pgBadger-
Global information
- Generated on Sun Mar 1 04:59:20 2026
- Log file: /home/postgres/pg_data/data/pg_log/postgresql-2026-03-01_060000.log
- Parsed 566,448 log entries in 19s
- Log start from 2026-03-01 06:00:00 to 2026-03-01 06:59:18
-
Overview
Global Stats
- 972 Number of unique normalized queries
- 108,027 Number of queries
- 2h38m16s Total query duration
- 2026-03-01 06:00:00 First query
- 2026-03-01 06:59:18 Last query
- 4,994 queries/s at 2026-03-01 06:05:02 Query peak
- 2h38m16s Total query duration
- 2s122ms Prepare/parse total duration
- 20s555ms Bind total duration
- 2h37m53s Execute total duration
- 523 Number of events
- 5 Number of unique normalized events
- 356 Max number of times the same event was reported
- 0 Number of cancellation
- 28 Total number of automatic vacuums
- 40 Total number of automatic analyzes
- 442 Number temporary file
- 137.65 MiB Max size of temporary file
- 6.36 MiB Average size of temporary file
- 1,614 Total number of sessions
- 12 sessions at 2026-03-01 06:58:09 Session peak
- 1d30m17s Total duration of sessions
- 54s657ms Average duration of sessions
- 66 Average queries per session
- 5s883ms Average queries duration per session
- 48s774ms Average idle time per session
- 1,614 Total number of connections
- 28 connections/s at 2026-03-01 06:03:48 Connection peak
- 6 Total number of databases
SQL Traffic
Key values
- 4,994 queries/s Query Peak
- 2026-03-01 06:05:02 Date
SELECT Traffic
Key values
- 2,481 queries/s Query Peak
- 2026-03-01 06:05:02 Date
INSERT/UPDATE/DELETE Traffic
Key values
- 105 queries/s Query Peak
- 2026-03-01 06:28:49 Date
Queries duration
Key values
- 2h38m16s 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
↑ Back to the top of the General Activity tableDay Hour Count Min duration Max duration Avg duration Latency Percentile(90) Latency Percentile(95) Latency Percentile(99) Mar 01 06 108,027 0ms 13m50s 87ms 4m16s 5m39s 31m56s Day Hour SELECT COPY TO Average Duration Latency Percentile(90) Latency Percentile(95) Latency Percentile(99) Mar 01 06 34,917 601 0ms 0ms 0ms 0ms Day Hour INSERT UPDATE DELETE COPY FROM Average Duration Latency Percentile(90) Latency Percentile(95) Latency Percentile(99) Mar 01 06 12,416 456 13 78 0ms 0ms 0ms 0ms Day Hour Prepare Bind Bind/Prepare Percentage of prepare Mar 01 06 8,604 20,554 2.39 16.67% Day Hour Count Average / Second Mar 01 06 1,614 0.45/s Day Hour Count Average Duration Average idle time Mar 01 06 1,614 54s657ms 48s788ms -
Connections
Established Connections
Key values
- 28 connections Connection Peak
- 2026-03-01 06:03:48 Date
Connections per database
Key values
- acaweb_fx Main Database
- 1,614 connections Total
Connections per user
Key values
- postgres Main User
- 1,614 connections Total
Connections per host
Key values
- 192.168.4.142 Main host with 543 connections
- 1,614 Total connections
-
Sessions
Simultaneous sessions
Key values
- 12 sessions Session Peak
- 2026-03-01 06:58:09 Date
Histogram of session times
Key values
- 1,214 0-500ms duration
Sessions per database
Key values
- acaweb_fx Main Database
- 1,614 sessions Total
Sessions per user
Key values
- postgres Main User
- 1,614 sessions Total
Sessions per host
Key values
- 192.168.4.142 Main Host
- 1,614 sessions Total
Host Count Total Duration Average Duration 127.0.0.1 110 35m25s 19s320ms 192.168.0.114 2 10m 5m 192.168.0.216 116 7m6s 3s673ms 192.168.0.74 50 1h46m52s 2m8s 192.168.1.145 10 173ms 17ms 192.168.1.15 16 18m46s 1m10s 192.168.1.20 31 11h9m40s 21m36s 192.168.1.231 20 9h53m8s 29m39s 192.168.1.239 54 284ms 5ms 192.168.1.90 6 49ms 8ms 192.168.2.126 18 5s79ms 282ms 192.168.3.199 38 21s680ms 570ms 192.168.4.142 543 7m32s 832ms 192.168.4.33 70 1m9s 996ms 192.168.4.98 330 22s520ms 68ms [local] 200 19m46s 5s932ms -
Checkpoints / Restartpoints
Checkpoints Buffers
Key values
- 7,820 buffers Checkpoint Peak
- 2026-03-01 06:44:16 Date
- 209.899 seconds Highest write time
- 0.966 seconds Sync time
Checkpoints Wal files
Key values
- 3 files Wal files usage Peak
- 2026-03-01 06:44:16 Date
Checkpoints distance
Key values
- 109.40 Mo Distance Peak
- 2026-03-01 06:44:16 Date
Checkpoints Activity
↑ Back to the top of the Checkpoint Activity tableDay Hour Written buffers Write time Sync time Total time Mar 01 06 20,826 1,614.96s 1.034s 1,616.324s Day Hour Added Removed Recycled Synced files Longest sync Average sync Mar 01 06 0 0 14 1,336 0.940s 0.005s Day Hour Count Avg time (sec) Mar 01 06 0 0s Day Hour Mean distance Mean estimate Mar 01 06 18,406.33 kB 33,090.83 kB -
Temporary Files
Size of temporary files
Key values
- 104.33 MiB Temp Files size Peak
- 2026-03-01 06:17:31 Date
Number of temporary files
Key values
- 28 per second Temp Files Peak
- 2026-03-01 06:32:21 Date
Temporary Files Activity
↑ Back to the top of the Temporary Files Activity tableDay Hour Count Total size Average size Mar 01 06 442 2.74 GiB 6.36 MiB Queries generating the most temporary files (N)
Rank Count Total size Min size Max size Avg size Query 1 47 190.41 MiB 4.04 MiB 4.06 MiB 4.05 MiB select resultuid from relevance_fibonacci_results order by resultuid desc limit ?), fr as ( select a.*, rr.age, rr.relevant from fibonacci_results a left outer join relevance_fibonacci_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) end), all_results as ( select fr.resultuid as resultuid, fr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, fr.pattern as pattern_name, fr.timed as timed, fr.patternendtime as identified, dtt.timezone as timezone, fr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when fr.age is not null then fr.age when fr.resultuid <= rm.resultuid then ? else ? end as age, case when fr.relevant is not null then fr.relevant when fr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from fr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = fr.symbolid inner join symbols s on fr.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on fr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join downloadersymbolsettings dss on fr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left join lateral calc_fib_signal_filter (fr.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where fr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (fr.simulation = ? or fr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or fr.pattern in (...)) and (? = ? or fr.patternlengthbars <= ?) and (? = ? or (? = ? and fr.timed > cast(? as timestamp)) or (? = ? and fr.timed < cast(? as timestamp)))), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc;-
SELECT resultuid FROM relevance_fibonacci_results ORDER BY resultuid DESC LIMIT 1), fr AS ( SELECT a.*, rr.age, rr.relevant from fibonacci_results a LEFT OUTER JOIN relevance_fibonacci_results rr on a.resultuid = rr.resultuid WHERE CASE WHEN FALSE = $1 THEN true ELSE a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) END), all_results AS ( SELECT fr.resultuid AS resultuid, fr.direction AS direction, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, fr.pattern AS pattern_name, fr.timed AS timed, fr.patternendtime AS identified, dtt.timezone AS timezone, fr.patternlengthbars AS length, g.basegroupname, newLevels.filtered, CASE WHEN fr.age IS NOT NULL THEN fr.age WHEN fr.resultuid <= rm.resultuid THEN 11 ELSE 0 END as age, CASE WHEN fr.relevant IS NOT NULL THEN fr.relevant WHEN fr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip FROM fr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $2 AND bsl.symbolid = fr.symbolid INNER JOIN symbols s ON fr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN symbolgroup sg on fr.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid INNER JOIN downloadersymbolsettings dss ON fr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN rar_max rm ON 1 = 1 LEFT JOIN LATERAL calc_fib_signal_filter (fr.resultuid) newLevels on true LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' WHERE fr.gmttimefound > now() - INTERVAL '7 DAYS' AND dss.enabled = 1 AND s.deleted = 0 AND (fr.simulation = 0 OR fr.simulation IS NULL) AND ($3 = 0 OR s.timegranularity in ($4, $5, $6, $7, $8, $9, $10)) AND ($11 = 0 OR s.exchange in ($12)) AND ($13 = 0 OR coalesce(bim.code, s.symbol) in ($14, $15, $16, $17, $18, $19, $20, $21, $22, $23, $24, $25, $26, $27, $28, $29, $30, $31, $32, $33, $34, $35, $36, $37, $38, $39, $40, $41, $42, $43, $44, $45, $46, $47, $48, $49, $50, $51, $52, $53, $54, $55, $56, $57, $58, $59, $60, $61, $62, $63, $64, $65, $66, $67, $68, $69, $70, $71, $72, $73, $74, $75, $76, $77, $78, $79, $80, $81, $82, $83, $84, $85, $86, $87, $88, $89, $90, $91, $92, $93, $94, $95, $96, $97, $98, $99, $100, $101, $102, $103, $104, $105, $106, $107, $108, $109, $110, $111, $112, $113, $114, $115, $116, $117, $118, $119, $120, $121, $122, $123, $124, $125, $126, $127, $128, $129, $130, $131, $132, $133, $134, $135, $136, $137, $138, $139, $140, $141, $142, $143, $144, $145, $146, $147, $148, $149, $150, $151, $152, $153, $154, $155, $156, $157, $158, $159, $160, $161, $162, $163, $164, $165, $166, $167, $168, $169, $170, $171, $172, $173, $174, $175, $176, $177, $178, $179, $180, $181, $182, $183, $184, $185, $186, $187, $188, $189, $190, $191, $192, $193, $194, $195, $196, $197, $198, $199, $200, $201, $202, $203, $204, $205, $206, $207, $208, $209, $210, $211, $212, $213, $214, $215, $216, $217, $218, $219, $220, $221, $222, $223, $224, $225, $226, $227, $228, $229, $230, $231, $232, $233, $234, $235, $236, $237, $238, $239, $240, $241, $242, $243, $244, $245, $246, $247, $248, $249, $250, $251, $252, $253, $254, $255, $256, $257, $258, $259, $260, $261, $262, $263, $264, $265, $266, $267, $268, $269, $270, $271, $272, $273, $274, $275, $276, $277, $278, $279, $280, $281, $282, $283, $284, $285, $286, $287, $288, $289, $290, $291, $292, $293, $294, $295, $296, $297, $298, $299, $300, $301, $302, $303, $304, $305, $306, $307, $308, $309, $310, $311, $312, $313, $314, $315, $316, $317, $318, $319, $320, $321, $322, $323)) AND ($324 = 0 OR fr.pattern in ($325)) AND ($326 = 0 OR fr.patternlengthbars <= $327) AND ($328 = 0 OR ($329 = 1 AND fr.timed > cast('1970-01-01' as timestamp)) OR ($330 = 2 AND fr.timed < cast('1970-01-01' as timestamp)))), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results WHERE (FALSE = $331 OR relevant = 1) AND ($332 = 0 OR age <= $333) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC;
Date: 2026-03-01 06:00:48 Duration: 0ms
2 46 169.74 MiB 3.41 MiB 4.20 MiB 3.69 MiB select resultuid from relevance_consecutivecandles_results order by resultuid desc limit ?), all_results as ( select ccr.resultuid as resultuid, ccr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, ccr.patternendtime as identified, dtt.timezone as timezone, ccr.qtyconsecutivecandles as length, g.basegroupname, case when rcr.age is not null then rcr.age when ccr.resultuid <= rm.resultuid then ? else ? end as age, case when rcr.relevant is not null then rcr.relevant when ccr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip, newlevels.filtered from consecutivecandles_results ccr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = ccr.symbolid inner join symbols s on ccr.symbolid = s.symbolid and s.nonliquid = ? inner join downloadersymbolsettings dss on ccr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on ccr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join rar_max rm on ? = ? left outer join relevance_consecutivecandles_results rcr on rcr.resultuid = ccr.resultuid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? left join lateral calc_cc_signal_filter (ccr.resultuid) newlevels on true where ccr.gmttimefound > now() - interval ? and s.deleted = ? and (ccr.simulation = ? or ccr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or ccr.patternlengthbars <= ?)), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc;-
SELECT resultuid FROM relevance_consecutivecandles_results ORDER BY resultuid DESC LIMIT 1), all_results AS ( SELECT ccr.resultuid AS resultuid, ccr.direction AS direction, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, ccr.patternendtime AS identified, dtt.timezone AS timezone, ccr.qtyconsecutivecandles AS length, g.basegroupname, CASE WHEN rcr.age IS NOT NULL THEN rcr.age WHEN ccr.resultuid <= rm.resultuid THEN 1 ELSE 0 END as age, CASE WHEN rcr.relevant IS NOT NULL THEN rcr.relevant WHEN ccr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip, newLevels.filtered FROM consecutivecandles_results ccr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $1 AND bsl.symbolid = ccr.symbolid INNER JOIN symbols s ON ccr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN downloadersymbolsettings dss ON ccr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN symbolgroup sg on ccr.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid INNER JOIN rar_max rm ON 1 = 1 LEFT OUTER JOIN relevance_consecutivecandles_results rcr ON rcr.resultuid = ccr.resultuid LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' LEFT JOIN LATERAL calc_cc_signal_filter (ccr.resultuid) newLevels on true WHERE ccr.gmttimefound > now() - INTERVAL '7 DAYS' AND s.deleted = 0 AND (ccr.simulation = 0 OR ccr.simulation IS NULL) AND ($2 = 0 OR s.timegranularity in ($3, $4, $5, $6, $7, $8, $9)) AND ($10 = 0 OR s.exchange in ($11)) AND ($12 = 0 OR coalesce(bim.code, s.symbol) in ($13, $14, $15, $16, $17, $18, $19, $20, $21, $22, $23, $24, $25, $26, $27, $28, $29, $30, $31, $32, $33, $34, $35, $36, $37, $38, $39, $40, $41, $42, $43, $44, $45, $46, $47, $48, $49, $50, $51, $52, $53, $54, $55, $56, $57, $58, $59, $60, $61, $62, $63, $64, $65, $66, $67, $68, $69, $70, $71, $72, $73, $74, $75, $76, $77, $78, $79, $80, $81, $82, $83, $84, $85, $86, $87, $88, $89, $90, $91, $92, $93, $94, $95, $96, $97, $98, $99, $100, $101, $102, $103, $104, $105, $106, $107, $108, $109, $110, $111, $112, $113, $114, $115, $116, $117, $118, $119, $120, $121, $122, $123, $124, $125, $126, $127, $128, $129, $130, $131, $132, $133, $134, $135, $136, $137, $138, $139, $140, $141, $142, $143, $144, $145, $146, $147, $148, $149, $150, $151, $152, $153, $154, $155, $156, $157, $158, $159, $160, $161, $162, $163, $164, $165, $166, $167, $168, $169, $170, $171, $172, $173, $174, $175, $176, $177, $178, $179, $180, $181, $182, $183, $184, $185, $186, $187, $188, $189, $190, $191, $192, $193, $194, $195, $196, $197, $198, $199, $200, $201, $202, $203, $204, $205, $206, $207, $208, $209, $210, $211, $212, $213, $214, $215, $216, $217, $218, $219, $220, $221, $222, $223, $224, $225, $226, $227, $228, $229, $230, $231, $232, $233, $234, $235, $236, $237, $238, $239, $240, $241, $242, $243, $244, $245, $246, $247, $248, $249, $250, $251, $252, $253, $254, $255, $256, $257, $258, $259, $260, $261, $262, $263, $264, $265, $266, $267, $268, $269, $270, $271, $272, $273, $274, $275, $276, $277, $278, $279, $280, $281, $282, $283, $284, $285, $286, $287, $288, $289, $290, $291, $292, $293, $294, $295, $296, $297, $298, $299, $300, $301, $302, $303, $304, $305, $306, $307, $308, $309, $310, $311, $312, $313, $314, $315, $316, $317, $318, $319, $320, $321, $322)) AND ($323 = 0 OR ccr.patternlengthbars <= $324)), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results WHERE (FALSE = $325 OR relevant = 1) AND ($326 = 0 OR age <= $327) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC;
Date: 2026-03-01 06:01:11 Duration: 0ms
3 24 79.98 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 06:03:22 Duration: 0ms
4 13 505.17 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 06:01:13 Duration: 0ms
5 13 968.60 MiB 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 06:01:15 Duration: 0ms
6 4 550.58 MiB 137.64 MiB 137.65 MiB 137.65 MiB select updateresultsmaterializedview ();-
select updateresultsmaterializedview ();
Date: 2026-03-01 06:02:13 Duration: 0ms
7 4 333.53 MiB 83.38 MiB 83.39 MiB 83.38 MiB select updateageforrelevantresults ();-
select updateageforrelevantresults ();
Date: 2026-03-01 06:02:05 Duration: 0ms
Queries generating the largest temporary files
Rank Size Query 1 137.65 MiB select updateresultsmaterializedview ();[ Date: 2026-03-01 06:32:28 ]
2 137.65 MiB select updateresultsmaterializedview ();[ Date: 2026-03-01 06:47:24 ]
3 137.65 MiB select updateresultsmaterializedview ();[ Date: 2026-03-01 06:17:25 ]
4 137.64 MiB select updateresultsmaterializedview ();[ Date: 2026-03-01 06:02:13 ]
5 83.39 MiB select updateageforrelevantresults ();[ Date: 2026-03-01 06:02:05 ]
6 83.38 MiB select updateageforrelevantresults ();[ Date: 2026-03-01 06:32:15 ]
7 83.38 MiB select updateageforrelevantresults ();[ Date: 2026-03-01 06:47:13 ]
8 83.38 MiB select updateageforrelevantresults ();[ Date: 2026-03-01 06:17:14 ]
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 06:01:15 ]
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 06:03:15 ]
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 06:19:08 ]
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 06:20:15 ]
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 06:26:17 ]
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 06:31:18 ]
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 06:33:17 ]
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 06:35:15 ]
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 06:41:18 ]
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 06:46:15 ]
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 06:48:15 ]
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 06:50:15 ]
-
Vacuums
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 (13) Main table analyzed (database acaweb_fx)
- 40 analyzes Total
Table Number of analyzes acaweb_fx.public.solr_relevance_old 13 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.public.latest_t15_candle_view 2 acaweb_fx.pg_catalog.pg_index 1 acaweb_fx.public.solr_imports 1 socialmedia.public.processes 1 acaweb_fx.public.latest_candle_datetime_per_receng 1 Total 40 Vacuums per table
Key values
- public.solr_relevance_old (13) Main table vacuumed on database acaweb_fx
- 28 vacuums Total
Index Buffer usage Skipped WAL usage Table Vacuums scans hits misses dirtied pins frozen records full page bytes acaweb_fx.public.solr_relevance_old 13 13 10,733 0 41 0 0 8,531 257 2,300,460 acaweb_fx.pg_catalog.pg_type 3 3 549 0 44 0 0 216 33 194,097 acaweb_fx.public.datafeeds_latestrun 3 0 363 0 5 0 0 37 6 36,646 acaweb_fx.pg_catalog.pg_attribute 3 3 2,762 0 272 0 201 1,145 256 1,496,220 acaweb_fx.pg_catalog.pg_class 2 2 899 0 89 0 0 260 86 450,815 acaweb_fx.pg_catalog.pg_index 1 1 105 0 11 0 0 28 11 83,234 acaweb_fx.pg_catalog.pg_statistic 1 1 1,032 0 249 0 628 479 230 861,785 acaweb_fx.pg_catalog.pg_depend 1 1 403 0 72 0 59 182 61 320,737 acaweb_fx.public.latest_t15_candle_view 1 1 71 0 4 0 0 6 1 8,712 Total 28 25 16,917 2,229 787 0 888 10,884 941 5,752,706 Tuples removed per table
Key values
- public.solr_relevance_old (81370) Main table with removed tuples on database acaweb_fx
- 90029 tuples Total removed
Index Tuples Pages Table Vacuums scans removed remain not yet removable removed remain acaweb_fx.public.solr_relevance_old 13 13 81,370 81,483 0 0 2,655 acaweb_fx.pg_catalog.pg_attribute 3 3 5,776 33,270 1,320 20 799 acaweb_fx.pg_catalog.pg_depend 1 1 893 14,341 0 0 144 acaweb_fx.pg_catalog.pg_type 3 3 797 4,520 176 0 132 acaweb_fx.pg_catalog.pg_statistic 1 1 528 3,991 0 0 1,194 acaweb_fx.pg_catalog.pg_class 2 2 409 3,415 115 0 300 acaweb_fx.public.datafeeds_latestrun 3 0 166 42 0 0 48 acaweb_fx.public.latest_t15_candle_view 1 1 51 20 8 0 1 acaweb_fx.pg_catalog.pg_index 1 1 39 813 0 0 22 Total 28 25 90,029 141,895 1,619 20 5,295 Pages removed per table
Key values
- pg_catalog.pg_attribute (20) Main table with removed pages on database acaweb_fx
- 20 pages Total removed
Table Number of vacuums Index scans Tuples removed Pages removed acaweb_fx.pg_catalog.pg_attribute 3 3 5776 20 acaweb_fx.pg_catalog.pg_index 1 1 39 0 acaweb_fx.pg_catalog.pg_type 3 3 797 0 acaweb_fx.public.datafeeds_latestrun 3 0 166 0 acaweb_fx.pg_catalog.pg_statistic 1 1 528 0 acaweb_fx.pg_catalog.pg_depend 1 1 893 0 acaweb_fx.public.latest_t15_candle_view 1 1 51 0 acaweb_fx.public.solr_relevance_old 13 13 81370 0 acaweb_fx.pg_catalog.pg_class 2 2 409 0 Total 28 25 90,029 20 Autovacuum Activity
↑ Back to the top of the Autovacuum Activity tableDay Hour VACUUMs ANALYZEs Mar 01 06 28 40 - 0 sec Highest CPU-cost vacuum
-
Locks
Locks by types
Key values
- AccessShareLock Main Lock Type
- 11 locks Total
Most frequent waiting queries (N)
Rank Count Total time Min time Max time Avg duration Query 1 9 15m12s 57s885ms 1m59s 1m41s select datetimeupdate from latest_candle_datetime_per_receng where recognitionengine ilike ?;-
SELECT datetimeupdate FROM latest_candle_datetime_per_receng WHERE recognitionengine ILIKE 'AXIORY - 1';
Date: 2026-03-01 06:19:03
2 1 13m50s 13m50s 13m50s 13m50s truncate table solr_relevance_old;-
TRUNCATE TABLE solr_relevance_old;
Date: 2026-03-01 06:19:02
3 1 2m 2m 2m 2m refresh materialized view latest_candle_datetime_per_receng;-
refresh materialized view latest_candle_datetime_per_receng;
Date: 2026-03-01 06:19:02
Queries that waited the most
Rank Wait time Query 1 13m50s TRUNCATE TABLE solr_relevance_old;[ Date: 2026-03-01 06:19:02 ]
2 2m refresh materialized view latest_candle_datetime_per_receng;[ Date: 2026-03-01 06:19:02 ]
3 1m59s SELECT datetimeupdate FROM latest_candle_datetime_per_receng WHERE recognitionengine ILIKE 'PEPPERSTONE - 1';[ Date: 2026-03-01 06:19:03 ]
4 1m58s SELECT datetimeupdate FROM latest_candle_datetime_per_receng WHERE recognitionengine ILIKE 'Alpari - 1';[ Date: 2026-03-01 06:19:03 ]
5 1m56s SELECT datetimeupdate FROM latest_candle_datetime_per_receng WHERE recognitionengine ILIKE 'IQFEED_FX - 1';[ Date: 2026-03-01 06:19:03 ]
6 1m49s SELECT datetimeupdate FROM latest_candle_datetime_per_receng WHERE recognitionengine ILIKE 'AXIORY - 1';[ Date: 2026-03-01 06:19:03 ]
7 1m39s SELECT datetimeupdate FROM latest_candle_datetime_per_receng WHERE recognitionengine ILIKE 'ATFX - 1';[ Date: 2026-03-01 06:19:03 ]
8 1m37s SELECT datetimeupdate FROM latest_candle_datetime_per_receng WHERE recognitionengine ILIKE 'HOTFOREX - 1';[ Date: 2026-03-01 06:19:03 ]
9 1m36s SELECT datetimeupdate FROM latest_candle_datetime_per_receng WHERE recognitionengine ILIKE 'MILLENNIUMPF - 1';[ Date: 2026-03-01 06:19:03 ]
10 1m36s SELECT datetimeupdate FROM latest_candle_datetime_per_receng WHERE recognitionengine ILIKE 'FPMARKETS - 1';[ Date: 2026-03-01 06:19:03 ]
11 57s885ms SELECT datetimeupdate FROM latest_candle_datetime_per_receng WHERE recognitionengine ILIKE 'ICMARKETS-AU-MT5 - 1';[ Date: 2026-03-01 06:19:03 ]
-
Queries
Queries by type
Key values
- 34,917 Total read queries
- 16,686 Total write queries
Queries by database
Key values
- unknown Main database
- 107,107 Requests
- 2h37m53s (unknown)
- Main time consuming database
Database Request type Count Duration acaweb_fx Total 844 0ms copy from 65 0ms copy to 26 0ms cte 82 0ms ddl 13 0ms delete 13 0ms others 212 0ms select 65 0ms tcl 332 0ms update 36 0ms acaweb_fx_integer Total 1 0ms select 1 0ms postgres Total 3 0ms select 3 0ms socialmedia Total 71 0ms select 71 0ms translations Total 1 0ms select 1 0ms unknown Total 107,107 2h37m53s copy from 13 0ms copy to 575 0ms cte 2,358 0ms insert 12,416 0ms others 2,425 0ms select 34,776 0ms tcl 337 0ms update 420 0ms Queries by user
Key values
- unknown Main user
- 107,107 Requests
User Request type Count Duration postgres Total 920 0ms copy from 65 0ms copy to 26 0ms cte 82 0ms ddl 13 0ms delete 13 0ms others 212 0ms select 141 0ms tcl 332 0ms update 36 0ms unknown Total 107,107 2h37m53s copy from 13 0ms copy to 575 0ms cte 2,358 0ms insert 12,416 0ms others 2,425 0ms select 34,776 0ms tcl 337 0ms update 420 0ms Duration by user
Key values
- 2h37m53s (unknown) Main time consuming user
User Request type Count Duration postgres Total 920 0ms copy from 65 0ms copy to 26 0ms cte 82 0ms ddl 13 0ms delete 13 0ms others 212 0ms select 141 0ms tcl 332 0ms update 36 0ms unknown Total 107,107 2h37m53s copy from 13 0ms copy to 575 0ms cte 2,358 0ms insert 12,416 0ms others 2,425 0ms select 34,776 0ms tcl 337 0ms update 420 0ms Queries by host
Key values
- unknown Main host
- 108,027 Requests
- 2h37m53s (unknown)
- Main time consuming host
Queries by application
Key values
- unknown Main application
- 107,717 Requests
- 2h37m53s (unknown)
- Main time consuming application
Application Request type Count Duration pg_dump Total 5 0ms select 5 0ms psql Total 305 0ms copy from 65 0ms copy to 26 0ms cte 82 0ms ddl 13 0ms delete 13 0ms others 4 0ms select 66 0ms update 36 0ms unknown Total 107,717 2h37m53s copy from 13 0ms copy to 575 0ms cte 2,358 0ms insert 12,416 0ms others 2,633 0ms select 34,846 0ms tcl 669 0ms update 420 0ms Number of cancelled queries
Key values
- 1 per second Cancelled query Peak
- 2026-03-01 06:13:25 Date
Number of cancelled queries (5 minutes period)
NO DATASET
-
Top Queries
Histogram of query times
Key values
- 33,618 0-1ms duration
Slowest individual queries
Rank Duration Query NO DATASET
Time consuming queries
Rank Total duration Times executed Min duration Max duration Avg duration Query 1 0ms 1 0ms 0ms 0ms lock table public.stats_hrs_summary_by_groups in access share mode;Times Reported Time consuming queries #1
Day Hour Count Duration Avg duration Mar 01 06 1 0ms 0ms 2 0ms 2 0ms 0ms 0ms copy public.powerstats_trumpet (symbolid, dayofweek, fromtime, enddate, stddev_15, ave_15, stddev_30, ave_30, stddev_60, ave_60, stddev_240, ave_240, stddev_1440, ave_1440) to stdout;Times Reported Time consuming queries #2
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 3 0ms 2 0ms 0ms 0ms select last_value, is_called from public.stats_by_groups_statsid_seq;Times Reported Time consuming queries #3
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 4 0ms 2 0ms 0ms 0ms lock table public.timezones_korean in access share mode;Times Reported Time consuming queries #4
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 5 0ms 12 0ms 0ms 0ms select key, value from datasources ds inner join datasourceparams dsp on ds.id = dsp.datasourceid where ds.name = ?;Times Reported Time consuming queries #5
Day Hour Count Duration Avg duration Mar 01 06 12 0ms 0ms 6 0ms 2 0ms 0ms 0ms copy public.t30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived, pricesadjusted, relevanceprocessed, recengprocessedadjusted, historical) to stdout;Times Reported Time consuming queries #6
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 7 0ms 1 0ms 0ms 0ms copy public.processresults (id, processid, resultdate, shorttext, longtext, title, test) to stdout;Times Reported Time consuming queries #7
Day Hour Count Duration Avg duration Mar 01 06 1 0ms 0ms 8 0ms 2,451 0ms 0ms 0ms insert into t60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;Times Reported Time consuming queries #8
Day Hour Count Duration Avg duration Mar 01 06 2,451 0ms 0ms 9 0ms 2 0ms 0ms 0ms select last_value, is_called from public.keylevels_results_resultuid_seq19;Times Reported Time consuming queries #9
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 10 0ms 2 0ms 0ms 0ms copy archive.whatshot (whid, brokerid, whdatetime, jobcompletiontime) to stdout;Times Reported Time consuming queries #10
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 11 0ms 5 0ms 0ms 0ms set synchronize_seqscans to off;Times Reported Time consuming queries #11
Day Hour Count Duration Avg duration Mar 01 06 5 0ms 0ms 12 0ms 1 0ms 0ms 0ms select last_value, is_called from public."ProcessLocaleParameters_id_seq";Times Reported Time consuming queries #12
Day Hour Count Duration Avg duration Mar 01 06 1 0ms 0ms 13 0ms 976 0ms 0ms 0ms select oid, tableoid, pol.polname, pol.polcmd, pol.polpermissive, case when pol.polroles = ? then null else pg_catalog.array_to_string(array ( select pg_catalog.quote_ident(rolname) from pg_catalog.pg_roles where oid = any (pol.polroles)), ?) end as polroles, pg_catalog.pg_get_expr(pol.polqual, pol.polrelid) as polqual, pg_catalog.pg_get_expr(pol.polwithcheck, pol.polrelid) as polwithcheck from pg_catalog.pg_policy pol where polrelid = ?;Times Reported Time consuming queries #13
Day Hour Count Duration Avg duration Mar 01 06 976 0ms 0ms 14 0ms 2 0ms 0ms 0ms lock table public.timezones_turkish in access share mode;Times Reported Time consuming queries #14
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 15 0ms 2 0ms 0ms 0ms copy public.timezones_greek (timezoneid, timezone) to stdout;Times Reported Time consuming queries #15
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 16 0ms 2 0ms 0ms 0ms lock table public.futures_symbols in access share mode;Times Reported Time consuming queries #16
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 17 0ms 2 0ms 0ms 0ms copy archive.autochartist_results (resultid, bandwidth, pattern, gmttimefound, direction, initialtrend, breakout, volumeincrease, noise, symmetry, patternstarttime, patternendtime, patternstartprice, patternendprice, resx0, resx1, supportx0, supportx1, resy0, resy1, supporty0, supporty1, supportgradient, resgradient, riskreward, qtytp, predictionpricefrom, predictionpriceto, predictiontimefrom, predictiontimeto, patternquality, trendchange, maxmovementafterbreakout, latestbaratbreakouttime, latestbaratbreakoutprice, patternlengthbars, temporarypattern, symbolid, resultuid, relevancestartdistance, simulation, writtendatetime, old_resultuid) to stdout;Times Reported Time consuming queries #17
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 18 0ms 2 0ms 0ms 0ms copy public.satelliteauthentication (id, satelliteserverid, brokerid, authenticationstatus, maketradeurl, notifications, validusername, validpassword, enabled) to stdout;Times Reported Time consuming queries #18
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 19 0ms 2 0ms 0ms 0ms lock table archive.patternresultsage in access share mode;Times Reported Time consuming queries #19
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 20 0ms 1 0ms 0ms 0ms copy public.processes (id, locale, region, schedule, enabled, live, lastmodified, lastrun, contenttypeid, brokerid, uuid) to stdout;Times Reported Time consuming queries #20
Day Hour Count Duration Avg duration Mar 01 06 1 0ms 0ms Most frequent queries (N)
Rank Times executed Total duration Min duration Max duration Avg duration Query 1 4,532 0ms 0ms 0ms 0ms select ?;Times Reported Time consuming queries #1
Day Hour Count Duration Avg duration Mar 01 06 4,532 0ms 0ms 2 4,093 0ms 0ms 0ms 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 06 4,093 0ms 0ms 3 3,293 0ms 0ms 0ms 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 06 3,293 0ms 0ms 4 2,789 0ms 0ms 0ms 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 06 2,789 0ms 0ms 5 2,460 0ms 0ms 0ms 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 06 2,460 0ms 0ms 6 2,451 0ms 0ms 0ms 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 06 2,451 0ms 0ms 7 1,854 0ms 0ms 0ms 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 06 1,854 0ms 0ms 8 1,664 0ms 0ms 0ms 0ms select pg_catalog.format_type(?::pg_catalog.oid, null);Times Reported Time consuming queries #8
Day Hour Count Duration Avg duration Mar 01 06 1,664 0ms 0ms 9 1,603 0ms 0ms 0ms 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 #9
Day Hour Count Duration Avg duration Mar 01 06 1,603 0ms 0ms 10 1,185 0ms 0ms 0ms 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 06 1,185 0ms 0ms 11 1,185 0ms 0ms 0ms 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 #11
Day Hour Count Duration Avg duration Mar 01 06 1,185 0ms 0ms 12 1,185 0ms 0ms 0ms 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 #12
Day Hour Count Duration Avg duration Mar 01 06 1,185 0ms 0ms 13 1,185 0ms 0ms 0ms 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 06 1,185 0ms 0ms 14 1,185 0ms 0ms 0ms 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 #14
Day Hour Count Duration Avg duration Mar 01 06 1,185 0ms 0ms 15 1,185 0ms 0ms 0ms 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 #15
Day Hour Count Duration Avg duration Mar 01 06 1,185 0ms 0ms 16 976 0ms 0ms 0ms 0ms select oid, tableoid, pol.polname, pol.polcmd, pol.polpermissive, case when pol.polroles = ? then null else pg_catalog.array_to_string(array ( select pg_catalog.quote_ident(rolname) from pg_catalog.pg_roles where oid = any (pol.polroles)), ?) end as polroles, pg_catalog.pg_get_expr(pol.polqual, pol.polrelid) as polqual, pg_catalog.pg_get_expr(pol.polwithcheck, pol.polrelid) as polwithcheck from pg_catalog.pg_policy pol where polrelid = ?;Times Reported Time consuming queries #16
Day Hour Count Duration Avg duration Mar 01 06 976 0ms 0ms 17 935 0ms 0ms 0ms 0ms select datetimeupdate from latest_candle_datetime_per_receng where recognitionengine ilike ?;Times Reported Time consuming queries #17
Day Hour Count Duration Avg duration Mar 01 06 935 0ms 0ms -
SELECT datetimeupdate FROM latest_candle_datetime_per_receng WHERE recognitionengine ILIKE 'AXIORY - 1';
Date: 2026-03-01 06:19:03 Duration: 0ms
18 786 0ms 0ms 0ms 0ms select a.attnum, a.attname, a.atttypmod, a.attstattarget, a.attstorage, t.typstorage, a.attnotnull, a.atthasdef, a.attisdropped, a.attlen, a.attalign, a.attislocal, pg_catalog.format_type(t.oid, a.atttypmod) as atttypname, a.attgenerated, case when a.atthasmissing and not a.attisdropped then a.attmissingval else null end as attmissingval, a.attidentity, pg_catalog.array_to_string(array ( select pg_catalog.quote_ident(option_name) || ? || pg_catalog.quote_literal(option_value) from pg_catalog.pg_options_to_table(attfdwoptions) order by option_name), e ?) as attfdwoptions, case when a.attcollation <> t.typcollation then a.attcollation else ? end as attcollation, array_to_string(a.attoptions, ?) as attoptions from pg_catalog.pg_attribute a left join pg_catalog.pg_type t on a.atttypid = t.oid where a.attrelid = ?::pg_catalog.oid and a.attnum > ?::pg_catalog.int2 order by a.attnum;Times Reported Time consuming queries #18
Day Hour Count Duration Avg duration Mar 01 06 786 0ms 0ms 19 682 0ms 0ms 0ms 0ms set extra_float_digits = ?;Times Reported Time consuming queries #19
Day Hour Count Duration Avg duration Mar 01 06 682 0ms 0ms 20 676 0ms 0ms 0ms 0ms select at.attname, ( select pg_catalog.array_agg(acl order by row_n) from ( select acl, row_n from pg_catalog.unnest(coalesce(at.attacl, pg_catalog.acldefault(?, c.relowner))) with ordinality as perm (acl, row_n) where not exists ( select ? from pg_catalog.unnest(coalesce(pip.initprivs, pg_catalog.acldefault(?, c.relowner))) as init (init_acl) where acl = init_acl)) as foo) as attacl, ( select pg_catalog.array_agg(acl order by row_n) from ( select acl, row_n from pg_catalog.unnest(coalesce(pip.initprivs, pg_catalog.acldefault(?, c.relowner))) with ordinality as initp (acl, row_n) where not exists ( select ? from pg_catalog.unnest(coalesce(at.attacl, pg_catalog.acldefault(?, c.relowner))) as permp (orig_acl) where acl = orig_acl)) as foo) as rattacl, null as initattacl, null as initrattacl from pg_catalog.pg_attribute at join pg_catalog.pg_class c on (at.attrelid = c.oid) left join pg_catalog.pg_init_privs pip on (at.attrelid = pip.objoid and pip.classoid = ?::pg_catalog.regclass and at.attnum = pip.objsubid) where at.attrelid = ?::pg_catalog.oid and not at.attisdropped and (( select pg_catalog.array_agg(acl order by row_n) from ( select acl, row_n from pg_catalog.unnest(coalesce(at.attacl, pg_catalog.acldefault(?, c.relowner))) with ordinality as perm (acl, row_n) where not exists ( select ? from pg_catalog.unnest(coalesce(pip.initprivs, pg_catalog.acldefault(?, c.relowner))) as init (init_acl) where acl = init_acl)) as foo) is not null or ( select pg_catalog.array_agg(acl order by row_n) from ( select acl, row_n from pg_catalog.unnest(coalesce(pip.initprivs, pg_catalog.acldefault(?, c.relowner))) with ordinality as initp (acl, row_n) where not exists ( select ? from pg_catalog.unnest(coalesce(at.attacl, pg_catalog.acldefault(?, c.relowner))) as permp (orig_acl) where acl = orig_acl)) as foo) is not null or null is not null or null is not null) order by at.attnum;Times Reported Time consuming queries #20
Day Hour Count Duration Avg duration Mar 01 06 676 0ms 0ms Normalized slowest queries (N)
Rank Min duration Max duration Avg duration Times executed Total duration Query 1 0ms 0ms 0ms 1 0ms lock table public.stats_hrs_summary_by_groups in access share mode;Times Reported Time consuming queries #1
Day Hour Count Duration Avg duration Mar 01 06 1 0ms 0ms 2 0ms 0ms 0ms 2 0ms copy public.powerstats_trumpet (symbolid, dayofweek, fromtime, enddate, stddev_15, ave_15, stddev_30, ave_30, stddev_60, ave_60, stddev_240, ave_240, stddev_1440, ave_1440) to stdout;Times Reported Time consuming queries #2
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 3 0ms 0ms 0ms 2 0ms select last_value, is_called from public.stats_by_groups_statsid_seq;Times Reported Time consuming queries #3
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 4 0ms 0ms 0ms 2 0ms lock table public.timezones_korean in access share mode;Times Reported Time consuming queries #4
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 5 0ms 0ms 0ms 12 0ms select key, value from datasources ds inner join datasourceparams dsp on ds.id = dsp.datasourceid where ds.name = ?;Times Reported Time consuming queries #5
Day Hour Count Duration Avg duration Mar 01 06 12 0ms 0ms 6 0ms 0ms 0ms 2 0ms copy public.t30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived, pricesadjusted, relevanceprocessed, recengprocessedadjusted, historical) to stdout;Times Reported Time consuming queries #6
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 7 0ms 0ms 0ms 1 0ms copy public.processresults (id, processid, resultdate, shorttext, longtext, title, test) to stdout;Times Reported Time consuming queries #7
Day Hour Count Duration Avg duration Mar 01 06 1 0ms 0ms 8 0ms 0ms 0ms 2,451 0ms insert into t60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;Times Reported Time consuming queries #8
Day Hour Count Duration Avg duration Mar 01 06 2,451 0ms 0ms 9 0ms 0ms 0ms 2 0ms select last_value, is_called from public.keylevels_results_resultuid_seq19;Times Reported Time consuming queries #9
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 10 0ms 0ms 0ms 2 0ms copy archive.whatshot (whid, brokerid, whdatetime, jobcompletiontime) to stdout;Times Reported Time consuming queries #10
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 11 0ms 0ms 0ms 5 0ms set synchronize_seqscans to off;Times Reported Time consuming queries #11
Day Hour Count Duration Avg duration Mar 01 06 5 0ms 0ms 12 0ms 0ms 0ms 1 0ms select last_value, is_called from public."ProcessLocaleParameters_id_seq";Times Reported Time consuming queries #12
Day Hour Count Duration Avg duration Mar 01 06 1 0ms 0ms 13 0ms 0ms 0ms 976 0ms select oid, tableoid, pol.polname, pol.polcmd, pol.polpermissive, case when pol.polroles = ? then null else pg_catalog.array_to_string(array ( select pg_catalog.quote_ident(rolname) from pg_catalog.pg_roles where oid = any (pol.polroles)), ?) end as polroles, pg_catalog.pg_get_expr(pol.polqual, pol.polrelid) as polqual, pg_catalog.pg_get_expr(pol.polwithcheck, pol.polrelid) as polwithcheck from pg_catalog.pg_policy pol where polrelid = ?;Times Reported Time consuming queries #13
Day Hour Count Duration Avg duration Mar 01 06 976 0ms 0ms 14 0ms 0ms 0ms 2 0ms lock table public.timezones_turkish in access share mode;Times Reported Time consuming queries #14
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 15 0ms 0ms 0ms 2 0ms copy public.timezones_greek (timezoneid, timezone) to stdout;Times Reported Time consuming queries #15
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 16 0ms 0ms 0ms 2 0ms lock table public.futures_symbols in access share mode;Times Reported Time consuming queries #16
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 17 0ms 0ms 0ms 2 0ms copy archive.autochartist_results (resultid, bandwidth, pattern, gmttimefound, direction, initialtrend, breakout, volumeincrease, noise, symmetry, patternstarttime, patternendtime, patternstartprice, patternendprice, resx0, resx1, supportx0, supportx1, resy0, resy1, supporty0, supporty1, supportgradient, resgradient, riskreward, qtytp, predictionpricefrom, predictionpriceto, predictiontimefrom, predictiontimeto, patternquality, trendchange, maxmovementafterbreakout, latestbaratbreakouttime, latestbaratbreakoutprice, patternlengthbars, temporarypattern, symbolid, resultuid, relevancestartdistance, simulation, writtendatetime, old_resultuid) to stdout;Times Reported Time consuming queries #17
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 18 0ms 0ms 0ms 2 0ms copy public.satelliteauthentication (id, satelliteserverid, brokerid, authenticationstatus, maketradeurl, notifications, validusername, validpassword, enabled) to stdout;Times Reported Time consuming queries #18
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 19 0ms 0ms 0ms 2 0ms lock table archive.patternresultsage in access share mode;Times Reported Time consuming queries #19
Day Hour Count Duration Avg duration Mar 01 06 2 0ms 0ms 20 0ms 0ms 0ms 1 0ms copy public.processes (id, locale, region, schedule, enabled, live, lastmodified, lastrun, contenttypeid, brokerid, uuid) to stdout;Times Reported Time consuming queries #20
Day Hour Count Duration Avg duration Mar 01 06 1 0ms 0ms Time consuming prepare
Rank Total duration Times executed Min duration Max duration Avg duration Query 1 694ms 477 0ms 2ms 1ms SELECT symbolid, ;Times Reported Time consuming prepare #1
Day Hour Count Duration Avg duration Mar 01 06 477 694ms 1ms -
SELECT symbolid, ;
Date: 2026-03-01 06:01:17 Duration: 2ms Database: postgres
-
SELECT symbolid, ;
Date: 2026-03-01 06:45:48 Duration: 2ms Database: postgres
-
SELECT symbolid, ;
Date: 2026-03-01 06:46:56 Duration: 2ms Database: postgres
2 450ms 512 0ms 9ms 0ms WITH rar_max as ( ;Times Reported Time consuming prepare #2
Day Hour Count Duration Avg duration 06 512 450ms 0ms -
WITH rar_max as ( ;
Date: 2026-03-01 06:19:16 Duration: 9ms Database: postgres
-
WITH rar_max as ( ;
Date: 2026-03-01 06:02:25 Duration: 7ms Database: postgres
-
WITH rar_max as ( ;
Date: 2026-03-01 06:19:16 Duration: 5ms Database: postgres
3 219ms 2,362 0ms 0ms 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 prepare #3
Day Hour Count Duration Avg duration 06 2,362 219ms 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 06:36:51 Duration: 0ms Database: postgres
-
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 06:11:44 Duration: 0ms Database: postgres
-
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 06:21:21 Duration: 0ms Database: postgres
4 187ms 2,433 0ms 0ms 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 prepare #4
Day Hour Count Duration Avg duration 06 2,433 187ms 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 06:41:04 Duration: 0ms Database: postgres
-
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 06:12:00 Duration: 0ms Database: postgres
-
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 06:33:00 Duration: 0ms Database: postgres
5 145ms 454 0ms 3ms 0ms SELECT ;Times Reported Time consuming prepare #5
Day Hour Count Duration Avg duration 06 454 145ms 0ms -
SELECT ;
Date: 2026-03-01 06:19:31 Duration: 3ms Database: postgres
-
SELECT ;
Date: 2026-03-01 06:19:31 Duration: 2ms Database: postgres
-
SELECT ;
Date: 2026-03-01 06:15:27 Duration: 1ms Database: postgres
6 137ms 127 0ms 1ms 1ms SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;Times Reported Time consuming prepare #6
Day Hour Count Duration Avg duration 06 127 137ms 1ms -
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-03-01 06:45:35 Duration: 1ms Database: postgres
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-03-01 06:30:51 Duration: 1ms Database: postgres
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-03-01 06:30:24 Duration: 1ms Database: postgres
7 95ms 682 0ms 0ms 0ms SET extra_float_digits = 3;Times Reported Time consuming prepare #7
Day Hour Count Duration Avg duration 06 682 95ms 0ms -
SET extra_float_digits = 3;
Date: 2026-03-01 06:45:48 Duration: 0ms Database: postgres
-
SET extra_float_digits = 3;
Date: 2026-03-01 06:19:31 Duration: 0ms Database: postgres
-
SET extra_float_digits = 3;
Date: 2026-03-01 06:02:25 Duration: 0ms Database: postgres
8 78ms 433 0ms 0ms 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 prepare #8
Day Hour Count Duration Avg duration 06 433 78ms 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 06:20:04 Duration: 0ms Database: postgres
-
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 06:26:05 Duration: 0ms Database: postgres
-
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 06:31:16 Duration: 0ms Database: postgres
9 48ms 18 1ms 6ms 2ms 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 prepare #9
Day Hour Count Duration Avg duration 06 18 48ms 2ms -
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 06:40:03 Duration: 6ms 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 06:20:02 Duration: 4ms 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 06:11:01 Duration: 2ms Database: postgres
10 15ms 6 2ms 4ms 2ms select client_addr, count(1) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by client_addr, setting having (client_addr is not null OR (client_addr is null and count(1) > (cast(setting as numeric) / 3 * 2))) order by count desc;Times Reported Time consuming prepare #10
Day Hour Count Duration Avg duration 06 6 15ms 2ms -
select client_addr, count(1) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by client_addr, setting having (client_addr is not null OR (client_addr is null and count(1) > (cast(setting as numeric) / 3 * 2))) order by count desc;
Date: 2026-03-01 06:10:05 Duration: 4ms Database: postgres
-
select client_addr, count(1) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by client_addr, setting having (client_addr is not null OR (client_addr is null and count(1) > (cast(setting as numeric) / 3 * 2))) order by count desc;
Date: 2026-03-01 06:30:04 Duration: 2ms Database: postgres
-
select client_addr, count(1) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by client_addr, setting having (client_addr is not null OR (client_addr is null and count(1) > (cast(setting as numeric) / 3 * 2))) order by count desc;
Date: 2026-03-01 06:20:04 Duration: 2ms Database: postgres
11 13ms 128 0ms 0ms 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 prepare #11
Day Hour Count Duration Avg duration 06 128 13ms 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 06:32:21 Duration: 0ms Database: postgres
-
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 06:05:34 Duration: 0ms Database: postgres
-
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 06:35:33 Duration: 0ms Database: postgres
12 11ms 96 0ms 0ms 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 prepare #12
Day Hour Count Duration Avg duration 06 96 11ms 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 06:47:30 Duration: 0ms Database: postgres
-
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 06:16:25 Duration: 0ms Database: postgres
-
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 06:01:29 Duration: 0ms Database: postgres
13 9ms 197 0ms 0ms 0ms select 1;Times Reported Time consuming prepare #13
Day Hour Count Duration Avg duration 06 197 9ms 0ms -
select 1;
Date: 2026-03-01 06:02:25 Duration: 0ms Database: postgres
-
select 1;
Date: 2026-03-01 06:02:08 Duration: 0ms Database: postgres
-
select 1;
Date: 2026-03-01 06:02:25 Duration: 0ms Database: postgres
14 7ms 670 0ms 0ms 0ms SET application_name = 'PostgreSQL JDBC Driver';Times Reported Time consuming prepare #14
Day Hour Count Duration Avg duration 06 670 7ms 0ms -
SET application_name = 'PostgreSQL JDBC Driver';
Date: 2026-03-01 06:01:22 Duration: 0ms Database: postgres
-
SET application_name = 'PostgreSQL JDBC Driver';
Date: 2026-03-01 06:17:50 Duration: 0ms Database: postgres
-
SET application_name = 'PostgreSQL JDBC Driver';
Date: 2026-03-01 06:36:50 Duration: 0ms Database: postgres
15 3ms 2 1ms 2ms 1ms SELECT * FROM datafeed_symbols WHERE classname ilike 'ICMARKETS-AU-MT5' LIMIT $1;Times Reported Time consuming prepare #15
Day Hour Count Duration Avg duration 06 2 3ms 1ms -
SELECT * FROM datafeed_symbols WHERE classname ilike 'ICMARKETS-AU-MT5' LIMIT $1;
Date: 2026-03-01 06:51:50 Duration: 2ms Database: postgres
-
SELECT * FROM datafeed_symbols WHERE classname ilike 'ICMARKETS-AU-MT5' LIMIT $1;
Date: 2026-03-01 06:21:50 Duration: 1ms Database: postgres
16 0ms 3 0ms 0ms 0ms SELECT DISTINCT ON (basegroupname, symbol) ;Times Reported Time consuming prepare #16
Day Hour Count Duration Avg duration 06 3 0ms 0ms -
SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-03-01 06:31:25 Duration: 0ms Database: postgres
-
SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-03-01 06:02:21 Duration: 0ms Database: postgres
-
SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-03-01 06:01:01 Duration: 0ms Database: postgres
17 0ms 3 0ms 0ms 0ms SELECT DISTINCT ;Times Reported Time consuming prepare #17
Day Hour Count Duration Avg duration 06 3 0ms 0ms -
SELECT DISTINCT ;
Date: 2026-03-01 06:02:24 Duration: 0ms Database: postgres
-
SELECT DISTINCT ;
Date: 2026-03-01 06:01:00 Duration: 0ms Database: postgres
-
SELECT DISTINCT ;
Date: 2026-03-01 06:01:01 Duration: 0ms Database: postgres
18 0ms 1 0ms 0ms 0ms SELECT (cast(substring(tz.gmoffset from 1 for 3) as float) * 60 + cast(substring(tz.gmoffset from 5 for 2) as float)) / 60 as offset FROM timezones tz WHERE tz.timezone = $1;Times Reported Time consuming prepare #18
Day Hour Count Duration Avg duration 06 1 0ms 0ms -
SELECT (cast(substring(tz.gmoffset from 1 for 3) as float) * 60 + cast(substring(tz.gmoffset from 5 for 2) as float)) / 60 as offset FROM timezones tz WHERE tz.timezone = $1;
Date: 2026-03-01 06:00:55 Duration: 0ms Database: postgres
Time consuming bind
Rank Total duration Times executed Min duration Max duration Avg duration Query 1 15s968ms 1,468 0ms 59ms 10ms WITH rar_max as ( ;Times Reported Time consuming bind #1
Day Hour Count Duration Avg duration Mar 01 06 1,468 15s968ms 10ms -
WITH rar_max as ( ;
Date: 2026-03-01 06:05:42 Duration: 59ms Database: postgres parameters: $1 = 't', $2 = '538', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '125', $14 = 'ANGLO', $15 = 'BARC', $16 = 'BAY', $17 = 'BPLON', $18 = 'HSBCL', $19 = 'LLOY', $20 = 'RIO', $21 = 'RollsRoyce', $22 = 'TESCO', $23 = 'VOD', $24 = 'AUDCAD', $25 = 'AUDCHF', $26 = 'AUDJPY', $27 = 'AUDNZD', $28 = 'AUDUSD', $29 = 'CADCHF', $30 = 'CADJPY', $31 = 'CHFJPY', $32 = 'EURAUD', $33 = 'EURCAD', $34 = 'EURCHF', $35 = 'EURDKK', $36 = 'EURGBP', $37 = 'EURHUF', $38 = 'EURJPY', $39 = 'EURNOK', $40 = 'EURNZD', $41 = 'EURPLN', $42 = 'EURUSD', $43 = 'GBPAUD', $44 = 'GBPCAD', $45 = 'GBPCHF', $46 = 'GBPJPY', $47 = 'GBPNZD', $48 = 'GBPUSD', $49 = 'GBPZAR', $50 = 'NZDCAD', $51 = 'NZDCHF', $52 = 'NZDJPY', $53 = 'NZDUSD', $54 = 'USDCAD', $55 = 'USDCHF', $56 = 'USDCNH', $57 = 'USDCZK', $58 = 'USDDKK', $59 = 'USDHKD', $60 = 'USDHUF', $61 = 'USDJPY', $62 = 'USDMXN', $63 = 'USDNOK', $64 = 'USDPLN', $65 = 'USDSEK', $66 = 'USDSGD', $67 = 'USDTRY', $68 = 'USDZAR', $69 = 'XAGEUR', $70 = 'XAGUSD', $71 = 'XAUEUR', $72 = 'XAUUSD', $73 = 'ZARJPY', $74 = 'Cocoa', $75 = 'Coffee', $76 = 'Copper', $77 = 'Palladium', $78 = 'Platinum', $79 = 'Sugar', $80 = 'UKOIL', $81 = 'USOIL', $82 = 'AUS200', $83 = 'FRA40', $84 = 'JPN225', $85 = 'NETH25', $86 = 'SPA35', $87 = 'SUI20', $88 = 'UK100', $89 = 'USA100', $90 = 'USA30', $91 = 'USDIndex', $92 = 'ALCOA', $93 = 'ALIBABA', $94 = 'AMAZON', $95 = 'AMEX', $96 = 'APPLE', $97 = 'BBVA', $98 = 'BOA', $99 = 'BOEING', $100 = 'CHEVRON', $101 = 'CISCO', $102 = 'COKE', $103 = 'EBAY', $104 = 'GE', $105 = 'GOOGLE', $106 = 'GS', $107 = 'HLT', $108 = 'IBM', $109 = 'ILMN', $110 = 'INTEL', $111 = 'Iberdrola', $112 = 'MCARD', $113 = 'MCDON', $114 = 'META', $115 = 'MSFT', $116 = 'Mapfre', $117 = 'Netflix', $118 = 'PFIZER', $119 = 'QCOM', $120 = 'Santander', $121 = 'TEVA', $122 = 'Telefonica', $123 = 'Tesla', $124 = 'AUDUSD', $125 = 'EURGBP', $126 = 'EURUSD', $127 = 'GBPUSD', $128 = 'NZDUSD', $129 = 'USDCAD', $130 = 'USDCHF', $131 = 'USDJPY', $132 = 'Adidas', $133 = 'Bayer', $134 = 'Daimler', $135 = 'Danone', $136 = 'LVMH', $137 = 'Lufthansa', $138 = 'Volksw', $139 = '0', $140 = '', $141 = '0', $142 = '0', $143 = '0', $144 = '700', $145 = '700', $146 = 't', $147 = '10', $148 = '10'
-
WITH rar_max as ( ;
Date: 2026-03-01 06:05:55 Duration: 41ms Database: postgres parameters: $1 = 't', $2 = '538', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '125', $14 = 'ANGLO', $15 = 'BARC', $16 = 'BAY', $17 = 'BPLON', $18 = 'HSBCL', $19 = 'LLOY', $20 = 'RIO', $21 = 'RollsRoyce', $22 = 'TESCO', $23 = 'VOD', $24 = 'AUDCAD', $25 = 'AUDCHF', $26 = 'AUDJPY', $27 = 'AUDNZD', $28 = 'AUDUSD', $29 = 'CADCHF', $30 = 'CADJPY', $31 = 'CHFJPY', $32 = 'EURAUD', $33 = 'EURCAD', $34 = 'EURCHF', $35 = 'EURDKK', $36 = 'EURGBP', $37 = 'EURHUF', $38 = 'EURJPY', $39 = 'EURNOK', $40 = 'EURNZD', $41 = 'EURPLN', $42 = 'EURUSD', $43 = 'GBPAUD', $44 = 'GBPCAD', $45 = 'GBPCHF', $46 = 'GBPJPY', $47 = 'GBPNZD', $48 = 'GBPUSD', $49 = 'GBPZAR', $50 = 'NZDCAD', $51 = 'NZDCHF', $52 = 'NZDJPY', $53 = 'NZDUSD', $54 = 'USDCAD', $55 = 'USDCHF', $56 = 'USDCNH', $57 = 'USDCZK', $58 = 'USDDKK', $59 = 'USDHKD', $60 = 'USDHUF', $61 = 'USDJPY', $62 = 'USDMXN', $63 = 'USDNOK', $64 = 'USDPLN', $65 = 'USDSEK', $66 = 'USDSGD', $67 = 'USDTRY', $68 = 'USDZAR', $69 = 'XAGEUR', $70 = 'XAGUSD', $71 = 'XAUEUR', $72 = 'XAUUSD', $73 = 'ZARJPY', $74 = 'Cocoa', $75 = 'Coffee', $76 = 'Copper', $77 = 'Palladium', $78 = 'Platinum', $79 = 'Sugar', $80 = 'UKOIL', $81 = 'USOIL', $82 = 'AUS200', $83 = 'FRA40', $84 = 'JPN225', $85 = 'NETH25', $86 = 'SPA35', $87 = 'SUI20', $88 = 'UK100', $89 = 'USA100', $90 = 'USA30', $91 = 'USDIndex', $92 = 'ALCOA', $93 = 'ALIBABA', $94 = 'AMAZON', $95 = 'AMEX', $96 = 'APPLE', $97 = 'BBVA', $98 = 'BOA', $99 = 'BOEING', $100 = 'CHEVRON', $101 = 'CISCO', $102 = 'COKE', $103 = 'EBAY', $104 = 'GE', $105 = 'GOOGLE', $106 = 'GS', $107 = 'HLT', $108 = 'IBM', $109 = 'ILMN', $110 = 'INTEL', $111 = 'Iberdrola', $112 = 'MCARD', $113 = 'MCDON', $114 = 'META', $115 = 'MSFT', $116 = 'Mapfre', $117 = 'Netflix', $118 = 'PFIZER', $119 = 'QCOM', $120 = 'Santander', $121 = 'TEVA', $122 = 'Telefonica', $123 = 'Tesla', $124 = 'AUDUSD', $125 = 'EURGBP', $126 = 'EURUSD', $127 = 'GBPUSD', $128 = 'NZDUSD', $129 = 'USDCAD', $130 = 'USDCHF', $131 = 'USDJPY', $132 = 'Adidas', $133 = 'Bayer', $134 = 'Daimler', $135 = 'Danone', $136 = 'LVMH', $137 = 'Lufthansa', $138 = 'Volksw', $139 = '0', $140 = '', $141 = '700', $142 = '700', $143 = '0', $144 = '0', $145 = '0', $146 = 't', $147 = '10', $148 = '10'
-
WITH rar_max as ( ;
Date: 2026-03-01 06:16:06 Duration: 39ms Database: postgres parameters: $1 = '538', $2 = '7', $3 = '15', $4 = '30', $5 = '60', $6 = '120', $7 = '240', $8 = '480', $9 = '1440', $10 = '0', $11 = '', $12 = '125', $13 = 'ANGLO', $14 = 'BARC', $15 = 'BAY', $16 = 'BPLON', $17 = 'HSBCL', $18 = 'LLOY', $19 = 'RIO', $20 = 'RollsRoyce', $21 = 'TESCO', $22 = 'VOD', $23 = 'AUDCAD', $24 = 'AUDCHF', $25 = 'AUDJPY', $26 = 'AUDNZD', $27 = 'AUDUSD', $28 = 'CADCHF', $29 = 'CADJPY', $30 = 'CHFJPY', $31 = 'EURAUD', $32 = 'EURCAD', $33 = 'EURCHF', $34 = 'EURDKK', $35 = 'EURGBP', $36 = 'EURHUF', $37 = 'EURJPY', $38 = 'EURNOK', $39 = 'EURNZD', $40 = 'EURPLN', $41 = 'EURUSD', $42 = 'GBPAUD', $43 = 'GBPCAD', $44 = 'GBPCHF', $45 = 'GBPJPY', $46 = 'GBPNZD', $47 = 'GBPUSD', $48 = 'GBPZAR', $49 = 'NZDCAD', $50 = 'NZDCHF', $51 = 'NZDJPY', $52 = 'NZDUSD', $53 = 'USDCAD', $54 = 'USDCHF', $55 = 'USDCNH', $56 = 'USDCZK', $57 = 'USDDKK', $58 = 'USDHKD', $59 = 'USDHUF', $60 = 'USDJPY', $61 = 'USDMXN', $62 = 'USDNOK', $63 = 'USDPLN', $64 = 'USDSEK', $65 = 'USDSGD', $66 = 'USDTRY', $67 = 'USDZAR', $68 = 'XAGEUR', $69 = 'XAGUSD', $70 = 'XAUEUR', $71 = 'XAUUSD', $72 = 'ZARJPY', $73 = 'Cocoa', $74 = 'Coffee', $75 = 'Copper', $76 = 'Palladium', $77 = 'Platinum', $78 = 'Sugar', $79 = 'UKOIL', $80 = 'USOIL', $81 = 'AUS200', $82 = 'FRA40', $83 = 'JPN225', $84 = 'NETH25', $85 = 'SPA35', $86 = 'SUI20', $87 = 'UK100', $88 = 'USA100', $89 = 'USA30', $90 = 'USDIndex', $91 = 'ALCOA', $92 = 'ALIBABA', $93 = 'AMAZON', $94 = 'AMEX', $95 = 'APPLE', $96 = 'BBVA', $97 = 'BOA', $98 = 'BOEING', $99 = 'CHEVRON', $100 = 'CISCO', $101 = 'COKE', $102 = 'EBAY', $103 = 'GE', $104 = 'GOOGLE', $105 = 'GS', $106 = 'HLT', $107 = 'IBM', $108 = 'ILMN', $109 = 'INTEL', $110 = 'Iberdrola', $111 = 'MCARD', $112 = 'MCDON', $113 = 'META', $114 = 'MSFT', $115 = 'Mapfre', $116 = 'Netflix', $117 = 'PFIZER', $118 = 'QCOM', $119 = 'Santander', $120 = 'TEVA', $121 = 'Telefonica', $122 = 'Tesla', $123 = 'AUDUSD', $124 = 'EURGBP', $125 = 'EURUSD', $126 = 'GBPUSD', $127 = 'NZDUSD', $128 = 'USDCAD', $129 = 'USDCHF', $130 = 'USDJPY', $131 = 'Adidas', $132 = 'Bayer', $133 = 'Daimler', $134 = 'Danone', $135 = 'LVMH', $136 = 'Lufthansa', $137 = 'Volksw', $138 = '700', $139 = '700', $140 = 't', $141 = '10', $142 = '10'
2 1s498ms 4,313 0ms 17ms 0ms SELECT ;Times Reported Time consuming bind #2
Day Hour Count Duration Avg duration 06 4,313 1s498ms 0ms -
SELECT ;
Date: 2026-03-01 06:46:40 Duration: 17ms Database: postgres parameters: $1 = '515840243198780300'
-
SELECT ;
Date: 2026-03-01 06:46:40 Duration: 15ms Database: postgres parameters: $1 = '515840243198780300'
-
SELECT ;
Date: 2026-03-01 06:19:16 Duration: 13ms Database: postgres parameters: $1 = '500991628216059200', $2 = '0'
3 1s218ms 477 0ms 5ms 2ms SELECT symbolid, ;Times Reported Time consuming bind #3
Day Hour Count Duration Avg duration 06 477 1s218ms 2ms -
SELECT symbolid, ;
Date: 2026-03-01 06:10:03 Duration: 5ms Database: postgres parameters: $1 = 'BDSWISS', $2 = '60', $3 = 'NAS100'
-
SELECT symbolid, ;
Date: 2026-03-01 06:10:03 Duration: 4ms Database: postgres parameters: $1 = 'BDSWISS', $2 = '15', $3 = 'NAS100'
-
SELECT symbolid, ;
Date: 2026-03-01 06:10:03 Duration: 4ms Database: postgres parameters: $1 = 'BDSWISS', $2 = '30', $3 = 'NAS100'
4 424ms 45 4ms 33ms 9ms WITH last_candle AS ( ;Times Reported Time consuming bind #4
Day Hour Count Duration Avg duration 06 45 424ms 9ms -
WITH last_candle AS ( ;
Date: 2026-03-01 06:32:00 Duration: 33ms Database: postgres parameters: $1 = '558', $2 = '558'
-
WITH last_candle AS ( ;
Date: 2026-03-01 06:36:01 Duration: 22ms Database: postgres parameters: $1 = '667', $2 = '667'
-
WITH last_candle AS ( ;
Date: 2026-03-01 06:12:00 Duration: 22ms Database: postgres parameters: $1 = '558', $2 = '558'
5 259ms 10 0ms 48ms 25ms with wh_patitioned as ( ;Times Reported Time consuming bind #5
Day Hour Count Duration Avg duration 06 10 259ms 25ms -
with wh_patitioned as ( ;
Date: 2026-03-01 06:10:02 Duration: 48ms 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 06:15:02 Duration: 42ms 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 06:05:03 Duration: 38ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'
6 214ms 127 1ms 2ms 1ms SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;Times Reported Time consuming bind #6
Day Hour Count Duration Avg duration 06 127 214ms 1ms -
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-03-01 06:31:31 Duration: 2ms Database: postgres parameters: $1 = 'FPMARKETS'
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-03-01 06:16:29 Duration: 2ms Database: postgres parameters: $1 = 'ICMARKETS'
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-03-01 06:45:44 Duration: 2ms Database: postgres parameters: $1 = 'MILLENNIUMPF'
7 185ms 4,432 0ms 6ms 0ms select 1;Times Reported Time consuming bind #7
Day Hour Count Duration Avg duration 06 4,432 185ms 0ms -
select 1;
Date: 2026-03-01 06:51:48 Duration: 6ms Database: postgres
-
select 1;
Date: 2026-03-01 06:36:20 Duration: 6ms Database: postgres
-
select 1;
Date: 2026-03-01 06:05:40 Duration: 5ms Database: postgres
8 181ms 2,451 0ms 0ms 0ms INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;Times Reported Time consuming bind #8
Day Hour Count Duration Avg duration 06 2,451 181ms 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 06:36:51 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 22:00:00', $2 = '237.625', $3 = '239.315', $4 = '237.595', $5 = '239.09', $6 = '1156', $7 = '515840249429919300', $8 = '0', $9 = '2026-03-01 06:36:51.171', $10 = '2026-03-01 06:36:51.171', $11 = '237.625', $12 = '239.315', $13 = '237.595', $14 = '239.09', $15 = '1156', $16 = '0', $17 = '2026-03-01 06:36:51.171', $18 = '2026-03-01 06:36:51.171'
-
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 06:11:44 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 21:00:00', $2 = '127.91', $3 = '127.91', $4 = '127.24', $5 = '127.6', $6 = '1948', $7 = '515840247919887300', $8 = '0', $9 = '2026-03-01 06:11:44.129', $10 = '2026-03-01 06:11:44.061', $11 = '127.91', $12 = '127.91', $13 = '127.24', $14 = '127.6', $15 = '1948', $16 = '0', $17 = '2026-03-01 06:11:44.129', $18 = '2026-03-01 06:11:44.061'
-
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 06:11:46 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 06:11:46.211', $10 = '2026-03-01 06:11:46.095', $11 = '9191.4', $12 = '9203.4', $13 = '9184.3', $14 = '9196.4', $15 = '8819', $16 = '0', $17 = '2026-03-01 06:11:46.211', $18 = '2026-03-01 06:11:46.095'
9 173ms 48 0ms 18ms 3ms WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;Times Reported Time consuming bind #9
Day Hour Count Duration Avg duration 06 48 173ms 3ms -
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;
Date: 2026-03-01 06:51:52 Duration: 18ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
-
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;
Date: 2026-03-01 06:35:52 Duration: 18ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
-
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;
Date: 2026-03-01 06:06:35 Duration: 12ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
10 161ms 2,460 0ms 0ms 0ms INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;Times Reported Time consuming bind #10
Day Hour Count Duration Avg duration 06 2,460 161ms 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 06:10:45 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 06:10:45.722', $10 = '2026-03-01 06:10:45.605', $11 = '9196.25', $12 = '9197.45', $13 = '9185.4', $14 = '9190.4', $15 = '3873', $16 = '0', $17 = '2026-03-01 06:10:45.722', $18 = '2026-03-01 06:10:45.605'
-
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 06:41:04 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 23:00:00', $2 = '48920.55', $3 = '48929.08', $4 = '48873.55', $5 = '48874.4', $6 = '7215', $7 = '515840248000726300', $8 = '0', $9 = '2026-03-01 06:41:04.978', $10 = '2026-03-01 06:41:04.9', $11 = '48920.55', $12 = '48929.08', $13 = '48873.55', $14 = '48874.4', $15 = '7215', $16 = '0', $17 = '2026-03-01 06:41:04.978', $18 = '2026-03-01 06:41:04.9'
-
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 06:33:00 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 18:00:00', $2 = '26583.3', $3 = '26614.8', $4 = '26536.8', $5 = '26539', $6 = '8584', $7 = '515840247933633300', $8 = '0', $9 = '2026-03-01 06:33:00.157', $10 = '2026-03-01 06:33:00.078', $11 = '26583.3', $12 = '26614.8', $13 = '26536.8', $14 = '26539', $15 = '8584', $16 = '0', $17 = '2026-03-01 06:33:00.158', $18 = '2026-03-01 06:33:00.078'
11 112ms 2,789 0ms 0ms 0ms INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;Times Reported Time consuming bind #11
Day Hour Count Duration Avg duration 06 2,789 112ms 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 06:17:20 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 23:30:00', $2 = '58554.5', $3 = '58624.5', $4 = '58551.5', $5 = '58571.5', $6 = '194', $7 = '515840245921261300', $8 = '0', $9 = '2026-03-01 06:17:20.812', $10 = '2026-03-01 06:17:20.72', $11 = '58554.5', $12 = '58624.5', $13 = '58551.5', $14 = '58571.5', $15 = '194', $16 = '0', $17 = '2026-03-01 06:17:20.812', $18 = '2026-03-01 06:17:20.72'
-
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 06:26:05 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 06:26:05.417', $10 = '2026-03-01 06:26:05.317', $11 = '48906.55', $12 = '48908.55', $13 = '48873.55', $14 = '48874.4', $15 = '3213', $16 = '0', $17 = '2026-03-01 06:26:05.417', $18 = '2026-03-01 06:26:05.317'
-
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 06:40:46 Duration: 0ms Database: postgres parameters: $1 = '2026-02-27 21:30:00', $2 = '9190.45', $3 = '9191.45', $4 = '9179.9', $5 = '9180.4', $6 = '1867', $7 = '515840248015086300', $8 = '0', $9 = '2026-03-01 06:40:46.82', $10 = '2026-03-01 06:40:46.734', $11 = '9190.45', $12 = '9191.45', $13 = '9179.9', $14 = '9180.4', $15 = '1867', $16 = '0', $17 = '2026-03-01 06:40:46.82', $18 = '2026-03-01 06:40:46.734'
12 29ms 54 0ms 10ms 0ms /*server.CPResult*/ SELECT patternid, resy0, resy1, supporty0, supporty1, predictiontimeto, patternstarttime, s.symbolid, resx0, resx1, supportx0, supportx1, symbol, longname, shortname, timegranularity, patternendtime, pattern, a.direction, trendchange, patternlengthbars, patternquality, resultuid as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, exchange, breakout, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM autochartist_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern where resultuid = $1;Times Reported Time consuming bind #12
Day Hour Count Duration Avg duration 06 54 29ms 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 06:09:49 Duration: 10ms Database: postgres parameters: $1 = '607753511461270301'
-
/*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 06:13:50 Duration: 4ms Database: postgres parameters: $1 = '607753397521982301'
-
/*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 06:36:18 Duration: 3ms Database: postgres parameters: $1 = '607753400627997301'
13 16ms 16 0ms 4ms 1ms /*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 #13
Day Hour Count Duration Avg duration 06 16 16ms 1ms -
/*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 06:36:39 Duration: 4ms Database: postgres parameters: $1 = '607753571244898303'
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/*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 06:06:38 Duration: 4ms Database: postgres parameters: $1 = '607759055687756303'
-
/*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 06:12:10 Duration: 3ms Database: postgres parameters: $1 = '607754166998661303'
14 14ms 128 0ms 0ms 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 #14
Day Hour Count Duration Avg duration 06 128 14ms 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 06:20:03 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 06:20:03.092', $10 = '2026-03-01 06:20:03.091', $11 = '58640', $12 = '58825', $13 = '58390', $14 = '58635', $15 = '6318', $16 = '0', $17 = '2026-03-01 06:20:03.092', $18 = '2026-03-01 06:20:03.091'
-
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 06:32:21 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 06:32:21.668', $10 = '2026-03-01 06:32:21.668', $11 = '58640', $12 = '58825', $13 = '58390', $14 = '58635', $15 = '6318', $16 = '0', $17 = '2026-03-01 06:32:21.668', $18 = '2026-03-01 06:32:21.668'
-
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 06:05:34 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 06:05:34.353', $10 = '2026-03-01 06:05:34.352', $11 = '58640', $12 = '58825', $13 = '58390', $14 = '58635', $15 = '6318', $16 = '0', $17 = '2026-03-01 06:05:34.353', $18 = '2026-03-01 06:05:34.352'
15 14ms 3 3ms 5ms 4ms SELECT DISTINCT ON (basegroupname, symbol) ;Times Reported Time consuming bind #15
Day Hour Count Duration Avg duration 06 3 14ms 4ms -
SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-03-01 06:02:21 Duration: 5ms Database: postgres parameters: $1 = '667', $2 = '667'
-
SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-03-01 06:31:25 Duration: 5ms Database: postgres parameters: $1 = '627', $2 = '627'
-
SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-03-01 06:01:01 Duration: 3ms Database: postgres parameters: $1 = '627', $2 = '627'
16 12ms 18 0ms 0ms 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 #16
Day Hour Count Duration Avg duration 06 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 06:11:01 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 06:31:09 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 06:51:01 Duration: 0ms Database: postgres
17 10ms 96 0ms 0ms 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 06 96 10ms 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 06:30:25 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 06:30:25.136', $10 = '2026-03-01 06:30:25.136', $11 = '242.507', $12 = '249.399', $13 = '242.4075', $14 = '248.3385', $15 = '65346', $16 = '0', $17 = '2026-03-01 06:30:25.137', $18 = '2026-03-01 06:30:25.136'
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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 06:30:28 Duration: 0ms Database: postgres parameters: $1 = '2026-02-28 00:00:00', $2 = '81.46855', $3 = '85.0299', $4 = '77.1469', $5 = '84.8777', $6 = '150410', $7 = '515840249476835300', $8 = '0', $9 = '2026-03-01 06:30:28.939', $10 = '2026-03-01 06:30:28.938', $11 = '81.46855', $12 = '85.0299', $13 = '77.1469', $14 = '84.8777', $15 = '150410', $16 = '0', $17 = '2026-03-01 06:30:28.939', $18 = '2026-03-01 06:30:28.938'
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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 06:17:23 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 06:17:23.96', $10 = '2026-03-01 06:17:23.96', $11 = '242.507', $12 = '249.399', $13 = '242.4075', $14 = '248.3385', $15 = '65346', $16 = '0', $17 = '2026-03-01 06:17:23.96', $18 = '2026-03-01 06:17:23.96'
18 7ms 4 0ms 7ms 1ms /*server.FibonacciResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, Exchange AS e, longname as lo, shortname as sho, timegranularity AS tg, p.PatternID AS pid, Direction AS d, PatternStartTime AS pst, PatternEndTime AS pet, PatternStartPrice AS psp, PatternEndPrice AS pep, priceX as px, timeX as tx, priceA as pa, timeA as ta, priceB as pb, timeB as tb, priceC as pc, timeC as tc, priceD as pd, timeD as td, averagequality as aq, timequality as tq, 1 - errormargin as rq, 1 - noise as c, target10 as t10, target06 as t06, target16 as t16, target07 as t07, target12 as t12, target03 as t03, target05 as t05, PatternLengthBars AS l, temporarypattern as tp, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM Fibonacci_Results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid INNER JOIN fibonaccipatterns p on a.pattern = p.patternname where resultuid = $1 and dtt.dayofweek = 3;Times Reported Time consuming bind #18
Day Hour Count Duration Avg duration 06 4 7ms 1ms -
/*server.FibonacciResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, Exchange AS e, longname as lo, shortname as sho, timegranularity AS tg, p.PatternID AS pid, Direction AS d, PatternStartTime AS pst, PatternEndTime AS pet, PatternStartPrice AS psp, PatternEndPrice AS pep, priceX as px, timeX as tx, priceA as pa, timeA as ta, priceB as pb, timeB as tb, priceC as pc, timeC as tc, priceD as pd, timeD as td, averagequality as aq, timequality as tq, 1 - errormargin as rq, 1 - noise as c, target10 as t10, target06 as t06, target16 as t16, target07 as t07, target12 as t12, target03 as t03, target05 as t05, PatternLengthBars AS l, temporarypattern as tp, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM Fibonacci_Results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid INNER JOIN fibonaccipatterns p on a.pattern = p.patternname where resultuid = $1 and dtt.dayofweek = 3;
Date: 2026-03-01 06:09:32 Duration: 7ms Database: postgres parameters: $1 = '607753396281766302'
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/*server.FibonacciResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, Exchange AS e, longname as lo, shortname as sho, timegranularity AS tg, p.PatternID AS pid, Direction AS d, PatternStartTime AS pst, PatternEndTime AS pet, PatternStartPrice AS psp, PatternEndPrice AS pep, priceX as px, timeX as tx, priceA as pa, timeA as ta, priceB as pb, timeB as tb, priceC as pc, timeC as tc, priceD as pd, timeD as td, averagequality as aq, timequality as tq, 1 - errormargin as rq, 1 - noise as c, target10 as t10, target06 as t06, target16 as t16, target07 as t07, target12 as t12, target03 as t03, target05 as t05, PatternLengthBars AS l, temporarypattern as tp, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM Fibonacci_Results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid INNER JOIN fibonaccipatterns p on a.pattern = p.patternname where resultuid = $1 and dtt.dayofweek = 3;
Date: 2026-03-01 06:16:48 Duration: 0ms Database: postgres parameters: $1 = '607753279496924302'
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/*server.FibonacciResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, Exchange AS e, longname as lo, shortname as sho, timegranularity AS tg, p.PatternID AS pid, Direction AS d, PatternStartTime AS pst, PatternEndTime AS pet, PatternStartPrice AS psp, PatternEndPrice AS pep, priceX as px, timeX as tx, priceA as pa, timeA as ta, priceB as pb, timeB as tb, priceC as pc, timeC as tc, priceD as pd, timeD as td, averagequality as aq, timequality as tq, 1 - errormargin as rq, 1 - noise as c, target10 as t10, target06 as t06, target16 as t16, target07 as t07, target12 as t12, target03 as t03, target05 as t05, PatternLengthBars AS l, temporarypattern as tp, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM Fibonacci_Results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid INNER JOIN fibonaccipatterns p on a.pattern = p.patternname where resultuid = $1 and dtt.dayofweek = 3;
Date: 2026-03-01 06:32:18 Duration: 0ms Database: postgres parameters: $1 = '607753454956084302'
19 7ms 44 0ms 2ms 0ms SELECT * FROM ( SELECT PriceDateTime, Open, High, Low, Close, Volume, BSF FROM T15 WHERE symbolid = $1 AND (BSF = 0 OR BSF IS NULL) ORDER BY PriceDateTime DESC LIMIT 1050) a ORDER BY PriceDateTime ASC;Times Reported Time consuming bind #19
Day Hour Count Duration Avg duration 06 44 7ms 0ms -
SELECT * FROM ( SELECT PriceDateTime, Open, High, Low, Close, Volume, BSF FROM T15 WHERE symbolid = $1 AND (BSF = 0 OR BSF IS NULL) ORDER BY PriceDateTime DESC LIMIT 1050) a ORDER BY PriceDateTime ASC;
Date: 2026-03-01 06:09:32 Duration: 2ms Database: postgres parameters: $1 = '515840243268101300'
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SELECT * FROM ( SELECT PriceDateTime, Open, High, Low, Close, Volume, BSF FROM T15 WHERE symbolid = $1 AND (BSF = 0 OR BSF IS NULL) ORDER BY PriceDateTime DESC LIMIT 1050) a ORDER BY PriceDateTime ASC;
Date: 2026-03-01 06:18:08 Duration: 0ms Database: postgres parameters: $1 = '515840243912230300'
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SELECT * FROM ( SELECT PriceDateTime, Open, High, Low, Close, Volume, BSF FROM T15 WHERE symbolid = $1 AND (BSF = 0 OR BSF IS NULL) ORDER BY PriceDateTime DESC LIMIT 1050) a ORDER BY PriceDateTime ASC;
Date: 2026-03-01 06:32:36 Duration: 0ms Database: postgres parameters: $1 = '515840243870885300'
20 7ms 68 0ms 1ms 0ms SELECT df.absolutetimezoneoffset FROM datafeedstimetable df INNER JOIN downloadersymbolsettings dss ON df.classname = dss.classname WHERE dss.symbolid = $1 GROUP BY df.absolutetimezoneoffset LIMIT 1;Times Reported Time consuming bind #20
Day Hour Count Duration Avg duration 06 68 7ms 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 06:37:48 Duration: 1ms Database: postgres parameters: $1 = '500991628208440200'
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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 06:32:36 Duration: 1ms Database: postgres parameters: $1 = '515840243870885300'
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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 06:48:09 Duration: 0ms Database: postgres parameters: $1 = '515840243870885300'
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Events
Log levels
Key values
- 173,820 Log entries
Events distribution
Key values
- 0 PANIC entries
- 0 FATAL entries
- 523 ERROR entries
- 0 WARNING entries
Most Frequent Errors/Events
Key values
- 356 Max number of times the same event was reported
- 523 Total events found
Rank Times reported Error 1 356 ERROR: pg_stat_statements must be loaded via shared_preload_libraries
Times Reported Most Frequent Error / Event #1
Day Hour Count Mar 01 06 356 - ERROR: pg_stat_statements must be loaded via shared_preload_libraries
Statement: /* service='datadog-agent' */ SELECT COUNT(*) FROM pg_stat_statements(false)
Date: 2026-03-01 06:00:02
2 67 LOG: process ... still waiting for AccessShareLock on relation ... of database ... after ... ms
Times Reported Most Frequent Error / Event #2
Day Hour Count Mar 01 06 67 - LOG: process 26045 still waiting for AccessShareLock on relation 5883477 of database 5881926 after 1000.041 ms
- LOG: process 26045 still waiting for AccessShareLock on relation 5883477 of database 5881926 after 1000.054 ms
- LOG: process 26045 still waiting for AccessShareLock on relation 5883477 of database 5881926 after 1000.052 ms
Detail: Process holding the lock: 27911. Wait queue: 27972, 26045.
Statement: /* service='datadog-agent' */ 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 0 END as index_size, CASE WHEN C.reltoastrelid > 0 THEN pg_relation_size(C.reltoastrelid) ELSE 0 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 = 'relation' WHERE NOT (nspname = ANY('{pg_catalog,information_schema}')) AND (L.relation IS NULL OR L.mode <> 'AccessExclusiveLock' OR NOT L.granted) AND relkind = 'r' AND (( relname ~ '.*' )) LIMIT 300) as sDate: 2026-03-01 06:05:22
Detail: Process holding the lock: 27911. Wait queue: 27972, 26045.
Statement: /* service='datadog-agent' */ 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 0 END as index_size, CASE WHEN C.reltoastrelid > 0 THEN pg_relation_size(C.reltoastrelid) ELSE 0 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 = 'relation' WHERE NOT (nspname = ANY('{pg_catalog,information_schema}')) AND (L.relation IS NULL OR L.mode <> 'AccessExclusiveLock' OR NOT L.granted) AND relkind = 'r' AND (( relname ~ '.*' )) LIMIT 300) as sDate: 2026-03-01 06:05:37
Detail: Process holding the lock: 27911. Wait queue: 27972, 26045.
Statement: /* service='datadog-agent' */ 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 0 END as index_size, CASE WHEN C.reltoastrelid > 0 THEN pg_relation_size(C.reltoastrelid) ELSE 0 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 = 'relation' WHERE NOT (nspname = ANY('{pg_catalog,information_schema}')) AND (L.relation IS NULL OR L.mode <> 'AccessExclusiveLock' OR NOT L.granted) AND relkind = 'r' AND (( relname ~ '.*' )) LIMIT 300) as sDate: 2026-03-01 06:05:52
3 55 ERROR: canceling statement due to statement timeout
Times Reported Most Frequent Error / Event #3
Day Hour Count Mar 01 06 55 - ERROR: canceling statement due to statement timeout
Statement: /* service='datadog-agent' */ 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 0 END as index_size, CASE WHEN C.reltoastrelid > 0 THEN pg_relation_size(C.reltoastrelid) ELSE 0 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 = 'relation' WHERE NOT (nspname = ANY('{pg_catalog,information_schema}')) AND (L.relation IS NULL OR L.mode <> 'AccessExclusiveLock' OR NOT L.granted) AND relkind = 'r' AND (( relname ~ '.*' )) LIMIT 300) as s
Date: 2026-03-01 06:05:26
4 43 ERROR: schema "..." does not exist
Times Reported Most Frequent Error / Event #4
Day Hour Count Mar 01 06 43 - ERROR: schema "datadog" does not exist at character 38
Statement: /* service='datadog-agent' */ SELECT datadog.explain_statement($stmt$SELECT * FROM pg_stat_activity$stmt$)
Date: 2026-03-01 06:00:20
5 2 LOG: process ... still waiting for AccessExclusiveLock on relation ... of database ... after ... ms
Times Reported Most Frequent Error / Event #5
Day Hour Count Mar 01 06 2 - LOG: process 27972 still waiting for AccessExclusiveLock on relation 5883477 of database 5881926 after 1000.048 ms
- LOG: process 31269 still waiting for AccessExclusiveLock on relation 5894441 of database 5881926 after 1000.060 ms
Detail: Process holding the lock: 27911. Wait queue: 27972.
Statement: TRUNCATE TABLE solr_relevance_old;Date: 2026-03-01 06:05:12
Detail: Process holding the lock: 27911. Wait queue: 31269.
Statement: refresh materialized view latest_candle_datetime_per_recengDate: 2026-03-01 06:17:02