZDCHF', $152 = 'NZDJPY', $153 = 'NZDUSD', $154 = 'USDCAD', $155 = 'USDCHF', $156 = 'USDCNH', $157 = 'USDDKK', $158 = 'USDJPY', $159 = 'USDMXN', $160 = 'USDNOK', $161 = 'USDPLN', $162 = 'USDSEK', $163 = 'USDSGD', $164 = 'USDZAR', $165 = 'ZARJPY', $166 = 'BTCEUR', $167 = 'BTCGBP', $168 = 'BTCUSD', $169 = 'DASHUSD', $170 = 'EOSUSD', $171 = 'ETHEUR', $172 = 'ETHGBP', $173 = 'ETHUSD', $174 = 'IOTAUSD', $175 = 'LTCEUR', $176 = 'LTCUSD', $177 = 'NEOUSD', $178 = 'OMGUSD', $179 = 'TRXUSD', $180 = 'XMRUSD', $181 = 'XRPUSD', $182 = 'ZECUSD', $183 = 'XAGUSD', $184 = 'XAUEUR', $185 = 'XAUUSD', $186 = 'XPTUSD', $187 = 'CL_BRENT', $188 = 'USOIL', $189 = '#ALVG', $190 = '#BAYGn', $191 = '#BMWG', $192 = '#BNPP', $193 = '#CBKG', $194 = '#DAIGn', $195 = '#VOWG', $196 = 'AUS_200', $197 = 'ESP_35', $198 = 'EUR_50', $199 = 'FRA_40', $200 = 'GBR_100', $201 = 'HKG_50', $202 = 'NAS100', $203 = 'SPX500', $204 = 'US30', $205 = '#ADBE', $206 = '#AMZN', $207 = '#APPL', $208 = '#BA', $209 = '#BABA', $210 = '#CAT', $211 = '#DIS', $212 = '#EA', $213 = '#FB', $214 = '#FDX', $215 = '#GE', $216 = '#GM', $217 = '#GOOGL', $218 = '#GS', $219 = '#INTC', $220 = '#JPM', $221 = '#KO', $222 = '#MSFT', $223 = '#NFLX', $224 = '#TSLA', $225 = '#WMT', $226 = '#XOM', $227 = '0', $228 = '', $229 = '0', $230 = '0', $231 = '0', $232 = '500', $233 = '500', $234 = 't', $235 = '10', $236 = '10'
WITH rar_max as ( ;

Date: 2026-02-24 09:02:20 Duration: 47ms Database: postgres parameters: $1 = 't', $2 = '621', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '1', $14 = 'EURUSD', $15 = '0', $16 = '', $17 = '0', $18 = '0', $19 = '0', $20 = '0', $21 = '0', $22 = 't', $23 = '10', $24 = '10'
WITH rar_max as ( ;

Date: 2026-02-24 09:21:02 Duration: 44ms Database: postgres parameters: $1 = 't', $2 = '689', $3 = '0', $4 = '0', $5 = '0', $6 = '', $7 = '0', $8 = '', $9 = '0', $10 = '', $11 = '0', $12 = '0', $13 = '0', $14 = '0', $15 = '0', $16 = 't', $17 = '0', $18 = '0'

2

12s231ms

29,701

Details

0ms

15ms

0ms

SELECT ;

Times Reported Time consuming bind #2

Day	Hour	Count	Duration	Avg duration
	09	29,701	12s231ms	0ms

SELECT ;

Date: 2026-02-24 09:30:04 Duration: 15ms Database: postgres parameters: $1 = '0', $2 = '0', $3 = '515840243250972300'
SELECT ;

Date: 2026-02-24 09:30:04 Duration: 14ms Database: postgres parameters: $1 = '558', $2 = '0', $3 = '0', $4 = 'USDCZK', $5 = 'USDCZK'
SELECT ;

Date: 2026-02-24 09:00:01 Duration: 13ms Database: postgres parameters: $1 = '667', $2 = '667', $3 = '500991627559626200'

3

2s497ms

1,150

Details

1ms

15ms

2ms

SELECT symbolid, ;

Times Reported Time consuming bind #3

Day	Hour	Count	Duration	Avg duration
	09	1,150	2s497ms	2ms

SELECT symbolid, ;

Date: 2026-02-24 09:02:55 Duration: 15ms Database: postgres parameters: $1 = 'MILLENNIUMPF', $2 = '15', $3 = 'AUDJPY.ID'
SELECT symbolid, ;

Date: 2026-02-24 09:55:50 Duration: 8ms Database: postgres parameters: $1 = 'BDSWISS', $2 = '15', $3 = 'NAS100', $4 = 'NEOUSD'
SELECT symbolid, ;

Date: 2026-02-24 09:30:38 Duration: 6ms Database: postgres parameters: $1 = 'BDSWISS', $2 = '30', $3 = 'AUDNZD', $4 = 'AUDCHF', $5 = 'AUDJPY', $6 = 'BTCGBP', $7 = 'AUS_200', $8 = 'BTCEUR', $9 = 'AUDUSD'

4

1s371ms

752

Details

1ms

6ms

1ms

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

Times Reported Time consuming bind #4

Day	Hour	Count	Duration	Avg duration
	09	752	1s371ms	1ms

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

Date: 2026-02-24 09:30:47 Duration: 6ms Database: postgres parameters: $1 = 'AXIORY'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-02-24 09:30:33 Duration: 6ms Database: postgres parameters: $1 = 'ICMARKETS'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-02-24 09:30:35 Duration: 4ms Database: postgres parameters: $1 = 'AXIORY'

5

1s272ms

230

Details

0ms

25ms

5ms

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

Times Reported Time consuming bind #5

Day	Hour	Count	Duration	Avg duration
	09	230	1s272ms	5ms

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

Date: 2026-02-24 09:01:53 Duration: 25ms Database: postgres parameters: $1 = '1436', $2 = '1436'
select distinct s.statsid as statsid, sy.exchange as name;

Date: 2026-02-24 09:41:03 Duration: 24ms Database: postgres parameters: $1 = '1436', $2 = '1436'
select distinct s.statsid as statsid, sy.exchange as name;

Date: 2026-02-24 09:12:06 Duration: 21ms Database: postgres parameters: $1 = '1436', $2 = '1436'

6

743ms

37,348

Details

0ms

14ms

0ms

select 1;

Times Reported Time consuming bind #6

Day	Hour	Count	Duration	Avg duration
	09	37,348	743ms	0ms

select 1;

Date: 2026-02-24 09:35:35 Duration: 14ms Database: postgres
select 1;

Date: 2026-02-24 09:57:57 Duration: 9ms Database: postgres
select 1;

Date: 2026-02-24 09:50:41 Duration: 7ms Database: postgres

7

693ms

26

Details

0ms

54ms

26ms

with wh_patitioned as ( ;

Times Reported Time consuming bind #7

Day	Hour	Count	Duration	Avg duration
	09	26	693ms	26ms

with wh_patitioned as ( ;

Date: 2026-02-24 09:10:36 Duration: 54ms 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: 2026-02-24 09:03:15 Duration: 46ms 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: 2026-02-24 09:03:18 Duration: 46ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'

8

640ms

54

Details

0ms

20ms

11ms

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

Times Reported Time consuming bind #8

Day	Hour	Count	Duration	Avg duration
	09	54	640ms	11ms

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

Date: 2026-02-24 09:12:24 Duration: 20ms 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: 2026-02-24 09:59:03 Duration: 20ms 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: 2026-02-24 09:33:04 Duration: 20ms Database: postgres parameters: $1 = '689', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'

9

632ms

8,189

Details

0ms

1ms

0ms

select category, ;

Times Reported Time consuming bind #9

Day	Hour	Count	Duration	Avg duration
	09	8,189	632ms	0ms

select category, ;

Date: 2026-02-24 09:50:49 Duration: 1ms Database: postgres parameters: $1 = '515852059313898307', $2 = 'symbol', $3 = 'BTCUSD', $4 = 'AUDJPY', $5 = 'USDMXN', $6 = 'EURZAR', $7 = 'USDTHB', $8 = 'USDJPY', $9 = 'NZDJPY', $10 = 'GBPJPY', $11 = 'USDZAR', $12 = 'USDHUF', $13 = 'CHFJPY', $14 = 'CADJPY', $15 = 'SGDJPY', $16 = 'EURJPY', $17 = 'GBPSEK', $18 = 'EURHKD', $19 = 'SEKJPY', $20 = 'USDSEK', $21 = 'EURTRY', $22 = 'EURSEK', $23 = 'USDDKK', $24 = 'USDNOK', $25 = 'NOKJPY', $26 = 'GBPDKK', $27 = 'EURNOK', $28 = 'USDCZK', $29 = 'GBPNOK', $30 = 'USDPLN', $31 = 'BTCUSD', $32 = 'GBPAUD', $33 = 'GBPNZD', $34 = 'EURPLN', $35 = 'EURAUD', $36 = 'USDCNH', $37 = 'EURNZD', $38 = 'USDTRY', $39 = 'GBPCAD', $40 = 'EURGBP', $41 = 'USDCAD', $42 = 'SEKJPY', $43 = 'EURNOK', $44 = 'USDHUF', $45 = 'GBPCAD', $46 = 'NOKJPY', $47 = 'GBPNOK', $48 = 'GBPSGD', $49 = 'CHFSGD', $50 = 'EURCAD', $51 = 'USDZAR', $52 = 'USDMXN', $53 = '515852059313898307', $54 = 'symbol', $55 = 'BTCUSD', $56 = 'AUDJPY', $57 = 'USDMXN', $58 = 'EURZAR', $59 = 'USDTHB', $60 = 'USDJPY', $61 = 'NZDJPY', $62 = 'GBPJPY', $63 = 'USDZAR', $64 = 'USDHUF', $65 = 'CHFJPY', $66 = 'CADJPY', $67 = 'SGDJPY', $68 = 'EURJPY', $69 = 'GBPSEK', $70 = 'EURHKD', $71 = 'SEKJPY', $72 = 'USDSEK', $73 = 'EURTRY', $74 = 'EURSEK', $75 = 'USDDKK', $76 = 'USDNOK', $77 = 'NOKJPY', $78 = 'GBPDKK', $79 = 'EURNOK', $80 = 'USDCZK', $81 = 'GBPNOK', $82 = 'USDPLN', $83 = 'BTCUSD', $84 = 'GBPAUD', $85 = 'GBPNZD', $86 = 'EURPLN', $87 = 'EURAUD', $88 = 'USDCNH', $89 = 'EURNZD', $90 = 'USDTRY', $91 = 'GBPCAD', $92 = 'EURGBP', $93 = 'USDCAD', $94 = 'SEKJPY', $95 = 'EURNOK', $96 = 'USDHUF', $97 = 'GBPCAD', $98 = 'NOKJPY', $99 = 'GBPNOK', $100 = 'GBPSGD', $101 = 'CHFSGD', $102 = 'EURCAD', $103 = 'USDZAR', $104 = 'USDMXN'
select category, ;

Date: 2026-02-24 09:50:35 Duration: 1ms Database: postgres parameters: $1 = '515852059305993307', $2 = 'symbol', $3 = 'XAUUSD', $4 = 'XNGUSD', $5 = 'XAGUSD', $6 = 'XAGEUR', $7 = 'US30', $8 = 'US2000', $9 = 'JP225', $10 = 'US500', $11 = 'UK100', $12 = 'XBRUSD', $13 = 'XTIUSD', $14 = 'XAUEUR', $15 = 'XPTUSD', $16 = 'USTEC', $17 = 'AUS200', $18 = 'XPDUSD', $19 = 'CHINA50', $20 = 'F40', $21 = 'STOXX50', $22 = 'IT40', $23 = 'XNGUSD', $24 = 'US2000', $25 = 'HK50', $26 = 'XPDUSD', $27 = 'XTIUSD', $28 = 'UK100', $29 = 'US30', $30 = 'XAGUSD', $31 = 'XBRUSD', $32 = 'XAUUSD', $33 = 'XAGEUR', $34 = 'XPTUSD', $35 = 'JP225', $36 = 'AUS200', $37 = 'USTEC', $38 = 'XAUEUR', $39 = 'US500', $40 = 'CHINA50', $41 = 'ES35', $42 = 'STOXX50', $43 = 'F40', $44 = 'HK50', $45 = 'ES35', $46 = 'IT40', $47 = '515852059305993307', $48 = 'symbol', $49 = 'XAUUSD', $50 = 'XNGUSD', $51 = 'XAGUSD', $52 = 'XAGEUR', $53 = 'US30', $54 = 'US2000', $55 = 'JP225', $56 = 'US500', $57 = 'UK100', $58 = 'XBRUSD', $59 = 'XTIUSD', $60 = 'XAUEUR', $61 = 'XPTUSD', $62 = 'USTEC', $63 = 'AUS200', $64 = 'XPDUSD', $65 = 'CHINA50', $66 = 'F40', $67 = 'STOXX50', $68 = 'IT40', $69 = 'XNGUSD', $70 = 'US2000', $71 = 'HK50', $72 = 'XPDUSD', $73 = 'XTIUSD', $74 = 'UK100', $75 = 'US30', $76 = 'XAGUSD', $77 = 'XBRUSD', $78 = 'XAUUSD', $79 = 'XAGEUR', $80 = 'XPTUSD', $81 = 'JP225', $82 = 'AUS200', $83 = 'USTEC', $84 = 'XAUEUR', $85 = 'US500', $86 = 'CHINA50', $87 = 'ES35', $88 = 'STOXX50', $89 = 'F40', $90 = 'HK50', $91 = 'ES35', $92 = 'IT40'
select category, ;

Date: 2026-02-24 09:59:08 Duration: 1ms Database: postgres parameters: $1 = '515852059324736307', $2 = 'symbol', $3 = 'USDMXN', $4 = 'CHFZAR', $5 = 'AUDJPY', $6 = 'CHFJPY', $7 = 'USDJPY', $8 = 'USDZAR', $9 = 'ZARJPY', $10 = 'CADJPY', $11 = 'NZDJPY', $12 = 'GBPZAR', $13 = 'EURCNH', $14 = 'AUDZAR', $15 = 'TRYJPY', $16 = 'EURMXN', $17 = 'USDHUF', $18 = 'GBPJPY', $19 = 'EURNOK', $20 = 'EURZAR', $21 = 'USDNOK', $22 = 'SGDJPY', $23 = 'EURSEK', $24 = 'USDCZK', $25 = 'EURHKD', $26 = 'CHFHUF', $27 = 'USDSEK', $28 = 'EURTRY', $29 = 'USDDKK', $30 = 'NZDSEK', $31 = 'EURJPY', $32 = 'EURHUF', $33 = 'USDPLN', $34 = 'GBPNZD', $35 = 'USDCNH', $36 = 'EURCZK', $37 = 'USDILS', $38 = 'EURPLN', $39 = 'EURGBP', $40 = 'GBPAUD', $41 = 'EURCZK', $42 = 'EURNZD', $43 = 'TRYJPY', $44 = 'EURAUD', $45 = 'ZARJPY', $46 = 'EURHUF', $47 = 'CHFHUF', $48 = 'GBPCAD', $49 = 'USDCAD', $50 = 'EURDKK', $51 = 'USDZAR', $52 = 'USDTRY', $53 = '515852059324736307', $54 = 'symbol', $55 = 'USDMXN', $56 = 'CHFZAR', $57 = 'AUDJPY', $58 = 'CHFJPY', $59 = 'USDJPY', $60 = 'USDZAR', $61 = 'ZARJPY', $62 = 'CADJPY', $63 = 'NZDJPY', $64 = 'GBPZAR', $65 = 'EURCNH', $66 = 'AUDZAR', $67 = 'TRYJPY', $68 = 'EURMXN', $69 = 'USDHUF', $70 = 'GBPJPY', $71 = 'EURNOK', $72 = 'EURZAR', $73 = 'USDNOK', $74 = 'SGDJPY', $75 = 'EURSEK', $76 = 'USDCZK', $77 = 'EURHKD', $78 = 'CHFHUF', $79 = 'USDSEK', $80 = 'EURTRY', $81 = 'USDDKK', $82 = 'NZDSEK', $83 = 'EURJPY', $84 = 'EURHUF', $85 = 'USDPLN', $86 = 'GBPNZD', $87 = 'USDCNH', $88 = 'EURCZK', $89 = 'USDILS', $90 = 'EURPLN', $91 = 'EURGBP', $92 = 'GBPAUD', $93 = 'EURCZK', $94 = 'EURNZD', $95 = 'TRYJPY', $96 = 'EURAUD', $97 = 'ZARJPY', $98 = 'EURHUF', $99 = 'CHFHUF', $100 = 'GBPCAD', $101 = 'USDCAD', $102 = 'EURDKK', $103 = 'USDZAR', $104 = 'USDTRY'

10

568ms

76

Details

4ms

14ms

7ms

WITH last_candle AS ( ;

Times Reported Time consuming bind #10

Day	Hour	Count	Duration	Avg duration
	09	76	568ms	7ms

WITH last_candle AS ( ;

Date: 2026-02-24 09:11:03 Duration: 14ms Database: postgres parameters: $1 = '538', $2 = '538'
WITH last_candle AS ( ;

Date: 2026-02-24 09:20:30 Duration: 13ms Database: postgres parameters: $1 = '621', $2 = '621'
WITH last_candle AS ( ;

Date: 2026-02-24 09:32:00 Duration: 13ms Database: postgres parameters: $1 = '558', $2 = '558'

11

515ms

12

Details

28ms

48ms

42ms

with sym_info as ( ;

Times Reported Time consuming bind #11

Day	Hour	Count	Duration	Avg duration
	09	12	515ms	42ms

with sym_info as ( ;

Date: 2026-02-24 09:51:47 Duration: 48ms Database: postgres parameters: $1 = '620', $2 = 'Forex', $3 = 'Forex', $4 = '620', $5 = 'Forex', $6 = '620', $7 = '620', $8 = 'Forex', $9 = '620'
with sym_info as ( ;

Date: 2026-02-24 09:21:43 Duration: 47ms Database: postgres parameters: $1 = '620', $2 = 'Forex', $3 = 'Forex', $4 = '620', $5 = 'Forex', $6 = '620', $7 = '620', $8 = 'Forex', $9 = '620'
with sym_info as ( ;

Date: 2026-02-24 09:51:50 Duration: 44ms Database: postgres parameters: $1 = '617', $2 = 'Forex', $3 = 'Forex', $4 = '617', $5 = 'Forex', $6 = '617', $7 = '617', $8 = 'Forex', $9 = '617'

12

305ms

431

Details

0ms

8ms

0ms

SELECT absolutetimezoneoffset;

Times Reported Time consuming bind #12

Day	Hour	Count	Duration	Avg duration
	09	431	305ms	0ms

SELECT absolutetimezoneoffset;

Date: 2026-02-24 09:02:32 Duration: 8ms Database: postgres parameters: $1 = '974', $2 = 'Market Overview'
SELECT absolutetimezoneoffset;

Date: 2026-02-24 09:50:32 Duration: 2ms Database: postgres parameters: $1 = '558', $2 = 'BROKER'
SELECT absolutetimezoneoffset;

Date: 2026-02-24 09:01:56 Duration: 1ms Database: postgres parameters: $1 = '538', $2 = 'Shares EU'

13

260ms

2,996

Details

0ms

2ms

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 #13

Day	Hour	Count	Duration	Avg duration
	09	2,996	260ms	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: 2026-02-24 09:30:02 Duration: 2ms Database: postgres parameters: $1 = '2026-02-24 10:00:00', $2 = '48873.83', $3 = '48906.95', $4 = '48869.79', $5 = '48897.89', $6 = '1836', $7 = '515840249388074300', $8 = '0', $9 = '2026-02-24 09:30:02.838', $10 = '2026-02-24 09:30:02.836', $11 = '48873.83', $12 = '48906.95', $13 = '48869.79', $14 = '48897.89', $15 = '1836', $16 = '0', $17 = '2026-02-24 09:30:02.838', $18 = '2026-02-24 09:30:02.836'
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: 2026-02-24 09:30:36 Duration: 1ms Database: postgres parameters: $1 = '2026-02-24 09:00:00', $2 = '5173.26', $3 = '5183.16', $4 = '5164.31', $5 = '5182.57', $6 = '7394', $7 = '515840230627771300', $8 = '0', $9 = '2026-02-24 09:30:36.898', $10 = '2026-02-24 09:30:36.898', $11 = '5173.26', $12 = '5183.16', $13 = '5164.31', $14 = '5182.57', $15 = '7394', $16 = '0', $17 = '2026-02-24 09:30:36.898', $18 = '2026-02-24 09:30:36.898'
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: 2026-02-24 09:30:36 Duration: 1ms Database: postgres parameters: $1 = '2026-02-24 09:00:00', $2 = '11.2742', $3 = '11.28333', $4 = '11.26519', $5 = '11.27903', $6 = '8365', $7 = '515840243193398300', $8 = '0', $9 = '2026-02-24 09:30:36.23', $10 = '2026-02-24 09:30:36.229', $11 = '11.2742', $12 = '11.28333', $13 = '11.26519', $14 = '11.27903', $15 = '8365', $16 = '0', $17 = '2026-02-24 09:30:36.23', $18 = '2026-02-24 09:30:36.229'

14

248ms

5,203

Details

0ms

1ms

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 #14

Day	Hour	Count	Duration	Avg duration
	09	5,203	248ms	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: 2026-02-24 09:30:39 Duration: 1ms Database: postgres parameters: $1 = '2026-02-24 09:15:00', $2 = '0.59575', $3 = '0.59603', $4 = '0.59552', $5 = '0.59593', $6 = '1216', $7 = '515840245857519300', $8 = '0', $9 = '2026-02-24 09:30:38.991', $10 = '2026-02-24 09:30:38.752', $11 = '0.59575', $12 = '0.59603', $13 = '0.59552', $14 = '0.59593', $15 = '1216', $16 = '0', $17 = '2026-02-24 09:30:38.991', $18 = '2026-02-24 09:30:38.752'
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: 2026-02-24 09:30:39 Duration: 1ms Database: postgres parameters: $1 = '2026-02-24 09:15:00', $2 = '0.46232', $3 = '0.4624', $4 = '0.46221', $5 = '0.46234', $6 = '1195', $7 = '515840245883447300', $8 = '0', $9 = '2026-02-24 09:30:38.991', $10 = '2026-02-24 09:30:38.752', $11 = '0.46232', $12 = '0.4624', $13 = '0.46221', $14 = '0.46234', $15 = '1195', $16 = '0', $17 = '2026-02-24 09:30:38.991', $18 = '2026-02-24 09:30:38.752'
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: 2026-02-24 09:30:46 Duration: 0ms Database: postgres parameters: $1 = '2026-02-24 09:15:00', $2 = '1.90802', $3 = '1.90837', $4 = '1.90717', $5 = '1.90752', $6 = '2237', $7 = '515840247889651300', $8 = '0', $9 = '2026-02-24 09:30:46.428', $10 = '2026-02-24 09:30:46.225', $11 = '1.90802', $12 = '1.90837', $13 = '1.90717', $14 = '1.90752', $15 = '2237', $16 = '0', $17 = '2026-02-24 09:30:46.428', $18 = '2026-02-24 09:30:46.225'

15

183ms

2,006

Details

0ms

7ms

0ms

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

Times Reported Time consuming bind #15

Day	Hour	Count	Duration	Avg duration
	09	2,006	183ms	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: 2026-02-24 09:02:33 Duration: 7ms Database: postgres parameters: $1 = '2026-02-24 09:00:00', $2 = '12.1805', $3 = '12.2825', $4 = '12.148', $5 = '12.1985', $6 = '6354', $7 = '515840249403205300', $8 = '0', $9 = '2026-02-24 09:02:33.923', $10 = '2026-02-24 09:02:33.923', $11 = '12.1805', $12 = '12.2825', $13 = '12.148', $14 = '12.1985', $15 = '6354', $16 = '0', $17 = '2026-02-24 09:02:33.923', $18 = '2026-02-24 09:02:33.923'
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-02-24 09:30:35 Duration: 0ms Database: postgres parameters: $1 = '2026-02-23 23:00:00', $2 = '88.8995', $3 = '88.9045', $4 = '87.985', $5 = '88.2055', $6 = '5865', $7 = '515840230625682300', $8 = '0', $9 = '2026-02-24 09:30:35.475', $10 = '2026-02-24 09:30:35.337', $11 = '88.8995', $12 = '88.9045', $13 = '87.985', $14 = '88.2055', $15 = '5865', $16 = '0', $17 = '2026-02-24 09:30:35.475', $18 = '2026-02-24 09:30:35.337'
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: 2026-02-24 09:11:46 Duration: 0ms Database: postgres parameters: $1 = '2026-02-23 22:00:00', $2 = '8494.78', $3 = '8501.88', $4 = '8490.38', $5 = '8498.88', $6 = '6874', $7 = '515840247902184300', $8 = '0', $9 = '2026-02-24 09:11:46.306', $10 = '2026-02-24 09:11:46.174', $11 = '8494.78', $12 = '8501.88', $13 = '8490.38', $14 = '8498.88', $15 = '6874', $16 = '0', $17 = '2026-02-24 09:11:46.306', $18 = '2026-02-24 09:11:46.174'

16

96ms

31

Details

1ms

19ms

3ms

WITH rcr_max as ( ;

Times Reported Time consuming bind #16

Day	Hour	Count	Duration	Avg duration
	09	31	96ms	3ms

WITH rcr_max as ( ;

Date: 2026-02-24 09:15:41 Duration: 19ms Database: postgres parameters: $1 = '607732870649693305', $2 = '607732870649693305', $3 = '607732870649693305'
WITH rcr_max as ( ;

Date: 2026-02-24 09:30:40 Duration: 15ms Database: postgres parameters: $1 = '607732870649693305', $2 = '607732870649693305', $3 = '607732870649693305'
WITH rcr_max as ( ;

Date: 2026-02-24 09:00:40 Duration: 9ms Database: postgres parameters: $1 = '607732870649693305', $2 = '607732870649693305', $3 = '607732870649693305'

17

89ms

15

Details

3ms

11ms

5ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Times Reported Time consuming bind #17

Day	Hour	Count	Duration	Avg duration
	09	15	89ms	5ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-02-24 09:02:32 Duration: 11ms Database: postgres parameters: $1 = '667', $2 = '667'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-02-24 09:02:47 Duration: 9ms Database: postgres parameters: $1 = '538', $2 = '538'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-02-24 09:02:32 Duration: 9ms Database: postgres parameters: $1 = '667', $2 = '667'

18

88ms

84

Details

0ms

2ms

1ms

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

Times Reported Time consuming bind #18

Day	Hour	Count	Duration	Avg duration
	09	84	88ms	1ms

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

Date: 2026-02-24 09:40:07 Duration: 2ms Database: postgres parameters: $1 = '689', $2 = 'XAUUSD', $3 = '689'
SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;

Date: 2026-02-24 09:50:41 Duration: 2ms Database: postgres parameters: $1 = '558', $2 = 'US30', $3 = '558'
SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;

Date: 2026-02-24 09:18:16 Duration: 1ms Database: postgres parameters: $1 = '558', $2 = 'EURUSD', $3 = '558'

19

65ms

268

Details

0ms

7ms

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 #19

Day	Hour	Count	Duration	Avg duration
	09	268	65ms	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: 2026-02-24 09:50:06 Duration: 7ms Database: postgres parameters: $1 = '607733225045412301'
/*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: 2026-02-24 09:50:46 Duration: 4ms Database: postgres parameters: $1 = '607732869468936301'
/*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: 2026-02-24 09:35:42 Duration: 4ms Database: postgres parameters: $1 = '607730039343463301'

20

49ms

72

Details

0ms

4ms

0ms

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

Times Reported Time consuming bind #20

Day	Hour	Count	Duration	Avg duration
	09	72	49ms	0ms

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

Date: 2026-02-24 09:35:15 Duration: 4ms Database: postgres parameters: $1 = '607733047338356302'
/*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: 2026-02-24 09:51:47 Duration: 4ms Database: postgres parameters: $1 = '607733225420850302'
/*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: 2026-02-24 09:34:09 Duration: 3ms Database: postgres parameters: $1 = '607732635821256302'

Log levels

Key values

546,961 Log entries

Chart
Table

Type	Count	Percentage
CONTEXT	636	0.12%
DETAIL	124,932	22.84%
ERROR	408	0.07%
LOG	419,795	76.75%
STATEMENT	1,190	0.22%

Events distribution

Key values

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

Most Frequent Errors/Events

Key values

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

Rank

Times reported

Error

1

360

Details

ERROR: pg_stat_statements must be loaded via shared_preload_libraries

Times Reported Most Frequent Error / Event #1

Day	Hour	Count
Feb 24	09	360

ERROR: pg_stat_statements must be loaded via shared_preload_libraries

2

48

Details

ERROR: schema "..." does not exist

Times Reported Most Frequent Error / Event #2

Day	Hour	Count
Feb 24	09	48

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

Global information

Overview

Global Stats

SQL Traffic

Key values

SELECT Traffic

Key values

INSERT/UPDATE/DELETE Traffic

Key values

Queries duration

Key values

Prepared queries ratio

Key values

General Activity

Connections

Established Connections

Key values

Connections per database

Key values

Connections per user

Key values

Connections per host

Key values

Sessions

Simultaneous sessions

Key values

Histogram of session times

Key values

Sessions per database

Key values

Sessions per user

Key values

Sessions per host

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Sessions per application

Key values

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

Key values

Temporary Files

Size of temporary files

Key values

Number of temporary files

Key values

Temporary Files Activity

Queries generating the most temporary files (N)

Queries generating the largest temporary files

Vacuums

Vacuums / Analyzes Distribution

Key values

Analyzes per table

Key values

Vacuums per table

Key values

Tuples removed per table

Key values

Pages removed per table

Key values

Autovacuum Activity

Locks

Locks by types

Key values

Type of locks

Most frequent waiting queries (N)

Queries that waited the most

Queries

Queries by type

Key values

Queries by database

Key values

Queries by user

Key values

Duration by user