Search Results

Search found 8465 results on 339 pages for '40 love'.

Page 59/339 | < Previous Page | 55 56 57 58 59 60 61 62 63 64 65 66  | Next Page >

• Why i disconnect every few seconds? using USB wireless adapter

  - by Rev3rse

  i know it's for ubuntu questions..but mint and ubuntu are very similiar and i had the same problem with linux ubuntu too..so i think this is the right place for my question anyway i don't have experience with drivers and other things,after installing Linux on my machine( i did dist-upgrade btw) everything seem to be great because i didn't have to install any driver, after a while i realized that my connection stop after few minutes(actually it shows that I'm connected but it's not) so i have to reconnect and after few minutes it disconnect again. I'm using Alfa USB wireless adapter AWS036H, and my Linux version is 11 i think the driver i'm using is Realtek i searched in the Internet and i found nothing. these are some outputs of few things people usually ask for: Note: I'm NOT using a laptop. dmsg: [19445.604448] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=2.174.220.77 DST=192.168.1.6 LEN=52 TOS=0x00 PREC=0x00 TTL=104 ID=10466 DF PROTO=TCP SPT=55150 DPT=6881 WINDOW=8192 RES=0x00 SYN URGP=0 [19448.164050] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=192.168.1.254 DST=192.168.1.6 LEN=56 TOS=0x00 PREC=0x00 TTL=255 ID=41982 PROTO=ICMP TYPE=3 CODE=0 [SRC=192.168.1.6 DST=91.189.88.33 LEN=52 TOS=0x00 PREC=0x00 TTL=63 ID=7566 DF PROTO=TCP INCOMPLETE [8 bytes] ] [19465.079565] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=80.128.216.31 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=113 ID=5100 DF PROTO=TCP SPT=50169 DPT=6881 WINDOW=8192 RES=0x00 SYN URGP=0 [19486.270328] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=90.130.13.122 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=109 ID=22207 PROTO=UDP SPT=6881 DPT=6881 LEN=28 [19497.480522] wlan0: deauthenticating from 00:24:c8:4b:46:e0 by local choice (reason=3) [19497.593276] cfg80211: All devices are disconnected, going to restore regulatory settings [19497.593282] cfg80211: Restoring regulatory settings [19497.593346] cfg80211: Calling CRDA to update world regulatory domain [19497.638740] cfg80211: Updating information on frequency 2412 MHz for a 20 MHz width channel with regulatory rule: [19497.638745] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [19497.638749] cfg80211: Updating information on frequency 2417 MHz for a 20 MHz width channel with regulatory rule: [19497.638753] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [19497.638756] cfg80211: Updating information on frequency 2422 MHz for a 20 MHz width channel with regulatory rule: [19497.638760] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [19497.638763] cfg80211: Updating information on frequency 2427 MHz for a 20 MHz width channel with regulatory rule: [19497.638766] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [19497.638770] cfg80211: Updating information on frequency 2432 MHz for a 20 MHz width channel with regulatory rule: [19497.638773] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [19497.638776] cfg80211: Updating information on frequency 2437 MHz for a 20 MHz width channel with regulatory rule: [19497.638780] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [19497.638783] cfg80211: Updating information on frequency 2442 MHz for a 20 MHz width channel with regulatory rule: [19497.638787] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [19497.638790] cfg80211: Updating information on frequency 2447 MHz for a 20 MHz width channel with regulatory rule: [19497.638794] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [19497.638797] cfg80211: Updating information on frequency 2452 MHz for a 20 MHz width channel with regulatory rule: [19497.638801] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [19497.638804] cfg80211: Updating information on frequency 2457 MHz for a 20 MHz width channel with regulatory rule: [19497.638807] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [19497.638811] cfg80211: Updating information on frequency 2462 MHz for a 20 MHz width channel with regulatory rule: [19497.638814] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [19497.638817] cfg80211: Updating information on frequency 2467 MHz for a 20 MHz width channel with regulatory rule: [19497.638821] cfg80211: 2457000 KHz - 2482000 KHz @ KHz), (300 mBi, 2000 mBm) [19497.638824] cfg80211: Updating information on frequency 2472 MHz for a 20 MHz width channel with regulatory rule: [19497.638828] cfg80211: 2457000 KHz - 2482000 KHz @ KHz), (300 mBi, 2000 mBm) [19497.638831] cfg80211: Updating information on frequency 2484 MHz for a 20 MHz width channel with regulatory rule: [19497.638835] cfg80211: 2474000 KHz - 2494000 KHz @ KHz), (300 mBi, 2000 mBm) [19497.638838] cfg80211: World regulatory domain updated: [19497.638841] cfg80211: (start_freq - end_freq @ bandwidth), (max_antenna_gain, max_eirp) [19497.638845] cfg80211: (2402000 KHz - 2472000 KHz @ 40000 KHz), (300 mBi, 2000 mBm) [19497.638848] cfg80211: (2457000 KHz - 2482000 KHz @ 20000 KHz), (300 mBi, 2000 mBm) [19497.638852] cfg80211: (2474000 KHz - 2494000 KHz @ 20000 KHz), (300 mBi, 2000 mBm) [19497.638855] cfg80211: (5170000 KHz - 5250000 KHz @ 40000 KHz), (300 mBi, 2000 mBm) [19497.638859] cfg80211: (5735000 KHz - 5835000 KHz @ 40000 KHz), (300 mBi, 2000 mBm) [19513.145150] wlan0: authenticate with 00:24:c8:4b:46:e0 (try 1) [19513.146910] wlan0: authenticated [19513.252775] wlan0: associate with 00:24:c8:4b:46:e0 (try 1) [19513.255149] wlan0: RX AssocResp from 00:24:c8:4b:46:e0 (capab=0x411 status=0 aid=2) [19513.255154] wlan0: associated [19515.675091] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=91.79.8.40 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x20 TTL=110 ID=42720 DF PROTO=TCP SPT=1945 DPT=6881 WINDOW=65535 RES=0x00 SYN URGP=0 [19525.684312] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=78.13.80.169 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=109 ID=49890 DF PROTO=TCP SPT=53401 DPT=6881 WINDOW=16384 RES=0x00 SYN URGP=0 [19551.856766] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=85.228.39.93 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=103 ID=1162 PROTO=UDP SPT=6881 DPT=6881 LEN=28 [19564.623005] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=90.202.21.238 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=114 ID=17881 PROTO=UDP SPT=6881 DPT=6881 LEN=28 [19584.855364] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=2.49.151.87 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=117 ID=31716 PROTO=UDP SPT=6881 DPT=6881 LEN=28 [19604.688647] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=109.225.124.155 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=112 ID=6656 PROTO=UDP SPT=6881 DPT=6881 LEN=28 [19626.362529] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=81.184.50.41 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=114 ID=23241 DF PROTO=TCP SPT=1416 DPT=6881 WINDOW=65535 RES=0x00 SYN URGP=0 [19645.040906] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=92.250.245.244 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=51 ID=0 DF PROTO=TCP SPT=50061 DPT=6881 WINDOW=16384 RES=0x00 SYN URGP=0 [19665.212659] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=87.183.3.18 DST=192.168.1.6 LEN=52 TOS=0x00 PREC=0x00 TTL=111 ID=1689 DF PROTO=TCP SPT=62817 DPT=6881 WINDOW=8192 RES=0x00 SYN URGP=0 [19685.036415] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=78.13.80.169 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=109 ID=50638 DF PROTO=TCP SPT=49624 DPT=6881 WINDOW=16384 RES=0x00 SYN URGP=0 [19705.487915] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=217.122.17.82 DST=192.168.1.6 LEN=56 TOS=0x00 PREC=0x00 TTL=112 ID=19070 DF PROTO=TCP SPT=54795 DPT=6881 WINDOW=8192 RES=0x00 SYN URGP=0 [19726.779185] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=80.88.116.239 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=109 ID=32168 DF PROTO=TCP SPT=57330 DPT=6881 WINDOW=8192 RES=0x00 SYN URGP=0 [19744.755673] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=109.124.5.43 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=113 ID=2288 DF PROTO=TCP SPT=6475 DPT=6881 WINDOW=65535 RES=0x00 SYN URGP=0 [19764.449183] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=79.216.35.19 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=113 ID=4281 PROTO=UDP SPT=6881 DPT=6881 LEN=28 [19784.456189] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=81.82.25.149 DST=192.168.1.6 LEN=52 TOS=0x00 PREC=0x00 TTL=114 ID=1866 DF PROTO=TCP SPT=59507 DPT=6881 WINDOW=8192 RES=0x00 SYN URGP=0 [19804.836687] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=81.56.199.3 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=108 ID=14749 PROTO=UDP SPT=6881 DPT=6881 LEN=28 [19824.812685] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=186.28.7.159 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=107 ID=44686 PROTO=UDP SPT=23418 DPT=6881 LEN=28 [19847.683314] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=78.13.80.169 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=108 ID=63046 DF PROTO=TCP SPT=52192 DPT=6881 WINDOW=16384 RES=0x00 SYN URGP=0 [19884.711455] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=84.146.24.238 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=113 ID=27914 PROTO=UDP SPT=6881 DPT=6881 LEN=28 [19884.983589] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=2.107.130.61 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=112 ID=7742 PROTO=UDP SPT=6881 DPT=6881 LEN=28 [19905.681078] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=95.21.11.121 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=114 ID=31775 PROTO=UDP SPT=6881 DPT=6881 LEN=28 [19926.035707] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=109.76.132.55 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=113 ID=28140 DF PROTO=TCP SPT=51905 DPT=6881 WINDOW=8192 RES=0x00 SYN URGP=0 [19945.668326] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=188.92.0.197 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=113 ID=7865 PROTO=UDP SPT=6881 DPT=6881 LEN=28 [19967.200339] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=83.252.102.172 DST=192.168.1.6 LEN=52 TOS=0x00 PREC=0x00 TTL=105 ID=28408 DF PROTO=TCP SPT=63505 DPT=6881 WINDOW=8192 RES=0x00 SYN URGP=0 [19999.752732] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=79.166.171.200 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=110 ID=36405 PROTO=UDP SPT=6881 DPT=6881 LEN=28 [20007.928719] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=79.235.59.16 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=112 ID=46415 DF PROTO=TCP SPT=4537 DPT=6881 WINDOW=16384 RES=0x00 SYN URGP=0 [20026.181726] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=81.182.169.36 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=106 ID=25126 PROTO=UDP SPT=6881 DPT=6881 LEN=28 [20048.845358] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=87.66.118.104 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=111 ID=18068 DF PROTO=TCP SPT=49928 DPT=6881 WINDOW=8192 RES=0x00 SYN URGP=0 [20064.341857] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=77.2.63.153 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=107 ID=7242 PROTO=UDP SPT=6881 DPT=6881 LEN=28 [20090.093490] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=93.16.17.210 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=108 ID=894 PROTO=UDP SPT=6881 DPT=6881 LEN=28 [20104.443995] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=89.83.235.99 DST=192.168.1.6 LEN=52 TOS=0x00 PREC=0x00 TTL=114 ID=17295 DF PROTO=TCP SPT=58979 DPT=6881 WINDOW=8192 RES=0x00 SYN URGP=0 [20128.625374] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=81.62.91.79 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=107 ID=21793 DF PROTO=TCP SPT=51446 DPT=6881 WINDOW=8192 RES=0x00 SYN URGP=0 [20151.055506] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=84.135.217.213 DST=192.168.1.6 LEN=52 TOS=0x00 PREC=0x00 TTL=112 ID=32452 DF PROTO=TCP SPT=55136 DPT=6881 WINDOW=8192 RES=0x00 SYN URGP=0 [20164.618874] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=91.79.8.40 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x20 TTL=110 ID=47784 DF PROTO=TCP SPT=2422 DPT=6881 WINDOW=65535 RES=0x00 SYN URGP=0 [20184.337745] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=83.252.212.71 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=107 ID=14544 PROTO=UDP SPT=6881 DPT=6881 LEN=28 [20205.007512] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=91.62.158.247 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=110 ID=21562 DF PROTO=TCP SPT=3933 DPT=6881 WINDOW=65535 RES=0x00 SYN URGP=0 [20225.204018] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=84.146.24.238 DST=192.168.1.6 LEN=52 TOS=0x00 PREC=0x00 TTL=113 ID=15045 DF PROTO=TCP SPT=49630 DPT=6881 WINDOW=8192 RES=0x00 SYN URGP=0 [20244.842290] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=82.82.190.168 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=112 ID=23741 DF PROTO=TCP SPT=50766 DPT=6881 WINDOW=8192 RES=0x00 SYN URGP=0 [20266.701649] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=88.153.108.124 DST=192.168.1.6 LEN=48 TOS=0x02 PREC=0x00 TTL=111 ID=206 DF PROTO=TCP SPT=2451 DPT=6881 WINDOW=65535 RES=0x00 SYN URGP=0 [20286.305414] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=78.240.86.73 DST=192.168.1.6 LEN=52 TOS=0x00 PREC=0x00 TTL=107 ID=325 DF PROTO=TCP SPT=65184 DPT=6881 WINDOW=8192 RES=0x00 SYN URGP=0 [20294.293989] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=192.168.1.254 DST=192.168.1.6 LEN=56 TOS=0x00 PREC=0x00 TTL=255 ID=43133 PROTO=ICMP TYPE=3 CODE=0 [SRC=192.168.1.6 DST=91.189.88.33 LEN=52 TOS=0x00 PREC=0x00 TTL=63 ID=56899 DF PROTO=TCP INCOMPLETE [8 bytes] ] [20294.297015] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=192.168.1.254 DST=192.168.1.6 LEN=56 TOS=0x00 PREC=0x00 TTL=255 ID=43134 PROTO=ICMP TYPE=3 CODE=0 [SRC=192.168.1.6 DST=91.189.88.40 LEN=52 TOS=0x00 PREC=0x00 TTL=63 ID=12080 DF PROTO=TCP INCOMPLETE [8 bytes] ] [20294.297242] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=192.168.1.254 DST=192.168.1.6 LEN=56 TOS=0x00 PREC=0x00 TTL=255 ID=43135 PROTO=ICMP TYPE=3 CODE=0 [SRC=192.168.1.6 DST=91.189.88.33 LEN=52 TOS=0x00 PREC=0x00 TTL=63 ID=25195 DF PROTO=TCP INCOMPLETE [8 bytes] ] [20295.478338] wlan0: deauthenticating from 00:24:c8:4b:46:e0 by local choice (reason=3) [20295.552735] cfg80211: All devices are disconnected, going to restore regulatory settings [20295.552742] cfg80211: Restoring regulatory settings [20295.552748] cfg80211: Calling CRDA to update world regulatory domain [20295.680635] cfg80211: Updating information on frequency 2412 MHz for a 20 MHz width channel with regulatory rule: [20295.680641] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [20295.680644] cfg80211: Updating information on frequency 2417 MHz for a 20 MHz width channel with regulatory rule: [20295.680648] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [20295.680652] cfg80211: Updating information on frequency 2422 MHz for a 20 MHz width channel with regulatory rule: [20295.680655] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [20295.680658] cfg80211: Updating information on frequency 2427 MHz for a 20 MHz width channel with regulatory rule: [20295.680662] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [20295.680665] cfg80211: Updating information on frequency 2432 MHz for a 20 MHz width channel with regulatory rule: [20295.680669] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [20295.680672] cfg80211: Updating information on frequency 2437 MHz for a 20 MHz width channel with regulatory rule: [20295.680676] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [20295.680679] cfg80211: Updating information on frequency 2442 MHz for a 20 MHz width channel with regulatory rule: [20295.680683] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [20295.680687] cfg80211: Updating information on frequency 2447 MHz for a 20 MHz width channel with regulatory rule: [20295.680690] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [20295.680693] cfg80211: Updating information on frequency 2452 MHz for a 20 MHz width channel with regulatory rule: [20295.680697] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [20295.680700] cfg80211: Updating information on frequency 2457 MHz for a 20 MHz width channel with regulatory rule: [20295.680704] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [20295.680708] cfg80211: Updating information on frequency 2462 MHz for a 20 MHz width channel with regulatory rule: [20295.680711] cfg80211: 2402000 KHz - 2472000 KHz @ KHz), (300 mBi, 2000 mBm) [20295.680715] cfg80211: Updating information on frequency 2467 MHz for a 20 MHz width channel with regulatory rule: [20295.680718] cfg80211: 2457000 KHz - 2482000 KHz @ KHz), (300 mBi, 2000 mBm) [20295.680722] cfg80211: Updating information on frequency 2472 MHz for a 20 MHz width channel with regulatory rule: [20295.680725] cfg80211: 2457000 KHz - 2482000 KHz @ KHz), (300 mBi, 2000 mBm) [20295.680728] cfg80211: Updating information on frequency 2484 MHz for a 20 MHz width channel with regulatory rule: [20295.680732] cfg80211: 2474000 KHz - 2494000 KHz @ KHz), (300 mBi, 2000 mBm) [20295.680736] cfg80211: World regulatory domain updated: [20295.680738] cfg80211: (start_freq - end_freq @ bandwidth), (max_antenna_gain, max_eirp) [20295.680742] cfg80211: (2402000 KHz - 2472000 KHz @ 40000 KHz), (300 mBi, 2000 mBm) [20295.680745] cfg80211: (2457000 KHz - 2482000 KHz @ 20000 KHz), (300 mBi, 2000 mBm) [20295.680749] cfg80211: (2474000 KHz - 2494000 KHz @ 20000 KHz), (300 mBi, 2000 mBm) [20295.680752] cfg80211: (5170000 KHz - 5250000 KHz @ 40000 KHz), (300 mBi, 2000 mBm) [20295.680756] cfg80211: (5735000 KHz - 5835000 KHz @ 40000 KHz), (300 mBi, 2000 mBm) [20306.009341] wlan0: authenticate with 00:24:c8:4b:46:e0 (try 1) [20306.011225] wlan0: authenticated [20306.118095] wlan0: associate with 00:24:c8:4b:46:e0 (try 1) [20306.120963] wlan0: RX AssocResp from 00:24:c8:4b:46:e0 (capab=0x411 status=0 aid=2) [20306.120967] wlan0: associated [20307.364427] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=87.91.101.130 DST=192.168.1.6 LEN=64 TOS=0x00 PREC=0x00 TTL=49 ID=36839 DF PROTO=TCP SPT=62492 DPT=6881 WINDOW=65535 RES=0x00 SYN URGP=0 [20310.914290] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=192.168.1.254 DST=192.168.1.6 LEN=56 TOS=0x00 PREC=0x00 TTL=255 ID=43180 PROTO=ICMP TYPE=3 CODE=0 [SRC=192.168.1.6 DST=91.189.88.33 LEN=52 TOS=0x00 PREC=0x00 TTL=63 ID=56900 DF PROTO=TCP INCOMPLETE [8 bytes] ] [20310.936634] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=192.168.1.254 DST=192.168.1.6 LEN=56 TOS=0x00 PREC=0x00 TTL=255 ID=43181 PROTO=ICMP TYPE=3 CODE=0 [SRC=192.168.1.6 DST=91.189.88.40 LEN=52 TOS=0x00 PREC=0x00 TTL=63 ID=12081 DF PROTO=TCP INCOMPLETE [8 bytes] ] [20310.939017] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=192.168.1.254 DST=192.168.1.6 LEN=56 TOS=0x00 PREC=0x00 TTL=255 ID=43182 PROTO=ICMP TYPE=3 CODE=0 [SRC=192.168.1.6 DST=91.189.88.33 LEN=52 TOS=0x00 PREC=0x00 TTL=63 ID=25196 DF PROTO=TCP INCOMPLETE [8 bytes] ] [20325.941050] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=217.118.78.99 DST=192.168.1.6 LEN=48 TOS=0x00 PREC=0x00 TTL=113 ID=4407 PROTO=UDP SPT=2970 DPT=6881 LEN=28 [20328.801724] [UFW BLOCK] IN=wlan0 OUT= MAC=00:c0:ca:44:62:d1:00:24:c8:4b:46:e0:08:00 SRC=192.168.1.254 DST=192.168.1.6 LEN=56 TOS=0x00 PREC=0x00 TTL=255 ID=43196 PROTO=ICMP TYPE=3 CODE=0 [SRC=192.168.1.6 DST=91.189.88.33 LEN=52 TOS=0x00 PREC=0x00 TTL=63 ID=56901 DF PROTO=TCP INCOMPLETE [8 bytes] ] ... inxi -N Network: Card-1 Realtek RTL8101E/RTL8102E PCI Express Fast Ethernet controller driver r8169 Card-2 Realtek RTL-8139/8139C/8139C+ driver 8139too /usr/lib/linuxmint/mintWifi/mintWifi.py ------------------------- * I. scanning WIFI PCI devices... ------------------------- * II. querying ndiswrapper... ------------------------- * III. querying iwconfig... lo no wireless extensions. eth0 no wireless extensions. eth1 no wireless extensions. wlan0 IEEE 802.11bg ESSID:"Home" Mode:Managed Frequency:2.437 GHz Access Point: 00:24:C8:4B:46:E0 Bit Rate=54 Mb/s Tx-Power=20 dBm Retry long limit:7 RTS thr:off Fragment thr:off Power Management:off Link Quality=68/70 Signal level=-42 dBm Rx invalid nwid:0 Rx invalid crypt:0 Rx invalid frag:0 Tx excessive retries:0 Invalid misc:1132 Missed beacon:0 ------------------------- * IV. querying ifconfig... eth0 Link encap:Ethernet HWaddr 00:1f:d0:c9:b8:8e UP BROADCAST MULTICAST MTU:1500 Metric:1 RX packets:0 errors:0 dropped:0 overruns:0 frame:0 TX packets:0 errors:0 dropped:0 overruns:0 carrier:0 collisions:0 txqueuelen:1000 RX bytes:0 (0.0 B) TX bytes:0 (0.0 B) Interrupt:43 Base address:0x4000 eth1 Link encap:Ethernet HWaddr 00:0e:2e:77:88:16 UP BROADCAST MULTICAST MTU:1500 Metric:1 RX packets:0 errors:0 dropped:0 overruns:0 frame:0 TX packets:0 errors:0 dropped:0 overruns:0 carrier:0 collisions:0 txqueuelen:1000 RX bytes:0 (0.0 B) TX bytes:0 (0.0 B) Interrupt:19 Base address:0xd000 lo Link encap:Local Loopback inet addr:127.0.0.1 Mask:255.0.0.0 inet6 addr: ::1/128 Scope:Host UP LOOPBACK RUNNING MTU:16436 Metric:1 RX packets:10696 errors:0 dropped:0 overruns:0 frame:0 TX packets:10696 errors:0 dropped:0 overruns:0 carrier:0 collisions:0 txqueuelen:0 RX bytes:3823011 (3.8 MB) TX bytes:3823011 (3.8 MB) wlan0 Link encap:Ethernet HWaddr 00:c0:ca:44:62:d1 inet addr:192.168.1.6 Bcast:255.255.255.255 Mask:255.255.255.0 inet6 addr: fe80::2c0:caff:fe44:62d1/64 Scope:Link UP BROADCAST RUNNING MULTICAST MTU:1500 Metric:1 RX packets:90424 errors:0 dropped:0 overruns:0 frame:0 TX packets:65201 errors:0 dropped:0 overruns:0 carrier:0 collisions:0 txqueuelen:1000 RX bytes:98024465 (98.0 MB) TX bytes:10345450 (10.3 MB) ------------------------- * V. querying DHCP... lspci 00:00.0 Host bridge: Intel Corporation 82G33/G31/P35/P31 Express DRAM Controller (rev 10) 00:01.0 PCI bridge: Intel Corporation 82G33/G31/P35/P31 Express PCI Express Root Port (rev 10) 00:1b.0 Audio device: Intel Corporation N10/ICH 7 Family High Definition Audio Controller (rev 01) 00:1c.0 PCI bridge: Intel Corporation N10/ICH 7 Family PCI Express Port 1 (rev 01) 00:1c.1 PCI bridge: Intel Corporation N10/ICH 7 Family PCI Express Port 2 (rev 01) 00:1d.0 USB Controller: Intel Corporation N10/ICH 7 Family USB UHCI Controller #1 (rev 01) 00:1d.1 USB Controller: Intel Corporation N10/ICH 7 Family USB UHCI Controller #2 (rev 01) 00:1d.2 USB Controller: Intel Corporation N10/ICH 7 Family USB UHCI Controller #3 (rev 01) 00:1d.3 USB Controller: Intel Corporation N10/ICH 7 Family USB UHCI Controller #4 (rev 01) 00:1d.7 USB Controller: Intel Corporation N10/ICH 7 Family USB2 EHCI Controller (rev 01) 00:1e.0 PCI bridge: Intel Corporation 82801 PCI Bridge (rev e1) 00:1f.0 ISA bridge: Intel Corporation 82801GB/GR (ICH7 Family) LPC Interface Bridge (rev 01) 00:1f.2 IDE interface: Intel Corporation N10/ICH7 Family SATA IDE Controller (rev 01) 00:1f.3 SMBus: Intel Corporation N10/ICH 7 Family SMBus Controller (rev 01) 01:00.0 VGA compatible controller: nVidia Corporation G96 [GeForce 9400 GT] (rev a1) 03:00.0 Ethernet controller: Realtek Semiconductor Co., Ltd. RTL8101E/RTL8102E PCI Express Fast Ethernet controller (rev 02) 04:01.0 Ethernet controller: Realtek Semiconductor Co., Ltd. RTL-8139/8139C/8139C+ (rev 10) lsmod Module Size Used by ipt_REJECT 12512 1 ipt_LOG 12784 5 xt_limit 12541 7 xt_tcpudp 12531 8 ipt_addrtype 12535 4 xt_state 12514 7 ip6table_filter 12711 1 ip6_tables 22545 1 ip6table_filter nf_nat_irc 12542 0 nf_conntrack_irc 13138 1 nf_nat_irc nf_nat_ftp 12548 0 nf_nat 24827 2 nf_nat_irc,nf_nat_ftp nf_conntrack_ipv4 19024 9 nf_nat nf_defrag_ipv4 12649 1 nf_conntrack_ipv4 nf_conntrack_ftp 13106 1 nf_nat_ftp nf_conntrack 69744 7 xt_state,nf_nat_irc,nf_conntrack_irc,nf_nat_ftp,nf_nat,nf_conntrack_ipv4,nf_conntrack_ftp iptable_filter 12706 1 ip_tables 18125 1 iptable_filter x_tables 21907 10 ipt_REJECT,ipt_LOG,xt_limit,xt_tcpudp,ipt_addrtype,xt_state,ip6table_filter,ip6_tables,iptable_filter,ip_tables nls_utf8 12493 10 udf 83795 1 crc_itu_t 12627 1 udf usb_storage 43946 1 uas 17676 0 snd_seq_dummy 12686 0 cryptd 19801 0 aes_i586 16956 1 aes_generic 38023 1 aes_i586 binfmt_misc 13213 1 dm_crypt 22463 0 vesafb 13449 1 nvidia 9766978 44 arc4 12473 2 rtl8187 56206 0 mac80211 257001 1 rtl8187 cfg80211 156212 2 rtl8187,mac80211 ppdev 12849 0 snd_hda_codec_realtek 255882 1 parport_pc 32111 1 psmouse 73312 0 eeprom_93cx6 12653 1 rtl8187 snd_hda_intel 24113 5 snd_hda_codec 90901 2 snd_hda_codec_realtek,snd_hda_intel snd_hwdep 13274 1 snd_hda_codec snd_pcm 80042 3 snd_hda_intel,snd_hda_codec snd_seq_midi 13132 0 snd_rawmidi 25269 1 snd_seq_midi snd_seq_midi_event 14475 1 snd_seq_midi snd_seq 51291 3 snd_seq_dummy,snd_seq_midi,snd_seq_midi_event snd_timer 28659 2 snd_pcm,snd_seq snd_seq_device 14110 4 snd_seq_dummy,snd_seq_midi,snd_rawmidi,snd_seq joydev 17322 0 snd 55295 18 snd_hda_codec_realtek,snd_hda_intel,snd_hda_codec,snd_hwdep,snd_pcm,snd_rawmidi,snd_seq,snd_timer,snd_seq_device serio_raw 12990 0 soundcore 12600 1 snd snd_page_alloc 14073 2 snd_hda_intel,snd_pcm lp 13349 0 parport 36746 3 ppdev,parport_pc,lp usbhid 41704 0 hid 77084 1 usbhid dm_raid45 88410 0 xor 21860 1 dm_raid45 btrfs 527388 0 zlib_deflate 26594 1 btrfs libcrc32c 12543 1 btrfs 8139too 23208 0 8139cp 22497 0 r8169 42534 0 floppy 60032 0

  Read the article

• SQL Server 2008 uses half the CPU’s

  - by ACALVETT

  I recently got my hands on a couple of 4 socket servers with Intel E7-4870's (10 cores per cpu) and with hyper threading enabled that gave me 80 logical CPU's. The server has Windows 2008 R2 SP1 along with SQL 2008 (Currently we can not deploy SQL 2008 R2 for the application being hosted). When SQL Server started I noticed only 2 NUMA nodes were configured and 40 logical cores where there should have been 4 NUMA nodes and 80 logical cores (see below). The problem is caused by that fact that...(read more)

  Read the article

• Infinite loop during A* algorithm

  - by Tashu

  The A* algorithm is used by enemies to have a path to the goal. It's working but when sometimes I placed a tower in a grid (randomly) it produces a stack overflow error. The A* algorithm would iterate the enemy and find its path and pass the list to the enemy's path. I added debug logs and the list that I'm getting it looks like it would arrive from start cell to goal cell. Here's the log - 06-19 19:26:41.982: DEBUG/findEnemyPath, enemy X:Y(4281): X2.8256836:Y3.5 06-19 19:26:41.990: DEBUG/findEnemyPath, grid X:Y(4281): X3:Y2 06-19 19:26:41.990: DEBUG/START CELL ID:(4281): 38 06-19 19:26:41.990: DEBUG/GOAL CELL ID:(4281): 47 06-19 19:26:41.990: DEBUG/Best : 38(4281): passThrough:0.0 06-19 19:26:41.990: DEBUG/Neighbor's Parent:(4281): 38 06-19 19:26:41.990: DEBUG/Neighbor's Parent:(4281): 38 06-19 19:26:41.990: DEBUG/Neighbor's Parent:(4281): 38 06-19 19:26:41.990: DEBUG/Neighbor's Parent:(4281): 38 06-19 19:26:41.990: DEBUG/Best : 39(4281): passThrough:8.875 06-19 19:26:41.990: DEBUG/Neighbor's Parent:(4281): 39 06-19 19:26:41.990: DEBUG/Neighbor's Parent:(4281): 39 06-19 19:26:41.990: DEBUG/Neighbor's Parent:(4281): 39 06-19 19:26:41.990: DEBUG/Best : 40(4281): passThrough:7.9375 06-19 19:26:41.990: DEBUG/Neighbor's Parent:(4281): 40 06-19 19:26:41.990: DEBUG/Neighbor's Parent:(4281): 40 06-19 19:26:41.990: DEBUG/Best : 52(4281): passThrough:8.9375 06-19 19:26:41.990: DEBUG/Neighbor's Parent:(4281): 52 06-19 19:26:41.990: DEBUG/Neighbor's Parent:(4281): 52 06-19 19:26:41.990: DEBUG/Best : 53(4281): passThrough:7.96875 06-19 19:26:41.990: DEBUG/Neighbor's Parent:(4281): 53 06-19 19:26:41.990: DEBUG/Best : 28(4281): passThrough:8.9375 06-19 19:26:41.990: DEBUG/Neighbor's Parent:(4281): 28 06-19 19:26:41.990: DEBUG/Best : 65(4281): passThrough:8.984375 06-19 19:26:41.990: DEBUG/Neighbor's Parent:(4281): 65 06-19 19:26:41.990: DEBUG/Neighbor's Parent:(4281): 65 06-19 19:26:41.990: DEBUG/Best : 66(4281): passThrough:7.9921875 06-19 19:26:41.990: DEBUG/Neighbor's Parent:(4281): 66 06-19 19:26:42.000: DEBUG/Best : 78(4281): passThrough:8.99609375 06-19 19:26:42.000: DEBUG/Neighbor's Parent:(4281): 78 06-19 19:26:42.000: DEBUG/Best : 79(4281): passThrough:7.998046875 06-19 19:26:42.000: DEBUG/Neighbor's Parent:(4281): 79 06-19 19:26:42.000: DEBUG/Best : 80(4281): passThrough:6.9990234375 06-19 19:26:42.000: DEBUG/Neighbor's Parent:(4281): 80 06-19 19:26:42.000: DEBUG/Neighbor's Parent:(4281): 80 06-19 19:26:42.000: DEBUG/Best : 81(4281): passThrough:5.99951171875 06-19 19:26:42.000: DEBUG/Neighbor's Parent:(4281): 81 06-19 19:26:42.000: DEBUG/Neighbor's Parent:(4281): 81 06-19 19:26:42.000: DEBUG/Best : 82(4281): passThrough:4.999755859375 06-19 19:26:42.000: DEBUG/Neighbor's Parent:(4281): 82 06-19 19:26:42.000: DEBUG/Neighbor's Parent:(4281): 82 06-19 19:26:42.000: DEBUG/Best : 83(4281): passThrough:3.9998779296875 06-19 19:26:42.000: DEBUG/Neighbor's Parent:(4281): 83 06-19 19:26:42.000: DEBUG/Best : 71(4281): passThrough:2.99993896484375 06-19 19:26:42.000: DEBUG/Neighbor's Parent:(4281): 71 06-19 19:26:42.000: DEBUG/Best : 59(4281): passThrough:1.99951171875 06-19 19:26:42.000: DEBUG/Neighbor's Parent:(4281): 59 06-19 19:26:42.000: DEBUG/Neighbor's Parent:(4281): 59 06-19 19:26:42.000: DEBUG/Neighbor's Parent:(4281): 59 06-19 19:26:42.000: DEBUG/Best : 47(4281): passThrough:0.99951171875 Then, the goal cell would be iterating its parent till start cell to break off the loop. private void populateBestList(Cell cell, List<Cell> bestList) { bestList.add(cell); if (cell.parent.start == false) { Log.d("ID:", ""+cell.id); Log.d("ParentID:", ""+cell.parent.id); populateBestList(cell.parent, bestList); } return; } The log with error above would show like this - 06-19 19:26:42.010: DEBUG/ID:(4281): 47 06-19 19:26:42.010: DEBUG/ParentID:(4281): 59 06-19 19:26:42.010: DEBUG/ID:(4281): 59 06-19 19:26:42.010: DEBUG/ParentID:(4281): 71 06-19 19:26:42.010: DEBUG/ID:(4281): 71 06-19 19:26:42.010: DEBUG/ParentID:(4281): 59 06-19 19:26:42.010: DEBUG/ID:(4281): 59 06-19 19:26:42.010: DEBUG/ParentID:(4281): 71 06-19 19:26:42.010: DEBUG/ID:(4281): 71 71 and 59 would switch over and goes on. I thought the grid is the issue due to the fact that enemies are using the single grid so I make the parent, start, and goal clear before starting the A* algorithm for an enemy. for(int i = 0; i < GRID_HEIGHT; i++) { for(int j = 0; j < GRID_WIDTH; j++) { grid[i][j].parent = null; grid[i][j].start = false; grid[i][j].goal = false; } } That didn't work. I thought it might be something related to this code, but not sure if I'm on right track - neighbor.parent = best; openList.remove(neighbor); closedList.remove(neighbor); openList.add(0, neighbor); Here's the code of the A* algorithm - private List<Cell> findEnemyPath(Enemy enemy) { for(int i = 0; i < GRID_HEIGHT; i++) { for(int j = 0; j < GRID_WIDTH; j++) { grid[i][j].parent = null; grid[i][j].start = false; grid[i][j].goal = false; } } List<Cell> openList = new ArrayList<Cell>(); List<Cell> closedList = new ArrayList<Cell>(); List<Cell> bestList = new ArrayList<Cell>(); int width = (int)Math.floor(enemy.position.x); int height = (int)Math.floor(enemy.position.y); width = (width < 0) ? 0 : width; height = (height < 0) ? 0 : height; Log.d("findEnemyPath, enemy X:Y", "X"+enemy.position.x+":"+"Y"+enemy.position.y); Log.d("findEnemyPath, grid X:Y", "X"+height+":"+"Y"+width); Cell start = grid[height][width]; Cell goal = grid[ENEMY_GOAL_HEIGHT][ENEMY_GOAL_WIDTH]; if(start.id != goal.id) { Log.d("START CELL ID: ", ""+start.id); Log.d("GOAL CELL ID: ", ""+goal.id); //Log.d("findEnemyPath, grid X:Y", "X"+start.position.x+":"+"Y"+start.position.y); start.start = true; goal.goal = true; openList.add(start); while(openList.size() > 0) { Cell best = findBestPassThrough(openList, goal); //Log.d("ID:", ""+best.id); openList.remove(best); closedList.add(best); if (best.goal) { System.out.println("Found Goal"); System.out.println(bestList.size()); populateBestList(goal, bestList); /* for(Cell cell : bestList) { Log.d("ID:", ""+cell.id); Log.d("ParentID:", ""+cell.parent.id); } */ Collections.reverse(bestList); Cell exit = new Cell(13.5f, 3.5f, 1, 1); exit.isExit = true; bestList.add(exit); //Log.d("PathList", "Enemy ID : " + enemy.id); return bestList; } else { List<Cell> neighbors = getNeighbors(best); for (Cell neighbor : neighbors) { if(neighbor.isTower) { continue; } if (openList.contains(neighbor)) { Cell tmpCell = new Cell(neighbor.position.x, neighbor.position.y, 1, 1); tmpCell.parent = best; if (tmpCell.getPassThrough(goal) >= neighbor.getPassThrough(goal)) { continue; } } if (closedList.contains(neighbor)) { Cell tmpCell = new Cell(neighbor.position.x, neighbor.position.y, 1, 1); tmpCell.parent = best; if (tmpCell.getPassThrough(goal) >= neighbor.getPassThrough(goal)) { continue; } } Log.d("Neighbor's Parent: ", ""+best.id); neighbor.parent = best; openList.remove(neighbor); closedList.remove(neighbor); openList.add(0, neighbor); } } } } Log.d("Cannot find a path", ""); return null; }

  Read the article

• Improving Partitioned Table Join Performance

  - by Paul White

  The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) );   CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY);   WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50);   -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE);   -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory:   Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   CREATE TABLE #P ( partition_number integer PRIMARY KEY);   INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1;   SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals;   DROP TABLE #P;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(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) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated  – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

  Read the article

• Why I don’t need to go on the SQLCruise

  - by Jonathan Kehayias

  Brent Ozar ( Blog | Twitter ) and Tim Mitchell ( Blog | Twitter ) are putting on a new type of event in the month of August after SQL Saturday #40 in South Florida July, 31st , properly named SQLCruise .  The concept is great, at least in my opinion, you pay for a cruise, get to have a break, and at the same time attend a mini-conference on SQL Server with training provided by two great speakers.  The cost is relatively affordable, so what could possibly make it better?  How about...(read more)

  Read the article

• Why I don’t need to go on the SQLCruise

  - by Jonathan Kehayias

  Brent Ozar ( Blog | Twitter ) and Tim Ford ( Blog | Twitter ) are putting on a new type of event in the month of August after SQL Saturday #40 in South Florida July, 31st , properly named SQLCruise . The concept is great, at least in my opinion, you pay for a cruise, get to have a break, and at the same time attend a mini-conference on SQL Server with training provided by two great speakers. The cost is relatively affordable, so what could possibly make it better? How about a sponsor offering up...(read more)

  Read the article

• Can anyone recommend a Google SERP tracker?

  - by Haroldo

  I want to track my website's position in Google's search results for around 50 keywords/phrases and I am looking to a nice web service or Windows application to automate this process. Ideally, I want to see pretty Javascript or Flash line graphs for my keywords and their positions. I'm currently free-trialing Raven Tools and Sheer SEO but I am not particularly impressed with either. My budget is up to £25-30/$30-40 per month for a decent rank checker.

  Read the article

• SQL SERVER – Introduction to SQL Server 2014 In-Memory OLTP

  - by Pinal Dave

  In SQL Server 2014 Microsoft has introduced a new database engine component called In-Memory OLTP aka project “Hekaton” which is fully integrated into the SQL Server Database Engine. It is optimized for OLTP workloads accessing memory resident data. In-memory OLTP helps us create memory optimized tables which in turn offer significant performance improvement for our typical OLTP workload. The main objective of memory optimized table is to ensure that highly transactional tables could live in memory and remain in memory forever without even losing out a single record. The most significant part is that it still supports majority of our Transact-SQL statement. Transact-SQL stored procedures can be compiled to machine code for further performance improvements on memory-optimized tables. This engine is designed to ensure higher concurrency and minimal blocking. In-Memory OLTP alleviates the issue of locking, using a new type of multi-version optimistic concurrency control. It also substantially reduces waiting for log writes by generating far less log data and needing fewer log writes. Points to remember Memory-optimized tables refer to tables using the new data structures and key words added as part of In-Memory OLTP. Disk-based tables refer to your normal tables which we used to create in SQL Server since its inception. These tables use a fixed size 8 KB pages that need to be read from and written to disk as a unit. Natively compiled stored procedures refer to an object Type which is new and is supported by in-memory OLTP engine which convert it into machine code, which can further improve the data access performance for memory –optimized tables. Natively compiled stored procedures can only reference memory-optimized tables, they can’t be used to reference any disk –based table. Interpreted Transact-SQL stored procedures, which is what SQL Server has always used. Cross-container transactions refer to transactions that reference both memory-optimized tables and disk-based tables. Interop refers to interpreted Transact-SQL that references memory-optimized tables. Using In-Memory OLTP In-Memory OLTP engine has been available as part of SQL Server 2014 since June 2013 CTPs. Installation of In-Memory OLTP is part of the SQL Server setup application. The In-Memory OLTP components can only be installed with a 64-bit edition of SQL Server 2014 hence they are not available with 32-bit editions. Creating Databases Any database that will store memory-optimized tables must have a MEMORY_OPTIMIZED_DATA filegroup. This filegroup is specifically designed to store the checkpoint files needed by SQL Server to recover the memory-optimized tables, and although the syntax for creating the filegroup is almost the same as for creating a regular filestream filegroup, it must also specify the option CONTAINS MEMORY_OPTIMIZED_DATA. Here is an example of a CREATE DATABASE statement for a database that can support memory-optimized tables: CREATE DATABASE InMemoryDB ON PRIMARY(NAME = [InMemoryDB_data], FILENAME = 'D:\data\InMemoryDB_data.mdf', size=500MB), FILEGROUP [SampleDB_mod_fg] CONTAINS MEMORY_OPTIMIZED_DATA (NAME = [InMemoryDB_mod_dir], FILENAME = 'S:\data\InMemoryDB_mod_dir'), (NAME = [InMemoryDB_mod_dir], FILENAME = 'R:\data\InMemoryDB_mod_dir') LOG ON (name = [SampleDB_log], Filename='L:\log\InMemoryDB_log.ldf', size=500MB) COLLATE Latin1_General_100_BIN2; Above example code creates files on three different drives (D:  S: and R:) for the data files and in memory storage so if you would like to run this code kindly change the drive and folder locations as per your convenience. Also notice that binary collation was specified as Windows (non-SQL). BIN2 collation is the only collation support at this point for any indexes on memory optimized tables. It is also possible to add a MEMORY_OPTIMIZED_DATA file group to an existing database, use the below command to achieve the same. ALTER DATABASE AdventureWorks2012 ADD FILEGROUP hekaton_mod CONTAINS MEMORY_OPTIMIZED_DATA; GO ALTER DATABASE AdventureWorks2012 ADD FILE (NAME='hekaton_mod', FILENAME='S:\data\hekaton_mod') TO FILEGROUP hekaton_mod; GO Creating Tables There is no major syntactical difference between creating a disk based table or a memory –optimized table but yes there are a few restrictions and a few new essential extensions. Essentially any memory-optimized table should use the MEMORY_OPTIMIZED = ON clause as shown in the Create Table query example. DURABILITY clause (SCHEMA_AND_DATA or SCHEMA_ONLY) Memory-optimized table should always be defined with a DURABILITY value which can be either SCHEMA_AND_DATA or  SCHEMA_ONLY the former being the default. A memory-optimized table defined with DURABILITY=SCHEMA_ONLY will not persist the data to disk which means the data durability is compromised whereas DURABILITY= SCHEMA_AND_DATA ensures that data is also persisted along with the schema. Indexing Memory Optimized Table A memory-optimized table must always have an index for all tables created with DURABILITY= SCHEMA_AND_DATA and this can be achieved by declaring a PRIMARY KEY Constraint at the time of creating a table. The following example shows a PRIMARY KEY index created as a HASH index, for which a bucket count must also be specified. CREATE TABLE Mem_Table ( [Name] VARCHAR(32) NOT NULL PRIMARY KEY NONCLUSTERED HASH WITH (BUCKET_COUNT = 100000), [City] VARCHAR(32) NULL, [State_Province] VARCHAR(32) NULL, [LastModified] DATETIME NOT NULL, ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA); Now as you can see in the above query example we have used the clause MEMORY_OPTIMIZED = ON to make sure that it is considered as a memory optimized table and not just a normal table and also used the DURABILITY Clause= SCHEMA_AND_DATA which means it will persist data along with metadata and also you can notice this table has a PRIMARY KEY mentioned upfront which is also a mandatory clause for memory-optimized tables. We will talk more about HASH Indexes and BUCKET_COUNT in later articles on this topic which will be focusing more on Row and Index storage on Memory-Optimized tables. So stay tuned for that as well. Now as we covered the basics of Memory Optimized tables and understood the key things to remember while using memory optimized tables, let’s explore more using examples to understand the Performance gains using memory-optimized tables. I will be using the database which i created earlier in this article i.e. InMemoryDB in the below Demo Exercise. USE InMemoryDB GO -- Creating a disk based table CREATE TABLE dbo.Disktable ( Id INT IDENTITY, Name CHAR(40) ) GO CREATE NONCLUSTERED INDEX IX_ID ON dbo.Disktable (Id) GO -- Creating a memory optimized table with similar structure and DURABILITY = SCHEMA_AND_DATA CREATE TABLE dbo.Memorytable_durable ( Id INT NOT NULL PRIMARY KEY NONCLUSTERED Hash WITH (bucket_count =1000000), Name CHAR(40) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA) GO -- Creating an another memory optimized table with similar structure but DURABILITY = SCHEMA_Only CREATE TABLE dbo.Memorytable_nondurable ( Id INT NOT NULL PRIMARY KEY NONCLUSTERED Hash WITH (bucket_count =1000000), Name CHAR(40) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_only) GO -- Now insert 100000 records in dbo.Disktable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Disktable(Name) VALUES('sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END -- Do the same inserts for Memory table dbo.Memorytable_durable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Memorytable_durable VALUES(@i_t, 'sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END -- Now finally do the same inserts for Memory table dbo.Memorytable_nondurable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Memorytable_nondurable VALUES(@i_t, 'sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END The above 3 Inserts took 1.20 minutes, 54 secs, and 2 secs respectively to insert 100000 records on my machine with 8 Gb RAM. This proves the point that memory-optimized tables can definitely help businesses achieve better performance for their highly transactional business table and memory- optimized tables with Durability SCHEMA_ONLY is even faster as it does not bother persisting its data to disk which makes it supremely fast. Koenig Solutions is one of the few organizations which offer IT training on SQL Server 2014 and all its updates. Now, I leave the decision on using memory_Optimized tables on you, I hope you like this article and it helped you understand  the fundamentals of IN-Memory OLTP . Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: Koenig

  Read the article

• Smile sort son « Guide Michelin » 2013 de l'Open-Source, le Livre Blanc gratuit s'enrichit de rubriques sur le Cloud et le Big Data

  Smile sort son « Guide Michelin » 2013 de l'Open-Source Le Livre Blanc gratuit s'enrichit de rubriques sur le Cloud et le Big Data Pour l'édition 2013 de son Guide de référence sur l'open source, Smile a enrichit son Livre Blanc (285 pages) d'une trentaine de nouvelles solutions et de deux nouvelles rubriques (Cloud et Big Data). Plus 300 solutions y sont recensées (dont 200 évaluées dans le détail) dans plus de 40 domaines d'applications, répartis en trois « dimensions » (Infrastructure, Développement et couches intermédiaires, Applications). Le livre se présente sous la forme de fiches de présentation (version du produit, site web, aute...

  Read the article

• How to install Windows 8 to dual boot with Windows 7/XP?

  - by Gopinath

  Microsoft released Windows 8 beta(customer preview) few days ago and yesterday I had a chance to install it on one of my home computers. My home PC is running on Windows 7 and I would like to install Windows 8 side by side so that I can dual boot. The installation process was pretty simple and with in 40 minutes my PC was up and running with beautiful Windows 8 OS along with Windows 7. In this post I want to share my experience and provide information for you to install Windows 8. 1. Identify a drive  with at least 20 GB of space – Identify one of the drives on your hard disk that can be used to install Windows 8. Delete all the files or preferably quick format it and make sure that it has at least 20 GB of free space. Rename the drive name to Windows 8 so that it will be helpful to identify the destination drive during installation process. 2. Download Windows 8 installer ISO– Go to Microsoft’s website and download Windows 8 ISO file which is approximately 2.5 GB file(32 bit English version). 3. Create Windows 8 bootable USB/DVD – Its advised to launch Windows 8 installer using a bootable USB or DVD for enabling dual boot instead of unzipping the ISO file and launching the setup from Windows 7 OS. Also consider creating bootable USB instead of bootable DVD to save a disc. To create bootable USB/DVD follow these steps Download and install the Windows 7 DVD / USB tool available at microsoftstore.com Launch the utility and follow the onscreen instructions where you would be asked to choose the ISO file(point to file downloaded in step 2) and choose a USB drive or DVD as destination. The onscreen instructions are very simple and you would be able to complete it in 20 minutes time. So now you have Windows 8 installation setup on your USB drive or DVD. 4. Change BIOS settings to boot from USB/DVD – Restart your PC and open BIOS configuration settings key by pressing F2 or  F12 or DELETE key (the key depends on your computer manufacturer). Go to boot sequence options and make sure that USB/DVD is ahead of hard disk in the boot sequence. Save the settings and restart the PC. 5. Install Windows 8 – After the restart you should be straight into Windows 8 installation screen. Follow the onscreen instructions and install Windows 8 on the drive that is identified during step 1. When prompted for product serial key enter NF32V-Q9P3W-7DR7Y-JGWRW-JFCK8. The installer would restart couple of times during the installation process. On the first restart, make sure that you remove USB/DVD. Windows 8 installation process is pretty simple and very quick. The complete process of creating bootable USB and installation should complete in 30 – 40 minutes time.

  Read the article

• A conversation with Paul Rademacher and Mano Marks, Google Maps API Office Hours

  A conversation with Paul Rademacher and Mano Marks, Google Maps API Office Hours This is a conversation between Paul Rademacher and Mano Marks on April 24th, 2012. Paul created the first Google Maps Mashup, housingmaps.com, and discusses his latest project, Stratocam, which allows users to find and display beautiful satellite and aerial imagery with the Google Maps API. From: GoogleDevelopers Views: 1199 11 ratings Time: 40:08 More in Science & Technology

  Read the article

• Google I/O 2011: Building Web Apps for Google TV

  Google I/O 2011: Building Web Apps for Google TV Chris Wilson, Daniels Lee Learn about the Google TV platform and the opportunity to build web apps for the platform using HTML5 or Flash. Session includes an overview of the platform, best practices, demos, and a discussion about the opportunities for developers to build killer apps for Google TV. From: GoogleDevelopers Views: 4653 17 ratings Time: 56:40 More in Science & Technology

  Read the article

• Google I/O 2011: Optimizing Android Apps with Google Analytics

  Google I/O 2011: Optimizing Android Apps with Google Analytics Nick Mihailovski, Philip Mui, Jim Cotugno Thousands of apps have taken advantage of Google Analytics' native Android tracking capabilities to improve the adoption and usability of Andriod Apps. This session covers best practices for tracking apps on mobile, TV and other devices. We'll also show you how to gain actionable insights from new tracking and reporting capabilities. From: GoogleDevelopers Views: 6819 34 ratings Time: 47:40 More in Science & Technology

  Read the article

• Of C# Iterators and Performance

  - by James Michael Hare

  Some of you reading this will be wondering, "what is an iterator" and think I'm locked in the world of C++.  Nope, I'm talking C# iterators.  No, not enumerators, iterators.   So, for those of you who do not know what iterators are in C#, I will explain it in summary, and for those of you who know what iterators are but are curious of the performance impacts, I will explore that as well.   Iterators have been around for a bit now, and there are still a bunch of people who don't know what they are or what they do.  I don't know how many times at work I've had a code review on my code and have someone ask me, "what's that yield word do?"   Basically, this post came to me as I was writing some extension methods to extend IEnumerable<T> -- I'll post some of the fun ones in a later post.  Since I was filtering the resulting list down, I was using the standard C# iterator concept; but that got me wondering: what are the performance implications of using an iterator versus returning a new enumeration?   So, to begin, let's look at a couple of methods.  This is a new (albeit contrived) method called Every(...).  The goal of this method is to access and enumeration and return every nth item in the enumeration (including the first).  So Every(2) would return items 0, 2, 4, 6, etc.   Now, if you wanted to write this in the traditional way, you may come up with something like this:       public static IEnumerable<T> Every<T>(this IEnumerable<T> list, int interval)     {         List<T> newList = new List<T>();         int count = 0;           foreach (var i in list)         {             if ((count++ % interval) == 0)             {                 newList.Add(i);             }         }           return newList;     }     So basically this method takes any IEnumerable<T> and returns a new IEnumerable<T> that contains every nth item.  Pretty straight forward.   The problem?  Well, Every<T>(...) will construct a list containing every nth item whether or not you care.  What happens if you were searching this result for a certain item and find that item after five tries?  You would have generated the rest of the list for nothing.   Enter iterators.  This C# construct uses the yield keyword to effectively defer evaluation of the next item until it is asked for.  This can be very handy if the evaluation itself is expensive or if there's a fair chance you'll never want to fully evaluate a list.   We see this all the time in Linq, where many expressions are chained together to do complex processing on a list.  This would be very expensive if each of these expressions evaluated their entire possible result set on call.    Let's look at the same example function, this time using an iterator:       public static IEnumerable<T> Every<T>(this IEnumerable<T> list, int interval)     {         int count = 0;         foreach (var i in list)         {             if ((count++ % interval) == 0)             {                 yield return i;             }         }     }   Notice it does not create a new return value explicitly, the only evidence of a return is the "yield return" statement.  What this means is that when an item is requested from the enumeration, it will enter this method and evaluate until it either hits a yield return (in which case that item is returned) or until it exits the method or hits a yield break (in which case the iteration ends.   Behind the scenes, this is all done with a class that the CLR creates behind the scenes that keeps track of the state of the iteration, so that every time the next item is asked for, it finds that item and then updates the current position so it knows where to start at next time.   It doesn't seem like a big deal, does it?  But keep in mind the key point here: it only returns items as they are requested. Thus if there's a good chance you will only process a portion of the return list and/or if the evaluation of each item is expensive, an iterator may be of benefit.   This is especially true if you intend your methods to be chainable similar to the way Linq methods can be chained.    For example, perhaps you have a List<int> and you want to take every tenth one until you find one greater than 10.  We could write that as:       List<int> someList = new List<int>();         // fill list here         someList.Every(10).TakeWhile(i => i <= 10);     Now is the difference more apparent?  If we use the first form of Every that makes a copy of the list.  It's going to copy the entire list whether we will need those items or not, that can be costly!    With the iterator version, however, it will only take items from the list until it finds one that is > 10, at which point no further items in the list are evaluated.   So, sounds neat eh?  But what's the cost is what you're probably wondering.  So I ran some tests using the two forms of Every above on lists varying from 5 to 500,000 integers and tried various things.    Now, iteration isn't free.  If you are more likely than not to iterate the entire collection every time, iterator has some very slight overhead:   Copy vs Iterator on 100% of Collection (10,000 iterations) Collection Size Num Iterated Type Total ms 5 5 Copy 5 5 5 Iterator 5 50 50 Copy 28 50 50 Iterator 27 500 500 Copy 227 500 500 Iterator 247 5000 5000 Copy 2266 5000 5000 Iterator 2444 50,000 50,000 Copy 24,443 50,000 50,000 Iterator 24,719 500,000 500,000 Copy 250,024 500,000 500,000 Iterator 251,521   Notice that when iterating over the entire produced list, the times for the iterator are a little better for smaller lists, then getting just a slight bit worse for larger lists.  In reality, given the number of items and iterations, the result is near negligible, but just to show that iterators come at a price.  However, it should also be noted that the form of Every that returns a copy will have a left-over collection to garbage collect.   However, if we only partially evaluate less and less through the list, the savings start to show and make it well worth the overhead.  Let's look at what happens if you stop looking after 80% of the list:   Copy vs Iterator on 80% of Collection (10,000 iterations) Collection Size Num Iterated Type Total ms 5 4 Copy 5 5 4 Iterator 5 50 40 Copy 27 50 40 Iterator 23 500 400 Copy 215 500 400 Iterator 200 5000 4000 Copy 2099 5000 4000 Iterator 1962 50,000 40,000 Copy 22,385 50,000 40,000 Iterator 19,599 500,000 400,000 Copy 236,427 500,000 400,000 Iterator 196,010       Notice that the iterator form is now operating quite a bit faster.  But the savings really add up if you stop on average at 50% (which most searches would typically do):     Copy vs Iterator on 50% of Collection (10,000 iterations) Collection Size Num Iterated Type Total ms 5 2 Copy 5 5 2 Iterator 4 50 25 Copy 25 50 25 Iterator 16 500 250 Copy 188 500 250 Iterator 126 5000 2500 Copy 1854 5000 2500 Iterator 1226 50,000 25,000 Copy 19,839 50,000 25,000 Iterator 12,233 500,000 250,000 Copy 208,667 500,000 250,000 Iterator 122,336   Now we see that if we only expect to go on average 50% into the results, we tend to shave off around 40% of the time.  And this is only for one level deep.  If we are using this in a chain of query expressions it only adds to the savings.   So my recommendation?  If you have a resonable expectation that someone may only want to partially consume your enumerable result, I would always tend to favor an iterator.  The cost if they iterate the whole thing does not add much at all -- and if they consume only partially, you reap some really good performance gains.   Next time I'll discuss some of my favorite extensions I've created to make development life a little easier and maintainability a little better.

  Read the article

• GDD-BR 2010 [1D] Tim Bray - Android Ecosystem and What's New

  GDD-BR 2010 [1D] Tim Bray - Android Ecosystem and What's New Speaker: Tim Bray Track: Android Time slot: D[13:50 - 14:35] Room: 1 Level: 101 This talk combines an introduction to the Android ecosystem with a description of what's new in it. The ecosystem includes the technology, developer community, Android Market, and of course the huge population of Android users. From: GoogleDevelopers Views: 25 1 ratings Time: 41:40 More in Science & Technology

  Read the article

• The winning combination: Oracle VM Server for x86 + Oracle Sun Fire HW

  - by Karim Berrah

  You might be wondering why OVM Server for x86 (OVM/x86 here and below) should be seriously considered as a nice (business point of view) alternative to standard Hypervisors, if you are virtualizing Oracle Software, especially if you are planning to move to Oracle x86 Hardware (rackmount or blades). Well, let see some "not well known" facts that might interest you and help you in saving more money for your entire company (and not only the Virtulization team). Fact 1: OVM/x86 is considered as a hard partitionning technology (check page 2 of Oracle Server Partitionning Licencing Policies), so if you are buying new servers based on the latest INTEL Xeon E7 CPUs (10 cores per Socket) and have some licencing issues in deploying further Oracle SW, because you are using a hypervisor not recognized as a hard partitionning technology (like VMware), then you need to check here how to do it with OVM . This might help you to continue to deploy your Oracle DB instances on new x86 HW (12 cores, 40 cores, 64 cores servers) in a reasonable way, without having to pay licences for 12 CPU, 40 CPUs or 64 CPUs. You might also consider migrating your legacy Oracle DB DBs to a virtualized environment like OVM/x86 an recover some CPU licences, that can be reused somewhere else in production. Fact 2: OVM/x86 is free to use, without any extra licence for any specific feature (LiveMigration, High Availability, Embedded Management Console). If you want to use it on non Oracle HW, there is a support fee per  system and per year, that is much below VMware support (Oracle VM Premier Limited Support for systems up to 2 CPUs, and Oracle VM Premier Support for any bigger system, independently on the number of populated sockets). Fact 3: support is included with your Oracle x86 HW support (OPS for systems)  and you can re-install on you system Oracle Linux, Oracle Solaris or Oracle VM server for x86, without beeing charged, an keeping the same support level. Fact 4: it is less expensive to virtualize Oracle Linux or Oracle Solaris on OVM/x86 with Oracle HW that any other similar solution with VMware, because all the VMs are then supported and licenced when you buy Oracle HW with OPS. Fact 5: Oracle VM Templates bring you many Virtual Machines already installed, patched and optimized for various Oracle applications. And to be more specific, those templates are fully supported by Oracle, which is not really true when it comes to another hypervisor. By optimized VM Kernel, I mean PV drivers, OVM-ready kernels in the VM, single source clock for all the VMS, better memory management of the VM ... Fact 6: there is no extra costs for a management console. OVM comes with a free OVM Manager package for Linux.  More infos: Latest announcement of OVM/x86 update 2.2.2 A short flash demo of OVM server for x86 A short flash demo on OVM Templates and Virtual Assembly Builder Oracle Linux Support and Oracle VM Support Global Price List  ISVs: Benefits for Independant Sofwtare Vendors (ISVs) in using OVM/x86 Consultant Services: Advanced Customer Services for OVM/x86  Technical Features Best practices and Guideline for OVM with Oracle Blades Reduce TCO and get more Value from your x86 Infrastructure

  Read the article

• Silverlight MEF – Download On Demand

  - by PeterTweed

  Take the Slalom Challenge at www.slalomchallenge.com! A common challenge with building complex applications in Silverlight is the initial download size of the xap file.  MEF enables us to build composable applications that allows us to build complex composite applications.  Wouldn’t it be great if we had a mechanism to spilt out components into different Silverlight applications in separate xap files and download the separate xap file only if needed?   MEF gives us the ability to do this.  This post will cover the basics needed to build such a composite application split between different silerlight applications and download the referenced silverlight application only when needed. Steps: 1.     Create a Silverlight 4 application 2.     Add references to the following assemblies: System.ComponentModel.Composition.dll System.ComponentModel.Composition.Initialization.dll 3.     Add a new Silverlight 4 application called ExternalSilverlightApplication to the solution that was created in step 1.  Ensure the new application is hosted in the web application for the solution and choose to not create a test page for the new application. 4.     Delete the App.xaml and MainPage.xaml files – they aren’t needed. 5.     Add references to the following assemblies in the ExternalSilverlightApplication project: System.ComponentModel.Composition.dll System.ComponentModel.Composition.Initialization.dll 6.     Ensure the two references above have their Copy Local values set to false.  As we will have these two assmblies in the original Silverlight application, we will have no need to include them in the built ExternalSilverlightApplication build. 7.     Add a new user control called LeftControl to the ExternalSilverlightApplication project. 8.     Replace the LayoutRoot Grid with the following xaml:     <Grid x:Name="LayoutRoot" Background="Beige" Margin="40" >         <Button Content="Left Content" Margin="30"></Button>     </Grid> 9.     Add the following statement to the top of the LeftControl.xaml.cs file using System.ComponentModel.Composition; 10.   Add the following attribute to the LeftControl class     [Export(typeof(LeftControl))]   This attribute tells MEF that the type LeftControl will be exported – i.e. made available for other applications to import and compose into the application. 11.   Add a new user control called RightControl to the ExternalSilverlightApplication project. 12.   Replace the LayoutRoot Grid with the following xaml:     <Grid x:Name="LayoutRoot" Background="Green" Margin="40"  >         <TextBlock Margin="40" Foreground="White" Text="Right Control" FontSize="16" VerticalAlignment="Center" HorizontalAlignment="Center" ></TextBlock>     </Grid> 13.   Add the following statement to the top of the RightControl.xaml.cs file using System.ComponentModel.Composition; 14.   Add the following attribute to the RightControl class     [Export(typeof(RightControl))] 15.   In your original Silverlight project add a reference to the ExternalSilverlightApplication project. 16.   Change the reference to the ExternalSilverlightApplication project to have it’s Copy Local value = false.  This will ensure that the referenced ExternalSilverlightApplication Silverlight application is not included in the original Silverlight application package when it it built.  The ExternalSilverlightApplication Silverlight application therefore has to be downloaded on demand by the original Silverlight application for it’s controls to be used. 1.     In your original Silverlight project add the following xaml to the LayoutRoot Grid in MainPage.xaml:         <Grid.RowDefinitions>             <RowDefinition Height="65*" />             <RowDefinition Height="235*" />         </Grid.RowDefinitions>         <Button Name="LoaderButton" Content="Download External Controls" Click="Button_Click"></Button>         <StackPanel Grid.Row="1" Orientation="Horizontal" HorizontalAlignment="Center" >             <Border Name="LeftContent" Background="Red" BorderBrush="Gray" CornerRadius="20"></Border>             <Border Name="RightContent" Background="Red" BorderBrush="Gray" CornerRadius="20"></Border>         </StackPanel>       The borders will hold the controls that will be downlaoded, imported and composed via MEF when the button is clicked. 2.     Add the following statement to the top of the MainPage.xaml.cs file using System.ComponentModel.Composition; 3.     Add the following properties to the MainPage class:         [Import(typeof(LeftControl))]         public LeftControl LeftUserControl { get; set; }         [Import(typeof(RightControl))]         public RightControl RightUserControl { get; set; }   This defines properties accepting LeftControl and RightControl types.  The attrributes are used to tell MEF the discovered type that should be applied to the property when composition occurs. 17.   Add the following event handler for the button click to the MainPage.xaml.cs file:         private void Button_Click(object sender, RoutedEventArgs e)         {                   DeploymentCatalog deploymentCatalog =     new DeploymentCatalog("ExternalSilverlightApplication.xap");                   CompositionHost.Initialize(deploymentCatalog);                   deploymentCatalog.DownloadCompleted += (s, i) =>                 {                     if (i.Error == null)                     {                         CompositionInitializer.SatisfyImports(this);                           LeftContent.Child = LeftUserControl;                         RightContent.Child = RightUserControl;                         LoaderButton.IsEnabled = false;                     }                 };                   deploymentCatalog.DownloadAsync();         } This is where the magic happens!  The deploymentCatalog object is pointed to the ExternalSilverlightApplication.xap file.  It is then associated with the CompositionHost initialization.  As the download will be asynchronous, an eventhandler is created for the DownloadCompleted event.  The deploymentCatalog object is then told to start the asynchronous download. The event handler that executes when the download is completed uses the CompositionInitializer.SatisfyImports() function to tell MEF to satisfy the Imports for the current class.  It is at this point that the LeftUserControl and RightUserControl properties are initialized with composed objects from the downloaded ExternalSilverlightApplication.xap package. 18.   Run the application click the Download External Controls button and see the controls defined in the ExternalSilverlightApplication application loaded into the original Silverlight application. Congratulations!  You have implemented download on demand capabilities for composite applications using the MEF DeploymentCatalog class.  You are now able to segment your applications into separate xap file for deployment.

  Read the article

• No root file system is defined error after installation

  - by LearnCode

  I installed ubuntu through Wubi and once i rebooted I get no root file system defined error. here's the output of the boot_info_script.Could anyone point me out where the error is. Boot Info Script 0.60 from 17 May 2011 ============================= Boot Info Summary: =============================== => Windows is installed in the MBR of /dev/sda. => Windows is installed in the MBR of /dev/sdb. sda1: __________________________________________________________________________ File system: ntfs Boot sector type: Windows Vista/7 Boot sector info: No errors found in the Boot Parameter Block. Operating System: Windows 7 Boot files: /bootmgr /Boot/BCD /Windows/System32/winload.exe /ntldr /ntdetect.com /wubildr /ubuntu/winboot/wubildr /wubildr.mbr /ubuntu/winboot/wubildr.mbr /ubuntu/disks/root.disk /ubuntu/disks/swap.disk sda1/Wubi: _____________________________________________________________________ File system: Boot sector type: Unknown Boot sector info: Mounting failed: mount: unknown filesystem type '' sda2: __________________________________________________________________________ File system: vfat Boot sector type: Unknown Boot sector info: No errors found in the Boot Parameter Block. Operating System: Boot files: /boot.ini /ntldr /NTDETECT.COM sdb1: __________________________________________________________________________ File system: ntfs Boot sector type: Windows Vista/7 Boot sector info: No errors found in the Boot Parameter Block. Operating System: Boot files: ============================ Drive/Partition Info: ============================= Drive: sda _____________________________________________________________________ Disk /dev/sda: 160.0 GB, 160041885696 bytes 240 heads, 63 sectors/track, 20673 cylinders, total 312581808 sectors Units = sectors of 1 * 512 = 512 bytes Sector size (logical/physical): 512 bytes / 512 bytes Partition Boot Start Sector End Sector # of Sectors Id System /dev/sda1 * 63 301,250,879 301,250,817 7 NTFS / exFAT / HPFS /dev/sda2 301,250,943 312,575,759 11,324,817 c W95 FAT32 (LBA) GUID Partition Table detected, but does not seem to be used. Partition Start Sector End Sector # of Sectors System /dev/sda1 323,465,741,313,502,988275,962,973,585-323,465,465,350,529,402 - /dev/sda2 242,728,591,638,290,720578,721,383,108,845,578335,992,791,470,554,859 - /dev/sda3 1,827,498,311,425,204,2562,091,935,274,843,009,907264,436,963,417,805,652 - /dev/sda4 579,711,218,081,401,3572,006,665,459,744,645,1521,426,954,241,663,243,796 - /dev/sda11 270,286,346,402,038,1183,786,543,326,404,525,9543,516,256,980,002,487,837 - /dev/sda12 4,179,681,002,230,769,6684,179,389,374,010,033,387-291,628,220,736,280 - /dev/sda13 232,556,480,979,456,1311,160,152,593,793,119,235927,596,112,813,663,105 - /dev/sda14 98,342,784,050,266,9183,691,264,578,843,725,1953,592,921,794,793,458,278 - /dev/sda15 2,307,845,219,957,882,4961,850,841,032,955,276,350-457,004,187,002,606,145 - /dev/sda16 512,592,046,878,946,497368,458,231,024,779,444-144,133,815,854,167,052 - /dev/sda17 2,504,135,232,870,384,3923,665,087,872,719,320,8291,160,952,639,848,936,438 - /dev/sda18 3,783,181,605,270,691,304122,034,509,624,708,942-3,661,147,095,645,982,361 - /dev/sda19 3,519,661,520,275,829,5122,376,243,094,723,723,587-1,143,418,425,552,105,924 - /dev/sda20 3,867,920,076,859,0744,494,691,111,933,625,1044,490,823,191,856,766,031 - /dev/sda21 1,500,144,061,909,253,7612,511,182,033,846,676,3401,011,037,971,937,422,580 - /dev/sda22 13,035,625,499,900,0062,360,168,613,941,394,9472,347,132,988,441,494,942 - /dev/sda23 4,228,978,682,068,599,48813,159,423,631,648,263-4,215,819,258,436,951,224 - /dev/sda24 3,695,955,742,872,046,9084,561,928,726,501,845,776865,972,983,629,798,869 - /dev/sda25 1,297,460,286,683,948,0461,444,350,486,339,417,957146,890,199,655,469,912 - /dev/sda26 1,228,858,248,533,131,831 0-1,228,858,248,533,131,830 - /dev/sda121 3,189,184,846,146,487,1461,849,820,258,006,914,852-1,339,364,588,139,572,293 - /dev/sda122 1,226,215,547,991,800,578389,781,518,734,546,300-836,434,029,257,254,277 - /dev/sda123 3,851,660,168,574,583,4654,046,215,657,583,031,556194,555,489,008,448,092 - /dev/sda124 1,197,460,980,174,153,341699,103,965,005,093,246-498,357,015,169,060,094 - Drive: sdb _____________________________________________________________________ Disk /dev/sdb: 750.2 GB, 750153367552 bytes 255 heads, 63 sectors/track, 91200 cylinders, total 1465143296 sectors Units = sectors of 1 * 512 = 512 bytes Sector size (logical/physical): 512 bytes / 512 bytes Partition Boot Start Sector End Sector # of Sectors Id System /dev/sdb1 2,048 1,465,143,295 1,465,141,248 7 NTFS / exFAT / HPFS "blkid" output: ________________________________________________________________ Device UUID TYPE LABEL /dev/loop0 iso9660 Ubuntu 11.04 amd64 /dev/loop1 squashfs /dev/sda1 E814B55B14B52E06 ntfs /dev/sda2 01CD-023B vfat HP_RECOVERY /dev/sdb1 7836F22A36F1E8D0 ntfs Elements ================================ Mount points: ================================= Device Mount_Point Type Options /dev/loop0 /cdrom iso9660 (ro,noatime) /dev/loop1 /rofs squashfs (ro,noatime) /dev/sdb1 /mnt fuseblk (rw,nosuid,nodev,allow_other,blksize=4096) ================================ sda2/boot.ini: ================================ -------------------------------------------------------------------------------- [boot loader] timeout=0 default=C:\CMDCONS\BOOTSECT.DAT [operating systems] multi(0)disk(0)rdisk(0)partition(1)\WINDOWS="Microsoft Windows XP Professional" /fastdetect C:\CMDCONS\BOOTSECT.DAT="Microsoft Windows Recovery Console" /cmdcons -------------------------------------------------------------------------------- ======================== Unknown MBRs/Boot Sectors/etc: ======================== Unknown GPT Partiton Type c104043000e9b9040dff24b580010100 Unknown GPT Partiton Type 46313020746f20737461727420746865 Unknown GPT Partiton Type 65727920706172746974696f6e207761 Unknown GPT Partiton Type 727920706172746974696f6e0d0a0000 Unknown GPT Partiton Type 000f84e5f7668b162404e82804744066 Unknown GPT Partiton Type ce01e8dc038bfe66391624047505e8d9 Unknown GPT Partiton Type 0345086603f0e881030bd2740333d240 Unknown GPT Partiton Type bece01e8db0287fec645041266895508 Unknown GPT Partiton Type 01f60634010175078b363b01e854f5e8 Unknown GPT Partiton Type 313825740ffec03865107408fec03824 Unknown GPT Partiton Type 02f60634014074088bfdbece01e85101 Unknown GPT Partiton Type 263401f9e894f30f858ef4e8e201e8ec Unknown GPT Partiton Type f7e960f35245434f5645525966606633 Unknown GPT Partiton Type 660faf1e00106603dac3668b0e001066 Unknown GPT Partiton Type 8bfd386d04740583c710e2f6c36660c6 Unknown GPT Partiton Type 04ebf132c0b91000f3aac3bf0c04ebf3 Unknown GPT Partiton Type 02662bc1660fb71e0e02662bc366031e Unknown GPT Partiton Type f4b40ebb0700b901003c08751381ff25 Unknown GPT Partiton Type 534f465448494e4b90653f62011b0100 Unknown GPT Partiton Type 0b050900027777772e68702e636f6d00 Unknown GPT Partiton Type d441a0f5030003000ecb744a08bb3746 Unknown GPT Partiton Type f8579a116b4a7aa931cde97a4b9b5c09 Unknown GPT Partiton Type 7229990415b77c0a1970e7e824237a3a Unknown GPT Partiton Type afb6e34d6b4bd8c7c0eada19a9786cc3 Unknown BootLoader on sda1/Wubi 00000000 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 |0000000000000000| * 00000200 Unknown BootLoader on sda2 00000000 e9 a7 00 52 45 43 4f 56 45 52 59 00 02 08 20 00 |...RECOVERY... .| 00000010 02 00 00 00 00 f8 00 00 3f 00 f0 00 7f b9 f4 11 |........?.......| 00000020 8c cd ac 00 1e 2b 00 00 00 00 00 00 02 00 00 00 |.....+..........| 00000030 01 00 06 00 00 00 00 00 00 00 00 00 00 00 00 00 |................| 00000040 80 00 29 3b 02 cd 01 20 20 20 20 20 20 20 20 20 |..);... | 00000050 20 20 46 41 54 33 32 20 20 20 8b d0 c1 e2 02 80 | FAT32 ......| 00000060 e6 01 66 c1 e8 07 66 3b 46 f8 74 2a 66 89 46 f8 |..f...f;F.t*f.F.| 00000070 66 03 46 f4 66 0f b6 5e 28 80 e3 0f 74 0f 3a 5e |f.F.f..^(...t.:^| 00000080 10 0f 83 90 00 66 0f af 5e 24 66 03 c3 bb e0 07 |.....f..^$f.....| 00000090 b9 01 00 e8 cf 00 8b da 66 8b 87 00 7e 66 25 ff |........f...~f%.| 000000a0 ff ff 0f 66 3d f8 ff ff 0f c3 33 c9 8e d9 8e c1 |...f=.....3.....| 000000b0 8e d1 66 bc f4 7b 00 00 bd 00 7c 66 0f b6 46 10 |..f..{....|f..F.| 000000c0 66 f7 66 24 66 0f b7 56 0e 66 03 56 1c 66 89 56 |f.f$f..V.f.V.f.V| 000000d0 f4 66 03 c2 66 89 46 fc 66 c7 46 f8 ff ff ff ff |.f..f.F.f.F.....| 000000e0 66 8b 46 2c 66 50 e8 af 00 bb 70 00 b9 01 00 e8 |f.F,fP....p.....| 000000f0 73 00 bf 00 07 b1 0b be a9 7d f3 a6 74 2a 03 f9 |s........}..t*..| 00000100 83 c7 15 81 ff 00 09 72 ec 66 40 4a 75 db 66 58 |[email protected]| 00000110 e8 47 ff 72 cf be b4 7d ac 84 c0 74 09 b4 0e bb |.G.r...}...t....| 00000120 07 00 cd 10 eb f2 cd 19 66 58 ff 75 09 ff 75 0f |........fX.u..u.| 00000130 66 58 bb 00 20 66 83 f8 02 72 da 66 3d f8 ff ff |fX.. f...r.f=...| 00000140 0f 73 d2 66 50 e8 50 00 0f b6 4e 0d e8 16 00 c1 |.s.fP.P...N.....| 00000150 e1 05 03 d9 66 58 53 e8 00 ff 5b 72 d8 8a 56 40 |....fXS...[r..V@| 00000160 ea 00 00 00 20 66 60 66 6a 00 66 50 53 6a 00 66 |.... f`fj.fPSj.f| 00000170 68 10 00 01 00 8b f4 b8 00 42 8a 56 40 cd 13 be |h........B.V@...| 00000180 c7 7d 72 94 67 83 44 24 06 20 66 67 ff 44 24 08 |.}r.g.D$. fg.D$.| 00000190 e2 e3 83 c4 10 66 61 c3 66 48 66 48 66 0f b6 56 |.....fa.fHfHf..V| 000001a0 0d 66 f7 e2 66 03 46 fc c3 4e 54 4c 44 52 20 20 |.f..f.F..NTLDR | 000001b0 20 20 20 20 0d 0a 4e 6f 20 53 79 73 74 65 6d 20 | ..No System | 000001c0 44 69 73 6b 20 6f 72 0d 0a 44 69 73 6b 20 49 2f |Disk or..Disk I/| 000001d0 4f 20 65 72 72 6f 72 0d 0a 50 72 65 73 73 20 61 |O error..Press a| 000001e0 20 6b 65 79 20 74 6f 20 72 65 73 74 61 72 74 0d | key to restart.| 000001f0 0a 00 00 00 00 00 00 00 00 00 00 00 00 00 55 aa |..............U.| 00000200 =============================== StdErr Messages: =============================== umount: /isodevice: device is busy. (In some cases useful info about processes that use the device is found by lsof(8) or fuser(1))

  Read the article

• Rapid Planning: Next Generation MRP

  - by john.bermudez

  MRP has been a mainstay of manufacturing systems for 40 years. MRP evolved from simple inventory planning systems to become the heart of the MRPII systems which eventually became ERP. While the applications surrounding it have become broader, more sophisticated and web-based, MRP continues to operate in the loneliness of the Saturday night batch window quietly exploding bills of materials and logging exceptions for hours. During this same 40 years, manufacturing business processes have seen countless changes and improvements including JIT, TQM, Six Sigma, Flow Manufacturing, Lean Manufacturing and Supply Chain Management. Although much logic has been added to MRP to deal with new manufacturing processes, it has not been able to keep up with the real-time pace of today's supply chain. As a result, planners have devised ingenious ways to trick MRP to handle new processes but often need to dump the output into spreadsheets of their own design in the hope of wrestling thousands of exceptions to ground. Oracle's new Rapid Planning application is just what companies still running MRP have been waiting for! The newest member of the Value Chain Planning product line, Rapid Planning is designed to empower planners with comprehensive supply planning that runs online in minutes, not hours. It enables a planner simulate the incremental impact of a new order or re-run an entire plan in a separate sandbox. Rapid Planning does a complete multi-level bill of material explosion like MRP but plans orders considering material and capacity constraints. Considering material and capacity constraints in planning can help you quickly reduce inventory and improve on-time shipments. Rapid Planning is an APS application that leverages years of Oracle development experience and customer feedback. Rather than rely exclusively on black-box heuristics, Rapid Planning is designed to give planners the computing power to use their industry experience and business knowledge to improve MRP. For example, Rapid Planning has a powerful worksheet user interface with built-in query capability that allows the planner to locate the orders she is interested in and use a mass update function to make quick work of large changes. The planner can save these queries and unique user interface to personalize their planning environment. Most importantly, Rapid Planning is designed to do supply planning in today's dynamic supply chain environment. It can be used to supplement MRP or replace MRP entirely. It generates plans that provide order-by-order details with aggregate key performance indicators that enable planners to quickly assess the overall business impact of a plan. To find out more about how Rapid Planning can help improve your MRP, please contact me at [email protected] or your Oracle Account Manager.

  Read the article

• OBIEE 11.1.1 - BI Design Best Practices Whitepaper V1.2

  - by Nicolas Barasz

  Oracle BI Principles. Repository design best practices. Dashboards and reports design best practices. 10g Upgrade considerations. This new version includes 40 more slides than the previous one. Multiple new best practices specific to 11g and a lot of new information about upgrade from 10g. Click here to download (Right click or option-click the link and choose "Save As..." to download this pdf file)

  Read the article

• Wifi too slow on 11.10/12.04

  - by Jorge Pinho

  As the title above mentions, i've some issues with wireless connection on both ubuntu 11.10 and 12.04. I've installed the latest drivers (Atheros AR928x), like "compact-wireless package" on several machines including ubuntu 11.10. The connection still too slow. My internet connection is 100 mb so, using WLAN connection, it should give me 70/80 mb of signal... instead of the 40/50 mb that i'm experience right now.. Do you have a solution to increase the signal?

  Read the article

• How to reinstall Ubuntu keeping my data intact?

  - by Santosh Linkha

  I want to reinstall Ubuntu keeping my data intact. I have 160 GB hardrive (sata or pata I don't know but it's slim and made in China) with a 40 GB ext3 partition, a 4GB swap memory and 3 other partition with a FAT32 file system. I have around 4GB space on my drive where Linux is installed. I'd like to keep the data intact, especially the Downloads folder, desktop, and /var/www; And I no longer have access to any other machines or external storage devices.

  Read the article

• The musical instrument software developer

  - by Peter Mortensen

  There is a correlation between playing a musical instrument and being a great software developer (the same for mathematics). But what is the causation (if any)? That is, should a software developer learn to play a musical instrument to become a better software developer? Or does proficiency in software development make it more likely that an interest in performing on a musical instrument will develop? Update: a very similar question was asked in podcast .NET Rocks, episode 614 (from Øredev 2010), 35 min 40 secs.

  Read the article

• T-SQL Tuesday 24: Ode to Composable Code

  - by merrillaldrich

  I love the T-SQL Tuesday tradition, started by Adam Machanic and hosted this month by Brad Shulz . I am a little pressed for time this month, so today’s post is a short ode to how I love saving time with Composable Code in SQL. Composability is one of the very best features of SQL, but sometimes gets picked on due to both real and imaginary performance worries. I like to pick composable solutions when I can, while keeping the perf issues in mind, because they are just so handy and eliminate so much...(read more)

  Read the article

• Creando un Menu Accordeon con Ajax

  - by jaullo

  Ajax, es uno de los grandes componentes nacidos para utilizar en asp.net que brinda gran cantidad de funcionalidades y potencia nuestras aplicaciones, brindando sencilles y agilidad. Este post, esta dedicado a la creación de un menú tipo accordeon con ajax. Como bien sabemos, para poder utilizar cualquiera de los componentes ajax, es necesario que exista un scriptmanager registrado en nuestra página, el cual será el encargado de manejar nuestros controles. Entonces, lo primero que haremos será crear nuestro script manager.  <asp:ScriptManager ID="ScriptManager1" runat="server">     </asp:ScriptManager> Seguidamente definimos nuestro elemento accordeon y establecemos algunas de sus propiedades básicas:   <cc1:Accordion ID="AccordionCtrl" runat="server"         SelectedIndex="0" HeaderCssClass="accordionHeader"         ContentCssClass="accordionContent" AutoSize="None"         FadeTransitions="true" TransitionDuration="250"     FramesPerSecond="40" Para que nuestro accordeon funcione debemos declarar PANES dentro de el, estos panes serán los encargados de contener los elementos, vinculos o información que deseamos mostrar.   <Panes>                 <cc1:AccordionPane ID="AccordionPane0" runat="server">                     <Header>Matenimiento</Header>                     <Content>                         <li><a href="mypagina.aspx">My página de prueba</a></li>                                                                                          </Content>                 </cc1:AccordionPane> Como vemos podemos declarar tantos accordionPanes como queramos, cada accordionPane representa un elemento de categoría dentro del accordeon. Por útlimo debemos cerrar los elementos panel y accordion que abrirmos inicialmente.  </Panes>  </cc1:Accordion> Nuesto ejemplo finalmente completo debería verse así: <asp:ScriptManager ID="ScriptManager1" runat="server">     </asp:ScriptManager>         <cc1:Accordion ID="AccordionCtrl" runat="server"         SelectedIndex="0" HeaderCssClass="accordionHeader"         ContentCssClass="accordionContent" AutoSize="None"         FadeTransitions="true" TransitionDuration="250"     FramesPerSecond="40" >             <Panes>                 <cc1:AccordionPane ID="AccordionPane0" runat="server">                     <Header>Matenimiento</Header>                     <Content>                         <li><a href="mypagina.aspx">My página de prueba</a></li>                                                                                          </Content>                 </cc1:AccordionPane>                                                             </Panes>         </cc1:Accordion>         De esta forma, nuestro Menu tipo accordeon debería estar funcionando, una forma sencilla y agil de crear un menú en asp.net con Ajax.

  Read the article

< Previous Page | 55 56 57 58 59 60 61 62 63 64 65 66  | Next Page >