pgBadger :: PostgreSQL Log Analyzer

Global information

Generated on Fri Mar 6 12:59:45 2026
Log file: /home/postgres/pg_data/data/pg_log/postgresql-2026-03-06_140000.log
Parsed 1,632,657 log entries in 43s
Log start from 2026-03-06 14:00:00 to 2026-03-06 14:59:43

Global Stats

316 Number of unique normalized queries
214,005 Number of queries
1h56m15s Total query duration
2026-03-06 14:00:00 First query
2026-03-06 14:59:43 Last query
4,059 queries/s at 2026-03-06 14:30:04 Query peak

1h56m15s Total query duration
6s42ms Prepare/parse total duration
40s697ms Bind total duration
1h55m28s Execute total duration

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

37 Total number of automatic vacuums
57 Total number of automatic analyzes

815 Number temporary file
171.21 MiB Max size of temporary file
7.28 MiB Average size of temporary file

2,865 Total number of sessions
13 sessions at 2026-03-06 14:58:48 Session peak
1d22h19m31s Total duration of sessions
58s209ms Average duration of sessions
74 Average queries per session
2s434ms Average queries duration per session
55s775ms Average idle time per session

2,870 Total number of connections
28 connections/s at 2026-03-06 14:18:48 Connection peak
3 Total number of databases

SQL Traffic

Key values

4,059 queries/s Query Peak
2026-03-06 14:30:04 Date

SELECT Traffic

Key values

1,985 queries/s Query Peak
2026-03-06 14:30:04 Date

INSERT/UPDATE/DELETE Traffic

Key values

189 queries/s Query Peak
2026-03-06 14:00:57 Date

Queries duration

Key values

1h56m15s Total query duration

Prepared queries ratio

Key values

0.00 Ratio of bind vs prepare
0.00 % Ratio between prepared and "usual" statements

General Activity

Day	Hour	Count	Min duration	Max duration	Avg duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Mar 06	14	214,005	0ms	39s349ms	32ms	3m14s	3m26s	4m25s

Day	Hour	SELECT	COPY TO	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Mar 06	14	70,698	26	0ms	0ms	0ms	0ms

Day	Hour	INSERT	UPDATE	DELETE	COPY FROM	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Mar 06	14	24,033	2,842	16	96	0ms	0ms	0ms	0ms

Day	Hour	Prepare	Bind	Bind/Prepare	Percentage of prepare
Mar 06	14	17,252	66,368	3.85	16.78%

Day	Hour	Count	Average / Second
Mar 06	14	2,870	0.80/s

Day	Hour	Count	Average Duration	Average idle time
Mar 06	14	2,865	58s209ms	55s791ms

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Established Connections

Key values

28 connections Connection Peak
2026-03-06 14:18:48 Date

Connections per database

Key values

acaweb_fx Main Database
2,870 connections Total

Chart
Table

Database	User	Count
acaweb_fx		2,719
	postgres	2,363
	psql	356
acaweb_v		1
	psql	1
socialmedia		150
	pgAdmin 4 - CONN:6114615	1
	pgAdmin 4 - DB:socialmedia	1
	postgres	148

Connections per user

Key values

postgres Main User
2,870 connections Total

Chart
Table

User	Count
pgAdmin 4 - CONN:6114615	1
pgAdmin 4 - DB:socialmedia	1
postgres	2,511
psql	357

Connections per host

Key values

192.168.4.142 Main host with 1343 connections
2,870 Total connections

Chart
Table

Host	Count
104.30.164.187	13
127.0.0.1	112
192.168.0.114	10
192.168.0.216	106
192.168.0.74	97
192.168.1.127	56
192.168.1.145	90
192.168.1.15	60
192.168.1.20	109
192.168.1.239	18
192.168.1.90	76
192.168.2.126	38
192.168.2.182	24
192.168.3.199	36
192.168.4.142	1,343
192.168.4.150	10
192.168.4.153	4
192.168.4.204	4
192.168.4.207	4
192.168.4.222	1
192.168.4.238	12
192.168.4.33	71
192.168.4.57	1
192.168.4.98	330
[local]	245

Simultaneous sessions

Key values

13 sessions Session Peak
2026-03-06 14:58:48 Date

Histogram of session times

Key values

2,427 0-500ms duration

Chart
Table

Range	Count	Percentage
0ms-500ms	2,427	84.71%
500ms-1s	113	3.94%
1s-30s	207	7.23%
30s-1m	16	0.56%
1m-10m	38	1.33%
10m-30m	47	1.64%
30m-1h	5	0.17%
1h-8h	12	0.42%
> 8h	0	0.00%

Sessions per database

Key values

acaweb_fx Main Database
2,865 sessions Total

Chart
Table

Database	User	Count	Total Duration	Average Duration
acaweb_fx	2,719	1d21h8m14s	59s762ms
socialmedia	146	1h11m16s	29s293ms

Sessions per user

Key values

postgres Main User
2,865 sessions Total

Chart
Table

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

Sessions per host

Key values

192.168.4.142 Main Host
2,865 sessions Total

Chart
Table

Host	Count	Total Duration	Average Duration
104.30.164.187	10	12m7s	1m12s
127.0.0.1	112	5s805ms	51ms
192.168.0.114	9	52m30s	5m50s
192.168.0.216	106	3m23s	1s920ms
192.168.0.74	97	2h12m28s	1m21s
192.168.1.127	56	36s495ms	651ms
192.168.1.145	90	4h1m4s	2m40s
192.168.1.15	60	4h14m32s	4m14s
192.168.1.20	109	14h22m34s	7m54s
192.168.1.239	18	134ms	7ms
192.168.1.90	76	36s394ms	478ms
192.168.2.126	38	6s517ms	171ms
192.168.2.182	24	6s194ms	258ms
192.168.3.199	36	1s299ms	36ms
192.168.4.142	1,343	6m39s	297ms
192.168.4.150	10	20h33s	2h3s
192.168.4.153	4	32ms	8ms
192.168.4.204	4	15s925ms	3s981ms
192.168.4.207	4	34ms	8ms
192.168.4.222	1	1m4s	1m4s
192.168.4.238	12	15s443ms	1s286ms
192.168.4.33	71	6m2s	5s110ms
192.168.4.57	1	178ms	178ms
192.168.4.98	330	12s879ms	39ms
[local]	244	4m13s	1s39ms

Sessions per application

Key values

unknown Main Host
2,865 sessions Total

Chart
Table

Application	Count	Total Duration	Average Duration
unknown	2,865	1d22h19m31s	58s209ms

Checkpoints Buffers

Key values

10,076 buffers Checkpoint Peak
2026-03-06 14:05:06 Date
209.897 seconds Highest write time
0.059 seconds Sync time

Checkpoints Wal files

Key values

4 files Wal files usage Peak
2026-03-06 14:10:06 Date

Checkpoints distance

Key values

119.01 Mo Distance Peak
2026-03-06 14:05:06 Date

Checkpoints Activity

Day	Hour	Written buffers	Write time	Sync time	Total time
Mar 06	14	41,793	2,015.541s	0.22s	2,016.065s

Day	Hour	Added	Removed	Recycled	Synced files	Longest sync	Average sync
Mar 06	14	0	0	22	1,967	0.059s	0s

Day	Hour	Count	Avg time (sec)
Mar 06	14	0	0s

Day	Hour	Mean distance	Mean estimate
Mar 06	14	30,261.83 kB	48,534.08 kB

↑ Back to the top of the Checkpoint Activity table

Checkpoint causes

Key values

time (12) Main checkpoint cause
12 checkpoints Total

Chart
Table

Cause	Number of checkpoints
time	12
Total	12

Size of temporary files

Key values

184.79 MiB Temp Files size Peak
2026-03-06 14:10:08 Date

Number of temporary files

Key values

30 per second Temp Files Peak
2026-03-06 14:47:08 Date

Temporary Files Activity

Temp Files

Day	Hour	Count	Total size	Average size
Mar 06	14	815	5.80 GiB	7.28 MiB

↑ Back to the top of the Temporary Files Activity table

Queries generating the most temporary files (N)

Rank	Count	Total size	Min size	Max size	Avg size	Query
1	73	367.74 MiB	5.01 MiB	5.07 MiB	5.04 MiB	select resultuid from relevance_fibonacci_results order by resultuid desc limit ?), fr as ( select a., rr.age, rr.relevant from fibonacci_results a left outer join relevance_fibonacci_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) end), all_results as ( select fr.resultuid as resultuid, fr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, fr.pattern as pattern_name, fr.timed as timed, fr.patternendtime as identified, dtt.timezone as timezone, fr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when fr.age is not null then fr.age when fr.resultuid <= rm.resultuid then ? else ? end as age, case when fr.relevant is not null then fr.relevant when fr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from fr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = fr.symbolid inner join symbols s on fr.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on fr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join downloadersymbolsettings dss on fr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left join lateral calc_fib_signal_filter (fr.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where fr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (fr.simulation = ? or fr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or fr.pattern in (...)) and (? = ? or fr.patternlengthbars <= ?) and (? = ? or (? = ? and fr.timed > cast(? as timestamp)) or (? = ? and fr.timed < cast(? as timestamp)))), results as ( select distinct on (symbolid) from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc; SELECT resultuid FROM relevance_fibonacci_results ORDER BY resultuid DESC LIMIT 1), fr AS ( SELECT a., rr.age, rr.relevant from fibonacci_results a LEFT OUTER JOIN relevance_fibonacci_results rr on a.resultuid = rr.resultuid WHERE CASE WHEN FALSE = $1 THEN true ELSE a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) END), all_results AS ( SELECT fr.resultuid AS resultuid, fr.direction AS direction, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, fr.pattern AS pattern_name, fr.timed AS timed, fr.patternendtime AS identified, dtt.timezone AS timezone, fr.patternlengthbars AS length, g.basegroupname, newLevels.filtered, CASE WHEN fr.age IS NOT NULL THEN fr.age WHEN fr.resultuid <= rm.resultuid THEN 11 ELSE 0 END as age, CASE WHEN fr.relevant IS NOT NULL THEN fr.relevant WHEN fr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip FROM fr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $2 AND bsl.symbolid = fr.symbolid INNER JOIN symbols s ON fr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN symbolgroup sg on fr.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid INNER JOIN downloadersymbolsettings dss ON fr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN rar_max rm ON 1 = 1 LEFT JOIN LATERAL calc_fib_signal_filter (fr.resultuid) newLevels on true LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' WHERE fr.gmttimefound > now() - INTERVAL '7 DAYS' AND dss.enabled = 1 AND s.deleted = 0 AND (fr.simulation = 0 OR fr.simulation IS NULL) AND ($3 = 0 OR s.timegranularity in ($4, $5, $6, $7, $8, $9, $10)) AND ($11 = 0 OR s.exchange in ($12)) AND ($13 = 0 OR coalesce(bim.code, s.symbol) in ($14, $15, $16, $17, $18, $19, $20, $21, $22, $23, $24, $25, $26, $27, $28, $29, $30, $31, $32, $33, $34, $35, $36, $37, $38, $39, $40, $41, $42, $43, $44, $45, $46, $47, $48, $49, $50, $51, $52, $53, $54, $55, $56, $57, $58, $59, $60, $61, $62, $63, $64, $65, $66, $67, $68, $69, $70, $71, $72, $73, $74, $75, $76, $77, $78, $79, $80, $81, $82, $83, $84, $85, $86, $87, $88, $89, $90, $91, $92, $93, $94, $95, $96, $97, $98, $99, $100, $101, $102, $103, $104, $105, $106, $107, $108, $109, $110, $111, $112, $113, $114, $115, $116, $117, $118, $119, $120, $121, $122, $123, $124, $125, $126, $127, $128, $129, $130, $131, $132, $133, $134, $135, $136, $137, $138, $139, $140, $141, $142, $143, $144, $145, $146, $147, $148, $149, $150, $151, $152, $153, $154, $155, $156, $157, $158, $159, $160, $161, $162, $163, $164, $165, $166, $167, $168, $169, $170, $171, $172, $173, $174, $175, $176, $177, $178, $179, $180, $181, $182, $183, $184, $185, $186, $187, $188, $189, $190, $191, $192, $193, $194, $195, $196, $197, $198, $199, $200, $201, $202, $203, $204, $205, $206, $207, $208, $209, $210, $211, $212, $213, $214, $215, $216, $217, $218, $219, $220, $221, $222, $223, $224, $225, $226, $227, $228, $229, $230, $231, $232, $233, $234, $235, $236, $237, $238, $239, $240, $241, $242, $243, $244, $245, $246, $247, $248, $249, $250, $251, $252, $253, $254, $255, $256, $257, $258, $259, $260, $261, $262, $263, $264, $265, $266, $267, $268, $269, $270, $271, $272, $273, $274, $275, $276, $277, $278, $279, $280, $281, $282, $283, $284, $285, $286, $287, $288, $289, $290, $291, $292, $293, $294, $295, $296, $297, $298, $299, $300, $301, $302, $303, $304, $305, $306, $307, $308, $309, $310, $311, $312, $313, $314, $315, $316, $317, $318, $319, $320, $321, $322, $323)) AND ($324 = 0 OR fr.pattern in ($325)) AND ($326 = 0 OR fr.patternlengthbars <= $327) AND ($328 = 0 OR ($329 = 1 AND fr.timed > cast('1970-01-01' as timestamp)) OR ($330 = 2 AND fr.timed < cast('1970-01-01' as timestamp)))), results AS ( SELECT DISTINCT ON (symbolid) FROM all_results WHERE (FALSE = $331 OR relevant = 1) AND ($332 = 0 OR age <= $333) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-03-06 14:00:48 Duration: 0ms
2	29	1.65 GiB	7.67 MiB	171.21 MiB	58.29 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: 2026-03-06 14:00:06 Duration: 0ms
3	22	184.40 MiB	8.35 MiB	8.44 MiB	8.38 MiB	jr.resultuid as resultuid, jr.direction as direction, jr.patternendtime as identified, jr.patternlengthbars as length, jr.patternstarttime as patternstarttime, case when jr.trendchangeid = ? then ? else ? end as trendchange, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, jp.patternname as pattern_name, dtt.timezone as timezone, ? as age, cps.pip, g.basegroupname from japsticks_results jr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = jr.symbolid inner join relevance_japsticks_results rar on rar.resultuid = jr.resultuid inner join symbols s on jr.symbolid = s.symbolid and s.nonliquid = ? inner join japsticks_patterns jp on jr.patternid = jp.id inner join downloadersymbolsettings dss on jr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on s.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where jr.gmttimefound > now() - interval ? and s.deleted = ? and (jr.simulation = ? or jr.simulation is null) and (rar.relevant = ?) --and (semicolon_age = ? or rar.age <= semicolon_age) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or jp.patternname in (...)) and (? = ? or jr.patternlengthbars <= ?) ), results as ( select distinct on (symbolid) * from all_results order by symbolid, resultuid ) select * from results order by identified desc, length desc ; jr.resultuid AS resultuid, jr.direction AS direction, jr.patternendtime AS identified, jr.patternlengthbars AS length, jr.patternstarttime AS patternstarttime, case when jr.trendchangeid = 1 then 'Continuation' else 'Reversal' end AS trendchange, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, jp.patternname AS pattern_name, dtt.timezone AS timezone, 0 AS age, cps.pip, g.basegroupname FROM japsticks_results jr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $1 AND bsl.symbolid = jr.symbolid INNER JOIN relevance_japsticks_results rar ON rar.resultuid = jr.resultuid INNER JOIN symbols s ON jr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN japsticks_patterns jp ON jr.patternid = jp.id INNER JOIN downloadersymbolsettings dss ON jr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN symbolgroup sg on s.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' WHERE jr.gmttimefound > now() - INTERVAL '7 DAYS' AND s.deleted = 0 AND (jr.simulation = 0 OR jr.simulation IS NULL) AND (rar.relevant = 1) --AND (semicolon_age = 0 OR rar.age <= semicolon_age) AND ($2 = 0 OR s.timegranularity in ($3)) AND ($4 = 0 OR s.exchange in ($5)) AND ($6 = 0 OR coalesce(bim.code, s.symbol) in ($7)) AND ($8 = 0 OR jp.patternname in ($9)) AND ($10 = 0 OR jr.patternlengthbars <= $11)), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-03-06 14:03:05 Duration: 0ms
4	20	64.78 MiB	3.11 MiB	3.41 MiB	3.24 MiB	select resultuid from relevance_consecutivecandles_results order by resultuid desc limit ?), all_results as ( select ccr.resultuid as resultuid, ccr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, ccr.patternendtime as identified, dtt.timezone as timezone, ccr.qtyconsecutivecandles as length, g.basegroupname, case when rcr.age is not null then rcr.age when ccr.resultuid <= rm.resultuid then ? else ? end as age, case when rcr.relevant is not null then rcr.relevant when ccr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip, newlevels.filtered from consecutivecandles_results ccr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = ccr.symbolid inner join symbols s on ccr.symbolid = s.symbolid and s.nonliquid = ? inner join downloadersymbolsettings dss on ccr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on ccr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join rar_max rm on ? = ? left outer join relevance_consecutivecandles_results rcr on rcr.resultuid = ccr.resultuid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? left join lateral calc_cc_signal_filter (ccr.resultuid) newlevels on true where ccr.gmttimefound > now() - interval ? and s.deleted = ? and (ccr.simulation = ? or ccr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or ccr.patternlengthbars <= ?)), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc; SELECT resultuid FROM relevance_consecutivecandles_results ORDER BY resultuid DESC LIMIT 1), all_results AS ( SELECT ccr.resultuid AS resultuid, ccr.direction AS direction, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, ccr.patternendtime AS identified, dtt.timezone AS timezone, ccr.qtyconsecutivecandles AS length, g.basegroupname, CASE WHEN rcr.age IS NOT NULL THEN rcr.age WHEN ccr.resultuid <= rm.resultuid THEN 1 ELSE 0 END as age, CASE WHEN rcr.relevant IS NOT NULL THEN rcr.relevant WHEN ccr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip, newLevels.filtered FROM consecutivecandles_results ccr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $1 AND bsl.symbolid = ccr.symbolid INNER JOIN symbols s ON ccr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN downloadersymbolsettings dss ON ccr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN symbolgroup sg on ccr.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid INNER JOIN rar_max rm ON 1 = 1 LEFT OUTER JOIN relevance_consecutivecandles_results rcr ON rcr.resultuid = ccr.resultuid LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' LEFT JOIN LATERAL calc_cc_signal_filter (ccr.resultuid) newLevels on true WHERE ccr.gmttimefound > now() - INTERVAL '7 DAYS' AND s.deleted = 0 AND (ccr.simulation = 0 OR ccr.simulation IS NULL) AND ($2 = 0 OR s.timegranularity in ($3, $4, $5, $6, $7, $8, $9)) AND ($10 = 0 OR s.exchange in ($11)) AND ($12 = 0 OR coalesce(bim.code, s.symbol) in ($13, $14, $15, $16, $17, $18, $19, $20, $21, $22, $23, $24, $25, $26, $27, $28, $29, $30, $31, $32, $33, $34, $35, $36, $37, $38, $39, $40, $41, $42, $43, $44, $45, $46, $47, $48, $49, $50, $51, $52, $53, $54, $55, $56, $57, $58, $59, $60, $61, $62, $63, $64, $65, $66, $67, $68, $69, $70, $71, $72, $73, $74, $75, $76, $77, $78, $79, $80, $81, $82, $83, $84, $85, $86, $87, $88, $89, $90, $91, $92, $93, $94, $95, $96, $97, $98, $99, $100, $101, $102, $103, $104, $105, $106, $107, $108, $109, $110, $111, $112, $113, $114, $115, $116, $117, $118, $119, $120, $121, $122, $123, $124, $125, $126, $127, $128, $129, $130, $131, $132, $133, $134, $135, $136, $137, $138, $139, $140, $141, $142, $143, $144, $145, $146, $147, $148, $149, $150, $151, $152, $153, $154, $155, $156, $157, $158, $159, $160, $161, $162, $163, $164, $165, $166, $167, $168, $169, $170, $171, $172, $173, $174, $175, $176, $177, $178, $179, $180, $181, $182, $183, $184, $185, $186, $187, $188, $189, $190, $191, $192, $193, $194, $195, $196, $197, $198, $199, $200, $201, $202, $203, $204, $205, $206, $207, $208, $209, $210, $211, $212, $213, $214, $215, $216, $217, $218, $219, $220, $221, $222, $223, $224, $225, $226, $227, $228, $229, $230, $231, $232, $233, $234, $235, $236, $237, $238, $239, $240, $241, $242, $243, $244, $245, $246, $247, $248, $249, $250, $251, $252, $253, $254, $255, $256, $257, $258, $259, $260, $261, $262, $263, $264, $265, $266, $267, $268, $269, $270, $271, $272, $273, $274, $275, $276, $277, $278, $279, $280, $281, $282, $283, $284, $285, $286, $287, $288, $289, $290, $291, $292, $293, $294, $295, $296, $297, $298, $299, $300, $301, $302, $303, $304, $305, $306, $307, $308, $309, $310, $311, $312, $313, $314, $315, $316, $317, $318, $319, $320, $321, $322)) AND ($323 = 0 OR ccr.patternlengthbars <= $324)), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results WHERE (FALSE = $325 OR relevant = 1) AND ($326 = 0 OR age <= $327) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-03-06 14:01:08 Duration: 0ms
5	16	739.35 MiB	46.20 MiB	46.21 MiB	46.21 MiB	update solr_relevance_old set new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total from ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total from whatshot_probability where type = ?) sub where result_uid = sub.resultuid; UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type = 'cp') sub WHERE result_uid = sub.resultuid; Date: 2026-03-06 14:01:12 Duration: 0ms
6	16	1.23 GiB	78.45 MiB	78.45 MiB	78.45 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit ?) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then ? else ? end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then ? else ? end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike ? inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike ?) sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike ?; update solr_relevance_old set newrelevant = ? where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike ? and a.resultuid is null); update solr_relevance_old set new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total from ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total from whatshot_probability where type in (...)) sub where result_uid = sub.resultuid; with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; Date: 2026-03-06 14:01:15 Duration: 0ms
7	8	1.13 GiB	145.00 MiB	145.06 MiB	145.03 MiB	select updateresultsmaterializedview (); select updateresultsmaterializedview (); Date: 2026-03-06 14:02:16 Duration: 0ms
8	4	321.26 MiB	80.25 MiB	80.42 MiB	80.32 MiB	select updateageforrelevantresults (); select updateageforrelevantresults (); Date: 2026-03-06 14:02:05 Duration: 0ms

Queries generating the largest temporary files

Rank	Size	Query
1	171.21 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: 2026-03-06 14:00:06 ]
2	161.05 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: 2026-03-06 14:20:06 ]
3	145.06 MiB	select updateresultsmaterializedview (); [ Date: 2026-03-06 14:32:15 ]
4	145.05 MiB	select updateresultsmaterializedview (); [ Date: 2026-03-06 14:02:16 ]
5	145.05 MiB	select updateresultsmaterializedview (); [ Date: 2026-03-06 14:17:13 ]
6	145.04 MiB	select updateresultsmaterializedview (); [ Date: 2026-03-06 14:20:32 ]
7	145.04 MiB	select updateresultsmaterializedview (); [ Date: 2026-03-06 14:05:32 ]
8	145.00 MiB	select updateresultsmaterializedview (); [ Date: 2026-03-06 14:47:13 ]
9	145.00 MiB	select updateresultsmaterializedview (); [ Date: 2026-03-06 14:35:32 ]
10	145.00 MiB	select updateresultsmaterializedview (); [ Date: 2026-03-06 14:50:33 ]
11	115.79 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: 2026-03-06 14:50:04 ]
12	110.98 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: 2026-03-06 14:40:05 ]
13	104.28 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: 2026-03-06 14:30:05 ]
14	86.84 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: 2026-03-06 14:30:08 ]
15	80.42 MiB	select updateageforrelevantresults (); [ Date: 2026-03-06 14:02:05 ]
16	80.34 MiB	select updateageforrelevantresults (); [ Date: 2026-03-06 14:32:05 ]
17	80.25 MiB	select updateageforrelevantresults (); [ Date: 2026-03-06 14:47:05 ]
18	80.25 MiB	select updateageforrelevantresults (); [ Date: 2026-03-06 14:17:05 ]
19	79.15 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: 2026-03-06 14:50:05 ]
20	78.45 MiB	with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid; [ Date: 2026-03-06 14:01:15 ]

Vacuums / Analyzes Distribution

Key values

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

Analyzes per table

Key values

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

Chart
Table

Table	Number of analyzes
acaweb_fx.public.solr_relevance_old	16
acaweb_fx.pg_catalog.pg_attribute	6
acaweb_fx.public.relevance_keylevels_results	4
acaweb_fx.pg_catalog.pg_class	4
acaweb_fx.public.relevance_fibonacci_results	4
acaweb_fx.pg_catalog.pg_type	4
acaweb_fx.public.relevance_autochartist_results	4
acaweb_fx.public.datafeeds_latestrun	3
acaweb_fx.pg_catalog.pg_index	2
acaweb_fx.public.latest_t15_candle_view	2
acaweb_fx.pg_catalog.pg_depend	2
acaweb_fx.public.autochartist_symbolupdates	1
acaweb_fx.public.solr_imports	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.instrument_precision	1
Total	57

Vacuums per table

Key values

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

Chart
Table

		Index	Buffer usage			Skipped		WAL usage
Table	Vacuums	scans	hits	misses	dirtied	pins	frozen	records	full page	bytes
acaweb_fx.public.solr_relevance_old	16	16	13,974	0	135	0	0	10,095	2,309	10,376,072
acaweb_fx.pg_catalog.pg_attribute	4	4	3,464	0	105	0	268	1,313	89	690,695
acaweb_fx.pg_toast.pg_toast_2619	2	2	309	0	56	0	0	195	52	188,325
acaweb_fx.pg_catalog.pg_type	2	2	312	0	58	0	0	154	34	194,675
acaweb_fx.public.datafeeds_latestrun	2	0	240	0	7	0	0	21	6	31,063
acaweb_fx.public.relevance_keylevels_results	2	2	7,357	0	320	4	163	1,845	261	801,461
acaweb_fx.pg_catalog.pg_class	2	2	942	0	69	0	0	309	57	331,337
acaweb_fx.public.relevance_autochartist_results	2	2	6,929	0	143	2	460	1,546	128	434,218
acaweb_fx.public.relevance_fibonacci_results	2	2	2,345	0	65	2	90	422	47	199,554
acaweb_fx.pg_catalog.pg_statistic	1	1	974	0	198	0	582	487	173	713,118
acaweb_fx.public.latest_t15_candle_view	1	1	66	0	2	0	0	6	4	10,899
acaweb_fx.public.instrument_precision	1	0	126	0	3	0	0	15	1	9,365
Total	37	34	37,038	18,142	1,161	8	1,563	16,408	3,161	13,980,782

Tuples removed per table

Key values

public.solr_relevance_old (68688) Main table with removed tuples on database acaweb_fx
81523 tuples Total removed

Chart
Table

		Index	Tuples			Pages
Table	Vacuums	scans	removed	remain	not yet removable	removed	remain
acaweb_fx.public.solr_relevance_old	16	16	68,688	97,154	0	0	3,443
acaweb_fx.pg_catalog.pg_attribute	4	4	5,641	43,276	411	0	1,054
acaweb_fx.public.relevance_autochartist_results	2	2	2,591	18,385	0	0	760
acaweb_fx.public.relevance_keylevels_results	2	2	2,183	23,396	0	0	558
acaweb_fx.pg_catalog.pg_type	2	2	731	2,909	3	0	88
acaweb_fx.pg_catalog.pg_statistic	1	1	564	3,754	0	0	1,194
acaweb_fx.public.relevance_fibonacci_results	2	2	528	3,088	0	0	204
acaweb_fx.pg_catalog.pg_class	2	2	280	3,315	5	0	300
acaweb_fx.pg_toast.pg_toast_2619	2	2	151	335	3	0	106
acaweb_fx.public.datafeeds_latestrun	2	0	104	40	12	0	32
acaweb_fx.public.latest_t15_candle_view	1	1	62	12	0	0	1
acaweb_fx.public.instrument_precision	1	0	0	1,569	0	0	12
Total	37	34	81,523	197,233	434	0	7,752

Pages removed per table

Key values

unknown (0) Main table with removed pages on database unknown
0 pages Total removed

Chart
Table

Pages removed per tables

NO DATASET

Table	Number of vacuums	Index scans	Tuples removed
acaweb_fx.pg_toast.pg_toast_2619	2	2	151
acaweb_fx.pg_catalog.pg_type	2	2	731
acaweb_fx.public.datafeeds_latestrun	2	0	104
acaweb_fx.pg_catalog.pg_statistic	1	1	564
acaweb_fx.pg_catalog.pg_attribute	4	4	5641
acaweb_fx.public.latest_t15_candle_view	1	1	62
acaweb_fx.public.relevance_keylevels_results	2	2	2183
acaweb_fx.public.instrument_precision	1	0	0
acaweb_fx.pg_catalog.pg_class	2	2	280
acaweb_fx.public.solr_relevance_old	16	16	68688
acaweb_fx.public.relevance_autochartist_results	2	2	2591
acaweb_fx.public.relevance_fibonacci_results	2	2	528
Total	37	34	81,523

Autovacuum Activity

Vacuums / Analyzes

Day	Hour	VACUUMs	ANALYZEs
Mar 06	14	37	57

↑ Back to the top of the Autovacuum Activity table

Locks by types

Key values

unknown Main Lock Type
0 locks Total

Chart
Table

Type of locks

NO DATASET

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

Most frequent waiting queries (N)

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

Queries that waited the most

Rank	Wait time	Query
NO DATASET

Queries by type

Key values

70,698 Total read queries
32,109 Total write queries

Chart
Table

Type	Count	Percentage
SELECT	70,698	33.04%
INSERT	24,033	11.23%
UPDATE	2,842	1.33%
DELETE	16	0.01%
COPY FROM	96	0.04%
COPY TO	26	0.01%
CTE	4,288	2.00%
DDL	18	0.01%
TCL	790	0.37%
CURSOR	0	0.00%
OTHERS	111,198	51.96%

Queries by database

Key values

unknown Main database
212,985 Requests
1h55m28s (unknown)
Main time consuming database

Chart
Table

Database	Request type	Count	Duration
acaweb_fx	Total	870	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	188	0ms
	select	72	0ms
	tcl	330	0ms
	update	38	0ms
socialmedia	Total	150	0ms
	others	58	0ms
	select	82	0ms
	tcl	10	0ms
unknown	Total	212,985	1h55m28s
	copy from	16	0ms
	cte	4,184	0ms
	ddl	2	0ms
	insert	24,033	0ms
	others	4,148	0ms
	select	70,544	0ms
	tcl	450	0ms
	update	2,804	0ms

Queries by user

Key values

unknown Main user
212,985 Requests

Queries Chart
Table

User	Request type	Count	Duration
postgres	Total	1,020	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	246	0ms
	select	154	0ms
	tcl	340	0ms
	update	38	0ms
unknown	Total	212,985	1h55m28s
	copy from	16	0ms
	cte	4,184	0ms
	ddl	2	0ms
	insert	24,033	0ms
	others	4,148	0ms
	select	70,544	0ms
	tcl	450	0ms
	update	2,804	0ms

Duration by user

Key values

1h55m28s (unknown) Main time consuming user

Duration Chart
Table

User	Request type	Count	Duration
postgres	Total	1,020	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	246	0ms
	select	154	0ms
	tcl	340	0ms
	update	38	0ms
unknown	Total	212,985	1h55m28s
	copy from	16	0ms
	cte	4,184	0ms
	ddl	2	0ms
	insert	24,033	0ms
	others	4,148	0ms
	select	70,544	0ms
	tcl	450	0ms
	update	2,804	0ms

Queries by host

Key values

unknown Main host
214,005 Requests
1h55m28s (unknown)
Main time consuming host

Chart
Table

Host	Request type	Count	Duration
unknown	Total	214,005	1h55m28s
	copy from	96	0ms
	copy to	26	0ms
	cte	4,288	0ms
	ddl	18	0ms
	delete	16	0ms
	insert	24,033	0ms
	others	4,394	0ms
	select	70,698	0ms
	tcl	790	0ms
	update	2,842	0ms

Queries by application

Key values

unknown Main application
213,647 Requests
1h55m28s (unknown)
Main time consuming application

Chart
Table

Application	Request type	Count	Duration
pgAdmin 4 - CONN:6114615	Total	1	0ms
	others	1	0ms
pgAdmin 4 - DB:socialmedia	Total	1	0ms
	others	1	0ms
psql	Total	356	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	4	0ms
	select	72	0ms
	update	38	0ms
unknown	Total	213,647	1h55m28s
	copy from	16	0ms
	cte	4,184	0ms
	ddl	2	0ms
	insert	24,033	0ms
	others	4,388	0ms
	select	70,626	0ms
	tcl	790	0ms
	update	2,804	0ms

Number of cancelled queries

Key values

0 per second Cancelled query Peak
2026-03-06 14:57:51 Date

Number of cancelled queries (5 minutes period)

NO DATASET

Histogram of query times

Key values

63,639 0-1ms duration

Chart
Table

Range	Count	Percentage
0-1ms	63,639	59.58%
1-5ms	36,654	34.32%
5-10ms	3,054	2.86%
10-25ms	1,479	1.38%
25-50ms	268	0.25%
50-100ms	283	0.26%
100-500ms	409	0.38%
500-1000ms	114	0.11%
1000-10000ms	708	0.66%
> 10000ms	196	0.18%

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

49

Details

0ms

select key, value from datasources ds inner join datasourceparams dsp on ds.id = dsp.datasourceid where ds.name = ?;

Times Reported Time consuming queries #1

Day	Hour	Count	Duration	Avg duration
Mar 06	14	49	0ms	0ms

2

0ms

4

Details

0ms

select "public"."processparameters"."id" AS "id", "public"."processparameters"."processid" AS "processid", "public"."processparameters"."key" AS "key", "public"."processparameters"."value" AS "value" from "public"."processparameters" where "public"."processparameters"."id" = ? and "public"."processparameters"."id" = ? limit ? offset ?;

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Mar 06	14	4	0ms	0ms

3

0ms

1

Details

0ms

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

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1	0ms	0ms

4

0ms

42

Details

0ms

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

Times Reported Time consuming queries #4

Day	Hour	Count	Duration	Avg duration
Mar 06	14	42	0ms	0ms

5

0ms

1,974

Details

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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
Mar 06	14	1,974	0ms	0ms

6

0ms

4

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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
Mar 06	14	4	0ms	0ms

7

0ms

4

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select updaterelevantforrelevantresults ();

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Mar 06	14	4	0ms	0ms

8

0ms

1

Details

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insert into executionlogs(executionid, status, message, details, detailtype) values(?, ?, ?, ?s biggest movers\", \"has_results\": true, \"data_Heading\": \"Biggest Movers\", \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"output_format\": \"png\", \"snapshot_time\": ?, \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"The biggest winners are: - Intuit Inc: +?.?%\\\\n - Expedia Group Inc.: +?.?%\\\\n - LyondellBasell Industries NV: +?.?%\\\\n - Crowdstrike Holdings Inc: +?.?%\\\\n - Dow Inc: +?.?%\\\\n - ServiceNow Inc: +?.?%\\\\n - Palantir Technologies Inc.: +?.?%\\\\n - CF Industries Holdings Inc: +?.?%\\\\n - Autodesk Inc: +?.?%\\\\n - Gartner Inc: +?.?%\\\\n. The biggest losers are: - The AES Corporation: (?.?%)\\\\n - United Airlines Holdings Inc: (?.?%)\\\\n - Ford Motor Company: (?.?%)\\\\n - Norwegian Cruise Line Holdings Ltd: (?.?%)\\\\n - Southwest Airlines Company: (?.?%)\\\\n - Carnival Corporation: (?.?%)\\\\n - Estee Lauder Companies Inc: (?.?%)\\\\n - Mohawk Industries Inc: (?.?%)\\\\n - Elevance Health Inc: (?.?%)\\\\n - NRG Energy Inc.: (?.?%)\\\\n\", \"dictionary_Corn\": \"Corn\", \"dictionary_Gold\": \"Gold\", \"dictionary_Hour\": \"Hour\", \"dictionary_Open\": \"Open\", \"dictionary_Time\": \"Time\", \"text_short_text\": \"The biggest winners are: Intuit Inc: +?.?%, Expedia Group Inc.: +?.?%, LyondellBasell Industries NV: +?.?%, Crowdstrike Holdings Inc: +?.?%, Dow Inc: +?.?%, ServiceNow Inc: +?.?%, Palantir Technologies Inc.: +?.?%, CF Industries Holdings Inc: +?.?%, Autodesk Inc: +?.?%, Gartner Inc: +?.?%. The biggest losers are: The AES Corporation: (?.?%), United Airlines Holdings Inc: (?.?%), Ford Motor Company: (?.?%), Norwegian Cruise Line Holdings Ltd: (?.?%), Southwest Airlines Company: (?.?%), Carnival Corporation: (?.?%), Estee Lauder Companies Inc: (?.?%), Mohawk Industries Inc: (?.?%), Elevance Health Inc: (?.?%), NRG Energy Inc.: (?.?%)\", \"data_OpenHeading\": \"Open\", \"dictionary_Close\": \"Close\", \"dictionary_Daily\": \"Daily\", \"dictionary_Event\": \"Event\", \"dictionary_Hours\": \"Hours\", \"dictionary_Price\": \"Price\", \"dictionary_Wheat\": \"Wheat\", \"quantity_results\": ?, \"data_CloseHeading\": \"Close\", \"data_losers_1_CIK\": \"?\", \"data_losers_1_LEI\": \"?NUNNB?D?COUIRE?\", \"data_losers_2_CIK\": \"?\", \"data_losers_2_LEI\": \"?DA?B?DD?\", \"data_losers_3_CIK\": \"?\", \"data_losers_3_LEI\": \"?S?OYHG?MQM?VUIC?\", \"data_losers_4_CIK\": \"?\", \"data_losers_4_LEI\": null, \"data_losers_5_CIK\": \"?\", \"data_losers_5_LEI\": \"UDTZ?G?STFETI?HGH?\", \"data_losers_6_CIK\": \"?\", \"data_losers_6_LEI\": null, \"data_losers_7_CIK\": \"?\", \"data_losers_7_LEI\": \"?VFZ?XJ?NUPU?\", \"data_losers_8_CIK\": \"?\", \"data_losers_8_LEI\": \"?JI?MG?Q?\", \"data_losers_9_CIK\": \"?\", \"data_losers_9_LEI\": \"?MYN?XMYQH?CTMTH?\", \"dictionary_Actual\": \"Actual\", \"dictionary_Change\": \"Change\", \"dictionary_Coffee\": \"Coffee\", \"dictionary_Friday\": \"Friday\", \"dictionary_Monday\": \"Monday\", \"dictionary_Silver\": \"Silver\", \"dictionary_Sunday\": \"Sunday\", \"dictionary_Target\": \"Target\", \"dictionary_dd MMM\": \"dd MMM\", \"params_mode_value\": ?, \"params_uuid_value\": \"a7bf3e8e-eaf9-476c-aead-d432e4fa63e5\", \"data_ChangeHeading\": \"Change\", \"data_losers_10_CIK\": \"?\", \"data_losers_10_LEI\": \"?E?UPK?SW?M?XY?I?\", \"data_losers_1_Code\": \"AES\", \"data_losers_1_ISIN\": \"US?H?\", \"data_losers_1_Name\": \"The AES Corporation\", \"data_losers_1_Type\": \"Common Stock\", \"data_losers_1_code\": \"AES\", \"data_losers_1_open\": ?.?, \"data_losers_2_Code\": \"UAL\", \"data_losers_2_ISIN\": \"US?\", \"data_losers_2_Name\": \"United Airlines Holdings Inc\", \"data_losers_2_Type\": \"Common Stock\", \"data_losers_2_code\": \"UAL\", \"data_losers_2_open\": ?.?, \"data_losers_3_Code\": \"F\", \"data_losers_3_ISIN\": \"US?\", \"data_losers_3_Name\": \"Ford Motor Company\", \"data_losers_3_Type\": \"Common Stock\", \"data_losers_3_code\": \"F\", \"data_losers_3_open\": ?.?, \"data_losers_4_Code\": \"NCLH\", \"data_losers_4_ISIN\": \"USG?\", \"data_losers_4_Name\": \"Norwegian Cruise Line Holdings Ltd\", \"data_losers_4_Type\": \"Common Stock\", \"data_losers_4_code\": \"NCLH\", \"data_losers_4_open\": ?.?, \"data_losers_5_Code\": \"LUV\", \"data_losers_5_ISIN\": \"US?\", \"data_losers_5_Name\": \"Southwest Airlines Company\", \"data_losers_5_Type\": \"Common Stock\", \"data_losers_5_code\": \"LUV\", \"data_losers_5_open\": ?.?, \"data_losers_6_Code\": \"CCL\", \"data_losers_6_ISIN\": \"US?\", \"data_losers_6_Name\": \"Carnival Corporation\", \"data_losers_6_Type\": \"Common Stock\", \"data_losers_6_code\": \"CCL\", \"data_losers_6_open\": ?.?, \"data_losers_7_Code\": \"EL\", \"data_losers_7_ISIN\": \"US?\", \"data_losers_7_Name\": \"Estee Lauder Companies Inc\", \"data_losers_7_Type\": \"Common Stock\", \"data_losers_7_code\": \"EL\", \"data_losers_7_open\": ?.?, \"data_losers_8_Code\": \"MHK\", \"data_losers_8_ISIN\": \"US?\", \"data_losers_8_Name\": \"Mohawk Industries Inc\", \"data_losers_8_Type\": \"Common Stock\", \"data_losers_8_code\": \"MHK\", \"data_losers_8_open\": ?.?, \"data_losers_9_Code\": \"ELV\", \"data_losers_9_ISIN\": \"US?\", \"data_losers_9_Name\": \"Elevance Health Inc\", \"data_losers_9_Type\": \"Common Stock\", \"data_losers_9_code\": \"ELV\", \"data_losers_9_open\": ?.?, \"data_winners_1_CIK\": \"?\", \"data_winners_1_LEI\": \"VI?HBPH?XSFMB?E?M?\", \"data_winners_2_CIK\": \"?\", \"data_winners_2_LEI\": \"CI?MUJI?USF?V?NJ?H?\", \"data_winners_3_CIK\": \"?\", \"data_winners_3_LEI\": null, \"data_winners_4_CIK\": \"?\", \"data_winners_4_LEI\": \"?YBY?K?KM?HX?\", \"data_winners_5_CIK\": \"?\", \"data_winners_5_LEI\": \"?S?INSLK?IP?\", \"data_winners_6_CIK\": \"?\", \"data_winners_6_LEI\": \"?HJTQM?M?E?G?\", \"data_winners_7_CIK\": \"?\", \"data_winners_7_LEI\": \"?UVN?B?BBDHO?\", \"data_winners_8_CIK\": \"?\", \"data_winners_8_LEI\": \"?CG?YAQFZ?JMV?\", \"data_winners_9_CIK\": \"?\", \"data_winners_9_LEI\": \"FRKKVKAIQEF?FCSTPG?\", \"dictionary_909_DAX\": \"GER?\", \"dictionary_AUD/USD\": \"AUD/USD\", \"dictionary_Company\": \"Company\", \"dictionary_EUR/USD\": \"EUR/USD\", \"dictionary_GBP/USD\": \"GBP/USD\", \"dictionary_Indices\": \"Indices\", \"dictionary_Minutes\": \"Minutes\", \"dictionary_NZD/USD\": \"NZD/USD\", \"dictionary_Tuesday\": \"Tuesday\", \"dictionary_USD/CAD\": \"USD/CAD\", \"dictionary_USD/CHF\": \"USD/CHF\", \"dictionary_USD/JPY\": \"USD/JPY\", \"data_losers_10_Code\": \"NRG\", \"data_losers_10_ISIN\": \"US?\", \"data_losers_10_Name\": \"NRG Energy Inc.\", \"data_losers_10_Type\": \"Common Stock\", \"data_losers_10_code\": \"NRG\", \"data_losers_10_open\": ?.?, \"data_losers_1_CUSIP\": \"?H?\", \"data_losers_1_Phone\": \"? ? ?\", \"data_losers_1_close\": ?.?, \"data_losers_2_CUSIP\": \"?\", \"data_losers_2_Phone\": \"? ? ?\", \"data_losers_2_close\": ?.?, \"data_losers_3_CUSIP\": \"?\", \"data_losers_3_Phone\": \"? ? ?\", \"data_losers_3_close\": ?.?, \"data_losers_4_CUSIP\": \"G?\", \"data_losers_4_Phone\": \"? ? ?\", \"data_losers_4_close\": ?.?, \"data_losers_5_CUSIP\": \"?\", \"data_losers_5_Phone\": \"? ? ?\", \"data_losers_5_close\": ?.?, \"data_losers_6_CUSIP\": \"?\", \"data_losers_6_Phone\": \"? ? ?\", \"data_losers_6_close\": ?.?, \"data_losers_7_CUSIP\": \"?\", \"data_losers_7_Phone\": \"? ? ?\", \"data_losers_7_close\": ?.?, \"data_losers_8_CUSIP\": \"?\", \"data_losers_8_Phone\": \"? ? ?\", \"data_losers_8_close\": ?.?, \"data_losers_9_CUSIP\": \"?B?\", \"data_losers_9_Phone\": \"? ? ?\", \"data_losers_9_close\": ?.?, \"data_winners_10_CIK\": \"?\", \"data_winners_10_LEI\": \"PP?B?R?BFB?O?HH?\", \"data_winners_1_Code\": \"INTU\", \"data_winners_1_ISIN\": \"US?\", \"data_winners_1_Name\": \"Intuit Inc\", \"data_winners_1_Type\": \"Common Stock\", \"data_winners_1_code\": \"INTU\", \"data_winners_1_open\": ?.?, \"data_winners_2_Code\": \"EXPE\", \"data_winners_2_ISIN\": \"US?P?\", \"data_winners_2_Name\": \"Expedia Group Inc.\", \"data_winners_2_Type\": \"Common Stock\", \"data_winners_2_code\": \"EXPE\", \"data_winners_2_open\": ?.?, \"data_winners_3_Code\": \"LYB\", \"data_winners_3_ISIN\": \"USN?\", \"data_winners_3_Name\": \"LyondellBasell Industries NV\", \"data_winners_3_Type\": \"Common Stock\", \"data_winners_3_code\": \"LYB\", \"data_winners_3_open\": ?.?, \"data_winners_4_Code\": \"CRWD\", \"data_winners_4_ISIN\": \"US?C?\", \"data_winners_4_Name\": \"Crowdstrike Holdings Inc\", \"data_winners_4_Type\": \"Common Stock\", \"data_winners_4_code\": \"CRWD\", \"data_winners_4_open\": ?.?, \"data_winners_5_Code\": \"DOW\", \"data_winners_5_ISIN\": \"US?\", \"data_winners_5_Name\": \"Dow Inc\", \"data_winners_5_Type\": \"Common Stock\", \"data_winners_5_code\": \"DOW\", \"data_winners_5_open\": ?.?, \"data_winners_6_Code\": \"NOW\", \"data_winners_6_ISIN\": \"US?P?\", \"data_winners_6_Name\": \"ServiceNow Inc\", \"data_winners_6_Type\": \"Common Stock\", \"data_winners_6_code\": \"NOW\", \"data_winners_6_open\": ?, \"data_winners_7_Code\": \"PLTR\", \"data_winners_7_ISIN\": \"US?A?\", \"data_winners_7_Name\": \"Palantir Technologies Inc.\", \"data_winners_7_Type\": \"Common Stock\", \"data_winners_7_code\": \"PLTR\", \"data_winners_7_open\": ?.?, \"data_winners_8_Code\": \"CF\", \"data_winners_8_ISIN\": \"US?\", \"data_winners_8_Name\": \"CF Industries Holdings Inc\", \"data_winners_8_Type\": \"Common Stock\", \"data_winners_8_code\": \"CF\", \"data_winners_8_open\": ?.?, \"data_winners_9_Code\": \"ADSK\", \"data_winners_9_ISIN\": \"US?\", \"data_winners_9_Name\": \"Autodesk Inc\", \"data_winners_9_Type\": \"Common Stock\", \"data_winners_9_code\": \"ADSK\", \"data_winners_9_open\": ?.?, \"dictionary_909_Gold\": \"XAUUSD\", \"dictionary_Estimate\": \"Estimate\", \"dictionary_Expected\": \"Expected\", \"dictionary_FTSE ?\": \"FTSE ?\", \"dictionary_Interval\": \"Interval\", \"dictionary_KeyLevel\": \"Key Level\", \"dictionary_Previous\": \"Previous\", \"dictionary_Saturday\": \"Saturday\", \"dictionary_Thursday\": \"Thursday\", \"dictionary_estimate\": \"estimate\", \"dictionary_previous\": \"previous\", \"params_locale_value\": \"en\", \"params_region_value\": \"Region1\", \"data_losers_10_CUSIP\": \"?\", \"data_losers_10_Phone\": \"? ? ?\", \"data_losers_10_close\": ?.?, \"data_losers_1_Sector\": \"Utilities\", \"data_losers_1_WebURL\": \"https://www.aes.com\", \"data_losers_1_change\": ?.?, \"data_losers_1_ticker\": \"AES.US\", \"data_losers_2_Sector\": \"Industrials\", \"data_losers_2_WebURL\": \"https://www.united.com\", \"data_losers_2_change\": ?.?, \"data_losers_2_ticker\": \"UAL.US\", \"data_losers_3_Sector\": \"Consumer Cyclical\", \"data_losers_3_WebURL\": \"https://www.ford.com\", \"data_losers_3_change\": ?.?, \"data_losers_3_ticker\": \"F.US\", \"data_losers_4_Sector\": \"Consumer Cyclical\", \"data_losers_4_WebURL\": \"https://www.nclhltd.com\", \"data_losers_4_change\": ?.?, \"data_losers_4_ticker\": \"NCLH.US\", \"data_losers_5_Sector\": \"Industrials\", \"data_losers_5_WebURL\": \"https://www.southwest.com\", \"data_losers_5_change\": ?.?, \"data_losers_5_ticker\": \"LUV.US\", \"data_losers_6_Sector\": \"Consumer Cyclical\", \"data_losers_6_WebURL\": \"https://www.carnivalcorp.com\", \"data_losers_6_change\": ?.?, \"data_losers_6_ticker\": \"CCL.US\", \"data_losers_7_Sector\": \"Consumer Defensive\", \"data_losers_7_WebURL\": \"https://www.elcompanies.com\", \"data_losers_7_change\": ?.?, \"data_losers_7_ticker\": \"EL.US\", \"data_losers_8_Sector\": \"Consumer Cyclical\", \"data_losers_8_WebURL\": \"https://www.mohawkind.com\", \"data_losers_8_change\": ?.?, \"data_losers_8_ticker\": \"MHK.US\", \"data_losers_9_Sector\": \"Healthcare\", \"data_losers_9_WebURL\": \"https://www.elevancehealth.com\", \"data_losers_9_change\": ?.?, \"data_losers_9_ticker\": \"ELV.US\", \"data_winners_10_Code\": \"IT\", \"data_winners_10_ISIN\": \"US?\", \"data_winners_10_Name\": \"Gartner Inc\", \"data_winners_10_Type\": \"Common Stock\", \"data_winners_10_code\": \"IT\", \"data_winners_10_open\": ?.?, \"data_winners_1_CUSIP\": \"?\", \"data_winners_1_Phone\": \"? ? ?\", \"data_winners_1_close\": ?.?, \"data_winners_2_CUSIP\": \"?P?\", \"data_winners_2_Phone\": \"? ? ?\", \"data_winners_2_close\": ?.?, \"data_winners_3_CUSIP\": \"N?\", \"data_winners_3_Phone\": \"? ? ? ?\", \"data_winners_3_close\": ?.?, \"data_winners_4_CUSIP\": \"?C?\", \"data_winners_4_Phone\": \"? ? ?\", \"data_winners_4_close\": ?.?, \"data_winners_5_CUSIP\": \"?\", \"data_winners_5_Phone\": \"? ? ?\", \"data_winners_5_close\": ?.?, \"data_winners_6_CUSIP\": \"?P?\", \"data_winners_6_Phone\": \"? ? ?\", \"data_winners_6_close\": ?.?, \"data_winners_7_CUSIP\": \"?A?\", \"data_winners_7_Phone\": \"? ? ?\", \"data_winners_7_close\": ?.?, \"data_winners_8_CUSIP\": \"?\", \"data_winners_8_Phone\": \"? ? ?\", \"data_winners_8_close\": ?.?, \"data_winners_9_CUSIP\": \"?\", \"data_winners_9_Phone\": \"? ? ?\", \"data_winners_9_close\": ?.?, \"dictionary_Consensus\": \"Consensus\", \"dictionary_Crude oil\": \"Crude oil\", \"dictionary_Fibonacci\": \"Fibonacci Patterns\", \"dictionary_Hang Seng\": \"Hang Seng\", \"dictionary_Stop_Loss\": \"Stop-Loss\", \"dictionary_Wednesday\": \"Wednesday\", \"dictionary_consensus\": \"consensus\", \"dictionary_dd MMM, h\": \"dd MMM, h\", \"params_request_value\": \"BiggestMoversWeekly\", \"params_tickers_value\": 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Top Gallant Road, Stamford, CT, United States, ?-7700\", \"data_winners_10_IPODate\": \"?-10-04\", \"data_winners_10_LogoURL\": \"https://eodhistoricaldata.comhttps://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/blank.svg\", \"data_winners_1_Exchange\": \"NASDAQ\", \"data_winners_1_GicGroup\": \"Software & Services\", \"data_winners_1_Industry\": \"Software - Application\", \"data_winners_1_OpenFigi\": \"BBG?BH?DV?\", \"data_winners_1_exchange\": \"US\", \"data_winners_2_Exchange\": \"NASDAQ\", \"data_winners_2_GicGroup\": \"Consumer Services\", \"data_winners_2_Industry\": \"Travel Services\", \"data_winners_2_OpenFigi\": \"BBG?QY?XZ?\", \"data_winners_2_exchange\": \"US\", \"data_winners_3_Exchange\": \"NYSE\", \"data_winners_3_GicGroup\": \"Materials\", \"data_winners_3_Industry\": \"Specialty Chemicals\", \"data_winners_3_OpenFigi\": \"BBG?WCFV?\", \"data_winners_3_exchange\": \"US\", \"data_winners_4_Exchange\": \"NASDAQ\", \"data_winners_4_GicGroup\": \"Software & Services\", \"data_winners_4_Industry\": \"Software - Infrastructure\", \"data_winners_4_OpenFigi\": \"BBG?BLYKS?\", \"data_winners_4_exchange\": \"US\", \"data_winners_5_Exchange\": \"NYSE\", \"data_winners_5_GicGroup\": \"Materials\", \"data_winners_5_Industry\": \"Chemicals\", \"data_winners_5_OpenFigi\": \"BBG?BN?\", \"data_winners_5_exchange\": \"US\", \"data_winners_6_Exchange\": \"NYSE\", \"data_winners_6_GicGroup\": \"Software & Services\", \"data_winners_6_Industry\": \"Software - Application\", \"data_winners_6_OpenFigi\": \"BBG?M?R?\", \"data_winners_6_exchange\": \"US\", \"data_winners_7_Exchange\": \"NASDAQ\", \"data_winners_7_GicGroup\": \"Software & Services\", \"data_winners_7_Industry\": \"Software - Infrastructure\", \"data_winners_7_OpenFigi\": \"BBG?N?QR?\", \"data_winners_7_exchange\": \"US\", \"data_winners_8_Exchange\": \"NYSE\", \"data_winners_8_GicGroup\": \"Materials\", \"data_winners_8_Industry\": \"Agricultural Inputs\", \"data_winners_8_OpenFigi\": \"BBG?BWJFZ?\", \"data_winners_8_exchange\": \"US\", \"data_winners_9_Exchange\": \"NASDAQ\", \"data_winners_9_GicGroup\": \"Software & Services\", \"data_winners_9_Industry\": \"Software - Application\", \"data_winners_9_OpenFigi\": \"BBG?BM?HL?\", \"data_winners_9_exchange\": \"US\", \"dictionary_909_FTSE ?\": \"UK?\", \"dictionary_BeforeMarket\": \"Before Market \", \"dictionary_ChartPattern\": \"Chart Pattern\", \"dictionary_Corn Futures\": \"Corn Futures\", \"dictionary_Gold Futures\": \"Gold Futures\", \"dictionary_Last12events\": \"Last ? events\", \"dictionary_RoundNumbers\": \"Round Number\", \"dictionary_Target_Level\": \"Target Level\", \"dictionary_TuesdayShort\": \"Tue\", \"params_DateFormat_value\": \"dd MMM yyyy\", \"params_disclaimer_value\": \"This is a Test\", \"params_down_color_value\": null, \"data_losers_10_GicSector\": \"Utilities\", \"data_losers_10_UpdatedAt\": \"?-03-05\", \"data_losers_1_CountryISO\": \"US\", \"data_losers_1_IsDelisted\": false, \"data_losers_1_change_str\": \"(?.?%)\", \"data_losers_2_CountryISO\": \"US\", \"data_losers_2_IsDelisted\": false, \"data_losers_2_change_str\": \"(?.?%)\", \"data_losers_3_CountryISO\": \"US\", \"data_losers_3_IsDelisted\": false, \"data_losers_3_change_str\": \"(?.?%)\", \"data_losers_4_CountryISO\": \"US\", \"data_losers_4_IsDelisted\": false, \"data_losers_4_change_str\": \"(?.?%)\", \"data_losers_5_CountryISO\": \"US\", \"data_losers_5_IsDelisted\": false, \"data_losers_5_change_str\": \"(?.?%)\", \"data_losers_6_CountryISO\": \"US\", \"data_losers_6_IsDelisted\": false, \"data_losers_6_change_str\": \"(?.?%)\", \"data_losers_7_CountryISO\": \"US\", \"data_losers_7_IsDelisted\": false, \"data_losers_7_change_str\": \"(?.?%)\", \"data_losers_8_CountryISO\": \"US\", \"data_losers_8_IsDelisted\": false, \"data_losers_8_change_str\": \"(?.?%)\", \"data_losers_9_CountryISO\": \"US\", \"data_losers_9_IsDelisted\": false, \"data_losers_9_change_str\": \"(?.?%)\", \"data_winners_10_Exchange\": \"NYSE\", \"data_winners_10_GicGroup\": \"Software & Services\", \"data_winners_10_Industry\": \"Information Technology Services\", \"data_winners_10_OpenFigi\": \"BBG?BB?D?\", \"data_winners_10_exchange\": \"US\", \"data_winners_1_GicSector\": \"Information Technology\", \"data_winners_1_UpdatedAt\": \"?-03-05\", \"data_winners_2_GicSector\": \"Consumer Discretionary\", \"data_winners_2_UpdatedAt\": \"?-03-06\", \"data_winners_3_GicSector\": \"Materials\", \"data_winners_3_UpdatedAt\": \"?-03-05\", \"data_winners_4_GicSector\": \"Information Technology\", \"data_winners_4_UpdatedAt\": \"?-03-05\", \"data_winners_5_GicSector\": \"Materials\", \"data_winners_5_UpdatedAt\": \"?-03-06\", \"data_winners_6_GicSector\": \"Information Technology\", \"data_winners_6_UpdatedAt\": \"?-03-06\", \"data_winners_7_GicSector\": \"Information Technology\", \"data_winners_7_UpdatedAt\": \"?-03-06\", \"data_winners_8_GicSector\": \"Materials\", \"data_winners_8_UpdatedAt\": \"?-03-05\", \"data_winners_9_GicSector\": \"Information Technology\", \"data_winners_9_UpdatedAt\": \"?-03-05\", \"dictionary_909_Crude oil\": \"XTIUSD\", \"dictionary_909_Hang Seng\": \"HK?\", \"dictionary_BiggestLosers\": \"Biggest Losers\", \"dictionary_BiggestMovers\": \"Biggest Movers\", \"dictionary_MarketSummary\": \"Market Summary\", \"dictionary_SaturdayShort\": \"Sat\", \"dictionary_Target_Period\": \"Target Period\", \"dictionary_ThursdayShort\": \"Thu\", \"dictionary_Wheat Futures\": \"Wheat Futures\", \"data_losers_10_CountryISO\": \"US\", \"data_losers_10_IsDelisted\": false, \"data_losers_10_change_str\": \"(?.?%)\", \"data_losers_1_CountryName\": \"USA\", \"data_losers_1_Description\": \"The AES Corporation, together with its subsidiaries, operates as a power generation and utility company in the United States and internationally. The company owns and/or operates power plants to generate and sell power to customers, such as utilities, industrial users, and other intermediaries; owns and/or operates utilities to generate or purchase, distribute, transmit, and sell electricity to end-user customers in the residential, commercial, industrial, and governmental sectors; and generates and sells electricity on the wholesale market. It uses various fuels and technologies to generate electricity, such as coal, gas, hydro, wind, solar, and biomass, as well as renewables comprising energy storage and landfill gas. The company owns and/or operates a generation portfolio of approximately ?,? megawatts and distributes power to ?.? million customers. The company was formerly known as Applied Energy Services, Inc. and changed its name to The AES Corporation in April ?. The AES Corporation was incorporated in ? and is headquartered in Arlington, Virginia.\", \"data_losers_1_GicIndustry\": \"Independent Power and Renewable Electricity Producers\", \"data_losers_1_OpenHeading\": \"Open\", \"data_losers_2_CountryName\": \"USA\", \"data_losers_2_Description\": \"United Airlines Holdings, Inc., through its subsidiaries, provides air transportation services in the United States, Canada, Atlantic, the Pacific, and Latin America. It transports people and cargo through its mainline and regional fleets. The company also offers ground handling, flight academy, frequent flyer award non-travel redemptions, and maintenance services for third parties. In addition, it provides freight and mail transportation services to commercial businesses, freight forwarders, logistics firms, and national postal services, as well as loyalty programs. The company distributes its products through direct channels, such as the Company\?s mobile app; and traditional travel agencies, online travel agencies, and other intermediaries. The company was formerly known as United Continental Holdings, Inc. and changed its name to United Airlines Holdings, Inc. in June ?. United Airlines Holdings, Inc. was incorporated in ? and is based in Chicago, Illinois.\", \"data_losers_2_GicIndustry\": \"Passenger Airlines\", \"data_losers_2_OpenHeading\": \"Open\", \"data_losers_3_CountryName\": \"USA\", \"data_losers_3_Description\": \"Ford Motor Company develops, delivers, and services Ford trucks, sport utility vehicles, commercial vans and cars, and Lincoln luxury vehicles in the United States, Canada, the United Kingdom, Mexico, and internationally. It operates through Ford Blue, Ford Model e, Ford Pro, and Ford Credit segments. The company sells Ford and Lincoln internal combustion engine and hybrid vehicles, electric vehicles, service parts, accessories, and digital services for retail customers; develops EV and digital vehicle technologies, and software; and provides telematics and EV charging solutions. It also sells Ford and Lincoln vehicles, service parts, and accessories through distributors and dealers, as well as through dealerships to commercial fleet customers, daily rental car companies, and governments. In addition, it engages in vehicle-related financing and leasing activities to and through automotive dealers. Further, the company provides retail installment sale contracts for new and used vehicles; and direct financing leases for new vehicles to retail and commercial customers, such as leasing companies, government entities, daily rental companies, and fleet customers. Additionally, it offers wholesale loans to dealers to finance the purchase of vehicle inventory; and loans to dealers to finance working capital and enhance dealership facilities, purchase dealership real estate, and other dealer vehicle programs. Ford Motor Company was incorporated in ? and is based in Dearborn, Michigan.\", \"data_losers_3_GicIndustry\": \"Automobiles\", \"data_losers_3_OpenHeading\": \"Open\", \"data_losers_4_CountryName\": \"USA\", \"data_losers_4_Description\": \"Norwegian Cruise Line Holdings Ltd., together with its subsidiaries, operates as a cruise company in North America, Europe, the Asia-Pacific, and internationally. It operates the Norwegian Cruise Line, Oceania Cruises, and Regent Seven Seas Cruises brands. The company\? actions and processes into tools for humans and LLM-driven agents. The company was incorporated in ? and is headquartered in Aventura, Florida.\", \"data_winners_7_GicIndustry\": \"Software\", \"data_winners_7_OpenHeading\": \"Open\", \"data_winners_8_CountryName\": \"USA\", \"data_winners_8_Description\": \"CF Industries Holdings, Inc., together with its subsidiaries, engages in the production of ammonia in North America, Europe, and internationally. It operates through Ammonia, Granular Urea, UAN, AN, and Other segments. The company offers ammonia products; nitrogen products, such as granular urea, urea ammonium nitrate solution, and ammonium nitrate; diesel exhaust fluid, urea liquor, and nitric acid products. It serves cooperatives, retailers, independent fertilizer distributors, traders, wholesalers, and industrial users. The company was founded in ? and is headquartered in Northbrook, Illinois.\", \"data_winners_8_GicIndustry\": \"Chemicals\", \"data_winners_8_OpenHeading\": \"Open\", \"data_winners_9_CountryName\": \"USA\", \"data_winners_9_Description\": \"Autodesk, Inc. provides ?D design, engineering, and entertainment technology solutions worldwide. It offers AutoCAD Civil ?D, a surveying, design, analysis, and documentation solution; Autodesk Build, a toolset for managing, sharing, and accessing project documents for streamlined workflows between the office, trailer, and jobsite; Revit, a software built for building information modeling to help professionals design, build, and maintain energy-efficient buildings; Autodesk BIM Collaborate Pro, cloud-based design collaboration and design management software; BuildingConnected, a SaaS preconstruction solution; and Tandem, a cloud-based platform that transforms the built asset lifecycle. The company also provides AutoCAD software, a customizable and extensible CAD application for professional design, drafting, detailing, and visualization; AutoCAD LT, a drafting and detailing software; Fusion, a ?D CAD, computer-aided manufacturing, and computer-aided engineering tool; Inventor, a software solution that offers a set of tools for ?D mechanical design, simulation, analysis, tooling, visualization, and documentation; product design and manufacturing collection tools; and Vault, a data management software for managing data in one central location, accelerate design processes, and streamline internal/external collaboration. It offers Flow Production Tracking, a cloud-based production management software; Maya software that offers ?D modeling, animation, effects, rendering, and compositing solutions for film and video artists, game developers, and design visualization professionals; Media and Entertainment Collection that offers end-to-end creative tools for entertainment creation; and ?ds Max software, which provides ?D modeling, animation, and rendering solutions. The company sells its products and services through a network of resellers and distributors. Autodesk, Inc. was incorporated in ? and is headquartered in San Francisco, California.\", \"data_winners_9_GicIndustry\": \"Software\", \"data_winners_9_OpenHeading\": \"Open\", \"dictionary_909_Natural gas\": \"XNGUSD\", \"dictionary_AUD/USD Futures\": \"AUD/USD Futures\", \"dictionary_EUR/USD Futures\": \"EUR/USD Futures\", \"dictionary_ExtremeMovement\": \"Extreme Movement\", \"dictionary_GBP/USD Futures\": \"GBP/USD Futures\", \"dictionary_NZD/USD Futures\": \"NZD/USD Futures\", \"dictionary_Soybean Futures\": \"Soybean Futures\", \"dictionary_USD/CAD Futures\": \"USD/CAD Futures\", \"dictionary_USD/CHF Futures\": \"USD/CHF Futures\", \"dictionary_USD/JPY Futures\": \"USD/JPY Futures\", \"data_losers_10_CloseHeading\": \"Close\", \"data_losers_10_CurrencyCode\": \"USD\", \"data_losers_10_CurrencyName\": \"US Dollar\", \"data_losers_10_HomeCategory\": \"Domestic\", \"data_losers_1_ChangeHeading\": \"Change\", \"data_losers_1_FiscalYearEnd\": \"December\", \"data_losers_1_PrimaryTicker\": \"AES.US\", \"data_losers_2_ChangeHeading\": \"Change\", \"data_losers_2_FiscalYearEnd\": \"December\", \"data_losers_2_PrimaryTicker\": \"UAL.US\", \"data_losers_3_ChangeHeading\": \"Change\", \"data_losers_3_FiscalYearEnd\": \"December\", \"data_losers_3_PrimaryTicker\": \"F.US\", \"data_losers_4_ChangeHeading\": \"Change\", \"data_losers_4_FiscalYearEnd\": \"December\", \"data_losers_4_PrimaryTicker\": \"NCLH.US\", \"data_losers_5_ChangeHeading\": \"Change\", \"data_losers_5_FiscalYearEnd\": \"December\", \"data_losers_5_PrimaryTicker\": \"LUV.US\", \"data_losers_6_ChangeHeading\": \"Change\", \"data_losers_6_FiscalYearEnd\": \"November\", \"data_losers_6_PrimaryTicker\": \"CCL.US\", \"data_losers_7_ChangeHeading\": \"Change\", \"data_losers_7_FiscalYearEnd\": \"June\", \"data_losers_7_PrimaryTicker\": \"EL.US\", \"data_losers_8_ChangeHeading\": \"Change\", \"data_losers_8_FiscalYearEnd\": \"December\", \"data_losers_8_PrimaryTicker\": \"MHK.US\", \"data_losers_9_ChangeHeading\": \"Change\", \"data_losers_9_FiscalYearEnd\": \"December\", \"data_losers_9_PrimaryTicker\": \"ELV.US\", \"data_winners_10_CountryName\": \"USA\", \"data_winners_10_Description\": \"Gartner, Inc. provides business and technology insights for decisions and performance on an organization\?s Economic Events\", \"dictionary_Zeromarkets ?_Gold\": \"XAUUSD\", \"data_losers_10_FullTimeEmployees\": ?, \"data_losers_1_nocolor_change_str\": \"(?.?%)\", \"data_losers_2_nocolor_change_str\": \"(?.?%)\", \"data_losers_3_nocolor_change_str\": \"(?.?%)\", \"data_losers_4_nocolor_change_str\": \"(?.?%)\", \"data_losers_5_nocolor_change_str\": \"(?.?%)\", \"data_losers_6_nocolor_change_str\": \"(?.?%)\", \"data_losers_7_nocolor_change_str\": \"(?.?%)\", \"data_losers_8_nocolor_change_str\": \"(?.?%)\", \"data_losers_9_nocolor_change_str\": \"(?.?%)\", \"data_winners_10_EmployerIdNumber\": \"?-3099750\", \"data_winners_1_FullTimeEmployees\": ?, \"data_winners_2_FullTimeEmployees\": ?, \"data_winners_3_FullTimeEmployees\": ?, \"data_winners_4_FullTimeEmployees\": ?, \"data_winners_5_FullTimeEmployees\": ?, \"data_winners_6_FullTimeEmployees\": ?, \"data_winners_7_FullTimeEmployees\": ?, \"data_winners_8_FullTimeEmployees\": ?, \"data_winners_9_FullTimeEmployees\": ?, \"dictionary_ExpectedMovementRange\": \"Expected movement range during event is between {from} and {to}\", \"dictionary_WTI Crude Oil Futures\": \"WTI Crude Oil Futures\", \"dictionary_highimpactevent_title\": \"{eventname}\?s Market Summary\", \"dictionary_Today\?s biggest movers\", \"dictionary_Zeromarkets ?_Silver\": \"XAGUSD\", \"dictionary_upcomingearnings_title\": \"Earnings announcements between {fromdate} and {todate}.\", \"params_creatomate_snapshots_value\": \"?.?,?.?\", \"data_winners_10_nocolor_change_str\": \"+?.?%\", \"dictionary_Brent Crude Oil Futures\": \"Brent Crude Oil Futures\", \"dictionary_The biggest losers are:\": \"The biggest losers are:\", \"dictionary_ThisWeeksEconomicEvents\": \"This Week\?{eventname}\? is being released in {countryname} in the next {hours_ahead} hours. The expected value for this release is {consensus}. {description}\", \"params_probability_of_posting_value\": ?, \"data_losers_10_InternationalDomestic\": \"Domestic\", \"data_losers_10_change_str.fill_color\": \"#?\", \"data_winners_1_InternationalDomestic\": \"Domestic\", \"data_winners_1_change_str.fill_color\": \"#?\", \"data_winners_2_InternationalDomestic\": \"Domestic\", \"data_winners_2_change_str.fill_color\": \"#?\", \"data_winners_3_InternationalDomestic\": \"Domestic\", \"data_winners_3_change_str.fill_color\": \"#?\", \"data_winners_4_InternationalDomestic\": null, \"data_winners_4_change_str.fill_color\": \"#?\", \"data_winners_5_InternationalDomestic\": \"Domestic\", \"data_winners_5_change_str.fill_color\": \"#?\", \"data_winners_6_InternationalDomestic\": \"Domestic\", \"data_winners_6_change_str.fill_color\": \"#?\", \"data_winners_7_InternationalDomestic\": null, \"data_winners_7_change_str.fill_color\": \"#?\", \"data_winners_8_InternationalDomestic\": \"Domestic\", \"data_winners_8_change_str.fill_color\": \"#?\", \"data_winners_9_InternationalDomestic\": \"Domestic\", \"data_winners_9_change_str.fill_color\": \"#?\", \"dictionary_909_WTI Crude Oil Futures\": \"WTI\", \"dictionary_E-mini NASDAQ? Futures\": \"E-mini NASDAQ? Futures\", \"dictionary_Nikkei ? Dollar Futures\": \"Nikkei ? Dollar Futures\", \"dictionary_ThisWeeksEarningsReleases\": \"This Week\? is being released in {countryname}. The expected value for this release is {consensus}.\", \"data_winners_10_InternationalDomestic\": \"Domestic\", \"data_winners_10_change_str.fill_color\": \"#?\", \"dictionary_Nohighimpacteconomicevents\": \"No high impact economic events\", \"dictionary_This week\?s biggest movers\", \"dictionary_Zeromarkets ?_Nasdaq ?\": \"US?\", \"dictionary_Zeromarkets ?_Nikkei ?\": \"JP?\", \"dictionary_volatilitywarning_longtext\": \"{eventname}\?s economic events:\", \"dictionary_Zeromarkets ?_Natural gas\": \"XNGUSD\", \"dictionary_volatilitywarning_shorttext\": \"{eventname}\?s biggest movers are:\": \"This week\?'s biggest movers\", \"has_results\": true, \"data_Heading\": \"Biggest Movers\", \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"output_format\": \"png\", \"snapshot_time\": ?, \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"The biggest winners are: - Intuit Inc: +?.?%\\n - Expedia Group Inc.: +?.?%\\n - LyondellBasell Industries NV: +?.?%\\n - Crowdstrike Holdings Inc: +?.?%\\n - Dow Inc: +?.?%\\n - ServiceNow Inc: +?.?%\\n - Palantir Technologies Inc.: +?.?%\\n - CF Industries Holdings Inc: +?.?%\\n - Autodesk Inc: +?.?%\\n - Gartner Inc: +?.?%\\n. The biggest losers are: - The AES Corporation: (?.?%)\\n - United Airlines Holdings Inc: (?.?%)\\n - Ford Motor Company: (?.?%)\\n - Norwegian Cruise Line Holdings Ltd: (?.?%)\\n - Southwest Airlines Company: (?.?%)\\n - Carnival Corporation: (?.?%)\\n - Estee Lauder Companies Inc: (?.?%)\\n - Mohawk Industries Inc: (?.?%)\\n - Elevance Health Inc: (?.?%)\\n - NRG Energy Inc.: (?.?%)\\n\", \"dictionary_Corn\": \"Corn\", \"dictionary_Gold\": \"Gold\", \"dictionary_Hour\": \"Hour\", \"dictionary_Open\": \"Open\", \"dictionary_Time\": \"Time\", \"text_short_text\": \"The biggest winners are: Intuit Inc: +?.?%, Expedia Group Inc.: +?.?%, LyondellBasell Industries NV: +?.?%, Crowdstrike Holdings Inc: +?.?%, Dow Inc: +?.?%, ServiceNow Inc: +?.?%, Palantir Technologies Inc.: +?.?%, CF Industries Holdings Inc: +?.?%, Autodesk Inc: +?.?%, Gartner Inc: +?.?%. The biggest losers are: The AES Corporation: (?.?%), United Airlines Holdings Inc: (?.?%), Ford Motor Company: (?.?%), Norwegian Cruise Line Holdings Ltd: (?.?%), Southwest Airlines Company: (?.?%), Carnival Corporation: (?.?%), Estee Lauder Companies Inc: (?.?%), Mohawk Industries Inc: (?.?%), Elevance Health Inc: (?.?%), NRG Energy Inc.: (?.?%)\", \"data_OpenHeading\": \"Open\", \"dictionary_Close\": \"Close\", \"dictionary_Daily\": \"Daily\", \"dictionary_Event\": \"Event\", \"dictionary_Hours\": \"Hours\", \"dictionary_Price\": \"Price\", \"dictionary_Wheat\": \"Wheat\", \"quantity_results\": ?, \"data_CloseHeading\": \"Close\", \"data_losers_1_CIK\": \"?\", \"data_losers_1_LEI\": \"?NUNNB?D?COUIRE?\", \"data_losers_2_CIK\": \"?\", \"data_losers_2_LEI\": \"?DA?B?DD?\", \"data_losers_3_CIK\": \"?\", \"data_losers_3_LEI\": \"?S?OYHG?MQM?VUIC?\", \"data_losers_4_CIK\": \"?\", \"data_losers_4_LEI\": null, \"data_losers_5_CIK\": \"?\", \"data_losers_5_LEI\": \"UDTZ?G?STFETI?HGH?\", \"data_losers_6_CIK\": \"?\", \"data_losers_6_LEI\": null, \"data_losers_7_CIK\": \"?\", \"data_losers_7_LEI\": \"?VFZ?XJ?NUPU?\", \"data_losers_8_CIK\": \"?\", \"data_losers_8_LEI\": \"?JI?MG?Q?\", \"data_losers_9_CIK\": \"?\", \"data_losers_9_LEI\": \"?MYN?XMYQH?CTMTH?\", \"dictionary_Actual\": \"Actual\", \"dictionary_Change\": \"Change\", \"dictionary_Coffee\": \"Coffee\", \"dictionary_Friday\": \"Friday\", \"dictionary_Monday\": \"Monday\", \"dictionary_Silver\": \"Silver\", \"dictionary_Sunday\": \"Sunday\", \"dictionary_Target\": \"Target\", \"dictionary_dd MMM\": \"dd MMM\", \"params_mode_value\": ?, \"params_uuid_value\": \"a7bf3e8e-eaf9-476c-aead-d432e4fa63e5\", \"data_ChangeHeading\": \"Change\", \"data_losers_10_CIK\": \"?\", \"data_losers_10_LEI\": \"?E?UPK?SW?M?XY?I?\", \"data_losers_1_Code\": \"AES\", \"data_losers_1_ISIN\": \"US?H?\", \"data_losers_1_Name\": \"The AES Corporation\", \"data_losers_1_Type\": \"Common Stock\", \"data_losers_1_code\": \"AES\", \"data_losers_1_open\": ?.?, \"data_losers_2_Code\": \"UAL\", \"data_losers_2_ISIN\": \"US?\", \"data_losers_2_Name\": \"United Airlines Holdings Inc\", \"data_losers_2_Type\": \"Common Stock\", \"data_losers_2_code\": \"UAL\", \"data_losers_2_open\": ?.?, \"data_losers_3_Code\": \"F\", \"data_losers_3_ISIN\": \"US?\", \"data_losers_3_Name\": \"Ford Motor Company\", \"data_losers_3_Type\": \"Common Stock\", \"data_losers_3_code\": \"F\", \"data_losers_3_open\": ?.?, \"data_losers_4_Code\": \"NCLH\", \"data_losers_4_ISIN\": \"USG?\", \"data_losers_4_Name\": \"Norwegian Cruise Line Holdings Ltd\", \"data_losers_4_Type\": \"Common Stock\", \"data_losers_4_code\": \"NCLH\", \"data_losers_4_open\": ?.?, \"data_losers_5_Code\": \"LUV\", \"data_losers_5_ISIN\": \"US?\", \"data_losers_5_Name\": \"Southwest Airlines Company\", \"data_losers_5_Type\": \"Common Stock\", \"data_losers_5_code\": \"LUV\", \"data_losers_5_open\": ?.?, \"data_losers_6_Code\": \"CCL\", \"data_losers_6_ISIN\": \"US?\", \"data_losers_6_Name\": \"Carnival Corporation\", \"data_losers_6_Type\": \"Common Stock\", \"data_losers_6_code\": \"CCL\", \"data_losers_6_open\": ?.?, \"data_losers_7_Code\": \"EL\", \"data_losers_7_ISIN\": \"US?\", \"data_losers_7_Name\": \"Estee Lauder Companies Inc\", \"data_losers_7_Type\": \"Common Stock\", \"data_losers_7_code\": \"EL\", \"data_losers_7_open\": ?.?, \"data_losers_8_Code\": \"MHK\", \"data_losers_8_ISIN\": \"US?\", \"data_losers_8_Name\": \"Mohawk Industries Inc\", \"data_losers_8_Type\": \"Common Stock\", \"data_losers_8_code\": \"MHK\", \"data_losers_8_open\": ?.?, \"data_losers_9_Code\": \"ELV\", \"data_losers_9_ISIN\": \"US?\", \"data_losers_9_Name\": \"Elevance Health Inc\", \"data_losers_9_Type\": \"Common Stock\", \"data_losers_9_code\": \"ELV\", \"data_losers_9_open\": ?.?, \"data_winners_1_CIK\": \"?\", \"data_winners_1_LEI\": \"VI?HBPH?XSFMB?E?M?\", \"data_winners_2_CIK\": \"?\", \"data_winners_2_LEI\": \"CI?MUJI?USF?V?NJ?H?\", \"data_winners_3_CIK\": \"?\", \"data_winners_3_LEI\": null, \"data_winners_4_CIK\": \"?\", \"data_winners_4_LEI\": \"?YBY?K?KM?HX?\", \"data_winners_5_CIK\": \"?\", \"data_winners_5_lei[...];

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1	0ms	0ms

9

0ms

56

Details

0ms

set datestyle = iso;

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Mar 06	14	56	0ms	0ms

10

0ms

1

Details

0ms

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

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1	0ms	0ms

11

0ms

56

Details

0ms

set client_encoding to ?;

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Mar 06	14	56	0ms	0ms

12

0ms

2

Details

0ms

select "public"."executions"."id" AS "id", "public"."executions"."processid" AS "processid", "public"."executions"."executiondate" AS "executiondate", "public"."executions"."errorcount" AS "errorcount", "public"."executions"."warningcount" AS "warningcount", "public"."executions"."isrunning" AS "isrunning", "public"."executions"."response" AS "response", "public"."executions"."live" AS "live", "public"."executions"."has_results" AS "has_results", "LT?"."id" AS "LA?" from "public"."executions" left outer join "public"."processes" "LT?" on "LT?"."id" = "public"."executions"."processid" where (processid = ?) order by "public"."executions"."id" desc limit ? offset ?;

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Mar 06	14	2	0ms	0ms

13

0ms

18

Details

0ms

select cast(count(*) / cast(setting as numeric) * ? as int) from pg_stat_activity, pg_settings where name = ? group by setting;

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Mar 06	14	18	0ms	0ms

14

0ms

2

Details

0ms

select count(*) from "public"."executions" left outer join "public"."processes" "LT?" on "LT?"."id" = "public"."executions"."processid" where (processid = ?);

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Mar 06	14	2	0ms	0ms

15

0ms

1

Details

0ms

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

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1	0ms	0ms

16

0ms

395

Details

0ms

commit;

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Mar 06	14	395	0ms	0ms

17

0ms

1

Details

0ms

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

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1	0ms	0ms

18

0ms

280

Details

0ms

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

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Mar 06	14	280	0ms	0ms

19

0ms

239

Details

0ms

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

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Mar 06	14	239	0ms	0ms

20

0ms

239

Details

0ms

select system_identifier from pg_control_system ();

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Mar 06	14	239	0ms	0ms

Most frequent queries (N)

Rank

Times executed

Total duration

Min duration

Max duration

Avg duration

Query

1

19,465

Details

0ms

select ?;

Times Reported Time consuming queries #1

Day	Hour	Count	Duration	Avg duration
Mar 06	14	19,465	0ms	0ms

2

12,021

Details

0ms

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

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Mar 06	14	12,021	0ms	0ms

3

10,219

Details

0ms

select s.symbolid as id, s.symbol as name, s.exchange as exchange, s.timegranularity as interval, dtt.timezone as timezone from symbols s inner join downloadersymbolsettings dss on dss.symbolid = s.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join brokersymbollist bsl on bsl.symbolid = s.symbolid where bsl.brokerid = ? and (? = ? or s.timegranularity = ?) and (s.symbol = ? or dss.downloadersymbol = ?) and dss.enabled = ?;

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Mar 06	14	10,219	0ms	0ms

4

5,905

Details

0ms

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

Times Reported Time consuming queries #4

Day	Hour	Count	Duration	Avg duration
Mar 06	14	5,905	0ms	0ms

5

5,025

Details

0ms

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

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Mar 06	14	5,025	0ms	0ms

6

3,737

Details

0ms

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

Times Reported Time consuming queries #6

Day	Hour	Count	Duration	Avg duration
Mar 06	14	3,737	0ms	0ms

7

3,314

Details

0ms

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

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Mar 06	14	3,314	0ms	0ms

8

3,185

Details

0ms

select ew.processid, "Errors", "Warnings" from quantity_errors_warnings_perprocess ew;

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Mar 06	14	3,185	0ms	0ms

9

2,898

Details

0ms

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

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Mar 06	14	2,898	0ms	0ms

10

2,329

Details

0ms

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

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Mar 06	14	2,329	0ms	0ms

11

2,314

Details

0ms

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

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Mar 06	14	2,314	0ms	0ms

12

1,974

Details

0ms

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

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1,974	0ms	0ms

13

1,809

Details

0ms

set extra_float_digits = ?;

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1,809	0ms	0ms

14

1,783

Details

0ms

set application_name = ?;

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1,783	0ms	0ms

15

1,361

Details

0ms

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

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1,361	0ms	0ms

16

1,196

Details

0ms

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

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1,196	0ms	0ms

17

1,195

Details

0ms

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

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1,195	0ms	0ms

18

1,195

Details

0ms

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

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1,195	0ms	0ms

19

1,195

Details

0ms

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

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1,195	0ms	0ms

20

1,195

Details

0ms

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

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1,195	0ms	0ms

Normalized slowest queries (N)

Rank

Min duration

Max duration

Avg duration

Times executed

Total duration

Query

1

0ms

49

Details

0ms

select key, value from datasources ds inner join datasourceparams dsp on ds.id = dsp.datasourceid where ds.name = ?;

Times Reported Time consuming queries #1

Day	Hour	Count	Duration	Avg duration
Mar 06	14	49	0ms	0ms

2

0ms

4

Details

0ms

select "public"."processparameters"."id" AS "id", "public"."processparameters"."processid" AS "processid", "public"."processparameters"."key" AS "key", "public"."processparameters"."value" AS "value" from "public"."processparameters" where "public"."processparameters"."id" = ? and "public"."processparameters"."id" = ? limit ? offset ?;

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Mar 06	14	4	0ms	0ms

3

0ms

1

Details

0ms

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

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1	0ms	0ms

4

0ms

42

Details

0ms

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

Times Reported Time consuming queries #4

Day	Hour	Count	Duration	Avg duration
Mar 06	14	42	0ms	0ms

5

0ms

1,974

Details

0ms

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

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1,974	0ms	0ms

6

0ms

4

Details

0ms

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

Times Reported Time consuming queries #6

Day	Hour	Count	Duration	Avg duration
Mar 06	14	4	0ms	0ms

7

0ms

4

Details

0ms

select updaterelevantforrelevantresults ();

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Mar 06	14	4	0ms	0ms

8

0ms

1

Details

0ms

insert into executionlogs(executionid, status, message, details, detailtype) values(?, ?, ?, ?s biggest movers\", \"has_results\": true, \"data_Heading\": \"Biggest Movers\", \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"output_format\": \"png\", \"snapshot_time\": ?, \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"The biggest winners are: - Intuit Inc: +?.?%\\\\n - Expedia Group Inc.: +?.?%\\\\n - LyondellBasell Industries NV: +?.?%\\\\n - Crowdstrike Holdings Inc: +?.?%\\\\n - Dow Inc: +?.?%\\\\n - ServiceNow Inc: +?.?%\\\\n - Palantir Technologies Inc.: +?.?%\\\\n - CF Industries Holdings Inc: +?.?%\\\\n - Autodesk Inc: +?.?%\\\\n - Gartner Inc: +?.?%\\\\n. The biggest losers are: - The AES Corporation: (?.?%)\\\\n - United Airlines Holdings Inc: (?.?%)\\\\n - Ford Motor Company: (?.?%)\\\\n - Norwegian Cruise Line Holdings Ltd: (?.?%)\\\\n - Southwest Airlines Company: (?.?%)\\\\n - Carnival Corporation: (?.?%)\\\\n - Estee Lauder Companies Inc: (?.?%)\\\\n - Mohawk Industries Inc: (?.?%)\\\\n - Elevance Health Inc: (?.?%)\\\\n - NRG Energy Inc.: (?.?%)\\\\n\", \"dictionary_Corn\": \"Corn\", \"dictionary_Gold\": \"Gold\", \"dictionary_Hour\": \"Hour\", \"dictionary_Open\": \"Open\", \"dictionary_Time\": \"Time\", \"text_short_text\": \"The biggest winners are: Intuit Inc: +?.?%, Expedia Group Inc.: +?.?%, LyondellBasell Industries NV: +?.?%, Crowdstrike Holdings Inc: +?.?%, Dow Inc: +?.?%, ServiceNow Inc: +?.?%, Palantir Technologies Inc.: +?.?%, CF Industries Holdings Inc: +?.?%, Autodesk Inc: +?.?%, Gartner Inc: +?.?%. The biggest losers are: The AES Corporation: (?.?%), United Airlines Holdings Inc: (?.?%), Ford Motor Company: (?.?%), Norwegian Cruise Line Holdings Ltd: (?.?%), Southwest Airlines Company: (?.?%), Carnival Corporation: (?.?%), Estee Lauder Companies Inc: (?.?%), Mohawk Industries Inc: (?.?%), Elevance Health Inc: (?.?%), NRG Energy Inc.: (?.?%)\", \"data_OpenHeading\": \"Open\", \"dictionary_Close\": \"Close\", \"dictionary_Daily\": \"Daily\", \"dictionary_Event\": \"Event\", \"dictionary_Hours\": \"Hours\", \"dictionary_Price\": \"Price\", \"dictionary_Wheat\": \"Wheat\", \"quantity_results\": ?, \"data_CloseHeading\": \"Close\", \"data_losers_1_CIK\": \"?\", \"data_losers_1_LEI\": \"?NUNNB?D?COUIRE?\", \"data_losers_2_CIK\": \"?\", \"data_losers_2_LEI\": \"?DA?B?DD?\", \"data_losers_3_CIK\": \"?\", \"data_losers_3_LEI\": \"?S?OYHG?MQM?VUIC?\", \"data_losers_4_CIK\": \"?\", \"data_losers_4_LEI\": null, \"data_losers_5_CIK\": \"?\", \"data_losers_5_LEI\": \"UDTZ?G?STFETI?HGH?\", \"data_losers_6_CIK\": \"?\", \"data_losers_6_LEI\": null, \"data_losers_7_CIK\": \"?\", \"data_losers_7_LEI\": \"?VFZ?XJ?NUPU?\", \"data_losers_8_CIK\": \"?\", \"data_losers_8_LEI\": \"?JI?MG?Q?\", \"data_losers_9_CIK\": \"?\", \"data_losers_9_LEI\": \"?MYN?XMYQH?CTMTH?\", \"dictionary_Actual\": \"Actual\", \"dictionary_Change\": \"Change\", \"dictionary_Coffee\": \"Coffee\", \"dictionary_Friday\": \"Friday\", \"dictionary_Monday\": \"Monday\", \"dictionary_Silver\": \"Silver\", \"dictionary_Sunday\": \"Sunday\", \"dictionary_Target\": \"Target\", \"dictionary_dd MMM\": \"dd MMM\", \"params_mode_value\": ?, \"params_uuid_value\": \"a7bf3e8e-eaf9-476c-aead-d432e4fa63e5\", \"data_ChangeHeading\": \"Change\", \"data_losers_10_CIK\": \"?\", \"data_losers_10_LEI\": \"?E?UPK?SW?M?XY?I?\", \"data_losers_1_Code\": \"AES\", \"data_losers_1_ISIN\": \"US?H?\", \"data_losers_1_Name\": \"The AES Corporation\", \"data_losers_1_Type\": \"Common Stock\", \"data_losers_1_code\": \"AES\", \"data_losers_1_open\": ?.?, \"data_losers_2_Code\": \"UAL\", \"data_losers_2_ISIN\": \"US?\", \"data_losers_2_Name\": \"United Airlines Holdings Inc\", \"data_losers_2_Type\": \"Common Stock\", \"data_losers_2_code\": \"UAL\", \"data_losers_2_open\": ?.?, \"data_losers_3_Code\": \"F\", \"data_losers_3_ISIN\": \"US?\", \"data_losers_3_Name\": \"Ford Motor Company\", \"data_losers_3_Type\": \"Common Stock\", \"data_losers_3_code\": \"F\", \"data_losers_3_open\": ?.?, \"data_losers_4_Code\": \"NCLH\", \"data_losers_4_ISIN\": \"USG?\", \"data_losers_4_Name\": \"Norwegian Cruise Line Holdings Ltd\", \"data_losers_4_Type\": \"Common Stock\", \"data_losers_4_code\": \"NCLH\", \"data_losers_4_open\": ?.?, \"data_losers_5_Code\": \"LUV\", \"data_losers_5_ISIN\": \"US?\", \"data_losers_5_Name\": \"Southwest Airlines Company\", \"data_losers_5_Type\": \"Common Stock\", \"data_losers_5_code\": \"LUV\", \"data_losers_5_open\": ?.?, \"data_losers_6_Code\": \"CCL\", \"data_losers_6_ISIN\": \"US?\", \"data_losers_6_Name\": \"Carnival Corporation\", \"data_losers_6_Type\": \"Common Stock\", \"data_losers_6_code\": \"CCL\", \"data_losers_6_open\": ?.?, \"data_losers_7_Code\": \"EL\", \"data_losers_7_ISIN\": \"US?\", \"data_losers_7_Name\": \"Estee Lauder Companies Inc\", \"data_losers_7_Type\": \"Common Stock\", \"data_losers_7_code\": \"EL\", \"data_losers_7_open\": ?.?, \"data_losers_8_Code\": \"MHK\", \"data_losers_8_ISIN\": \"US?\", \"data_losers_8_Name\": \"Mohawk Industries Inc\", \"data_losers_8_Type\": \"Common Stock\", \"data_losers_8_code\": \"MHK\", \"data_losers_8_open\": ?.?, \"data_losers_9_Code\": \"ELV\", \"data_losers_9_ISIN\": \"US?\", \"data_losers_9_Name\": \"Elevance Health Inc\", \"data_losers_9_Type\": \"Common Stock\", \"data_losers_9_code\": \"ELV\", \"data_losers_9_open\": ?.?, \"data_winners_1_CIK\": \"?\", \"data_winners_1_LEI\": \"VI?HBPH?XSFMB?E?M?\", \"data_winners_2_CIK\": \"?\", \"data_winners_2_LEI\": \"CI?MUJI?USF?V?NJ?H?\", \"data_winners_3_CIK\": \"?\", \"data_winners_3_LEI\": null, \"data_winners_4_CIK\": \"?\", \"data_winners_4_LEI\": \"?YBY?K?KM?HX?\", \"data_winners_5_CIK\": \"?\", \"data_winners_5_LEI\": \"?S?INSLK?IP?\", \"data_winners_6_CIK\": \"?\", \"data_winners_6_LEI\": \"?HJTQM?M?E?G?\", \"data_winners_7_CIK\": \"?\", \"data_winners_7_LEI\": \"?UVN?B?BBDHO?\", \"data_winners_8_CIK\": \"?\", \"data_winners_8_LEI\": \"?CG?YAQFZ?JMV?\", \"data_winners_9_CIK\": \"?\", \"data_winners_9_LEI\": \"FRKKVKAIQEF?FCSTPG?\", \"dictionary_909_DAX\": \"GER?\", \"dictionary_AUD/USD\": \"AUD/USD\", \"dictionary_Company\": \"Company\", \"dictionary_EUR/USD\": \"EUR/USD\", \"dictionary_GBP/USD\": \"GBP/USD\", \"dictionary_Indices\": \"Indices\", \"dictionary_Minutes\": \"Minutes\", \"dictionary_NZD/USD\": \"NZD/USD\", \"dictionary_Tuesday\": \"Tuesday\", \"dictionary_USD/CAD\": \"USD/CAD\", \"dictionary_USD/CHF\": \"USD/CHF\", \"dictionary_USD/JPY\": \"USD/JPY\", \"data_losers_10_Code\": \"NRG\", \"data_losers_10_ISIN\": \"US?\", \"data_losers_10_Name\": \"NRG Energy Inc.\", \"data_losers_10_Type\": \"Common Stock\", \"data_losers_10_code\": \"NRG\", \"data_losers_10_open\": ?.?, \"data_losers_1_CUSIP\": \"?H?\", \"data_losers_1_Phone\": \"? ? ?\", \"data_losers_1_close\": ?.?, \"data_losers_2_CUSIP\": \"?\", \"data_losers_2_Phone\": \"? ? ?\", \"data_losers_2_close\": ?.?, \"data_losers_3_CUSIP\": \"?\", \"data_losers_3_Phone\": \"? ? ?\", \"data_losers_3_close\": ?.?, \"data_losers_4_CUSIP\": \"G?\", \"data_losers_4_Phone\": \"? ? ?\", \"data_losers_4_close\": ?.?, \"data_losers_5_CUSIP\": \"?\", \"data_losers_5_Phone\": \"? ? ?\", \"data_losers_5_close\": ?.?, \"data_losers_6_CUSIP\": \"?\", \"data_losers_6_Phone\": \"? ? ?\", \"data_losers_6_close\": ?.?, \"data_losers_7_CUSIP\": \"?\", \"data_losers_7_Phone\": \"? ? ?\", \"data_losers_7_close\": ?.?, \"data_losers_8_CUSIP\": \"?\", \"data_losers_8_Phone\": \"? ? ?\", \"data_losers_8_close\": ?.?, \"data_losers_9_CUSIP\": \"?B?\", \"data_losers_9_Phone\": \"? ? ?\", \"data_losers_9_close\": ?.?, \"data_winners_10_CIK\": \"?\", \"data_winners_10_LEI\": \"PP?B?R?BFB?O?HH?\", \"data_winners_1_Code\": \"INTU\", \"data_winners_1_ISIN\": \"US?\", \"data_winners_1_Name\": \"Intuit Inc\", \"data_winners_1_Type\": \"Common Stock\", \"data_winners_1_code\": \"INTU\", \"data_winners_1_open\": ?.?, \"data_winners_2_Code\": \"EXPE\", \"data_winners_2_ISIN\": \"US?P?\", \"data_winners_2_Name\": \"Expedia Group Inc.\", \"data_winners_2_Type\": \"Common Stock\", \"data_winners_2_code\": \"EXPE\", \"data_winners_2_open\": ?.?, \"data_winners_3_Code\": \"LYB\", \"data_winners_3_ISIN\": \"USN?\", \"data_winners_3_Name\": \"LyondellBasell Industries NV\", \"data_winners_3_Type\": \"Common Stock\", \"data_winners_3_code\": \"LYB\", \"data_winners_3_open\": ?.?, \"data_winners_4_Code\": \"CRWD\", \"data_winners_4_ISIN\": \"US?C?\", \"data_winners_4_Name\": \"Crowdstrike Holdings Inc\", \"data_winners_4_Type\": \"Common Stock\", \"data_winners_4_code\": \"CRWD\", \"data_winners_4_open\": ?.?, \"data_winners_5_Code\": \"DOW\", \"data_winners_5_ISIN\": \"US?\", \"data_winners_5_Name\": \"Dow Inc\", \"data_winners_5_Type\": \"Common Stock\", \"data_winners_5_code\": \"DOW\", \"data_winners_5_open\": ?.?, \"data_winners_6_Code\": \"NOW\", \"data_winners_6_ISIN\": \"US?P?\", \"data_winners_6_Name\": \"ServiceNow Inc\", \"data_winners_6_Type\": \"Common Stock\", \"data_winners_6_code\": \"NOW\", \"data_winners_6_open\": ?, \"data_winners_7_Code\": \"PLTR\", \"data_winners_7_ISIN\": \"US?A?\", \"data_winners_7_Name\": \"Palantir Technologies Inc.\", \"data_winners_7_Type\": \"Common Stock\", \"data_winners_7_code\": \"PLTR\", \"data_winners_7_open\": ?.?, \"data_winners_8_Code\": \"CF\", \"data_winners_8_ISIN\": \"US?\", \"data_winners_8_Name\": \"CF Industries Holdings Inc\", \"data_winners_8_Type\": \"Common Stock\", \"data_winners_8_code\": \"CF\", \"data_winners_8_open\": ?.?, \"data_winners_9_Code\": \"ADSK\", \"data_winners_9_ISIN\": \"US?\", \"data_winners_9_Name\": \"Autodesk Inc\", \"data_winners_9_Type\": \"Common Stock\", \"data_winners_9_code\": \"ADSK\", \"data_winners_9_open\": ?.?, \"dictionary_909_Gold\": \"XAUUSD\", \"dictionary_Estimate\": \"Estimate\", \"dictionary_Expected\": \"Expected\", \"dictionary_FTSE ?\": \"FTSE ?\", \"dictionary_Interval\": \"Interval\", \"dictionary_KeyLevel\": \"Key Level\", \"dictionary_Previous\": \"Previous\", \"dictionary_Saturday\": \"Saturday\", \"dictionary_Thursday\": \"Thursday\", \"dictionary_estimate\": \"estimate\", \"dictionary_previous\": \"previous\", \"params_locale_value\": \"en\", \"params_region_value\": \"Region1\", \"data_losers_10_CUSIP\": \"?\", \"data_losers_10_Phone\": \"? ? ?\", \"data_losers_10_close\": ?.?, \"data_losers_1_Sector\": \"Utilities\", \"data_losers_1_WebURL\": \"https://www.aes.com\", \"data_losers_1_change\": ?.?, \"data_losers_1_ticker\": \"AES.US\", \"data_losers_2_Sector\": \"Industrials\", \"data_losers_2_WebURL\": \"https://www.united.com\", \"data_losers_2_change\": ?.?, \"data_losers_2_ticker\": \"UAL.US\", \"data_losers_3_Sector\": \"Consumer Cyclical\", \"data_losers_3_WebURL\": \"https://www.ford.com\", \"data_losers_3_change\": ?.?, \"data_losers_3_ticker\": \"F.US\", \"data_losers_4_Sector\": \"Consumer Cyclical\", \"data_losers_4_WebURL\": \"https://www.nclhltd.com\", \"data_losers_4_change\": ?.?, \"data_losers_4_ticker\": \"NCLH.US\", \"data_losers_5_Sector\": \"Industrials\", \"data_losers_5_WebURL\": \"https://www.southwest.com\", \"data_losers_5_change\": ?.?, \"data_losers_5_ticker\": \"LUV.US\", \"data_losers_6_Sector\": \"Consumer Cyclical\", \"data_losers_6_WebURL\": \"https://www.carnivalcorp.com\", \"data_losers_6_change\": ?.?, \"data_losers_6_ticker\": \"CCL.US\", \"data_losers_7_Sector\": \"Consumer Defensive\", \"data_losers_7_WebURL\": \"https://www.elcompanies.com\", \"data_losers_7_change\": ?.?, \"data_losers_7_ticker\": \"EL.US\", \"data_losers_8_Sector\": \"Consumer Cyclical\", \"data_losers_8_WebURL\": \"https://www.mohawkind.com\", \"data_losers_8_change\": ?.?, \"data_losers_8_ticker\": \"MHK.US\", \"data_losers_9_Sector\": \"Healthcare\", \"data_losers_9_WebURL\": \"https://www.elevancehealth.com\", \"data_losers_9_change\": ?.?, \"data_losers_9_ticker\": \"ELV.US\", \"data_winners_10_Code\": \"IT\", \"data_winners_10_ISIN\": \"US?\", \"data_winners_10_Name\": \"Gartner Inc\", \"data_winners_10_Type\": \"Common Stock\", \"data_winners_10_code\": \"IT\", \"data_winners_10_open\": ?.?, \"data_winners_1_CUSIP\": \"?\", \"data_winners_1_Phone\": \"? ? ?\", \"data_winners_1_close\": ?.?, \"data_winners_2_CUSIP\": \"?P?\", \"data_winners_2_Phone\": \"? ? ?\", \"data_winners_2_close\": ?.?, \"data_winners_3_CUSIP\": \"N?\", \"data_winners_3_Phone\": \"? ? ? ?\", \"data_winners_3_close\": ?.?, \"data_winners_4_CUSIP\": \"?C?\", \"data_winners_4_Phone\": \"? ? ?\", \"data_winners_4_close\": ?.?, \"data_winners_5_CUSIP\": \"?\", \"data_winners_5_Phone\": \"? ? ?\", \"data_winners_5_close\": ?.?, \"data_winners_6_CUSIP\": \"?P?\", \"data_winners_6_Phone\": \"? ? ?\", \"data_winners_6_close\": ?.?, \"data_winners_7_CUSIP\": \"?A?\", \"data_winners_7_Phone\": \"? ? ?\", \"data_winners_7_close\": ?.?, \"data_winners_8_CUSIP\": \"?\", \"data_winners_8_Phone\": \"? ? ?\", \"data_winners_8_close\": ?.?, \"data_winners_9_CUSIP\": \"?\", \"data_winners_9_Phone\": \"? ? ?\", \"data_winners_9_close\": ?.?, \"dictionary_Consensus\": \"Consensus\", \"dictionary_Crude oil\": \"Crude oil\", \"dictionary_Fibonacci\": \"Fibonacci Patterns\", \"dictionary_Hang Seng\": \"Hang Seng\", \"dictionary_Stop_Loss\": \"Stop-Loss\", \"dictionary_Wednesday\": \"Wednesday\", \"dictionary_consensus\": \"consensus\", \"dictionary_dd MMM, h\": \"dd MMM, h\", \"params_request_value\": \"BiggestMoversWeekly\", \"params_tickers_value\": 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Top Gallant Road, Stamford, CT, United States, ?-7700\", \"data_winners_10_IPODate\": \"?-10-04\", \"data_winners_10_LogoURL\": \"https://eodhistoricaldata.comhttps://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/blank.svg\", \"data_winners_1_Exchange\": \"NASDAQ\", \"data_winners_1_GicGroup\": \"Software & Services\", \"data_winners_1_Industry\": \"Software - Application\", \"data_winners_1_OpenFigi\": \"BBG?BH?DV?\", \"data_winners_1_exchange\": \"US\", \"data_winners_2_Exchange\": \"NASDAQ\", \"data_winners_2_GicGroup\": \"Consumer Services\", \"data_winners_2_Industry\": \"Travel Services\", \"data_winners_2_OpenFigi\": \"BBG?QY?XZ?\", \"data_winners_2_exchange\": \"US\", \"data_winners_3_Exchange\": \"NYSE\", \"data_winners_3_GicGroup\": \"Materials\", \"data_winners_3_Industry\": \"Specialty Chemicals\", \"data_winners_3_OpenFigi\": \"BBG?WCFV?\", \"data_winners_3_exchange\": \"US\", \"data_winners_4_Exchange\": \"NASDAQ\", \"data_winners_4_GicGroup\": \"Software & Services\", \"data_winners_4_Industry\": \"Software - Infrastructure\", \"data_winners_4_OpenFigi\": \"BBG?BLYKS?\", \"data_winners_4_exchange\": \"US\", \"data_winners_5_Exchange\": \"NYSE\", \"data_winners_5_GicGroup\": \"Materials\", \"data_winners_5_Industry\": \"Chemicals\", \"data_winners_5_OpenFigi\": \"BBG?BN?\", \"data_winners_5_exchange\": \"US\", \"data_winners_6_Exchange\": \"NYSE\", \"data_winners_6_GicGroup\": \"Software & Services\", \"data_winners_6_Industry\": \"Software - Application\", \"data_winners_6_OpenFigi\": \"BBG?M?R?\", \"data_winners_6_exchange\": \"US\", \"data_winners_7_Exchange\": \"NASDAQ\", \"data_winners_7_GicGroup\": \"Software & Services\", \"data_winners_7_Industry\": \"Software - Infrastructure\", \"data_winners_7_OpenFigi\": \"BBG?N?QR?\", \"data_winners_7_exchange\": \"US\", \"data_winners_8_Exchange\": \"NYSE\", \"data_winners_8_GicGroup\": \"Materials\", \"data_winners_8_Industry\": \"Agricultural Inputs\", \"data_winners_8_OpenFigi\": \"BBG?BWJFZ?\", \"data_winners_8_exchange\": \"US\", \"data_winners_9_Exchange\": \"NASDAQ\", \"data_winners_9_GicGroup\": \"Software & Services\", \"data_winners_9_Industry\": \"Software - Application\", \"data_winners_9_OpenFigi\": \"BBG?BM?HL?\", \"data_winners_9_exchange\": \"US\", \"dictionary_909_FTSE ?\": \"UK?\", \"dictionary_BeforeMarket\": \"Before Market \", \"dictionary_ChartPattern\": \"Chart Pattern\", \"dictionary_Corn Futures\": \"Corn Futures\", \"dictionary_Gold Futures\": \"Gold Futures\", \"dictionary_Last12events\": \"Last ? events\", \"dictionary_RoundNumbers\": \"Round Number\", \"dictionary_Target_Level\": \"Target Level\", \"dictionary_TuesdayShort\": \"Tue\", \"params_DateFormat_value\": \"dd MMM yyyy\", \"params_disclaimer_value\": \"This is a Test\", \"params_down_color_value\": null, \"data_losers_10_GicSector\": \"Utilities\", \"data_losers_10_UpdatedAt\": \"?-03-05\", \"data_losers_1_CountryISO\": \"US\", \"data_losers_1_IsDelisted\": false, \"data_losers_1_change_str\": \"(?.?%)\", \"data_losers_2_CountryISO\": \"US\", \"data_losers_2_IsDelisted\": false, \"data_losers_2_change_str\": \"(?.?%)\", \"data_losers_3_CountryISO\": \"US\", \"data_losers_3_IsDelisted\": false, \"data_losers_3_change_str\": \"(?.?%)\", \"data_losers_4_CountryISO\": \"US\", \"data_losers_4_IsDelisted\": false, \"data_losers_4_change_str\": \"(?.?%)\", \"data_losers_5_CountryISO\": \"US\", \"data_losers_5_IsDelisted\": false, \"data_losers_5_change_str\": \"(?.?%)\", \"data_losers_6_CountryISO\": \"US\", \"data_losers_6_IsDelisted\": false, \"data_losers_6_change_str\": \"(?.?%)\", \"data_losers_7_CountryISO\": \"US\", \"data_losers_7_IsDelisted\": false, \"data_losers_7_change_str\": \"(?.?%)\", \"data_losers_8_CountryISO\": \"US\", \"data_losers_8_IsDelisted\": false, \"data_losers_8_change_str\": \"(?.?%)\", \"data_losers_9_CountryISO\": \"US\", \"data_losers_9_IsDelisted\": false, \"data_losers_9_change_str\": \"(?.?%)\", \"data_winners_10_Exchange\": \"NYSE\", \"data_winners_10_GicGroup\": \"Software & Services\", \"data_winners_10_Industry\": \"Information Technology Services\", \"data_winners_10_OpenFigi\": \"BBG?BB?D?\", \"data_winners_10_exchange\": \"US\", \"data_winners_1_GicSector\": \"Information Technology\", \"data_winners_1_UpdatedAt\": \"?-03-05\", \"data_winners_2_GicSector\": \"Consumer Discretionary\", \"data_winners_2_UpdatedAt\": \"?-03-06\", \"data_winners_3_GicSector\": \"Materials\", \"data_winners_3_UpdatedAt\": \"?-03-05\", \"data_winners_4_GicSector\": \"Information Technology\", \"data_winners_4_UpdatedAt\": \"?-03-05\", \"data_winners_5_GicSector\": \"Materials\", \"data_winners_5_UpdatedAt\": \"?-03-06\", \"data_winners_6_GicSector\": \"Information Technology\", \"data_winners_6_UpdatedAt\": \"?-03-06\", \"data_winners_7_GicSector\": \"Information Technology\", \"data_winners_7_UpdatedAt\": \"?-03-06\", \"data_winners_8_GicSector\": \"Materials\", \"data_winners_8_UpdatedAt\": \"?-03-05\", \"data_winners_9_GicSector\": \"Information Technology\", \"data_winners_9_UpdatedAt\": \"?-03-05\", \"dictionary_909_Crude oil\": \"XTIUSD\", \"dictionary_909_Hang Seng\": \"HK?\", \"dictionary_BiggestLosers\": \"Biggest Losers\", \"dictionary_BiggestMovers\": \"Biggest Movers\", \"dictionary_MarketSummary\": \"Market Summary\", \"dictionary_SaturdayShort\": \"Sat\", \"dictionary_Target_Period\": \"Target Period\", \"dictionary_ThursdayShort\": \"Thu\", \"dictionary_Wheat Futures\": \"Wheat Futures\", \"data_losers_10_CountryISO\": \"US\", \"data_losers_10_IsDelisted\": false, \"data_losers_10_change_str\": \"(?.?%)\", \"data_losers_1_CountryName\": \"USA\", \"data_losers_1_Description\": \"The AES Corporation, together with its subsidiaries, operates as a power generation and utility company in the United States and internationally. The company owns and/or operates power plants to generate and sell power to customers, such as utilities, industrial users, and other intermediaries; owns and/or operates utilities to generate or purchase, distribute, transmit, and sell electricity to end-user customers in the residential, commercial, industrial, and governmental sectors; and generates and sells electricity on the wholesale market. It uses various fuels and technologies to generate electricity, such as coal, gas, hydro, wind, solar, and biomass, as well as renewables comprising energy storage and landfill gas. The company owns and/or operates a generation portfolio of approximately ?,? megawatts and distributes power to ?.? million customers. The company was formerly known as Applied Energy Services, Inc. and changed its name to The AES Corporation in April ?. The AES Corporation was incorporated in ? and is headquartered in Arlington, Virginia.\", \"data_losers_1_GicIndustry\": \"Independent Power and Renewable Electricity Producers\", \"data_losers_1_OpenHeading\": \"Open\", \"data_losers_2_CountryName\": \"USA\", \"data_losers_2_Description\": \"United Airlines Holdings, Inc., through its subsidiaries, provides air transportation services in the United States, Canada, Atlantic, the Pacific, and Latin America. It transports people and cargo through its mainline and regional fleets. The company also offers ground handling, flight academy, frequent flyer award non-travel redemptions, and maintenance services for third parties. In addition, it provides freight and mail transportation services to commercial businesses, freight forwarders, logistics firms, and national postal services, as well as loyalty programs. The company distributes its products through direct channels, such as the Company\?s mobile app; and traditional travel agencies, online travel agencies, and other intermediaries. The company was formerly known as United Continental Holdings, Inc. and changed its name to United Airlines Holdings, Inc. in June ?. United Airlines Holdings, Inc. was incorporated in ? and is based in Chicago, Illinois.\", \"data_losers_2_GicIndustry\": \"Passenger Airlines\", \"data_losers_2_OpenHeading\": \"Open\", \"data_losers_3_CountryName\": \"USA\", \"data_losers_3_Description\": \"Ford Motor Company develops, delivers, and services Ford trucks, sport utility vehicles, commercial vans and cars, and Lincoln luxury vehicles in the United States, Canada, the United Kingdom, Mexico, and internationally. It operates through Ford Blue, Ford Model e, Ford Pro, and Ford Credit segments. The company sells Ford and Lincoln internal combustion engine and hybrid vehicles, electric vehicles, service parts, accessories, and digital services for retail customers; develops EV and digital vehicle technologies, and software; and provides telematics and EV charging solutions. It also sells Ford and Lincoln vehicles, service parts, and accessories through distributors and dealers, as well as through dealerships to commercial fleet customers, daily rental car companies, and governments. In addition, it engages in vehicle-related financing and leasing activities to and through automotive dealers. Further, the company provides retail installment sale contracts for new and used vehicles; and direct financing leases for new vehicles to retail and commercial customers, such as leasing companies, government entities, daily rental companies, and fleet customers. Additionally, it offers wholesale loans to dealers to finance the purchase of vehicle inventory; and loans to dealers to finance working capital and enhance dealership facilities, purchase dealership real estate, and other dealer vehicle programs. Ford Motor Company was incorporated in ? and is based in Dearborn, Michigan.\", \"data_losers_3_GicIndustry\": \"Automobiles\", \"data_losers_3_OpenHeading\": \"Open\", \"data_losers_4_CountryName\": \"USA\", \"data_losers_4_Description\": \"Norwegian Cruise Line Holdings Ltd., together with its subsidiaries, operates as a cruise company in North America, Europe, the Asia-Pacific, and internationally. It operates the Norwegian Cruise Line, Oceania Cruises, and Regent Seven Seas Cruises brands. The company\? actions and processes into tools for humans and LLM-driven agents. The company was incorporated in ? and is headquartered in Aventura, Florida.\", \"data_winners_7_GicIndustry\": \"Software\", \"data_winners_7_OpenHeading\": \"Open\", \"data_winners_8_CountryName\": \"USA\", \"data_winners_8_Description\": \"CF Industries Holdings, Inc., together with its subsidiaries, engages in the production of ammonia in North America, Europe, and internationally. It operates through Ammonia, Granular Urea, UAN, AN, and Other segments. The company offers ammonia products; nitrogen products, such as granular urea, urea ammonium nitrate solution, and ammonium nitrate; diesel exhaust fluid, urea liquor, and nitric acid products. It serves cooperatives, retailers, independent fertilizer distributors, traders, wholesalers, and industrial users. The company was founded in ? and is headquartered in Northbrook, Illinois.\", \"data_winners_8_GicIndustry\": \"Chemicals\", \"data_winners_8_OpenHeading\": \"Open\", \"data_winners_9_CountryName\": \"USA\", \"data_winners_9_Description\": \"Autodesk, Inc. provides ?D design, engineering, and entertainment technology solutions worldwide. It offers AutoCAD Civil ?D, a surveying, design, analysis, and documentation solution; Autodesk Build, a toolset for managing, sharing, and accessing project documents for streamlined workflows between the office, trailer, and jobsite; Revit, a software built for building information modeling to help professionals design, build, and maintain energy-efficient buildings; Autodesk BIM Collaborate Pro, cloud-based design collaboration and design management software; BuildingConnected, a SaaS preconstruction solution; and Tandem, a cloud-based platform that transforms the built asset lifecycle. The company also provides AutoCAD software, a customizable and extensible CAD application for professional design, drafting, detailing, and visualization; AutoCAD LT, a drafting and detailing software; Fusion, a ?D CAD, computer-aided manufacturing, and computer-aided engineering tool; Inventor, a software solution that offers a set of tools for ?D mechanical design, simulation, analysis, tooling, visualization, and documentation; product design and manufacturing collection tools; and Vault, a data management software for managing data in one central location, accelerate design processes, and streamline internal/external collaboration. It offers Flow Production Tracking, a cloud-based production management software; Maya software that offers ?D modeling, animation, effects, rendering, and compositing solutions for film and video artists, game developers, and design visualization professionals; Media and Entertainment Collection that offers end-to-end creative tools for entertainment creation; and ?ds Max software, which provides ?D modeling, animation, and rendering solutions. The company sells its products and services through a network of resellers and distributors. Autodesk, Inc. was incorporated in ? and is headquartered in San Francisco, California.\", \"data_winners_9_GicIndustry\": \"Software\", \"data_winners_9_OpenHeading\": \"Open\", \"dictionary_909_Natural gas\": \"XNGUSD\", \"dictionary_AUD/USD Futures\": \"AUD/USD Futures\", \"dictionary_EUR/USD Futures\": \"EUR/USD Futures\", \"dictionary_ExtremeMovement\": \"Extreme Movement\", \"dictionary_GBP/USD Futures\": \"GBP/USD Futures\", \"dictionary_NZD/USD Futures\": \"NZD/USD Futures\", \"dictionary_Soybean Futures\": \"Soybean Futures\", \"dictionary_USD/CAD Futures\": \"USD/CAD Futures\", \"dictionary_USD/CHF Futures\": \"USD/CHF Futures\", \"dictionary_USD/JPY Futures\": \"USD/JPY Futures\", \"data_losers_10_CloseHeading\": \"Close\", \"data_losers_10_CurrencyCode\": \"USD\", \"data_losers_10_CurrencyName\": \"US Dollar\", \"data_losers_10_HomeCategory\": \"Domestic\", \"data_losers_1_ChangeHeading\": \"Change\", \"data_losers_1_FiscalYearEnd\": \"December\", \"data_losers_1_PrimaryTicker\": \"AES.US\", \"data_losers_2_ChangeHeading\": \"Change\", \"data_losers_2_FiscalYearEnd\": \"December\", \"data_losers_2_PrimaryTicker\": \"UAL.US\", \"data_losers_3_ChangeHeading\": \"Change\", \"data_losers_3_FiscalYearEnd\": \"December\", \"data_losers_3_PrimaryTicker\": \"F.US\", \"data_losers_4_ChangeHeading\": \"Change\", \"data_losers_4_FiscalYearEnd\": \"December\", \"data_losers_4_PrimaryTicker\": \"NCLH.US\", \"data_losers_5_ChangeHeading\": \"Change\", \"data_losers_5_FiscalYearEnd\": \"December\", \"data_losers_5_PrimaryTicker\": \"LUV.US\", \"data_losers_6_ChangeHeading\": \"Change\", \"data_losers_6_FiscalYearEnd\": \"November\", \"data_losers_6_PrimaryTicker\": \"CCL.US\", \"data_losers_7_ChangeHeading\": \"Change\", \"data_losers_7_FiscalYearEnd\": \"June\", \"data_losers_7_PrimaryTicker\": \"EL.US\", \"data_losers_8_ChangeHeading\": \"Change\", \"data_losers_8_FiscalYearEnd\": \"December\", \"data_losers_8_PrimaryTicker\": \"MHK.US\", \"data_losers_9_ChangeHeading\": \"Change\", \"data_losers_9_FiscalYearEnd\": \"December\", \"data_losers_9_PrimaryTicker\": \"ELV.US\", \"data_winners_10_CountryName\": \"USA\", \"data_winners_10_Description\": \"Gartner, Inc. provides business and technology insights for decisions and performance on an organization\?s Economic Events\", \"dictionary_Zeromarkets ?_Gold\": \"XAUUSD\", \"data_losers_10_FullTimeEmployees\": ?, \"data_losers_1_nocolor_change_str\": \"(?.?%)\", \"data_losers_2_nocolor_change_str\": \"(?.?%)\", \"data_losers_3_nocolor_change_str\": \"(?.?%)\", \"data_losers_4_nocolor_change_str\": \"(?.?%)\", \"data_losers_5_nocolor_change_str\": \"(?.?%)\", \"data_losers_6_nocolor_change_str\": \"(?.?%)\", \"data_losers_7_nocolor_change_str\": \"(?.?%)\", \"data_losers_8_nocolor_change_str\": \"(?.?%)\", \"data_losers_9_nocolor_change_str\": \"(?.?%)\", \"data_winners_10_EmployerIdNumber\": \"?-3099750\", \"data_winners_1_FullTimeEmployees\": ?, \"data_winners_2_FullTimeEmployees\": ?, \"data_winners_3_FullTimeEmployees\": ?, \"data_winners_4_FullTimeEmployees\": ?, \"data_winners_5_FullTimeEmployees\": ?, \"data_winners_6_FullTimeEmployees\": ?, \"data_winners_7_FullTimeEmployees\": ?, \"data_winners_8_FullTimeEmployees\": ?, \"data_winners_9_FullTimeEmployees\": ?, \"dictionary_ExpectedMovementRange\": \"Expected movement range during event is between {from} and {to}\", \"dictionary_WTI Crude Oil Futures\": \"WTI Crude Oil Futures\", \"dictionary_highimpactevent_title\": \"{eventname}\?s Market Summary\", \"dictionary_Today\?s biggest movers\", \"dictionary_Zeromarkets ?_Silver\": \"XAGUSD\", \"dictionary_upcomingearnings_title\": \"Earnings announcements between {fromdate} and {todate}.\", \"params_creatomate_snapshots_value\": \"?.?,?.?\", \"data_winners_10_nocolor_change_str\": \"+?.?%\", \"dictionary_Brent Crude Oil Futures\": \"Brent Crude Oil Futures\", \"dictionary_The biggest losers are:\": \"The biggest losers are:\", \"dictionary_ThisWeeksEconomicEvents\": \"This Week\?{eventname}\? is being released in {countryname} in the next {hours_ahead} hours. The expected value for this release is {consensus}. {description}\", \"params_probability_of_posting_value\": ?, \"data_losers_10_InternationalDomestic\": \"Domestic\", \"data_losers_10_change_str.fill_color\": \"#?\", \"data_winners_1_InternationalDomestic\": \"Domestic\", \"data_winners_1_change_str.fill_color\": \"#?\", \"data_winners_2_InternationalDomestic\": \"Domestic\", \"data_winners_2_change_str.fill_color\": \"#?\", \"data_winners_3_InternationalDomestic\": \"Domestic\", \"data_winners_3_change_str.fill_color\": \"#?\", \"data_winners_4_InternationalDomestic\": null, \"data_winners_4_change_str.fill_color\": \"#?\", \"data_winners_5_InternationalDomestic\": \"Domestic\", \"data_winners_5_change_str.fill_color\": \"#?\", \"data_winners_6_InternationalDomestic\": \"Domestic\", \"data_winners_6_change_str.fill_color\": \"#?\", \"data_winners_7_InternationalDomestic\": null, \"data_winners_7_change_str.fill_color\": \"#?\", \"data_winners_8_InternationalDomestic\": \"Domestic\", \"data_winners_8_change_str.fill_color\": \"#?\", \"data_winners_9_InternationalDomestic\": \"Domestic\", \"data_winners_9_change_str.fill_color\": \"#?\", \"dictionary_909_WTI Crude Oil Futures\": \"WTI\", \"dictionary_E-mini NASDAQ? Futures\": \"E-mini NASDAQ? Futures\", \"dictionary_Nikkei ? Dollar Futures\": \"Nikkei ? Dollar Futures\", \"dictionary_ThisWeeksEarningsReleases\": \"This Week\? is being released in {countryname}. The expected value for this release is {consensus}.\", \"data_winners_10_InternationalDomestic\": \"Domestic\", \"data_winners_10_change_str.fill_color\": \"#?\", \"dictionary_Nohighimpacteconomicevents\": \"No high impact economic events\", \"dictionary_This week\?s biggest movers\", \"dictionary_Zeromarkets ?_Nasdaq ?\": \"US?\", \"dictionary_Zeromarkets ?_Nikkei ?\": \"JP?\", \"dictionary_volatilitywarning_longtext\": \"{eventname}\?s economic events:\", \"dictionary_Zeromarkets ?_Natural gas\": \"XNGUSD\", \"dictionary_volatilitywarning_shorttext\": \"{eventname}\?s biggest movers are:\": \"This week\?'s biggest movers\", \"has_results\": true, \"data_Heading\": \"Biggest Movers\", \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"output_format\": \"png\", \"snapshot_time\": ?, \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"The biggest winners are: - Intuit Inc: +?.?%\\n - Expedia Group Inc.: +?.?%\\n - LyondellBasell Industries NV: +?.?%\\n - Crowdstrike Holdings Inc: +?.?%\\n - Dow Inc: +?.?%\\n - ServiceNow Inc: +?.?%\\n - Palantir Technologies Inc.: +?.?%\\n - CF Industries Holdings Inc: +?.?%\\n - Autodesk Inc: +?.?%\\n - Gartner Inc: +?.?%\\n. The biggest losers are: - The AES Corporation: (?.?%)\\n - United Airlines Holdings Inc: (?.?%)\\n - Ford Motor Company: (?.?%)\\n - Norwegian Cruise Line Holdings Ltd: (?.?%)\\n - Southwest Airlines Company: (?.?%)\\n - Carnival Corporation: (?.?%)\\n - Estee Lauder Companies Inc: (?.?%)\\n - Mohawk Industries Inc: (?.?%)\\n - Elevance Health Inc: (?.?%)\\n - NRG Energy Inc.: (?.?%)\\n\", \"dictionary_Corn\": \"Corn\", \"dictionary_Gold\": \"Gold\", \"dictionary_Hour\": \"Hour\", \"dictionary_Open\": \"Open\", \"dictionary_Time\": \"Time\", \"text_short_text\": \"The biggest winners are: Intuit Inc: +?.?%, Expedia Group Inc.: +?.?%, LyondellBasell Industries NV: +?.?%, Crowdstrike Holdings Inc: +?.?%, Dow Inc: +?.?%, ServiceNow Inc: +?.?%, Palantir Technologies Inc.: +?.?%, CF Industries Holdings Inc: +?.?%, Autodesk Inc: +?.?%, Gartner Inc: +?.?%. The biggest losers are: The AES Corporation: (?.?%), United Airlines Holdings Inc: (?.?%), Ford Motor Company: (?.?%), Norwegian Cruise Line Holdings Ltd: (?.?%), Southwest Airlines Company: (?.?%), Carnival Corporation: (?.?%), Estee Lauder Companies Inc: (?.?%), Mohawk Industries Inc: (?.?%), Elevance Health Inc: (?.?%), NRG Energy Inc.: (?.?%)\", \"data_OpenHeading\": \"Open\", \"dictionary_Close\": \"Close\", \"dictionary_Daily\": \"Daily\", \"dictionary_Event\": \"Event\", \"dictionary_Hours\": \"Hours\", \"dictionary_Price\": \"Price\", \"dictionary_Wheat\": \"Wheat\", \"quantity_results\": ?, \"data_CloseHeading\": \"Close\", \"data_losers_1_CIK\": \"?\", \"data_losers_1_LEI\": \"?NUNNB?D?COUIRE?\", \"data_losers_2_CIK\": \"?\", \"data_losers_2_LEI\": \"?DA?B?DD?\", \"data_losers_3_CIK\": \"?\", \"data_losers_3_LEI\": \"?S?OYHG?MQM?VUIC?\", \"data_losers_4_CIK\": \"?\", \"data_losers_4_LEI\": null, \"data_losers_5_CIK\": \"?\", \"data_losers_5_LEI\": \"UDTZ?G?STFETI?HGH?\", \"data_losers_6_CIK\": \"?\", \"data_losers_6_LEI\": null, \"data_losers_7_CIK\": \"?\", \"data_losers_7_LEI\": \"?VFZ?XJ?NUPU?\", \"data_losers_8_CIK\": \"?\", \"data_losers_8_LEI\": \"?JI?MG?Q?\", \"data_losers_9_CIK\": \"?\", \"data_losers_9_LEI\": \"?MYN?XMYQH?CTMTH?\", \"dictionary_Actual\": \"Actual\", \"dictionary_Change\": \"Change\", \"dictionary_Coffee\": \"Coffee\", \"dictionary_Friday\": \"Friday\", \"dictionary_Monday\": \"Monday\", \"dictionary_Silver\": \"Silver\", \"dictionary_Sunday\": \"Sunday\", \"dictionary_Target\": \"Target\", \"dictionary_dd MMM\": \"dd MMM\", \"params_mode_value\": ?, \"params_uuid_value\": \"a7bf3e8e-eaf9-476c-aead-d432e4fa63e5\", \"data_ChangeHeading\": \"Change\", \"data_losers_10_CIK\": \"?\", \"data_losers_10_LEI\": \"?E?UPK?SW?M?XY?I?\", \"data_losers_1_Code\": \"AES\", \"data_losers_1_ISIN\": \"US?H?\", \"data_losers_1_Name\": \"The AES Corporation\", \"data_losers_1_Type\": \"Common Stock\", \"data_losers_1_code\": \"AES\", \"data_losers_1_open\": ?.?, \"data_losers_2_Code\": \"UAL\", \"data_losers_2_ISIN\": \"US?\", \"data_losers_2_Name\": \"United Airlines Holdings Inc\", \"data_losers_2_Type\": \"Common Stock\", \"data_losers_2_code\": \"UAL\", \"data_losers_2_open\": ?.?, \"data_losers_3_Code\": \"F\", \"data_losers_3_ISIN\": \"US?\", \"data_losers_3_Name\": \"Ford Motor Company\", \"data_losers_3_Type\": \"Common Stock\", \"data_losers_3_code\": \"F\", \"data_losers_3_open\": ?.?, \"data_losers_4_Code\": \"NCLH\", \"data_losers_4_ISIN\": \"USG?\", \"data_losers_4_Name\": \"Norwegian Cruise Line Holdings Ltd\", \"data_losers_4_Type\": \"Common Stock\", \"data_losers_4_code\": \"NCLH\", \"data_losers_4_open\": ?.?, \"data_losers_5_Code\": \"LUV\", \"data_losers_5_ISIN\": \"US?\", \"data_losers_5_Name\": \"Southwest Airlines Company\", \"data_losers_5_Type\": \"Common Stock\", \"data_losers_5_code\": \"LUV\", \"data_losers_5_open\": ?.?, \"data_losers_6_Code\": \"CCL\", \"data_losers_6_ISIN\": \"US?\", \"data_losers_6_Name\": \"Carnival Corporation\", \"data_losers_6_Type\": \"Common Stock\", \"data_losers_6_code\": \"CCL\", \"data_losers_6_open\": ?.?, \"data_losers_7_Code\": \"EL\", \"data_losers_7_ISIN\": \"US?\", \"data_losers_7_Name\": \"Estee Lauder Companies Inc\", \"data_losers_7_Type\": \"Common Stock\", \"data_losers_7_code\": \"EL\", \"data_losers_7_open\": ?.?, \"data_losers_8_Code\": \"MHK\", \"data_losers_8_ISIN\": \"US?\", \"data_losers_8_Name\": \"Mohawk Industries Inc\", \"data_losers_8_Type\": \"Common Stock\", \"data_losers_8_code\": \"MHK\", \"data_losers_8_open\": ?.?, \"data_losers_9_Code\": \"ELV\", \"data_losers_9_ISIN\": \"US?\", \"data_losers_9_Name\": \"Elevance Health Inc\", \"data_losers_9_Type\": \"Common Stock\", \"data_losers_9_code\": \"ELV\", \"data_losers_9_open\": ?.?, \"data_winners_1_CIK\": \"?\", \"data_winners_1_LEI\": \"VI?HBPH?XSFMB?E?M?\", \"data_winners_2_CIK\": \"?\", \"data_winners_2_LEI\": \"CI?MUJI?USF?V?NJ?H?\", \"data_winners_3_CIK\": \"?\", \"data_winners_3_LEI\": null, \"data_winners_4_CIK\": \"?\", \"data_winners_4_LEI\": \"?YBY?K?KM?HX?\", \"data_winners_5_CIK\": \"?\", \"data_winners_5_lei[...];

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1	0ms	0ms

9

0ms

56

Details

0ms

set datestyle = iso;

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Mar 06	14	56	0ms	0ms

10

0ms

1

Details

0ms

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

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1	0ms	0ms

11

0ms

56

Details

0ms

set client_encoding to ?;

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Mar 06	14	56	0ms	0ms

12

0ms

2

Details

0ms

select "public"."executions"."id" AS "id", "public"."executions"."processid" AS "processid", "public"."executions"."executiondate" AS "executiondate", "public"."executions"."errorcount" AS "errorcount", "public"."executions"."warningcount" AS "warningcount", "public"."executions"."isrunning" AS "isrunning", "public"."executions"."response" AS "response", "public"."executions"."live" AS "live", "public"."executions"."has_results" AS "has_results", "LT?"."id" AS "LA?" from "public"."executions" left outer join "public"."processes" "LT?" on "LT?"."id" = "public"."executions"."processid" where (processid = ?) order by "public"."executions"."id" desc limit ? offset ?;

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Mar 06	14	2	0ms	0ms

13

0ms

18

Details

0ms

select cast(count(*) / cast(setting as numeric) * ? as int) from pg_stat_activity, pg_settings where name = ? group by setting;

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Mar 06	14	18	0ms	0ms

14

0ms

2

Details

0ms

select count(*) from "public"."executions" left outer join "public"."processes" "LT?" on "LT?"."id" = "public"."executions"."processid" where (processid = ?);

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Mar 06	14	2	0ms	0ms

15

0ms

1

Details

0ms

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

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1	0ms	0ms

16

0ms

395

Details

0ms

commit;

Times Reported Time consuming queries #16

Day	Hour	Count	Duration	Avg duration
Mar 06	14	395	0ms	0ms

17

0ms

1

Details

0ms

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

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Mar 06	14	1	0ms	0ms

18

0ms

280

Details

0ms

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

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Mar 06	14	280	0ms	0ms

19

0ms

239

Details

0ms

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

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Mar 06	14	239	0ms	0ms

20

0ms

239

Details

0ms

select system_identifier from pg_control_system ();

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Mar 06	14	239	0ms	0ms

Time consuming prepare


Date: 2026-03-06 14:28:41
Duration: 54ms
Database: postgres
Date: 2026-03-06 14:17:36
Duration: 2ms
Database: postgres
Date: 2026-03-06 14:51:59
Duration: 13ms
Database: postgres
Date: 2026-03-06 14:30:05
Duration: 2ms
Database: postgres
Date: 2026-03-06 14:53:37
Duration: 10ms
Database: postgres
Date: 2026-03-06 14:41:50
Duration: 0ms
Database: postgres
Date: 2026-03-06 14:05:22
Duration: 0ms
Database: postgres
Date: 2026-03-06 14:45:51
Duration: 0ms
Database: postgres
Date: 2026-03-06 14:51:42
Duration: 7ms
Database: postgres
Date: 2026-03-06 14:04:00
Duration: 4ms
Database: postgres
Date: 2026-03-06 14:02:13
Duration: 2ms
Database: postgres
Date: 2026-03-06 14:13:56
Duration: 0ms
Database: postgres
Date: 2026-03-06 14:24:00
Duration: 1ms
Database: postgres
Date: 2026-03-06 14:20:04
Duration: 3ms
Database: postgres
Date: 2026-03-06 14:13:22
Duration: 0ms
Database: postgres
Date: 2026-03-06 14:00:21
Duration: 1ms
Database: postgres
Date: 2026-03-06 14:40:04
Duration: 3ms
Database: postgres
Date: 2026-03-06 14:13:21
Duration: 3ms
Database: postgres
Date: 2026-03-06 14:20:03
Duration: 3ms
Database: postgres
Date: 2026-03-06 14:24:00
Duration: 0ms
Database: postgres

Time consuming bind

Rank

Total duration

Times executed

Min duration

Max duration

Avg duration

Query

1

23s487ms

3,161

Details

0ms

46ms

7ms

WITH rar_max as ( ;

Times Reported Time consuming bind #1

Day	Hour	Count	Duration	Avg duration
Mar 06	14	3,161	23s487ms	7ms

WITH rar_max as ( ;

Date: 2026-03-06 14:38:05 Duration: 46ms Database: postgres parameters: $1 = 't', $2 = '689', $3 = '0', $4 = '0', $5 = '0', $6 = '', $7 = '0', $8 = '', $9 = '0', $10 = '', $11 = '0', $12 = '0', $13 = '0', $14 = '0', $15 = '0', $16 = 't', $17 = '0', $18 = '0'
WITH rar_max as ( ;

Date: 2026-03-06 14:37:45 Duration: 45ms Database: postgres parameters: $1 = 't', $2 = '689', $3 = '0', $4 = '0', $5 = '0', $6 = '', $7 = '0', $8 = '', $9 = '0', $10 = '', $11 = '0', $12 = '0', $13 = '0', $14 = '0', $15 = '0', $16 = 't', $17 = '0', $18 = '0'
WITH rar_max as ( ;

Date: 2026-03-06 14:43:34 Duration: 44ms Database: postgres parameters: $1 = '607790967606884301', $2 = '607790967606884301', $3 = '607790967606884301'

2

9s513ms

25,611

Details

0ms

11ms

0ms

SELECT ;

Times Reported Time consuming bind #2

Day	Hour	Count	Duration	Avg duration
	14	25,611	9s513ms	0ms

SELECT ;

Date: 2026-03-06 14:53:37 Duration: 11ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '515840243203999300'
SELECT ;

Date: 2026-03-06 14:44:42 Duration: 9ms Database: postgres parameters: $1 = '689', $2 = '60', $3 = '60', $4 = 'USDCHF', $5 = 'USDCHF'
SELECT ;

Date: 2026-03-06 14:45:04 Duration: 9ms Database: postgres parameters: $1 = '515840243239577300'

3

3s65ms

1,319

Details

1ms

4ms

2ms

SELECT symbolid, ;

Times Reported Time consuming bind #3

Day	Hour	Count	Duration	Avg duration
	14	1,319	3s65ms	2ms

SELECT symbolid, ;

Date: 2026-03-06 14:02:25 Duration: 4ms Database: postgres parameters: $1 = 'MILLENNIUMPF', $2 = '15', $3 = 'EURUSD', $4 = 'EURNZD.FX', $5 = 'EURUSD.FX'
SELECT symbolid, ;

Date: 2026-03-06 14:02:17 Duration: 4ms Database: postgres parameters: $1 = 'MILLENNIUMPF', $2 = '15', $3 = 'EURCAD.FX', $4 = 'EURAUD.ID', $5 = 'EURCAD'
SELECT symbolid, ;

Date: 2026-03-06 14:16:14 Duration: 3ms Database: postgres parameters: $1 = 'MILLENNIUMPF', $2 = '15', $3 = 'DOW30', $4 = 'EURAUD'

4

737ms

460

Details

1ms

2ms

1ms

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

Times Reported Time consuming bind #4

Day	Hour	Count	Duration	Avg duration
	14	460	737ms	1ms

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

Date: 2026-03-06 14:02:13 Duration: 2ms Database: postgres parameters: $1 = 'ATFX'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-03-06 14:02:29 Duration: 2ms Database: postgres parameters: $1 = 'MILLENNIUMPF'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-03-06 14:30:20 Duration: 2ms Database: postgres parameters: $1 = 'GLOBALGTMT5'

5

590ms

75

Details

4ms

14ms

7ms

WITH last_candle AS ( ;

Times Reported Time consuming bind #5

Day	Hour	Count	Duration	Avg duration
	14	75	590ms	7ms

WITH last_candle AS ( ;

Date: 2026-03-06 14:52:04 Duration: 14ms Database: postgres parameters: $1 = '621', $2 = '621'
WITH last_candle AS ( ;

Date: 2026-03-06 14:36:01 Duration: 14ms Database: postgres parameters: $1 = '621', $2 = '621'
WITH last_candle AS ( ;

Date: 2026-03-06 14:32:00 Duration: 13ms Database: postgres parameters: $1 = '558', $2 = '558'

6

509ms

45

Details

0ms

20ms

11ms

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

Times Reported Time consuming bind #6

Day	Hour	Count	Duration	Avg duration
	14	45	509ms	11ms

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

Date: 2026-03-06 14:13:02 Duration: 20ms Database: postgres parameters: $1 = '689', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Date: 2026-03-06 14:07:45 Duration: 20ms Database: postgres parameters: $1 = '689', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Date: 2026-03-06 14:34:36 Duration: 19ms Database: postgres parameters: $1 = '689', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'

7

470ms

19,358

Details

0ms

5ms

0ms

select 1;

Times Reported Time consuming bind #7

Day	Hour	Count	Duration	Avg duration
	14	19,358	470ms	0ms

select 1;

Date: 2026-03-06 14:07:41 Duration: 5ms Database: postgres
select 1;

Date: 2026-03-06 14:51:25 Duration: 5ms Database: postgres
select 1;

Date: 2026-03-06 14:05:31 Duration: 4ms Database: postgres

8

457ms

22

Details

0ms

58ms

20ms

with wh_patitioned as ( ;

Times Reported Time consuming bind #8

Day	Hour	Count	Duration	Avg duration
	14	22	457ms	20ms

with wh_patitioned as ( ;

Date: 2026-03-06 14:38:02 Duration: 58ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'
with wh_patitioned as ( ;

Date: 2026-03-06 14:37:19 Duration: 37ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'
with wh_patitioned as ( ;

Date: 2026-03-06 14:02:08 Duration: 36ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'

9

428ms

12

Details

28ms

44ms

35ms

with sym_info as ( ;

Times Reported Time consuming bind #9

Day	Hour	Count	Duration	Avg duration
	14	12	428ms	35ms

with sym_info as ( ;

Date: 2026-03-06 14:51:45 Duration: 44ms 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: 2026-03-06 14:51:50 Duration: 44ms 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: 2026-03-06 14:51:54 Duration: 44ms Database: postgres parameters: $1 = '692', $2 = 'Forex', $3 = 'Forex', $4 = '692', $5 = 'Forex', $6 = '692', $7 = '692', $8 = 'Forex', $9 = '692'

10

229ms

5,025

Details

0ms

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

Times Reported Time consuming bind #10

Day	Hour	Count	Duration	Avg duration
	14	5,025	229ms	0ms

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

Date: 2026-03-06 14:47:42 Duration: 0ms Database: postgres parameters: $1 = '2026-03-06 14:30:00', $2 = '1.57921', $3 = '1.579525', $4 = '1.578875', $5 = '1.57913', $6 = '1968', $7 = '515840230465070300', $8 = '0', $9 = '2026-03-06 14:47:41.999', $10 = '2026-03-06 14:47:41.904', $11 = '1.57921', $12 = '1.579525', $13 = '1.578875', $14 = '1.57913', $15 = '1968', $16 = '0', $17 = '2026-03-06 14:47:41.999', $18 = '2026-03-06 14:47:41.904'
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-03-06 14:47:58 Duration: 0ms Database: postgres parameters: $1 = '2026-03-06 14:30:00', $2 = '210.438', $3 = '210.558', $4 = '210.383', $5 = '210.534', $6 = '1827', $7 = '515840230494922300', $8 = '0', $9 = '2026-03-06 14:47:58.243', $10 = '2026-03-06 14:47:58.127', $11 = '210.438', $12 = '210.558', $13 = '210.383', $14 = '210.534', $15 = '1827', $16 = '0', $17 = '2026-03-06 14:47:58.243', $18 = '2026-03-06 14:47:58.127'
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-03-06 14:56:52 Duration: 0ms Database: postgres parameters: $1 = '2026-03-06 14:30:00', $2 = '6786.7', $3 = '6789.1', $4 = '6777.3', $5 = '6778.8', $6 = '2192', $7 = '515840248032019300', $8 = '0', $9 = '2026-03-06 14:56:52.122', $10 = '2026-03-06 14:56:52.056', $11 = '6786.7', $12 = '6789.1', $13 = '6777.3', $14 = '6778.8', $15 = '2192', $16 = '0', $17 = '2026-03-06 14:56:52.123', $18 = '2026-03-06 14:56:52.056'

11

217ms

2,898

Details

0ms

INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Times Reported Time consuming bind #11

Day	Hour	Count	Duration	Avg duration
	14	2,898	217ms	0ms

INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-03-06 14:32:00 Duration: 0ms Database: postgres parameters: $1 = '2026-03-06 15:00:00', $2 = '24.50105', $3 = '24.50885', $4 = '24.3489', $5 = '24.4806', $6 = '13680', $7 = '515840249404704300', $8 = '0', $9 = '2026-03-06 14:32:00.9', $10 = '2026-03-06 14:32:00.9', $11 = '24.50105', $12 = '24.50885', $13 = '24.3489', $14 = '24.4806', $15 = '13680', $16 = '0', $17 = '2026-03-06 14:32:00.9', $18 = '2026-03-06 14:32:00.9'
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-03-06 14:41:50 Duration: 0ms Database: postgres parameters: $1 = '2026-03-06 13:30:00', $2 = '25366.3', $3 = '25392.5', $4 = '25315.7', $5 = '25335.9', $6 = '9430', $7 = '515840247933633300', $8 = '0', $9 = '2026-03-06 14:41:50.205', $10 = '2026-03-06 14:41:50.098', $11 = '25366.3', $12 = '25392.5', $13 = '25315.7', $14 = '25335.9', $15 = '9430', $16 = '0', $17 = '2026-03-06 14:41:50.205', $18 = '2026-03-06 14:41:50.098'
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-03-06 14:32:39 Duration: 0ms Database: postgres parameters: $1 = '2026-03-06 14:00:00', $2 = '82.26', $3 = '82.72', $4 = '82.125', $5 = '82.33', $6 = '2820', $7 = '515840230623610300', $8 = '0', $9 = '2026-03-06 14:32:39.913', $10 = '2026-03-06 14:32:39.912', $11 = '82.26', $12 = '82.72', $13 = '82.125', $14 = '82.33', $15 = '2820', $16 = '0', $17 = '2026-03-06 14:32:39.913', $18 = '2026-03-06 14:32:39.912'

12

159ms

1,974

Details

0ms

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

Times Reported Time consuming bind #12

Day	Hour	Count	Duration	Avg duration
	14	1,974	159ms	0ms

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

Date: 2026-03-06 14:11:54 Duration: 0ms Database: postgres parameters: $1 = '2026-03-06 13:00:00', $2 = '47811.15', $3 = '47879.65', $4 = '47687.15', $5 = '47713.15', $6 = '13070', $7 = '515840248000890300', $8 = '0', $9 = '2026-03-06 14:11:54.436', $10 = '2026-03-06 14:11:54.317', $11 = '47811.15', $12 = '47879.65', $13 = '47687.15', $14 = '47713.15', $15 = '13070', $16 = '0', $17 = '2026-03-06 14:11:54.436', $18 = '2026-03-06 14:11:54.317'
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-03-06 14:02:19 Duration: 0ms Database: postgres parameters: $1 = '2026-03-06 13:00:00', $2 = '52.36', $3 = '52.38', $4 = '51.86', $5 = '51.94', $6 = '790', $7 = '515840246024652300', $8 = '0', $9 = '2026-03-06 14:02:19.632', $10 = '2026-03-06 14:02:19.632', $11 = '52.36', $12 = '52.38', $13 = '51.86', $14 = '51.94', $15 = '790', $16 = '0', $17 = '2026-03-06 14:02:19.632', $18 = '2026-03-06 14:02:19.632'
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;

Date: 2026-03-06 14:00:03 Duration: 0ms Database: postgres parameters: $1 = '2026-03-06 13:00:00', $2 = '110.68', $3 = '110.748', $4 = '110.429', $5 = '110.462', $6 = '9887', $7 = '515840230426101300', $8 = '0', $9 = '2026-03-06 14:00:03.731', $10 = '2026-03-06 14:00:03.73', $11 = '110.68', $12 = '110.748', $13 = '110.429', $14 = '110.462', $15 = '9887', $16 = '0', $17 = '2026-03-06 14:00:03.731', $18 = '2026-03-06 14:00:03.73'

13

95ms

231

Details

0ms

1ms

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 #13

Day	Hour	Count	Duration	Avg duration
	14	231	95ms	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: 2026-03-06 14:13:22 Duration: 1ms 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: 2026-03-06 14:13:23 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: 2026-03-06 14:13:22 Duration: 0ms Database: postgres

14

79ms

78

Details

0ms

2ms

1ms

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

Times Reported Time consuming bind #14

Day	Hour	Count	Duration	Avg duration
	14	78	79ms	1ms

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

Date: 2026-03-06 14:14:04 Duration: 2ms Database: postgres parameters: $1 = '689', $2 = 'XAUUSD', $3 = '689'
SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;

Date: 2026-03-06 14:05:31 Duration: 2ms Database: postgres parameters: $1 = '558', $2 = 'USDPLN', $3 = '558'
SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;

Date: 2026-03-06 14:32:14 Duration: 2ms Database: postgres parameters: $1 = '538', $2 = 'XAUUSDr', $3 = '538'

15

75ms

382

Details

0ms

4ms

0ms

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

Times Reported Time consuming bind #15

Day	Hour	Count	Duration	Avg duration
	14	382	75ms	0ms

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

Date: 2026-03-06 14:52:16 Duration: 4ms Database: postgres parameters: $1 = '607790787362620301'
/*server.CPResult*/ SELECT patternid, resy0, resy1, supporty0, supporty1, predictiontimeto, patternstarttime, s.symbolid, resx0, resx1, supportx0, supportx1, symbol, longname, shortname, timegranularity, patternendtime, pattern, a.direction, trendchange, patternlengthbars, patternquality, resultuid as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, exchange, breakout, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM autochartist_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern where resultuid = $1;

Date: 2026-03-06 14:20:53 Duration: 4ms Database: postgres parameters: $1 = '607790788818278301'
/*server.CPResult*/ SELECT patternid, resy0, resy1, supporty0, supporty1, predictiontimeto, patternstarttime, s.symbolid, resx0, resx1, supportx0, supportx1, symbol, longname, shortname, timegranularity, patternendtime, pattern, a.direction, trendchange, patternlengthbars, patternquality, resultuid as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, exchange, breakout, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM autochartist_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern where resultuid = $1;

Date: 2026-03-06 14:20:24 Duration: 4ms Database: postgres parameters: $1 = '607790791227810301'

16

58ms

11

Details

3ms

8ms

5ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Times Reported Time consuming bind #16

Day	Hour	Count	Duration	Avg duration
	14	11	58ms	5ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-03-06 14:59:36 Duration: 8ms Database: postgres parameters: $1 = '667', $2 = '667'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-03-06 14:00:00 Duration: 6ms Database: postgres parameters: $1 = '667', $2 = '667'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-03-06 14:06:00 Duration: 6ms Database: postgres parameters: $1 = '627', $2 = '627'

17

54ms

12

Details

1ms

10ms

4ms

WITH pre_symbols AS ( /* find relevant symbols */ ;

Times Reported Time consuming bind #17

Day	Hour	Count	Duration	Avg duration
	14	12	54ms	4ms

WITH pre_symbols AS ( /* find relevant symbols */ ;

Date: 2026-03-06 14:13:21 Duration: 10ms 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: 2026-03-06 14:13:21 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: 2026-03-06 14:00:42 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'

18

38ms

110

Details

0ms

4ms

0ms

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

Times Reported Time consuming bind #18

Day	Hour	Count	Duration	Avg duration
	14	110	38ms	0ms

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

Date: 2026-03-06 14:05:32 Duration: 4ms Database: postgres parameters: $1 = '607790317209147303'
/*server.KeyLevelResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, longname, shortname, Exchange AS e, timegranularity AS tg, a.PatternID AS pid, a.direction AS d, a.patternprice as pp, atbaridentified AS pet, CASE WHEN (x9 != '') THEN x9 WHEN (x8 != '') THEN x8 WHEN (x7 != '') THEN x7 WHEN (x6 != '') THEN x6 WHEN (x5 != '') THEN x5 WHEN (x4 != '') THEN x4 WHEN (x3 != '') THEN x3 WHEN (x2 != '') THEN x2 END AS pst, PatternPrice AS patp, x0, x1, x2, CASE WHEN (x3 != '') THEN x3 ELSE '1900-01-01' END as x3, CASE WHEN (x4 != '') THEN x4 ELSE '1900-01-01' END as x4, CASE WHEN (x5 != '') THEN x5 ELSE '1900-01-01' END as x5, CASE WHEN (x6 != '') THEN x6 ELSE '1900-01-01' END as x6, CASE WHEN (x7 != '') THEN x7 ELSE '1900-01-01' END as x7, CASE WHEN (x8 != '') THEN x8 ELSE '1900-01-01' END as x8, CASE WHEN (x9 != '') THEN x9 ELSE '1900-01-01' END as x9, errorMargin as erm, breakoutprice as pE, breakoutbars as be, breakout, atbaridentified as atBar, PatternLengthBars AS l, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz, approachingtimestamp AS apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb FROM keylevels_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid INNER JOIN hrspatterns p on a.patternid = p.patternid where resultuid = $1 and dtt.dayofweek = 3;

Date: 2026-03-06 14:35:29 Duration: 2ms Database: postgres parameters: $1 = '607790850124033303'
/*server.KeyLevelResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, longname, shortname, Exchange AS e, timegranularity AS tg, a.PatternID AS pid, a.direction AS d, a.patternprice as pp, atbaridentified AS pet, CASE WHEN (x9 != '') THEN x9 WHEN (x8 != '') THEN x8 WHEN (x7 != '') THEN x7 WHEN (x6 != '') THEN x6 WHEN (x5 != '') THEN x5 WHEN (x4 != '') THEN x4 WHEN (x3 != '') THEN x3 WHEN (x2 != '') THEN x2 END AS pst, PatternPrice AS patp, x0, x1, x2, CASE WHEN (x3 != '') THEN x3 ELSE '1900-01-01' END as x3, CASE WHEN (x4 != '') THEN x4 ELSE '1900-01-01' END as x4, CASE WHEN (x5 != '') THEN x5 ELSE '1900-01-01' END as x5, CASE WHEN (x6 != '') THEN x6 ELSE '1900-01-01' END as x6, CASE WHEN (x7 != '') THEN x7 ELSE '1900-01-01' END as x7, CASE WHEN (x8 != '') THEN x8 ELSE '1900-01-01' END as x8, CASE WHEN (x9 != '') THEN x9 ELSE '1900-01-01' END as x9, errorMargin as erm, breakoutprice as pE, breakoutbars as be, breakout, atbaridentified as atBar, PatternLengthBars AS l, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz, approachingtimestamp AS apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb FROM keylevels_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid INNER JOIN hrspatterns p on a.patternid = p.patternid where resultuid = $1 and dtt.dayofweek = 3;

Date: 2026-03-06 14:50:26 Duration: 2ms Database: postgres parameters: $1 = '607790849476774303'

19

36ms

48

Details

0ms

select feedname, to_char(latestrxtime, 'yyyy-mm-dd HH24:MI'), to_char(LatestDBWriteTime, 'yyyy-mm-dd HH24:MI'), to_char(LatestStartupTime, 'yyyy-mm-dd HH24:MI'), StartupTimeInMinutes, dm.source_type, dm.transport_type, case when latestrxtime < (CURRENT_TIMESTAMP - 5 * interval '1 minute') then 'X' else 'OK' end, case when (feedname ilike '%_EOD' OR feedname ilike 'IQFEED_DAILIES' or feedname ilike 'YAHOO%' or feedname ilike 'QUANDL_FUTURES%' or feedname ilike 'BAR_CHART') then case when LatestDBWriteTime < (CURRENT_TIMESTAMP - 24 * interval '1 hour') then 'X' else 'OK' end else case when (LatestDBWriteTime < (CURRENT_TIMESTAMP - 15 * interval '1 minute') and LatestStartupTime < (CURRENT_TIMESTAMP - 30 * interval '1 minute')) OR latestrxtime < CURRENT_TIMESTAMP - interval '2 hour' then 'X' else 'OK' end end as statusDB, comment from datafeeds_latestrun dlr left outer join datafeeds df on dlr.feedname ilike df.name inner join datafeeds_metadata dm on df.metadata_id = dm.id order by feedname;

Times Reported Time consuming bind #19

Day	Hour	Count	Duration	Avg duration
	14	48	36ms	0ms

select feedname, to_char(latestrxtime, 'yyyy-mm-dd HH24:MI'), to_char(LatestDBWriteTime, 'yyyy-mm-dd HH24:MI'), to_char(LatestStartupTime, 'yyyy-mm-dd HH24:MI'), StartupTimeInMinutes, dm.source_type, dm.transport_type, case when latestrxtime < (CURRENT_TIMESTAMP - 5 * interval '1 minute') then 'X' else 'OK' end, case when (feedname ilike '%_EOD' OR feedname ilike 'IQFEED_DAILIES' or feedname ilike 'YAHOO%' or feedname ilike 'QUANDL_FUTURES%' or feedname ilike 'BAR_CHART') then case when LatestDBWriteTime < (CURRENT_TIMESTAMP - 24 * interval '1 hour') then 'X' else 'OK' end else case when (LatestDBWriteTime < (CURRENT_TIMESTAMP - 15 * interval '1 minute') and LatestStartupTime < (CURRENT_TIMESTAMP - 30 * interval '1 minute')) OR latestrxtime < CURRENT_TIMESTAMP - interval '2 hour' then 'X' else 'OK' end end as statusDB, comment from datafeeds_latestrun dlr left outer join datafeeds df on dlr.feedname ilike df.name inner join datafeeds_metadata dm on df.metadata_id = dm.id order by feedname;

Date: 2026-03-06 14:24:00 Duration: 0ms Database: postgres
select feedname, to_char(latestrxtime, 'yyyy-mm-dd HH24:MI'), to_char(LatestDBWriteTime, 'yyyy-mm-dd HH24:MI'), to_char(LatestStartupTime, 'yyyy-mm-dd HH24:MI'), StartupTimeInMinutes, dm.source_type, dm.transport_type, case when latestrxtime < (CURRENT_TIMESTAMP - 5 * interval '1 minute') then 'X' else 'OK' end, case when (feedname ilike '%_EOD' OR feedname ilike 'IQFEED_DAILIES' or feedname ilike 'YAHOO%' or feedname ilike 'QUANDL_FUTURES%' or feedname ilike 'BAR_CHART') then case when LatestDBWriteTime < (CURRENT_TIMESTAMP - 24 * interval '1 hour') then 'X' else 'OK' end else case when (LatestDBWriteTime < (CURRENT_TIMESTAMP - 15 * interval '1 minute') and LatestStartupTime < (CURRENT_TIMESTAMP - 30 * interval '1 minute')) OR latestrxtime < CURRENT_TIMESTAMP - interval '2 hour' then 'X' else 'OK' end end as statusDB, comment from datafeeds_latestrun dlr left outer join datafeeds df on dlr.feedname ilike df.name inner join datafeeds_metadata dm on df.metadata_id = dm.id order by feedname;

Date: 2026-03-06 14:47:22 Duration: 0ms Database: postgres
select feedname, to_char(latestrxtime, 'yyyy-mm-dd HH24:MI'), to_char(LatestDBWriteTime, 'yyyy-mm-dd HH24:MI'), to_char(LatestStartupTime, 'yyyy-mm-dd HH24:MI'), StartupTimeInMinutes, dm.source_type, dm.transport_type, case when latestrxtime < (CURRENT_TIMESTAMP - 5 * interval '1 minute') then 'X' else 'OK' end, case when (feedname ilike '%_EOD' OR feedname ilike 'IQFEED_DAILIES' or feedname ilike 'YAHOO%' or feedname ilike 'QUANDL_FUTURES%' or feedname ilike 'BAR_CHART') then case when LatestDBWriteTime < (CURRENT_TIMESTAMP - 24 * interval '1 hour') then 'X' else 'OK' end else case when (LatestDBWriteTime < (CURRENT_TIMESTAMP - 15 * interval '1 minute') and LatestStartupTime < (CURRENT_TIMESTAMP - 30 * interval '1 minute')) OR latestrxtime < CURRENT_TIMESTAMP - interval '2 hour' then 'X' else 'OK' end end as statusDB, comment from datafeeds_latestrun dlr left outer join datafeeds df on dlr.feedname ilike df.name inner join datafeeds_metadata dm on df.metadata_id = dm.id order by feedname;

Date: 2026-03-06 14:17:16 Duration: 0ms Database: postgres

20

33ms

57

Details

0ms

3ms

0ms

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

Times Reported Time consuming bind #20

Day	Hour	Count	Duration	Avg duration
	14	57	33ms	0ms

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

Date: 2026-03-06 14:02:32 Duration: 3ms Database: postgres parameters: $1 = '607790554181386302'
/*server.FibonacciResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, Exchange AS e, longname as lo, shortname as sho, timegranularity AS tg, p.PatternID AS pid, Direction AS d, PatternStartTime AS pst, PatternEndTime AS pet, PatternStartPrice AS psp, PatternEndPrice AS pep, priceX as px, timeX as tx, priceA as pa, timeA as ta, priceB as pb, timeB as tb, priceC as pc, timeC as tc, priceD as pd, timeD as td, averagequality as aq, timequality as tq, 1 - errormargin as rq, 1 - noise as c, target10 as t10, target06 as t06, target16 as t16, target07 as t07, target12 as t12, target03 as t03, target05 as t05, PatternLengthBars AS l, temporarypattern as tp, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM Fibonacci_Results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid INNER JOIN fibonaccipatterns p on a.pattern = p.patternname where resultuid = $1 and dtt.dayofweek = 3;

Date: 2026-03-06 14:35:51 Duration: 2ms Database: postgres parameters: $1 = '607790673395821302'
/*server.FibonacciResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, Exchange AS e, longname as lo, shortname as sho, timegranularity AS tg, p.PatternID AS pid, Direction AS d, PatternStartTime AS pst, PatternEndTime AS pet, PatternStartPrice AS psp, PatternEndPrice AS pep, priceX as px, timeX as tx, priceA as pa, timeA as ta, priceB as pb, timeB as tb, priceC as pc, timeC as tc, priceD as pd, timeD as td, averagequality as aq, timequality as tq, 1 - errormargin as rq, 1 - noise as c, target10 as t10, target06 as t06, target16 as t16, target07 as t07, target12 as t12, target03 as t03, target05 as t05, PatternLengthBars AS l, temporarypattern as tp, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM Fibonacci_Results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid INNER JOIN fibonaccipatterns p on a.pattern = p.patternname where resultuid = $1 and dtt.dayofweek = 3;

Date: 2026-03-06 14:22:11 Duration: 2ms Database: postgres parameters: $1 = '607790675208695302'

Log levels

Key values

395,086 Log entries

Chart
Table

Type	Count	Percentage
CONTEXT	540	0.14%
DETAIL	85,888	21.74%
ERROR	397	0.10%
FATAL	1	0.00%
LOG	307,186	77.75%
STATEMENT	1,074	0.27%

Events distribution

Key values

0 PANIC entries
1 FATAL entries
397 ERROR entries
0 WARNING entries

Most Frequent Errors/Events

Key values

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

Rank

Times reported

Error

1

358

Details

ERROR: pg_stat_statements must be loaded via shared_preload_libraries

Times Reported Most Frequent Error / Event #1

Day	Hour	Count
Mar 06	14	358

ERROR: pg_stat_statements must be loaded via shared_preload_libraries

2

38

Details

ERROR: schema "..." does not exist

Times Reported Most Frequent Error / Event #2

Day	Hour	Count
Mar 06	14	38

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

3

1

Details

ERROR: view "..." does not exist

Times Reported Most Frequent Error / Event #3

Day	Hour	Count
Mar 06	14	1

ERROR: view "currencypips_view" does not exist

4

1

Details

FATAL: database "..." does not exist

Times Reported Most Frequent Error / Event #4

Day	Hour	Count
Mar 06	14	1

FATAL: database "acaweb_v" does not exist

Global information

Overview

Global Stats

SQL Traffic

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SELECT Traffic

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INSERT/UPDATE/DELETE Traffic

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Queries duration

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Prepared queries ratio

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General Activity

Connections

Established Connections

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Connections per database

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Connections per user

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Connections per host

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Sessions

Simultaneous sessions

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Histogram of session times

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Sessions per database

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Sessions per user

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Sessions per host

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Sessions per application

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Checkpoints / Restartpoints

Checkpoints Buffers

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Checkpoints Wal files

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Checkpoints distance

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Checkpoints Activity

Checkpoint causes

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Temporary Files

Size of temporary files

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Number of temporary files

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Temporary Files Activity

Queries generating the most temporary files (N)

Queries generating the largest temporary files

Vacuums

Vacuums / Analyzes Distribution

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Analyzes per table

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Vacuums per table

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Tuples removed per table

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Pages removed per table

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Pages removed per tables

Autovacuum Activity

Locks

Locks by types

Key values

Type of locks

Most frequent waiting queries (N)

Queries that waited the most

Queries

Queries by type

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Queries by database

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Queries by user

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