pgBadger :: PostgreSQL Log Analyzer

Global information

Generated on Fri Mar 20 07:59:53 2026
Log file: /home/postgres/pg_data/data/pg_log/postgresql-2026-03-20_090000.log
Parsed 2,172,357 log entries in 52s
Log start from 2026-03-20 09:00:00 to 2026-03-20 09:59:51

Global Stats

254 Number of unique normalized queries
219,243 Number of queries
1h52m26s Total query duration
2026-03-20 09:00:01 First query
2026-03-20 09:59:51 Last query
3,948 queries/s at 2026-03-20 09:01:21 Query peak

1h52m26s Total query duration
8s305ms Prepare/parse total duration
49s549ms Bind total duration
1h51m28s Execute total duration

239 Number of events
1 Number of unique normalized events
239 Max number of times the same event was reported
0 Number of cancellation

37 Total number of automatic vacuums
60 Total number of automatic analyzes

1,737 Number temporary file
622.83 MiB Max size of temporary file
81.51 MiB Average size of temporary file

2,897 Total number of sessions
33 sessions at 2026-03-20 09:53:48 Session peak
25d15h15m36s Total duration of sessions
12m44s Average duration of sessions
75 Average queries per session
2s328ms Average queries duration per session
12m42s Average idle time per session

2,899 Total number of connections
27 connections/s at 2026-03-20 09:48:48 Connection peak
3 Total number of databases

SQL Traffic

Key values

3,948 queries/s Query Peak
2026-03-20 09:01:21 Date

SELECT Traffic

Key values

1,959 queries/s Query Peak
2026-03-20 09:01:21 Date

INSERT/UPDATE/DELETE Traffic

Key values

139 queries/s Query Peak
2026-03-20 09:00:53 Date

Queries duration

Key values

1h52m26s 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 20	09	219,243	0ms	37s284ms	30ms	3m23s	3m47s	4m39s

Day	Hour	SELECT	COPY TO	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Mar 20	09	72,997	26	0ms	0ms	0ms	0ms

Day	Hour	INSERT	UPDATE	DELETE	COPY FROM	Average Duration	Latency Percentile(90)	Latency Percentile(95)	Latency Percentile(99)
Mar 20	09	19,780	2,208	16	96	0ms	0ms	0ms	0ms

Day	Hour	Prepare	Bind	Bind/Prepare	Percentage of prepare
Mar 20	09	20,281	86,220	4.25	19.25%

Day	Hour	Count	Average / Second
Mar 20	09	2,899	0.81/s

Day	Hour	Count	Average Duration	Average idle time
Mar 20	09	2,897	12m44s	12m42s

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Established Connections

Key values

27 connections Connection Peak
2026-03-20 09:48:48 Date

Connections per database

Key values

acaweb_fx Main Database
2,899 connections Total

Chart
Table

Database	User	Count
acaweb_fx		2,817
	postgres	2,463
	psql	354
socialmedia		82
	postgres	82

Connections per user

Key values

postgres Main User
2,899 connections Total

Chart
Table

User	Count
postgres	2,545
psql	354

Connections per host

Key values

192.168.4.142 Main host with 1196 connections
2,899 Total connections

Chart
Table

Host	Count
104.30.164.187	20
127.0.0.1	110
182.165.1.54	2
192.168.0.114	1
192.168.0.216	103
192.168.0.74	176
192.168.0.84	2
192.168.1.131	2
192.168.1.145	47
192.168.1.15	306
192.168.1.20	70
192.168.1.238	2
192.168.1.239	14
192.168.1.90	70
192.168.2.126	48
192.168.2.182	12
192.168.3.199	36
192.168.4.110	4
192.168.4.118	1
192.168.4.129	4
192.168.4.142	1,196
192.168.4.150	10
192.168.4.177	4
192.168.4.222	1
192.168.4.238	8
192.168.4.28	4
192.168.4.33	72
192.168.4.98	330
[local]	244

Simultaneous sessions

Key values

33 sessions Session Peak
2026-03-20 09:53:48 Date

Histogram of session times

Key values

2,382 0-500ms duration

Chart
Table

Range	Count	Percentage
0ms-500ms	2,382	82.22%
500ms-1s	114	3.94%
1s-30s	252	8.70%
30s-1m	8	0.28%
1m-10m	53	1.83%
10m-30m	40	1.38%
30m-1h	3	0.10%
1h-8h	16	0.55%
> 8h	29	1.00%

Sessions per database

Key values

acaweb_fx Main Database
2,897 sessions Total

Chart
Table

Database	User	Count	Total Duration	Average Duration
acaweb_fx	2,825	25d15h3m20s	13m3s
socialmedia	72	12m16s	10s228ms

Sessions per user

Key values

postgres Main User
2,897 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
2,897 sessions Total

Chart
Table

Host	Count	Total Duration	Average Duration
127.0.0.1	110	11s95ms	100ms
182.165.1.54	2	23h26m20s	11h43m10s
192.168.0.114	1	5m	5m
192.168.0.171	8	5d13h5m22s	16h38m10s
192.168.0.216	103	1m23s	812ms
192.168.0.74	176	2d21h38m7s	23m44s
192.168.0.84	2	23h59m7s	11h59m33s
192.168.1.131	2	23h59m5s	11h59m32s
192.168.1.145	47	2d1h55m43s	1h3m44s
192.168.1.15	306	2d4h43m29s	10m20s
192.168.1.154	9	5d12h27m10s	14h43m1s
192.168.1.20	71	2d13h8m7s	51m39s
192.168.1.238	2	23h59m10s	11h59m35s
192.168.1.239	14	70ms	5ms
192.168.1.90	70	40s239ms	574ms
192.168.2.126	48	16s583ms	345ms
192.168.2.182	12	1s418ms	118ms
192.168.3.199	36	1s428ms	39ms
192.168.4.110	4	38s170ms	9s542ms
192.168.4.118	1	214ms	214ms
192.168.4.129	4	35ms	8ms
192.168.4.142	1,196	7m41s	385ms
192.168.4.150	10	20h18m3s	2h1m48s
192.168.4.177	4	35ms	8ms
192.168.4.222	1	56s902ms	56s902ms
192.168.4.238	8	1m26s	10s857ms
192.168.4.28	4	32ms	8ms
192.168.4.33	72	12m16s	10s228ms
192.168.4.98	330	14s41ms	42ms
[local]	244	4m59s	1s228ms

Sessions per application

Key values

unknown Main Host
2,897 sessions Total

Chart
Table

Application	Count	Total Duration	Average Duration
unknown	2,897	25d15h15m36s	12m44s

Checkpoints Buffers

Key values

7,403 buffers Checkpoint Peak
2026-03-20 09:08:59 Date
209.628 seconds Highest write time
2.429 seconds Sync time

Checkpoints Wal files

Key values

5 files Wal files usage Peak
2026-03-20 09:08:59 Date

Checkpoints distance

Key values

151.33 Mo Distance Peak
2026-03-20 09:08:59 Date

Checkpoints Activity

Day	Hour	Written buffers	Write time	Sync time	Total time
Mar 20	09	29,009	1,893.677s	4.684s	1,913.27s

Day	Hour	Added	Removed	Recycled	Synced files	Longest sync	Average sync
Mar 20	09	0	0	19	1,906	2.412s	0.023s

Day	Hour	Count	Avg time (sec)
Mar 20	09	0	0s

Day	Hour	Mean distance	Mean estimate
Mar 20	09	26,170.00 kB	57,677.67 kB

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Checkpoint causes

Key values

time (12) Main checkpoint cause
12 checkpoints Total

Chart
Table

Cause	Number of checkpoints
time	12
Total	12

Size of temporary files

Key values

644.59 MiB Temp Files size Peak
2026-03-20 09:05:14 Date

Number of temporary files

Key values

31 per second Temp Files Peak
2026-03-20 09:47:09 Date

Temporary Files Activity

Temp Files

Day	Hour	Count	Total size	Average size
Mar 20	09	1,737	138.27 GiB	81.51 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	240	132.04 GiB	303.66 MiB	622.83 MiB	563.35 MiB	classname, case when latestdbwritetime < current_timestamp - interval ? then ? else ? end as is_stale from latest_t15_candle_view order by classname; classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; Date: 2026-03-20 09:00:00 Duration: 0ms
2	119	223.44 MiB	280.66 KiB	4.14 MiB	1.88 MiB	with rar_max as ( select resultuid from relevance_autochartist_results order by resultuid desc limit ? ), ar as ( select a., rr.age, rr.relevant from autochartist_results a left outer join relevance_autochartist_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_autochartist_results) end ), all_results as ( select ar.resultuid as resultuid, ar.direction as direction, ar.predictiontimeto as predictiontimeto, ar.predictionpricefrom as predictionpricefrom, ar.predictionpriceto as predictionpriceto, cp.pip as pip, 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, ar.pattern as pattern_name, ar.breakout as breakout, ar.patternendtime as identified, dtt.timezone as timezone, ar.patternlengthbars as length, g.basegroupname, newlevels.profit, newlevels.stop, newlevels.filtered, case when ar.age is not null then ar.age when ar.resultuid <= rm.resultuid then ? else ? end as age, case when ar.relevant is not null then ar.relevant when ar.resultuid <= rm.resultuid then ? else ? end as relevant from ar inner join symbols s on ar.symbolid = s.symbolid and s.nonliquid = ? inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = s.symbolid inner join symbolgroup sg on bsl.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 sg.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 currencypips cp on s.symbol = cp.symbol left join lateral calc_cp_signal (ar.resultuid) newlevels on true left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where ar.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (ar.simulation = ? or ar.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or ar.pattern in (...)) and (? = ? or (? = ? and ar.breakout >= ?) or (? = ? and ar.breakout < ?)) and (? = ? or ar.patternlengthbars <= ?) and newlevels.filtered = false and ar.patternstarttime >= 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 ; WITH rar_max as ( SELECT resultuid FROM relevance_autochartist_results ORDER BY resultuid DESC LIMIT 1 ), ar AS ( SELECT a., rr.age, rr.relevant from autochartist_results a LEFT OUTER JOIN relevance_autochartist_results rr on a.resultuid = rr.resultuid WHERE CASE WHEN FALSE = $1 THEN true ELSE a.resultuid > ( select min(resultuid) from relevance_autochartist_results) END ), all_results AS ( SELECT ar.resultuid AS resultuid, ar.direction AS direction, ar.predictiontimeto AS predictiontimeto, ar.predictionpricefrom AS predictionpricefrom, ar.predictionpriceto AS predictionpriceto, cp.pip AS pip, 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, ar.pattern AS pattern_name, ar.breakout AS breakout, ar.patternendtime AS identified, dtt.timezone AS timezone, ar.patternlengthbars AS length, g.basegroupname, newLevels.profit, newLevels.stop, newLevels.filtered, CASE WHEN ar.age IS NOT NULL THEN ar.age WHEN ar.resultuid <= rm.resultuid THEN 11 ELSE 0 END as age, CASE WHEN ar.relevant IS NOT NULL THEN ar.relevant WHEN ar.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant FROM ar INNER JOIN symbols s ON ar.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN brokersymbollist bsl ON bsl.brokerid = $2 AND bsl.symbolid = s.symbolid INNER JOIN symbolgroup sg on bsl.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 sg.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 OUTER JOIN autochartist_symbolupdates au on dss.symbolid = au.symbolid LEFT OUTER JOIN currencypips cp ON s.symbol = cp.symbol LEFT JOIN LATERAL calc_cp_signal (ar.resultuid) newLevels on true LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' WHERE ar.gmttimefound > now() - INTERVAL '7 DAYS' AND dss.enabled = 1 AND s.deleted = 0 AND (ar.simulation = 0 OR ar.simulation IS NULL) AND ($3 = 0 OR s.timegranularity in ($4)) AND ($5 = 0 OR s.exchange in ($6)) AND ($7 = 0 OR coalesce(bim.code, s.symbol) in ($8)) AND ($9 = 0 OR ar.pattern in ($10)) AND ($11 = 0 OR ($12 = 1 AND ar.breakout >= 0) OR ($13 = 2 AND ar.breakout < 0)) AND ($14 = 0 OR ar.patternlengthbars <= $15) and newLevels.filtered = false AND ar.patternstarttime >= coalesce(au.earliestpricedatetime, '1900-01-01'::timestamp without time zone) -- To make sure patternstarttime is in our t-tables ), results AS ( SELECT DISTINCT ON (symbolid) FROM all_results WHERE (FALSE = $16 OR relevant = 1) AND ($17 = 0 OR age <= $18) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-03-20 09:00:02 Duration: 0ms
3	55	250.46 MiB	4.35 MiB	4.90 MiB	4.55 MiB	select resultuid from relevance_fibonacci_results order by resultuid desc limit ?), fr as ( select a., rr.age, rr.relevant from fibonacci_results a left outer join relevance_fibonacci_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) end), all_results as ( select fr.resultuid as resultuid, fr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, fr.pattern as pattern_name, fr.timed as timed, fr.patternendtime as identified, dtt.timezone as timezone, fr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when fr.age is not null then fr.age when fr.resultuid <= rm.resultuid then ? else ? end as age, case when fr.relevant is not null then fr.relevant when fr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from fr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = fr.symbolid inner join symbols s on fr.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on fr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join downloadersymbolsettings dss on fr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left join lateral calc_fib_signal_filter (fr.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where fr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (fr.simulation = ? or fr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or fr.pattern in (...)) and (? = ? or fr.patternlengthbars <= ?) and (? = ? or (? = ? and fr.timed > cast(? as timestamp)) or (? = ? and fr.timed < cast(? as timestamp)))), results as ( select distinct on (symbolid) from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc; SELECT resultuid FROM relevance_fibonacci_results ORDER BY resultuid DESC LIMIT 1), fr AS ( SELECT a., rr.age, rr.relevant from fibonacci_results a LEFT OUTER JOIN relevance_fibonacci_results rr on a.resultuid = rr.resultuid WHERE CASE WHEN FALSE = $1 THEN true ELSE a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) END), all_results AS ( SELECT fr.resultuid AS resultuid, fr.direction AS direction, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, fr.pattern AS pattern_name, fr.timed AS timed, fr.patternendtime AS identified, dtt.timezone AS timezone, fr.patternlengthbars AS length, g.basegroupname, newLevels.filtered, CASE WHEN fr.age IS NOT NULL THEN fr.age WHEN fr.resultuid <= rm.resultuid THEN 11 ELSE 0 END as age, CASE WHEN fr.relevant IS NOT NULL THEN fr.relevant WHEN fr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip FROM fr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $2 AND bsl.symbolid = fr.symbolid INNER JOIN symbols s ON fr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN symbolgroup sg on fr.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid INNER JOIN downloadersymbolsettings dss ON fr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN rar_max rm ON 1 = 1 LEFT JOIN LATERAL calc_fib_signal_filter (fr.resultuid) newLevels on true LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' WHERE fr.gmttimefound > now() - INTERVAL '7 DAYS' AND dss.enabled = 1 AND s.deleted = 0 AND (fr.simulation = 0 OR fr.simulation IS NULL) AND ($3 = 0 OR s.timegranularity in ($4)) AND ($5 = 0 OR s.exchange in ($6)) AND ($7 = 0 OR coalesce(bim.code, s.symbol) in ($8)) AND ($9 = 0 OR fr.pattern in ($10)) AND ($11 = 0 OR fr.patternlengthbars <= $12) AND ($13 = 0 OR ($14 = 1 AND fr.timed > cast('1970-01-01' as timestamp)) OR ($15 = 2 AND fr.timed < cast('1970-01-01' as timestamp)))), results AS ( SELECT DISTINCT ON (symbolid) FROM all_results WHERE (FALSE = $16 OR relevant = 1) AND ($17 = 0 OR age <= $18) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC; Date: 2026-03-20 09:00:12 Duration: 0ms
4	29	1.67 GiB	9.84 MiB	187.99 MiB	58.83 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: 2026-03-20 09:00:06 Duration: 0ms
5	18	63.39 MiB	3.41 MiB	3.75 MiB	3.52 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: 2026-03-20 09:01:13 Duration: 0ms
6	18	28.26 MiB	137.65 KiB	4.11 MiB	1.57 MiB	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 ? ; WITH rar_max as ( SELECT resultuid FROM relevance_keylevels_results ORDER BY resultuid DESC LIMIT 1 ), 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 = $1 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 11 ELSE 0 END as age, CASE WHEN kr.relevant IS NOT NULL THEN kr.relevant WHEN kr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip FROM kr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $2 AND bsl.symbolid = kr.symbolid INNER JOIN symbols s ON bsl.symbolid = s.symbolid AND s.nonliquid = 0 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 = 3 INNER JOIN rar_max rm ON 1 = 1 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 = 'OUTBOUND' WHERE kr.gmttimefound > now() - INTERVAL '7 DAYS' AND dss.enabled = 1 AND s.deleted = 0 AND (kr.simulation = 0 OR kr.simulation IS NULL) AND ($3 = 0 OR s.timegranularity in ($4)) AND ($5 = 0 OR s.exchange in ($6)) AND ($7 = 0 OR coalesce(bim.code, s.symbol) in ($8)) AND ($9 = 0 OR p.patternname in ($10)) AND ($11 = 0 OR kr.patternclassid in ($12)) AND ($13 = 0 OR kr.patternlengthbars <= $14) AND kr.patternstarttime::timestamp without time zone >= coalesce(au.earliestpricedatetime, '1900-01-01'::timestamp without time zone) -- To make sure patternstarttime is in our t-tables ), results AS ( SELECT DISTINCT ON (symbolid) FROM all_results WHERE (FALSE = $15 OR relevant = 1) AND ($16 = 0 OR age <= $17) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC LIMIT 200; Date: 2026-03-20 09:00:28 Duration: 0ms
7	16	623.75 MiB	38.98 MiB	38.98 MiB	38.98 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: 2026-03-20 09:01:14 Duration: 0ms
8	16	1.23 GiB	78.63 MiB	78.64 MiB	78.64 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: 2026-03-20 09:01:17 Duration: 0ms
9	8	1.11 GiB	141.93 MiB	141.98 MiB	141.95 MiB	select updateresultsmaterializedview (); select updateresultsmaterializedview (); Date: 2026-03-20 09:02:15 Duration: 0ms
10	4	315.41 MiB	78.82 MiB	78.90 MiB	78.85 MiB	select updateageforrelevantresults (); select updateageforrelevantresults (); Date: 2026-03-20 09:02:05 Duration: 0ms
11	3	24.96 MiB	8.32 MiB	8.33 MiB	8.32 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: 2026-03-20 09:00:54 Duration: 0ms

Queries generating the largest temporary files

Rank	Size	Query
1	622.83 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:34:13 ]
2	622.83 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:34:44 ]
3	622.83 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:34:58 ]
4	622.83 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:43:29 ]
5	622.83 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:44:14 ]
6	622.83 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:45:29 ]
7	622.83 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:48:14 ]
8	622.83 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:48:44 ]
9	622.83 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:57:14 ]
10	622.83 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:58:59 ]
11	622.83 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:59:29 ]
12	620.73 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:27:29 ]
13	620.73 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:42:14 ]
14	620.73 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:42:29 ]
15	617.73 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:10:16 ]
16	617.73 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:36:44 ]
17	617.73 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:50:44 ]
18	617.73 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:54:14 ]
19	617.73 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:58:00 ]
20	617.73 MiB	classname, CASE WHEN latestdbwritetime < current_timestamp - interval '18 hours' THEN 1 ELSE 0 END AS is_stale FROM latest_t15_candle_view order by classname; [ Date: 2026-03-20 09:58:15 ]

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)
60 analyzes Total

Chart
Table

Table	Number of analyzes
acaweb_fx.public.solr_relevance_old	16
acaweb_fx.pg_catalog.pg_attribute	8
acaweb_fx.pg_catalog.pg_class	6
acaweb_fx.public.relevance_keylevels_results	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
Total	60

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	10,894	0	55	0	0	8,139	879	4,666,094
acaweb_fx.pg_catalog.pg_type	3	3	467	0	119	0	0	232	83	353,692
acaweb_fx.pg_catalog.pg_attribute	3	3	2,610	0	322	0	201	1,021	332	1,848,823
acaweb_fx.pg_toast.pg_toast_2619	2	2	266	0	78	0	0	233	69	283,739
acaweb_fx.public.datafeeds_latestrun	2	0	240	0	6	0	0	26	6	27,884
acaweb_fx.public.relevance_keylevels_results	2	2	7,422	0	312	3	237	1,520	248	836,420
acaweb_fx.pg_catalog.pg_class	2	2	910	0	86	0	0	261	84	457,019
acaweb_fx.public.relevance_autochartist_results	2	2	6,353	0	272	3	524	1,033	168	556,658
acaweb_fx.public.relevance_fibonacci_results	2	2	2,372	0	56	2	112	349	33	169,060
acaweb_fx.public.autochartist_symbolupdates	1	1	20,783	0	4,127	1	38,079	5,929	4,675	2,040,226
acaweb_fx.pg_catalog.pg_statistic	1	1	1,036	0	126	0	582	463	118	511,521
acaweb_fx.public.latest_t15_candle_view	1	1	66	0	1	0	0	6	1	9,073
Total	37	35	53,419	48,909	5,560	9	39,735	19,212	6,696	11,760,209

Tuples removed per table

Key values

public.solr_relevance_old (66621) Main table with removed tuples on database acaweb_fx
83332 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	66,621	75,807	0	0	2,575
acaweb_fx.public.autochartist_symbolupdates	1	1	6,468	45,470	117	0	40,691
acaweb_fx.pg_catalog.pg_attribute	3	3	4,535	31,958	0	30	781
acaweb_fx.public.relevance_keylevels_results	2	2	1,948	22,487	0	0	558
acaweb_fx.public.relevance_autochartist_results	2	2	1,151	14,612	0	0	760
acaweb_fx.pg_catalog.pg_type	3	3	1,002	4,365	0	0	135
acaweb_fx.pg_catalog.pg_statistic	1	1	612	3,728	0	0	1,194
acaweb_fx.public.relevance_fibonacci_results	2	2	378	2,743	0	0	204
acaweb_fx.pg_catalog.pg_class	2	2	270	3,312	0	0	300
acaweb_fx.pg_toast.pg_toast_2619	2	2	158	342	10	0	104
acaweb_fx.public.datafeeds_latestrun	2	0	127	28	0	0	32
acaweb_fx.public.latest_t15_candle_view	1	1	62	12	0	0	1
Total	37	35	83,332	204,864	127	30	47,335

Pages removed per table

Key values

pg_catalog.pg_attribute (30) Main table with removed pages on database acaweb_fx
30 pages Total removed

Chart
Table

Table	Number of vacuums	Index scans	Tuples removed	Pages removed
acaweb_fx.pg_catalog.pg_attribute	3	3	4535	30
acaweb_fx.pg_toast.pg_toast_2619	2	2	158	0
acaweb_fx.pg_catalog.pg_type	3	3	1002	0
acaweb_fx.public.datafeeds_latestrun	2	0	127	0
acaweb_fx.public.autochartist_symbolupdates	1	1	6468	0
acaweb_fx.pg_catalog.pg_statistic	1	1	612	0
acaweb_fx.public.latest_t15_candle_view	1	1	62	0
acaweb_fx.public.relevance_keylevels_results	2	2	1948	0
acaweb_fx.pg_catalog.pg_class	2	2	270	0
acaweb_fx.public.solr_relevance_old	16	16	66621	0
acaweb_fx.public.relevance_autochartist_results	2	2	1151	0
acaweb_fx.public.relevance_fibonacci_results	2	2	378	0
Total	37	35	83,332	30

Autovacuum Activity

Vacuums / Analyzes

Day	Hour	VACUUMs	ANALYZEs
Mar 20	09	37	60

↑ 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

72,997 Total read queries
32,363 Total write queries

Chart
Table

Type	Count	Percentage
SELECT	72,997	33.30%
INSERT	19,780	9.02%
UPDATE	2,208	1.01%
DELETE	16	0.01%
COPY FROM	96	0.04%
COPY TO	26	0.01%
CTE	9,559	4.36%
DDL	16	0.01%
TCL	662	0.30%
CURSOR	0	0.00%
OTHERS	113,883	51.94%

Queries by database

Key values

unknown Main database
218,321 Requests
1h51m28s (unknown)
Main time consuming database

Chart
Table

Database	Request type	Count	Duration
acaweb_fx	Total	850	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	169	0ms
	select	72	0ms
	tcl	331	0ms
	update	36	0ms
socialmedia	Total	72	0ms
	select	72	0ms
unknown	Total	218,321	1h51m28s
	copy from	16	0ms
	cte	9,455	0ms
	insert	19,780	0ms
	others	4,212	0ms
	select	72,853	0ms
	tcl	331	0ms
	update	2,172	0ms

Queries by user

Key values

unknown Main user
218,321 Requests

Queries Chart
Table

User	Request type	Count	Duration
postgres	Total	922	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	169	0ms
	select	144	0ms
	tcl	331	0ms
	update	36	0ms
unknown	Total	218,321	1h51m28s
	copy from	16	0ms
	cte	9,455	0ms
	insert	19,780	0ms
	others	4,212	0ms
	select	72,853	0ms
	tcl	331	0ms
	update	2,172	0ms

Duration by user

Key values

1h51m28s (unknown) Main time consuming user

Duration Chart
Table

User	Request type	Count	Duration
postgres	Total	922	0ms
	copy from	80	0ms
	copy to	26	0ms
	cte	104	0ms
	ddl	16	0ms
	delete	16	0ms
	others	169	0ms
	select	144	0ms
	tcl	331	0ms
	update	36	0ms
unknown	Total	218,321	1h51m28s
	copy from	16	0ms
	cte	9,455	0ms
	insert	19,780	0ms
	others	4,212	0ms
	select	72,853	0ms
	tcl	331	0ms
	update	2,172	0ms

Queries by host

Key values

unknown Main host
219,243 Requests
1h51m28s (unknown)
Main time consuming host

Chart
Table

Host	Request type	Count	Duration
unknown	Total	219,243	1h51m28s
	copy from	96	0ms
	copy to	26	0ms
	cte	9,559	0ms
	ddl	16	0ms
	delete	16	0ms
	insert	19,780	0ms
	others	4,381	0ms
	select	72,997	0ms
	tcl	662	0ms
	update	2,208	0ms

Queries by application

Key values

unknown Main application
218,889 Requests
1h51m28s (unknown)
Main time consuming application

Chart
Table

Application	Request type	Count	Duration
psql	Total	354	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	36	0ms
unknown	Total	218,889	1h51m28s
	copy from	16	0ms
	cte	9,455	0ms
	insert	19,780	0ms
	others	4,377	0ms
	select	72,925	0ms
	tcl	662	0ms
	update	2,172	0ms

Number of cancelled queries

Key values

1 per second Cancelled query Peak
2026-03-20 09:02:59 Date

Number of cancelled queries (5 minutes period)

NO DATASET

Histogram of query times

Key values

73,929 0-1ms duration

Chart
Table

Range	Count	Percentage
0-1ms	73,929	67.51%
1-5ms	17,996	16.43%
5-10ms	2,019	1.84%
10-25ms	1,139	1.04%
25-50ms	8,223	7.51%
50-100ms	3,342	3.05%
100-500ms	1,405	1.28%
500-1000ms	588	0.54%
1000-10000ms	699	0.64%
> 10000ms	162	0.15%

Slowest individual queries

Rank	Duration	Query
NO DATASET

Time consuming queries

Rank

Total duration

Times executed

Min duration

Max duration

Avg duration

Query

1

0ms

19

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 20	09	19	0ms	0ms

2

0ms

166

Details

0ms

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

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Mar 20	09	166	0ms	0ms

3

0ms

1

Details

0ms

update executions set isrunning = false, has_results = true, response = ? 's immune system against cancer and infections in the United States and internationally. It offers Retrocyte Display, an antibody expression platform for the identification of fully human and humanized monoclonal antibodies; and display technologies. It develops QS ? Stimulon adjuvant, a saponin - based vaccine adjuvant. The company also develops Balstilimab, a programmed death receptor - 1 (PD ?) blocking antibody; AGEN ?, a human Fc - enhanced cytotoxic T - lymphocyte antigen ? (CTLA ?) blocking antibody that is in Phase ? trials in metastatic colorectal cancer (mCRC), pancreatic cancer, and melanoma; AGEN ?, a CD ? monospecific antibody that is in Phase ? b clinical trial; AGEN ?, a CD ? / TGF u00df TRAP antibody; AGEN ?, an ILT ? monospecific antibody that is in clinical development; and BMS ?, a TIGIT bispecific antibodies. In addition, it develops INCAGN ?, a GITR agonist; INCAGN ?, a TIM ? monoclonal antibodies; INCAGN ?, a LAG ? monospecific antibody; MK ?, a monospecific antibody targeting ILT ? that is in Phase ? clinical trial; UGN ?, a zalifrelimab intravesical solution for the treatment of cancers of the urinary tract t; and AGEN ?.The company operates under the Agenus, MiNK, Prophage, Retrocyte Display, and STIMULON trademarks. It has collaborations with Bristol - Myers Squibb Company, Betta Pharmaceuticals Co., Ltd., UroGen Pharma Ltd., Gilead Sciences, Inc., Incyte Corporation, and Merck Sharp & Dohme. The company was formerly known as Antigenics Inc. and changed its name to Agenus Inc. in January ?.Agenus Inc. was founded in ? and is headquartered in Lexington, Massachusetts. ", " Address ": " ? Forbes Road, Lexington, MA, United States, ? - 7305 ", " Phone ": " ? ? ? ", " WebURL ": " https: // www.agenusbio.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / AGEN.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 03 - 19 ", " ticker ": " AGEN.US ", " code ": " AGEN ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " }, " ? ": { " Code ": " NSA ", " Type ": " Common Stock ", " Name ": " National Storage Affiliates Trust ", " Exchange ": " NYSE ", " CurrencyCode ": " USD ", " CurrencyName ": " US Dollar ", " CurrencySymbol ": " $ ", " CountryName ": " USA ", " CountryISO ": " US ", " OpenFigi ": " BBG ? VN ? ", " ISIN ": " US ? ", " LEI ": null, " PrimaryTicker ": " NSA.US ", " CUSIP ": " ? ", " CIK ": " ? ", " EmployerIdNumber ": " ? - 5053858 ", " FiscalYearEnd ": " December ", " IPODate ": " ? - 04 - 23 ", " InternationalDomestic ": " Domestic ", " Sector ": " Real Estate ", " Industry ": " REIT - Industrial ", " GicSector ": " Real Estate ", " GicGroup ": " Equity Real Estate Investment Trusts ( REITs) ", " GicIndustry ": " Specialized REITs ", " GicSubIndustry ": " Self - Storage REITs ", " HomeCategory ": " Domestic Primary ", " IsDelisted ": false, " Description ": " National Storage Affiliates Trust is a real estate investment trust headquartered in Greenwood Village, Colorado, focused on the ownership, operation and acquisition of self - storage properties predominantly located within the top ? metropolitan statistical areas throughout the United States. As of September ?, ?, the Company held ownership interests in and operated ?, ? self - storage properties, located in ? states and Puerto Rico with approximately ?.? million rentable square feet. NSA is one of the largest owners and operators of self - storage properties among public and private companies in the United States. ", " Address ": " ? East Prentice Avenue, Greenwood Village, CO, United States, ? ", " Phone ": " ? ? ? ", " WebURL ": " https: // www.nationalstorageaffiliates.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / NSA.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 03 - 19 ", " ticker ": " NSA.US ", " code ": " NSA ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " }, " ? ": { " Code ": " VET ", " Type ": " Common Stock ", " Name ": " Vermilion Energy Inc. ", " Exchange ": " NYSE ", " CurrencyCode ": " USD ", " CurrencyName ": " US Dollar ", " CurrencySymbol ": " $ ", " CountryName ": " USA ", " CountryISO ": " US ", " OpenFigi ": " BBG ? PL ? C ? ", " ISIN ": " CA ? ", " LEI ": null, " PrimaryTicker ": " VET.TO ", " CUSIP ": " ? ", " CIK ": " ? ", " EmployerIdNumber ": " % % OBJNAME ? % % FiscalYearEnd ": " December ", " IPODate ": " ? - 09 - 14 ", " InternationalDomestic ": " Domestic ", " Sector ": " Energy ", " Industry ": " Oil & Gas E & P ", " GicSector ": " Energy ", " GicGroup ": " Energy ", " GicIndustry ": " Oil, Gas & Consumable Fuels ", " GicSubIndustry ": " Oil & Gas Exploration & Production ", " HomeCategory ": " Canadian ", " IsDelisted ": false, " Description ": " Vermilion Energy Inc., engages in petroleum and natural gas, focuses on the acquisition, exploration, development, and optimization of producing properties in North America, Europe, and Australia. Its properties are located in the West Pembina region of West Central Alberta, Canada; southwest Bordeaux and Paris Basin in France; the Netherlands; Germany; Ireland; Croatia; Slovakia; Hungary; and Australia. The company was founded in ? and is headquartered in Calgary, Canada. ", " Address ": " ? - ? rd Avenue S.W., Calgary, AB, Canada, T ? P ? R ? ", " Phone ": " ? ? ? ", " WebURL ": " https: // www.vermilionenergy.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / VET.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 03 - 19 ", " ticker ": " VET.US ", " code ": " VET ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " } }, " Heading ": " Mayores Movimientos ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " Date ": " ? mar ? " }, " text ": { " title ": " Los mayores movimientos de esta semana ", " short_text ": " Los mayores ganadores son: Tillys Inc: + ?.? %, Aemetis Inc: + ?.? %, Adecoagro SA: + ?.? %, Tower Semiconductor Ltd: + ?.? %, Kosmos Energy Ltd: + ?.? %, SolarEdge Technologies Inc: + ?.? %, Microvision Inc: + ?.? %, Agenus Inc: + ?.? %, National Storage Affiliates Trust: + ?.? %, Vermilion Energy Inc. : + ?.? %.Los mayores perdedores son: Aldeyra The: (?.? %), Akari Therapeutics PLC: (?.? %), American Vanguard Corporation: (?.? %), Tencent Music Entertainment Group: (?.? %), Canadian Solar Inc: (?.? %), Atara Biotherapeutics Inc: (?.? %), Curis Inc: (?.? %), Niu Technologies: (?.? %), GSI Technology Inc: (?.? %), Voyager Therapeutics Inc: (?.? %) ", " long_text ": " Los mayores ganadores son: - Tillys Inc: + ?.? % n - Aemetis Inc: + ?.? % n - Adecoagro SA: + ?.? % n - Tower Semiconductor Ltd: + ?.? % n - Kosmos Energy Ltd: + ?.? % n - SolarEdge Technologies Inc: + ?.? % n - Microvision Inc: + ?.? % n - Agenus Inc: + ?.? % n - National Storage Affiliates Trust: + ?.? % n - Vermilion Energy Inc. : + ?.? % n. Los mayores perdedores son: - Aldeyra The: (?.? %) n - Akari Therapeutics PLC: (?.? %) n - American Vanguard Corporation: (?.? %) n - Tencent Music Entertainment Group: (?.? %) n - Canadian Solar Inc: (?.? %) n - Atara Biotherapeutics Inc: (?.? %) n - Curis Inc: (?.? %) n - Niu Technologies: (?.? %) n - GSI Technology Inc: (?.? %) n - Voyager Therapeutics Inc: (?.? %) n " }, " warnings ": [], " errors ": [], " has_results ": true, " quantity_results ": ?, " creatomate_response ": { " ? _id ": " e35b76f7 - 96fd - 427b - 9f2c - 969fcbe8c5ad ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / e35b76f7 - 96fd - 427b - 9f2c - 969fcbe8c5ad.png ", " ? _template_id ": " d547776f - f504 - 4737 - 8de1 - 1a357f411a3c ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (ES) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ? af6 - 382b - 438a - 86a4 - 28cf0e35fb6a ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? af6 - 382b - 438a - 86a4 - 28cf0e35fb6a.png ", " ? _template_id ": " d547776f - f504 - 4737 - 8de1 - 1a357f411a3c ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (ES) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ? d7a442a - 9b5d - 4c7e - 9fa5 - ce2c5a731195 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? d7a442a - 9b5d - 4c7e - 9fa5 - ce2c5a731195.mp4 ", " ? _template_id ": " d547776f - f504 - 4737 - 8de1 - 1a357f411a3c ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (ES) ", " ? _output_format ": " mp4 ", " ? _frame_rate ": ? }, " image_api ": { " latest ": [ " https: // api.autochartist.com / social_media / image / c996c29b - 47fd - 4a3e - 9eda - aca16a6f39aa ? broker_id = ?& item = ? ", " https: // api.autochartist.com / social_media / image / c996c29b - 47fd - 4a3e - 9eda - aca16a6f39aa ? broker_id = ?& item = ? ", " https: // api.autochartist.com / social_media / image / c996c29b - 47fd - 4a3e - 9eda - aca16a6f39aa ? broker_id = ?& item = ? " ], " snapshot ": [ " https: // api.autochartist.com / social_media / image / c996c29b - 47fd - 4a3e - 9eda - aca1[...];

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Mar 20	09	1	0ms	0ms

4

0ms

274

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 20	09	274	0ms	0ms

5

0ms

2,074

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 20	09	2,074	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 20	09	4	0ms	0ms

7

0ms

4

Details

0ms

select updaterelevantforrelevantresults ();

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Mar 20	09	4	0ms	0ms

8

0ms

4

Details

0ms

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

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Mar 20	09	4	0ms	0ms

9

0ms

744

Details

0ms

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

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Mar 20	09	744	0ms	0ms

10

0ms

1

Details

0ms

insert into executionlogs (executionid, status, message, details, detailtype) values (?, ?, ?, ? 's immune system against cancer and infections in the United States and internationally. It offers Retrocyte Display, an antibody expression platform for the identification of fully human and humanized monoclonal antibodies; and display technologies. It develops QS ? Stimulon adjuvant, a saponin - based vaccine adjuvant. The company also develops Balstilimab, a programmed death receptor - 1 (PD ?) blocking antibody; AGEN ?, a human Fc - enhanced cytotoxic T - lymphocyte antigen ? (CTLA ?) blocking antibody that is in Phase ? trials in metastatic colorectal cancer (mCRC), pancreatic cancer, and melanoma; AGEN ?, a CD ? monospecific antibody that is in Phase ? b clinical trial; AGEN ?, a CD ? / TGF u00df TRAP antibody; AGEN ?, an ILT ? monospecific antibody that is in clinical development; and BMS ?, a TIGIT bispecific antibodies. In addition, it develops INCAGN ?, a GITR agonist; INCAGN ?, a TIM ? monoclonal antibodies; INCAGN ?, a LAG ? monospecific antibody; MK ?, a monospecific antibody targeting ILT ? that is in Phase ? clinical trial; UGN ?, a zalifrelimab intravesical solution for the treatment of cancers of the urinary tract t; and AGEN ?.The company operates under the Agenus, MiNK, Prophage, Retrocyte Display, and STIMULON trademarks. It has collaborations with Bristol - Myers Squibb Company, Betta Pharmaceuticals Co., Ltd., UroGen Pharma Ltd., Gilead Sciences, Inc., Incyte Corporation, and Merck Sharp & Dohme. The company was formerly known as Antigenics Inc. and changed its name to Agenus Inc. in January ?.Agenus Inc. was founded in ? and is headquartered in Lexington, Massachusetts. ", " Address ": " ? Forbes Road, Lexington, MA, United States, ? - 7305 ", " Phone ": " ? ? ? ", " WebURL ": " https: // www.agenusbio.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / AGEN.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 03 - 19 ", " ticker ": " AGEN.US ", " code ": " AGEN ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " }, " ? ": { " Code ": " NSA ", " Type ": " Common Stock ", " Name ": " National Storage Affiliates Trust ", " Exchange ": " NYSE ", " CurrencyCode ": " USD ", " CurrencyName ": " US Dollar ", " CurrencySymbol ": " $ ", " CountryName ": " USA ", " CountryISO ": " US ", " OpenFigi ": " BBG ? VN ? ", " ISIN ": " US ? ", " LEI ": null, " PrimaryTicker ": " NSA.US ", " CUSIP ": " ? ", " CIK ": " ? ", " EmployerIdNumber ": " ? - 5053858 ", " FiscalYearEnd ": " December ", " IPODate ": " ? - 04 - 23 ", " InternationalDomestic ": " Domestic ", " Sector ": " Real Estate ", " Industry ": " REIT - Industrial ", " GicSector ": " Real Estate ", " GicGroup ": " Equity Real Estate Investment Trusts ( REITs) ", " GicIndustry ": " Specialized REITs ", " GicSubIndustry ": " Self - Storage REITs ", " HomeCategory ": " Domestic Primary ", " IsDelisted ": false, " Description ": " National Storage Affiliates Trust is a real estate investment trust headquartered in Greenwood Village, Colorado, focused on the ownership, operation and acquisition of self - storage properties predominantly located within the top ? metropolitan statistical areas throughout the United States. As of September ?, ?, the Company held ownership interests in and operated ?, ? self - storage properties, located in ? states and Puerto Rico with approximately ?.? million rentable square feet. NSA is one of the largest owners and operators of self - storage properties among public and private companies in the United States. ", " Address ": " ? East Prentice Avenue, Greenwood Village, CO, United States, ? ", " Phone ": " ? ? ? ", " WebURL ": " https: // www.nationalstorageaffiliates.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / NSA.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 03 - 19 ", " ticker ": " NSA.US ", " code ": " NSA ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " }, " ? ": { " Code ": " VET ", " Type ": " Common Stock ", " Name ": " Vermilion Energy Inc. ", " Exchange ": " NYSE ", " CurrencyCode ": " USD ", " CurrencyName ": " US Dollar ", " CurrencySymbol ": " $ ", " CountryName ": " USA ", " CountryISO ": " US ", " OpenFigi ": " BBG ? PL ? C ? ", " ISIN ": " CA ? ", " LEI ": null, " PrimaryTicker ": " VET.TO ", " CUSIP ": " ? ", " CIK ": " ? ", " EmployerIdNumber ": " % % OBJNAME ? % % FiscalYearEnd ": " December ", " IPODate ": " ? - 09 - 14 ", " InternationalDomestic ": " Domestic ", " Sector ": " Energy ", " Industry ": " Oil & Gas E & P ", " GicSector ": " Energy ", " GicGroup ": " Energy ", " GicIndustry ": " Oil, Gas & Consumable Fuels ", " GicSubIndustry ": " Oil & Gas Exploration & Production ", " HomeCategory ": " Canadian ", " IsDelisted ": false, " Description ": " Vermilion Energy Inc., engages in petroleum and natural gas, focuses on the acquisition, exploration, development, and optimization of producing properties in North America, Europe, and Australia. Its properties are located in the West Pembina region of West Central Alberta, Canada; southwest Bordeaux and Paris Basin in France; the Netherlands; Germany; Ireland; Croatia; Slovakia; Hungary; and Australia. The company was founded in ? and is headquartered in Calgary, Canada. ", " Address ": " ? - ? rd Avenue S.W., Calgary, AB, Canada, T ? P ? R ? ", " Phone ": " ? ? ? ", " WebURL ": " https: // www.vermilionenergy.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / VET.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 03 - 19 ", " ticker ": " VET.US ", " code ": " VET ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " } }, " Heading ": " Mayores Movimientos ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " Date ": " ? mar ? " }, " text ": { " title ": " Los mayores movimientos de esta semana ", " short_text ": " Los mayores ganadores son: Tillys Inc: + ?.? %, Aemetis Inc: + ?.? %, Adecoagro SA: + ?.? %, Tower Semiconductor Ltd: + ?.? %, Kosmos Energy Ltd: + ?.? %, SolarEdge Technologies Inc: + ?.? %, Microvision Inc: + ?.? %, Agenus Inc: + ?.? %, National Storage Affiliates Trust: + ?.? %, Vermilion Energy Inc. : + ?.? %.Los mayores perdedores son: Aldeyra The: (?.? %), Akari Therapeutics PLC: (?.? %), American Vanguard Corporation: (?.? %), Tencent Music Entertainment Group: (?.? %), Canadian Solar Inc: (?.? %), Atara Biotherapeutics Inc: (?.? %), Curis Inc: (?.? %), Niu Technologies: (?.? %), GSI Technology Inc: (?.? %), Voyager Therapeutics Inc: (?.? %) ", " long_text ": " Los mayores ganadores son: - Tillys Inc: + ?.? % n - Aemetis Inc: + ?.? % n - Adecoagro SA: + ?.? % n - Tower Semiconductor Ltd: + ?.? % n - Kosmos Energy Ltd: + ?.? % n - SolarEdge Technologies Inc: + ?.? % n - Microvision Inc: + ?.? % n - Agenus Inc: + ?.? % n - National Storage Affiliates Trust: + ?.? % n - Vermilion Energy Inc. : + ?.? % n. Los mayores perdedores son: - Aldeyra The: (?.? %) n - Akari Therapeutics PLC: (?.? %) n - American Vanguard Corporation: (?.? %) n - Tencent Music Entertainment Group: (?.? %) n - Canadian Solar Inc: (?.? %) n - Atara Biotherapeutics Inc: (?.? %) n - Curis Inc: (?.? %) n - Niu Technologies: (?.? %) n - GSI Technology Inc: (?.? %) n - Voyager Therapeutics Inc: (?.? %) n " }, " warnings ": [], " errors ": [], " has_results ": true, " quantity_results ": ?, " creatomate_response ": { " ? _id ": " e35b76f7 - 96fd - 427b - 9f2c - 969fcbe8c5ad ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / e35b76f7 - 96fd - 427b - 9f2c - 969fcbe8c5ad.png ", " ? _template_id ": " d547776f - f504 - 4737 - 8de1 - 1a357f411a3c ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (ES) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ? af6 - 382b - 438a - 86a4 - 28cf0e35fb6a ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? af6 - 382b - 438a - 86a4 - 28cf0e35fb6a.png ", " ? _template_id ": " d547776f - f504 - 4737 - 8de1 - 1a357f411a3c ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (ES) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ? d7a442a - 9b5d - 4c7e - 9fa5 - ce2c5a731195 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? d7a442a - 9b5d - 4c7e - 9fa5 - ce2c5a731195.mp4 ", " ? _template_id ": " d547776f - f504 - 4737 - 8de1 - 1a357f411a3c ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (ES) ", " ? _output_format ": " mp4 ", " ? _frame_rate ": ? }, " image_api ": { " latest ": [ " https: // api.autochartist.com / social_media / image / c996c29b - 47fd - 4a3e - 9eda - aca16a6f39aa ? broker_id = ?& item = ? ", " https: // api.autochartist.com / social_media / image / c996c29b - 47fd - 4a3e - 9eda - aca16a6f39aa ? broker_id = ?& item = ? ", " https: // api.autochartist.com / social_media / image / c996c29b - 47fd - 4a3e - 9eda - aca16a6f39aa ? broker_id = ?& item = ? " ], " snapshot ": [ " https: // api.aut[...];

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Mar 20	09	1	0ms	0ms

11

0ms

1,749

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 20	09	1,749	0ms	0ms

12

0ms

239

Details

0ms

select * from ( select timegranularity, extract(day from (max(patternendtime) - min(patternendtime))) from fibonacci_results a inner join symbols s on a.symbolid = s.symbolid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid where timegranularity = ? and (dss.archive is null or dss.archive is true) group by timegranularity union select timegranularity, extract(day from (max(patternendtime) - min(patternendtime))) from fibonacci_results a inner join symbols s on a.symbolid = s.symbolid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid where timegranularity = ? and (dss.archive is null or dss.archive is true) group by timegranularity union select timegranularity, extract(day from (max(patternendtime) - min(patternendtime))) from fibonacci_results a inner join symbols s on a.symbolid = s.symbolid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid where timegranularity = ? and (dss.archive is null or dss.archive is true) group by timegranularity union select timegranularity, extract(day from (max(patternendtime) - min(patternendtime))) from fibonacci_results a inner join symbols s on a.symbolid = s.symbolid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid where timegranularity = ? and (dss.archive is null or dss.archive is true) group by timegranularity union select timegranularity, extract(day from (max(patternendtime) - min(patternendtime))) from fibonacci_results a inner join symbols s on a.symbolid = s.symbolid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid where timegranularity = ? and (dss.archive is null or dss.archive is true) group by timegranularity) as k order by timegranularity;

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Mar 20	09	239	0ms	0ms

13

0ms

13

Details

0ms

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

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Mar 20	09	13	0ms	0ms

14

0ms

2

Details

0ms

select t.pricedatetime as pricedatetime, t.open as open, t.high as high, t.low as low, t.close "..." close, t.volume as volume, t.bsf as bsf from t1440 t where symbolid = ? and (bsf = ? or bsf is null) order by pricedatetime desc limit ?;

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Mar 20	09	2	0ms	0ms

15

0ms

240

Details

0ms

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

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Mar 20	09	240	0ms	0ms

16

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 #16

Day	Hour	Count	Duration	Avg duration
Mar 20	09	18	0ms	0ms

17

0ms

418

Details

0ms

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

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Mar 20	09	418	0ms	0ms

18

0ms

230

Details

0ms

select distinct s.statsid as statsid, sy.exchange as name from stats s inner join broker b on s.brokerid = b.brokerid inner join brokerconfig bc on b.brokerid = bc.brokerid inner join stats_symbols ss on s.statsid = ss.statsid inner join downloadersymbolsettings dss on ss.symbolid = dss.symbolid inner join symbols sy on dss.symbolid = sy.symbolid where dss.enabled = ? and s.brokerid is not null and b.brokerid = ? and s.groupingtype ilike ? and s.description ilike ? || b.name || ? and s.description ilike ? || sy.exchange || ? and s.description not ilike ? || ? || ? union all select distinct s.statsid as statsid, basegroupname as name from stats s inner join broker b on s.brokerid = b.brokerid inner join brokerconfig bc on b.brokerid = bc.brokerid inner join stats_symbols ss on s.statsid = ss.statsid inner join downloadersymbolsettings dss on ss.symbolid = dss.symbolid inner join symbolgroup sg on dss.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bg.brokerid = b.brokerid where dss.enabled = ? and s.brokerid is not null and b.brokerid = ? and s.groupingtype not ilike ? and s.description ilike ? || b.name || ? and s.description ilike ? || g.basegroupname || ? and s.description not ilike ? || ? || ?;

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Mar 20	09	230	0ms	0ms

19

0ms

30

Details

0ms

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

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Mar 20	09	30	0ms	0ms

20

0ms

331

Details

0ms

commit;

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Mar 20	09	331	0ms	0ms

Most frequent queries (N)

Rank

Times executed

Total duration

Min duration

Max duration

Avg duration

Query

1

31,732

Details

0ms

select ?;

Times Reported Time consuming queries #1

Day	Hour	Count	Duration	Avg duration
Mar 20	09	31,732	0ms	0ms

2

6,430

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 20	09	6,430	0ms	0ms

3

5,323

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 #3

Day	Hour	Count	Duration	Avg duration
Mar 20	09	5,323	0ms	0ms

4

5,204

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 #4

Day	Hour	Count	Duration	Avg duration
Mar 20	09	5,204	0ms	0ms

5

4,602

Details

0ms

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

Times Reported Time consuming queries #5

Day	Hour	Count	Duration	Avg duration
Mar 20	09	4,602	0ms	0ms

6

3,541

Details

0ms

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

Times Reported Time consuming queries #6

Day	Hour	Count	Duration	Avg duration
Mar 20	09	3,541	0ms	0ms

7

3,091

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 #7

Day	Hour	Count	Duration	Avg duration
Mar 20	09	3,091	0ms	0ms

8

2,697

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 #8

Day	Hour	Count	Duration	Avg duration
Mar 20	09	2,697	0ms	0ms

9

2,508

Details

0ms

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

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Mar 20	09	2,508	0ms	0ms

10

2,484

Details

0ms

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

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Mar 20	09	2,484	0ms	0ms

11

2,268

Details

0ms

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

Times Reported Time consuming queries #11

Day	Hour	Count	Duration	Avg duration
Mar 20	09	2,268	0ms	0ms

12

2,074

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 20	09	2,074	0ms	0ms

13

2,063

Details

0ms

select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as resultuid, s.symbol, pattern as patternname, timegranularity as interval, patternlengthbars as length, patternendtime, direction, breakout, predictiontimeto, predictionpricefrom, predictionpriceto, patternstartprice, resy1, supporty1, dtt.timezone, cps.pip, newlevels.profit from autochartist_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern left join currencypips cps on cps.symbol = s.symbol left join lateral calc_cp_signal (a.resultuid) newlevels on true where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Mar 20	09	2,063	0ms	0ms

14

1,915

Details

0ms

set extra_float_digits = ?;

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Mar 20	09	1,915	0ms	0ms

15

1,880

Details

0ms

set application_name = ?;

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Mar 20	09	1,880	0ms	0ms

16

1,796

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 #16

Day	Hour	Count	Duration	Avg duration
Mar 20	09	1,796	0ms	0ms

17

1,751

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 #17

Day	Hour	Count	Duration	Avg duration
Mar 20	09	1,751	0ms	0ms

18

1,749

Details

0ms

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

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Mar 20	09	1,749	0ms	0ms

19

1,365

Details

0ms

with rar_max as ( select resultuid from relevance_fibonacci_results order by resultuid desc limit ? ) select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as ruid, s.symbolid as sid, s.symbol as sym, 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, ? - errormargin as rq, ? - 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, case when rar.age is not null then rar.age when a.resultuid <= rm.resultuid then ? else ? end as age, case when rar.relevant is not null then rar.relevant when a.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip, newlevels.filtered 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 inner join rar_max rm on ? = ? left outer join relevance_fibonacci_results rar on a.resultuid = rar.resultuid left join currencypips cps on cps.symbol = s.symbol left join lateral calc_fib_signal_filter (a.resultuid) newlevels on true where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Mar 20	09	1,365	0ms	0ms

20

1,197

Details

0ms

select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as resultuid, s.symbol, a.patternprice, atbaridentified as patternendtime, breakout, p.patternname, dtt.timezone, a.direction, case when a.patternclassid = ? then a.predictionpricefrom else a.patternprice end as predictionpricefrom, case when a.patternclassid = ? then a.predictionpriceto else a.patternprice end as predictionpriceto, s.timegranularity as interval, patternlengthbars as length, cps.pip from keylevels_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join hrspatterns p on a.patternid = p.patternid left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Mar 20	09	1,197	0ms	0ms

Normalized slowest queries (N)

Rank

Min duration

Max duration

Avg duration

Times executed

Total duration

Query

1

0ms

19

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 20	09	19	0ms	0ms

2

0ms

166

Details

0ms

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

Times Reported Time consuming queries #2

Day	Hour	Count	Duration	Avg duration
Mar 20	09	166	0ms	0ms

3

0ms

1

Details

0ms

update executions set isrunning = false, has_results = true, response = ? 's immune system against cancer and infections in the United States and internationally. It offers Retrocyte Display, an antibody expression platform for the identification of fully human and humanized monoclonal antibodies; and display technologies. It develops QS ? Stimulon adjuvant, a saponin - based vaccine adjuvant. The company also develops Balstilimab, a programmed death receptor - 1 (PD ?) blocking antibody; AGEN ?, a human Fc - enhanced cytotoxic T - lymphocyte antigen ? (CTLA ?) blocking antibody that is in Phase ? trials in metastatic colorectal cancer (mCRC), pancreatic cancer, and melanoma; AGEN ?, a CD ? monospecific antibody that is in Phase ? b clinical trial; AGEN ?, a CD ? / TGF u00df TRAP antibody; AGEN ?, an ILT ? monospecific antibody that is in clinical development; and BMS ?, a TIGIT bispecific antibodies. In addition, it develops INCAGN ?, a GITR agonist; INCAGN ?, a TIM ? monoclonal antibodies; INCAGN ?, a LAG ? monospecific antibody; MK ?, a monospecific antibody targeting ILT ? that is in Phase ? clinical trial; UGN ?, a zalifrelimab intravesical solution for the treatment of cancers of the urinary tract t; and AGEN ?.The company operates under the Agenus, MiNK, Prophage, Retrocyte Display, and STIMULON trademarks. It has collaborations with Bristol - Myers Squibb Company, Betta Pharmaceuticals Co., Ltd., UroGen Pharma Ltd., Gilead Sciences, Inc., Incyte Corporation, and Merck Sharp & Dohme. The company was formerly known as Antigenics Inc. and changed its name to Agenus Inc. in January ?.Agenus Inc. was founded in ? and is headquartered in Lexington, Massachusetts. ", " Address ": " ? Forbes Road, Lexington, MA, United States, ? - 7305 ", " Phone ": " ? ? ? ", " WebURL ": " https: // www.agenusbio.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / AGEN.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 03 - 19 ", " ticker ": " AGEN.US ", " code ": " AGEN ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " }, " ? ": { " Code ": " NSA ", " Type ": " Common Stock ", " Name ": " National Storage Affiliates Trust ", " Exchange ": " NYSE ", " CurrencyCode ": " USD ", " CurrencyName ": " US Dollar ", " CurrencySymbol ": " $ ", " CountryName ": " USA ", " CountryISO ": " US ", " OpenFigi ": " BBG ? VN ? ", " ISIN ": " US ? ", " LEI ": null, " PrimaryTicker ": " NSA.US ", " CUSIP ": " ? ", " CIK ": " ? ", " EmployerIdNumber ": " ? - 5053858 ", " FiscalYearEnd ": " December ", " IPODate ": " ? - 04 - 23 ", " InternationalDomestic ": " Domestic ", " Sector ": " Real Estate ", " Industry ": " REIT - Industrial ", " GicSector ": " Real Estate ", " GicGroup ": " Equity Real Estate Investment Trusts ( REITs) ", " GicIndustry ": " Specialized REITs ", " GicSubIndustry ": " Self - Storage REITs ", " HomeCategory ": " Domestic Primary ", " IsDelisted ": false, " Description ": " National Storage Affiliates Trust is a real estate investment trust headquartered in Greenwood Village, Colorado, focused on the ownership, operation and acquisition of self - storage properties predominantly located within the top ? metropolitan statistical areas throughout the United States. As of September ?, ?, the Company held ownership interests in and operated ?, ? self - storage properties, located in ? states and Puerto Rico with approximately ?.? million rentable square feet. NSA is one of the largest owners and operators of self - storage properties among public and private companies in the United States. ", " Address ": " ? East Prentice Avenue, Greenwood Village, CO, United States, ? ", " Phone ": " ? ? ? ", " WebURL ": " https: // www.nationalstorageaffiliates.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / NSA.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 03 - 19 ", " ticker ": " NSA.US ", " code ": " NSA ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " }, " ? ": { " Code ": " VET ", " Type ": " Common Stock ", " Name ": " Vermilion Energy Inc. ", " Exchange ": " NYSE ", " CurrencyCode ": " USD ", " CurrencyName ": " US Dollar ", " CurrencySymbol ": " $ ", " CountryName ": " USA ", " CountryISO ": " US ", " OpenFigi ": " BBG ? PL ? C ? ", " ISIN ": " CA ? ", " LEI ": null, " PrimaryTicker ": " VET.TO ", " CUSIP ": " ? ", " CIK ": " ? ", " EmployerIdNumber ": " % % OBJNAME ? % % FiscalYearEnd ": " December ", " IPODate ": " ? - 09 - 14 ", " InternationalDomestic ": " Domestic ", " Sector ": " Energy ", " Industry ": " Oil & Gas E & P ", " GicSector ": " Energy ", " GicGroup ": " Energy ", " GicIndustry ": " Oil, Gas & Consumable Fuels ", " GicSubIndustry ": " Oil & Gas Exploration & Production ", " HomeCategory ": " Canadian ", " IsDelisted ": false, " Description ": " Vermilion Energy Inc., engages in petroleum and natural gas, focuses on the acquisition, exploration, development, and optimization of producing properties in North America, Europe, and Australia. Its properties are located in the West Pembina region of West Central Alberta, Canada; southwest Bordeaux and Paris Basin in France; the Netherlands; Germany; Ireland; Croatia; Slovakia; Hungary; and Australia. The company was founded in ? and is headquartered in Calgary, Canada. ", " Address ": " ? - ? rd Avenue S.W., Calgary, AB, Canada, T ? P ? R ? ", " Phone ": " ? ? ? ", " WebURL ": " https: // www.vermilionenergy.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / VET.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 03 - 19 ", " ticker ": " VET.US ", " code ": " VET ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " } }, " Heading ": " Mayores Movimientos ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " Date ": " ? mar ? " }, " text ": { " title ": " Los mayores movimientos de esta semana ", " short_text ": " Los mayores ganadores son: Tillys Inc: + ?.? %, Aemetis Inc: + ?.? %, Adecoagro SA: + ?.? %, Tower Semiconductor Ltd: + ?.? %, Kosmos Energy Ltd: + ?.? %, SolarEdge Technologies Inc: + ?.? %, Microvision Inc: + ?.? %, Agenus Inc: + ?.? %, National Storage Affiliates Trust: + ?.? %, Vermilion Energy Inc. : + ?.? %.Los mayores perdedores son: Aldeyra The: (?.? %), Akari Therapeutics PLC: (?.? %), American Vanguard Corporation: (?.? %), Tencent Music Entertainment Group: (?.? %), Canadian Solar Inc: (?.? %), Atara Biotherapeutics Inc: (?.? %), Curis Inc: (?.? %), Niu Technologies: (?.? %), GSI Technology Inc: (?.? %), Voyager Therapeutics Inc: (?.? %) ", " long_text ": " Los mayores ganadores son: - Tillys Inc: + ?.? % n - Aemetis Inc: + ?.? % n - Adecoagro SA: + ?.? % n - Tower Semiconductor Ltd: + ?.? % n - Kosmos Energy Ltd: + ?.? % n - SolarEdge Technologies Inc: + ?.? % n - Microvision Inc: + ?.? % n - Agenus Inc: + ?.? % n - National Storage Affiliates Trust: + ?.? % n - Vermilion Energy Inc. : + ?.? % n. Los mayores perdedores son: - Aldeyra The: (?.? %) n - Akari Therapeutics PLC: (?.? %) n - American Vanguard Corporation: (?.? %) n - Tencent Music Entertainment Group: (?.? %) n - Canadian Solar Inc: (?.? %) n - Atara Biotherapeutics Inc: (?.? %) n - Curis Inc: (?.? %) n - Niu Technologies: (?.? %) n - GSI Technology Inc: (?.? %) n - Voyager Therapeutics Inc: (?.? %) n " }, " warnings ": [], " errors ": [], " has_results ": true, " quantity_results ": ?, " creatomate_response ": { " ? _id ": " e35b76f7 - 96fd - 427b - 9f2c - 969fcbe8c5ad ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / e35b76f7 - 96fd - 427b - 9f2c - 969fcbe8c5ad.png ", " ? _template_id ": " d547776f - f504 - 4737 - 8de1 - 1a357f411a3c ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (ES) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ? af6 - 382b - 438a - 86a4 - 28cf0e35fb6a ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? af6 - 382b - 438a - 86a4 - 28cf0e35fb6a.png ", " ? _template_id ": " d547776f - f504 - 4737 - 8de1 - 1a357f411a3c ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (ES) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ? d7a442a - 9b5d - 4c7e - 9fa5 - ce2c5a731195 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? d7a442a - 9b5d - 4c7e - 9fa5 - ce2c5a731195.mp4 ", " ? _template_id ": " d547776f - f504 - 4737 - 8de1 - 1a357f411a3c ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (ES) ", " ? _output_format ": " mp4 ", " ? _frame_rate ": ? }, " image_api ": { " latest ": [ " https: // api.autochartist.com / social_media / image / c996c29b - 47fd - 4a3e - 9eda - aca16a6f39aa ? broker_id = ?& item = ? ", " https: // api.autochartist.com / social_media / image / c996c29b - 47fd - 4a3e - 9eda - aca16a6f39aa ? broker_id = ?& item = ? ", " https: // api.autochartist.com / social_media / image / c996c29b - 47fd - 4a3e - 9eda - aca16a6f39aa ? broker_id = ?& item = ? " ], " snapshot ": [ " https: // api.autochartist.com / social_media / image / c996c29b - 47fd - 4a3e - 9eda - aca1[...];

Times Reported Time consuming queries #3

Day	Hour	Count	Duration	Avg duration
Mar 20	09	1	0ms	0ms

4

0ms

274

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 20	09	274	0ms	0ms

5

0ms

2,074

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 20	09	2,074	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 20	09	4	0ms	0ms

7

0ms

4

Details

0ms

select updaterelevantforrelevantresults ();

Times Reported Time consuming queries #7

Day	Hour	Count	Duration	Avg duration
Mar 20	09	4	0ms	0ms

8

0ms

4

Details

0ms

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

Times Reported Time consuming queries #8

Day	Hour	Count	Duration	Avg duration
Mar 20	09	4	0ms	0ms

9

0ms

744

Details

0ms

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

Times Reported Time consuming queries #9

Day	Hour	Count	Duration	Avg duration
Mar 20	09	744	0ms	0ms

10

0ms

1

Details

0ms

insert into executionlogs (executionid, status, message, details, detailtype) values (?, ?, ?, ? 's immune system against cancer and infections in the United States and internationally. It offers Retrocyte Display, an antibody expression platform for the identification of fully human and humanized monoclonal antibodies; and display technologies. It develops QS ? Stimulon adjuvant, a saponin - based vaccine adjuvant. The company also develops Balstilimab, a programmed death receptor - 1 (PD ?) blocking antibody; AGEN ?, a human Fc - enhanced cytotoxic T - lymphocyte antigen ? (CTLA ?) blocking antibody that is in Phase ? trials in metastatic colorectal cancer (mCRC), pancreatic cancer, and melanoma; AGEN ?, a CD ? monospecific antibody that is in Phase ? b clinical trial; AGEN ?, a CD ? / TGF u00df TRAP antibody; AGEN ?, an ILT ? monospecific antibody that is in clinical development; and BMS ?, a TIGIT bispecific antibodies. In addition, it develops INCAGN ?, a GITR agonist; INCAGN ?, a TIM ? monoclonal antibodies; INCAGN ?, a LAG ? monospecific antibody; MK ?, a monospecific antibody targeting ILT ? that is in Phase ? clinical trial; UGN ?, a zalifrelimab intravesical solution for the treatment of cancers of the urinary tract t; and AGEN ?.The company operates under the Agenus, MiNK, Prophage, Retrocyte Display, and STIMULON trademarks. It has collaborations with Bristol - Myers Squibb Company, Betta Pharmaceuticals Co., Ltd., UroGen Pharma Ltd., Gilead Sciences, Inc., Incyte Corporation, and Merck Sharp & Dohme. The company was formerly known as Antigenics Inc. and changed its name to Agenus Inc. in January ?.Agenus Inc. was founded in ? and is headquartered in Lexington, Massachusetts. ", " Address ": " ? Forbes Road, Lexington, MA, United States, ? - 7305 ", " Phone ": " ? ? ? ", " WebURL ": " https: // www.agenusbio.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / AGEN.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 03 - 19 ", " ticker ": " AGEN.US ", " code ": " AGEN ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " }, " ? ": { " Code ": " NSA ", " Type ": " Common Stock ", " Name ": " National Storage Affiliates Trust ", " Exchange ": " NYSE ", " CurrencyCode ": " USD ", " CurrencyName ": " US Dollar ", " CurrencySymbol ": " $ ", " CountryName ": " USA ", " CountryISO ": " US ", " OpenFigi ": " BBG ? VN ? ", " ISIN ": " US ? ", " LEI ": null, " PrimaryTicker ": " NSA.US ", " CUSIP ": " ? ", " CIK ": " ? ", " EmployerIdNumber ": " ? - 5053858 ", " FiscalYearEnd ": " December ", " IPODate ": " ? - 04 - 23 ", " InternationalDomestic ": " Domestic ", " Sector ": " Real Estate ", " Industry ": " REIT - Industrial ", " GicSector ": " Real Estate ", " GicGroup ": " Equity Real Estate Investment Trusts ( REITs) ", " GicIndustry ": " Specialized REITs ", " GicSubIndustry ": " Self - Storage REITs ", " HomeCategory ": " Domestic Primary ", " IsDelisted ": false, " Description ": " National Storage Affiliates Trust is a real estate investment trust headquartered in Greenwood Village, Colorado, focused on the ownership, operation and acquisition of self - storage properties predominantly located within the top ? metropolitan statistical areas throughout the United States. As of September ?, ?, the Company held ownership interests in and operated ?, ? self - storage properties, located in ? states and Puerto Rico with approximately ?.? million rentable square feet. NSA is one of the largest owners and operators of self - storage properties among public and private companies in the United States. ", " Address ": " ? East Prentice Avenue, Greenwood Village, CO, United States, ? ", " Phone ": " ? ? ? ", " WebURL ": " https: // www.nationalstorageaffiliates.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / NSA.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 03 - 19 ", " ticker ": " NSA.US ", " code ": " NSA ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " }, " ? ": { " Code ": " VET ", " Type ": " Common Stock ", " Name ": " Vermilion Energy Inc. ", " Exchange ": " NYSE ", " CurrencyCode ": " USD ", " CurrencyName ": " US Dollar ", " CurrencySymbol ": " $ ", " CountryName ": " USA ", " CountryISO ": " US ", " OpenFigi ": " BBG ? PL ? C ? ", " ISIN ": " CA ? ", " LEI ": null, " PrimaryTicker ": " VET.TO ", " CUSIP ": " ? ", " CIK ": " ? ", " EmployerIdNumber ": " % % OBJNAME ? % % FiscalYearEnd ": " December ", " IPODate ": " ? - 09 - 14 ", " InternationalDomestic ": " Domestic ", " Sector ": " Energy ", " Industry ": " Oil & Gas E & P ", " GicSector ": " Energy ", " GicGroup ": " Energy ", " GicIndustry ": " Oil, Gas & Consumable Fuels ", " GicSubIndustry ": " Oil & Gas Exploration & Production ", " HomeCategory ": " Canadian ", " IsDelisted ": false, " Description ": " Vermilion Energy Inc., engages in petroleum and natural gas, focuses on the acquisition, exploration, development, and optimization of producing properties in North America, Europe, and Australia. Its properties are located in the West Pembina region of West Central Alberta, Canada; southwest Bordeaux and Paris Basin in France; the Netherlands; Germany; Ireland; Croatia; Slovakia; Hungary; and Australia. The company was founded in ? and is headquartered in Calgary, Canada. ", " Address ": " ? - ? rd Avenue S.W., Calgary, AB, Canada, T ? P ? R ? ", " Phone ": " ? ? ? ", " WebURL ": " https: // www.vermilionenergy.com ", " LogoURL ": " https: // eodhistoricaldata.com / img / logos / US / VET.png ", " FullTimeEmployees ": ?, " UpdatedAt ": " ? - 03 - 19 ", " ticker ": " VET.US ", " code ": " VET ", " exchange ": " US ", " open ": ?.?, " close ": ?.?, " change ": ?.?, " change_percent ": ?.?, " change_str ": " + ?.? % ", " nocolor_change_str ": " + ?.? % ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " change_str.fill_color ": " # ? " } }, " Heading ": " Mayores Movimientos ", " CloseHeading ": " Cerca ", " OpenHeading ": " Abierto ", " ChangeHeading ": " Cambio ", " Date ": " ? mar ? " }, " text ": { " title ": " Los mayores movimientos de esta semana ", " short_text ": " Los mayores ganadores son: Tillys Inc: + ?.? %, Aemetis Inc: + ?.? %, Adecoagro SA: + ?.? %, Tower Semiconductor Ltd: + ?.? %, Kosmos Energy Ltd: + ?.? %, SolarEdge Technologies Inc: + ?.? %, Microvision Inc: + ?.? %, Agenus Inc: + ?.? %, National Storage Affiliates Trust: + ?.? %, Vermilion Energy Inc. : + ?.? %.Los mayores perdedores son: Aldeyra The: (?.? %), Akari Therapeutics PLC: (?.? %), American Vanguard Corporation: (?.? %), Tencent Music Entertainment Group: (?.? %), Canadian Solar Inc: (?.? %), Atara Biotherapeutics Inc: (?.? %), Curis Inc: (?.? %), Niu Technologies: (?.? %), GSI Technology Inc: (?.? %), Voyager Therapeutics Inc: (?.? %) ", " long_text ": " Los mayores ganadores son: - Tillys Inc: + ?.? % n - Aemetis Inc: + ?.? % n - Adecoagro SA: + ?.? % n - Tower Semiconductor Ltd: + ?.? % n - Kosmos Energy Ltd: + ?.? % n - SolarEdge Technologies Inc: + ?.? % n - Microvision Inc: + ?.? % n - Agenus Inc: + ?.? % n - National Storage Affiliates Trust: + ?.? % n - Vermilion Energy Inc. : + ?.? % n. Los mayores perdedores son: - Aldeyra The: (?.? %) n - Akari Therapeutics PLC: (?.? %) n - American Vanguard Corporation: (?.? %) n - Tencent Music Entertainment Group: (?.? %) n - Canadian Solar Inc: (?.? %) n - Atara Biotherapeutics Inc: (?.? %) n - Curis Inc: (?.? %) n - Niu Technologies: (?.? %) n - GSI Technology Inc: (?.? %) n - Voyager Therapeutics Inc: (?.? %) n " }, " warnings ": [], " errors ": [], " has_results ": true, " quantity_results ": ?, " creatomate_response ": { " ? _id ": " e35b76f7 - 96fd - 427b - 9f2c - 969fcbe8c5ad ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / e35b76f7 - 96fd - 427b - 9f2c - 969fcbe8c5ad.png ", " ? _template_id ": " d547776f - f504 - 4737 - 8de1 - 1a357f411a3c ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (ES) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ? af6 - 382b - 438a - 86a4 - 28cf0e35fb6a ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? af6 - 382b - 438a - 86a4 - 28cf0e35fb6a.png ", " ? _template_id ": " d547776f - f504 - 4737 - 8de1 - 1a357f411a3c ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (ES) ", " ? _output_format ": " png ", " ? _frame_rate ": ?, " ? _id ": " ? d7a442a - 9b5d - 4c7e - 9fa5 - ce2c5a731195 ", " ? _status ": " planned ", " ? _url ": " https: // f002.backblazeb2.com / file / creatomate - c8xg3hsxdu / ? d7a442a - 9b5d - 4c7e - 9fa5 - ce2c5a731195.mp4 ", " ? _template_id ": " d547776f - f504 - 4737 - 8de1 - 1a357f411a3c ", " ? _template_name ": " Biggest stock gainers and losers MP ? (Instagram ?) (ES) ", " ? _output_format ": " mp4 ", " ? _frame_rate ": ? }, " image_api ": { " latest ": [ " https: // api.autochartist.com / social_media / image / c996c29b - 47fd - 4a3e - 9eda - aca16a6f39aa ? broker_id = ?& item = ? ", " https: // api.autochartist.com / social_media / image / c996c29b - 47fd - 4a3e - 9eda - aca16a6f39aa ? broker_id = ?& item = ? ", " https: // api.autochartist.com / social_media / image / c996c29b - 47fd - 4a3e - 9eda - aca16a6f39aa ? broker_id = ?& item = ? " ], " snapshot ": [ " https: // api.aut[...];

Times Reported Time consuming queries #10

Day	Hour	Count	Duration	Avg duration
Mar 20	09	1	0ms	0ms

11

0ms

1,749

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 20	09	1,749	0ms	0ms

12

0ms

239

Details

0ms

select * from ( select timegranularity, extract(day from (max(patternendtime) - min(patternendtime))) from fibonacci_results a inner join symbols s on a.symbolid = s.symbolid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid where timegranularity = ? and (dss.archive is null or dss.archive is true) group by timegranularity union select timegranularity, extract(day from (max(patternendtime) - min(patternendtime))) from fibonacci_results a inner join symbols s on a.symbolid = s.symbolid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid where timegranularity = ? and (dss.archive is null or dss.archive is true) group by timegranularity union select timegranularity, extract(day from (max(patternendtime) - min(patternendtime))) from fibonacci_results a inner join symbols s on a.symbolid = s.symbolid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid where timegranularity = ? and (dss.archive is null or dss.archive is true) group by timegranularity union select timegranularity, extract(day from (max(patternendtime) - min(patternendtime))) from fibonacci_results a inner join symbols s on a.symbolid = s.symbolid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid where timegranularity = ? and (dss.archive is null or dss.archive is true) group by timegranularity union select timegranularity, extract(day from (max(patternendtime) - min(patternendtime))) from fibonacci_results a inner join symbols s on a.symbolid = s.symbolid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid where timegranularity = ? and (dss.archive is null or dss.archive is true) group by timegranularity) as k order by timegranularity;

Times Reported Time consuming queries #12

Day	Hour	Count	Duration	Avg duration
Mar 20	09	239	0ms	0ms

13

0ms

13

Details

0ms

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

Times Reported Time consuming queries #13

Day	Hour	Count	Duration	Avg duration
Mar 20	09	13	0ms	0ms

14

0ms

2

Details

0ms

select t.pricedatetime as pricedatetime, t.open as open, t.high as high, t.low as low, t.close "..." close, t.volume as volume, t.bsf as bsf from t1440 t where symbolid = ? and (bsf = ? or bsf is null) order by pricedatetime desc limit ?;

Times Reported Time consuming queries #14

Day	Hour	Count	Duration	Avg duration
Mar 20	09	2	0ms	0ms

15

0ms

240

Details

0ms

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

Times Reported Time consuming queries #15

Day	Hour	Count	Duration	Avg duration
Mar 20	09	240	0ms	0ms

16

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 #16

Day	Hour	Count	Duration	Avg duration
Mar 20	09	18	0ms	0ms

17

0ms

418

Details

0ms

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

Times Reported Time consuming queries #17

Day	Hour	Count	Duration	Avg duration
Mar 20	09	418	0ms	0ms

18

0ms

230

Details

0ms

select distinct s.statsid as statsid, sy.exchange as name from stats s inner join broker b on s.brokerid = b.brokerid inner join brokerconfig bc on b.brokerid = bc.brokerid inner join stats_symbols ss on s.statsid = ss.statsid inner join downloadersymbolsettings dss on ss.symbolid = dss.symbolid inner join symbols sy on dss.symbolid = sy.symbolid where dss.enabled = ? and s.brokerid is not null and b.brokerid = ? and s.groupingtype ilike ? and s.description ilike ? || b.name || ? and s.description ilike ? || sy.exchange || ? and s.description not ilike ? || ? || ? union all select distinct s.statsid as statsid, basegroupname as name from stats s inner join broker b on s.brokerid = b.brokerid inner join brokerconfig bc on b.brokerid = bc.brokerid inner join stats_symbols ss on s.statsid = ss.statsid inner join downloadersymbolsettings dss on ss.symbolid = dss.symbolid inner join symbolgroup sg on dss.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bg.brokerid = b.brokerid where dss.enabled = ? and s.brokerid is not null and b.brokerid = ? and s.groupingtype not ilike ? and s.description ilike ? || b.name || ? and s.description ilike ? || g.basegroupname || ? and s.description not ilike ? || ? || ?;

Times Reported Time consuming queries #18

Day	Hour	Count	Duration	Avg duration
Mar 20	09	230	0ms	0ms

19

0ms

30

Details

0ms

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

Times Reported Time consuming queries #19

Day	Hour	Count	Duration	Avg duration
Mar 20	09	30	0ms	0ms

20

0ms

331

Details

0ms

commit;

Times Reported Time consuming queries #20

Day	Hour	Count	Duration	Avg duration
Mar 20	09	331	0ms	0ms

Time consuming prepare


Date: 2026-03-20 09:30:23
Duration: 25ms
Database: postgres
Date: 2026-03-20 09:15:51
Duration: 3ms
Database: postgres
Date: 2026-03-20 09:30:39
Duration: 6ms
Database: postgres
Date: 2026-03-20 09:00:40
Duration: 4ms
Database: postgres
Date: 2026-03-20 09:15:35
Duration: 17ms
Database: postgres
Date: 2026-03-20 09:00:40
Duration: 0ms
Database: postgres
Date: 2026-03-20 09:15:55
Duration: 3ms
Database: postgres
Date: 2026-03-20 09:16:01
Duration: 2ms
Database: postgres
Date: 2026-03-20 09:39:07
Duration: 0ms
Database: postgres
Date: 2026-03-20 09:27:54
Duration: 22ms
Database: postgres
Date: 2026-03-20 09:16:00
Duration: 4ms
Database: postgres
Date: 2026-03-20 09:50:52
Duration: 1ms
Database: postgres
Date: 2026-03-20 09:06:43
Duration: 7ms
Database: postgres
Date: 2026-03-20 09:11:03
Duration: 3ms
Database: postgres
Date: 2026-03-20 09:13:08
Duration: 0ms
Database: postgres
Date: 2026-03-20 09:56:12
Duration: 1ms
Database: postgres
Date: 2026-03-20 09:35:56
Duration: 5ms
Database: postgres
Date: 2026-03-20 09:10:59
Duration: 3ms
Database: postgres
Date: 2026-03-20 09:30:20
Duration: 3ms
Database: postgres
Date: 2026-03-20 09:27:59
Duration: 0ms
Database: postgres

Time consuming bind

Rank

Total duration

Times executed

Min duration

Max duration

Avg duration

Query

1

31s564ms

8,448

Details

0ms

134ms

3ms

WITH rar_max as ( ;

Times Reported Time consuming bind #1

Day	Hour	Count	Duration	Avg duration
Mar 20	09	8,448	31s564ms	3ms

WITH rar_max as ( ;

Date: 2026-03-20 09:10:55 Duration: 134ms Database: postgres parameters: $1 = 't', $2 = '627', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '213', $14 = '#ADBE', $15 = '#ALVG', $16 = '#AMZN', $17 = '#APPL', $18 = '#BA', $19 = '#BABA', $20 = '#BAYGn', $21 = '#BMWG', $22 = '#BNPP', $23 = '#CAT', $24 = '#CBKG', $25 = '#DAIGn', $26 = '#DIS', $27 = '#EA', $28 = '#FB', $29 = '#FDX', $30 = '#GE', $31 = '#GM', $32 = '#GOOGL', $33 = '#GS', $34 = '#INTC', $35 = '#JPM', $36 = '#KO', $37 = '#META', $38 = '#MSFT', $39 = '#NFLX', $40 = '#TSLA', $41 = '#VOWG', $42 = '#WMT', $43 = '#XOM', $44 = 'AUDCAD', $45 = 'AUDCHF', $46 = 'AUDJPY', $47 = 'AUDNZD', $48 = 'AUDUSD', $49 = 'AUS_200', $50 = 'BTCEUR', $51 = 'BTCGBP', $52 = 'BTCUSD', $53 = 'CADCHF', $54 = 'CADJPY', $55 = 'CHFJPY', $56 = 'CL_BRENT', $57 = 'DASHUSD', $58 = 'EOSUSD', $59 = 'ESP_35', $60 = 'ETHEUR', $61 = 'ETHGBP', $62 = 'ETHUSD', $63 = 'EURAUD', $64 = 'EURCAD', $65 = 'EURCHF', $66 = 'EURGBP', $67 = 'EURJPY', $68 = 'EURMXN', $69 = 'EURNOK', $70 = 'EURNZD', $71 = 'EURPLN', $72 = 'EURSEK', $73 = 'EURTRY', $74 = 'EURUSD', $75 = 'EUR_50', $76 = 'FRA_40', $77 = 'GBPAUD', $78 = 'GBPCAD', $79 = 'GBPCHF', $80 = 'GBPJPY', $81 = 'GBPNZD', $82 = 'GBPUSD', $83 = 'GBPZAR', $84 = 'GBR_100', $85 = 'HKDJPY', $86 = 'HKG_50', $87 = 'IOTAUSD', $88 = 'LTCEUR', $89 = 'LTCUSD', $90 = 'NAS100', $91 = 'NEOUSD', $92 = 'NOKJPY', $93 = 'NZDCAD', $94 = 'NZDCHF', $95 = 'NZDJPY', $96 = 'NZDUSD', $97 = 'OMGUSD', $98 = 'SPX500', $99 = 'TRXUSD', $100 = 'US30', $101 = 'USDCAD', $102 = 'USDCHF', $103 = 'USDCNH', $104 = 'USDDKK', $105 = 'USDJPY', $106 = 'USDMXN', $107 = 'USDNOK', $108 = 'USDPLN', $109 = 'USDSEK', $110 = 'USDSGD', $111 = 'USDZAR', $112 = 'USOIL', $113 = 'XAGUSD', $114 = 'XAUEUR', $115 = 'XAUUSD', $116 = 'XMRUSD', $117 = 'XPTUSD', $118 = 'XRPUSD', $119 = 'ZARJPY', $120 = 'ZECUSD', $121 = 'AUDCAD', $122 = 'AUDCHF', $123 = 'AUDJPY', $124 = 'AUDNZD', $125 = 'AUDUSD', $126 = 'CADCHF', $127 = 'CADJPY', $128 = 'CHFJPY', $129 = 'EURAUD', $130 = 'EURCAD', $131 = 'EURCHF', $132 = 'EURGBP', $133 = 'EURJPY', $134 = 'EURMXN', $135 = 'EURNOK', $136 = 'EURNZD', $137 = 'EURPLN', $138 = 'EURSEK', $139 = 'EURTRY', $140 = 'EURUSD', $141 = 'GBPAUD', $142 = 'GBPCAD', $143 = 'GBPCHF', $144 = 'GBPJPY', $145 = 'GBPNZD', $146 = 'GBPUSD', $147 = 'GBPZAR', $148 = 'HKDJPY', $149 = 'NOKJPY', $150 = 'NZDCAD', $151 = 'NZDCHF', $152 = 'NZDJPY', $153 = 'NZDUSD', $154 = 'USDCAD', $155 = 'USDCHF', $156 = 'USDCNH', $157 = 'USDDKK', $158 = 'USDJPY', $159 = 'USDMXN', $160 = 'USDNOK', $161 = 'USDPLN', $162 = 'USDSEK', $163 = 'USDSGD', $164 = 'USDZAR', $165 = 'ZARJPY', $166 = 'BTCEUR', $167 = 'BTCGBP', $168 = 'BTCUSD', $169 = 'DASHUSD', $170 = 'EOSUSD', $171 = 'ETHEUR', $172 = 'ETHGBP', $173 = 'ETHUSD', $174 = 'IOTAUSD', $175 = 'LTCEUR', $176 = 'LTCUSD', $177 = 'NEOUSD', $178 = 'OMGUSD', $179 = 'TRXUSD', $180 = 'XMRUSD', $181 = 'XRPUSD', $182 = 'ZECUSD', $183 = 'XAGUSD', $184 = 'XAUEUR', $185 = 'XAUUSD', $186 = 'XPTUSD', $187 = 'CL_BRENT', $188 = 'USOIL', $189 = '#ALVG', $190 = '#BAYGn', $191 = '#BMWG', $192 = '#BNPP', $193 = '#CBKG', $194 = '#DAIGn', $195 = '#VOWG', $196 = 'AUS_200', $197 = 'ESP_35', $198 = 'EUR_50', $199 = 'FRA_40', $200 = 'GBR_100', $201 = 'HKG_50', $202 = 'NAS100', $203 = 'SPX500', $204 = 'US30', $205 = '#ADBE', $206 = '#AMZN', $207 = '#APPL', $208 = '#BA', $209 = '#BABA', $210 = '#CAT', $211 = '#DIS', $212 = '#EA', $213 = '#FB', $214 = '#FDX', $215 = '#GE', $216 = '#GM', $217 = '#GOOGL', $218 = '#GS', $219 = '#INTC', $220 = '#JPM', $221 = '#KO', $222 = '#MSFT', $223 = '#NFLX', $224 = '#TSLA', $225 = '#WMT', $226 = '#XOM', $227 = '0', $228 = '', $229 = '0', $230 = '0', $231 = '0', $232 = '700', $233 = '700', $234 = 't', $235 = '10', $236 = '10'
WITH rar_max as ( ;

Date: 2026-03-20 09:30:39 Duration: 82ms Database: postgres parameters: $1 = '607867819166780302', $2 = '607867819166780302', $3 = '607867819166780302'
WITH rar_max as ( ;

Date: 2026-03-20 09:10:55 Duration: 80ms Database: postgres parameters: $1 = 't', $2 = '627', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '213', $14 = '#ADBE', $15 = '#ALVG', $16 = '#AMZN', $17 = '#APPL', $18 = '#BA', $19 = '#BABA', $20 = '#BAYGn', $21 = '#BMWG', $22 = '#BNPP', $23 = '#CAT', $24 = '#CBKG', $25 = '#DAIGn', $26 = '#DIS', $27 = '#EA', $28 = '#FB', $29 = '#FDX', $30 = '#GE', $31 = '#GM', $32 = '#GOOGL', $33 = '#GS', $34 = '#INTC', $35 = '#JPM', $36 = '#KO', $37 = '#META', $38 = '#MSFT', $39 = '#NFLX', $40 = '#TSLA', $41 = '#VOWG', $42 = '#WMT', $43 = '#XOM', $44 = 'AUDCAD', $45 = 'AUDCHF', $46 = 'AUDJPY', $47 = 'AUDNZD', $48 = 'AUDUSD', $49 = 'AUS_200', $50 = 'BTCEUR', $51 = 'BTCGBP', $52 = 'BTCUSD', $53 = 'CADCHF', $54 = 'CADJPY', $55 = 'CHFJPY', $56 = 'CL_BRENT', $57 = 'DASHUSD', $58 = 'EOSUSD', $59 = 'ESP_35', $60 = 'ETHEUR', $61 = 'ETHGBP', $62 = 'ETHUSD', $63 = 'EURAUD', $64 = 'EURCAD', $65 = 'EURCHF', $66 = 'EURGBP', $67 = 'EURJPY', $68 = 'EURMXN', $69 = 'EURNOK', $70 = 'EURNZD', $71 = 'EURPLN', $72 = 'EURSEK', $73 = 'EURTRY', $74 = 'EURUSD', $75 = 'EUR_50', $76 = 'FRA_40', $77 = 'GBPAUD', $78 = 'GBPCAD', $79 = 'GBPCHF', $80 = 'GBPJPY', $81 = 'GBPNZD', $82 = 'GBPUSD', $83 = 'GBPZAR', $84 = 'GBR_100', $85 = 'HKDJPY', $86 = 'HKG_50', $87 = 'IOTAUSD', $88 = 'LTCEUR', $89 = 'LTCUSD', $90 = 'NAS100', $91 = 'NEOUSD', $92 = 'NOKJPY', $93 = 'NZDCAD', $94 = 'NZDCHF', $95 = 'NZDJPY', $96 = 'NZDUSD', $97 = 'OMGUSD', $98 = 'SPX500', $99 = 'TRXUSD', $100 = 'US30', $101 = 'USDCAD', $102 = 'USDCHF', $103 = 'USDCNH', $104 = 'USDDKK', $105 = 'USDJPY', $106 = 'USDMXN', $107 = 'USDNOK', $108 = 'USDPLN', $109 = 'USDSEK', $110 = 'USDSGD', $111 = 'USDZAR', $112 = 'USOIL', $113 = 'XAGUSD', $114 = 'XAUEUR', $115 = 'XAUUSD', $116 = 'XMRUSD', $117 = 'XPTUSD', $118 = 'XRPUSD', $119 = 'ZARJPY', $120 = 'ZECUSD', $121 = 'AUDCAD', $122 = 'AUDCHF', $123 = 'AUDJPY', $124 = 'AUDNZD', $125 = 'AUDUSD', $126 = 'CADCHF', $127 = 'CADJPY', $128 = 'CHFJPY', $129 = 'EURAUD', $130 = 'EURCAD', $131 = 'EURCHF', $132 = 'EURGBP', $133 = 'EURJPY', $134 = 'EURMXN', $135 = 'EURNOK', $136 = 'EURNZD', $137 = 'EURPLN', $138 = 'EURSEK', $139 = 'EURTRY', $140 = 'EURUSD', $141 = 'GBPAUD', $142 = 'GBPCAD', $143 = 'GBPCHF', $144 = 'GBPJPY', $145 = 'GBPNZD', $146 = 'GBPUSD', $147 = 'GBPZAR', $148 = 'HKDJPY', $149 = 'NOKJPY', $150 = 'NZDCAD', $151 = 'NZDCHF', $152 = 'NZDJPY', $153 = 'NZDUSD', $154 = 'USDCAD', $155 = 'USDCHF', $156 = 'USDCNH', $157 = 'USDDKK', $158 = 'USDJPY', $159 = 'USDMXN', $160 = 'USDNOK', $161 = 'USDPLN', $162 = 'USDSEK', $163 = 'USDSGD', $164 = 'USDZAR', $165 = 'ZARJPY', $166 = 'BTCEUR', $167 = 'BTCGBP', $168 = 'BTCUSD', $169 = 'DASHUSD', $170 = 'EOSUSD', $171 = 'ETHEUR', $172 = 'ETHGBP', $173 = 'ETHUSD', $174 = 'IOTAUSD', $175 = 'LTCEUR', $176 = 'LTCUSD', $177 = 'NEOUSD', $178 = 'OMGUSD', $179 = 'TRXUSD', $180 = 'XMRUSD', $181 = 'XRPUSD', $182 = 'ZECUSD', $183 = 'XAGUSD', $184 = 'XAUEUR', $185 = 'XAUUSD', $186 = 'XPTUSD', $187 = 'CL_BRENT', $188 = 'USOIL', $189 = '#ALVG', $190 = '#BAYGn', $191 = '#BMWG', $192 = '#BNPP', $193 = '#CBKG', $194 = '#DAIGn', $195 = '#VOWG', $196 = 'AUS_200', $197 = 'ESP_35', $198 = 'EUR_50', $199 = 'FRA_40', $200 = 'GBR_100', $201 = 'HKG_50', $202 = 'NAS100', $203 = 'SPX500', $204 = 'US30', $205 = '#ADBE', $206 = '#AMZN', $207 = '#APPL', $208 = '#BA', $209 = '#BABA', $210 = '#CAT', $211 = '#DIS', $212 = '#EA', $213 = '#FB', $214 = '#FDX', $215 = '#GE', $216 = '#GM', $217 = '#GOOGL', $218 = '#GS', $219 = '#INTC', $220 = '#JPM', $221 = '#KO', $222 = '#MSFT', $223 = '#NFLX', $224 = '#TSLA', $225 = '#WMT', $226 = '#XOM', $227 = '0', $228 = '', $229 = '0', $230 = '0', $231 = '0', $232 = '700', $233 = '700', $234 = 't', $235 = '10', $236 = '10'

2

7s101ms

17,754

Details

0ms

29ms

0ms

SELECT ;

Times Reported Time consuming bind #2

Day	Hour	Count	Duration	Avg duration
	09	17,754	7s101ms	0ms

SELECT ;

Date: 2026-03-20 09:13:08 Duration: 29ms Database: postgres parameters: $1 = '606715250665520300'
SELECT ;

Date: 2026-03-20 09:47:44 Duration: 23ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '515840243250333300'
SELECT ;

Date: 2026-03-20 09:18:42 Duration: 13ms Database: postgres parameters: $1 = '558', $2 = '0', $3 = '0', $4 = 'SGDJPY', $5 = 'SGDJPY'

3

2s938ms

1,157

Details

1ms

7ms

2ms

SELECT symbolid, ;

Times Reported Time consuming bind #3

Day	Hour	Count	Duration	Avg duration
	09	1,157	2s938ms	2ms

SELECT symbolid, ;

Date: 2026-03-20 09:55:53 Duration: 7ms Database: postgres parameters: $1 = 'BDSWISS', $2 = '15', $3 = 'US30'
SELECT symbolid, ;

Date: 2026-03-20 09:15:59 Duration: 7ms Database: postgres parameters: $1 = 'MILLENNIUMPF', $2 = '15', $3 = 'GBPUSD.ID', $4 = 'NASDAQ100', $5 = 'GBPUSD.FX'
SELECT symbolid, ;

Date: 2026-03-20 09:01:37 Duration: 5ms Database: postgres parameters: $1 = 'MILLENNIUMPF', $2 = '15', $3 = 'EURCAD.FX', $4 = 'EURCAD.ID', $5 = 'EURCHF'

4

1s593ms

230

Details

0ms

44ms

6ms

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

Times Reported Time consuming bind #4

Day	Hour	Count	Duration	Avg duration
	09	230	1s593ms	6ms

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

Date: 2026-03-20 09:01:14 Duration: 44ms Database: postgres parameters: $1 = '1506', $2 = '1506'
select distinct s.statsid as statsid, sy.exchange as name;

Date: 2026-03-20 09:10:59 Duration: 36ms Database: postgres parameters: $1 = '1436', $2 = '1436'
select distinct s.statsid as statsid, sy.exchange as name;

Date: 2026-03-20 09:01:14 Duration: 32ms Database: postgres parameters: $1 = '1508', $2 = '1508'

5

1s37ms

511

Details

1ms

7ms

2ms

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

Times Reported Time consuming bind #5

Day	Hour	Count	Duration	Avg duration
	09	511	1s37ms	2ms

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

Date: 2026-03-20 09:16:14 Duration: 7ms Database: postgres parameters: $1 = 'GLOBALGTMT5'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-03-20 09:46:14 Duration: 5ms Database: postgres parameters: $1 = 'GLOBALGTMT5'
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;

Date: 2026-03-20 09:00:56 Duration: 3ms Database: postgres parameters: $1 = 'MILLENNIUMPF'

6

733ms

31,623

Details

0ms

9ms

0ms

select 1;

Times Reported Time consuming bind #6

Day	Hour	Count	Duration	Avg duration
	09	31,623	733ms	0ms

select 1;

Date: 2026-03-20 09:22:53 Duration: 9ms Database: postgres
select 1;

Date: 2026-03-20 09:15:48 Duration: 6ms Database: postgres
select 1;

Date: 2026-03-20 09:22:56 Duration: 6ms Database: postgres

7

733ms

20

Details

22ms

52ms

36ms

with wh_patitioned as ( ;

Times Reported Time consuming bind #7

Day	Hour	Count	Duration	Avg duration
	09	20	733ms	36ms

with wh_patitioned as ( ;

Date: 2026-03-20 09:35:57 Duration: 52ms 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: 2026-03-20 09:35:59 Duration: 47ms 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: 2026-03-20 09:22:53 Duration: 45ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'

8

716ms

7,946

Details

0ms

2ms

0ms

select category, ;

Times Reported Time consuming bind #8

Day	Hour	Count	Duration	Avg duration
	09	7,946	716ms	0ms

select category, ;

Date: 2026-03-20 09:01:22 Duration: 2ms Database: postgres parameters: $1 = '515852059319772307', $2 = 'pattern', $3 = '515852059319772307', $4 = 'pattern'
select category, ;

Date: 2026-03-20 09:11:06 Duration: 1ms Database: postgres parameters: $1 = '601729875360207307', $2 = 'symbol', $3 = 'USDSEK', $4 = 'USDMXN', $5 = 'AUDJPY', $6 = 'USDZAR', $7 = 'CADJPY', $8 = 'XAUUSD', $9 = 'NZDJPY', $10 = 'XAGEUR', $11 = 'XAGUSD', $12 = 'USDHUF', $13 = 'USDJPY', $14 = 'ZARJPY', $15 = 'XAUEUR', $16 = 'GBPZAR', $17 = 'EURJPY', $18 = 'GBPJPY', $19 = 'CHFJPY', $20 = 'USDCZK', $21 = 'USDPLN', $22 = 'USDDKK', $23 = 'GBPAUD', $24 = 'EURHUF', $25 = 'USDNOK', $26 = 'GBPNZD', $27 = 'USDCNH', $28 = 'EURNOK', $29 = 'USDTRY', $30 = 'EURHUF', $31 = 'USDCAD', $32 = 'ZARJPY', $33 = 'EURPLN', $34 = 'EURGBP', $35 = 'EURNZD', $36 = 'CADCHF', $37 = 'EURCHF', $38 = 'EURAUD', $39 = 'GBPCAD', $40 = 'USDHUF', $41 = 'USDSGD', $42 = 'USDHKD', $43 = 'AUDNZD', $44 = 'CADJPY', $45 = 'EURCAD', $46 = 'USDMXN', $47 = 'GBPCAD', $48 = 'USDZAR', $49 = 'GBPCHF', $50 = 'EURCAD', $51 = 'GBPUSD', $52 = 'EURNZD', $53 = '601729875360207307', $54 = 'symbol', $55 = 'USDSEK', $56 = 'USDMXN', $57 = 'AUDJPY', $58 = 'USDZAR', $59 = 'CADJPY', $60 = 'XAUUSD', $61 = 'NZDJPY', $62 = 'XAGEUR', $63 = 'XAGUSD', $64 = 'USDHUF', $65 = 'USDJPY', $66 = 'ZARJPY', $67 = 'XAUEUR', $68 = 'GBPZAR', $69 = 'EURJPY', $70 = 'GBPJPY', $71 = 'CHFJPY', $72 = 'USDCZK', $73 = 'USDPLN', $74 = 'USDDKK', $75 = 'GBPAUD', $76 = 'EURHUF', $77 = 'USDNOK', $78 = 'GBPNZD', $79 = 'USDCNH', $80 = 'EURNOK', $81 = 'USDTRY', $82 = 'EURHUF', $83 = 'USDCAD', $84 = 'ZARJPY', $85 = 'EURPLN', $86 = 'EURGBP', $87 = 'EURNZD', $88 = 'CADCHF', $89 = 'EURCHF', $90 = 'EURAUD', $91 = 'GBPCAD', $92 = 'USDHUF', $93 = 'USDSGD', $94 = 'USDHKD', $95 = 'AUDNZD', $96 = 'CADJPY', $97 = 'EURCAD', $98 = 'USDMXN', $99 = 'GBPCAD', $100 = 'USDZAR', $101 = 'GBPCHF', $102 = 'EURCAD', $103 = 'GBPUSD', $104 = 'EURNZD'
select category, ;

Date: 2026-03-20 09:01:17 Duration: 1ms Database: postgres parameters: $1 = '515852059313898307', $2 = 'symbol', $3 = 'BTCUSD', $4 = 'USDMXN', $5 = 'AUDJPY', $6 = 'USDZAR', $7 = 'USDJPY', $8 = 'EURZAR', $9 = 'USDTHB', $10 = 'GBPJPY', $11 = 'NZDJPY', $12 = 'CADJPY', $13 = 'CHFJPY', $14 = 'USDHUF', $15 = 'EURJPY', $16 = 'USDSEK', $17 = 'EURTRY', $18 = 'SGDJPY', $19 = 'SEKJPY', $20 = 'EURHKD', $21 = 'GBPSEK', $22 = 'USDNOK', $23 = 'USDDKK', $24 = 'EURSEK', $25 = 'GBPDKK', $26 = 'GBPNOK', $27 = 'NOKJPY', $28 = 'USDCZK', $29 = 'EURNOK', $30 = 'USDPLN', $31 = 'BTCUSD', $32 = 'GBPAUD', $33 = 'GBPNZD', $34 = 'EURNZD', $35 = 'EURPLN', $36 = 'USDCNH', $37 = 'EURAUD', $38 = 'USDTRY', $39 = 'EURGBP', $40 = 'GBPCAD', $41 = 'USDCAD', $42 = 'SEKJPY', $43 = 'GBPCAD', $44 = 'EURNOK', $45 = 'NOKJPY', $46 = 'USDHUF', $47 = 'AUDNZD', $48 = 'GBPSGD', $49 = 'CHFSGD', $50 = 'GBPNOK', $51 = 'USDMXN', $52 = 'EURSGD', $53 = '515852059313898307', $54 = 'symbol', $55 = 'BTCUSD', $56 = 'USDMXN', $57 = 'AUDJPY', $58 = 'USDZAR', $59 = 'USDJPY', $60 = 'EURZAR', $61 = 'USDTHB', $62 = 'GBPJPY', $63 = 'NZDJPY', $64 = 'CADJPY', $65 = 'CHFJPY', $66 = 'USDHUF', $67 = 'EURJPY', $68 = 'USDSEK', $69 = 'EURTRY', $70 = 'SGDJPY', $71 = 'SEKJPY', $72 = 'EURHKD', $73 = 'GBPSEK', $74 = 'USDNOK', $75 = 'USDDKK', $76 = 'EURSEK', $77 = 'GBPDKK', $78 = 'GBPNOK', $79 = 'NOKJPY', $80 = 'USDCZK', $81 = 'EURNOK', $82 = 'USDPLN', $83 = 'BTCUSD', $84 = 'GBPAUD', $85 = 'GBPNZD', $86 = 'EURNZD', $87 = 'EURPLN', $88 = 'USDCNH', $89 = 'EURAUD', $90 = 'USDTRY', $91 = 'EURGBP', $92 = 'GBPCAD', $93 = 'USDCAD', $94 = 'SEKJPY', $95 = 'GBPCAD', $96 = 'EURNOK', $97 = 'NOKJPY', $98 = 'USDHUF', $99 = 'AUDNZD', $100 = 'GBPSGD', $101 = 'CHFSGD', $102 = 'GBPNOK', $103 = 'USDMXN', $104 = 'EURSGD'

9

398ms

49

Details

4ms

14ms

8ms

WITH last_candle AS ( ;

Times Reported Time consuming bind #9

Day	Hour	Count	Duration	Avg duration
	09	49	398ms	8ms

WITH last_candle AS ( ;

Date: 2026-03-20 09:15:04 Duration: 14ms Database: postgres parameters: $1 = '667', $2 = '667'
WITH last_candle AS ( ;

Date: 2026-03-20 09:16:00 Duration: 14ms Database: postgres parameters: $1 = '558', $2 = '558'
WITH last_candle AS ( ;

Date: 2026-03-20 09:04:00 Duration: 12ms Database: postgres parameters: $1 = '558', $2 = '558'

10

331ms

419

Details

0ms

2ms

0ms

SELECT absolutetimezoneoffset;

Times Reported Time consuming bind #10

Day	Hour	Count	Duration	Avg duration
	09	419	331ms	0ms

SELECT absolutetimezoneoffset;

Date: 2026-03-20 09:01:22 Duration: 2ms Database: postgres parameters: $1 = '690', $2 = 'FOREX'
SELECT absolutetimezoneoffset;

Date: 2026-03-20 09:01:21 Duration: 1ms Database: postgres parameters: $1 = '689', $2 = 'FOREX'
SELECT absolutetimezoneoffset;

Date: 2026-03-20 09:01:22 Duration: 1ms Database: postgres parameters: $1 = '692', $2 = 'FOREX'

11

315ms

8

Details

29ms

45ms

39ms

with sym_info as ( ;

Times Reported Time consuming bind #11

Day	Hour	Count	Duration	Avg duration
	09	8	315ms	39ms

with sym_info as ( ;

Date: 2026-03-20 09:35:59 Duration: 45ms 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: 2026-03-20 09:36:09 Duration: 45ms Database: postgres parameters: $1 = '692', $2 = 'Forex', $3 = 'Forex', $4 = '692', $5 = 'Forex', $6 = '692', $7 = '692', $8 = 'Forex', $9 = '692'
with sym_info as ( ;

Date: 2026-03-20 09:06:53 Duration: 43ms Database: postgres parameters: $1 = '692', $2 = 'Forex', $3 = 'Forex', $4 = '692', $5 = 'Forex', $6 = '692', $7 = '692', $8 = 'Forex', $9 = '692'

12

309ms

28

Details

0ms

19ms

11ms

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

Times Reported Time consuming bind #12

Day	Hour	Count	Duration	Avg duration
	09	28	309ms	11ms

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

Date: 2026-03-20 09:26:30 Duration: 19ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Date: 2026-03-20 09:52:00 Duration: 18ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;

Date: 2026-03-20 09:18:19 Duration: 18ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'

13

263ms

5,323

Details

0ms

1ms

0ms

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

Times Reported Time consuming bind #13

Day	Hour	Count	Duration	Avg duration
	09	5,323	263ms	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: 2026-03-20 09:45:52 Duration: 1ms Database: postgres parameters: $1 = '2026-03-20 09:30:00', $2 = '1.20492', $3 = '1.20495', $4 = '1.20437', $5 = '1.20443', $6 = '1767', $7 = '515840233450398300', $8 = '0', $9 = '2026-03-20 09:45:52.402', $10 = '2026-03-20 09:45:52.093', $11 = '1.20492', $12 = '1.20495', $13 = '1.20437', $14 = '1.20443', $15 = '1767', $16 = '0', $17 = '2026-03-20 09:45:52.402', $18 = '2026-03-20 09:45:52.093'
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: 2026-03-20 09:45:23 Duration: 0ms Database: postgres parameters: $1 = '2026-03-20 09:30:00', $2 = '1.20504', $3 = '1.20507', $4 = '1.20432', $5 = '1.20433', $6 = '839', $7 = '515840217700230300', $8 = '0', $9 = '2026-03-20 09:45:23.315', $10 = '2026-03-20 09:45:23.074', $11 = '1.20504', $12 = '1.20507', $13 = '1.20432', $14 = '1.20433', $15 = '839', $16 = '0', $17 = '2026-03-20 09:45:23.315', $18 = '2026-03-20 09:45:23.074'
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: 2026-03-20 09:55:37 Duration: 0ms Database: postgres parameters: $1 = '2026-03-20 09:15:00', $2 = '8451.95', $3 = '8465.4', $4 = '8450.95', $5 = '8463.4', $6 = '1650', $7 = '515840248015086300', $8 = '0', $9 = '2026-03-20 09:55:37.648', $10 = '2026-03-20 09:55:37.567', $11 = '8451.95', $12 = '8465.4', $13 = '8450.95', $14 = '8463.4', $15 = '1650', $16 = '0', $17 = '2026-03-20 09:55:37.648', $18 = '2026-03-20 09:55:37.567'

14

251ms

3,091

Details

0ms

1ms

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 #14

Day	Hour	Count	Duration	Avg duration
	09	3,091	251ms	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: 2026-03-20 09:10:53 Duration: 1ms Database: postgres parameters: $1 = '2026-03-20 08:00:00', $2 = '46124.45', $3 = '46137.95', $4 = '46059.45', $5 = '46068.45', $6 = '1790', $7 = '515840248000726300', $8 = '0', $9 = '2026-03-20 09:10:53.989', $10 = '2026-03-20 09:10:53.847', $11 = '46124.45', $12 = '46137.95', $13 = '46059.45', $14 = '46068.45', $15 = '1790', $16 = '0', $17 = '2026-03-20 09:10:53.989', $18 = '2026-03-20 09:10:53.847'
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: 2026-03-20 09:32:36 Duration: 0ms Database: postgres parameters: $1 = '2026-03-20 08:30:00', $2 = '8442.4', $3 = '8442.75', $4 = '8426.8', $5 = '8432.4', $6 = '1876', $7 = '515840248015340300', $8 = '0', $9 = '2026-03-20 09:32:36.872', $10 = '2026-03-20 09:32:36.801', $11 = '8442.4', $12 = '8442.75', $13 = '8426.8', $14 = '8432.4', $15 = '1876', $16 = '0', $17 = '2026-03-20 09:32:36.872', $18 = '2026-03-20 09:32:36.801'
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: 2026-03-20 09:11:38 Duration: 0ms Database: postgres parameters: $1 = '2026-03-20 08:30:00', $2 = '8442.4', $3 = '8442.75', $4 = '8426.8', $5 = '8432.4', $6 = '1876', $7 = '515840248015340300', $8 = '0', $9 = '2026-03-20 09:11:38.286', $10 = '2026-03-20 09:11:38.179', $11 = '8442.4', $12 = '8442.75', $13 = '8426.8', $14 = '8432.4', $15 = '1876', $16 = '0', $17 = '2026-03-20 09:11:38.286', $18 = '2026-03-20 09:11:38.179'

15

181ms

2,074

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 #15

Day	Hour	Count	Duration	Avg duration
	09	2,074	181ms	0ms

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

Date: 2026-03-20 09:10:53 Duration: 0ms Database: postgres parameters: $1 = '2026-03-20 07:00:00', $2 = '46092.25', $3 = '46143.25', $4 = '46082.25', $5 = '46123.95', $6 = '3150', $7 = '515840248000890300', $8 = '0', $9 = '2026-03-20 09:10:53.962', $10 = '2026-03-20 09:10:53.857', $11 = '46092.25', $12 = '46143.25', $13 = '46082.25', $14 = '46123.95', $15 = '3150', $16 = '0', $17 = '2026-03-20 09:10:53.962', $18 = '2026-03-20 09:10:53.857'
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: 2026-03-20 09:02:28 Duration: 0ms Database: postgres parameters: $1 = '2026-03-19 20:00:00', $2 = '109.18', $3 = '110.56', $4 = '108.7', $5 = '109.74', $6 = '5357', $7 = '515840247879403300', $8 = '0', $9 = '2026-03-20 09:02:28.756', $10 = '2026-03-20 09:02:28.53', $11 = '109.18', $12 = '110.56', $13 = '108.7', $14 = '109.74', $15 = '5357', $16 = '0', $17 = '2026-03-20 09:02:28.756', $18 = '2026-03-20 09:02:28.53'
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: 2026-03-20 09:02:26 Duration: 0ms Database: postgres parameters: $1 = '2026-03-19 20:00:00', $2 = '244.24', $3 = '247.32', $4 = '244.22', $5 = '246.8', $6 = '2791', $7 = '515840247899857300', $8 = '0', $9 = '2026-03-20 09:02:26.585', $10 = '2026-03-20 09:02:26.516', $11 = '244.24', $12 = '247.32', $13 = '244.22', $14 = '246.8', $15 = '2791', $16 = '0', $17 = '2026-03-20 09:02:26.585', $18 = '2026-03-20 09:02:26.516'

16

103ms

211

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 #16

Day	Hour	Count	Duration	Avg duration
	09	211	103ms	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: 2026-03-20 09:13:08 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: 2026-03-20 09:13:06 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: 2026-03-20 09:13:06 Duration: 0ms Database: postgres

17

91ms

34

Details

1ms

17ms

2ms

WITH rcr_max as ( ;

Times Reported Time consuming bind #17

Day	Hour	Count	Duration	Avg duration
	09	34	91ms	2ms

WITH rcr_max as ( ;

Date: 2026-03-20 09:45:20 Duration: 17ms Database: postgres parameters: $1 = '607868527615176305', $2 = '607868527615176305', $3 = '607868527615176305'
WITH rcr_max as ( ;

Date: 2026-03-20 09:30:20 Duration: 13ms Database: postgres parameters: $1 = '607868527615176305', $2 = '607868527615176305', $3 = '607868527615176305'
WITH rcr_max as ( ;

Date: 2026-03-20 09:00:02 Duration: 7ms Database: postgres parameters: $1 = '607868527615176305', $2 = '607868527615176305', $3 = '607868527615176305'

18

89ms

16

Details

3ms

9ms

5ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Times Reported Time consuming bind #18

Day	Hour	Count	Duration	Avg duration
	09	16	89ms	5ms

SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-03-20 09:10:55 Duration: 9ms Database: postgres parameters: $1 = '627', $2 = '627'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-03-20 09:10:54 Duration: 8ms Database: postgres parameters: $1 = '627', $2 = '627'
SELECT DISTINCT ON (basegroupname, symbol) ;

Date: 2026-03-20 09:01:02 Duration: 7ms Database: postgres parameters: $1 = '627', $2 = '627'

19

66ms

12

Details

2ms

10ms

5ms

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

Times Reported Time consuming bind #19

Day	Hour	Count	Duration	Avg duration
	09	12	66ms	5ms

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

Date: 2026-03-20 09:13:04 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: 2026-03-20 09:13:05 Duration: 9ms 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: 2026-03-20 09:00:36 Duration: 8ms 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'

20

56ms

54

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 #20

Day	Hour	Count	Duration	Avg duration
	09	54	56ms	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: 2026-03-20 09:50:43 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: 2026-03-20 09:03:44 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: 2026-03-20 09:35:43 Duration: 2ms Database: postgres parameters: $1 = '689', $2 = 'XAUUSD', $3 = '689'

Log levels

Key values

438,672 Log entries

Chart
Table

Type	Count	Percentage
CONTEXT	412	0.09%
DETAIL	100,770	22.97%
ERROR	239	0.05%
LOG	336,320	76.67%
STATEMENT	931	0.21%

Events distribution

Key values

0 PANIC entries
0 FATAL entries
239 ERROR entries
0 WARNING entries

Most Frequent Errors/Events

Key values

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

Rank

Times reported

Error

1

239

Details

ERROR: canceling statement due to statement timeout

Times Reported Most Frequent Error / Event #1

Day	Hour	Count
Mar 20	09	239

ERROR: canceling statement due to statement timeout

Global information

Overview

Global Stats

SQL Traffic

Key values

SELECT Traffic

Key values

INSERT/UPDATE/DELETE Traffic

Key values

Queries duration

Key values

Prepared queries ratio

Key values

General Activity

Connections

Established Connections

Key values

Connections per database

Key values

Connections per user

Key values

Connections per host

Key values

Sessions

Simultaneous sessions

Key values

Histogram of session times

Key values

Sessions per database

Key values

Sessions per user

Key values

Sessions per host

Key values

Sessions per application

Key values

Checkpoints / Restartpoints

Checkpoints Buffers

Key values

Checkpoints Wal files

Key values

Checkpoints distance

Key values

Checkpoints Activity

Checkpoint causes

Key values

Temporary Files

Size of temporary files

Key values

Number of temporary files

Key values

Temporary Files Activity

Queries generating the most temporary files (N)

Queries generating the largest temporary files

Vacuums

Vacuums / Analyzes Distribution

Key values

Analyzes per table

Key values

Vacuums per table

Key values

Tuples removed per table

Key values

Pages removed per table

Key values

Autovacuum Activity

Locks

Locks by types

Key values

Type of locks

Most frequent waiting queries (N)

Queries that waited the most

Queries

Queries by type

Key values

Queries by database

Key values

Queries by user

Key values

Duration by user