pgBadger :: PostgreSQL Log Analyzer

Global information

Generated on Sun Mar 1 11:59:20 2026
Log file: /home/postgres/pg_data/data/pg_log/postgresql-2026-03-01_130000.log
Parsed 507,389 log entries in 19s
Log start from 2026-03-01 13:00:00 to 2026-03-01 13:59:18

Global Stats

176 Number of unique normalized queries
91,388 Number of queries
1h12m27s Total query duration
2026-03-01 13:00:00 First query
2026-03-01 13:59:18 Last query
1,239 queries/s at 2026-03-01 13:06:08 Query peak

1h12m27s Total query duration
2s167ms Prepare/parse total duration
13s906ms Bind total duration
1h12m11s Execute total duration

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

31 Total number of automatic vacuums
42 Total number of automatic analyzes

459 Number temporary file
137.67 MiB Max size of temporary file
6.98 MiB Average size of temporary file

1,767 Total number of sessions
13 sessions at 2026-03-01 13:58:48 Session peak
14h41m10s Total duration of sessions
29s921ms Average duration of sessions
51 Average queries per session
2s460ms Average queries duration per session
27s460ms Average idle time per session

1,770 Total number of connections
28 connections/s at 2026-03-01 13:03:48 Connection peak
3 Total number of databases

SQL Traffic

Key values

1,239 queries/s Query Peak
2026-03-01 13:06:08 Date

SELECT Traffic

Key values

618 queries/s Query Peak
2026-03-01 13:06:08 Date

INSERT/UPDATE/DELETE Traffic

Key values

99 queries/s Query Peak
2026-03-01 13:58:49 Date

Queries duration

Key values

1h12m27s 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)
Mar 01	13	91,388	0ms	21s94ms	47ms	3m10s	3m20s	4m1s

Day	Hour	SELECT	COPY TO	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Mar 01	13	28,210	26	0ms	0ms	0ms	0ms

Day	Hour	INSERT	UPDATE	DELETE	COPY FROM	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Mar 01	13	12,254	509	16	96	0ms	0ms	0ms	0ms

Day	Hour	Prepare	Bind	Bind/Prepare	Percentage of prepare
Mar 01	13	8,656	20,573	2.38	19.84%

Day	Hour	Count	Average / Second
Mar 01	13	1,770	0.49/s

Day	Hour	Count	Average Duration	Average idle time
Mar 01	13	1,767	29s921ms	27s469ms

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Established Connections

Key values

28 connections Connection Peak
2026-03-01 13:03:48 Date

Connections per database

Key values

acaweb_fx Main Database
1,770 connections Total

Chart
Table

Database	User	Count
acaweb_fx		1,694
	postgres	1,352
	psql	342
socialmedia		76
	postgres	76

Connections per user

Key values

postgres Main User
1,770 connections Total

Chart
Table

User	Count
postgres	1,428
psql	342

Connections per host

Key values

192.168.4.142 Main host with 730 connections
1,770 Total connections

Chart
Table

Host	Count
127.0.0.1	106
192.168.0.114	2
192.168.0.216	116
192.168.0.74	5
192.168.1.15	32
192.168.1.20	20
192.168.1.239	54
192.168.1.90	6
192.168.2.126	18
192.168.3.199	39
192.168.4.142	730
192.168.4.33	76
192.168.4.98	330
[local]	236

Simultaneous sessions

Key values

13 sessions Session Peak
2026-03-01 13:58:48 Date

Histogram of session times

Key values

1,403 0-500ms duration

Chart
Table

Range	Count	Percentage
0ms-500ms	1,403	79.40%
500ms-1s	112	6.34%
1s-30s	212	12.00%
30s-1m	10	0.57%
1m-10m	7	0.40%
10m-30m	20	1.13%
30m-1h	2	0.11%
1h-8h	1	0.06%
> 8h	0	0.00%

Sessions per database

Key values

acaweb_fx Main Database
1,767 sessions Total

Chart
Table

Database	User	Count	Total Duration	Average Duration
acaweb_fx	1,691	14h35m16s	31s56ms
socialmedia	76	5m53s	4s653ms

Sessions per user

Key values

postgres Main User
1,767 sessions Total

Chart
Table

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

Sessions per host

Key values

192.168.4.142 Main Host
1,767 sessions Total

Chart
Table

Host	Count	Total Duration	Average Duration
127.0.0.1	106	3s123ms	29ms
192.168.0.114	2	10m	5m
192.168.0.216	116	7m5s	3s671ms
192.168.0.74	3	2h38m14s	52m44s
192.168.1.15	31	1h37m15s	3m8s
192.168.1.20	20	9h51m56s	29m35s
192.168.1.239	54	325ms	6ms
192.168.1.90	6	53ms	8ms
192.168.2.126	18	5s54ms	280ms
192.168.3.199	39	31s546ms	808ms
192.168.4.142	730	6m54s	567ms
192.168.4.33	76	5m53s	4s653ms
192.168.4.98	330	13s397ms	40ms
[local]	236	2m57s	753ms

Sessions per application

Key values

unknown Main Host
1,767 sessions Total

Chart
Table

Application	Count	Total Duration	Average Duration
unknown	1,767	14h41m10s	29s921ms

Checkpoints Buffers

Key values

2,219 buffers Checkpoint Peak
2026-03-01 13:09:19 Date
209.713 seconds Highest write time
0.004 seconds Sync time

Checkpoints Wal files

Key values

2 files Wal files usage Peak
2026-03-01 13:54:16 Date

Checkpoints distance

Key values

60.33 Mo Distance Peak
2026-03-01 13:09:19 Date

Checkpoints Activity

Day	Hour	Written buffers	Write time	Sync time	Total time
Mar 01	13	14,415	1,555.333s	0.022s	1,555.621s

Day	Hour	Added	Removed	Recycled	Synced files	Longest sync	Average sync
Mar 01	13	0	0	11	1,280	0.004s	0s

Day	Hour	Count	Avg time (sec)
Mar 01	13	0	0s

Day	Hour	Mean distance	Mean estimate
Mar 01	13	15,721.67 kB	27,319.17 kB

↑ Back to the top of the Checkpoint Activity table

Checkpoint causes

Key values

time (12) Main checkpoint cause
12 checkpoints Total

Chart
Table

Cause	Number of checkpoints
time	12
Total	12

Size of temporary files

Key values

108.54 MiB Temp Files size Peak
2026-03-01 13:02:18 Date

Number of temporary files

Key values

30 per second Temp Files Peak
2026-03-01 13:02:09 Date

Temporary Files Activity

Temp Files

Day	Hour	Count	Total size	Average size
Mar 01	13	459	3.13 GiB	6.98 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	60	242.56 MiB	4.02 MiB	4.06 MiB	4.04 MiB	select resultuid from relevance_fibonacci_results order by resultuid desc limit ?), fr as ( select a., rr.age, rr.relevant from fibonacci_results a left outer join relevance_fibonacci_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) end), all_results as ( select fr.resultuid as resultuid, fr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, fr.pattern as pattern_name, fr.timed as timed, fr.patternendtime as identified, dtt.timezone as timezone, fr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when fr.age is not null then fr.age when fr.resultuid <= rm.resultuid then ? else ? end as age, case when fr.relevant is not null then fr.relevant when fr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from fr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = fr.symbolid inner join symbols s on fr.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on fr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join downloadersymbolsettings dss on fr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left join lateral calc_fib_signal_filter (fr.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where fr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (fr.simulation = ? or fr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or fr.pattern in (...)) and (? = ? or fr.patternlengthbars <= ?) and (? = ? or (? = ? and fr.timed > cast(? as timestamp)) or (? = ? and fr.timed < cast(? as timestamp)))), results as ( select distinct on (symbolid) from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc; SELECT resultuid FROM relevance_fibonacci_results ORDER BY resultuid DESC LIMIT 1), fr AS ( SELECT a., rr.age, rr.relevant from fibonacci_results a LEFT OUTER JOIN relevance_fibonacci_results rr on a.resultuid = rr.resultuid WHERE CASE WHEN FALSE = $1 THEN true ELSE a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) END), all_results AS ( SELECT fr.resultuid AS resultuid, fr.direction AS direction, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, fr.pattern AS pattern_name, fr.timed AS timed, fr.patternendtime AS identified, dtt.timezone AS timezone, fr.patternlengthbars AS length, g.basegroupname, newLevels.filtered, CASE WHEN fr.age IS NOT NULL THEN fr.age WHEN fr.resultuid <= rm.resultuid THEN 11 ELSE 0 END as age, CASE WHEN fr.relevant IS NOT NULL THEN fr.relevant WHEN fr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip FROM fr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $2 AND bsl.symbolid = fr.symbolid INNER JOIN symbols s ON fr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN symbolgroup sg on fr.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid INNER JOIN downloadersymbolsettings dss ON fr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN rar_max rm ON 1 = 1 LEFT JOIN LATERAL calc_fib_signal_filter (fr.resultuid) newLevels on true LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' WHERE fr.gmttimefound > now() - INTERVAL '7 DAYS' AND dss.enabled = 1 AND s.deleted = 0 AND (fr.simulation = 0 OR fr.simulation IS NULL) AND ($3 = 0 OR s.timegranularity in ($4, $5, $6, $7, $8, $9, $10)) AND ($11 = 0 OR s.exchange in ($12)) AND ($13 = 0 OR coalesce(bim.code, s.symbol) in ($14, $15, $16, $17, $18, $19, $20, $21, $22, $23, $24, $25, $26, $27, $28, $29, $30, $31, $32, $33, $34, $35, $36, $37, $38, $39, $40, $41, $42, $43, $44, $45, $46, $47, $48, $49, $50, $51, $52, $53, $54, $55, $56, $57, $58, $59, $60, $61, $62, $63, $64, $65, $66, $67, $68, $69, $70, $71, $72, $73, $74, $75, $76, $77, $78, $79, $80, $81, $82, $83, $84, $85, $86, $87, $88, $89, $90, $91, $92, $93, $94, $95, $96, $97, $98, $99, $100, $101, $102, $103, $104, $105, $106, $107, $108, $109, $110, $111, $112, $113, $114, $115, $116, $117, $118, $119, $120, $121, $122, $123, $124, $125, $126, $127, $128, $129, $130, $131, $132, $133, $134, $135, $136, $137, $138, $139, $140, $141, $142, $143, $144, $145, $146, $147, $148, $149, $150, $151, $152, $153, $154, $155, $156, $157, $158, $159, $160, $161, $162, $163, $164, $165, $166, $167, $168, $169, $170, $171, $172, $173, $174, $175, $176, $177, $178, $179, $180, $181, $182, $183, $184, $185, $186, $187, $188, $189, $190, $191, $192, $193, $194, $195, $196, $197, $198, $199, $200, $201, $202, $203, $204, $205, $206, $207, $208, $209, $210, $211, $212, $213, $214, $215, $216, $217, $218, $219, $220, $221, $222, $223, $224, $225, $226)) AND ($227 = 0 OR fr.pattern in ($228)) AND ($229 = 0 OR fr.patternlengthbars <= $230) AND ($231 = 0 OR ($232 = 1 AND fr.timed > cast('1970-01-01' as timestamp)) OR ($233 = 2 AND fr.timed < cast('1970-01-01' as timestamp)))), results AS ( SELECT DISTINCT ON (symbolid) FROM all_results WHERE (FALSE = $234 OR relevant = 1) AND ($235 = 0 OR age <= $236) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-03-01 13:00:44 Duration: 0ms
2	59	223.78 MiB	3.39 MiB	4.20 MiB	3.79 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: 2026-03-01 13:01:08 Duration: 0ms
3	16	621.75 MiB	38.86 MiB	38.86 MiB	38.86 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: 2026-03-01 13:01:12 Duration: 0ms
4	16	1.16 GiB	74.51 MiB	74.51 MiB	74.51 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: 2026-03-01 13:01:15 Duration: 0ms
5	10	33.33 MiB	3.33 MiB	3.33 MiB	3.33 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: 2026-03-01 13:36:49 Duration: 0ms
6	4	550.67 MiB	137.67 MiB	137.67 MiB	137.67 MiB	select updateresultsmaterializedview (); select updateresultsmaterializedview (); Date: 2026-03-01 13:02:13 Duration: 0ms
7	4	333.58 MiB	83.39 MiB	83.40 MiB	83.40 MiB	select updateageforrelevantresults (); select updateageforrelevantresults (); Date: 2026-03-01 13:02:05 Duration: 0ms

Queries generating the largest temporary files

Rank	Size	Query
1	137.67 MiB	select updateresultsmaterializedview (); [ Date: 2026-03-01 13:17:13 ]
2	137.67 MiB	select updateresultsmaterializedview (); [ Date: 2026-03-01 13:32:13 ]
3	137.67 MiB	select updateresultsmaterializedview (); [ Date: 2026-03-01 13:02:13 ]
4	137.67 MiB	select updateresultsmaterializedview (); [ Date: 2026-03-01 13:47:14 ]
5	83.40 MiB	select updateageforrelevantresults (); [ Date: 2026-03-01 13:02:05 ]
6	83.40 MiB	select updateageforrelevantresults (); [ Date: 2026-03-01 13:32:04 ]
7	83.39 MiB	select updateageforrelevantresults (); [ Date: 2026-03-01 13:17:04 ]
8	83.39 MiB	select updateageforrelevantresults (); [ Date: 2026-03-01 13:47:05 ]
9	74.51 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; [ Date: 2026-03-01 13:01:15 ]
10	74.51 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; [ Date: 2026-03-01 13:03:16 ]
11	74.51 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; [ Date: 2026-03-01 13:05:15 ]
12	74.51 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; [ Date: 2026-03-01 13:11:15 ]
13	74.51 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; [ Date: 2026-03-01 13:16:15 ]
14	74.51 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; [ Date: 2026-03-01 13:18:15 ]
15	74.51 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; [ Date: 2026-03-01 13:20:15 ]
16	74.51 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; [ Date: 2026-03-01 13:26:15 ]
17	74.51 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; [ Date: 2026-03-01 13:31:15 ]
18	74.51 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; [ Date: 2026-03-01 13:33:15 ]
19	74.51 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; [ Date: 2026-03-01 13:35:15 ]
20	74.51 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; [ Date: 2026-03-01 13:41:15 ]

Vacuums / Analyzes Distribution

Key values

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

Analyzes per table

Key values

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

Chart
Table

Table	Number of analyzes
acaweb_fx.public.solr_relevance_old	16
acaweb_fx.pg_catalog.pg_attribute	8
acaweb_fx.pg_catalog.pg_type	4
acaweb_fx.pg_catalog.pg_class	4
acaweb_fx.public.datafeeds_latestrun	3
acaweb_fx.pg_catalog.pg_depend	2
acaweb_fx.pg_catalog.pg_index	1
acaweb_fx.public.solr_imports	1
socialmedia.public.processes	1
acaweb_fx.public.latest_t15_candle_view	1
acaweb_fx.public.latest_candle_datetime_per_receng	1
Total	42

Vacuums per table

Key values

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

Chart
Table

		Index	Buffer usage			Skipped		WAL usage
Table	Vacuums	scans	hits	misses	dirtied	pins	frozen	records	full page	bytes
acaweb_fx.public.solr_relevance_old	16	16	13,152	0	51	0	0	8,456	1,089	5,378,456
acaweb_fx.public.datafeeds_latestrun	3	0	356	0	4	0	0	37	5	33,630
acaweb_fx.pg_catalog.pg_attribute	3	3	3,760	0	414	0	201	1,052	423	2,315,237
acaweb_fx.pg_catalog.pg_type	2	2	474	0	52	0	0	155	66	284,194
acaweb_fx.pg_catalog.pg_class	2	2	1,136	0	121	0	0	273	116	582,383
acaweb_fx.pg_toast.pg_toast_2619	1	1	212	0	38	0	0	107	34	119,725
acaweb_fx.pg_catalog.pg_statistic	1	1	1,035	0	140	0	606	373	133	548,562
acaweb_fx.public.solr_imports	1	1	67	0	2	0	0	6	2	14,161
acaweb_fx.pg_catalog.pg_depend	1	1	572	0	66	0	59	193	85	412,243
acaweb_fx.public.latest_t15_candle_view	1	1	67	0	1	0	0	6	1	9,055
Total	31	28	20,831	643	889	0	866	10,658	1,954	9,697,646

Tuples removed per table

Key values

public.solr_relevance_old (20411) Main table with removed tuples on database acaweb_fx
28542 tuples Total removed

Chart
Table

		Index	Tuples			Pages
Table	Vacuums	scans	removed	remain	not yet removable	removed	remain
acaweb_fx.public.solr_relevance_old	16	16	20,411	100,169	0	0	3,261
acaweb_fx.pg_catalog.pg_attribute	3	3	4,818	32,270	137	17	783
acaweb_fx.pg_catalog.pg_depend	1	1	1,228	14,341	0	0	144
acaweb_fx.pg_catalog.pg_type	2	2	783	2,902	6	0	88
acaweb_fx.pg_catalog.pg_statistic	1	1	716	3,962	0	0	1,194
acaweb_fx.pg_catalog.pg_class	2	2	247	3,300	0	0	300
acaweb_fx.public.datafeeds_latestrun	3	0	166	42	0	0	48
acaweb_fx.pg_toast.pg_toast_2619	1	1	63	164	0	0	51
acaweb_fx.public.latest_t15_candle_view	1	1	59	12	0	0	1
acaweb_fx.public.solr_imports	1	1	51	1	0	0	2
Total	31	28	28,542	157,163	143	17	5,872

Pages removed per table

Key values

pg_catalog.pg_attribute (17) Main table with removed pages on database acaweb_fx
17 pages Total removed

Chart
Table

Table	Number of vacuums	Index scans	Tuples removed	Pages removed
acaweb_fx.pg_catalog.pg_attribute	3	3	4818	17
acaweb_fx.pg_toast.pg_toast_2619	1	1	63	0
acaweb_fx.pg_catalog.pg_type	2	2	783	0
acaweb_fx.public.datafeeds_latestrun	3	0	166	0
acaweb_fx.pg_catalog.pg_statistic	1	1	716	0
acaweb_fx.public.solr_imports	1	1	51	0
acaweb_fx.pg_catalog.pg_depend	1	1	1228	0
acaweb_fx.public.latest_t15_candle_view	1	1	59	0
acaweb_fx.public.solr_relevance_old	16	16	20411	0
acaweb_fx.pg_catalog.pg_class	2	2	247	0
Total	31	28	28,542	17

Autovacuum Activity

Vacuums / Analyzes

Day	Hour	VACUUMs	ANALYZEs
Mar 01	13	31	42

↑ Back to the top of the Autovacuum Activity table

Locks by types

Key values

unknown Main Lock Type
0 locks Total

Chart
Table

Type of locks

NO DATASET

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

Most frequent waiting queries (N)

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

Queries that waited the most

Rank	Wait time	Query
NO DATASET

Queries by type

Key values

28,210 Total read queries
15,427 Total write queries

Chart
Table

Type	Count	Percentage
SELECT	28,210	30.87%
INSERT	12,254	13.41%
UPDATE	509	0.56%
DELETE	16	0.02%
COPY FROM	96	0.11%
COPY TO	26	0.03%
CTE	1,846	2.02%
DDL	16	0.02%
TCL	664	0.73%
CURSOR	0	0.00%
OTHERS	47,751	52.25%

Queries by database

Key values

unknown Main database
90,429 Requests
1h12m11s (unknown)
Main time consuming database

Chart
Table

Database	Request type	Count	Duration
acaweb_fx	Total	883	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	100	0ms
	ddl	16	0ms
	delete	16	0ms
	others	213	0ms
	select	64	0ms
	tcl	332	0ms
	update	36	0ms
socialmedia	Total	76	0ms
	select	76	0ms
unknown	Total	90,429	1h12m11s
	copy from	16	0ms
	cte	1,746	0ms
	insert	12,254	0ms
	others	2,040	0ms
	select	28,070	0ms
	tcl	332	0ms
	update	473	0ms

Queries by user

Key values

unknown Main user
90,429 Requests

Queries Chart
Table

User	Request type	Count	Duration
postgres	Total	959	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	100	0ms
	ddl	16	0ms
	delete	16	0ms
	others	213	0ms
	select	140	0ms
	tcl	332	0ms
	update	36	0ms
unknown	Total	90,429	1h12m11s
	copy from	16	0ms
	cte	1,746	0ms
	insert	12,254	0ms
	others	2,040	0ms
	select	28,070	0ms
	tcl	332	0ms
	update	473	0ms

Duration by user

Key values

1h12m11s (unknown) Main time consuming user

Duration Chart
Table

User	Request type	Count	Duration
postgres	Total	959	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	100	0ms
	ddl	16	0ms
	delete	16	0ms
	others	213	0ms
	select	140	0ms
	tcl	332	0ms
	update	36	0ms
unknown	Total	90,429	1h12m11s
	copy from	16	0ms
	cte	1,746	0ms
	insert	12,254	0ms
	others	2,040	0ms
	select	28,070	0ms
	tcl	332	0ms
	update	473	0ms

Queries by host

Key values

unknown Main host
91,388 Requests
1h12m11s (unknown)
Main time consuming host

Chart
Table

Host	Request type	Count	Duration
unknown	Total	91,388	1h12m11s
	copy from	96	0ms
	copy to	26	0ms
	cte	1,846	0ms
	ddl	16	0ms
	delete	16	0ms
	insert	12,254	0ms
	others	2,253	0ms
	select	28,210	0ms
	tcl	664	0ms
	update	509	0ms

Queries by application

Key values

unknown Main application
91,046 Requests
1h12m11s (unknown)
Main time consuming application

Chart
Table

Application	Request type	Count	Duration
psql	Total	342	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	100	0ms
	ddl	16	0ms
	delete	16	0ms
	others	4	0ms
	select	64	0ms
	update	36	0ms
unknown	Total	91,046	1h12m11s
	copy from	16	0ms
	cte	1,746	0ms
	insert	12,254	0ms
	others	2,249	0ms
	select	28,146	0ms
	tcl	664	0ms
	update	473	0ms

Number of cancelled queries

Key values

0 per second Cancelled query Peak
2026-03-01 13:54:05 Date

Number of cancelled queries (5 minutes period)

NO DATASET

Histogram of query times

Key values

27,224 0-1ms duration

Chart
Table

Range	Count	Percentage
0-1ms	27,224	59.84%
1-5ms	14,694	32.30%
5-10ms	1,254	2.76%
10-25ms	1,194	2.62%
25-50ms	160	0.35%
50-100ms	75	0.16%
100-500ms	227	0.50%
500-1000ms	89	0.20%
1000-10000ms	442	0.97%
> 10000ms	139	0.31%

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
Mar 01	13	26	0ms	0ms

2

0ms

133

Details

0ms

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

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Mar 01	13	133	0ms	0ms

3

0ms

2,378

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
Mar 01	13	2,378	0ms	0ms

4

0ms

180

Details

0ms

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

Times Reported Time consuming queries #4

Day	Hour	Count	Duration	Avg duration
Mar 01	13	180	0ms	0ms

5

0ms

49

Details

0ms

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

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Mar 01	13	49	0ms	0ms

6

0ms

6

Details

0ms

insert into resultmedia (processresultsid, type, name, filename) values (?, ?, ?, ?) returning id;

Times Reported Time consuming queries #6

Day	Hour	Count	Duration	Avg duration
Mar 01	13	6	0ms	0ms

7

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 #7

Day	Hour	Count	Duration	Avg duration
Mar 01	13	18	0ms	0ms

8

0ms

237

Details

0ms

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

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Mar 01	13	237	0ms	0ms

9

0ms

11

Details

0ms

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

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Mar 01	13	11	0ms	0ms

10

0ms

332

Details

0ms

commit;

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Mar 01	13	332	0ms	0ms

11

0ms

181

Details

0ms

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

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Mar 01	13	181	0ms	0ms

12

0ms

3

Details

0ms

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

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Mar 01	13	3	0ms	0ms

13

0ms

237

Details

0ms

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

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Mar 01	13	237	0ms	0ms

14

0ms

25

Details

0ms

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

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Mar 01	13	25	0ms	0ms

15

0ms

6

Details

0ms

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

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Mar 01	13	6	0ms	0ms

16

0ms

11

Details

0ms

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

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Mar 01	13	11	0ms	0ms

17

0ms

237

Details

0ms

select system_identifier from pg_control_system ();

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Mar 01	13	237	0ms	0ms

18

0ms

3

Details

0ms

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

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Mar 01	13	3	0ms	0ms

19

0ms

237

Details

0ms

select datname, confl_tablespace, confl_lock, confl_snapshot, confl_bufferpin, confl_deadlock from pg_stat_database_conflicts where datname not ilike ? and datname not ilike ? and datname not ilike ? and datname not ilike ?;

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Mar 01	13	237	0ms	0ms

20

0ms

132

Details

0ms

with rar_max as ( select resultuid from relevance_bigmovement_results order by resultuid desc limit ? ), all_results as ( select bmr.resultuid as resultuid, ? 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, bmr.patternendtime as identified, bmr.patternlengthbars, dtt.timezone as timezone, g.basegroupname, 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 brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = bmr.symbolid inner join symbols s on bmr.symbolid = s.symbolid and s.nonliquid = ? inner join downloadersymbolsettings dss on bmr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on bmr.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_bigmovement_results rbr on rbr.resultuid = bmr.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 = ? where bmr.gmttimefound > now() - interval ? and s.deleted = ? and (bmr.simulation = ? or bmr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or bmr.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, interval desc;

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Mar 01	13	132	0ms	0ms

Most frequent queries (N)

Rank

Times executed

Total duration

Min duration

Max duration

Avg duration

Query

1

4,452

Details

0ms

select ?;

Times Reported Time consuming queries #1

Day	Hour	Count	Duration	Avg duration
Mar 01	13	4,452	0ms	0ms

2

4,105

Details

0ms

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

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Mar 01	13	4,105	0ms	0ms

3

3,273

Details

0ms

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

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Mar 01	13	3,273	0ms	0ms

4

2,789

Details

0ms

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

Times Reported Time consuming queries #4

Day	Hour	Count	Duration	Avg duration
Mar 01	13	2,789	0ms	0ms

5

2,407

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 #5

Day	Hour	Count	Duration	Avg duration
Mar 01	13	2,407	0ms	0ms

6

2,378

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 #6

Day	Hour	Count	Duration	Avg duration
Mar 01	13	2,378	0ms	0ms

7

1,661

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 #7

Day	Hour	Count	Duration	Avg duration
Mar 01	13	1,661	0ms	0ms

8

1,561

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 #8

Day	Hour	Count	Duration	Avg duration
Mar 01	13	1,561	0ms	0ms

9

1,185

Details

0ms

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

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Mar 01	13	1,185	0ms	0ms

10

1,185

Details

0ms

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

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Mar 01	13	1,185	0ms	0ms

11

1,185

Details

0ms

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

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Mar 01	13	1,185	0ms	0ms

12

1,185

Details

0ms

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

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Mar 01	13	1,185	0ms	0ms

13

1,185

Details

0ms

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

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Mar 01	13	1,185	0ms	0ms

14

1,185

Details

0ms

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

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Mar 01	13	1,185	0ms	0ms

15

972

Details

0ms

select datetimeupdate from latest_candle_datetime_per_receng where recognitionengine ilike ?;

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Mar 01	13	972	0ms	0ms

16

799

Details

0ms

set extra_float_digits = ?;

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Mar 01	13	799	0ms	0ms

17

787

Details

0ms

set application_name = ?;

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Mar 01	13	787	0ms	0ms

18

608

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
Mar 01	13	608	0ms	0ms

19

544

Details

0ms

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

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Mar 01	13	544	0ms	0ms

20

356

Details

0ms

select count(*) from pg_stat_statements (false);

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Mar 01	13	356	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
Mar 01	13	26	0ms	0ms

2

0ms

133

Details

0ms

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

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Mar 01	13	133	0ms	0ms

3

0ms

2,378

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
Mar 01	13	2,378	0ms	0ms

4

0ms

180

Details

0ms

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

Times Reported Time consuming queries #4

Day	Hour	Count	Duration	Avg duration
Mar 01	13	180	0ms	0ms

5

0ms

49

Details

0ms

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

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Mar 01	13	49	0ms	0ms

6

0ms

6

Details

0ms

insert into resultmedia (processresultsid, type, name, filename) values (?, ?, ?, ?) returning id;

Times Reported Time consuming queries #6

Day	Hour	Count	Duration	Avg duration
Mar 01	13	6	0ms	0ms

7

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 #7

Day	Hour	Count	Duration	Avg duration
Mar 01	13	18	0ms	0ms

8

0ms

237

Details

0ms

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

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Mar 01	13	237	0ms	0ms

9

0ms

11

Details

0ms

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

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Mar 01	13	11	0ms	0ms

10

0ms

332

Details

0ms

commit;

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Mar 01	13	332	0ms	0ms

11

0ms

181

Details

0ms

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

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Mar 01	13	181	0ms	0ms

12

0ms

3

Details

0ms

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

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Mar 01	13	3	0ms	0ms

13

0ms

237

Details

0ms

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

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Mar 01	13	237	0ms	0ms

14

0ms

25

Details

0ms

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

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Mar 01	13	25	0ms	0ms

15

0ms

6

Details

0ms

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

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Mar 01	13	6	0ms	0ms

16

0ms

11

Details

0ms

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

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Mar 01	13	11	0ms	0ms

17

0ms

237

Details

0ms

select system_identifier from pg_control_system ();

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Mar 01	13	237	0ms	0ms

18

0ms

3

Details

0ms

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

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Mar 01	13	3	0ms	0ms

19

0ms

237

Details

0ms

select datname, confl_tablespace, confl_lock, confl_snapshot, confl_bufferpin, confl_deadlock from pg_stat_database_conflicts where datname not ilike ? and datname not ilike ? and datname not ilike ? and datname not ilike ?;

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Mar 01	13	237	0ms	0ms

20

0ms

132

Details

0ms

with rar_max as ( select resultuid from relevance_bigmovement_results order by resultuid desc limit ? ), all_results as ( select bmr.resultuid as resultuid, ? 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, bmr.patternendtime as identified, bmr.patternlengthbars, dtt.timezone as timezone, g.basegroupname, 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 brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = bmr.symbolid inner join symbols s on bmr.symbolid = s.symbolid and s.nonliquid = ? inner join downloadersymbolsettings dss on bmr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on bmr.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_bigmovement_results rbr on rbr.resultuid = bmr.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 = ? where bmr.gmttimefound > now() - interval ? and s.deleted = ? and (bmr.simulation = ? or bmr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or bmr.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, interval desc;

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Mar 01	13	132	0ms	0ms

Time consuming prepare


Date: 2026-03-01 13:01:05
Duration: 3ms
Database: postgres
Date: 2026-03-01 13:32:14
Duration: 8ms
Database: postgres
Date: 2026-03-01 13:01:46
Duration: 0ms
Database: postgres
Date: 2026-03-01 13:41:00
Duration: 0ms
Database: postgres
Date: 2026-03-01 13:45:58
Duration: 1ms
Database: postgres
Date: 2026-03-01 13:32:14
Duration: 3ms
Database: postgres
Date: 2026-03-01 13:41:53
Duration: 0ms
Database: postgres
Date: 2026-03-01 13:56:02
Duration: 0ms
Database: postgres
Date: 2026-03-01 13:11:01
Duration: 3ms
Database: postgres
Date: 2026-03-01 13:40:05
Duration: 2ms
Database: postgres
Date: 2026-03-01 13:47:51
Duration: 0ms
Database: postgres
Date: 2026-03-01 13:11:01
Duration: 0ms
Database: postgres
Date: 2026-03-01 13:36:50
Duration: 0ms
Database: postgres
Date: 2026-03-01 13:47:23
Duration: 0ms
Database: postgres
Date: 2026-03-01 13:56:37
Duration: 1ms
Database: postgres
Date: 2026-03-01 13:36:49
Duration: 0ms
Database: postgres
Date: 2026-03-01 13:57:32
Duration: 1ms
Database: postgres
Date: 2026-03-01 13:57:30
Duration: 2ms
Database: postgres
Date: 2026-03-01 13:21:50
Duration: 1ms
Database: postgres
Date: 2026-03-01 13:57:28
Duration: 0ms
Database: postgres

Time consuming bind

Rank

Total duration

Times executed

Min duration

Max duration

Avg duration

Query

1

9s528ms

909

Details

0ms

35ms

10ms

WITH rar_max as ( ;

Times Reported Time consuming bind #1

Day	Hour	Count	Duration	Avg duration
Mar 01	13	909	9s528ms	10ms

WITH rar_max as ( ;

Date: 2026-03-01 13:36:32 Duration: 35ms Database: postgres parameters: $1 = 't', $2 = '558', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '80', $14 = 'AUDSGD', $15 = 'CHFSGD', $16 = 'EURDKK', $17 = 'EURHKD', $18 = 'EURNOK', $19 = 'EURPLN', $20 = 'EURSEK', $21 = 'EURSGD', $22 = 'EURTRY', $23 = 'EURZAR', $24 = 'GBPDKK', $25 = 'GBPNOK', $26 = 'GBPSEK', $27 = 'GBPSGD', $28 = 'NOKJPY', $29 = 'NOKSEK', $30 = 'SEKJPY', $31 = 'SGDJPY', $32 = 'USDCNH', $33 = 'USDCZK', $34 = 'USDDKK', $35 = 'USDHKD', $36 = 'USDHUF', $37 = 'USDMXN', $38 = 'USDNOK', $39 = 'USDPLN', $40 = 'USDRUB', $41 = 'USDSEK', $42 = 'USDTHB', $43 = 'USDTRY', $44 = 'USDZAR', $45 = 'AUDUSD', $46 = 'EURUSD', $47 = 'GBPUSD', $48 = 'USDCAD', $49 = 'USDCHF', $50 = 'USDJPY', $51 = 'AUDCAD', $52 = 'AUDCHF', $53 = 'AUDJPY', $54 = 'AUDNZD', $55 = 'CADCHF', $56 = 'CADJPY', $57 = 'CHFJPY', $58 = 'EURAUD', $59 = 'EURCAD', $60 = 'EURCHF', $61 = 'EURGBP', $62 = 'EURJPY', $63 = 'EURNZD', $64 = 'GBPAUD', $65 = 'GBPCAD', $66 = 'GBPCHF', $67 = 'GBPJPY', $68 = 'GBPNZD', $69 = 'NZDCAD', $70 = 'NZDCHF', $71 = 'NZDJPY', $72 = 'NZDUSD', $73 = 'USDSGD', $74 = 'AUS200', $75 = 'DE30', $76 = 'ES35', $77 = 'F40', $78 = 'HK50', $79 = 'IT40', $80 = 'JP225', $81 = 'STOXX50', $82 = 'UK100', $83 = 'US2000', $84 = 'US30', $85 = 'US500', $86 = 'CHINA50', $87 = 'USTEC', $88 = 'XAGEUR', $89 = 'XAGUSD', $90 = 'XAUUSD', $91 = 'XAUEUR', $92 = 'XPDUSD', $93 = 'XPTUSD', $94 = '0', $95 = '', $96 = '0', $97 = '0', $98 = '0', $99 = '0', $100 = '0', $101 = 't', $102 = '10', $103 = '10'
WITH rar_max as ( ;

Date: 2026-03-01 13:05:56 Duration: 32ms Database: postgres parameters: $1 = 't', $2 = '667', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '84', $14 = 'AUDCAD', $15 = 'AUDCHF', $16 = 'AUDJPY', $17 = 'AUDNZD', $18 = 'AUDSGD', $19 = 'CADCHF', $20 = 'CADJPY', $21 = 'CHFJPY', $22 = 'EURAUD', $23 = 'EURCAD', $24 = 'EURCHF', $25 = 'EURCZK', $26 = 'EURGBP', $27 = 'EURHUF', $28 = 'EURJPY', $29 = 'EURNOK', $30 = 'EURNZD', $31 = 'EURPLN', $32 = 'EURSEK', $33 = 'EURSGD', $34 = 'EURTRY', $35 = 'EURZAR', $36 = 'GBPAUD', $37 = 'GBPCAD', $38 = 'GBPCHF', $39 = 'GBPJPY', $40 = 'GBPNZD', $41 = 'GBPPLN', $42 = 'GBPSEK', $43 = 'GBPSGD', $44 = 'NZDCAD', $45 = 'NZDCHF', $46 = 'NZDJPY', $47 = 'NZDSGD', $48 = 'USDCNH', $49 = 'USDCZK', $50 = 'USDHUF', $51 = 'USDNOK', $52 = 'USDPLN', $53 = 'USDSEK', $54 = 'USDSGD', $55 = 'USDTRY', $56 = 'USDZAR', $57 = 'WTI', $58 = 'XBRUSD', $59 = 'XTIUSD', $60 = 'BTCUSD', $61 = 'XAGAUD', $62 = 'XAGUSD', $63 = 'XAUAUD', $64 = 'XAUUSD', $65 = 'XPTUSD', $66 = 'XPDUSD', $67 = 'AUDUSD', $68 = 'EURUSD', $69 = 'GBPUSD', $70 = 'NZDUSD', $71 = 'USDCAD', $72 = 'USDCHF', $73 = 'USDHKD', $74 = 'USDJPY', $75 = 'AUS200', $76 = 'CHINA300', $77 = 'CHINA50', $78 = 'DJ30', $79 = 'ESP35t', $80 = 'EUR50', $81 = 'EURO50', $82 = 'FRA40', $83 = 'GDAXI', $84 = 'GDAXIm', $85 = 'HK50', $86 = 'ITA40', $87 = 'J225', $88 = 'JP225', $89 = 'NAS100', $90 = 'SING30', $91 = 'SPA35', $92 = 'STOXX50', $93 = 'SUI20', $94 = 'UK100', $95 = 'US100', $96 = 'US30', $97 = 'US500', $98 = '0', $99 = '', $100 = '0', $101 = '0', $102 = '0', $103 = '700', $104 = '700', $105 = 't', $106 = '10', $107 = '10'
WITH rar_max as ( ;

Date: 2026-03-01 13:20:53 Duration: 31ms Database: postgres parameters: $1 = 't', $2 = '558', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '80', $14 = 'AUDSGD', $15 = 'CHFSGD', $16 = 'EURDKK', $17 = 'EURHKD', $18 = 'EURNOK', $19 = 'EURPLN', $20 = 'EURSEK', $21 = 'EURSGD', $22 = 'EURTRY', $23 = 'EURZAR', $24 = 'GBPDKK', $25 = 'GBPNOK', $26 = 'GBPSEK', $27 = 'GBPSGD', $28 = 'NOKJPY', $29 = 'NOKSEK', $30 = 'SEKJPY', $31 = 'SGDJPY', $32 = 'USDCNH', $33 = 'USDCZK', $34 = 'USDDKK', $35 = 'USDHKD', $36 = 'USDHUF', $37 = 'USDMXN', $38 = 'USDNOK', $39 = 'USDPLN', $40 = 'USDRUB', $41 = 'USDSEK', $42 = 'USDTHB', $43 = 'USDTRY', $44 = 'USDZAR', $45 = 'AUDUSD', $46 = 'EURUSD', $47 = 'GBPUSD', $48 = 'USDCAD', $49 = 'USDCHF', $50 = 'USDJPY', $51 = 'AUDCAD', $52 = 'AUDCHF', $53 = 'AUDJPY', $54 = 'AUDNZD', $55 = 'CADCHF', $56 = 'CADJPY', $57 = 'CHFJPY', $58 = 'EURAUD', $59 = 'EURCAD', $60 = 'EURCHF', $61 = 'EURGBP', $62 = 'EURJPY', $63 = 'EURNZD', $64 = 'GBPAUD', $65 = 'GBPCAD', $66 = 'GBPCHF', $67 = 'GBPJPY', $68 = 'GBPNZD', $69 = 'NZDCAD', $70 = 'NZDCHF', $71 = 'NZDJPY', $72 = 'NZDUSD', $73 = 'USDSGD', $74 = 'AUS200', $75 = 'DE30', $76 = 'ES35', $77 = 'F40', $78 = 'HK50', $79 = 'IT40', $80 = 'JP225', $81 = 'STOXX50', $82 = 'UK100', $83 = 'US2000', $84 = 'US30', $85 = 'US500', $86 = 'CHINA50', $87 = 'USTEC', $88 = 'XAGEUR', $89 = 'XAGUSD', $90 = 'XAUUSD', $91 = 'XAUEUR', $92 = 'XPDUSD', $93 = 'XPTUSD', $94 = '0', $95 = '', $96 = '0', $97 = '0', $98 = '0', $99 = '0', $100 = '0', $101 = 't', $102 = '10', $103 = '10'

2

1s637ms

642

Details

0ms

11ms

2ms

SELECT symbolid, ;

Times Reported Time consuming bind #2

Day	Hour	Count	Duration	Avg duration
	13	642	1s637ms	2ms

SELECT symbolid, ;

Date: 2026-03-01 13:11:01 Duration: 11ms Database: postgres parameters: $1 = 'BDSWISS', $2 = '60', $3 = 'NAS100', $4 = 'HKG_50'
SELECT symbolid, ;

Date: 2026-03-01 13:01:05 Duration: 4ms Database: postgres parameters: $1 = 'BDSWISS', $2 = '15', $3 = 'SPX500', $4 = 'US30'
SELECT symbolid, ;

Date: 2026-03-01 13:00:35 Duration: 4ms Database: postgres parameters: $1 = 'GLOBALGTMT5', $2 = '15', $3 = 'DOTBTC', $4 = 'BTCXAU', $5 = 'BTCZAR', $6 = 'DOGUSD'

3

1s349ms

4,584

Details

0ms

6ms

0ms

SELECT ;

Times Reported Time consuming bind #3

Day	Hour	Count	Duration	Avg duration
	13	4,584	1s349ms	0ms

SELECT ;

Date: 2026-03-01 13:57:32 Duration: 6ms Database: postgres parameters: $1 = '500991628203869200'
SELECT ;

Date: 2026-03-01 13:57:32 Duration: 6ms Database: postgres parameters: $1 = '500991628203869200'
SELECT ;

Date: 2026-03-01 13:30:46 Duration: 5ms Database: postgres parameters: $1 = '515840243198780300'

4

197ms

31

Details

4ms

9ms

6ms

WITH last_candle AS ( ;

Times Reported Time consuming bind #4

Day	Hour	Count	Duration	Avg duration
	13	31	197ms	6ms

WITH last_candle AS ( ;

Date: 2026-03-01 13:57:30 Duration: 9ms Database: postgres parameters: $1 = '667', $2 = '667'
WITH last_candle AS ( ;

Date: 2026-03-01 13:16:00 Duration: 8ms Database: postgres parameters: $1 = '558', $2 = '558'
WITH last_candle AS ( ;

Date: 2026-03-01 13:16:00 Duration: 8ms Database: postgres parameters: $1 = '558', $2 = '558'

5

194ms

118

Details

1ms

2ms

1ms

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

Times Reported Time consuming bind #5

Day	Hour	Count	Duration	Avg duration
	13	118	194ms	1ms

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

Date: 2026-03-01 13:36:50 Duration: 2ms Database: postgres parameters: $1 = 'PEPPERSTONEMT5'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-03-01 13:45:54 Duration: 2ms Database: postgres parameters: $1 = 'BDSWISS'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-03-01 13:45:57 Duration: 2ms Database: postgres parameters: $1 = 'BDSWISS'

6

186ms

11

Details

0ms

23ms

16ms

with wh_patitioned as ( ;

Times Reported Time consuming bind #6

Day	Hour	Count	Duration	Avg duration
	13	11	186ms	16ms

with wh_patitioned as ( ;

Date: 2026-03-01 13:30:02 Duration: 23ms 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: 2026-03-01 13:25:03 Duration: 23ms 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: 2026-03-01 13:40:02 Duration: 23ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'

7

180ms

2,378

Details

0ms

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

Times Reported Time consuming bind #7

Day	Hour	Count	Duration	Avg duration
	13	2,378	180ms	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: 2026-03-01 13:01:46 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 20:00:00', $2 = '9191.4', $3 = '9203.4', $4 = '9184.3', $5 = '9196.4', $6 = '8819', $7 = '515840248015562300', $8 = '0', $9 = '2026-03-01 13:01:46.095', $10 = '2026-03-01 13:01:45.991', $11 = '9191.4', $12 = '9203.4', $13 = '9184.3', $14 = '9196.4', $15 = '8819', $16 = '0', $17 = '2026-03-01 13:01:46.095', $18 = '2026-03-01 13:01:45.991'
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: 2026-03-01 13:11:34 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 21:00:00', $2 = '109.26', $3 = '110.02', $4 = '108.9', $5 = '109.86', $6 = '4525', $7 = '515840247879403300', $8 = '0', $9 = '2026-03-01 13:11:34.066', $10 = '2026-03-01 13:11:34', $11 = '109.26', $12 = '110.02', $13 = '108.9', $14 = '109.86', $15 = '4525', $16 = '0', $17 = '2026-03-01 13:11:34.066', $18 = '2026-03-01 13:11:34'
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: 2026-03-01 13:11:30 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 21:00:00', $2 = '261.35', $3 = '262.65', $4 = '261.18', $5 = '262.24', $6 = '1783', $7 = '515840247899857300', $8 = '0', $9 = '2026-03-01 13:11:30.035', $10 = '2026-03-01 13:11:29.963', $11 = '261.35', $12 = '262.65', $13 = '261.18', $14 = '262.24', $15 = '1783', $16 = '0', $17 = '2026-03-01 13:11:30.035', $18 = '2026-03-01 13:11:29.963'

8

163ms

2,407

Details

0ms

5ms

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 #8

Day	Hour	Count	Duration	Avg duration
	13	2,407	163ms	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: 2026-03-01 13:46:52 Duration: 5ms Database: postgres parameters: $1 = '2026-02-27 22:30:00', $2 = '156.023', $3 = '156.179', $4 = '155.976', $5 = '156.178', $6 = '1762', $7 = '515840230603982300', $8 = '0', $9 = '2026-03-01 13:46:52.496', $10 = '2026-03-01 13:46:52.419', $11 = '156.023', $12 = '156.179', $13 = '155.976', $14 = '156.178', $15 = '1762', $16 = '0', $17 = '2026-03-01 13:46:52.496', $18 = '2026-03-01 13:46:52.419'
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: 2026-03-01 13:41:47 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 21:00:00', $2 = '9196.25', $3 = '9197.45', $4 = '9185.4', $5 = '9190.4', $6 = '3873', $7 = '515840248015340300', $8 = '0', $9 = '2026-03-01 13:41:47.333', $10 = '2026-03-01 13:41:47.27', $11 = '9196.25', $12 = '9197.45', $13 = '9185.4', $14 = '9190.4', $15 = '3873', $16 = '0', $17 = '2026-03-01 13:41:47.333', $18 = '2026-03-01 13:41:47.27'
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: 2026-03-01 13:32:03 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 23:00:00', $2 = '24931.78', $3 = '24935.03', $4 = '24891.15', $5 = '24920.03', $6 = '8919', $7 = '515840248039147300', $8 = '0', $9 = '2026-03-01 13:32:03.671', $10 = '2026-03-01 13:32:03.574', $11 = '24931.78', $12 = '24935.03', $13 = '24891.15', $14 = '24920.03', $15 = '8919', $16 = '0', $17 = '2026-03-01 13:32:03.671', $18 = '2026-03-01 13:32:03.574'

9

123ms

2,789

Details

0ms

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

Times Reported Time consuming bind #9

Day	Hour	Count	Duration	Avg duration
	13	2,789	123ms	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: 2026-03-01 13:56:02 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 23:15:00', $2 = '24916.03', $3 = '24928.03', $4 = '24891.15', $5 = '24920.03', $6 = '3923', $7 = '515840248038958300', $8 = '0', $9 = '2026-03-01 13:56:02.492', $10 = '2026-03-01 13:56:02.421', $11 = '24916.03', $12 = '24928.03', $13 = '24891.15', $14 = '24920.03', $15 = '3923', $16 = '0', $17 = '2026-03-01 13:56:02.492', $18 = '2026-03-01 13:56:02.421'
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: 2026-03-01 13:45:38 Duration: 0ms Database: postgres parameters: $1 = '2026-02-28 00:45:00', $2 = '1.66046', $3 = '1.661065', $4 = '1.65987', $5 = '1.660405', $6 = '756', $7 = '515840249382700300', $8 = '0', $9 = '2026-03-01 13:45:38.694', $10 = '2026-03-01 13:45:38.637', $11 = '1.66046', $12 = '1.661065', $13 = '1.65987', $14 = '1.660405', $15 = '756', $16 = '0', $17 = '2026-03-01 13:45:38.694', $18 = '2026-03-01 13:45:38.637'
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: 2026-03-01 13:56:06 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 23:15:00', $2 = '48906.55', $3 = '48908.55', $4 = '48873.55', $5 = '48874.4', $6 = '3213', $7 = '515840248000537300', $8 = '0', $9 = '2026-03-01 13:56:06.52', $10 = '2026-03-01 13:56:06.448', $11 = '48906.55', $12 = '48908.55', $13 = '48873.55', $14 = '48874.4', $15 = '3213', $16 = '0', $17 = '2026-03-01 13:56:06.52', $18 = '2026-03-01 13:56:06.448'

10

114ms

10

Details

9ms

12ms

11ms

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

Times Reported Time consuming bind #10

Day	Hour	Count	Duration	Avg duration
	13	10	114ms	11ms

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

Date: 2026-03-01 13:36:50 Duration: 12ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Date: 2026-03-01 13:36:49 Duration: 12ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Date: 2026-03-01 13:57:13 Duration: 12ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'

11

108ms

4,348

Details

0ms

5ms

0ms

select 1;

Times Reported Time consuming bind #11

Day	Hour	Count	Duration	Avg duration
	13	4,348	108ms	0ms

select 1;

Date: 2026-03-01 13:21:00 Duration: 5ms Database: postgres
select 1;

Date: 2026-03-01 13:36:31 Duration: 3ms Database: postgres
select 1;

Date: 2026-03-01 13:56:37 Duration: 2ms Database: postgres

12

26ms

5

Details

3ms

6ms

5ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Times Reported Time consuming bind #12

Day	Hour	Count	Duration	Avg duration
	13	5	26ms	5ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-03-01 13:21:00 Duration: 6ms Database: postgres parameters: $1 = '627', $2 = '627'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-03-01 13:56:37 Duration: 6ms Database: postgres parameters: $1 = '667', $2 = '667'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-03-01 13:57:22 Duration: 5ms Database: postgres parameters: $1 = '667', $2 = '667'

13

12ms

18

Details

0ms

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

Times Reported Time consuming bind #13

Day	Hour	Count	Duration	Avg duration
	13	18	12ms	0ms

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

Date: 2026-03-01 13:31:09 Duration: 0ms Database: postgres
select cast(count(*) / cast(setting as numeric) * 100 as int) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by setting;

Date: 2026-03-01 13:11:01 Duration: 0ms Database: postgres
select cast(count(*) / cast(setting as numeric) * 100 as int) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by setting;

Date: 2026-03-01 13:11:01 Duration: 0ms Database: postgres

14

11ms

97

Details

0ms

4ms

0ms

/*server.CPResult*/ SELECT patternid, resy0, resy1, supporty0, supporty1, predictiontimeto, patternstarttime, s.symbolid, resx0, resx1, supportx0, supportx1, symbol, longname, shortname, timegranularity, patternendtime, pattern, a.direction, trendchange, patternlengthbars, patternquality, resultuid as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, exchange, breakout, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM autochartist_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern where resultuid = $1;

Times Reported Time consuming bind #14

Day	Hour	Count	Duration	Avg duration
	13	97	11ms	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: 2026-03-01 13:35:43 Duration: 4ms Database: postgres parameters: $1 = '607753454586139301'
/*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: 2026-03-01 13:41:14 Duration: 0ms Database: postgres parameters: $1 = '607751748849065301'
/*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: 2026-03-01 13:21:02 Duration: 0ms Database: postgres parameters: $1 = '607753514987140301'

15

10ms

68

Details

0ms

INSERT INTO T240 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Times Reported Time consuming bind #15

Day	Hour	Count	Duration	Avg duration
	13	68	10ms	0ms

INSERT INTO T240 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-03-01 13:47:51 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 16:00:00', $2 = '58640', $3 = '58825', $4 = '58390', $5 = '58635', $6 = '6318', $7 = '515840230561612300', $8 = '0', $9 = '2026-03-01 13:47:51.281', $10 = '2026-03-01 13:47:51.28', $11 = '58640', $12 = '58825', $13 = '58390', $14 = '58635', $15 = '6318', $16 = '0', $17 = '2026-03-01 13:47:51.281', $18 = '2026-03-01 13:47:51.28'
INSERT INTO T240 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-03-01 13:05:33 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 16:00:00', $2 = '58640', $3 = '58825', $4 = '58390', $5 = '58635', $6 = '6318', $7 = '515840230561612300', $8 = '0', $9 = '2026-03-01 13:05:33.553', $10 = '2026-03-01 13:05:33.552', $11 = '58640', $12 = '58825', $13 = '58390', $14 = '58635', $15 = '6318', $16 = '0', $17 = '2026-03-01 13:05:33.553', $18 = '2026-03-01 13:05:33.552'
INSERT INTO T240 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-03-01 13:35:32 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 16:00:00', $2 = '58640', $3 = '58825', $4 = '58390', $5 = '58635', $6 = '6318', $7 = '515840230561612300', $8 = '0', $9 = '2026-03-01 13:35:32.184', $10 = '2026-03-01 13:35:32.184', $11 = '58640', $12 = '58825', $13 = '58390', $14 = '58635', $15 = '6318', $16 = '0', $17 = '2026-03-01 13:35:32.184', $18 = '2026-03-01 13:35:32.184'

16

7ms

1

Details

7ms

SELECT distinct g.basegroupname, coalesce(bim.code, s.symbol) as symbol;

Times Reported Time consuming bind #16

Day	Hour	Count	Duration	Avg duration
	13	1	7ms	7ms

SELECT distinct g.basegroupname, coalesce(bim.code, s.symbol) as symbol;

Date: 2026-03-01 13:18:52 Duration: 7ms Database: postgres parameters: $1 = '667'

17

6ms

36

Details

0ms

INSERT INTO T1440_underlying (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Times Reported Time consuming bind #17

Day	Hour	Count	Duration	Avg duration
	13	36	6ms	0ms

INSERT INTO T1440_underlying (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: 2026-03-01 13:02:25 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 00:00:00', $2 = '272.725', $3 = '272.765', $4 = '263.075', $5 = '264.245', $6 = '12186', $7 = '515840249386902300', $8 = '0', $9 = '2026-03-01 13:02:25.419', $10 = '2026-03-01 13:02:25.419', $11 = '272.725', $12 = '272.765', $13 = '263.075', $14 = '264.245', $15 = '12186', $16 = '0', $17 = '2026-03-01 13:02:25.419', $18 = '2026-03-01 13:02:25.419'
INSERT INTO T1440_underlying (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: 2026-03-01 13:47:23 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 00:00:00', $2 = '272.725', $3 = '272.765', $4 = '263.075', $5 = '264.245', $6 = '12186', $7 = '515840249386902300', $8 = '0', $9 = '2026-03-01 13:47:23.717', $10 = '2026-03-01 13:47:23.716', $11 = '272.725', $12 = '272.765', $13 = '263.075', $14 = '264.245', $15 = '12186', $16 = '0', $17 = '2026-03-01 13:47:23.717', $18 = '2026-03-01 13:47:23.716'
INSERT INTO T1440_underlying (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: 2026-03-01 13:45:32 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 00:00:00', $2 = '242.507', $3 = '249.399', $4 = '242.4075', $5 = '248.3385', $6 = '65346', $7 = '515840249404368300', $8 = '0', $9 = '2026-03-01 13:45:32.665', $10 = '2026-03-01 13:45:32.665', $11 = '242.507', $12 = '249.399', $13 = '242.4075', $14 = '248.3385', $15 = '65346', $16 = '0', $17 = '2026-03-01 13:45:32.665', $18 = '2026-03-01 13:45:32.665'

18

4ms

121

Details

0ms

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

Times Reported Time consuming bind #18

Day	Hour	Count	Duration	Avg duration
	13	121	4ms	0ms

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

Date: 2026-03-01 13:35:43 Duration: 0ms Database: postgres parameters: $1 = '515840233907979300'
SELECT df.absolutetimezoneoffset FROM datafeedstimetable df INNER JOIN downloadersymbolsettings dss ON df.classname = dss.classname WHERE dss.symbolid = $1 GROUP BY df.absolutetimezoneoffset LIMIT 1;

Date: 2026-03-01 13:18:14 Duration: 0ms Database: postgres parameters: $1 = '515840243281880300'
SELECT df.absolutetimezoneoffset FROM datafeedstimetable df INNER JOIN downloadersymbolsettings dss ON df.classname = dss.classname WHERE dss.symbolid = $1 GROUP BY df.absolutetimezoneoffset LIMIT 1;

Date: 2026-03-01 13:02:52 Duration: 0ms Database: postgres parameters: $1 = '515840243873007300'

19

4ms

2

Details

2ms

SELECT * FROM datafeed_symbols WHERE classname ilike 'ICMARKETS-AU-MT5' LIMIT $1;

Times Reported Time consuming bind #19

Day	Hour	Count	Duration	Avg duration
	13	2	4ms	2ms

SELECT * FROM datafeed_symbols WHERE classname ilike 'ICMARKETS-AU-MT5' LIMIT $1;

Date: 2026-03-01 13:21:50 Duration: 2ms Database: postgres parameters: $1 = '5000'
SELECT * FROM datafeed_symbols WHERE classname ilike 'ICMARKETS-AU-MT5' LIMIT $1;

Date: 2026-03-01 13:51:50 Duration: 2ms Database: postgres parameters: $1 = '5000'

20

4ms

33

Details

0ms

1ms

0ms

/*server.KeyLevelResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, longname, shortname, Exchange AS e, timegranularity AS tg, a.PatternID AS pid, a.direction AS d, a.patternprice as pp, atbaridentified AS pet, CASE WHEN (x9 != '') THEN x9 WHEN (x8 != '') THEN x8 WHEN (x7 != '') THEN x7 WHEN (x6 != '') THEN x6 WHEN (x5 != '') THEN x5 WHEN (x4 != '') THEN x4 WHEN (x3 != '') THEN x3 WHEN (x2 != '') THEN x2 END AS pst, PatternPrice AS patp, x0, x1, x2, CASE WHEN (x3 != '') THEN x3 ELSE '1900-01-01' END as x3, CASE WHEN (x4 != '') THEN x4 ELSE '1900-01-01' END as x4, CASE WHEN (x5 != '') THEN x5 ELSE '1900-01-01' END as x5, CASE WHEN (x6 != '') THEN x6 ELSE '1900-01-01' END as x6, CASE WHEN (x7 != '') THEN x7 ELSE '1900-01-01' END as x7, CASE WHEN (x8 != '') THEN x8 ELSE '1900-01-01' END as x8, CASE WHEN (x9 != '') THEN x9 ELSE '1900-01-01' END as x9, errorMargin as erm, breakoutprice as pE, breakoutbars as be, breakout, atbaridentified as atBar, PatternLengthBars AS l, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz, approachingtimestamp AS apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb FROM keylevels_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid INNER JOIN hrspatterns p on a.patternid = p.patternid where resultuid = $1 and dtt.dayofweek = 3;

Times Reported Time consuming bind #20

Day	Hour	Count	Duration	Avg duration
	13	33	4ms	0ms

/*server.KeyLevelResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, longname, shortname, Exchange AS e, timegranularity AS tg, a.PatternID AS pid, a.direction AS d, a.patternprice as pp, atbaridentified AS pet, CASE WHEN (x9 != '') THEN x9 WHEN (x8 != '') THEN x8 WHEN (x7 != '') THEN x7 WHEN (x6 != '') THEN x6 WHEN (x5 != '') THEN x5 WHEN (x4 != '') THEN x4 WHEN (x3 != '') THEN x3 WHEN (x2 != '') THEN x2 END AS pst, PatternPrice AS patp, x0, x1, x2, CASE WHEN (x3 != '') THEN x3 ELSE '1900-01-01' END as x3, CASE WHEN (x4 != '') THEN x4 ELSE '1900-01-01' END as x4, CASE WHEN (x5 != '') THEN x5 ELSE '1900-01-01' END as x5, CASE WHEN (x6 != '') THEN x6 ELSE '1900-01-01' END as x6, CASE WHEN (x7 != '') THEN x7 ELSE '1900-01-01' END as x7, CASE WHEN (x8 != '') THEN x8 ELSE '1900-01-01' END as x8, CASE WHEN (x9 != '') THEN x9 ELSE '1900-01-01' END as x9, errorMargin as erm, breakoutprice as pE, breakoutbars as be, breakout, atbaridentified as atBar, PatternLengthBars AS l, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz, approachingtimestamp AS apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb FROM keylevels_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid INNER JOIN hrspatterns p on a.patternid = p.patternid where resultuid = $1 and dtt.dayofweek = 3;

Date: 2026-03-01 13:39:24 Duration: 1ms Database: postgres parameters: $1 = '607753045418998303'
/*server.KeyLevelResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, longname, shortname, Exchange AS e, timegranularity AS tg, a.PatternID AS pid, a.direction AS d, a.patternprice as pp, atbaridentified AS pet, CASE WHEN (x9 != '') THEN x9 WHEN (x8 != '') THEN x8 WHEN (x7 != '') THEN x7 WHEN (x6 != '') THEN x6 WHEN (x5 != '') THEN x5 WHEN (x4 != '') THEN x4 WHEN (x3 != '') THEN x3 WHEN (x2 != '') THEN x2 END AS pst, PatternPrice AS patp, x0, x1, x2, CASE WHEN (x3 != '') THEN x3 ELSE '1900-01-01' END as x3, CASE WHEN (x4 != '') THEN x4 ELSE '1900-01-01' END as x4, CASE WHEN (x5 != '') THEN x5 ELSE '1900-01-01' END as x5, CASE WHEN (x6 != '') THEN x6 ELSE '1900-01-01' END as x6, CASE WHEN (x7 != '') THEN x7 ELSE '1900-01-01' END as x7, CASE WHEN (x8 != '') THEN x8 ELSE '1900-01-01' END as x8, CASE WHEN (x9 != '') THEN x9 ELSE '1900-01-01' END as x9, errorMargin as erm, breakoutprice as pE, breakoutbars as be, breakout, atbaridentified as atBar, PatternLengthBars AS l, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz, approachingtimestamp AS apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb FROM keylevels_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid INNER JOIN hrspatterns p on a.patternid = p.patternid where resultuid = $1 and dtt.dayofweek = 3;

Date: 2026-03-01 13:02:52 Duration: 0ms Database: postgres parameters: $1 = '607752458200990303'
/*server.KeyLevelResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, longname, shortname, Exchange AS e, timegranularity AS tg, a.PatternID AS pid, a.direction AS d, a.patternprice as pp, atbaridentified AS pet, CASE WHEN (x9 != '') THEN x9 WHEN (x8 != '') THEN x8 WHEN (x7 != '') THEN x7 WHEN (x6 != '') THEN x6 WHEN (x5 != '') THEN x5 WHEN (x4 != '') THEN x4 WHEN (x3 != '') THEN x3 WHEN (x2 != '') THEN x2 END AS pst, PatternPrice AS patp, x0, x1, x2, CASE WHEN (x3 != '') THEN x3 ELSE '1900-01-01' END as x3, CASE WHEN (x4 != '') THEN x4 ELSE '1900-01-01' END as x4, CASE WHEN (x5 != '') THEN x5 ELSE '1900-01-01' END as x5, CASE WHEN (x6 != '') THEN x6 ELSE '1900-01-01' END as x6, CASE WHEN (x7 != '') THEN x7 ELSE '1900-01-01' END as x7, CASE WHEN (x8 != '') THEN x8 ELSE '1900-01-01' END as x8, CASE WHEN (x9 != '') THEN x9 ELSE '1900-01-01' END as x9, errorMargin as erm, breakoutprice as pE, breakoutbars as be, breakout, atbaridentified as atBar, PatternLengthBars AS l, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz, approachingtimestamp AS apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb FROM keylevels_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid INNER JOIN hrspatterns p on a.patternid = p.patternid where resultuid = $1 and dtt.dayofweek = 3;

Date: 2026-03-01 13:03:07 Duration: 0ms Database: postgres parameters: $1 = '607752217844219303'

Log levels

Key values

157,132 Log entries

Chart
Table

Type	Count	Percentage
CONTEXT	297	0.19%
DETAIL	29,230	18.60%
ERROR	392	0.25%
LOG	126,492	80.50%
STATEMENT	721	0.46%

Events distribution

Key values

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

Most Frequent Errors/Events

Key values

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

Rank

Times reported

Error

1

356

Details

ERROR: pg_stat_statements must be loaded via shared_preload_libraries

Times Reported Most Frequent Error / Event #1

Day	Hour	Count
Mar 01	13	356

ERROR: pg_stat_statements must be loaded via shared_preload_libraries

2

36

Details

ERROR: schema "..." does not exist

Times Reported Most Frequent Error / Event #2

Day	Hour	Count
Mar 01	13	36

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

Global information

Overview

Global Stats

SQL Traffic

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SELECT Traffic

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INSERT/UPDATE/DELETE Traffic

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Queries duration

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Prepared queries ratio

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General Activity

Connections

Established Connections

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Connections per database

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Connections per user

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Connections per host

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Sessions

Simultaneous sessions

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Histogram of session times

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Sessions per database

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Sessions per user

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Sessions per host

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Sessions per application

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Checkpoints / Restartpoints

Checkpoints Buffers

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Checkpoints Wal files

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Checkpoints distance

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Checkpoints Activity

Checkpoint causes

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Temporary Files

Size of temporary files

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Number of temporary files

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Temporary Files Activity

Queries generating the most temporary files (N)

Queries generating the largest temporary files

Vacuums

Vacuums / Analyzes Distribution

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Analyzes per table

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Vacuums per table

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Tuples removed per table

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Pages removed per table

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Autovacuum Activity

Locks

Locks by types

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Type of locks

Most frequent waiting queries (N)

Queries that waited the most

Queries

Queries by type

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Queries by database

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Queries by user

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Duration by user