-
Global information
- Generated on Fri Feb 20 07:59:56 2026
- Log file: /home/postgres/pg_data/data/pg_log/postgresql-2026-02-20_090000.log
- Parsed 2,319,147 log entries in 55s
- Log start from 2026-02-20 09:00:00 to 2026-02-20 09:59:55
-
Overview
Global Stats
- 250 Number of unique normalized queries
- 258,825 Number of queries
- 1h28m37s Total query duration
- 2026-02-20 09:00:00 First query
- 2026-02-20 09:59:55 Last query
- 4,035 queries/s at 2026-02-20 09:10:53 Query peak
- 1h28m37s Total query duration
- 6s1ms Prepare/parse total duration
- 41s260ms Bind total duration
- 1h27m50s Execute total duration
- 401 Number of events
- 3 Number of unique normalized events
- 360 Max number of times the same event was reported
- 0 Number of cancellation
- 42 Total number of automatic vacuums
- 56 Total number of automatic analyzes
- 700 Number temporary file
- 609.82 MiB Max size of temporary file
- 9.00 MiB Average size of temporary file
- 2,682 Total number of sessions
- 13 sessions at 2026-02-20 09:58:48 Session peak
- 16d6h6m34s Total duration of sessions
- 8m43s Average duration of sessions
- 96 Average queries per session
- 1s982ms Average queries duration per session
- 8m41s Average idle time per session
- 2,672 Total number of connections
- 28 connections/s at 2026-02-20 09:18:48 Connection peak
- 3 Total number of databases
SQL Traffic
Key values
- 4,035 queries/s Query Peak
- 2026-02-20 09:10:53 Date
SELECT Traffic
Key values
- 2,000 queries/s Query Peak
- 2026-02-20 09:10:53 Date
INSERT/UPDATE/DELETE Traffic
Key values
- 156 queries/s Query Peak
- 2026-02-20 09:45:51 Date
Queries duration
Key values
- 1h28m37s 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) Feb 20 09 258,825 0ms 41s443ms 20ms 2m53s 3m45s 4m29s Day Hour SELECT COPY TO Average Duration Latency Percentile(90) Latency Percentile(95) Latency Percentile(99) Feb 20 09 87,226 26 0ms 0ms 0ms 0ms Day Hour INSERT UPDATE DELETE COPY FROM Average Duration Latency Percentile(90) Latency Percentile(95) Latency Percentile(99) Feb 20 09 25,389 2,114 16 96 0ms 0ms 0ms 0ms Day Hour Prepare Bind Bind/Prepare Percentage of prepare Feb 20 09 17,647 92,866 5.26 14.04% Day Hour Count Average / Second Feb 20 09 2,672 0.74/s Day Hour Count Average Duration Average idle time Feb 20 09 2,682 8m43s 8m41s -
Connections
Established Connections
Key values
- 28 connections Connection Peak
- 2026-02-20 09:18:48 Date
Connections per database
Key values
- acaweb_fx Main Database
- 2,672 connections Total
Connections per user
Key values
- postgres Main User
- 2,672 connections Total
Connections per host
Key values
- 192.168.4.142 Main host with 1238 connections
- 2,672 Total connections
Host Count 127.0.0.1 112 182.165.1.54 2 192.168.0.114 6 192.168.0.216 103 192.168.0.74 60 192.168.0.84 2 192.168.1.131 2 192.168.1.145 96 192.168.1.15 87 192.168.1.20 124 192.168.1.238 2 192.168.1.239 14 192.168.1.90 41 192.168.2.126 48 192.168.2.182 12 192.168.3.199 36 192.168.4.142 1,238 192.168.4.150 10 192.168.4.222 1 192.168.4.238 16 192.168.4.33 81 192.168.4.39 4 192.168.4.55 1 192.168.4.98 330 [local] 244 -
Sessions
Simultaneous sessions
Key values
- 13 sessions Session Peak
- 2026-02-20 09:58:48 Date
Histogram of session times
Key values
- 2,208 0-500ms duration
Sessions per database
Key values
- acaweb_fx Main Database
- 2,682 sessions Total
Sessions per user
Key values
- postgres Main User
- 2,682 sessions Total
Sessions per host
Key values
- 192.168.4.142 Main Host
- 2,682 sessions Total
Host Count Total Duration Average Duration 127.0.0.1 112 8s123ms 72ms 182.165.1.54 2 23h20m13s 11h40m6s 192.168.0.114 8 49m14s 6m9s 192.168.0.171 3 2d20h14m55s 22h44m58s 192.168.0.216 103 1m43s 1s2ms 192.168.0.74 60 7h56m29s 7m56s 192.168.0.84 2 23h59m21s 11h59m40s 192.168.1.131 2 23h59m20s 11h59m40s 192.168.1.145 97 2d10h54m31s 36m26s 192.168.1.15 87 8h48m7s 6m4s 192.168.1.154 3 2d20h14m54s 22h44m58s 192.168.1.20 125 2d13h4m1s 29m18s 192.168.1.238 2 23h59m27s 11h59m43s 192.168.1.239 14 101ms 7ms 192.168.1.90 41 36s964ms 901ms 192.168.2.126 48 17s979ms 374ms 192.168.2.182 12 1s119ms 93ms 192.168.3.199 36 1s398ms 38ms 192.168.4.142 1,238 7m5s 343ms 192.168.4.150 10 20h18m54s 2h1m53s 192.168.4.222 1 1m6s 1m6s 192.168.4.238 16 20s610ms 1s288ms 192.168.4.33 81 10m32s 7s806ms 192.168.4.39 4 27s176ms 6s794ms 192.168.4.55 1 162ms 162ms 192.168.4.98 330 14s582ms 44ms [local] 244 4m25s 1s89ms -
Checkpoints / Restartpoints
Checkpoints Buffers
Key values
- 12,070 buffers Checkpoint Peak
- 2026-02-20 09:07:32 Date
- 209.932 seconds Highest write time
- 0.006 seconds Sync time
Checkpoints Wal files
Key values
- 5 files Wal files usage Peak
- 2026-02-20 09:07:32 Date
Checkpoints distance
Key values
- 173.31 Mo Distance Peak
- 2026-02-20 09:07:32 Date
Checkpoints Activity
↑ Back to the top of the Checkpoint Activity tableDay Hour Written buffers Write time Sync time Total time Feb 20 09 44,554 1,836.254s 0.024s 1,836.599s Day Hour Added Removed Recycled Synced files Longest sync Average sync Feb 20 09 0 0 23 1,859 0.005s 0s Day Hour Count Avg time (sec) Feb 20 09 0 0s Day Hour Mean distance Mean estimate Feb 20 09 31,665.50 kB 69,311.67 kB -
Temporary Files
Size of temporary files
Key values
- 609.82 MiB Temp Files size Peak
- 2026-02-20 09:16:27 Date
Number of temporary files
Key values
- 30 per second Temp Files Peak
- 2026-02-20 09:17:09 Date
Temporary Files Activity
↑ Back to the top of the Temporary Files Activity tableDay Hour Count Total size Average size Feb 20 09 700 6.15 GiB 9.00 MiB Queries generating the most temporary files (N)
Rank Count Total size Min size Max size Avg size Query 1 45 185.31 MiB 3.82 MiB 4.20 MiB 4.12 MiB select resultuid from relevance_fibonacci_results order by resultuid desc limit ?), fr as ( select a.*, rr.age, rr.relevant from fibonacci_results a left outer join relevance_fibonacci_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) end), all_results as ( select fr.resultuid as resultuid, fr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, fr.pattern as pattern_name, fr.timed as timed, fr.patternendtime as identified, dtt.timezone as timezone, fr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when fr.age is not null then fr.age when fr.resultuid <= rm.resultuid then ? else ? end as age, case when fr.relevant is not null then fr.relevant when fr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from fr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = fr.symbolid inner join symbols s on fr.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on fr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join downloadersymbolsettings dss on fr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left join lateral calc_fib_signal_filter (fr.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where fr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (fr.simulation = ? or fr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or fr.pattern in (...)) and (? = ? or fr.patternlengthbars <= ?) and (? = ? or (? = ? and fr.timed > cast(? as timestamp)) or (? = ? and fr.timed < cast(? as timestamp)))), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc;-
SELECT resultuid FROM relevance_fibonacci_results ORDER BY resultuid DESC LIMIT 1), fr AS ( SELECT a.*, rr.age, rr.relevant from fibonacci_results a LEFT OUTER JOIN relevance_fibonacci_results rr on a.resultuid = rr.resultuid WHERE CASE WHEN FALSE = $1 THEN true ELSE a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) END), all_results AS ( SELECT fr.resultuid AS resultuid, fr.direction AS direction, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, fr.pattern AS pattern_name, fr.timed AS timed, fr.patternendtime AS identified, dtt.timezone AS timezone, fr.patternlengthbars AS length, g.basegroupname, newLevels.filtered, CASE WHEN fr.age IS NOT NULL THEN fr.age WHEN fr.resultuid <= rm.resultuid THEN 11 ELSE 0 END as age, CASE WHEN fr.relevant IS NOT NULL THEN fr.relevant WHEN fr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip FROM fr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $2 AND bsl.symbolid = fr.symbolid INNER JOIN symbols s ON fr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN symbolgroup sg on fr.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid INNER JOIN downloadersymbolsettings dss ON fr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN rar_max rm ON 1 = 1 LEFT JOIN LATERAL calc_fib_signal_filter (fr.resultuid) newLevels on true LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' WHERE fr.gmttimefound > now() - INTERVAL '7 DAYS' AND dss.enabled = 1 AND s.deleted = 0 AND (fr.simulation = 0 OR fr.simulation IS NULL) AND ($3 = 0 OR s.timegranularity in ($4, $5, $6, $7, $8, $9, $10)) AND ($11 = 0 OR s.exchange in ($12)) AND ($13 = 0 OR coalesce(bim.code, s.symbol) in ($14, $15, $16, $17, $18, $19, $20, $21, $22, $23, $24, $25, $26, $27, $28, $29, $30, $31, $32, $33, $34, $35, $36, $37, $38, $39, $40, $41, $42, $43, $44, $45, $46, $47, $48, $49, $50, $51, $52, $53, $54, $55, $56, $57, $58, $59, $60, $61, $62, $63, $64, $65, $66, $67, $68, $69, $70, $71, $72, $73, $74, $75, $76, $77, $78, $79, $80, $81, $82, $83, $84, $85, $86, $87, $88, $89, $90, $91, $92, $93, $94, $95, $96, $97, $98, $99, $100, $101, $102, $103, $104, $105, $106, $107, $108, $109, $110, $111, $112, $113, $114, $115, $116, $117, $118, $119, $120, $121, $122, $123, $124, $125, $126, $127, $128, $129, $130, $131, $132, $133, $134, $135, $136, $137, $138, $139, $140, $141, $142, $143, $144, $145, $146, $147, $148, $149, $150, $151, $152, $153, $154, $155, $156, $157, $158, $159, $160, $161, $162, $163, $164, $165, $166, $167, $168, $169, $170, $171, $172, $173, $174, $175, $176, $177, $178, $179, $180, $181, $182, $183, $184, $185, $186, $187, $188, $189, $190, $191, $192, $193, $194, $195, $196, $197, $198, $199, $200, $201, $202, $203, $204, $205, $206, $207, $208, $209, $210, $211, $212, $213, $214, $215, $216, $217, $218, $219, $220, $221, $222, $223, $224, $225, $226)) AND ($227 = 0 OR fr.pattern in ($228)) AND ($229 = 0 OR fr.patternlengthbars <= $230) AND ($231 = 0 OR ($232 = 1 AND fr.timed > cast('1970-01-01' as timestamp)) OR ($233 = 2 AND fr.timed < cast('1970-01-01' as timestamp)))), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results WHERE (FALSE = $234 OR relevant = 1) AND ($235 = 0 OR age <= $236) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC;
Date: 2026-02-20 09:01:06 Duration: 0ms
2 42 134.79 MiB 3.15 MiB 3.63 MiB 3.21 MiB select resultuid from relevance_consecutivecandles_results order by resultuid desc limit ?), all_results as ( select ccr.resultuid as resultuid, ccr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, ccr.patternendtime as identified, dtt.timezone as timezone, ccr.qtyconsecutivecandles as length, g.basegroupname, case when rcr.age is not null then rcr.age when ccr.resultuid <= rm.resultuid then ? else ? end as age, case when rcr.relevant is not null then rcr.relevant when ccr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip, newlevels.filtered from consecutivecandles_results ccr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = ccr.symbolid inner join symbols s on ccr.symbolid = s.symbolid and s.nonliquid = ? inner join downloadersymbolsettings dss on ccr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on ccr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join rar_max rm on ? = ? left outer join relevance_consecutivecandles_results rcr on rcr.resultuid = ccr.resultuid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? left join lateral calc_cc_signal_filter (ccr.resultuid) newlevels on true where ccr.gmttimefound > now() - interval ? and s.deleted = ? and (ccr.simulation = ? or ccr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or ccr.patternlengthbars <= ?)), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc;-
SELECT resultuid FROM relevance_consecutivecandles_results ORDER BY resultuid DESC LIMIT 1), all_results AS ( SELECT ccr.resultuid AS resultuid, ccr.direction AS direction, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, ccr.patternendtime AS identified, dtt.timezone AS timezone, ccr.qtyconsecutivecandles AS length, g.basegroupname, CASE WHEN rcr.age IS NOT NULL THEN rcr.age WHEN ccr.resultuid <= rm.resultuid THEN 1 ELSE 0 END as age, CASE WHEN rcr.relevant IS NOT NULL THEN rcr.relevant WHEN ccr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip, newLevels.filtered FROM consecutivecandles_results ccr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $1 AND bsl.symbolid = ccr.symbolid INNER JOIN symbols s ON ccr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN downloadersymbolsettings dss ON ccr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN symbolgroup sg on ccr.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid INNER JOIN rar_max rm ON 1 = 1 LEFT OUTER JOIN relevance_consecutivecandles_results rcr ON rcr.resultuid = ccr.resultuid LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' LEFT JOIN LATERAL calc_cc_signal_filter (ccr.resultuid) newLevels on true WHERE ccr.gmttimefound > now() - INTERVAL '7 DAYS' AND s.deleted = 0 AND (ccr.simulation = 0 OR ccr.simulation IS NULL) AND ($2 = 0 OR s.timegranularity in ($3, $4, $5, $6, $7, $8, $9)) AND ($10 = 0 OR s.exchange in ($11)) AND ($12 = 0 OR coalesce(bim.code, s.symbol) in ($13, $14, $15, $16, $17, $18, $19, $20, $21, $22, $23, $24, $25, $26, $27, $28, $29, $30, $31, $32, $33, $34, $35, $36, $37, $38, $39, $40, $41, $42, $43, $44, $45, $46, $47, $48, $49, $50, $51, $52, $53, $54, $55, $56, $57, $58, $59, $60, $61, $62, $63, $64, $65, $66, $67, $68, $69, $70, $71, $72, $73, $74, $75, $76, $77, $78, $79, $80, $81, $82, $83, $84, $85, $86, $87, $88, $89, $90, $91, $92, $93, $94, $95, $96, $97, $98, $99, $100, $101, $102, $103, $104, $105, $106, $107, $108, $109, $110, $111, $112, $113, $114, $115, $116, $117, $118, $119, $120, $121, $122, $123, $124, $125, $126, $127, $128, $129, $130, $131, $132, $133, $134, $135, $136, $137, $138, $139, $140, $141, $142, $143, $144, $145, $146, $147, $148, $149, $150, $151, $152, $153, $154, $155, $156, $157, $158, $159, $160, $161, $162, $163, $164, $165, $166, $167, $168, $169, $170, $171, $172, $173, $174, $175, $176, $177, $178, $179, $180, $181, $182, $183, $184, $185, $186, $187, $188, $189, $190, $191, $192, $193, $194, $195, $196, $197, $198, $199, $200, $201, $202, $203, $204, $205, $206, $207, $208, $209, $210, $211, $212, $213, $214, $215, $216, $217, $218, $219, $220, $221, $222, $223, $224, $225)) AND ($226 = 0 OR ccr.patternlengthbars <= $227)), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results WHERE (FALSE = $228 OR relevant = 1) AND ($229 = 0 OR age <= $230) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC;
Date: 2026-02-20 09:01:24 Duration: 0ms
3 30 1.66 GiB 5.46 MiB 165.33 MiB 56.49 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = ? ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = ? ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = ?) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, ?::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> ? ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = ?) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = ? where (ok.r is null or ok.r = ?) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = ?) and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > ? * ? and last.eventtimestamp > current_timestamp - interval ? and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval ?) and last.eventtimestamp > current_timestamp - interval ? and broker.r = ?;-
with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;
Date: 2026-02-20 09:00:07 Duration: 0ms
4 26 100.60 MiB 3.40 MiB 9.42 MiB 3.87 MiB jr.resultuid as resultuid, jr.direction as direction, jr.patternendtime as identified, jr.patternlengthbars as length, jr.patternstarttime as patternstarttime, case when jr.trendchangeid = ? then ? else ? end as trendchange, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, jp.patternname as pattern_name, dtt.timezone as timezone, ? as age, cps.pip, g.basegroupname from japsticks_results jr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = jr.symbolid inner join relevance_japsticks_results rar on rar.resultuid = jr.resultuid inner join symbols s on jr.symbolid = s.symbolid and s.nonliquid = ? inner join japsticks_patterns jp on jr.patternid = jp.id inner join downloadersymbolsettings dss on jr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on s.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where jr.gmttimefound > now() - interval ? and s.deleted = ? and (jr.simulation = ? or jr.simulation is null) and (rar.relevant = ?) --and (semicolon_age = ? or rar.age <= semicolon_age) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or jp.patternname in (...)) and (? = ? or jr.patternlengthbars <= ?) ), results as ( select distinct on (symbolid) * from all_results order by symbolid, resultuid ) select * from results order by identified desc, length desc ;-
jr.resultuid AS resultuid, jr.direction AS direction, jr.patternendtime AS identified, jr.patternlengthbars AS length, jr.patternstarttime AS patternstarttime, case when jr.trendchangeid = 1 then 'Continuation' else 'Reversal' end AS trendchange, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, jp.patternname AS pattern_name, dtt.timezone AS timezone, 0 AS age, cps.pip, g.basegroupname FROM japsticks_results jr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $1 AND bsl.symbolid = jr.symbolid INNER JOIN relevance_japsticks_results rar ON rar.resultuid = jr.resultuid INNER JOIN symbols s ON jr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN japsticks_patterns jp ON jr.patternid = jp.id INNER JOIN downloadersymbolsettings dss ON jr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN symbolgroup sg on s.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' WHERE jr.gmttimefound > now() - INTERVAL '7 DAYS' AND s.deleted = 0 AND (jr.simulation = 0 OR jr.simulation IS NULL) AND (rar.relevant = 1) --AND (semicolon_age = 0 OR rar.age <= semicolon_age) AND ($2 = 0 OR s.timegranularity in ($3)) AND ($4 = 0 OR s.exchange in ($5)) AND ($6 = 0 OR coalesce(bim.code, s.symbol) in ($7)) AND ($8 = 0 OR jp.patternname in ($9)) AND ($10 = 0 OR jr.patternlengthbars <= $11)), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC;
Date: 2026-02-20 09:01:58 Duration: 0ms
5 16 738.50 MiB 46.16 MiB 46.16 MiB 46.16 MiB update solr_relevance_old set new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total from ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total from whatshot_probability where type = ?) sub where result_uid = sub.resultuid;-
UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type = 'cp') sub WHERE result_uid = sub.resultuid;
Date: 2026-02-20 09:01:13 Duration: 0ms
6 16 1.22 GiB 78.35 MiB 78.36 MiB 78.36 MiB with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit ?) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then ? else ? end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then ? else ? end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike ? inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike ?) sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike ?; update solr_relevance_old set newrelevant = ? where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike ? and a.resultuid is null); update solr_relevance_old set new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total from ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total from whatshot_probability where type in (...)) sub where result_uid = sub.resultuid;-
with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid;
Date: 2026-02-20 09:01:16 Duration: 0ms
7 8 1.07 GiB 137.47 MiB 137.52 MiB 137.49 MiB select updateresultsmaterializedview ();-
select updateresultsmaterializedview ();
Date: 2026-02-20 09:02:15 Duration: 0ms
8 4 340.53 MiB 85.06 MiB 85.21 MiB 85.13 MiB select updateageforrelevantresults ();-
select updateageforrelevantresults ();
Date: 2026-02-20 09:02:05 Duration: 0ms
Queries generating the largest temporary files
Rank Size Query 1 165.33 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;[ Date: 2026-02-20 09:10:05 ]
2 157.52 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;[ Date: 2026-02-20 09:30:09 ]
3 156.33 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;[ Date: 2026-02-20 09:40:08 ]
4 137.52 MiB select updateresultsmaterializedview ();[ Date: 2026-02-20 09:32:20 ]
5 137.50 MiB select updateresultsmaterializedview ();[ Date: 2026-02-20 09:47:16 ]
6 137.50 MiB select updateresultsmaterializedview ();[ Date: 2026-02-20 09:02:15 ]
7 137.50 MiB select updateresultsmaterializedview ();[ Date: 2026-02-20 09:50:32 ]
8 137.49 MiB select updateresultsmaterializedview ();[ Date: 2026-02-20 09:17:15 ]
9 137.49 MiB select updateresultsmaterializedview ();[ Date: 2026-02-20 09:35:33 ]
10 137.48 MiB select updateresultsmaterializedview ();[ Date: 2026-02-20 09:20:32 ]
11 137.47 MiB select updateresultsmaterializedview ();[ Date: 2026-02-20 09:05:32 ]
12 107.33 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;[ Date: 2026-02-20 09:50:04 ]
13 92.02 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;[ Date: 2026-02-20 09:00:04 ]
14 91.54 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;[ Date: 2026-02-20 09:50:05 ]
15 85.21 MiB select updateageforrelevantresults ();[ Date: 2026-02-20 09:02:05 ]
16 85.18 MiB select updateageforrelevantresults ();[ Date: 2026-02-20 09:32:07 ]
17 85.08 MiB select updateageforrelevantresults ();[ Date: 2026-02-20 09:47:04 ]
18 85.06 MiB select updateageforrelevantresults ();[ Date: 2026-02-20 09:17:05 ]
19 79.13 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;[ Date: 2026-02-20 09:20:05 ]
20 78.36 MiB with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid;[ Date: 2026-02-20 09:11:15 ]
-
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 (16) Main table analyzed (database acaweb_fx)
- 56 analyzes Total
Table Number of analyzes acaweb_fx.public.solr_relevance_old 16 acaweb_fx.pg_catalog.pg_attribute 8 acaweb_fx.public.relevance_keylevels_results 4 acaweb_fx.pg_catalog.pg_class 4 acaweb_fx.pg_catalog.pg_type 4 acaweb_fx.public.relevance_autochartist_results 4 acaweb_fx.public.datafeeds_latestrun 3 acaweb_fx.public.relevance_fibonacci_results 3 acaweb_fx.public.latest_t15_candle_view 2 acaweb_fx.pg_catalog.pg_depend 2 acaweb_fx.pg_catalog.pg_index 1 acaweb_fx.public.autochartist_symbolupdates 1 acaweb_fx.public.latest_candle_datetime_per_receng 1 acaweb_fx.public.relevance_consecutivecandles_results 1 acaweb_fx.public.symbollatestupdatetime 1 acaweb_fx.public.t15 1 Total 56 Vacuums per table
Key values
- public.solr_relevance_old (16) Main table vacuumed on database acaweb_fx
- 42 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 16 16 14,274 0 51 0 0 10,882 16 1,905,074 acaweb_fx.public.datafeeds_latestrun 3 0 360 0 18 0 0 39 24 53,298 acaweb_fx.pg_catalog.pg_attribute 3 3 2,635 0 239 0 201 952 231 1,451,811 acaweb_fx.public.relevance_autochartist_results 3 3 10,144 0 114 4 723 1,785 95 387,172 acaweb_fx.public.relevance_fibonacci_results 3 3 3,592 0 30 4 150 421 15 80,504 acaweb_fx.pg_toast.pg_toast_2619 2 2 298 0 51 0 0 190 49 167,964 acaweb_fx.pg_catalog.pg_type 2 2 322 0 48 0 0 145 36 198,437 acaweb_fx.pg_catalog.pg_statistic 2 2 2,015 0 302 0 1,164 899 309 1,183,700 acaweb_fx.public.relevance_keylevels_results 2 2 7,399 0 183 4 174 2,000 168 604,115 acaweb_fx.pg_catalog.pg_class 2 2 926 0 96 0 0 286 87 488,957 acaweb_fx.public.autochartist_symbolupdates 1 1 21,126 0 2,684 3 38,520 5,374 2,649 1,207,962 acaweb_fx.public.solr_imports 1 1 65 0 2 0 0 6 2 14,141 acaweb_fx.pg_catalog.pg_depend 1 1 414 0 81 0 59 182 71 366,405 acaweb_fx.public.latest_t15_candle_view 1 1 93 0 1 0 0 6 1 9,025 Total 42 39 63,663 52,383 3,900 15 40,991 23,167 3,753 8,118,565 Tuples removed per table
Key values
- public.solr_relevance_old (79601) Main table with removed tuples on database acaweb_fx
- 96519 tuples Total removed
Index Tuples Pages Table Vacuums scans removed remain not yet removable removed remain acaweb_fx.public.solr_relevance_old 16 16 79,601 99,595 0 0 3,530 acaweb_fx.public.autochartist_symbolupdates 1 1 5,244 50,615 368 0 40,691 acaweb_fx.pg_catalog.pg_attribute 3 3 4,151 32,199 0 0 781 acaweb_fx.public.relevance_keylevels_results 2 2 2,643 24,650 0 0 558 acaweb_fx.public.relevance_autochartist_results 3 3 1,767 28,590 1,444 0 1,140 acaweb_fx.pg_catalog.pg_statistic 2 2 1,142 7,519 40 0 2,388 acaweb_fx.pg_catalog.pg_depend 1 1 604 14,432 0 0 142 acaweb_fx.pg_catalog.pg_type 2 2 601 2,932 36 0 86 acaweb_fx.pg_catalog.pg_class 2 2 199 3,354 54 0 300 acaweb_fx.public.datafeeds_latestrun 3 0 163 42 0 0 48 acaweb_fx.public.relevance_fibonacci_results 3 3 154 5,073 263 0 306 acaweb_fx.pg_toast.pg_toast_2619 2 2 144 336 0 0 104 acaweb_fx.public.latest_t15_candle_view 1 1 54 14 0 0 1 acaweb_fx.public.solr_imports 1 1 52 1 0 0 2 Total 42 39 96,519 269,352 2,205 0 50,077 Pages removed per table
Key values
- unknown (0) Main table with removed pages on database unknown
- 0 pages Total removed
Pages removed per tables
NO DATASET
Table Number of vacuums Index scans Tuples removed Pages removed acaweb_fx.pg_toast.pg_toast_2619 2 2 144 0 acaweb_fx.pg_catalog.pg_type 2 2 601 0 acaweb_fx.public.autochartist_symbolupdates 1 1 5244 0 acaweb_fx.public.datafeeds_latestrun 3 0 163 0 acaweb_fx.public.solr_imports 1 1 52 0 acaweb_fx.pg_catalog.pg_attribute 3 3 4151 0 acaweb_fx.pg_catalog.pg_statistic 2 2 1142 0 acaweb_fx.pg_catalog.pg_depend 1 1 604 0 acaweb_fx.public.latest_t15_candle_view 1 1 54 0 acaweb_fx.public.relevance_keylevels_results 2 2 2643 0 acaweb_fx.pg_catalog.pg_class 2 2 199 0 acaweb_fx.public.solr_relevance_old 16 16 79601 0 acaweb_fx.public.relevance_autochartist_results 3 3 1767 0 acaweb_fx.public.relevance_fibonacci_results 3 3 154 0 Total 42 39 96,519 0 Autovacuum Activity
↑ Back to the top of the Autovacuum Activity tableDay Hour VACUUMs ANALYZEs Feb 20 09 42 56 - 0 sec Highest CPU-cost vacuum
-
Locks
Locks by types
Key values
- unknown Main Lock Type
- 0 locks Total
Most frequent waiting queries (N)
Rank Count Total time Min time Max time Avg duration Query NO DATASET
Queries that waited the most
Rank Wait time Query NO DATASET
-
Queries
Queries by type
Key values
- 87,226 Total read queries
- 38,428 Total write queries
Queries by database
Key values
- unknown Main database
- 257,887 Requests
- 1h27m50s (unknown)
- Main time consuming database
Database Request type Count Duration acaweb_fx Total 852 0ms copy from 80 0ms copy to 26 0ms cte 104 0ms ddl 16 0ms delete 16 0ms others 169 0ms select 72 0ms tcl 331 0ms update 38 0ms socialmedia Total 86 0ms select 81 0ms tcl 5 0ms unknown Total 257,887 1h27m50s copy from 16 0ms cte 9,957 0ms insert 25,389 0ms others 3,790 0ms select 87,073 0ms tcl 374 0ms update 2,076 0ms Queries by user
Key values
- unknown Main user
- 257,887 Requests
User Request type Count Duration postgres Total 938 0ms copy from 80 0ms copy to 26 0ms cte 104 0ms ddl 16 0ms delete 16 0ms others 169 0ms select 153 0ms tcl 336 0ms update 38 0ms unknown Total 257,887 1h27m50s copy from 16 0ms cte 9,957 0ms insert 25,389 0ms others 3,790 0ms select 87,073 0ms tcl 374 0ms update 2,076 0ms Duration by user
Key values
- 1h27m50s (unknown) Main time consuming user
User Request type Count Duration postgres Total 938 0ms copy from 80 0ms copy to 26 0ms cte 104 0ms ddl 16 0ms delete 16 0ms others 169 0ms select 153 0ms tcl 336 0ms update 38 0ms unknown Total 257,887 1h27m50s copy from 16 0ms cte 9,957 0ms insert 25,389 0ms others 3,790 0ms select 87,073 0ms tcl 374 0ms update 2,076 0ms Queries by host
Key values
- unknown Main host
- 258,825 Requests
- 1h27m50s (unknown)
- Main time consuming host
Queries by application
Key values
- unknown Main application
- 258,469 Requests
- 1h27m50s (unknown)
- Main time consuming application
Number of cancelled queries
Key values
- 0 per second Cancelled query Peak
- 2026-02-20 09:19:50 Date
Number of cancelled queries (5 minutes period)
NO DATASET
-
Top Queries
Histogram of query times
Key values
- 92,424 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 28 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 #1
Day Hour Count Duration Avg duration Feb 20 09 28 0ms 0ms 2 0ms 1 0ms 0ms 0ms update executions set isrunning = false, has_results = true, response = ? 's biggest movers ", " short_text ": " The biggest winners are: Noodles & Company: + ?.? %, Luna Innovations Incorporated: + ?.? %, Ekso Bionics Holdings Inc: + ?.? %, Masimo Corporation: + ?.? %, American Well u00a0Corp: + ?.? %, Herbalife Nutrition Ltd: + ?.? %, Kratos Defense & Security Solutions: + ?.? %, PROG Holdings Inc: + ?.? %, Nabors Industries Ltd: + ?.? %, ELF Beauty Inc: + ?.? %.The biggest losers are: SimilarWeb Ltd: (?.? %), Ocular Therapeutix Inc: (?.? %), Esports Entertainment Group Inc: (?.? %), Genuine Parts Co: (?.? %), Pool Corporation: (?.? %), XWELL Inc. : (?.? %), Avis Budget Group Inc: (?.? %), Exicure Inc: (?.? %), Fiverr International Ltd: (?.? %), EPAM Systems Inc: (?.? %) ", " long_text ": " The biggest winners are: - Noodles & Company: + ?.? % n - Luna Innovations Incorporated: + ?.? % n - Ekso Bionics Holdings Inc: + ?.? % n - Masimo Corporation: + ?.? % n - American Well u00a0Corp: + ?.? % n - Herbalife Nutrition Ltd: + ?.? % n - Kratos Defense & Security Solutions: + ?.? % n - PROG Holdings Inc: + ?.? % n - Nabors Industries Ltd: + ?.? % n - ELF Beauty Inc: + ?.? % n. The biggest losers are: - SimilarWeb Ltd: (?.? %) n - Ocular Therapeutix Inc: (?.? %) n - Esports Entertainment Group Inc: (?.? %) n - Genuine Parts Co: (?.? %) n - Pool Corporation: (?.? %) n - XWELL Inc. : (?.? %) n - Avis Budget Group Inc: (?.? %) n - Exicure Inc: (?.? %) n - Fiverr International Ltd: (?.? %) n - EPAM Systems Inc: (?.? %) n " }, " warnings ": [], " errors ": [], " has_results ": true, " quantity_results ": ?, " creatomate_response ": { " ? _id ": " ? f4d921 - 82df - 47b4 - a53c - eb45f3d17b46 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? f4d921 - 82df - 47b4 - a53c - eb45f3d17b46.png ", " ? _template_id ": " ? c270d7 - 883c - 480e-8118 - 92af28ced303 ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (EN) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " a5e41de8 - fa31 - 4ce5 - 9882 - f54d83352896 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / a5e41de8 - fa31 - 4ce5 - 9882 - f54d83352896.png ", " ? _template_id ": " ? c270d7 - 883c - 480e-8118 - 92af28ced303 ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (EN) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ec5882ca - 8cfd - 4824 - b2bc - 8201982984c8 ", " ? _status ": " planned ", [...];Times Reported Time consuming queries #2
Day Hour Count Duration Avg duration Feb 20 09 1 0ms 0ms 3 0ms 173 0ms 0ms 0ms with rar_max as ( select resultuid from relevance_consecutivecandles_results order by resultuid desc limit ? ), all_results as ( select ccr.resultuid as resultuid, ccr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, ccr.patternendtime as identified, dtt.timezone as timezone, ccr.qtyconsecutivecandles as length, g.basegroupname, case when rcr.age is not null then rcr.age when ccr.resultuid <= rm.resultuid then ? else ? end as age, case when rcr.relevant is not null then rcr.relevant when ccr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip, newlevels.filtered from consecutivecandles_results ccr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = ccr.symbolid inner join symbols s on ccr.symbolid = s.symbolid and s.nonliquid = ? inner join downloadersymbolsettings dss on ccr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on ccr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join rar_max rm on ? = ? left outer join relevance_consecutivecandles_results rcr on rcr.resultuid = ccr.resultuid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? left join lateral calc_cc_signal_filter (ccr.resultuid) newlevels on true where ccr.gmttimefound > now() - interval ? and s.deleted = ? and (ccr.simulation = ? or ccr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or ccr.patternlengthbars <= ?) ), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc;Times Reported Time consuming queries #3
Day Hour Count Duration Avg duration Feb 20 09 173 0ms 0ms 4 0ms 13 0ms 0ms 0ms with rar_max as ( select resultuid from relevance_bigmovement_results order by resultuid desc limit ? ) select bmr.symbolid, patternstarttime, patternendtime, timegranularity, ? as direction, case when bmr.old_resultuid = ? then bmr.old_resultuid else bmr.resultuid end as uid, s.exchange, s.symbol, s.longname, s.shortname, dtt.timezone, bmr.patternmovement, bmr.statisticalmovement, bmr.fromprice, bmr.toprice, bmr.percentile, bmr.patternlengthbars, case when rbr.age is not null then rbr.age when bmr.resultuid <= rm.resultuid then ? else ? end as age, case when rbr.relevant is not null then rbr.relevant when bmr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from bigmovement_results bmr inner join downloadersymbolsettings dss on bmr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on bmr.symbolid = s.symbolid inner join rar_max rm on ? = ? left outer join relevance_bigmovement_results rbr on rbr.resultuid = bmr.resultuid left join currencypips cps on cps.symbol = s.symbol where (bmr.old_resultuid = ? or bmr.resultuid = ?) and dtt.dayofweek = ?;Times Reported Time consuming queries #4
Day Hour Count Duration Avg duration Feb 20 09 13 0ms 0ms 5 0ms 2,069 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 #5
Day Hour Count Duration Avg duration Feb 20 09 2,069 0ms 0ms 6 0ms 4 0ms 0ms 0ms select count(*) from datafeeds_latestrun where feedname ilike ? and ((latestrxtime > current_timestamp - interval ? and latestdbwritetime > current_timestamp - interval ?) or (latestdbwritetime > current_timestamp - interval ? and lateststartuptime > current_timestamp - interval ?));Times Reported Time consuming queries #6
Day Hour Count Duration Avg duration Feb 20 09 4 0ms 0ms 7 0ms 4 0ms 0ms 0ms select updaterelevantforrelevantresults ();Times Reported Time consuming queries #7
Day Hour Count Duration Avg duration Feb 20 09 4 0ms 0ms 8 0ms 574 0ms 0ms 0ms select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as resultuid, s.symbol, timegranularity as interval, direction as direction, patternendtime as patternendtime, patternstartprice as psp, patternendprice as pep, target03 as t03, target16 as t16, patternlengthbars as length, p.patternname as patternname, dtt.timezone, cps.pip from fibonacci_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join fibonaccipatterns p on a.pattern = p.patternname left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;Times Reported Time consuming queries #8
Day Hour Count Duration Avg duration Feb 20 09 574 0ms 0ms 9 0ms 4 0ms 0ms 0ms select psd.symbolid, psd.hourlysymbolid as intervalsymbolid, s.symbol, dtt.timezone, dtt.absolutetimezoneoffset, psh.hour as index, psh.ave, psh.stddev, (psh.ave - psh.stddev / ?.?) as low, (psh.ave + psh.stddev / ?.?) as high, ee.timezone as exchangetimezone, ee.mon_t1start as exchangestart, ee.mon_t1end as exchangeend from powerstats_symboldata psd inner join powerstats_hourly psh on psd.hourlysymbolid = psh.symbolid inner join symbols s on psh.symbolid = s.symbolid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join mat_ps_hourly_symbolid_max_enddate e on psh.enddate = e.enddate and psh.symbolid = e.symbolid inner join exchanges ee on ee.exchange = s.exchange where psd.symbolid = ? and dtt.dayofweek = ? order by hour asc;Times Reported Time consuming queries #9
Day Hour Count Duration Avg duration Feb 20 09 4 0ms 0ms 10 0ms 1,633 0ms 0ms 0ms update patternresultsrelevance set relevant = ?, saxo_relevant = ?, notrelevantpricedatetime = ?, reason = ? where uniqueindex = ? and relevant = ?;Times Reported Time consuming queries #10
Day Hour Count Duration Avg duration Feb 20 09 1,633 0ms 0ms 11 0ms 13 0ms 0ms 0ms insert into resultmedia (processresultsid, type, name, filename) values (?, ?, ?, ?) returning id;Times Reported Time consuming queries #11
Day Hour Count Duration Avg duration Feb 20 09 13 0ms 0ms 12 0ms 10 0ms 0ms 0ms select t.pricedatetime as pricedatetime, t.open as open, t.high as high, t.low as low, t.close "..." close, t.volume as volume, t.bsf as bsf from t1440 t where symbolid = ? and (bsf = ? or bsf is null) order by pricedatetime desc limit ?;Times Reported Time consuming queries #12
Day Hour Count Duration Avg duration Feb 20 09 10 0ms 0ms 13 0ms 18 0ms 0ms 0ms select cast(count(*) / cast(setting as numeric) * ? as int) from pg_stat_activity, pg_settings where name = ? group by setting;Times Reported Time consuming queries #13
Day Hour Count Duration Avg duration Feb 20 09 18 0ms 0ms 14 0ms 240 0ms 0ms 0ms select v.datname, c.relname, v.phase, v.heap_blks_total, v.heap_blks_scanned, v.heap_blks_vacuumed, v.index_vacuum_count, v.max_dead_tuples, v.num_dead_tuples from pg_stat_progress_vacuum as v join pg_class c on c.oid = v.relid;Times Reported Time consuming queries #14
Day Hour Count Duration Avg duration Feb 20 09 240 0ms 0ms 15 0ms 1 0ms 0ms 0ms insert into executionlogs (executionid, status, message, details, detailtype) values (?, ?, ?, ? 's Holdings Company, Inc. and changed its name to PROG Holdings, Inc. in December ?.PROG Holdings, Inc. was founded in ? and is based in Draper, Utah. ", " Address ": " ? West Data Drive, Draper, UT, United States, ? - 2315 ", " Phone ": " ? ? ? ", " WebURL ": " https: // progholdings.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / PRG.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 02 - 19 ", " ticker ": " PRG.US ", " code ": " PRG ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " }, " ? ": { " Code ": " NBR ", " Type ": " Common Stock ", " Name ": " Nabors Industries Ltd ", " Exchange ": " NYSE ", " CurrencyCode ": " USD ", " CurrencyName ": " US Dollar ", " CurrencySymbol ": " $ ", " CountryName ": " USA ", " CountryISO ": " US ", " OpenFigi ": " BBG ? BZTW ? ", " ISIN ": " BMG ? F ? ", " LEI ": null, " PrimaryTicker ": " NBR.US ", " CUSIP ": " G ? F ? ", " CIK ": " ? ", " EmployerIdNumber ": " ? - 0363970 ", " FiscalYearEnd ": " December ", " IPODate ": " ? - 02 - 28 ", " InternationalDomestic ": " International / Domestic ", " Sector ": " Energy ", " Industry ": " Oil & Gas Drilling ", " GicSector ": " Energy ", " GicGroup ": " Energy ", " GicIndustry ": " Energy Equipment & Services ", " GicSubIndustry ": " Oil & Gas Drilling ", " HomeCategory ": " Domestic Primary ", " IsDelisted ": false, " Description ": " Nabors Industries Ltd. provides drilling and drilling - related services for land - based and offshore oil and natural gas wells in the United States and internationally. The company operates through four segments: U.S. Drilling, International Drilling, Drilling Solutions, and Rig Technologies. The company offers tubular running services, including casing and tubing running, and torque monitoring; managed pressure drilling services; and drilling - bit steering systems and rig instrumentation software. The company also offers drilling systems comprising ROCKit, a directional steering control system; SmartNAV, a collaborative guidance and advisory platform; SmartSLIDE, a directional steering control system; and RigCLOUD, a digital infrastructure that integrate applications to deliver real - time insight into operations across the rig fleet. In addition, it operates a fleet of land - based drilling rigs and marketed platforms rigs; manufactures and sells top drives, catwalks, wrenches, drawworks, and other drilling related equipment, such as robotic systems and downhole tools; and provides aftermarket sales and services for the installed base of its equipment. Nabors Industries Ltd. was founded in ? and is based in Hamilton, Bermuda. ", " Address ": " Crown House, Hamilton, Bermuda, HM ? ", " Phone ": " ? - 292 - 1510 ", " WebURL ": " https: // www.nabors.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / NBR.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 02 - 19 ", " ticker ": " NBR.US ", " code ": " NBR ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " }, " ? ": { " Code ": " ELF ", " Type ": " Common Stock ", " Name ": " ELF Beauty Inc ", " Exchange ": " NYSE ", " CurrencyCode ": " USD ", " CurrencyName ": " US Dollar ", " CurrencySymbol ": " $ ", " CountryName ": " USA ", " CountryISO ": " US ", " OpenFigi ": " BBG ? DQ ? VYP ? ", " ISIN ": " US ? L ? ", " LEI ": " ? U ? K ? TCON ? C ? ", " PrimaryTicker ": " ELF.US ", " CUSIP ": " ? L ? ", " CIK ": " ? ", " EmployerIdNumber ": " ? - 4464131 ", " FiscalYearEnd ": " March ", " IPODate ": " ? - 09 - 22 ", " InternationalDomestic ": " Domestic ", " Sector ": " Consumer Defensive ", " Industry ": " Household & Personal Products ", " GicSector ": " Consumer Staples ", " GicGroup ": " Household & Personal Products ", " GicIndustry ": " Personal Care Products ", " GicSubIndustry ": " Personal Care Products ", " HomeCategory ": " Domestic ", " IsDelisted ": false, " Description ": " e.l.f. Beauty, Inc., a beauty company, provides cosmetics and skin care products worldwide. The company offers eye, lip, face, paw, and skin care products. It offers products under the e.l.f. Cosmetics, e.l.f. Skin, Well People, Naturium, and Keys Soulcare brand names. The company sells its products through national and international retailer and direct - to - consumer through its e - commerce channel. e.l.f. Beauty, Inc. was formerly known as J.A. Cosmetics Holdings, Inc. and changed its name to e.l.f. Beauty, Inc. in April ?.The company was founded in ? and is headquartered in Oakland, California. ", " Address ": " ? ? th Street, Oakland, CA, United States, ? ", " Phone ": " ? ? ? ", " WebURL ": " https: // www.elfbeauty.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / ELF.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 02 - 19 ", " ticker ": " ELF.US ", " code ": " ELF ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " } }, " Heading ": " Mayores Movimientos ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " Date ": " ? feb ? " }, " text ": { " title ": " Los mayores movimientos de esta semana ", " short_text ": " Los mayores ganadores son: Noodles & Company: + ?.? %, Luna Innovations Incorporated: + ?.? %, Ekso Bionics Holdings Inc: + ?.? %, Masimo Corporation: + ?.? %, American Well u00a0Corp: + ?.? %, Herbalife Nutrition Ltd: + ?.? %, Kratos Defense & Security Solutions: + ?.? %, PROG Holdings Inc: + ?.? %, Nabors Industries Ltd: + ?.? %, ELF Beauty Inc: + ?.? %.Los mayores perdedores son: SimilarWeb Ltd: (?.? %), Ocular Therapeutix Inc: (?.? %), Esports Entertainment Group Inc: (?.? %), Genuine Parts Co: (?.? %), Pool Corporation: (?.? %), XWELL Inc. : (?.? %), Avis Budget Group Inc: (?.? %), Exicure Inc: (?.? %), Fiverr International Ltd: (?.? %), EPAM Systems Inc: (?.? %) ", " long_text ": " Los mayores ganadores son: - Noodles & Company: + ?.? % n - Luna Innovations Incorporated: + ?.? % n - Ekso Bionics Holdings Inc: + ?.? % n - Masimo Corporation: + ?.? % n - American Well u00a0Corp: + ?.? % n - Herbalife Nutrition Ltd: + ?.? % n - Kratos Defense & Security Solutions: + ?.? % n - PROG Holdings Inc: + ?.? % n - Nabors Industries Ltd: + ?.? % n - ELF Beauty Inc: + ?.? % n. Los mayores perdedores son: - SimilarWeb Ltd: (?.? %) n - Ocular Therapeutix Inc: (?.? %) n - Esports Entertainment Group Inc: (?.? %) n - Genuine Parts Co: (?.? %) n - Pool Corporation: (?.? %) n - XWELL Inc. : (?.? %) n - Avis Budget Group Inc: (?.? %) n - Exicure Inc: (?.? %) n - Fiverr International Ltd: (?.? %) n - EPAM Systems Inc: (?.? %) n " }, " warnings ": [], " errors ": [], " has_results ": true, " quantity_results ": ?, " creatomate_response ": { " ? _id ": " d5a5dcd8 - 228e-4 c6a - 94fd - 187f9038ca6e ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate -[...];Times Reported Time consuming queries #15
Day Hour Count Duration Avg duration Feb 20 09 1 0ms 0ms 16 0ms 434 0ms 0ms 0ms select category, name, sum(total) as total, sum(correct) as correct, (cast(sum(correct) as float) / cast(sum(total) as float)) * ?.? as percentage, min("from") AS "from", max("to") AS "to" from ( select category, name, total, correct, percentage, "from", "to" from stats_summary where statsid = ? and category = lower(?) and name in (...) union select category, name, total, correct, percentage, "from", "to" from stats_hrs_summary where statsid = ? and category = lower(?) and name in (...) order by correct desc) as summdata group by category, name having sum(total) > ? order by name;Times Reported Time consuming queries #16
Day Hour Count Duration Avg duration Feb 20 09 434 0ms 0ms 17 0ms 225 0ms 0ms 0ms select distinct s.statsid as statsid, sy.exchange as name from stats s inner join broker b on s.brokerid = b.brokerid inner join brokerconfig bc on b.brokerid = bc.brokerid inner join stats_symbols ss on s.statsid = ss.statsid inner join downloadersymbolsettings dss on ss.symbolid = dss.symbolid inner join symbols sy on dss.symbolid = sy.symbolid where dss.enabled = ? and s.brokerid is not null and b.brokerid = ? and s.groupingtype ilike ? and s.description ilike ? || b.name || ? and s.description ilike ? || sy.exchange || ? and s.description not ilike ? || ? || ? union all select distinct s.statsid as statsid, basegroupname as name from stats s inner join broker b on s.brokerid = b.brokerid inner join brokerconfig bc on b.brokerid = bc.brokerid inner join stats_symbols ss on s.statsid = ss.statsid inner join downloadersymbolsettings dss on ss.symbolid = dss.symbolid inner join symbolgroup sg on dss.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bg.brokerid = b.brokerid where dss.enabled = ? and s.brokerid is not null and b.brokerid = ? and s.groupingtype not ilike ? and s.description ilike ? || b.name || ? and s.description ilike ? || g.basegroupname || ? and s.description not ilike ? || ? || ?;Times Reported Time consuming queries #17
Day Hour Count Duration Avg duration Feb 20 09 225 0ms 0ms 18 0ms 37 0ms 0ms 0ms select * from ( select pricedatetime, open, high, low, close, volume, bsf from t60 where symbolid = ? and (bsf = ? or bsf is null) order by pricedatetime desc limit ?) a order by pricedatetime asc;Times Reported Time consuming queries #18
Day Hour Count Duration Avg duration Feb 20 09 37 0ms 0ms 19 0ms 1 0ms 0ms 0ms insert into executionlogs (executionid, status, message, details, detailtype) values (?, ?, ?, ? 's biggest movers ", " short_text ": " The biggest winners are: Noodles & Company: + ?.? %, Luna Innovations Incorporated: + ?.? %, Ekso Bionics Holdings Inc: + ?.? %, Masimo Corporation: + ?.? %, American Well u00a0Corp: + ?.? %, Herbalife Nutrition Ltd: + ?.? %, Kratos Defense & Security Solutions: + ?.? %, PROG Holdings Inc: + ?.? %, Nabors Industries Ltd: + ?.? %, ELF Beauty Inc: + ?.? %.The biggest losers are: SimilarWeb Ltd: (?.? %), Ocular Therapeutix Inc: (?.? %), Esports Entertainment Group Inc: (?.? %), Genuine Parts Co: (?.? %), Pool Corporation: (?.? %), XWELL Inc. : (?.? %), Avis Budget Group Inc: (?.? %), Exicure Inc: (?.? %), Fiverr International Ltd: (?.? %), EPAM Systems Inc: (?.? %) ", " long_text ": " The biggest winners are: - Noodles & Company: + ?.? % n - Luna Innovations Incorporated: + ?.? % n - Ekso Bionics Holdings Inc: + ?.? % n - Masimo Corporation: + ?.? % n - American Well u00a0Corp: + ?.? % n - Herbalife Nutrition Ltd: + ?.? % n - Kratos Defense & Security Solutions: + ?.? % n - PROG Holdings Inc: + ?.? % n - Nabors Industries Ltd: + ?.? % n - ELF Beauty Inc: + ?.? % n. The biggest losers are: - SimilarWeb Ltd: (?.? %) n - Ocular Therapeutix Inc: (?.? %) n - Esports Entertainment Group Inc: (?.? %) n - Genuine Parts Co: (?.? %) n - Pool Corporation: (?.? %) n - XWELL Inc. : (?.? %) n - Avis Budget Group Inc: (?.? %) n - Exicure Inc: (?.? %) n - Fiverr International Ltd: (?.? %) n - EPAM Systems Inc: (?.? %) n " }, " warnings ": [], " errors ": [], " has_results ": true, " quantity_results ": ?, " creatomate_response ": { " ? _id ": " ? f4d921 - 82df - 47b4 - a53c - eb45f3d17b46 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? f4d921 - 82df - 47b4 - a53c - eb45f3d17b46.png ", " ? _template_id ": " ? c270d7 - 883c - 480e-8118 - 92af28ced303 ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (EN) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " a5e41de8 - fa31 - 4ce5 - 9882 - f54d83352896 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / a5e41de8 - fa31 - 4ce5 - 9882 - f54d83352896.png ", " ? _template_id ": " ? c270d7 - 883c - 480e-8118 - 92af28ced303 ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (EN) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ec5882ca - 8cfd - 4824[...];Times Reported Time consuming queries #19
Day Hour Count Duration Avg duration Feb 20 09 1 0ms 0ms 20 0ms 355 0ms 0ms 0ms commit;Times Reported Time consuming queries #20
Day Hour Count Duration Avg duration Feb 20 09 355 0ms 0ms Most frequent queries (N)
Rank Times executed Total duration Min duration Max duration Avg duration Query 1 34,579 0ms 0ms 0ms 0ms select ?;Times Reported Time consuming queries #1
Day Hour Count Duration Avg duration Feb 20 09 34,579 0ms 0ms 2 8,361 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 #2
Day Hour Count Duration Avg duration Feb 20 09 8,361 0ms 0ms 3 6,696 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 #3
Day Hour Count Duration Avg duration Feb 20 09 6,696 0ms 0ms 4 5,624 0ms 0ms 0ms 0ms insert into autochartist_results (resultid, symbolid, bandwidth, pattern, qtytp, gmttimefound, direction, initialtrend, breakout, volumeincrease, noise, symmetry, predictionpricefrom, predictionpriceto, predictiontimefrom, predictiontimeto, patternstarttime, patternendtime, patternstartprice, patternendprice, resx0, resx1, supportx0, supportx1, resy0, resy1, supporty0, supporty1, supportgradient, resgradient, riskreward, patternquality, trendchange, maxmovementafterbreakout, latestbaratbreakouttime, latestbaratbreakoutprice, patternlengthbars, temporarypattern, relevancestartdistance, simulation, writtendatetime) values (?, ?, ?.?, ?, ?, ?::timestamp without time zone, ?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?::timestamp without time zone, ?::timestamp without time zone, ?::timestamp without time zone, ?::timestamp without time zone, ?.?, ?.?, ?::timestamp without time zone, ?::timestamp without time zone, ?::timestamp without time zone, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?, ?.?, ?::timestamp without time zone, ?.?, ?, ?, ?.?, ?, current_timestamp::timestamp without time zone) on conflict do nothing;Times Reported Time consuming queries #4
Day Hour Count Duration Avg duration Feb 20 09 5,624 0ms 0ms 5 5,194 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 #5
Day Hour Count Duration Avg duration Feb 20 09 5,194 0ms 0ms 6 4,774 0ms 0ms 0ms 0ms select category, name, sum(total) as total, sum(correct) as correct, (cast(sum(correct) as float) / cast(sum(total) as float)) * ?.? as percentage, min("from") AS "from", max("to") AS "to" from ( select category, name, total, correct, percentage, "from", "to" from stats_summary where statsid = ? and category = lower(?) union select category, name, total, correct, percentage, "from", "to" from stats_hrs_summary where statsid = ? and category = lower(?) order by correct desc) as summdata group by category, name having sum(total) > ? order by name;Times Reported Time consuming queries #6
Day Hour Count Duration Avg duration Feb 20 09 4,774 0ms 0ms 7 4,762 0ms 0ms 0ms 0ms with rar_max as ( select resultuid from relevance_autochartist_results order by resultuid desc limit ? ) select a.symbolid, pattern, patternid, resy0, resy1, resx0, resx1, supporty0, supporty1, supportx0, supportx1, predictiontimeto, patternstarttime, timegranularity, patternendtime, direction, trendchange, patternlengthbars, patternquality, case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, s.exchange, s.symbol, s.longname, s.shortname, breakout, dtt.timezone, patternstartprice, patternendprice, qtytp, newlevels.profit, newlevels.stop, newlevels.filtered, case when rar.age is not null then rar.age when a.resultuid <= rm.resultuid then ? else ? end as age, case when rar.relevant is not null then rar.relevant when a.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from autochartist_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern inner join rar_max rm on ? = ? left outer join relevance_autochartist_results rar on rar.resultuid = a.resultuid left join lateral calc_cp_signal (a.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;Times Reported Time consuming queries #7
Day Hour Count Duration Avg duration Feb 20 09 4,762 0ms 0ms 8 4,242 0ms 0ms 0ms 0ms insert into executionlogs (executionid, status, message, details, detailtype) values (null, ?, ?, null, null);Times Reported Time consuming queries #8
Day Hour Count Duration Avg duration Feb 20 09 4,242 0ms 0ms 9 3,329 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 #9
Day Hour Count Duration Avg duration Feb 20 09 3,329 0ms 0ms 10 3,053 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 #10
Day Hour Count Duration Avg duration Feb 20 09 3,053 0ms 0ms 11 2,604 0ms 0ms 0ms 0ms select category, name, sum(total) as total, sum(correct) as correct, (cast(sum(correct) as float) / cast(sum(total) as float)) * ?.? as percentage, min("from") AS "from", max("to") AS "to" from ( select category, name, total, correct, percentage, "from", "to" from stats_hrsapproaches_summary where statsid = ? and category = lower(?) order by correct desc) as summdata group by category, name having sum(total) > ? order by name;Times Reported Time consuming queries #11
Day Hour Count Duration Avg duration Feb 20 09 2,604 0ms 0ms 12 2,516 0ms 0ms 0ms 0ms insert into keylevels_results (bandwidth, breakout, patternid, gmttimefound, approachingtimestamp, approachingregion, qtytp, patternlengthbars, patternprice, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, breakoutbars, breakoutprice, patternendtime, atbaridentified, atpriceidentified, errormargin, direction, symbolid, predictionpricefrom, predictionpriceto, predictiontimefrom, predictiontimebars, uniquepointsvalue, furthestprice, relevancestartdistance, patternclassid, patternstarttime, stoplosslevel, simulation, writtendatetime) values (?.?, ?, ?, ?::timestamp without time zone, ?, ?.?, ?, ?, ?.?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?.?, ?::timestamp without time zone, ?, ?.?, ?.?, ?, ?, ?.?, ?.?, ?::timestamp without time zone, ?, ?, ?.?, ?.?, ?, ?, ?.?, ?, current_timestamp::timestamp without time zone) on conflict do nothing;Times Reported Time consuming queries #12
Day Hour Count Duration Avg duration Feb 20 09 2,516 0ms 0ms 13 2,226 0ms 0ms 0ms 0ms select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as resultuid, s.symbol, pattern as patternname, timegranularity as interval, patternlengthbars as length, patternendtime, direction, breakout, predictiontimeto, predictionpricefrom, predictionpriceto, patternstartprice, resy1, supporty1, dtt.timezone, cps.pip, newlevels.profit from autochartist_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern left join currencypips cps on cps.symbol = s.symbol left join lateral calc_cp_signal (a.resultuid) newlevels on true where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;Times Reported Time consuming queries #13
Day Hour Count Duration Avg duration Feb 20 09 2,226 0ms 0ms 14 2,140 0ms 0ms 0ms 0ms with rar_max as ( select resultuid from relevance_keylevels_results order by resultuid desc limit ? ) select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as ruid, s.symbolid as sid, s.symbol as sym, longname, shortname, exchange as e, timegranularity as tg, a.patternid as pid, a.direction as d, a.patternprice as pp, atbaridentified as pet, case when (x9 != ?) then x9 when (x8 != ?) then x8 when (x7 != ?) then x7 when (x6 != ?) then x6 when (x5 != ?) then x5 when (x4 != ?) then x4 when (x3 != ?) then x3 when (x2 != ?) then x2 end as pst, patternprice as patp, x0, x1, x2, case when (x3 != ?) then x3 else ? end as x3, case when (x4 != ?) then x4 else ? end as x4, case when (x5 != ?) then x5 else ? end as x5, case when (x6 != ?) then x6 else ? end as x6, case when (x7 != ?) then x7 else ? end as x7, case when (x8 != ?) then x8 else ? end as x8, errormargin as erm, breakoutprice as pe, breakoutbars as be, breakout, atbaridentified as atbar, atpriceidentified as atprice, patternlengthbars as l, bandwidth as bw, qtytp as qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzos, dtt.timezone as timezone, approachingtimestamp as apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb, furthestprice as fp, newlevels.filtered, a.uniquepointsvalue as upv, case when rar.age is not null then rar.age when a.resultuid <= rm.resultuid then ? else ? end as age, case when rar.relevant is not null then rar.relevant when a.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from keylevels_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join hrspatterns p on a.patternid = p.patternid inner join rar_max rm on ? = ? left outer join relevance_keylevels_results rar on a.resultuid = rar.resultuid left join lateral calc_kl_signal_filter (a.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;Times Reported Time consuming queries #14
Day Hour Count Duration Avg duration Feb 20 09 2,140 0ms 0ms 15 2,069 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 #15
Day Hour Count Duration Avg duration Feb 20 09 2,069 0ms 0ms 16 2,065 0ms 0ms 0ms 0ms insert into fibonacci_results (bandwidth, pattern, gmttimefound, direction, patternstarttime, patternendtime, patternstartprice, patternendprice, qtytp, pricex, timex, pricea, timea, priceb, timeb, pricec, timec, priced, timed, averagequality, timequality, errormargin, patternlengthbars, target10, target06, target16, target07, target12, target05, target03, symbolid, noise, ratiosfound, temporarypattern, uniqueindex, completed, simulation, writtendatetime) values (?.?, ?, ?::timestamp without time zone, ?, ?::timestamp without time zone, ?::timestamp without time zone, ?.?, ?.?, ?, ?.?, ?::timestamp without time zone, ?.?, ?::timestamp without time zone, ?.?, ?::timestamp without time zone, ?.?, ?::timestamp without time zone, ?.?, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?, ?.?, ?, ?, ?, ?, ?, current_timestamp::timestamp without time zone) on conflict do nothing;Times Reported Time consuming queries #16
Day Hour Count Duration Avg duration Feb 20 09 2,065 0ms 0ms 17 1,692 0ms 0ms 0ms 0ms set extra_float_digits = ?;Times Reported Time consuming queries #17
Day Hour Count Duration Avg duration Feb 20 09 1,692 0ms 0ms 18 1,657 0ms 0ms 0ms 0ms set application_name = ?;Times Reported Time consuming queries #18
Day Hour Count Duration Avg duration Feb 20 09 1,657 0ms 0ms 19 1,633 0ms 0ms 0ms 0ms update patternresultsrelevance set relevant = ?, saxo_relevant = ?, notrelevantpricedatetime = ?, reason = ? where uniqueindex = ? and relevant = ?;Times Reported Time consuming queries #19
Day Hour Count Duration Avg duration Feb 20 09 1,633 0ms 0ms 20 1,200 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 #20
Day Hour Count Duration Avg duration Feb 20 09 1,200 0ms 0ms Normalized slowest queries (N)
Rank Min duration Max duration Avg duration Times executed Total duration Query 1 0ms 0ms 0ms 28 0ms select key, value from datasources ds inner join datasourceparams dsp on ds.id = dsp.datasourceid where ds.name = ?;Times Reported Time consuming queries #1
Day Hour Count Duration Avg duration Feb 20 09 28 0ms 0ms 2 0ms 0ms 0ms 1 0ms update executions set isrunning = false, has_results = true, response = ? 's biggest movers ", " short_text ": " The biggest winners are: Noodles & Company: + ?.? %, Luna Innovations Incorporated: + ?.? %, Ekso Bionics Holdings Inc: + ?.? %, Masimo Corporation: + ?.? %, American Well u00a0Corp: + ?.? %, Herbalife Nutrition Ltd: + ?.? %, Kratos Defense & Security Solutions: + ?.? %, PROG Holdings Inc: + ?.? %, Nabors Industries Ltd: + ?.? %, ELF Beauty Inc: + ?.? %.The biggest losers are: SimilarWeb Ltd: (?.? %), Ocular Therapeutix Inc: (?.? %), Esports Entertainment Group Inc: (?.? %), Genuine Parts Co: (?.? %), Pool Corporation: (?.? %), XWELL Inc. : (?.? %), Avis Budget Group Inc: (?.? %), Exicure Inc: (?.? %), Fiverr International Ltd: (?.? %), EPAM Systems Inc: (?.? %) ", " long_text ": " The biggest winners are: - Noodles & Company: + ?.? % n - Luna Innovations Incorporated: + ?.? % n - Ekso Bionics Holdings Inc: + ?.? % n - Masimo Corporation: + ?.? % n - American Well u00a0Corp: + ?.? % n - Herbalife Nutrition Ltd: + ?.? % n - Kratos Defense & Security Solutions: + ?.? % n - PROG Holdings Inc: + ?.? % n - Nabors Industries Ltd: + ?.? % n - ELF Beauty Inc: + ?.? % n. The biggest losers are: - SimilarWeb Ltd: (?.? %) n - Ocular Therapeutix Inc: (?.? %) n - Esports Entertainment Group Inc: (?.? %) n - Genuine Parts Co: (?.? %) n - Pool Corporation: (?.? %) n - XWELL Inc. : (?.? %) n - Avis Budget Group Inc: (?.? %) n - Exicure Inc: (?.? %) n - Fiverr International Ltd: (?.? %) n - EPAM Systems Inc: (?.? %) n " }, " warnings ": [], " errors ": [], " has_results ": true, " quantity_results ": ?, " creatomate_response ": { " ? _id ": " ? f4d921 - 82df - 47b4 - a53c - eb45f3d17b46 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? f4d921 - 82df - 47b4 - a53c - eb45f3d17b46.png ", " ? _template_id ": " ? c270d7 - 883c - 480e-8118 - 92af28ced303 ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (EN) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " a5e41de8 - fa31 - 4ce5 - 9882 - f54d83352896 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / a5e41de8 - fa31 - 4ce5 - 9882 - f54d83352896.png ", " ? _template_id ": " ? c270d7 - 883c - 480e-8118 - 92af28ced303 ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (EN) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ec5882ca - 8cfd - 4824 - b2bc - 8201982984c8 ", " ? _status ": " planned ", [...];Times Reported Time consuming queries #2
Day Hour Count Duration Avg duration Feb 20 09 1 0ms 0ms 3 0ms 0ms 0ms 173 0ms with rar_max as ( select resultuid from relevance_consecutivecandles_results order by resultuid desc limit ? ), all_results as ( select ccr.resultuid as resultuid, ccr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, ccr.patternendtime as identified, dtt.timezone as timezone, ccr.qtyconsecutivecandles as length, g.basegroupname, case when rcr.age is not null then rcr.age when ccr.resultuid <= rm.resultuid then ? else ? end as age, case when rcr.relevant is not null then rcr.relevant when ccr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip, newlevels.filtered from consecutivecandles_results ccr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = ccr.symbolid inner join symbols s on ccr.symbolid = s.symbolid and s.nonliquid = ? inner join downloadersymbolsettings dss on ccr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on ccr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join rar_max rm on ? = ? left outer join relevance_consecutivecandles_results rcr on rcr.resultuid = ccr.resultuid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? left join lateral calc_cc_signal_filter (ccr.resultuid) newlevels on true where ccr.gmttimefound > now() - interval ? and s.deleted = ? and (ccr.simulation = ? or ccr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or ccr.patternlengthbars <= ?) ), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc;Times Reported Time consuming queries #3
Day Hour Count Duration Avg duration Feb 20 09 173 0ms 0ms 4 0ms 0ms 0ms 13 0ms with rar_max as ( select resultuid from relevance_bigmovement_results order by resultuid desc limit ? ) select bmr.symbolid, patternstarttime, patternendtime, timegranularity, ? as direction, case when bmr.old_resultuid = ? then bmr.old_resultuid else bmr.resultuid end as uid, s.exchange, s.symbol, s.longname, s.shortname, dtt.timezone, bmr.patternmovement, bmr.statisticalmovement, bmr.fromprice, bmr.toprice, bmr.percentile, bmr.patternlengthbars, case when rbr.age is not null then rbr.age when bmr.resultuid <= rm.resultuid then ? else ? end as age, case when rbr.relevant is not null then rbr.relevant when bmr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from bigmovement_results bmr inner join downloadersymbolsettings dss on bmr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on bmr.symbolid = s.symbolid inner join rar_max rm on ? = ? left outer join relevance_bigmovement_results rbr on rbr.resultuid = bmr.resultuid left join currencypips cps on cps.symbol = s.symbol where (bmr.old_resultuid = ? or bmr.resultuid = ?) and dtt.dayofweek = ?;Times Reported Time consuming queries #4
Day Hour Count Duration Avg duration Feb 20 09 13 0ms 0ms 5 0ms 0ms 0ms 2,069 0ms insert into t60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;Times Reported Time consuming queries #5
Day Hour Count Duration Avg duration Feb 20 09 2,069 0ms 0ms 6 0ms 0ms 0ms 4 0ms select count(*) from datafeeds_latestrun where feedname ilike ? and ((latestrxtime > current_timestamp - interval ? and latestdbwritetime > current_timestamp - interval ?) or (latestdbwritetime > current_timestamp - interval ? and lateststartuptime > current_timestamp - interval ?));Times Reported Time consuming queries #6
Day Hour Count Duration Avg duration Feb 20 09 4 0ms 0ms 7 0ms 0ms 0ms 4 0ms select updaterelevantforrelevantresults ();Times Reported Time consuming queries #7
Day Hour Count Duration Avg duration Feb 20 09 4 0ms 0ms 8 0ms 0ms 0ms 574 0ms select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as resultuid, s.symbol, timegranularity as interval, direction as direction, patternendtime as patternendtime, patternstartprice as psp, patternendprice as pep, target03 as t03, target16 as t16, patternlengthbars as length, p.patternname as patternname, dtt.timezone, cps.pip from fibonacci_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join fibonaccipatterns p on a.pattern = p.patternname left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;Times Reported Time consuming queries #8
Day Hour Count Duration Avg duration Feb 20 09 574 0ms 0ms 9 0ms 0ms 0ms 4 0ms select psd.symbolid, psd.hourlysymbolid as intervalsymbolid, s.symbol, dtt.timezone, dtt.absolutetimezoneoffset, psh.hour as index, psh.ave, psh.stddev, (psh.ave - psh.stddev / ?.?) as low, (psh.ave + psh.stddev / ?.?) as high, ee.timezone as exchangetimezone, ee.mon_t1start as exchangestart, ee.mon_t1end as exchangeend from powerstats_symboldata psd inner join powerstats_hourly psh on psd.hourlysymbolid = psh.symbolid inner join symbols s on psh.symbolid = s.symbolid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join mat_ps_hourly_symbolid_max_enddate e on psh.enddate = e.enddate and psh.symbolid = e.symbolid inner join exchanges ee on ee.exchange = s.exchange where psd.symbolid = ? and dtt.dayofweek = ? order by hour asc;Times Reported Time consuming queries #9
Day Hour Count Duration Avg duration Feb 20 09 4 0ms 0ms 10 0ms 0ms 0ms 1,633 0ms update patternresultsrelevance set relevant = ?, saxo_relevant = ?, notrelevantpricedatetime = ?, reason = ? where uniqueindex = ? and relevant = ?;Times Reported Time consuming queries #10
Day Hour Count Duration Avg duration Feb 20 09 1,633 0ms 0ms 11 0ms 0ms 0ms 13 0ms insert into resultmedia (processresultsid, type, name, filename) values (?, ?, ?, ?) returning id;Times Reported Time consuming queries #11
Day Hour Count Duration Avg duration Feb 20 09 13 0ms 0ms 12 0ms 0ms 0ms 10 0ms select t.pricedatetime as pricedatetime, t.open as open, t.high as high, t.low as low, t.close "..." close, t.volume as volume, t.bsf as bsf from t1440 t where symbolid = ? and (bsf = ? or bsf is null) order by pricedatetime desc limit ?;Times Reported Time consuming queries #12
Day Hour Count Duration Avg duration Feb 20 09 10 0ms 0ms 13 0ms 0ms 0ms 18 0ms select cast(count(*) / cast(setting as numeric) * ? as int) from pg_stat_activity, pg_settings where name = ? group by setting;Times Reported Time consuming queries #13
Day Hour Count Duration Avg duration Feb 20 09 18 0ms 0ms 14 0ms 0ms 0ms 240 0ms select v.datname, c.relname, v.phase, v.heap_blks_total, v.heap_blks_scanned, v.heap_blks_vacuumed, v.index_vacuum_count, v.max_dead_tuples, v.num_dead_tuples from pg_stat_progress_vacuum as v join pg_class c on c.oid = v.relid;Times Reported Time consuming queries #14
Day Hour Count Duration Avg duration Feb 20 09 240 0ms 0ms 15 0ms 0ms 0ms 1 0ms insert into executionlogs (executionid, status, message, details, detailtype) values (?, ?, ?, ? 's Holdings Company, Inc. and changed its name to PROG Holdings, Inc. in December ?.PROG Holdings, Inc. was founded in ? and is based in Draper, Utah. ", " Address ": " ? West Data Drive, Draper, UT, United States, ? - 2315 ", " Phone ": " ? ? ? ", " WebURL ": " https: // progholdings.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / PRG.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 02 - 19 ", " ticker ": " PRG.US ", " code ": " PRG ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " }, " ? ": { " Code ": " NBR ", " Type ": " Common Stock ", " Name ": " Nabors Industries Ltd ", " Exchange ": " NYSE ", " CurrencyCode ": " USD ", " CurrencyName ": " US Dollar ", " CurrencySymbol ": " $ ", " CountryName ": " USA ", " CountryISO ": " US ", " OpenFigi ": " BBG ? BZTW ? ", " ISIN ": " BMG ? F ? ", " LEI ": null, " PrimaryTicker ": " NBR.US ", " CUSIP ": " G ? F ? ", " CIK ": " ? ", " EmployerIdNumber ": " ? - 0363970 ", " FiscalYearEnd ": " December ", " IPODate ": " ? - 02 - 28 ", " InternationalDomestic ": " International / Domestic ", " Sector ": " Energy ", " Industry ": " Oil & Gas Drilling ", " GicSector ": " Energy ", " GicGroup ": " Energy ", " GicIndustry ": " Energy Equipment & Services ", " GicSubIndustry ": " Oil & Gas Drilling ", " HomeCategory ": " Domestic Primary ", " IsDelisted ": false, " Description ": " Nabors Industries Ltd. provides drilling and drilling - related services for land - based and offshore oil and natural gas wells in the United States and internationally. The company operates through four segments: U.S. Drilling, International Drilling, Drilling Solutions, and Rig Technologies. The company offers tubular running services, including casing and tubing running, and torque monitoring; managed pressure drilling services; and drilling - bit steering systems and rig instrumentation software. The company also offers drilling systems comprising ROCKit, a directional steering control system; SmartNAV, a collaborative guidance and advisory platform; SmartSLIDE, a directional steering control system; and RigCLOUD, a digital infrastructure that integrate applications to deliver real - time insight into operations across the rig fleet. In addition, it operates a fleet of land - based drilling rigs and marketed platforms rigs; manufactures and sells top drives, catwalks, wrenches, drawworks, and other drilling related equipment, such as robotic systems and downhole tools; and provides aftermarket sales and services for the installed base of its equipment. Nabors Industries Ltd. was founded in ? and is based in Hamilton, Bermuda. ", " Address ": " Crown House, Hamilton, Bermuda, HM ? ", " Phone ": " ? - 292 - 1510 ", " WebURL ": " https: // www.nabors.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / NBR.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 02 - 19 ", " ticker ": " NBR.US ", " code ": " NBR ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " }, " ? ": { " Code ": " ELF ", " Type ": " Common Stock ", " Name ": " ELF Beauty Inc ", " Exchange ": " NYSE ", " CurrencyCode ": " USD ", " CurrencyName ": " US Dollar ", " CurrencySymbol ": " $ ", " CountryName ": " USA ", " CountryISO ": " US ", " OpenFigi ": " BBG ? DQ ? VYP ? ", " ISIN ": " US ? L ? ", " LEI ": " ? U ? K ? TCON ? C ? ", " PrimaryTicker ": " ELF.US ", " CUSIP ": " ? L ? ", " CIK ": " ? ", " EmployerIdNumber ": " ? - 4464131 ", " FiscalYearEnd ": " March ", " IPODate ": " ? - 09 - 22 ", " InternationalDomestic ": " Domestic ", " Sector ": " Consumer Defensive ", " Industry ": " Household & Personal Products ", " GicSector ": " Consumer Staples ", " GicGroup ": " Household & Personal Products ", " GicIndustry ": " Personal Care Products ", " GicSubIndustry ": " Personal Care Products ", " HomeCategory ": " Domestic ", " IsDelisted ": false, " Description ": " e.l.f. Beauty, Inc., a beauty company, provides cosmetics and skin care products worldwide. The company offers eye, lip, face, paw, and skin care products. It offers products under the e.l.f. Cosmetics, e.l.f. Skin, Well People, Naturium, and Keys Soulcare brand names. The company sells its products through national and international retailer and direct - to - consumer through its e - commerce channel. e.l.f. Beauty, Inc. was formerly known as J.A. Cosmetics Holdings, Inc. and changed its name to e.l.f. Beauty, Inc. in April ?.The company was founded in ? and is headquartered in Oakland, California. ", " Address ": " ? ? th Street, Oakland, CA, United States, ? ", " Phone ": " ? ? ? ", " WebURL ": " https: // www.elfbeauty.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / ELF.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 02 - 19 ", " ticker ": " ELF.US ", " code ": " ELF ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " } }, " Heading ": " Mayores Movimientos ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " Date ": " ? feb ? " }, " text ": { " title ": " Los mayores movimientos de esta semana ", " short_text ": " Los mayores ganadores son: Noodles & Company: + ?.? %, Luna Innovations Incorporated: + ?.? %, Ekso Bionics Holdings Inc: + ?.? %, Masimo Corporation: + ?.? %, American Well u00a0Corp: + ?.? %, Herbalife Nutrition Ltd: + ?.? %, Kratos Defense & Security Solutions: + ?.? %, PROG Holdings Inc: + ?.? %, Nabors Industries Ltd: + ?.? %, ELF Beauty Inc: + ?.? %.Los mayores perdedores son: SimilarWeb Ltd: (?.? %), Ocular Therapeutix Inc: (?.? %), Esports Entertainment Group Inc: (?.? %), Genuine Parts Co: (?.? %), Pool Corporation: (?.? %), XWELL Inc. : (?.? %), Avis Budget Group Inc: (?.? %), Exicure Inc: (?.? %), Fiverr International Ltd: (?.? %), EPAM Systems Inc: (?.? %) ", " long_text ": " Los mayores ganadores son: - Noodles & Company: + ?.? % n - Luna Innovations Incorporated: + ?.? % n - Ekso Bionics Holdings Inc: + ?.? % n - Masimo Corporation: + ?.? % n - American Well u00a0Corp: + ?.? % n - Herbalife Nutrition Ltd: + ?.? % n - Kratos Defense & Security Solutions: + ?.? % n - PROG Holdings Inc: + ?.? % n - Nabors Industries Ltd: + ?.? % n - ELF Beauty Inc: + ?.? % n. Los mayores perdedores son: - SimilarWeb Ltd: (?.? %) n - Ocular Therapeutix Inc: (?.? %) n - Esports Entertainment Group Inc: (?.? %) n - Genuine Parts Co: (?.? %) n - Pool Corporation: (?.? %) n - XWELL Inc. : (?.? %) n - Avis Budget Group Inc: (?.? %) n - Exicure Inc: (?.? %) n - Fiverr International Ltd: (?.? %) n - EPAM Systems Inc: (?.? %) n " }, " warnings ": [], " errors ": [], " has_results ": true, " quantity_results ": ?, " creatomate_response ": { " ? _id ": " d5a5dcd8 - 228e-4 c6a - 94fd - 187f9038ca6e ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate -[...];Times Reported Time consuming queries #15
Day Hour Count Duration Avg duration Feb 20 09 1 0ms 0ms 16 0ms 0ms 0ms 434 0ms select category, name, sum(total) as total, sum(correct) as correct, (cast(sum(correct) as float) / cast(sum(total) as float)) * ?.? as percentage, min("from") AS "from", max("to") AS "to" from ( select category, name, total, correct, percentage, "from", "to" from stats_summary where statsid = ? and category = lower(?) and name in (...) union select category, name, total, correct, percentage, "from", "to" from stats_hrs_summary where statsid = ? and category = lower(?) and name in (...) order by correct desc) as summdata group by category, name having sum(total) > ? order by name;Times Reported Time consuming queries #16
Day Hour Count Duration Avg duration Feb 20 09 434 0ms 0ms 17 0ms 0ms 0ms 225 0ms select distinct s.statsid as statsid, sy.exchange as name from stats s inner join broker b on s.brokerid = b.brokerid inner join brokerconfig bc on b.brokerid = bc.brokerid inner join stats_symbols ss on s.statsid = ss.statsid inner join downloadersymbolsettings dss on ss.symbolid = dss.symbolid inner join symbols sy on dss.symbolid = sy.symbolid where dss.enabled = ? and s.brokerid is not null and b.brokerid = ? and s.groupingtype ilike ? and s.description ilike ? || b.name || ? and s.description ilike ? || sy.exchange || ? and s.description not ilike ? || ? || ? union all select distinct s.statsid as statsid, basegroupname as name from stats s inner join broker b on s.brokerid = b.brokerid inner join brokerconfig bc on b.brokerid = bc.brokerid inner join stats_symbols ss on s.statsid = ss.statsid inner join downloadersymbolsettings dss on ss.symbolid = dss.symbolid inner join symbolgroup sg on dss.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bg.brokerid = b.brokerid where dss.enabled = ? and s.brokerid is not null and b.brokerid = ? and s.groupingtype not ilike ? and s.description ilike ? || b.name || ? and s.description ilike ? || g.basegroupname || ? and s.description not ilike ? || ? || ?;Times Reported Time consuming queries #17
Day Hour Count Duration Avg duration Feb 20 09 225 0ms 0ms 18 0ms 0ms 0ms 37 0ms select * from ( select pricedatetime, open, high, low, close, volume, bsf from t60 where symbolid = ? and (bsf = ? or bsf is null) order by pricedatetime desc limit ?) a order by pricedatetime asc;Times Reported Time consuming queries #18
Day Hour Count Duration Avg duration Feb 20 09 37 0ms 0ms 19 0ms 0ms 0ms 1 0ms insert into executionlogs (executionid, status, message, details, detailtype) values (?, ?, ?, ? 's biggest movers ", " short_text ": " The biggest winners are: Noodles & Company: + ?.? %, Luna Innovations Incorporated: + ?.? %, Ekso Bionics Holdings Inc: + ?.? %, Masimo Corporation: + ?.? %, American Well u00a0Corp: + ?.? %, Herbalife Nutrition Ltd: + ?.? %, Kratos Defense & Security Solutions: + ?.? %, PROG Holdings Inc: + ?.? %, Nabors Industries Ltd: + ?.? %, ELF Beauty Inc: + ?.? %.The biggest losers are: SimilarWeb Ltd: (?.? %), Ocular Therapeutix Inc: (?.? %), Esports Entertainment Group Inc: (?.? %), Genuine Parts Co: (?.? %), Pool Corporation: (?.? %), XWELL Inc. : (?.? %), Avis Budget Group Inc: (?.? %), Exicure Inc: (?.? %), Fiverr International Ltd: (?.? %), EPAM Systems Inc: (?.? %) ", " long_text ": " The biggest winners are: - Noodles & Company: + ?.? % n - Luna Innovations Incorporated: + ?.? % n - Ekso Bionics Holdings Inc: + ?.? % n - Masimo Corporation: + ?.? % n - American Well u00a0Corp: + ?.? % n - Herbalife Nutrition Ltd: + ?.? % n - Kratos Defense & Security Solutions: + ?.? % n - PROG Holdings Inc: + ?.? % n - Nabors Industries Ltd: + ?.? % n - ELF Beauty Inc: + ?.? % n. The biggest losers are: - SimilarWeb Ltd: (?.? %) n - Ocular Therapeutix Inc: (?.? %) n - Esports Entertainment Group Inc: (?.? %) n - Genuine Parts Co: (?.? %) n - Pool Corporation: (?.? %) n - XWELL Inc. : (?.? %) n - Avis Budget Group Inc: (?.? %) n - Exicure Inc: (?.? %) n - Fiverr International Ltd: (?.? %) n - EPAM Systems Inc: (?.? %) n " }, " warnings ": [], " errors ": [], " has_results ": true, " quantity_results ": ?, " creatomate_response ": { " ? _id ": " ? f4d921 - 82df - 47b4 - a53c - eb45f3d17b46 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? f4d921 - 82df - 47b4 - a53c - eb45f3d17b46.png ", " ? _template_id ": " ? c270d7 - 883c - 480e-8118 - 92af28ced303 ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (EN) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " a5e41de8 - fa31 - 4ce5 - 9882 - f54d83352896 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / a5e41de8 - fa31 - 4ce5 - 9882 - f54d83352896.png ", " ? _template_id ": " ? c270d7 - 883c - 480e-8118 - 92af28ced303 ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (EN) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ec5882ca - 8cfd - 4824[...];Times Reported Time consuming queries #19
Day Hour Count Duration Avg duration Feb 20 09 1 0ms 0ms 20 0ms 0ms 0ms 355 0ms commit;Times Reported Time consuming queries #20
Day Hour Count Duration Avg duration Feb 20 09 355 0ms 0ms Time consuming prepare
Rank Total duration Times executed Min duration Max duration Avg duration Query 1 1s722ms 1,898 0ms 9ms 0ms WITH rar_max as ( ;Times Reported Time consuming prepare #1
Day Hour Count Duration Avg duration Feb 20 09 1,898 1s722ms 0ms -
WITH rar_max as ( ;
Date: 2026-02-20 09:51:39 Duration: 9ms Database: postgres
-
WITH rar_max as ( ;
Date: 2026-02-20 09:43:44 Duration: 7ms Database: postgres
-
WITH rar_max as ( ;
Date: 2026-02-20 09:43:44 Duration: 6ms Database: postgres
2 1s672ms 1,195 0ms 3ms 1ms SELECT symbolid, ;Times Reported Time consuming prepare #2
Day Hour Count Duration Avg duration 09 1,195 1s672ms 1ms -
SELECT symbolid, ;
Date: 2026-02-20 09:32:11 Duration: 3ms Database: postgres
-
SELECT symbolid, ;
Date: 2026-02-20 09:47:21 Duration: 2ms Database: postgres
-
SELECT symbolid, ;
Date: 2026-02-20 09:46:02 Duration: 2ms Database: postgres
3 561ms 2,397 0ms 4ms 0ms SELECT ;Times Reported Time consuming prepare #3
Day Hour Count Duration Avg duration 09 2,397 561ms 0ms -
SELECT ;
Date: 2026-02-20 09:44:17 Duration: 4ms Database: postgres
-
SELECT ;
Date: 2026-02-20 09:51:47 Duration: 3ms Database: postgres
-
SELECT ;
Date: 2026-02-20 09:43:44 Duration: 3ms Database: postgres
4 451ms 434 0ms 1ms 1ms SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;Times Reported Time consuming prepare #4
Day Hour Count Duration Avg duration 09 434 451ms 1ms -
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-20 09:30:25 Duration: 1ms Database: postgres
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-20 09:30:44 Duration: 1ms Database: postgres
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-20 09:30:30 Duration: 1ms Database: postgres
5 290ms 2,893 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 #5
Day Hour Count Duration Avg duration 09 2,893 290ms 0ms -
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-20 09:11:49 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: 2026-02-20 09:00:55 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: 2026-02-20 09:10:51 Duration: 0ms Database: postgres
6 250ms 1,692 0ms 2ms 0ms SET extra_float_digits = 3;Times Reported Time consuming prepare #6
Day Hour Count Duration Avg duration 09 1,692 250ms 0ms -
SET extra_float_digits = 3;
Date: 2026-02-20 09:15:46 Duration: 2ms Database: postgres
-
SET extra_float_digits = 3;
Date: 2026-02-20 09:47:29 Duration: 1ms Database: postgres
-
SET extra_float_digits = 3;
Date: 2026-02-20 09:09:12 Duration: 0ms Database: postgres
7 199ms 1,905 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 #7
Day Hour Count Duration Avg duration 09 1,905 199ms 0ms -
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-20 09:11:29 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: 2026-02-20 09:11:47 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: 2026-02-20 09:02:46 Duration: 0ms Database: postgres
8 178ms 1,116 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 09 1,116 178ms 0ms -
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-20 09:41:52 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: 2026-02-20 09:31:55 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: 2026-02-20 09:47:10 Duration: 0ms Database: postgres
9 114ms 672 0ms 0ms 0ms select category, ;Times Reported Time consuming prepare #9
Day Hour Count Duration Avg duration 09 672 114ms 0ms -
select category, ;
Date: 2026-02-20 09:42:03 Duration: 0ms Database: postgres
-
select category, ;
Date: 2026-02-20 09:10:49 Duration: 0ms Database: postgres
-
select category, ;
Date: 2026-02-20 09:10:49 Duration: 0ms Database: postgres
10 92ms 16 4ms 7ms 5ms with sym_info as ( ;Times Reported Time consuming prepare #10
Day Hour Count Duration Avg duration 09 16 92ms 5ms -
with sym_info as ( ;
Date: 2026-02-20 09:51:43 Duration: 7ms Database: postgres
-
with sym_info as ( ;
Date: 2026-02-20 09:51:45 Duration: 7ms Database: postgres
-
with sym_info as ( ;
Date: 2026-02-20 09:51:50 Duration: 7ms Database: postgres
11 50ms 902 0ms 2ms 0ms select 1;Times Reported Time consuming prepare #11
Day Hour Count Duration Avg duration 09 902 50ms 0ms -
select 1;
Date: 2026-02-20 09:16:03 Duration: 2ms Database: postgres
-
select 1;
Date: 2026-02-20 09:07:13 Duration: 1ms Database: postgres
-
select 1;
Date: 2026-02-20 09:21:24 Duration: 1ms Database: postgres
12 44ms 18 1ms 3ms 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 #12
Day Hour Count Duration Avg duration 09 18 44ms 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: 2026-02-20 09:51:10 Duration: 3ms Database: postgres
-
select cast(count(*) / cast(setting as numeric) * 100 as int) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by setting;
Date: 2026-02-20 09:40:03 Duration: 3ms Database: postgres
-
select cast(count(*) / cast(setting as numeric) * 100 as int) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by setting;
Date: 2026-02-20 09:51:01 Duration: 2ms Database: postgres
13 42ms 26 0ms 5ms 1ms WITH last_candle AS ( ;Times Reported Time consuming prepare #13
Day Hour Count Duration Avg duration 09 26 42ms 1ms -
WITH last_candle AS ( ;
Date: 2026-02-20 09:07:30 Duration: 5ms Database: postgres
-
WITH last_candle AS ( ;
Date: 2026-02-20 09:15:06 Duration: 3ms Database: postgres
-
WITH last_candle AS ( ;
Date: 2026-02-20 09:16:00 Duration: 3ms Database: postgres
14 39ms 237 0ms 0ms 0ms SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;Times Reported Time consuming prepare #14
Day Hour Count Duration Avg duration 09 237 39ms 0ms -
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-02-20 09:12:50 Duration: 0ms Database: postgres
-
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-02-20 09:12:49 Duration: 0ms Database: postgres
-
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-02-20 09:12:50 Duration: 0ms Database: postgres
15 29ms 12 1ms 6ms 2ms with wh_patitioned as ( ;Times Reported Time consuming prepare #15
Day Hour Count Duration Avg duration 09 12 29ms 2ms -
with wh_patitioned as ( ;
Date: 2026-02-20 09:10:03 Duration: 6ms Database: postgres
-
with wh_patitioned as ( ;
Date: 2026-02-20 09:30:56 Duration: 5ms Database: postgres
-
with wh_patitioned as ( ;
Date: 2026-02-20 09:05:02 Duration: 4ms Database: postgres
16 24ms 40 0ms 2ms 0ms select distinct s.statsid as statsid, sy.exchange as name;Times Reported Time consuming prepare #16
Day Hour Count Duration Avg duration 09 40 24ms 0ms -
select distinct s.statsid as statsid, sy.exchange as name;
Date: 2026-02-20 09:10:48 Duration: 2ms Database: postgres
-
select distinct s.statsid as statsid, sy.exchange as name;
Date: 2026-02-20 09:00:45 Duration: 1ms Database: postgres
-
select distinct s.statsid as statsid, sy.exchange as name;
Date: 2026-02-20 09:10:51 Duration: 1ms Database: postgres
17 20ms 1,657 0ms 0ms 0ms SET application_name = 'PostgreSQL JDBC Driver';Times Reported Time consuming prepare #17
Day Hour Count Duration Avg duration 09 1,657 20ms 0ms -
SET application_name = 'PostgreSQL JDBC Driver';
Date: 2026-02-20 09:26:38 Duration: 0ms Database: postgres
-
SET application_name = 'PostgreSQL JDBC Driver';
Date: 2026-02-20 09:45:34 Duration: 0ms Database: postgres
-
SET application_name = 'PostgreSQL JDBC Driver';
Date: 2026-02-20 09:47:27 Duration: 0ms Database: postgres
18 15ms 24 0ms 0ms 0ms select count(*) from datafeed_restarter_events where is_current_entry = 1;Times Reported Time consuming prepare #18
Day Hour Count Duration Avg duration 09 24 15ms 0ms -
select count(*) from datafeed_restarter_events where is_current_entry = 1;
Date: 2026-02-20 09:30:06 Duration: 0ms Database: postgres
-
select count(*) from datafeed_restarter_events where is_current_entry = 1;
Date: 2026-02-20 09:25:04 Duration: 0ms Database: postgres
-
select count(*) from datafeed_restarter_events where is_current_entry = 1;
Date: 2026-02-20 09:20:07 Duration: 0ms Database: postgres
19 15ms 12 0ms 2ms 1ms WITH pre_symbols AS ( /* find relevant symbols */ ;Times Reported Time consuming prepare #19
Day Hour Count Duration Avg duration 09 12 15ms 1ms -
WITH pre_symbols AS ( /* find relevant symbols */ ;
Date: 2026-02-20 09:12:48 Duration: 2ms Database: postgres
-
WITH pre_symbols AS ( /* find relevant symbols */ ;
Date: 2026-02-20 09:00:38 Duration: 2ms Database: postgres
-
WITH pre_symbols AS ( /* find relevant symbols */ ;
Date: 2026-02-20 09:12:48 Duration: 2ms Database: postgres
20 15ms 6 2ms 3ms 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 #20
Day Hour Count Duration Avg duration 09 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: 2026-02-20 09:40:04 Duration: 3ms 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: 2026-02-20 09:10:03 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: 2026-02-20 09:50:04 Duration: 2ms Database: postgres
Time consuming bind
Rank Total duration Times executed Min duration Max duration Avg duration Query 1 24s34ms 9,046 0ms 67ms 2ms WITH rar_max as ( ;Times Reported Time consuming bind #1
Day Hour Count Duration Avg duration Feb 20 09 9,046 24s34ms 2ms -
WITH rar_max as ( ;
Date: 2026-02-20 09:36:06 Duration: 67ms Database: postgres parameters: $1 = 't', $2 = '667', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '84', $14 = 'AUDCAD', $15 = 'AUDCHF', $16 = 'AUDJPY', $17 = 'AUDNZD', $18 = 'AUDSGD', $19 = 'CADCHF', $20 = 'CADJPY', $21 = 'CHFJPY', $22 = 'EURAUD', $23 = 'EURCAD', $24 = 'EURCHF', $25 = 'EURCZK', $26 = 'EURGBP', $27 = 'EURHUF', $28 = 'EURJPY', $29 = 'EURNOK', $30 = 'EURNZD', $31 = 'EURPLN', $32 = 'EURSEK', $33 = 'EURSGD', $34 = 'EURTRY', $35 = 'EURZAR', $36 = 'GBPAUD', $37 = 'GBPCAD', $38 = 'GBPCHF', $39 = 'GBPJPY', $40 = 'GBPNZD', $41 = 'GBPPLN', $42 = 'GBPSEK', $43 = 'GBPSGD', $44 = 'NZDCAD', $45 = 'NZDCHF', $46 = 'NZDJPY', $47 = 'NZDSGD', $48 = 'USDCNH', $49 = 'USDCZK', $50 = 'USDHUF', $51 = 'USDNOK', $52 = 'USDPLN', $53 = 'USDSEK', $54 = 'USDSGD', $55 = 'USDTRY', $56 = 'USDZAR', $57 = 'WTI', $58 = 'XBRUSD', $59 = 'XTIUSD', $60 = 'BTCUSD', $61 = 'XAGAUD', $62 = 'XAGUSD', $63 = 'XAUAUD', $64 = 'XAUUSD', $65 = 'XPTUSD', $66 = 'XPDUSD', $67 = 'AUDUSD', $68 = 'EURUSD', $69 = 'GBPUSD', $70 = 'NZDUSD', $71 = 'USDCAD', $72 = 'USDCHF', $73 = 'USDHKD', $74 = 'USDJPY', $75 = 'AUS200', $76 = 'CHINA300', $77 = 'CHINA50', $78 = 'DJ30', $79 = 'ESP35t', $80 = 'EUR50', $81 = 'EURO50', $82 = 'FRA40', $83 = 'GDAXI', $84 = 'GDAXIm', $85 = 'HK50', $86 = 'ITA40', $87 = 'J225', $88 = 'JP225', $89 = 'NAS100', $90 = 'SING30', $91 = 'SPA35', $92 = 'STOXX50', $93 = 'SUI20', $94 = 'UK100', $95 = 'US100', $96 = 'US30', $97 = 'US500', $98 = '0', $99 = '', $100 = '700', $101 = '700', $102 = '0', $103 = '0', $104 = '0', $105 = 't', $106 = '10', $107 = '10'
-
WITH rar_max as ( ;
Date: 2026-02-20 09:36:04 Duration: 40ms Database: postgres parameters: $1 = 't', $2 = '558', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '80', $14 = 'AUDSGD', $15 = 'CHFSGD', $16 = 'EURDKK', $17 = 'EURHKD', $18 = 'EURNOK', $19 = 'EURPLN', $20 = 'EURSEK', $21 = 'EURSGD', $22 = 'EURTRY', $23 = 'EURZAR', $24 = 'GBPDKK', $25 = 'GBPNOK', $26 = 'GBPSEK', $27 = 'GBPSGD', $28 = 'NOKJPY', $29 = 'NOKSEK', $30 = 'SEKJPY', $31 = 'SGDJPY', $32 = 'USDCNH', $33 = 'USDCZK', $34 = 'USDDKK', $35 = 'USDHKD', $36 = 'USDHUF', $37 = 'USDMXN', $38 = 'USDNOK', $39 = 'USDPLN', $40 = 'USDRUB', $41 = 'USDSEK', $42 = 'USDTHB', $43 = 'USDTRY', $44 = 'USDZAR', $45 = 'AUDUSD', $46 = 'EURUSD', $47 = 'GBPUSD', $48 = 'USDCAD', $49 = 'USDCHF', $50 = 'USDJPY', $51 = 'AUDCAD', $52 = 'AUDCHF', $53 = 'AUDJPY', $54 = 'AUDNZD', $55 = 'CADCHF', $56 = 'CADJPY', $57 = 'CHFJPY', $58 = 'EURAUD', $59 = 'EURCAD', $60 = 'EURCHF', $61 = 'EURGBP', $62 = 'EURJPY', $63 = 'EURNZD', $64 = 'GBPAUD', $65 = 'GBPCAD', $66 = 'GBPCHF', $67 = 'GBPJPY', $68 = 'GBPNZD', $69 = 'NZDCAD', $70 = 'NZDCHF', $71 = 'NZDJPY', $72 = 'NZDUSD', $73 = 'USDSGD', $74 = 'AUS200', $75 = 'DE30', $76 = 'ES35', $77 = 'F40', $78 = 'HK50', $79 = 'IT40', $80 = 'JP225', $81 = 'STOXX50', $82 = 'UK100', $83 = 'US2000', $84 = 'US30', $85 = 'US500', $86 = 'CHINA50', $87 = 'USTEC', $88 = 'XAGEUR', $89 = 'XAGUSD', $90 = 'XAUUSD', $91 = 'XAUEUR', $92 = 'XPDUSD', $93 = 'XPTUSD', $94 = '0', $95 = '', $96 = '0', $97 = '0', $98 = '0', $99 = '0', $100 = '0', $101 = 't', $102 = '10', $103 = '10'
-
WITH rar_max as ( ;
Date: 2026-02-20 09:36:04 Duration: 38ms Database: postgres parameters: $1 = 't', $2 = '489', $3 = '0', $4 = '0', $5 = '0', $6 = '', $7 = '0', $8 = '', $9 = '0', $10 = '', $11 = '0', $12 = '0', $13 = '0', $14 = '0', $15 = '0', $16 = 't', $17 = '0', $18 = '0'
2 7s273ms 21,025 0ms 7ms 0ms SELECT ;Times Reported Time consuming bind #2
Day Hour Count Duration Avg duration 09 21,025 7s273ms 0ms -
SELECT ;
Date: 2026-02-20 09:35:39 Duration: 7ms Database: postgres parameters: $1 = '515840233444059300'
-
SELECT ;
Date: 2026-02-20 09:59:49 Duration: 6ms Database: postgres parameters: $1 = '500991628259001200'
-
SELECT ;
Date: 2026-02-20 09:15:08 Duration: 6ms Database: postgres parameters: $1 = '515840233428150300'
3 2s829ms 1,195 1ms 5ms 2ms SELECT symbolid, ;Times Reported Time consuming bind #3
Day Hour Count Duration Avg duration 09 1,195 2s829ms 2ms -
SELECT symbolid, ;
Date: 2026-02-20 09:47:21 Duration: 5ms Database: postgres parameters: $1 = 'MILLENNIUMPF', $2 = '15', $3 = 'NZDUSD', $4 = 'NZDUSD.ID', $5 = 'NZDUSD.FX'
-
SELECT symbolid, ;
Date: 2026-02-20 09:31:51 Duration: 4ms Database: postgres parameters: $1 = 'BDSWISS', $2 = '15', $3 = 'NAS100'
-
SELECT symbolid, ;
Date: 2026-02-20 09:32:01 Duration: 3ms Database: postgres parameters: $1 = 'GLOBALGTMT5', $2 = '15', $3 = 'NZDUSD', $4 = 'SHBUSD', $5 = 'LTCUSD', $6 = 'MATUSD', $7 = 'LTCJPY', $8 = 'JP225', $9 = 'SOLUSD', $10 = 'GTi12'
4 1s288ms 225 0ms 22ms 5ms select distinct s.statsid as statsid, sy.exchange as name;Times Reported Time consuming bind #4
Day Hour Count Duration Avg duration 09 225 1s288ms 5ms -
select distinct s.statsid as statsid, sy.exchange as name;
Date: 2026-02-20 09:10:48 Duration: 22ms Database: postgres parameters: $1 = '1436', $2 = '1436'
-
select distinct s.statsid as statsid, sy.exchange as name;
Date: 2026-02-20 09:10:51 Duration: 21ms Database: postgres parameters: $1 = '1436', $2 = '1436'
-
select distinct s.statsid as statsid, sy.exchange as name;
Date: 2026-02-20 09:00:45 Duration: 21ms Database: postgres parameters: $1 = '1436', $2 = '1436'
5 717ms 434 1ms 5ms 1ms SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;Times Reported Time consuming bind #5
Day Hour Count Duration Avg duration 09 434 717ms 1ms -
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-20 09:47:21 Duration: 5ms Database: postgres parameters: $1 = 'FPMARKETS'
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-20 09:30:56 Duration: 2ms Database: postgres parameters: $1 = 'HOTFOREX'
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-20 09:30:56 Duration: 2ms Database: postgres parameters: $1 = 'AXIORY'
6 658ms 8,246 0ms 1ms 0ms select category, ;Times Reported Time consuming bind #6
Day Hour Count Duration Avg duration 09 8,246 658ms 0ms -
select category, ;
Date: 2026-02-20 09:59:22 Duration: 1ms Database: postgres parameters: $1 = '515852059296080307', $2 = 'symbol', $3 = 'AUDJPY', $4 = 'NZDJPY', $5 = 'USDMXN', $6 = 'EURMXN', $7 = 'USDJPY', $8 = 'USDNOK', $9 = 'NOKJPY', $10 = 'GBPJPY', $11 = 'CADJPY', $12 = 'ZARJPY', $13 = 'USDZAR', $14 = 'CHFJPY', $15 = 'HKDJPY', $16 = 'EURNOK', $17 = 'GBPZAR', $18 = 'EURSEK', $19 = 'USDDKK', $20 = 'USDSEK', $21 = 'USDPLN', $22 = 'EURTRY', $23 = 'EURJPY', $24 = 'GBPAUD', $25 = 'USDCNH', $26 = 'GBPNZD', $27 = 'ZARJPY', $28 = 'EURPLN', $29 = 'EURAUD', $30 = 'EURNZD', $31 = 'EURGBP', $32 = 'GBPCAD', $33 = 'USDZAR', $34 = 'EURNOK', $35 = 'EURSEK', $36 = 'USDCAD', $37 = 'EURCAD', $38 = 'EURCHF', $39 = 'NOKJPY', $40 = 'USDMXN', $41 = 'GBPZAR', $42 = 'CADJPY', $43 = 'EURMXN', $44 = 'GBPUSD', $45 = 'HKDJPY', $46 = 'CADCHF', $47 = 'USDSEK', $48 = 'USDSGD', $49 = 'USDPLN', $50 = 'EURNZD', $51 = 'EURPLN', $52 = 'EURCAD', $53 = '515852059296080307', $54 = 'symbol', $55 = 'AUDJPY', $56 = 'NZDJPY', $57 = 'USDMXN', $58 = 'EURMXN', $59 = 'USDJPY', $60 = 'USDNOK', $61 = 'NOKJPY', $62 = 'GBPJPY', $63 = 'CADJPY', $64 = 'ZARJPY', $65 = 'USDZAR', $66 = 'CHFJPY', $67 = 'HKDJPY', $68 = 'EURNOK', $69 = 'GBPZAR', $70 = 'EURSEK', $71 = 'USDDKK', $72 = 'USDSEK', $73 = 'USDPLN', $74 = 'EURTRY', $75 = 'EURJPY', $76 = 'GBPAUD', $77 = 'USDCNH', $78 = 'GBPNZD', $79 = 'ZARJPY', $80 = 'EURPLN', $81 = 'EURAUD', $82 = 'EURNZD', $83 = 'EURGBP', $84 = 'GBPCAD', $85 = 'USDZAR', $86 = 'EURNOK', $87 = 'EURSEK', $88 = 'USDCAD', $89 = 'EURCAD', $90 = 'EURCHF', $91 = 'NOKJPY', $92 = 'USDMXN', $93 = 'GBPZAR', $94 = 'CADJPY', $95 = 'EURMXN', $96 = 'GBPUSD', $97 = 'HKDJPY', $98 = 'CADCHF', $99 = 'USDSEK', $100 = 'USDSGD', $101 = 'USDPLN', $102 = 'EURNZD', $103 = 'EURPLN', $104 = 'EURCAD'
-
select category, ;
Date: 2026-02-20 09:47:16 Duration: 1ms Database: postgres parameters: $1 = '601729875344536307', $2 = 'symbol', $3 = 'USDSEK', $4 = 'AUDJPY', $5 = 'USDMXN', $6 = 'USDZAR', $7 = 'CADJPY', $8 = 'XAUUSD', $9 = 'NZDJPY', $10 = 'USDHUF', $11 = 'XAGEUR', $12 = 'USDJPY', $13 = 'XAGUSD', $14 = 'XAUEUR', $15 = 'ZARJPY', $16 = 'GBPJPY', $17 = 'EURJPY', $18 = 'GBPZAR', $19 = 'USDCZK', $20 = 'CHFJPY', $21 = 'USDPLN', $22 = 'EURHUF', $23 = 'USDDKK', $24 = 'GBPNZD', $25 = 'USDCNH', $26 = 'GBPAUD', $27 = 'USDNOK', $28 = 'EURNOK', $29 = 'USDTRY', $30 = 'EURHUF', $31 = 'EURPLN', $32 = 'ZARJPY', $33 = 'USDCAD', $34 = 'EURGBP', $35 = 'GBPCAD', $36 = 'CADCHF', $37 = 'EURAUD', $38 = 'EURCHF', $39 = 'EURNZD', $40 = 'USDSGD', $41 = 'USDHUF', $42 = 'USDMXN', $43 = 'CADJPY', $44 = 'EURCAD', $45 = 'EURCAD', $46 = 'GBPCHF', $47 = 'USDHKD', $48 = 'GBPCAD', $49 = 'GBPUSD', $50 = 'AUDNZD', $51 = 'USDZAR', $52 = 'EURNZD', $53 = '601729875344536307', $54 = 'symbol', $55 = 'USDSEK', $56 = 'AUDJPY', $57 = 'USDMXN', $58 = 'USDZAR', $59 = 'CADJPY', $60 = 'XAUUSD', $61 = 'NZDJPY', $62 = 'USDHUF', $63 = 'XAGEUR', $64 = 'USDJPY', $65 = 'XAGUSD', $66 = 'XAUEUR', $67 = 'ZARJPY', $68 = 'GBPJPY', $69 = 'EURJPY', $70 = 'GBPZAR', $71 = 'USDCZK', $72 = 'CHFJPY', $73 = 'USDPLN', $74 = 'EURHUF', $75 = 'USDDKK', $76 = 'GBPNZD', $77 = 'USDCNH', $78 = 'GBPAUD', $79 = 'USDNOK', $80 = 'EURNOK', $81 = 'USDTRY', $82 = 'EURHUF', $83 = 'EURPLN', $84 = 'ZARJPY', $85 = 'USDCAD', $86 = 'EURGBP', $87 = 'GBPCAD', $88 = 'CADCHF', $89 = 'EURAUD', $90 = 'EURCHF', $91 = 'EURNZD', $92 = 'USDSGD', $93 = 'USDHUF', $94 = 'USDMXN', $95 = 'CADJPY', $96 = 'EURCAD', $97 = 'EURCAD', $98 = 'GBPCHF', $99 = 'USDHKD', $100 = 'GBPCAD', $101 = 'GBPUSD', $102 = 'AUDNZD', $103 = 'USDZAR', $104 = 'EURNZD'
-
select category, ;
Date: 2026-02-20 09:10:49 Duration: 1ms Database: postgres parameters: $1 = '515852059317765307', $2 = 'symbol', $3 = 'XAUUSD', $4 = 'DOW30', $5 = 'AUDJPY', $6 = 'GBPJPY', $7 = 'XAGUSD', $8 = 'USDJPY', $9 = 'SP500', $10 = 'NASDAQ100', $11 = 'OIL', $12 = 'NZDJPY', $13 = 'CHFJPY', $14 = 'EURJPY', $15 = 'GBPAUD', $16 = 'EURAUD', $17 = 'GBPCHF', $18 = 'EURCHF', $19 = 'USDCAD', $20 = 'EURCAD', $21 = 'EURGBP', $22 = 'OIL', $23 = 'GBPUSD', $24 = 'DOW30', $25 = 'EURCAD', $26 = 'XAGUSD', $27 = 'EURJPY', $28 = 'EURUSD', $29 = 'NZDUSD', $30 = 'AUDJPY', $31 = 'RK_SSI', $32 = 'GBPJPY', $33 = 'R_SSI', $34 = 'CHFJPY', $35 = 'SP500', $36 = 'GBPAUD', $37 = 'GBPUSD', $38 = 'EURUSD', $39 = 'USDCHF', $40 = 'EURAUD', $41 = 'XAUUSD', $42 = 'NZDJPY', $43 = 'AUDUSD', $44 = 'AUDNZD', $45 = 'USDJPY', $46 = 'USDCAD', $47 = 'NASDAQ100', $48 = 'AUDUSD', $49 = 'GBPCHF', $50 = 'NZDUSD', $51 = 'USDCHF', $52 = 'EURCHF', $53 = '515852059317765307', $54 = 'symbol', $55 = 'XAUUSD', $56 = 'DOW30', $57 = 'AUDJPY', $58 = 'GBPJPY', $59 = 'XAGUSD', $60 = 'USDJPY', $61 = 'SP500', $62 = 'NASDAQ100', $63 = 'OIL', $64 = 'NZDJPY', $65 = 'CHFJPY', $66 = 'EURJPY', $67 = 'GBPAUD', $68 = 'EURAUD', $69 = 'GBPCHF', $70 = 'EURCHF', $71 = 'USDCAD', $72 = 'EURCAD', $73 = 'EURGBP', $74 = 'OIL', $75 = 'GBPUSD', $76 = 'DOW30', $77 = 'EURCAD', $78 = 'XAGUSD', $79 = 'EURJPY', $80 = 'EURUSD', $81 = 'NZDUSD', $82 = 'AUDJPY', $83 = 'RK_SSI', $84 = 'GBPJPY', $85 = 'R_SSI', $86 = 'CHFJPY', $87 = 'SP500', $88 = 'GBPAUD', $89 = 'GBPUSD', $90 = 'EURUSD', $91 = 'USDCHF', $92 = 'EURAUD', $93 = 'XAUUSD', $94 = 'NZDJPY', $95 = 'AUDUSD', $96 = 'AUDNZD', $97 = 'USDJPY', $98 = 'USDCAD', $99 = 'NASDAQ100', $100 = 'AUDUSD', $101 = 'GBPCHF', $102 = 'NZDUSD', $103 = 'USDCHF', $104 = 'EURCHF'
7 649ms 34,468 0ms 4ms 0ms select 1;Times Reported Time consuming bind #7
Day Hour Count Duration Avg duration 09 34,468 649ms 0ms -
select 1;
Date: 2026-02-20 09:51:01 Duration: 4ms Database: postgres
-
select 1;
Date: 2026-02-20 09:35:54 Duration: 4ms Database: postgres
-
select 1;
Date: 2026-02-20 09:35:54 Duration: 4ms Database: postgres
8 582ms 16 28ms 46ms 36ms with sym_info as ( ;Times Reported Time consuming bind #8
Day Hour Count Duration Avg duration 09 16 582ms 36ms -
with sym_info as ( ;
Date: 2026-02-20 09:51:50 Duration: 46ms Database: postgres parameters: $1 = '627', $2 = 'Forex', $3 = 'Forex', $4 = '627', $5 = 'Forex', $6 = '627', $7 = '627', $8 = 'Forex', $9 = '627'
-
with sym_info as ( ;
Date: 2026-02-20 09:51:45 Duration: 46ms Database: postgres parameters: $1 = '617', $2 = 'Forex', $3 = 'Forex', $4 = '617', $5 = 'Forex', $6 = '617', $7 = '617', $8 = 'Forex', $9 = '617'
-
with sym_info as ( ;
Date: 2026-02-20 09:21:53 Duration: 45ms Database: postgres parameters: $1 = '692', $2 = 'Forex', $3 = 'Forex', $4 = '692', $5 = 'Forex', $6 = '692', $7 = '692', $8 = 'Forex', $9 = '692'
9 543ms 22 0ms 60ms 24ms with wh_patitioned as ( ;Times Reported Time consuming bind #9
Day Hour Count Duration Avg duration 09 22 543ms 24ms -
with wh_patitioned as ( ;
Date: 2026-02-20 09:30:56 Duration: 60ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'
-
with wh_patitioned as ( ;
Date: 2026-02-20 09:10:59 Duration: 44ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'
-
with wh_patitioned as ( ;
Date: 2026-02-20 09:15:13 Duration: 41ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'
10 492ms 42 0ms 20ms 11ms WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;Times Reported Time consuming bind #10
Day Hour Count Duration Avg duration 09 42 492ms 11ms -
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;
Date: 2026-02-20 09:32:21 Duration: 20ms Database: postgres parameters: $1 = '489', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
-
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;
Date: 2026-02-20 09:47:34 Duration: 19ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
-
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;
Date: 2026-02-20 09:32:20 Duration: 17ms Database: postgres parameters: $1 = '489', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
11 379ms 48 4ms 16ms 7ms WITH last_candle AS ( ;Times Reported Time consuming bind #11
Day Hour Count Duration Avg duration 09 48 379ms 7ms -
WITH last_candle AS ( ;
Date: 2026-02-20 09:36:01 Duration: 16ms Database: postgres parameters: $1 = '558', $2 = '558'
-
WITH last_candle AS ( ;
Date: 2026-02-20 09:36:01 Duration: 16ms Database: postgres parameters: $1 = '558', $2 = '558'
-
WITH last_candle AS ( ;
Date: 2026-02-20 09:07:30 Duration: 15ms Database: postgres parameters: $1 = '558', $2 = '558'
12 313ms 435 0ms 1ms 0ms SELECT absolutetimezoneoffset;Times Reported Time consuming bind #12
Day Hour Count Duration Avg duration 09 435 313ms 0ms -
SELECT absolutetimezoneoffset;
Date: 2026-02-20 09:10:50 Duration: 1ms Database: postgres parameters: $1 = '538', $2 = 'Shares EU'
-
SELECT absolutetimezoneoffset;
Date: 2026-02-20 09:10:53 Duration: 1ms Database: postgres parameters: $1 = '538', $2 = 'Shares EU'
-
SELECT absolutetimezoneoffset;
Date: 2026-02-20 09:42:01 Duration: 1ms Database: postgres parameters: $1 = '489', $2 = 'BROKER'
13 244ms 3,053 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 #13
Day Hour Count Duration Avg duration 09 3,053 244ms 0ms -
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-20 09:10:51 Duration: 0ms Database: postgres parameters: $1 = '2026-02-20 08:00:00', $2 = '24878.78', $3 = '24891.4', $4 = '24870.65', $5 = '24871.78', $6 = '5325', $7 = '515840248039147300', $8 = '0', $9 = '2026-02-20 09:10:51.472', $10 = '2026-02-20 09:10:51.394', $11 = '24878.78', $12 = '24891.4', $13 = '24870.65', $14 = '24871.78', $15 = '5325', $16 = '0', $17 = '2026-02-20 09:10:51.472', $18 = '2026-02-20 09:10:51.394'
-
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-20 09:11:49 Duration: 0ms Database: postgres parameters: $1 = '2026-02-19 22:30:00', $2 = '10651.5', $3 = '10662', $4 = '10651.25', $5 = '10660.65', $6 = '5254', $7 = '515840247999481300', $8 = '0', $9 = '2026-02-20 09:11:49.989', $10 = '2026-02-20 09:11:49.893', $11 = '10651.5', $12 = '10662', $13 = '10651.25', $14 = '10660.65', $15 = '5254', $16 = '0', $17 = '2026-02-20 09:11:49.989', $18 = '2026-02-20 09:11:49.893'
-
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-20 09:40:50 Duration: 0ms Database: postgres parameters: $1 = '2026-02-20 08:30:00', $2 = '24871.65', $3 = '24884.8', $4 = '24865.9', $5 = '24877.92', $6 = '4705', $7 = '515840248039147300', $8 = '0', $9 = '2026-02-20 09:40:50.198', $10 = '2026-02-20 09:40:50.113', $11 = '24871.65', $12 = '24884.8', $13 = '24865.9', $14 = '24877.92', $15 = '4705', $16 = '0', $17 = '2026-02-20 09:40:50.198', $18 = '2026-02-20 09:40:50.113'
14 241ms 5,194 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 #14
Day Hour Count Duration Avg duration 09 5,194 241ms 0ms -
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-20 09:41:52 Duration: 0ms Database: postgres parameters: $1 = '2026-02-20 09:15:00', $2 = '49474.79', $3 = '49478.79', $4 = '49463.29', $5 = '49474.79', $6 = '2052', $7 = '515840248000537300', $8 = '0', $9 = '2026-02-20 09:41:52.56', $10 = '2026-02-20 09:41:52.489', $11 = '49474.79', $12 = '49478.79', $13 = '49463.29', $14 = '49474.79', $15 = '2052', $16 = '0', $17 = '2026-02-20 09:41:52.56', $18 = '2026-02-20 09:41:52.489'
-
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-20 09:26:51 Duration: 0ms Database: postgres parameters: $1 = '2026-02-20 09:00:00', $2 = '24878.3', $3 = '24889.92', $4 = '24877.17', $5 = '24881.17', $6 = '2510', $7 = '515840248038958300', $8 = '0', $9 = '2026-02-20 09:26:51.043', $10 = '2026-02-20 09:26:50.966', $11 = '24878.3', $12 = '24889.92', $13 = '24877.17', $14 = '24881.17', $15 = '2510', $16 = '0', $17 = '2026-02-20 09:26:51.043', $18 = '2026-02-20 09:26:50.966'
-
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-20 09:47:10 Duration: 0ms Database: postgres parameters: $1 = '2026-02-20 10:30:00', $2 = '5015.755', $3 = '5018.115', $4 = '5012.805', $5 = '5016.365', $6 = '2330', $7 = '515840249390867300', $8 = '0', $9 = '2026-02-20 09:47:10.644', $10 = '2026-02-20 09:47:10.595', $11 = '5015.755', $12 = '5018.115', $13 = '5012.805', $14 = '5016.365', $15 = '2330', $16 = '0', $17 = '2026-02-20 09:47:10.644', $18 = '2026-02-20 09:47:10.595'
15 166ms 2,069 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 #15
Day Hour Count Duration Avg duration 09 2,069 166ms 0ms -
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-20 09:11:29 Duration: 0ms Database: postgres parameters: $1 = '2026-02-19 21:00:00', $2 = '114.73', $3 = '115.5', $4 = '114.71', $5 = '115.44', $6 = '2405', $7 = '515840247879403300', $8 = '0', $9 = '2026-02-20 09:11:29.768', $10 = '2026-02-20 09:11:29.672', $11 = '114.73', $12 = '115.5', $13 = '114.71', $14 = '115.44', $15 = '2405', $16 = '0', $17 = '2026-02-20 09:11:29.768', $18 = '2026-02-20 09:11:29.672'
-
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-20 09:11:47 Duration: 0ms Database: postgres parameters: $1 = '2026-02-19 22:00:00', $2 = '8395.63', $3 = '8414.8', $4 = '8395.33', $5 = '8411.38', $6 = '4064', $7 = '515840247902184300', $8 = '0', $9 = '2026-02-20 09:11:47.972', $10 = '2026-02-20 09:11:47.87', $11 = '8395.63', $12 = '8414.8', $13 = '8395.33', $14 = '8411.38', $15 = '4064', $16 = '0', $17 = '2026-02-20 09:11:47.972', $18 = '2026-02-20 09:11:47.87'
-
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-20 09:02:48 Duration: 0ms Database: postgres parameters: $1 = '2026-02-19 22:00:00', $2 = '10640.5', $3 = '10662', $4 = '10640.4', $5 = '10660.65', $6 = '10727', $7 = '515840247999972300', $8 = '0', $9 = '2026-02-20 09:02:48.893', $10 = '2026-02-20 09:02:48.788', $11 = '10640.5', $12 = '10662', $13 = '10640.4', $14 = '10660.65', $15 = '10727', $16 = '0', $17 = '2026-02-20 09:02:48.893', $18 = '2026-02-20 09:02:48.788'
16 97ms 237 0ms 0ms 0ms SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;Times Reported Time consuming bind #16
Day Hour Count Duration Avg duration 09 237 97ms 0ms -
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-02-20 09:12:49 Duration: 0ms Database: postgres
-
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-02-20 09:12:50 Duration: 0ms Database: postgres
-
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-02-20 09:12:50 Duration: 0ms Database: postgres
17 63ms 16 3ms 9ms 3ms SELECT DISTINCT ON (basegroupname, symbol) ;Times Reported Time consuming bind #17
Day Hour Count Duration Avg duration 09 16 63ms 3ms -
SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-02-20 09:10:51 Duration: 9ms Database: postgres parameters: $1 = '627', $2 = '627'
-
SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-02-20 09:03:33 Duration: 4ms Database: postgres parameters: $1 = '538', $2 = '538'
-
SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-02-20 09:48:39 Duration: 3ms Database: postgres parameters: $1 = '538', $2 = '538'
18 60ms 63 0ms 2ms 0ms SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;Times Reported Time consuming bind #18
Day Hour Count Duration Avg duration 09 63 60ms 0ms -
SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;
Date: 2026-02-20 09:31:55 Duration: 2ms Database: postgres parameters: $1 = '667', $2 = 'XAGUSD.r', $3 = '667'
-
SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;
Date: 2026-02-20 09:21:56 Duration: 1ms Database: postgres parameters: $1 = '621', $2 = 'USDJPY', $3 = '621'
-
SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;
Date: 2026-02-20 09:00:55 Duration: 1ms Database: postgres parameters: $1 = '667', $2 = 'XAGUSD.r', $3 = '667'
19 49ms 328 0ms 5ms 0ms /*server.CPResult*/ SELECT patternid, resy0, resy1, supporty0, supporty1, predictiontimeto, patternstarttime, s.symbolid, resx0, resx1, supportx0, supportx1, symbol, longname, shortname, timegranularity, patternendtime, pattern, a.direction, trendchange, patternlengthbars, patternquality, resultuid as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, exchange, breakout, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM autochartist_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern where resultuid = $1;Times Reported Time consuming bind #19
Day Hour Count Duration Avg duration 09 328 49ms 0ms -
/*server.CPResult*/ SELECT patternid, resy0, resy1, supporty0, supporty1, predictiontimeto, patternstarttime, s.symbolid, resx0, resx1, supportx0, supportx1, symbol, longname, shortname, timegranularity, patternendtime, pattern, a.direction, trendchange, patternlengthbars, patternquality, resultuid as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, exchange, breakout, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM autochartist_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern where resultuid = $1;
Date: 2026-02-20 09:51:41 Duration: 5ms Database: postgres parameters: $1 = '607710277717677301'
-
/*server.CPResult*/ SELECT patternid, resy0, resy1, supporty0, supporty1, predictiontimeto, patternstarttime, s.symbolid, resx0, resx1, supportx0, supportx1, symbol, longname, shortname, timegranularity, patternendtime, pattern, a.direction, trendchange, patternlengthbars, patternquality, resultuid as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, exchange, breakout, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM autochartist_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern where resultuid = $1;
Date: 2026-02-20 09:20:35 Duration: 3ms Database: postgres parameters: $1 = '607710160938891301'
-
/*server.CPResult*/ SELECT patternid, resy0, resy1, supporty0, supporty1, predictiontimeto, patternstarttime, s.symbolid, resx0, resx1, supportx0, supportx1, symbol, longname, shortname, timegranularity, patternendtime, pattern, a.direction, trendchange, patternlengthbars, patternquality, resultuid as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, exchange, breakout, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM autochartist_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern where resultuid = $1;
Date: 2026-02-20 09:05:36 Duration: 3ms Database: postgres parameters: $1 = '607709276789954301'
20 48ms 12 1ms 7ms 4ms WITH pre_symbols AS ( /* find relevant symbols */ ;Times Reported Time consuming bind #20
Day Hour Count Duration Avg duration 09 12 48ms 4ms -
WITH pre_symbols AS ( /* find relevant symbols */ ;
Date: 2026-02-20 09:12:48 Duration: 7ms Database: postgres parameters: $1 = '1018', $2 = 'ICMARKETS-AU-MT5', $3 = 'AAPL.NAS', $4 = 'ABBV.NYSE', $5 = 'AMCR.NYSE', $6 = 'AMP.NYSE', $7 = 'AMZN.NAS', $8 = 'ANZ.ASX', $9 = 'AUDJPY', $10 = 'AUDUSD', $11 = 'AUS200', $12 = 'BABA.NYSE', $13 = 'BIIB.NAS', $14 = 'BXB.ASX', $15 = 'CBA.ASX', $16 = 'CHINA50', $17 = 'CSL.ASX', $18 = 'DE30', $19 = 'ES35', $20 = 'EURCHF', $21 = 'EURGBP', $22 = 'EURUSD', $23 = 'F40', $24 = 'FMG.ASX', $25 = 'GBPJPY', $26 = 'GBPUSD', $27 = 'GOOG.NAS', $28 = 'HK50', $29 = 'IT40', $30 = 'JP225', $31 = 'KO.NYSE', $32 = 'MQG.ASX', $33 = 'MSFT.NAS', $34 = 'NAB.ASX', $35 = 'NFLX.NAS', $36 = 'PYPL.NAS', $37 = 'QBE.ASX', $38 = 'STOXX50', $39 = 'SUN.ASX', $40 = 'TCL.ASX', $41 = 'TLS.ASX', $42 = 'TSLA.NAS', $43 = 'UK100', $44 = 'UNH.NYSE', $45 = 'US2000', $46 = 'US30', $47 = 'US500', $48 = 'USDCAD', $49 = 'USDCHF', $50 = 'USDCNH', $51 = 'USDJPY', $52 = 'USTEC', $53 = 'WBC.ASX', $54 = 'WES.ASX', $55 = 'WOW.ASX', $56 = 'WPL.ASX', $57 = 'XAUEUR', $58 = 'XAUUSD', $59 = 'XBRUSD', $60 = 'XTIUSD', $61 = 'AAPL.NAS', $62 = 'ABBV.NYSE', $63 = 'AMCR.NYSE', $64 = 'AMP.NYSE', $65 = 'AMZN.NAS', $66 = 'ANZ.ASX', $67 = 'AUDJPY', $68 = 'AUDUSD', $69 = 'AUS200', $70 = 'BABA.NYSE', $71 = 'BIIB.NAS', $72 = 'BXB.ASX', $73 = 'CBA.ASX', $74 = 'CHINA50', $75 = 'CSL.ASX', $76 = 'DE30', $77 = 'ES35', $78 = 'EURCHF', $79 = 'EURGBP', $80 = 'EURUSD', $81 = 'F40', $82 = 'FMG.ASX', $83 = 'GBPJPY', $84 = 'GBPUSD', $85 = 'GOOG.NAS', $86 = 'HK50', $87 = 'IT40', $88 = 'JP225', $89 = 'KO.NYSE', $90 = 'MQG.ASX', $91 = 'MSFT.NAS', $92 = 'NAB.ASX', $93 = 'NFLX.NAS', $94 = 'PYPL.NAS', $95 = 'QBE.ASX', $96 = 'STOXX50', $97 = 'SUN.ASX', $98 = 'TCL.ASX', $99 = 'TLS.ASX', $100 = 'TSLA.NAS', $101 = 'UK100', $102 = 'UNH.NYSE', $103 = 'US2000', $104 = 'US30', $105 = 'US500', $106 = 'USDCAD', $107 = 'USDCHF', $108 = 'USDCNH', $109 = 'USDJPY', $110 = 'USTEC', $111 = 'WBC.ASX', $112 = 'WES.ASX', $113 = 'WOW.ASX', $114 = 'WPL.ASX', $115 = 'XAUEUR', $116 = 'XAUUSD', $117 = 'XBRUSD', $118 = 'XTIUSD', $119 = '5'
-
WITH pre_symbols AS ( /* find relevant symbols */ ;
Date: 2026-02-20 09:12:48 Duration: 7ms Database: postgres parameters: $1 = '1018', $2 = 'ICMARKETS-AU-MT5', $3 = 'AAPL.NAS', $4 = 'ABBV.NYSE', $5 = 'AMCR.NYSE', $6 = 'AMP.NYSE', $7 = 'AMZN.NAS', $8 = 'ANZ.ASX', $9 = 'AUDJPY', $10 = 'AUDUSD', $11 = 'AUS200', $12 = 'BABA.NYSE', $13 = 'BIIB.NAS', $14 = 'BXB.ASX', $15 = 'CBA.ASX', $16 = 'CHINA50', $17 = 'CSL.ASX', $18 = 'DE30', $19 = 'ES35', $20 = 'EURCHF', $21 = 'EURGBP', $22 = 'EURUSD', $23 = 'F40', $24 = 'FMG.ASX', $25 = 'GBPJPY', $26 = 'GBPUSD', $27 = 'GOOG.NAS', $28 = 'HK50', $29 = 'IT40', $30 = 'JP225', $31 = 'KO.NYSE', $32 = 'MQG.ASX', $33 = 'MSFT.NAS', $34 = 'NAB.ASX', $35 = 'NFLX.NAS', $36 = 'PYPL.NAS', $37 = 'QBE.ASX', $38 = 'STOXX50', $39 = 'SUN.ASX', $40 = 'TCL.ASX', $41 = 'TLS.ASX', $42 = 'TSLA.NAS', $43 = 'UK100', $44 = 'UNH.NYSE', $45 = 'US2000', $46 = 'US30', $47 = 'US500', $48 = 'USDCAD', $49 = 'USDCHF', $50 = 'USDCNH', $51 = 'USDJPY', $52 = 'USTEC', $53 = 'WBC.ASX', $54 = 'WES.ASX', $55 = 'WOW.ASX', $56 = 'WPL.ASX', $57 = 'XAUEUR', $58 = 'XAUUSD', $59 = 'XBRUSD', $60 = 'XTIUSD', $61 = 'AAPL.NAS', $62 = 'ABBV.NYSE', $63 = 'AMCR.NYSE', $64 = 'AMP.NYSE', $65 = 'AMZN.NAS', $66 = 'ANZ.ASX', $67 = 'AUDJPY', $68 = 'AUDUSD', $69 = 'AUS200', $70 = 'BABA.NYSE', $71 = 'BIIB.NAS', $72 = 'BXB.ASX', $73 = 'CBA.ASX', $74 = 'CHINA50', $75 = 'CSL.ASX', $76 = 'DE30', $77 = 'ES35', $78 = 'EURCHF', $79 = 'EURGBP', $80 = 'EURUSD', $81 = 'F40', $82 = 'FMG.ASX', $83 = 'GBPJPY', $84 = 'GBPUSD', $85 = 'GOOG.NAS', $86 = 'HK50', $87 = 'IT40', $88 = 'JP225', $89 = 'KO.NYSE', $90 = 'MQG.ASX', $91 = 'MSFT.NAS', $92 = 'NAB.ASX', $93 = 'NFLX.NAS', $94 = 'PYPL.NAS', $95 = 'QBE.ASX', $96 = 'STOXX50', $97 = 'SUN.ASX', $98 = 'TCL.ASX', $99 = 'TLS.ASX', $100 = 'TSLA.NAS', $101 = 'UK100', $102 = 'UNH.NYSE', $103 = 'US2000', $104 = 'US30', $105 = 'US500', $106 = 'USDCAD', $107 = 'USDCHF', $108 = 'USDCNH', $109 = 'USDJPY', $110 = 'USTEC', $111 = 'WBC.ASX', $112 = 'WES.ASX', $113 = 'WOW.ASX', $114 = 'WPL.ASX', $115 = 'XAUEUR', $116 = 'XAUUSD', $117 = 'XBRUSD', $118 = 'XTIUSD', $119 = '5'
-
WITH pre_symbols AS ( /* find relevant symbols */ ;
Date: 2026-02-20 09:00:38 Duration: 6ms Database: postgres parameters: $1 = '619', $2 = 'AXIORY', $3 = 'EURUSD', $4 = 'USDJPY', $5 = 'GBPUSD', $6 = 'AUDUSD', $7 = 'USDCHF', $8 = 'USDCAD', $9 = 'NZDUSD', $10 = 'GBPJPY', $11 = 'EURJPY', $12 = 'EURCHF', $13 = 'GBPCHF', $14 = 'EURCAD', $15 = 'GBPCAD', $16 = 'EURNZD', $17 = 'GBPNZD', $18 = 'CADJPY', $19 = 'CADCHF', $20 = 'CHFJPY', $21 = 'NZDJPY', $22 = 'XAUUSD', $23 = 'XAGUSD', $24 = 'EURUSD', $25 = 'USDJPY', $26 = 'GBPUSD', $27 = 'AUDUSD', $28 = 'USDCHF', $29 = 'USDCAD', $30 = 'NZDUSD', $31 = 'GBPJPY', $32 = 'EURJPY', $33 = 'EURCHF', $34 = 'GBPCHF', $35 = 'EURCAD', $36 = 'GBPCAD', $37 = 'EURNZD', $38 = 'GBPNZD', $39 = 'CADJPY', $40 = 'CADCHF', $41 = 'CHFJPY', $42 = 'NZDJPY', $43 = 'XAUUSD', $44 = 'XAGUSD', $45 = '5'
-
Events
Log levels
Key values
- 489,325 Log entries
Events distribution
Key values
- 0 PANIC entries
- 1 FATAL entries
- 400 ERROR entries
- 0 WARNING entries
Most Frequent Errors/Events
Key values
- 360 Max number of times the same event was reported
- 401 Total events found
Rank Times reported Error 1 360 ERROR: pg_stat_statements must be loaded via shared_preload_libraries
Times Reported Most Frequent Error / Event #1
Day Hour Count Feb 20 09 360 - ERROR: pg_stat_statements must be loaded via shared_preload_libraries
Statement: /* service='datadog-agent' */ SELECT COUNT(*) FROM pg_stat_statements(false)
Date: 2026-02-20 09:00:03
2 40 ERROR: schema "..." does not exist
Times Reported Most Frequent Error / Event #2
Day Hour Count Feb 20 09 40 - ERROR: schema "datadog" does not exist at character 38
Statement: /* service='datadog-agent' */ SELECT datadog.explain_statement($stmt$SELECT * FROM pg_stat_activity$stmt$)
Date: 2026-02-20 09:01:49
3 1 FATAL: connection to client lost
Times Reported Most Frequent Error / Event #3
Day Hour Count Feb 20 09 1 - FATAL: connection to client lost
Date: 2026-02-20 09:10:13