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  • XML/PHP : Content is not allowed in prolog

    - by Tristan
    Hello, i have this message error and i don't know where does the problem comes from: <?php include "DBconnection.class.php"; $sql = DBConnection::getInstance(); $requete = "SELECT g.siteweb, g.offreDedie, g.coupon, g.only_dedi, g.transparence, g.abonnement , s.GSP_nom as nom , COUNT(s.GSP_nom) as nb_votes, TRUNCATE(AVG(vote), 2) as qualite, TRUNCATE(AVG(prix), 2) as rapport, TRUNCATE(AVG(serviceClient), 2) as serviceCli, TRUNCATE(AVG(interface), 2) as interface, TRUNCATE(AVG(services), 2) as services FROM votes_serveur AS v INNER JOIN serveur AS s ON v.idServ = s.idServ INNER JOIN gsp AS g ON s.GSP_nom = g.nom WHERE s.valide = 1 GROUP BY s.GSP_nom"; $sql->query($requete); $xml = '<?xml version="1.0" encoding="UTF-8" ?>'; $xml .='<GamerCertified>'; while($row = $sql->fetchArray()){ $moyenne_services = ($row['services'] + $row['serviceCli'] + $row['interface'] ) / 3 ; $moyenne_services = round( $moyenne_services, 2); $moyenne_ge = ($row['services'] + $row['serviceCli'] + $row['interface'] + $row['qualite'] + $row['rapport'] ) / 5 ; $moyenne_ge = round( $moyenne_ge, 2); $xml .= '<GSP>'; $xml .= '<nom>'.$row["nom"].'</nom>'; $xml .= '<nombre-votes>'.$row["nb_votes"].'</nombre-votes>'; $xml .= '<services>'.$moyenne_services.'</services>'; $xml .= '<qualite>'.$row["qualite"].'</qualite>'; $xml .= '<prix>'.$row["rapport"].'</prix>'; $xml .= '<label-transparence>'.$row["transparence"].'</label-transparence>'; $xml .= '<moyenne-generale>'.$moyenne_ge.'</moyenne-generale>'; $xml .= '<serveurs-dedies>'.$row["offreDedie"].'</serveurs-dedies>'; $xml .= '</GSP>'; } $xml .= '</GamerCertified>'; echo $xml; Thanks

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  • XML/PHP : Content is not allowed in trailing section

    - by Tristan
    Hello, i have this message error and i don't know where does the problem comes from: <?php include "DBconnection.class.php"; $sql = DBConnection::getInstance(); $requete = "SELECT g.siteweb, g.offreDedie, g.coupon, g.only_dedi, g.transparence, g.abonnement , s.GSP_nom as nom , COUNT(s.GSP_nom) as nb_votes, TRUNCATE(AVG(vote), 2) as qualite, TRUNCATE(AVG(prix), 2) as rapport, TRUNCATE(AVG(serviceClient), 2) as serviceCli, TRUNCATE(AVG(interface), 2) as interface, TRUNCATE(AVG(services), 2) as services FROM votes_serveur AS v INNER JOIN serveur AS s ON v.idServ = s.idServ INNER JOIN gsp AS g ON s.GSP_nom = g.nom WHERE s.valide = 1 GROUP BY s.GSP_nom"; $sql->query($requete); $xml = '<?xml version="1.0" encoding="UTF-8" ?><GamerCertified>'; while($row = $sql->fetchArray()){ $moyenne_services = ($row['services'] + $row['serviceCli'] + $row['interface'] ) / 3 ; $moyenne_services = round( $moyenne_services, 2); $moyenne_ge = ($row['services'] + $row['serviceCli'] + $row['interface'] + $row['qualite'] + $row['rapport'] ) / 5 ; $moyenne_ge = round( $moyenne_ge, 2); $xml .= '<GSP>'; $xml .= '<nom>'.$row["nom"].'</nom>'; $xml .= '<nombre-votes>'.$row["nb_votes"].'</nombre-votes>'; $xml .= '<services>'.$moyenne_services.'</services>'; $xml .= '<qualite>'.$row["qualite"].'</qualite>'; $xml .= '<prix>'.$row["rapport"].'</prix>'; $xml .= '<label-transparence>'.$row["transparence"].'</label-transparence>'; $xml .= '<moyenne-generale>'.$moyenne_ge.'</moyenne-generale>'; $xml .= '<serveurs-dedies>'.$row["offreDedie"].'</serveurs-dedies>'; $xml .= '</GSP>'; } $xml .= '</GamerCertified>'; echo $xml; Thanks

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  • Getting timing consistency in Linux

    - by Jim Hunziker
    I can't seem to get a simple program (with lots of memory access) to achieve consistent timing in Linux. I'm using a 2.6 kernel, and the program is being run on a dual-core processor with realtime priority. I'm trying to disable cache effects by declaring the memory arrays as volatile. Below are the results and the program. What are some possible sources of the outliers? Results: Number of trials: 100 Range: 0.021732s to 0.085596s Average Time: 0.058094s Standard Deviation: 0.006944s Extreme Outliers (2 SDs away from mean): 7 Average Time, excluding extreme outliers: 0.059273s Program: #include <stdio.h> #include <stdlib.h> #include <math.h> #include <sched.h> #include <sys/time.h> #define NUM_POINTS 5000000 #define REPS 100 unsigned long long getTimestamp() { unsigned long long usecCount; struct timeval timeVal; gettimeofday(&timeVal, 0); usecCount = timeVal.tv_sec * (unsigned long long) 1000000; usecCount += timeVal.tv_usec; return (usecCount); } double convertTimestampToSecs(unsigned long long timestamp) { return (timestamp / (double) 1000000); } int main(int argc, char* argv[]) { unsigned long long start, stop; double times[REPS]; double sum = 0; double scale, avg, newavg, median; double stddev = 0; double maxval = -1.0, minval = 1000000.0; int i, j, freq, count; int outliers = 0; struct sched_param sparam; sched_getparam(getpid(), &sparam); sparam.sched_priority = sched_get_priority_max(SCHED_FIFO); sched_setscheduler(getpid(), SCHED_FIFO, &sparam); volatile float* data; volatile float* results; data = calloc(NUM_POINTS, sizeof(float)); results = calloc(NUM_POINTS, sizeof(float)); for (i = 0; i < REPS; ++i) { start = getTimestamp(); for (j = 0; j < NUM_POINTS; ++j) { results[j] = data[j]; } stop = getTimestamp(); times[i] = convertTimestampToSecs(stop-start); } free(data); free(results); for (i = 0; i < REPS; i++) { sum += times[i]; if (times[i] > maxval) maxval = times[i]; if (times[i] < minval) minval = times[i]; } avg = sum/REPS; for (i = 0; i < REPS; i++) stddev += (times[i] - avg)*(times[i] - avg); stddev /= REPS; stddev = sqrt(stddev); for (i = 0; i < REPS; i++) { if (times[i] > avg + 2*stddev || times[i] < avg - 2*stddev) { sum -= times[i]; outliers++; } } newavg = sum/(REPS-outliers); printf("Number of trials: %d\n", REPS); printf("Range: %fs to %fs\n", minval, maxval); printf("Average Time: %fs\n", avg); printf("Standard Deviation: %fs\n", stddev); printf("Extreme Outliers (2 SDs away from mean): %d\n", outliers); printf("Average Time, excluding extreme outliers: %fs\n", newavg); return 0; }

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  • Remove redundant SQL code

    - by Dave Jarvis
    Code The following code calculates the slope and intercept for a linear regression against a slathering of data. It then applies the equation y = mx + b against the same result set to calculate the value of the regression line for each row. Can the two separate sub-selects be joined so that the data and its slope/intercept are calculated without executing the data gathering part of the query twice? SELECT AVG(D.AMOUNT) as AMOUNT, Y.YEAR * ymxb.SLOPE + ymxb.INTERCEPT as REGRESSION_LINE, Y.YEAR as YEAR, MAKEDATE(Y.YEAR,1) as AMOUNT_DATE FROM CITY C, STATION S, YEAR_REF Y, MONTH_REF M, DAILY D, (SELECT ((avg(t.AMOUNT * t.YEAR)) - avg(t.AMOUNT) * avg(t.YEAR)) / (stddev( t.AMOUNT ) * stddev( t.YEAR )) as CORRELATION, ((sum(t.YEAR) * sum(t.AMOUNT)) - (count(1) * sum(t.YEAR * t.AMOUNT))) / (power(sum(t.YEAR), 2) - count(1) * sum(power(t.YEAR, 2))) as SLOPE, ((sum( t.YEAR ) * sum( t.YEAR * t.AMOUNT )) - (sum( t.AMOUNT ) * sum(power(t.YEAR, 2)))) / (power(sum(t.YEAR), 2) - count(1) * sum(power(t.YEAR, 2))) as INTERCEPT FROM ( SELECT AVG(D.AMOUNT) as AMOUNT, Y.YEAR as YEAR, MAKEDATE(Y.YEAR,1) as AMOUNT_DATE FROM CITY C, STATION S, YEAR_REF Y, MONTH_REF M, DAILY D WHERE $X{ IN, C.ID, CityCode } AND SQRT( POW( C.LATITUDE - S.LATITUDE, 2 ) + POW( C.LONGITUDE - S.LONGITUDE, 2 ) ) < $P{Radius} AND S.STATION_DISTRICT_ID = Y.STATION_DISTRICT_ID AND Y.YEAR BETWEEN 1900 AND 2009 AND M.YEAR_REF_ID = Y.ID AND M.CATEGORY_ID = $P{CategoryCode} AND M.ID = D.MONTH_REF_ID AND D.DAILY_FLAG_ID <> 'M' GROUP BY Y.YEAR ) t ) ymxb WHERE $X{ IN, C.ID, CityCode } AND SQRT( POW( C.LATITUDE - S.LATITUDE, 2 ) + POW( C.LONGITUDE - S.LONGITUDE, 2 ) ) < $P{Radius} AND S.STATION_DISTRICT_ID = Y.STATION_DISTRICT_ID AND Y.YEAR BETWEEN 1900 AND 2009 AND M.YEAR_REF_ID = Y.ID AND M.CATEGORY_ID = $P{CategoryCode} AND M.ID = D.MONTH_REF_ID AND D.DAILY_FLAG_ID <> 'M' GROUP BY Y.YEAR Question How do I execute the duplicate bits only once per query, instead of twice? The duplicate bit is the WHERE clause: $X{ IN, C.ID, CityCode } AND SQRT( POW( C.LATITUDE - S.LATITUDE, 2 ) + POW( C.LONGITUDE - S.LONGITUDE, 2 ) ) < $P{Radius} AND S.STATION_DISTRICT_ID = Y.STATION_DISTRICT_ID AND Y.YEAR BETWEEN 1900 AND 2009 AND M.YEAR_REF_ID = Y.ID AND M.CATEGORY_ID = $P{CategoryCode} AND M.ID = D.MONTH_REF_ID AND D.DAILY_FLAG_ID <> 'M' Related http://stackoverflow.com/questions/1595659/how-to-eliminate-duplicate-calculation-in-sql Thank you!

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  • Accessing mySQL from two ports: Problems with iptables

    - by marekventur
    Hi! I'm trying to make my mySQL-server (running on Ubuntu) listen on port 3306 and 110, because I would like to access it from a network with very few open ports. So far I've found this answer telling me to do iptables -t nat -A PREROUTING -i eth0 -p tcp --dport 110 -j REDIRECT --to-port 3306 but all I got is: # mysql -h mydomain.com -P 3306 -u username --password=xyz Welcome to the MySQL monitor. Commands end with ; or \g. Your MySQL connection id is 68863 Server version: 5.0.75-0ubuntu10.5 (Ubuntu) Type 'help;' or '\h' for help. Type '\c' to clear the buffer. mysql> exit Bye # mysql -h mydomain.com -P 110 -u username --password=xyz ERROR 2003 (HY000): Can't connect to MySQL server on 'mydomain.com' (111) I'm not an expert with iptables, so I not sure where to look for the problem. I'm googling around for quite some time, but haven't found anything to help me yet. This is what iptable tells me: # iptables -t nat -L -n -v Chain PREROUTING (policy ACCEPT 32M packets, 1674M bytes) pkts bytes target prot opt in out source destination 0 0 REDIRECT tcp -- eth0 * 0.0.0.0/0 0.0.0.0/0 tcp dpt:110 redir ports 3306 Chain POSTROUTING (policy ACCEPT 855K packets, 55M bytes) pkts bytes target prot opt in out source destination Chain OUTPUT (policy ACCEPT 837K packets, 54M bytes) pkts bytes target prot opt in out source destination # iptables -L -n -v Chain INPUT (policy DROP 7 packets, 340 bytes) pkts bytes target prot opt in out source destination 107K 5390K LOG all -- * * 0.0.0.0/0 0.0.0.0/0 state INVALID limit: avg 2/sec burst 5 LOG flags 0 level 4 prefix `INPUT INVALID ' 131K 6614K DROP all -- * * 0.0.0.0/0 0.0.0.0/0 state INVALID 0 0 MY_DROP tcp -- * * 0.0.0.0/0 0.0.0.0/0 tcp flags:0x3F/0x00 0 0 MY_DROP tcp -- * * 0.0.0.0/0 0.0.0.0/0 tcp flags:0x03/0x03 0 0 MY_DROP tcp -- * * 0.0.0.0/0 0.0.0.0/0 tcp flags:0x06/0x06 0 0 MY_DROP tcp -- * * 0.0.0.0/0 0.0.0.0/0 tcp flags:0x05/0x05 0 0 MY_DROP tcp -- * * 0.0.0.0/0 0.0.0.0/0 tcp flags:0x11/0x01 0 0 MY_DROP tcp -- * * 0.0.0.0/0 0.0.0.0/0 tcp flags:0x18/0x08 0 0 MY_DROP tcp -- * * 0.0.0.0/0 0.0.0.0/0 tcp flags:0x30/0x20 6948K 12G ACCEPT all -- lo * 0.0.0.0/0 0.0.0.0/0 151M 34G ACCEPT all -- * * 0.0.0.0/0 0.0.0.0/0 state RELATED,ESTABLISHED 32M 1666M ACCEPT tcp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW tcp dpt:80 1833 106K ACCEPT tcp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW tcp dpt:443 603 29392 ACCEPT tcp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW tcp dpt:25 1 60 ACCEPT tcp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW tcp dpt:465 24 1180 ACCEPT tcp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW tcp dpt:110 1 60 ACCEPT tcp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW tcp dpt:995 7919 400K ACCEPT tcp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW tcp dpt:143 1 60 ACCEPT tcp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW tcp dpt:993 0 0 ACCEPT tcp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW tcp dpt:119 1 60 ACCEPT tcp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW tcp dpt:53 7 517 ACCEPT udp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW udp dpt:53 1110 65364 ACCEPT tcp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW tcp dpt:21 139K 8313K ACCEPT tcp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW tcp dpt:22 10176 499K ACCEPT tcp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW tcp dpt:3306 2 80 ACCEPT udp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW udp dpt:123 0 0 ACCEPT tcp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW tcp dpt:6060 4 176 ACCEPT tcp -- venet0 * 0.0.0.0/0 0.0.0.0/0 state NEW tcp dpt:6667 20987 1179K MY_REJECT all -- * * 0.0.0.0/0 0.0.0.0/0 Chain FORWARD (policy DROP 0 packets, 0 bytes) pkts bytes target prot opt in out source destination Chain OUTPUT (policy DROP 0 packets, 0 bytes) pkts bytes target prot opt in out source destination 2159 284K LOG all -- * * 0.0.0.0/0 0.0.0.0/0 state INVALID limit: avg 2/sec burst 5 LOG flags 0 level 4 prefix `OUTPUT INVALID ' 2630 304K DROP all -- * * 0.0.0.0/0 0.0.0.0/0 state INVALID 6948K 12G ACCEPT all -- * lo 0.0.0.0/0 0.0.0.0/0 181M 34G ACCEPT all -- * * 0.0.0.0/0 0.0.0.0/0 state NEW,RELATED,ESTABLISHED 0 0 MY_REJECT all -- * * 0.0.0.0/0 0.0.0.0/0 Chain MY_DROP (7 references) pkts bytes target prot opt in out source destination 0 0 LOG all -- * * 0.0.0.0/0 0.0.0.0/0 limit: avg 2/sec burst 5 LOG flags 0 level 4 prefix `PORTSCAN DROP ' 0 0 DROP all -- * * 0.0.0.0/0 0.0.0.0/0 Chain MY_REJECT (2 references) pkts bytes target prot opt in out source destination 13806 652K LOG tcp -- * * 0.0.0.0/0 0.0.0.0/0 limit: avg 2/sec burst 5 LOG flags 0 level 4 prefix `REJECT TCP ' 18171 830K REJECT tcp -- * * 0.0.0.0/0 0.0.0.0/0 reject-with tcp-reset 912 242K LOG udp -- * * 0.0.0.0/0 0.0.0.0/0 limit: avg 2/sec burst 5 LOG flags 0 level 4 prefix `REJECT UDP ' 912 242K REJECT udp -- * * 0.0.0.0/0 0.0.0.0/0 reject-with icmp-port-unreachable 1904 107K LOG icmp -- * * 0.0.0.0/0 0.0.0.0/0 limit: avg 2/sec burst 5 LOG flags 0 level 4 prefix `DROP ICMP ' 1904 107K DROP icmp -- * * 0.0.0.0/0 0.0.0.0/0 0 0 LOG all -- * * 0.0.0.0/0 0.0.0.0/0 limit: avg 2/sec burst 5 LOG flags 0 level 4 prefix `REJECT OTHER ' 0 0 REJECT all -- * * 0.0.0.0/0 0.0.0.0/0 reject-with icmp-proto-unreachable Is there anyone who can give ma a hint where to look for the problem? Thank you!

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  • How to calculate the covariance in T-SQL

    - by Peter Larsson
    DECLARE @Sample TABLE         (             x INT NOT NULL,             y INT NOT NULL         ) INSERT  @Sample VALUES  (3, 9),         (2, 7),         (4, 12),         (5, 15),         (6, 17) ;WITH cteSource(x, xAvg, y, yAvg, n) AS (         SELECT  1E * x,                 AVG(1E * x) OVER (PARTITION BY (SELECT NULL)),                 1E * y,                 AVG(1E * y) OVER (PARTITION BY (SELECT NULL)),                 COUNT(*) OVER (PARTITION BY (SELECT NULL))         FROM    @Sample ) SELECT  SUM((x - xAvg) *(y - yAvg)) / MAX(n) AS [COVAR(x,y)] FROM    cteSource

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  • "Unable to open MRTG log file" error with nagios and mrtg

    - by Simone Magnaschi
    We have a strange issue with our setup of icinga / nagios and mrtg. Icinga is working great and has no problem, it can monitor basically everything without issues. We setup mrtg to gather bandwith data from our routers and switches. MRTG is working fine: it stores the log data in the /var/www/mrtg/ directory and displays the graph data via web. We assume so MRTG is doing great. We tried to setup bandwidth checks in nagios: define service{ use generic-service ; Inherit values from a template host_name zywall-agora service_description ZYWALL AGORA TRAFFICO check_command check_local_mrtgtraf!/var/www/mrtg/x.x.x.x_2.log!AVG!1000000,2000000!5000000,5000000!1000 check_interval 1 ; Check the service every 1 minute under normal conditions retry_interval 1 ; Re-check every minute until its final/hard state is determined } Where /var/www/mrtg/x.x.x.x_2.log is the correct log path file. We keep on getting Unable to open MRTG log file error in the test result in icinga web interface. We tried everything: give ownership to user nagios or icinga to the log file give chmod 777 to the file try to copy the file in another directory and give it full permission Same error. The strange thing is that if we use the command that nagios generate in a bash session the command works like a charm: /usr/lib64/nagios/plugins/check_mrtgtraf -F /var/www/mrtg/x.x.x.x_2.log -a AVG -w 10,20 -c 5000000,5000000 -e 10 Result: Traffic WARNING - Avg. In = 17.9 KB/s, Avg. Out = 5.0 KB/s|in=17.877930KB/s;10.000000;5000000.000000;0.000000 out=5.000000KB/s;20.000000;5000000.000000;0.000000 We ran that command line as root, as user nagios and as user icinga and all three worked ok. We thought that the command that nagios perform maybe has something wrong in it, so we debugged nagios but we found out that the generated command from nagios is the same as above. Searching on google for these kind of problem returns only issues of systems where mrtg is not installed or issues with the wrong path to the log file, but these seems not to be our case. We are stuck, can somebody help?

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  • Default browser hangs

    - by Craig Hinrichs
    Intermittent hangs would occur when I would use Internet Explorer to open a new main page or new tab to a site I know would be up. The browser would open and say "Waiting for site example.com" and do nothing more. If I closed the window and reopened it it would immediately connect. Over time I would have to close and reopen the window to get to the page. This would happen to any page, including Google. Got sick of it and started using Chrome. I recently upgraded my anti-virus and am now experiencing the same issue with Chrome. I use AVG for my antivirus. Empirically it seems that if I don't make Chrome my default browser I don't experience the issue. I tested this theory for over two hours yesterday. Possible issues I have found this could be but not confirmed yet: MTU settings are not correct. I am infected but my antivirus has not caught it (unlikely but possible) ?? I would like to think this is related to my antivirus but I am unsure how to verify. I don't like the idea of killing my antivirus if #2 is a possibility. I am looking for tips on how I can troubleshoot possible issues.

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  • Default Browser hangs (IE, Chrome)

    - by Craig Hinrichs
    IE was my default browser about three months ago when I started experiencing this issue. Intermittent hangs would occur when I would open a new main page or new tab to a site I know would be up. What I mean by a hang: The browser would open and say "Waiting for site " and do nothing more. If I closed the window and reopened it it would immediatly connect. Over time I would have to close and reopen the window to get to the page. This would happen to any page including Google. I finally got sick of it and started using chrome and I will never go back. I recently upgraded my anti-virus and now I am experiencing the same issue with Chrome. I use AVG for my antivirus. Empirically it seems if I don't make Chrome my default browser I don't experience the issue. I tested this theory for over two hours yesterday. Possible issues I have found this coudl be but not confirmed yet: MTU settings are not correct. I am infected but my antivirus has not caught it (unlikely but possible) ?? I would like to think this is related to my antivirus but I am unsure how to verify. I don't like the idea of killing my antivirus if #2 is a possibility. I am looking for tips on how I can trouble shoot possible issues. I am on Windows XP SP3 Thanks in advance.

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  • Performance considerations for common SQL queries

    - by Jim Giercyk
    Originally posted on: http://geekswithblogs.net/NibblesAndBits/archive/2013/10/16/performance-considerations-for-common-sql-queries.aspxSQL offers many different methods to produce the same results.  There is a never-ending debate between SQL developers as to the “best way” or the “most efficient way” to render a result set.  Sometimes these disputes even come to blows….well, I am a lover, not a fighter, so I decided to collect some data that will prove which way is the best and most efficient.  For the queries below, I downloaded the test database from SQLSkills:  http://www.sqlskills.com/sql-server-resources/sql-server-demos/.  There isn’t a lot of data, but enough to prove my point: dbo.member has 10,000 records, and dbo.payment has 15,554.  Our result set contains 6,706 records. The following queries produce an identical result set; the result set contains aggregate payment information for each member who has made more than 1 payment from the dbo.payment table and the first and last name of the member from the dbo.member table.   /*************/ /* Sub Query  */ /*************/ SELECT  a.[Member Number] ,         m.lastname ,         m.firstname ,         a.[Number Of Payments] ,         a.[Average Payment] ,         a.[Total Paid] FROM    ( SELECT    member_no 'Member Number' ,                     AVG(payment_amt) 'Average Payment' ,                     SUM(payment_amt) 'Total Paid' ,                     COUNT(Payment_No) 'Number Of Payments'           FROM      dbo.payment           GROUP BY  member_no           HAVING    COUNT(Payment_No) > 1         ) a         JOIN dbo.member m ON a.[Member Number] = m.member_no         /***************/ /* Cross Apply  */ /***************/ SELECT  ca.[Member Number] ,         m.lastname ,         m.firstname ,         ca.[Number Of Payments] ,         ca.[Average Payment] ,         ca.[Total Paid] FROM    dbo.member m         CROSS APPLY ( SELECT    member_no 'Member Number' ,                                 AVG(payment_amt) 'Average Payment' ,                                 SUM(payment_amt) 'Total Paid' ,                                 COUNT(Payment_No) 'Number Of Payments'                       FROM      dbo.payment                       WHERE     member_no = m.member_no                       GROUP BY  member_no                       HAVING    COUNT(Payment_No) > 1                     ) ca /********/                    /* CTEs  */ /********/ ; WITH    Payments           AS ( SELECT   member_no 'Member Number' ,                         AVG(payment_amt) 'Average Payment' ,                         SUM(payment_amt) 'Total Paid' ,                         COUNT(Payment_No) 'Number Of Payments'                FROM     dbo.payment                GROUP BY member_no                HAVING   COUNT(Payment_No) > 1              ),         MemberInfo           AS ( SELECT   p.[Member Number] ,                         m.lastname ,                         m.firstname ,                         p.[Number Of Payments] ,                         p.[Average Payment] ,                         p.[Total Paid]                FROM     dbo.member m                         JOIN Payments p ON m.member_no = p.[Member Number]              )     SELECT  *     FROM    MemberInfo /************************/ /* SELECT with Grouping   */ /************************/ SELECT  p.member_no 'Member Number' ,         m.lastname ,         m.firstname ,         COUNT(Payment_No) 'Number Of Payments' ,         AVG(payment_amt) 'Average Payment' ,         SUM(payment_amt) 'Total Paid' FROM    dbo.payment p         JOIN dbo.member m ON m.member_no = p.member_no GROUP BY p.member_no ,         m.lastname ,         m.firstname HAVING  COUNT(Payment_No) > 1   We can see what is going on in SQL’s brain by looking at the execution plan.  The Execution Plan will demonstrate which steps and in what order SQL executes those steps, and what percentage of batch time each query takes.  SO….if I execute all 4 of these queries in a single batch, I will get an idea of the relative time SQL takes to execute them, and how it renders the Execution Plan.  We can settle this once and for all.  Here is what SQL did with these queries:   Not only did the queries take the same amount of time to execute, SQL generated the same Execution Plan for each of them.  Everybody is right…..I guess we can all finally go to lunch together!  But wait a second, I may not be a fighter, but I AM an instigator.     Let’s see how a table variable stacks up.  Here is the code I executed: /********************/ /*  Table Variable  */ /********************/ DECLARE @AggregateTable TABLE     (       member_no INT ,       AveragePayment MONEY ,       TotalPaid MONEY ,       NumberOfPayments MONEY     ) INSERT  @AggregateTable         SELECT  member_no 'Member Number' ,                 AVG(payment_amt) 'Average Payment' ,                 SUM(payment_amt) 'Total Paid' ,                 COUNT(Payment_No) 'Number Of Payments'         FROM    dbo.payment         GROUP BY member_no         HAVING  COUNT(Payment_No) > 1   SELECT  at.member_no 'Member Number' ,         m.lastname ,         m.firstname ,         at.NumberOfPayments 'Number Of Payments' ,         at.AveragePayment 'Average Payment' ,         at.TotalPaid 'Total Paid' FROM    @AggregateTable at         JOIN dbo.member m ON m.member_no = at.member_no In the interest of keeping things in groupings of 4, I removed the last query from the previous batch and added the table variable query.  Here’s what I got:     Since we first insert into the table variable, then we read from it, the Execution Plan renders 2 steps.  BUT, the combination of the 2 steps is only 22% of the batch.  It is actually faster than the other methods even though it is treated as 2 separate queries in the Execution Plan.  The argument I often hear against Table Variables is that SQL only estimates 1 row for the table size in the Execution Plan.  While this is true, the estimate does not come in to play until you read from the table variable.  In this case, the table variable had 6,706 rows, but it still outperformed the other queries.  People argue that table variables should only be used for hash or lookup tables.  The fact is, you have control of what you put IN to the variable, so as long as you keep it within reason, these results suggest that a table variable is a viable alternative to sub-queries. If anyone does volume testing on this theory, I would be interested in the results.  My suspicion is that there is a breaking point where efficiency goes down the tubes immediately, and it would be interesting to see where the threshold is. Coding SQL is a matter of style.  If you’ve been around since they introduced DB2, you were probably taught a little differently than a recent computer science graduate.  If you have a company standard, I strongly recommend you follow it.    If you do not have a standard, generally speaking, there is no right or wrong answer when talking about the efficiency of these types of queries, and certainly no hard-and-fast rule.  Volume and infrastructure will dictate a lot when it comes to performance, so your results may vary in your environment.  Download the database and try it!

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  • ROracle support for TimesTen In-Memory Database

    - by Sam Drake
    Today's guest post comes from Jason Feldhaus, a Consulting Member of Technical Staff in the TimesTen Database organization at Oracle.  He shares with us a sample session using ROracle with the TimesTen In-Memory database.  Beginning in version 1.1-4, ROracle includes support for the Oracle Times Ten In-Memory Database, version 11.2.2. TimesTen is a relational database providing very fast and high throughput through its memory-centric architecture.  TimesTen is designed for low latency, high-volume data, and event and transaction management. A TimesTen database resides entirely in memory, so no disk I/O is required for transactions and query operations. TimesTen is used in applications requiring very fast and predictable response time, such as real-time financial services trading applications and large web applications. TimesTen can be used as the database of record or as a relational cache database to Oracle Database. ROracle provides an interface between R and the database, providing the rich functionality of the R statistical programming environment using the SQL query language. ROracle uses the OCI libraries to handle database connections, providing much better performance than standard ODBC.The latest ROracle enhancements include: Support for Oracle TimesTen In-Memory Database Support for Date-Time using R's POSIXct/POSIXlt data types RAW, BLOB and BFILE data type support Option to specify number of rows per fetch operation Option to prefetch LOB data Break support using Ctrl-C Statement caching support Times Ten 11.2.2 contains enhanced support for analytics workloads and complex queries: Analytic functions: AVG, SUM, COUNT, MAX, MIN, DENSE_RANK, RANK, ROW_NUMBER, FIRST_VALUE and LAST_VALUE Analytic clauses: OVER PARTITION BY and OVER ORDER BY Multidimensional grouping operators: Grouping clauses: GROUP BY CUBE, GROUP BY ROLLUP, GROUP BY GROUPING SETS Grouping functions: GROUP, GROUPING_ID, GROUP_ID WITH clause, which allows repeated references to a named subquery block Aggregate expressions over DISTINCT expressions General expressions that return a character string in the source or a pattern within the LIKE predicate Ability to order nulls first or last in a sort result (NULLS FIRST or NULLS LAST in the ORDER BY clause) Note: Some functionality is only available with Oracle Exalytics, refer to the TimesTen product licensing document for details. Connecting to TimesTen is easy with ROracle. Simply install and load the ROracle package and load the driver. > install.packages("ROracle") > library(ROracle) Loading required package: DBI > drv <- dbDriver("Oracle") Once the ROracle package is installed, create a database connection object and connect to a TimesTen direct driver DSN as the OS user. > conn <- dbConnect(drv, username ="", password="", dbname = "localhost/SampleDb_1122:timesten_direct") You have the option to report the server type - Oracle or TimesTen? > print (paste ("Server type =", dbGetInfo (conn)$serverType)) [1] "Server type = TimesTen IMDB" To create tables in the database using R data frame objects, use the function dbWriteTable. In the following example we write the built-in iris data frame to TimesTen. The iris data set is a small example data set containing 150 rows and 5 columns. We include it here not to highlight performance, but so users can easily run this example in their R session. > dbWriteTable (conn, "IRIS", iris, overwrite=TRUE, ora.number=FALSE) [1] TRUE Verify that the newly created IRIS table is available in the database. To list the available tables and table columns in the database, use dbListTables and dbListFields, respectively. > dbListTables (conn) [1] "IRIS" > dbListFields (conn, "IRIS") [1] "SEPAL.LENGTH" "SEPAL.WIDTH" "PETAL.LENGTH" "PETAL.WIDTH" "SPECIES" To retrieve a summary of the data from the database we need to save the results to a local object. The following call saves the results of the query as a local R object, iris.summary. The ROracle function dbGetQuery is used to execute an arbitrary SQL statement against the database. When connected to TimesTen, the SQL statement is processed completely within main memory for the fastest response time. > iris.summary <- dbGetQuery(conn, 'SELECT SPECIES, AVG ("SEPAL.LENGTH") AS AVG_SLENGTH, AVG ("SEPAL.WIDTH") AS AVG_SWIDTH, AVG ("PETAL.LENGTH") AS AVG_PLENGTH, AVG ("PETAL.WIDTH") AS AVG_PWIDTH FROM IRIS GROUP BY ROLLUP (SPECIES)') > iris.summary SPECIES AVG_SLENGTH AVG_SWIDTH AVG_PLENGTH AVG_PWIDTH 1 setosa 5.006000 3.428000 1.462 0.246000 2 versicolor 5.936000 2.770000 4.260 1.326000 3 virginica 6.588000 2.974000 5.552 2.026000 4 <NA> 5.843333 3.057333 3.758 1.199333 Finally, disconnect from the TimesTen Database. > dbCommit (conn) [1] TRUE > dbDisconnect (conn) [1] TRUE We encourage you download Oracle software for evaluation from the Oracle Technology Network. See these links for our software: Times Ten In-Memory Database,  ROracle.  As always, we welcome comments and questions on the TimesTen and  Oracle R technical forums.

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  • ROracle support for TimesTen In-Memory Database

    - by Sherry LaMonica
    Today's guest post comes from Jason Feldhaus, a Consulting Member of Technical Staff in the TimesTen Database organization at Oracle.  He shares with us a sample session using ROracle with the TimesTen In-Memory database.  Beginning in version 1.1-4, ROracle includes support for the Oracle Times Ten In-Memory Database, version 11.2.2. TimesTen is a relational database providing very fast and high throughput through its memory-centric architecture.  TimesTen is designed for low latency, high-volume data, and event and transaction management. A TimesTen database resides entirely in memory, so no disk I/O is required for transactions and query operations. TimesTen is used in applications requiring very fast and predictable response time, such as real-time financial services trading applications and large web applications. TimesTen can be used as the database of record or as a relational cache database to Oracle Database. ROracle provides an interface between R and the database, providing the rich functionality of the R statistical programming environment using the SQL query language. ROracle uses the OCI libraries to handle database connections, providing much better performance than standard ODBC.The latest ROracle enhancements include: Support for Oracle TimesTen In-Memory Database Support for Date-Time using R's POSIXct/POSIXlt data types RAW, BLOB and BFILE data type support Option to specify number of rows per fetch operation Option to prefetch LOB data Break support using Ctrl-C Statement caching support Times Ten 11.2.2 contains enhanced support for analytics workloads and complex queries: Analytic functions: AVG, SUM, COUNT, MAX, MIN, DENSE_RANK, RANK, ROW_NUMBER, FIRST_VALUE and LAST_VALUE Analytic clauses: OVER PARTITION BY and OVER ORDER BY Multidimensional grouping operators: Grouping clauses: GROUP BY CUBE, GROUP BY ROLLUP, GROUP BY GROUPING SETS Grouping functions: GROUP, GROUPING_ID, GROUP_ID WITH clause, which allows repeated references to a named subquery block Aggregate expressions over DISTINCT expressions General expressions that return a character string in the source or a pattern within the LIKE predicate Ability to order nulls first or last in a sort result (NULLS FIRST or NULLS LAST in the ORDER BY clause) Note: Some functionality is only available with Oracle Exalytics, refer to the TimesTen product licensing document for details. Connecting to TimesTen is easy with ROracle. Simply install and load the ROracle package and load the driver. > install.packages("ROracle") > library(ROracle) Loading required package: DBI > drv <- dbDriver("Oracle") Once the ROracle package is installed, create a database connection object and connect to a TimesTen direct driver DSN as the OS user. > conn <- dbConnect(drv, username ="", password="", dbname = "localhost/SampleDb_1122:timesten_direct") You have the option to report the server type - Oracle or TimesTen? > print (paste ("Server type =", dbGetInfo (conn)$serverType)) [1] "Server type = TimesTen IMDB" To create tables in the database using R data frame objects, use the function dbWriteTable. In the following example we write the built-in iris data frame to TimesTen. The iris data set is a small example data set containing 150 rows and 5 columns. We include it here not to highlight performance, but so users can easily run this example in their R session. > dbWriteTable (conn, "IRIS", iris, overwrite=TRUE, ora.number=FALSE) [1] TRUE Verify that the newly created IRIS table is available in the database. To list the available tables and table columns in the database, use dbListTables and dbListFields, respectively. > dbListTables (conn) [1] "IRIS" > dbListFields (conn, "IRIS") [1] "SEPAL.LENGTH" "SEPAL.WIDTH" "PETAL.LENGTH" "PETAL.WIDTH" "SPECIES" To retrieve a summary of the data from the database we need to save the results to a local object. The following call saves the results of the query as a local R object, iris.summary. The ROracle function dbGetQuery is used to execute an arbitrary SQL statement against the database. When connected to TimesTen, the SQL statement is processed completely within main memory for the fastest response time. > iris.summary <- dbGetQuery(conn, 'SELECT SPECIES, AVG ("SEPAL.LENGTH") AS AVG_SLENGTH, AVG ("SEPAL.WIDTH") AS AVG_SWIDTH, AVG ("PETAL.LENGTH") AS AVG_PLENGTH, AVG ("PETAL.WIDTH") AS AVG_PWIDTH FROM IRIS GROUP BY ROLLUP (SPECIES)') > iris.summary SPECIES AVG_SLENGTH AVG_SWIDTH AVG_PLENGTH AVG_PWIDTH 1 setosa 5.006000 3.428000 1.462 0.246000 2 versicolor 5.936000 2.770000 4.260 1.326000 3 virginica 6.588000 2.974000 5.552 2.026000 4 <NA> 5.843333 3.057333 3.758 1.199333 Finally, disconnect from the TimesTen Database. > dbCommit (conn) [1] TRUE > dbDisconnect (conn) [1] TRUE We encourage you download Oracle software for evaluation from the Oracle Technology Network. See these links for our software: Times Ten In-Memory Database,  ROracle.  As always, we welcome comments and questions on the TimesTen and  Oracle R technical forums.

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  • Using Subjects to Deploy Queries Dynamically

    - by Roman Schindlauer
    In the previous blog posting, we showed how to construct and deploy query fragments to a StreamInsight server, and how to re-use them later. In today’s posting we’ll integrate this pattern into a method of dynamically composing a new query with an existing one. The construct that enables this scenario in StreamInsight V2.1 is a Subject. A Subject lets me create a junction element in an existing query that I can tap into while the query is running. To set this up as an end-to-end example, let’s first define a stream simulator as our data source: var generator = myApp.DefineObservable(     (TimeSpan t) => Observable.Interval(t).Select(_ => new SourcePayload())); This ‘generator’ produces a new instance of SourcePayload with a period of t (system time) as an IObservable. SourcePayload happens to have a property of type double as its payload data. Let’s also define a sink for our example—an IObserver of double values that writes to the console: var console = myApp.DefineObserver(     (string label) => Observer.Create<double>(e => Console.WriteLine("{0}: {1}", label, e)))     .Deploy("ConsoleSink"); The observer takes a string as parameter which is used as a label on the console, so that we can distinguish the output of different sink instances. Note that we also deploy this observer, so that we can retrieve it later from the server from a different process. Remember how we defined the aggregation as an IQStreamable function in the previous article? We will use that as well: var avg = myApp     .DefineStreamable((IQStreamable<SourcePayload> s, TimeSpan w) =>         from win in s.TumblingWindow(w)         select win.Avg(e => e.Value))     .Deploy("AverageQuery"); Then we define the Subject, which acts as an observable sequence as well as an observer. Thus, we can feed a single source into the Subject and have multiple consumers—that can come and go at runtime—on the other side: var subject = myApp.CreateSubject("Subject", () => new Subject<SourcePayload>()); Subject are always deployed automatically. Their name is used to retrieve them from a (potentially) different process (see below). Note that the Subject as we defined it here doesn’t know anything about temporal streams. It is merely a sequence of SourcePayloads, without any notion of StreamInsight point events or CTIs. So in order to compose a temporal query on top of the Subject, we need to 'promote' the sequence of SourcePayloads into an IQStreamable of point events, including CTIs: var stream = subject.ToPointStreamable(     e => PointEvent.CreateInsert<SourcePayload>(e.Timestamp, e),     AdvanceTimeSettings.StrictlyIncreasingStartTime); In a later posting we will show how to use Subjects that have more awareness of time and can be used as a junction between QStreamables instead of IQbservables. Having turned the Subject into a temporal stream, we can now define the aggregate on this stream. We will use the IQStreamable entity avg that we defined above: var longAverages = avg(stream, TimeSpan.FromSeconds(5)); In order to run the query, we need to bind it to a sink, and bind the subject to the source: var standardQuery = longAverages     .Bind(console("5sec average"))     .With(generator(TimeSpan.FromMilliseconds(300)).Bind(subject)); Lastly, we start the process: standardQuery.Run("StandardProcess"); Now we have a simple query running end-to-end, producing results. What follows next is the crucial part of tapping into the Subject and adding another query that runs in parallel, using the same query definition (the “AverageQuery”) but with a different window length. We are assuming that we connected to the same StreamInsight server from a different process or even client, and thus have to retrieve the previously deployed entities through their names: // simulate the addition of a 'fast' query from a separate server connection, // by retrieving the aggregation query fragment // (instead of simply using the 'avg' object) var averageQuery = myApp     .GetStreamable<IQStreamable<SourcePayload>, TimeSpan, double>("AverageQuery"); // retrieve the input sequence as a subject var inputSequence = myApp     .GetSubject<SourcePayload, SourcePayload>("Subject"); // retrieve the registered sink var sink = myApp.GetObserver<string, double>("ConsoleSink"); // turn the sequence into a temporal stream var stream2 = inputSequence.ToPointStreamable(     e => PointEvent.CreateInsert<SourcePayload>(e.Timestamp, e),     AdvanceTimeSettings.StrictlyIncreasingStartTime); // apply the query, now with a different window length var shortAverages = averageQuery(stream2, TimeSpan.FromSeconds(1)); // bind new sink to query and run it var fastQuery = shortAverages     .Bind(sink("1sec average"))     .Run("FastProcess"); The attached solution demonstrates the sample end-to-end. Regards, The StreamInsight Team

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  • When to increase AWS RDS MySQL Server instance to larger CPU/RAM?

    - by rksprst
    I'm wondering at what stage do I need to increase the image for the RDS MySQL server to a larger CPU/RAM instance. The CPU utilization graph is near 0. The Avg Free Memory is around 150MB. The Avg Swap Usage is 420MB. Read Latency is 0-20ms/op it spikes up randomly. Avg write latency is on average 5ms/op but spikes up to 10-20ms/op. Are there some common rules here that I should follow? Thanks!

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  • Can't update scala on Gentoo

    - by xhochy
    As I wanted to test Scala 2.9.2 on my gentoo system I tried updated the package but ended up with this error. I can't figure out where the problem may be: Calculating dependencies ...... done! >>> Verifying ebuild manifests >>> Jobs: 0 of 1 complete, 1 running Load avg: 0.23, 0.16, 0.20 >>> Emerging (1 of 1) dev-lang/scala-2.9.2 >>> Jobs: 0 of 1 complete, 1 running Load avg: 0.23, 0.16, 0.20 >>> Failed to emerge dev-lang/scala-2.9.2, Log file: >>> Jobs: 0 of 1 complete, 1 running Load avg: 0.23, 0.16, 0.20 >>> '/var/tmp/portage/dev-lang/scala-2.9.2/temp/build.log' >>> Jobs: 0 of 1 complete, 1 running Load avg: 0.23, 0.16, 0.20 >>> Jobs: 0 of 1 complete, 1 running, 1 failed Load avg: 0.23, 0.16, 0.20 >>> Jobs: 0 of 1 complete, 1 failed Load avg: 0.23, 0.16, 0.20 * Package: dev-lang/scala-2.9.2 * Repository: gentoo * Maintainer: [email protected] * USE: amd64 elibc_glibc kernel_linux multilib userland_GNU * FEATURES: sandbox [01m[31;06m!!! ERROR: Couldn't find suitable VM. Possible invalid dependency string. Due to jdk-with-com-sun requiring a target of 1.7 but the virtual machines constrained by virtual/jdk-1.6 and/or this package requiring virtual(s) jdk-with-com-sun[0m * Unable to determine VM for building from dependencies: NV_DEPEND: virtual/jdk:1.6 java-virtuals/jdk-with-com-sun !binary? ( dev-java/ant-contrib:0 ) app-arch/xz-utils >=dev-java/java-config-2.1.9-r1 source? ( app-arch/zip ) >=dev-java/ant-core-1.7.0 dev-java/ant-nodeps >=dev-java/javatoolkit-0.3.0-r2 >=dev-lang/python-2.4 * ERROR: dev-lang/scala-2.9.2 failed (setup phase): * Failed to determine VM for building. * * Call stack: * ebuild.sh, line 93: Called pkg_setup * scala-2.9.2.ebuild, line 43: Called java-pkg-2_pkg_setup * java-pkg-2.eclass, line 53: Called java-pkg_init * java-utils-2.eclass, line 2187: Called java-pkg_switch-vm * java-utils-2.eclass, line 2674: Called die * The specific snippet of code: * die "Failed to determine VM for building." * * If you need support, post the output of `emerge --info '=dev-lang/scala-2.9.2'`, * the complete build log and the output of `emerge -pqv '=dev-lang/scala-2.9.2'`. !!! When you file a bug report, please include the following information: GENTOO_VM= CLASSPATH="" JAVA_HOME="" JAVACFLAGS="" COMPILER="" and of course, the output of emerge --info * The complete build log is located at '/var/tmp/portage/dev-lang/scala-2.9.2/temp/build.log'. * The ebuild environment file is located at '/var/tmp/portage/dev-lang/scala-2.9.2/temp/die.env'. * Working directory: '/var/tmp/portage/dev-lang/scala-2.9.2' * S: '/var/tmp/portage/dev-lang/scala-2.9.2/work/scala-2.9.2-sources' * Messages for package dev-lang/scala-2.9.2: * Unable to determine VM for building from dependencies: * ERROR: dev-lang/scala-2.9.2 failed (setup phase): * Failed to determine VM for building. * * Call stack: * ebuild.sh, line 93: Called pkg_setup * scala-2.9.2.ebuild, line 43: Called java-pkg-2_pkg_setup * java-pkg-2.eclass, line 53: Called java-pkg_init * java-utils-2.eclass, line 2187: Called java-pkg_switch-vm * java-utils-2.eclass, line 2674: Called die * The specific snippet of code: * die "Failed to determine VM for building." * * If you need support, post the output of `emerge --info '=dev-lang/scala-2.9.2'`, * the complete build log and the output of `emerge -pqv '=dev-lang/scala-2.9.2'`. * The complete build log is located at '/var/tmp/portage/dev-lang/scala-2.9.2/temp/build.log'. * The ebuild environment file is located at '/var/tmp/portage/dev-lang/scala-2.9.2/temp/die.env'. * Working directory: '/var/tmp/portage/dev-lang/scala-2.9.2' * S: '/var/tmp/portage/dev-lang/scala-2.9.2/work/scala-2.9.2-sources' The following eix output may help: % eix java-virtuals/jdk-with-com-sun [I] java-virtuals/jdk-with-com-sun Available versions: 20111111 {{ELIBC="FreeBSD"}} Installed versions: 20111111(16:08:51 18/04/12)(ELIBC="-FreeBSD") Homepage: http://www.gentoo.org Description: Virtual ebuilds that require internal com.sun classes from a JDK Both virtual jdks 1.6 and 1.7 are installed: % eix virtual/jdk [I] virtual/jdk Available versions: (1.4) ~1.4.2-r1[1] (1.5) 1.5.0 ~1.5.0-r3[1] (1.6) 1.6.0 1.6.0-r1 (1.7) (~)1.7.0 Installed versions: 1.6.0-r1(1.6)(23:22:48 10/11/12) 1.7.0(1.7)(23:21:09 10/11/12) Description: Virtual for JDK [1] "java-overlay" /var/lib/layman/java-overlay

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  • Justifying a memory upgrade, take 2

    - by AngryHacker
    Previously I asked a question on what metrics I should measure (e.g. before and after) to justify a memory upgrade. Perfmon was suggested. I'd like to know which specific perfmon counters I should be measuring. So far I got: PhysicalDisk/Avg. Disk Queue Length (for each drive) PhysicalDisk/Avg. Disk Write Queue Length (for each drive) PhysicalDisk/Avg. Disk Read Queue Length (for each drive) Processor/Processor Time% SQLServer:BufferManager/Buffer cache hit ratio What other ones should I use?

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  • Visits-PageViews-Bounce Rate-New Visitors-Visit Duration (Google Analytics), which one is top priority for seo?

    - by HOY
    This is the case: My site is getting a lot of trafic from an image (a company logo image) because this image is ranked 1.st in google search results for a company's title. (I have no idea how that happened) This image is must for my website, but it is not relevant with site content so irrelevant people search for the image and finds out about my site, so that I get interesting statistics: http://postimage.org/image/3oyvrjoz9/ Pros: Total Visits & Avg. New Visits Cons: Avg. Page/Visit, Avg. Visit Duration, Bounce Rate In summary I am confused if this image is helpful to my website ? Because I don't know the balance between those 5 statistics P.S: My website is 2 months old, and we are working on seo at the moment Another P.S: Kindly ask you to not provide assumtions, because I also have assumptions, I need real knowledge. Edit: Search Keyword is: arcelik logo Search Site: google.com.tr Search URL: https://www.google.com.tr/search?hl=en&q=arcelik+logo&bav=on.2,or.r_gc.r_pw.r_qf.&bvm=bv.41524429,d.Yms&biw=1366&bih=667&um=1&ie=UTF-8&tbm=isch&source=og&sa=N&tab=wi&ei=oZIDUfutAseVswa9zYHwCw

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  • My computer is playing audio without any program open

    - by super x man
    This is the weirdest thing ever that has happened to my computer (running Windows 7). I haven't installed anything lastly, except lavasoft adware antivirus When my computer stats then audio starts playing, mostly hip hop There is no programs opened. I tried killing all unknown processed, no success. I tried resetting the firewall options of the antivirus, no success. If I disconnect from internet, then it works. The antivirus is not detecting anything. Is somebody is streaming from another house? Is that possible? and making my life impossible. How can I check and stop this?

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  • Averaging initial values for rolling series

    - by Dave Jarvis
    Question Given a maximum sliding window size of 40 (i.e., the set of numbers in the list cannot exceed 40), what is the calculation to ensure a smooth averaging transition as the set size grows from 1 to 40? Problem Description Creating a trend line for a set of data has skewed initial values. The complete set of values is unknown at runtime: they are provided one at a time. It seems like a reverse-weighted average is required so that the initial values are averaged differently. In the image below the leftmost data for the trend line are incorrectly averaged. Current Solution Created a new type of ArrayList subclass that calculates the appropriate values and ensures its size never goes beyond the bounds of the sliding window: /** * A list of Double values that has a maximum capacity enforced by a sliding * window. Can calculate the average of its values. */ public class AveragingList extends ArrayList<Double> { private float slidingWindowSize = 0.0f; /** * The initial capacity is used for the sliding window size. * @param slidingWindowSize */ public AveragingList( int slidingWindowSize ) { super( slidingWindowSize ); setSlidingWindowSize( ( float )slidingWindowSize ); } public boolean add( Double d ) { boolean result = super.add( d ); // Prevent the list from exceeding the maximum sliding window size. // if( size() > getSlidingWindowSize() ) { remove( 0 ); } return result; } /** * Calculate the average. * * @return The average of the values stored in this list. */ public double average() { double result = 0.0; int size = size(); for( Double d: this ) { result += d.doubleValue(); } return (double)result / (double)size; } /** * Changes the maximum number of numbers stored in this list. * * @param slidingWindowSize New maximum number of values to remember. */ public void setSlidingWindowSize( float slidingWindowSize ) { this.slidingWindowSize = slidingWindowSize; } /** * Returns the number used to determine the maximum values this list can * store before it removes the first entry upon adding another value. * @return The maximum number of numbers stored in this list. */ public float getSlidingWindowSize() { return slidingWindowSize; } } Resulting Image Example Input The data comes into the function one value at a time. For example, data points (Data) and calculated averages (Avg) typically look as follows: Data: 17.0 Avg : 17.0 Data: 17.0 Avg : 17.0 Data: 5.0 Avg : 13.0 Data: 5.0 Avg : 11.0  Related Sites The following pages describe moving averages, but typically when all (or sufficient) data is known: http://www.cs.princeton.edu/introcs/15inout/MovingAverage.java.html http://stackoverflow.com/questions/2161815/r-zoo-series-sliding-window-calculation http://taragana.blogspot.com/ http://www.dreamincode.net/forums/index.php?showtopic=92508 http://blogs.sun.com/nickstephen/entry/dtrace_and_moving_rolling_averages

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  • SQL SERVER – How to Ignore Columnstore Index Usage in Query

    - by pinaldave
    Earlier I wrote about SQL SERVER – Fundamentals of Columnstore Index and very first question I received in email was as following. “We are using SQL Server 2012 CTP3 and so far so good. In our data warehouse solution we have created 1 non-clustered columnstore index on our large fact table. We have very unique situation but your article did not cover it. We are running few queries on our fact table which is working very efficiently but there is one query which earlier was running very fine but after creating this non-clustered columnstore index this query is running very slow. We dropped the columnstore index and suddenly this one query is running fast but other queries which were benefited by this columnstore index it is running slow. Any workaround in this situation?” In summary the question in simple words “How can we ignore using columnstore index in selective queries?” Very interesting question – you can use I can understand there may be the cases when columnstore index is not ideal and needs to be ignored the same. You can use the query hint IGNORE_NONCLUSTERED_COLUMNSTORE_INDEX to ignore the columnstore index. SQL Server Engine will use any other index which is best after ignoring the columnstore index. Here is the quick script to prove the same. We will first create sample database and then create columnstore index on the same. Once columnstore index is created we will write simple query. This query will use columnstore index. We will then show the usage of the query hint. USE AdventureWorks GO -- Create New Table CREATE TABLE [dbo].[MySalesOrderDetail]( [SalesOrderID] [int] NOT NULL, [SalesOrderDetailID] [int] NOT NULL, [CarrierTrackingNumber] [nvarchar](25) NULL, [OrderQty] [smallint] NOT NULL, [ProductID] [int] NOT NULL, [SpecialOfferID] [int] NOT NULL, [UnitPrice] [money] NOT NULL, [UnitPriceDiscount] [money] NOT NULL, [LineTotal] [numeric](38, 6) NOT NULL, [rowguid] [uniqueidentifier] NOT NULL, [ModifiedDate] [datetime] NOT NULL ) ON [PRIMARY] GO -- Create clustered index CREATE CLUSTERED INDEX [CL_MySalesOrderDetail] ON [dbo].[MySalesOrderDetail] ( [SalesOrderDetailID]) GO -- Create Sample Data Table -- WARNING: This Query may run upto 2-10 minutes based on your systems resources INSERT INTO [dbo].[MySalesOrderDetail] SELECT S1.* FROM Sales.SalesOrderDetail S1 GO 100 -- Create ColumnStore Index CREATE NONCLUSTERED COLUMNSTORE INDEX [IX_MySalesOrderDetail_ColumnStore] ON [MySalesOrderDetail] (UnitPrice, OrderQty, ProductID) GO Now we have created columnstore index so if we run following query it will use for sure the same index. -- Select Table with regular Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO We can specify Query Hint IGNORE_NONCLUSTERED_COLUMNSTORE_INDEX as described in following query and it will not use columnstore index. -- Select Table with regular Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID OPTION (IGNORE_NONCLUSTERED_COLUMNSTORE_INDEX) GO Let us clean up the database. -- Cleanup DROP INDEX [IX_MySalesOrderDetail_ColumnStore] ON [dbo].[MySalesOrderDetail] GO TRUNCATE TABLE dbo.MySalesOrderDetail GO DROP TABLE dbo.MySalesOrderDetail GO Again, make sure that you use hint sparingly and understanding the proper implication of the same. Make sure that you test it with and without hint and select the best option after review of your administrator. Here is the question for you – have you started to use SQL Server 2012 for your validation and development (not on production)? It will be interesting to know the answer. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Index, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Observing flow control idle time in TCP

    - by user12820842
    Previously I described how to observe congestion control strategies during transmission, and here I talked about TCP's sliding window approach for handling flow control on the receive side. A neat trick would now be to put the pieces together and ask the following question - how often is TCP transmission blocked by congestion control (send-side flow control) versus a zero-sized send window (which is the receiver saying it cannot process any more data)? So in effect we are asking whether the size of the receive window of the peer or the congestion control strategy may be sub-optimal. The result of such a problem would be that we have TCP data that we could be transmitting but we are not, potentially effecting throughput. So flow control is in effect: when the congestion window is less than or equal to the amount of bytes outstanding on the connection. We can derive this from args[3]-tcps_snxt - args[3]-tcps_suna, i.e. the difference between the next sequence number to send and the lowest unacknowledged sequence number; and when the window in the TCP segment received is advertised as 0 We time from these events until we send new data (i.e. args[4]-tcp_seq = snxt value when window closes. Here's the script: #!/usr/sbin/dtrace -s #pragma D option quiet tcp:::send / (args[3]-tcps_snxt - args[3]-tcps_suna) = args[3]-tcps_cwnd / { cwndclosed[args[1]-cs_cid] = timestamp; cwndsnxt[args[1]-cs_cid] = args[3]-tcps_snxt; @numclosed["cwnd", args[2]-ip_daddr, args[4]-tcp_dport] = count(); } tcp:::send / cwndclosed[args[1]-cs_cid] && args[4]-tcp_seq = cwndsnxt[args[1]-cs_cid] / { @meantimeclosed["cwnd", args[2]-ip_daddr, args[4]-tcp_dport] = avg(timestamp - cwndclosed[args[1]-cs_cid]); @stddevtimeclosed["cwnd", args[2]-ip_daddr, args[4]-tcp_dport] = stddev(timestamp - cwndclosed[args[1]-cs_cid]); @numclosed["cwnd", args[2]-ip_daddr, args[4]-tcp_dport] = count(); cwndclosed[args[1]-cs_cid] = 0; cwndsnxt[args[1]-cs_cid] = 0; } tcp:::receive / args[4]-tcp_window == 0 && (args[4]-tcp_flags & (TH_SYN|TH_RST|TH_FIN)) == 0 / { swndclosed[args[1]-cs_cid] = timestamp; swndsnxt[args[1]-cs_cid] = args[3]-tcps_snxt; @numclosed["swnd", args[2]-ip_saddr, args[4]-tcp_dport] = count(); } tcp:::send / swndclosed[args[1]-cs_cid] && args[4]-tcp_seq = swndsnxt[args[1]-cs_cid] / { @meantimeclosed["swnd", args[2]-ip_daddr, args[4]-tcp_sport] = avg(timestamp - swndclosed[args[1]-cs_cid]); @stddevtimeclosed["swnd", args[2]-ip_daddr, args[4]-tcp_sport] = stddev(timestamp - swndclosed[args[1]-cs_cid]); swndclosed[args[1]-cs_cid] = 0; swndsnxt[args[1]-cs_cid] = 0; } END { printf("%-6s %-20s %-8s %-25s %-8s %-8s\n", "Window", "Remote host", "Port", "TCP Avg WndClosed(ns)", "StdDev", "Num"); printa("%-6s %-20s %-8d %@-25d %@-8d %@-8d\n", @meantimeclosed, @stddevtimeclosed, @numclosed); } So this script will show us whether the peer's receive window size is preventing flow ("swnd" events) or whether congestion control is limiting flow ("cwnd" events). As an example I traced on a server with a large file transfer in progress via a webserver and with an active ssh connection running "find / -depth -print". Here is the output: ^C Window Remote host Port TCP Avg WndClosed(ns) StdDev Num cwnd 10.175.96.92 80 86064329 77311705 125 cwnd 10.175.96.92 22 122068522 151039669 81 So we see in this case, the congestion window closes 125 times for port 80 connections and 81 times for ssh. The average time the window is closed is 0.086sec for port 80 and 0.12sec for port 22. So if you wish to change congestion control algorithm in Oracle Solaris 11, a useful step may be to see if congestion really is an issue on your network. Scripts like the one posted above can help assess this, but it's worth reiterating that if congestion control is occuring, that's not necessarily a problem that needs fixing. Recall that congestion control is about controlling flow to prevent large-scale drops, so looking at congestion events in isolation doesn't tell us the whole story. For example, are we seeing more congestion events with one control algorithm, but more drops/retransmission with another? As always, it's best to start with measures of throughput and latency before arriving at a specific hypothesis such as "my congestion control algorithm is sub-optimal".

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  • C# performance analysis- how to count CPU cycles?

    - by Lirik
    Is this a valid way to do performance analysis? I want to get nanosecond accuracy and determine the performance of typecasting: class PerformanceTest { static double last = 0.0; static List<object> numericGenericData = new List<object>(); static List<double> numericTypedData = new List<double>(); static void Main(string[] args) { double totalWithCasting = 0.0; double totalWithoutCasting = 0.0; for (double d = 0.0; d < 1000000.0; ++d) { numericGenericData.Add(d); numericTypedData.Add(d); } Stopwatch stopwatch = new Stopwatch(); for (int i = 0; i < 10; ++i) { stopwatch.Start(); testWithTypecasting(); stopwatch.Stop(); totalWithCasting += stopwatch.ElapsedTicks; stopwatch.Start(); testWithoutTypeCasting(); stopwatch.Stop(); totalWithoutCasting += stopwatch.ElapsedTicks; } Console.WriteLine("Avg with typecasting = {0}", (totalWithCasting/10)); Console.WriteLine("Avg without typecasting = {0}", (totalWithoutCasting/10)); Console.ReadKey(); } static void testWithTypecasting() { foreach (object o in numericGenericData) { last = ((double)o*(double)o)/200; } } static void testWithoutTypeCasting() { foreach (double d in numericTypedData) { last = (d * d)/200; } } } The output is: Avg with typecasting = 468872.3 Avg without typecasting = 501157.9 I'm a little suspicious... it looks like there is nearly no impact on the performance. Is casting really that cheap?

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