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• Question SpeechSynthesizer.SetOutputToAudioStream audio format problem

  - by Chris Kugler

  Hi, I'm currently working on an application which requires transmission of speech encoded to a specific audio format. System.Speech.AudioFormat.SpeechAudioFormatInfo synthFormat = new System.Speech.AudioFormat.SpeechAudioFormatInfo(System.Speech.AudioFormat.EncodingFormat.Pcm, 8000, 16, 1, 16000, 2, null); This states that the audio is in PCM format, 8000 samples per second, 16 bits per sample, mono, 16000 average bytes per second, block alignment of 2. When I attempt to execute the following code there is nothing written to my MemoryStream instance; however when I change from 8000 samples per second up to 11025 the audio data is written successfully. SpeechSynthesizer synthesizer = new SpeechSynthesizer(); waveStream = new MemoryStream(); PromptBuilder pbuilder = new PromptBuilder(); PromptStyle pStyle = new PromptStyle(); pStyle.Emphasis = PromptEmphasis.None; pStyle.Rate = PromptRate.Fast; pStyle.Volume = PromptVolume.ExtraLoud; pbuilder.StartStyle(pStyle); pbuilder.StartParagraph(); pbuilder.StartVoice(VoiceGender.Male, VoiceAge.Teen, 2); pbuilder.StartSentence(); pbuilder.AppendText("This is some text."); pbuilder.EndSentence(); pbuilder.EndVoice(); pbuilder.EndParagraph(); pbuilder.EndStyle(); synthesizer.SetOutputToAudioStream(waveStream, synthFormat); synthesizer.Speak(pbuilder); synthesizer.SetOutputToNull(); There are no exceptions or errors recorded when using a sample rate of 8000 and I couldn't find anything useful in the documentation regarding SetOutputToAudioStream and why it succeeds at 11025 samples per second and not 8000. I have a workaround involving a wav file that I generated and converted to the correct sample rate using some sound editing tools, but I would like to generate the audio from within the application if I can. One particular point of interest was that the SpeechRecognitionEngine accepts that audio format and successfully recognized the speech in my synthesized wave file... Update: Recently discovered that this audio format succeeds for certain installed voices, but fails for others. It fails specifically for LH Michael and LH Michelle, and failure varies for certain voice settings defined in the PromptBuilder.

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• segmented reduction with scattered segments

  - by Christian Rau

  I got to solve a pretty standard problem on the GPU, but I'm quite new to practical GPGPU, so I'm looking for ideas to approach this problem. I have many points in 3-space which are assigned to a very small number of groups (each point belongs to one group), specifically 15 in this case (doesn't ever change). Now I want to compute the mean and covariance matrix of all the groups. So on the CPU it's roughly the same as: for each point p { mean[p.group] += p.pos; covariance[p.group] += p.pos * p.pos; ++count[p.group]; } for each group g { mean[g] /= count[g]; covariance[g] = covariance[g]/count[g] - mean[g]*mean[g]; } Since the number of groups is extremely small, the last step can be done on the CPU (I need those values on the CPU, anyway). The first step is actually just a segmented reduction, but with the segments scattered around. So the first idea I came up with, was to first sort the points by their groups. I thought about a simple bucket sort using atomic_inc to compute bucket sizes and per-point relocation indices (got a better idea for sorting?, atomics may not be the best idea). After that they're sorted by groups and I could possibly come up with an adaption of the segmented scan algorithms presented here. But in this special case, I got a very large amount of data per point (9-10 floats, maybe even doubles if the need arises), so the standard algorithms using a shared memory element per thread and a thread per point might make problems regarding per-multiprocessor resources as shared memory or registers (Ok, much more on compute capability 1.x than 2.x, but still). Due to the very small and constant number of groups I thought there might be better approaches. Maybe there are already existing ideas suited for these specific properties of such a standard problem. Or maybe my general approach isn't that bad and you got ideas for improving the individual steps, like a good sorting algorithm suited for a very small number of keys or some segmented reduction algorithm minimizing shared memory/register usage. I'm looking for general approaches and don't want to use external libraries. FWIW I'm using OpenCL, but it shouldn't really matter as the general concepts of GPU computing don't really differ over the major frameworks.

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• How to read oom-killer syslog messages?

  - by Grant

  I have a Ubuntu 12.04 server which sometimes dies completely - no SSH, no ping, nothing until it is physically rebooted. After the reboot, I see in syslog that the oom-killer killed, well, pretty much everything. There's a lot of detailed memory usage information in them. How do I read these logs to see what caused the OOM issue? The server has far more memory than it needs, so it shouldn't be running out of memory. Oct 25 07:28:04 nldedip4k031 kernel: [87946.529511] oom_kill_process: 9 callbacks suppressed Oct 25 07:28:04 nldedip4k031 kernel: [87946.529514] irqbalance invoked oom-killer: gfp_mask=0x80d0, order=0, oom_adj=0, oom_score_adj=0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529516] irqbalance cpuset=/ mems_allowed=0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529518] Pid: 948, comm: irqbalance Not tainted 3.2.0-55-generic-pae #85-Ubuntu Oct 25 07:28:04 nldedip4k031 kernel: [87946.529519] Call Trace: Oct 25 07:28:04 nldedip4k031 kernel: [87946.529525] [] dump_header.isra.6+0x85/0xc0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529528] [] oom_kill_process+0x5c/0x80 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529530] [] out_of_memory+0xc5/0x1c0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529532] [] __alloc_pages_nodemask+0x72c/0x740 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529535] [] __get_free_pages+0x1c/0x30 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529537] [] get_zeroed_page+0x12/0x20 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529541] [] fill_read_buffer.isra.8+0xaa/0xd0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529543] [] sysfs_read_file+0x7d/0x90 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529546] [] vfs_read+0x8c/0x160 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529548] [] ? fill_read_buffer.isra.8+0xd0/0xd0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529550] [] sys_read+0x3d/0x70 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529554] [] sysenter_do_call+0x12/0x28 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529555] Mem-Info: Oct 25 07:28:04 nldedip4k031 kernel: [87946.529556] DMA per-cpu: Oct 25 07:28:04 nldedip4k031 kernel: [87946.529557] CPU 0: hi: 0, btch: 1 usd: 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529558] CPU 1: hi: 0, btch: 1 usd: 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529560] CPU 2: hi: 0, btch: 1 usd: 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529561] CPU 3: hi: 0, btch: 1 usd: 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529562] CPU 4: hi: 0, btch: 1 usd: 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529563] CPU 5: hi: 0, btch: 1 usd: 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529564] CPU 6: hi: 0, btch: 1 usd: 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529565] CPU 7: hi: 0, btch: 1 usd: 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529566] Normal per-cpu: Oct 25 07:28:04 nldedip4k031 kernel: [87946.529567] CPU 0: hi: 186, btch: 31 usd: 179 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529568] CPU 1: hi: 186, btch: 31 usd: 182 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529569] CPU 2: hi: 186, btch: 31 usd: 132 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529570] CPU 3: hi: 186, btch: 31 usd: 175 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529571] CPU 4: hi: 186, btch: 31 usd: 91 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529572] CPU 5: hi: 186, btch: 31 usd: 173 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529573] CPU 6: hi: 186, btch: 31 usd: 159 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529574] CPU 7: hi: 186, btch: 31 usd: 164 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529575] HighMem per-cpu: Oct 25 07:28:04 nldedip4k031 kernel: [87946.529576] CPU 0: hi: 186, btch: 31 usd: 165 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529577] CPU 1: hi: 186, btch: 31 usd: 183 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529578] CPU 2: hi: 186, btch: 31 usd: 185 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529579] CPU 3: hi: 186, btch: 31 usd: 138 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529580] CPU 4: hi: 186, btch: 31 usd: 155 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529581] CPU 5: hi: 186, btch: 31 usd: 104 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529582] CPU 6: hi: 186, btch: 31 usd: 133 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529583] CPU 7: hi: 186, btch: 31 usd: 170 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529586] active_anon:5523 inactive_anon:354 isolated_anon:0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529586] active_file:2815 inactive_file:6849119 isolated_file:0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529587] unevictable:0 dirty:449 writeback:10 unstable:0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529587] free:1304125 slab_reclaimable:104672 slab_unreclaimable:3419 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529588] mapped:2661 shmem:138 pagetables:313 bounce:0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529591] DMA free:4252kB min:780kB low:972kB high:1168kB active_anon:0kB inactive_anon:0kB active_file:4kB inactive_file:0kB unevictable:0kB isolated(anon):0kB isolated(file):0kB present:15756kB mlocked:0kB dirty:0kB writeback:0kB mapped:0kB shmem:0kB slab_reclaimable:11564kB slab_unreclaimable:4kB kernel_stack:0kB pagetables:0kB unstable:0kB bounce:0kB writeback_tmp:0kB pages_scanned:1 all_unreclaimable? yes Oct 25 07:28:04 nldedip4k031 kernel: [87946.529594] lowmem_reserve[]: 0 869 32460 32460 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529599] Normal free:44052kB min:44216kB low:55268kB high:66324kB active_anon:0kB inactive_anon:0kB active_file:616kB inactive_file:568kB unevictable:0kB isolated(anon):0kB isolated(file):0kB present:890008kB mlocked:0kB dirty:0kB writeback:0kB mapped:4kB shmem:0kB slab_reclaimable:407124kB slab_unreclaimable:13672kB kernel_stack:992kB pagetables:0kB unstable:0kB bounce:0kB writeback_tmp:0kB pages_scanned:2083 all_unreclaimable? yes Oct 25 07:28:04 nldedip4k031 kernel: [87946.529602] lowmem_reserve[]: 0 0 252733 252733 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529606] HighMem free:5168196kB min:512kB low:402312kB high:804112kB active_anon:22092kB inactive_anon:1416kB active_file:10640kB inactive_file:27395920kB unevictable:0kB isolated(anon):0kB isolated(file):0kB present:32349872kB mlocked:0kB dirty:1796kB writeback:40kB mapped:10640kB shmem:552kB slab_reclaimable:0kB slab_unreclaimable:0kB kernel_stack:0kB pagetables:1252kB unstable:0kB bounce:0kB writeback_tmp:0kB pages_scanned:0 all_unreclaimable? no Oct 25 07:28:04 nldedip4k031 kernel: [87946.529609] lowmem_reserve[]: 0 0 0 0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529611] DMA: 6*4kB 6*8kB 6*16kB 5*32kB 5*64kB 4*128kB 2*256kB 1*512kB 0*1024kB 1*2048kB 0*4096kB = 4232kB Oct 25 07:28:04 nldedip4k031 kernel: [87946.529616] Normal: 297*4kB 180*8kB 119*16kB 73*32kB 67*64kB 47*128kB 35*256kB 13*512kB 5*1024kB 1*2048kB 1*4096kB = 44052kB Oct 25 07:28:04 nldedip4k031 kernel: [87946.529622] HighMem: 1*4kB 6*8kB 27*16kB 11*32kB 2*64kB 1*128kB 0*256kB 0*512kB 4*1024kB 1*2048kB 1260*4096kB = 5168196kB Oct 25 07:28:04 nldedip4k031 kernel: [87946.529627] 6852076 total pagecache pages Oct 25 07:28:04 nldedip4k031 kernel: [87946.529628] 0 pages in swap cache Oct 25 07:28:04 nldedip4k031 kernel: [87946.529629] Swap cache stats: add 0, delete 0, find 0/0 Oct 25 07:28:04 nldedip4k031 kernel: [87946.529630] Free swap = 3998716kB Oct 25 07:28:04 nldedip4k031 kernel: [87946.529631] Total swap = 3998716kB Oct 25 07:28:04 nldedip4k031 kernel: [87946.571914] 8437743 pages RAM Oct 25 07:28:04 nldedip4k031 kernel: [87946.571916] 8209409 pages HighMem Oct 25 07:28:04 nldedip4k031 kernel: [87946.571917] 159556 pages reserved Oct 25 07:28:04 nldedip4k031 kernel: [87946.571917] 6862034 pages shared Oct 25 07:28:04 nldedip4k031 kernel: [87946.571918] 123540 pages non-shared Oct 25 07:28:04 nldedip4k031 kernel: [87946.571919] [ pid ] uid tgid total_vm rss cpu oom_adj oom_score_adj name Oct 25 07:28:04 nldedip4k031 kernel: [87946.571927] [ 421] 0 421 709 152 3 0 0 upstart-udev-br Oct 25 07:28:04 nldedip4k031 kernel: [87946.571929] [ 429] 0 429 773 326 5 -17 -1000 udevd Oct 25 07:28:04 nldedip4k031 kernel: [87946.571931] [ 567] 0 567 772 224 4 -17 -1000 udevd Oct 25 07:28:04 nldedip4k031 kernel: [87946.571932] [ 568] 0 568 772 231 7 -17 -1000 udevd Oct 25 07:28:04 nldedip4k031 kernel: [87946.571934] [ 764] 0 764 712 103 1 0 0 upstart-socket- Oct 25 07:28:04 nldedip4k031 kernel: [87946.571936] [ 772] 103 772 815 164 5 0 0 dbus-daemon Oct 25 07:28:04 nldedip4k031 kernel: [87946.571938] [ 785] 0 785 1671 600 1 -17 -1000 sshd Oct 25 07:28:04 nldedip4k031 kernel: [87946.571940] [ 809] 101 809 7766 380 1 0 0 rsyslogd Oct 25 07:28:04 nldedip4k031 kernel: [87946.571942] [ 869] 0 869 1158 213 3 0 0 getty Oct 25 07:28:04 nldedip4k031 kernel: [87946.571943] [ 873] 0 873 1158 214 6 0 0 getty Oct 25 07:28:04 nldedip4k031 kernel: [87946.571945] [ 911] 0 911 1158 215 3 0 0 getty Oct 25 07:28:04 nldedip4k031 kernel: [87946.571947] [ 912] 0 912 1158 214 2 0 0 getty Oct 25 07:28:04 nldedip4k031 kernel: [87946.571949] [ 914] 0 914 1158 213 1 0 0 getty Oct 25 07:28:04 nldedip4k031 kernel: [87946.571950] [ 916] 0 916 618 86 1 0 0 atd Oct 25 07:28:04 nldedip4k031 kernel: [87946.571952] [ 917] 0 917 655 226 3 0 0 cron Oct 25 07:28:04 nldedip4k031 kernel: [87946.571954] [ 948] 0 948 902 159 3 0 0 irqbalance Oct 25 07:28:04 nldedip4k031 kernel: [87946.571956] [ 993] 0 993 1145 363 3 0 0 master Oct 25 07:28:04 nldedip4k031 kernel: [87946.571957] [ 1002] 104 1002 1162 333 1 0 0 qmgr Oct 25 07:28:04 nldedip4k031 kernel: [87946.571959] [ 1016] 0 1016 730 149 2 0 0 mdadm Oct 25 07:28:04 nldedip4k031 kernel: [87946.571961] [ 1057] 0 1057 6066 2160 3 0 0 /usr/sbin/apach Oct 25 07:28:04 nldedip4k031 kernel: [87946.571963] [ 1086] 0 1086 1158 213 3 0 0 getty Oct 25 07:28:04 nldedip4k031 kernel: [87946.571965] [ 1088] 33 1088 6191 1517 0 0 0 /usr/sbin/apach Oct 25 07:28:04 nldedip4k031 kernel: [87946.571967] [ 1089] 33 1089 6191 1451 1 0 0 /usr/sbin/apach Oct 25 07:28:04 nldedip4k031 kernel: [87946.571969] [ 1090] 33 1090 6175 1451 3 0 0 /usr/sbin/apach Oct 25 07:28:04 nldedip4k031 kernel: [87946.571971] [ 1091] 33 1091 6191 1451 1 0 0 /usr/sbin/apach Oct 25 07:28:04 nldedip4k031 kernel: [87946.571972] [ 1092] 33 1092 6191 1451 0 0 0 /usr/sbin/apach Oct 25 07:28:04 nldedip4k031 kernel: [87946.571974] [ 1109] 33 1109 6191 1517 0 0 0 /usr/sbin/apach Oct 25 07:28:04 nldedip4k031 kernel: [87946.571976] [ 1151] 33 1151 6191 1451 1 0 0 /usr/sbin/apach Oct 25 07:28:04 nldedip4k031 kernel: [87946.571978] [ 1201] 104 1201 1803 652 1 0 0 tlsmgr Oct 25 07:28:04 nldedip4k031 kernel: [87946.571980] [ 2475] 0 2475 2435 812 0 0 0 sshd Oct 25 07:28:04 nldedip4k031 kernel: [87946.571982] [ 2494] 0 2494 1745 839 1 0 0 bash Oct 25 07:28:04 nldedip4k031 kernel: [87946.571984] [ 2573] 0 2573 3394 1689 0 0 0 sshd Oct 25 07:28:04 nldedip4k031 kernel: [87946.571986] [ 2589] 0 2589 5014 457 3 0 0 rsync Oct 25 07:28:04 nldedip4k031 kernel: [87946.571988] [ 2590] 0 2590 7970 522 1 0 0 rsync Oct 25 07:28:04 nldedip4k031 kernel: [87946.571990] [ 2652] 104 2652 1150 326 5 0 0 pickup Oct 25 07:28:04 nldedip4k031 kernel: [87946.571992] Out of memory: Kill process 421 (upstart-udev-br) score 1 or sacrifice child Oct 25 07:28:04 nldedip4k031 kernel: [87946.572407] Killed process 421 (upstart-udev-br) total-vm:2836kB, anon-rss:156kB, file-rss:452kB Oct 25 07:28:04 nldedip4k031 kernel: [87946.573107] init: upstart-udev-bridge main process (421) killed by KILL signal Oct 25 07:28:04 nldedip4k031 kernel: [87946.573126] init: upstart-udev-bridge main process ended, respawning Oct 25 07:28:34 nldedip4k031 kernel: [87976.461570] irqbalance invoked oom-killer: gfp_mask=0x80d0, order=0, oom_adj=0, oom_score_adj=0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461573] irqbalance cpuset=/ mems_allowed=0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461576] Pid: 948, comm: irqbalance Not tainted 3.2.0-55-generic-pae #85-Ubuntu Oct 25 07:28:34 nldedip4k031 kernel: [87976.461578] Call Trace: Oct 25 07:28:34 nldedip4k031 kernel: [87976.461585] [] dump_header.isra.6+0x85/0xc0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461588] [] oom_kill_process+0x5c/0x80 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461591] [] out_of_memory+0xc5/0x1c0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461595] [] __alloc_pages_nodemask+0x72c/0x740 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461599] [] __get_free_pages+0x1c/0x30 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461602] [] get_zeroed_page+0x12/0x20 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461606] [] fill_read_buffer.isra.8+0xaa/0xd0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461609] [] sysfs_read_file+0x7d/0x90 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461613] [] vfs_read+0x8c/0x160 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461616] [] ? fill_read_buffer.isra.8+0xd0/0xd0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461619] [] sys_read+0x3d/0x70 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461624] [] sysenter_do_call+0x12/0x28 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461626] Mem-Info: Oct 25 07:28:34 nldedip4k031 kernel: [87976.461628] DMA per-cpu: Oct 25 07:28:34 nldedip4k031 kernel: [87976.461629] CPU 0: hi: 0, btch: 1 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461631] CPU 1: hi: 0, btch: 1 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461633] CPU 2: hi: 0, btch: 1 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461634] CPU 3: hi: 0, btch: 1 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461636] CPU 4: hi: 0, btch: 1 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461638] CPU 5: hi: 0, btch: 1 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461639] CPU 6: hi: 0, btch: 1 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461641] CPU 7: hi: 0, btch: 1 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461642] Normal per-cpu: Oct 25 07:28:34 nldedip4k031 kernel: [87976.461644] CPU 0: hi: 186, btch: 31 usd: 61 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461646] CPU 1: hi: 186, btch: 31 usd: 49 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461647] CPU 2: hi: 186, btch: 31 usd: 8 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461649] CPU 3: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461651] CPU 4: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461652] CPU 5: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461654] CPU 6: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461656] CPU 7: hi: 186, btch: 31 usd: 30 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461657] HighMem per-cpu: Oct 25 07:28:34 nldedip4k031 kernel: [87976.461658] CPU 0: hi: 186, btch: 31 usd: 4 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461660] CPU 1: hi: 186, btch: 31 usd: 204 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461662] CPU 2: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461663] CPU 3: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461665] CPU 4: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461667] CPU 5: hi: 186, btch: 31 usd: 31 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461668] CPU 6: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461670] CPU 7: hi: 186, btch: 31 usd: 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461674] active_anon:5441 inactive_anon:412 isolated_anon:0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461674] active_file:2668 inactive_file:6922842 isolated_file:0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461675] unevictable:0 dirty:836 writeback:0 unstable:0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461676] free:1231664 slab_reclaimable:105781 slab_unreclaimable:3399 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461677] mapped:2649 shmem:138 pagetables:313 bounce:0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461682] DMA free:4248kB min:780kB low:972kB high:1168kB active_anon:0kB inactive_anon:0kB active_file:0kB inactive_file:4kB unevictable:0kB isolated(anon):0kB isolated(file):0kB present:15756kB mlocked:0kB dirty:0kB writeback:0kB mapped:0kB shmem:0kB slab_reclaimable:11560kB slab_unreclaimable:4kB kernel_stack:0kB pagetables:0kB unstable:0kB bounce:0kB writeback_tmp:0kB pages_scanned:5687 all_unreclaimable? yes Oct 25 07:28:34 nldedip4k031 kernel: [87976.461686] lowmem_reserve[]: 0 869 32460 32460 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461693] Normal free:44184kB min:44216kB low:55268kB high:66324kB active_anon:0kB inactive_anon:0kB active_file:20kB inactive_file:1096kB unevictable:0kB isolated(anon):0kB isolated(file):0kB present:890008kB mlocked:0kB dirty:4kB writeback:0kB mapped:4kB shmem:0kB slab_reclaimable:411564kB slab_unreclaimable:13592kB kernel_stack:992kB pagetables:0kB unstable:0kB bounce:0kB writeback_tmp:0kB pages_scanned:1816 all_unreclaimable? yes Oct 25 07:28:34 nldedip4k031 kernel: [87976.461697] lowmem_reserve[]: 0 0 252733 252733 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461703] HighMem free:4878224kB min:512kB low:402312kB high:804112kB active_anon:21764kB inactive_anon:1648kB active_file:10652kB inactive_file:27690268kB unevictable:0kB isolated(anon):0kB isolated(file):0kB present:32349872kB mlocked:0kB dirty:3340kB writeback:0kB mapped:10592kB shmem:552kB slab_reclaimable:0kB slab_unreclaimable:0kB kernel_stack:0kB pagetables:1252kB unstable:0kB bounce:0kB writeback_tmp:0kB pages_scanned:0 all_unreclaimable? no Oct 25 07:28:34 nldedip4k031 kernel: [87976.461708] lowmem_reserve[]: 0 0 0 0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461711] DMA: 8*4kB 7*8kB 6*16kB 5*32kB 5*64kB 4*128kB 2*256kB 1*512kB 0*1024kB 1*2048kB 0*4096kB = 4248kB Oct 25 07:28:34 nldedip4k031 kernel: [87976.461719] Normal: 272*4kB 178*8kB 76*16kB 52*32kB 42*64kB 36*128kB 23*256kB 20*512kB 7*1024kB 2*2048kB 1*4096kB = 44176kB Oct 25 07:28:34 nldedip4k031 kernel: [87976.461727] HighMem: 1*4kB 45*8kB 31*16kB 24*32kB 5*64kB 3*128kB 1*256kB 2*512kB 4*1024kB 2*2048kB 1188*4096kB = 4877852kB Oct 25 07:28:34 nldedip4k031 kernel: [87976.461736] 6925679 total pagecache pages Oct 25 07:28:34 nldedip4k031 kernel: [87976.461737] 0 pages in swap cache Oct 25 07:28:34 nldedip4k031 kernel: [87976.461739] Swap cache stats: add 0, delete 0, find 0/0 Oct 25 07:28:34 nldedip4k031 kernel: [87976.461740] Free swap = 3998716kB Oct 25 07:28:34 nldedip4k031 kernel: [87976.461741] Total swap = 3998716kB Oct 25 07:28:34 nldedip4k031 kernel: [87976.524951] 8437743 pages RAM Oct 25 07:28:34 nldedip4k031 kernel: [87976.524953] 8209409 pages HighMem Oct 25 07:28:34 nldedip4k031 kernel: [87976.524954] 159556 pages reserved Oct 25 07:28:34 nldedip4k031 kernel: [87976.524955] 6936141 pages shared Oct 25 07:28:34 nldedip4k031 kernel: [87976.524956] 124602 pages non-shared Oct 25 07:28:34 nldedip4k031 kernel: [87976.524957] [ pid ] uid tgid total_vm rss cpu oom_adj oom_score_adj name Oct 25 07:28:34 nldedip4k031 kernel: [87976.524966] [ 429] 0 429 773 326 5 -17 -1000 udevd Oct 25 07:28:34 nldedip4k031 kernel: [87976.524968] [ 567] 0 567 772 224 4 -17 -1000 udevd Oct 25 07:28:34 nldedip4k031 kernel: [87976.524971] [ 568] 0 568 772 231 7 -17 -1000 udevd Oct 25 07:28:34 nldedip4k031 kernel: [87976.524973] [ 764] 0 764 712 103 3 0 0 upstart-socket- Oct 25 07:28:34 nldedip4k031 kernel: [87976.524976] [ 772] 103 772 815 164 2 0 0 dbus-daemon Oct 25 07:28:34 nldedip4k031 kernel: [87976.524979] [ 785] 0 785 1671 600 1 -17 -1000 sshd Oct 25 07:28:34 nldedip4k031 kernel: [87976.524981] [ 809] 101 809 7766 380 1 0 0 rsyslogd Oct 25 07:28:34 nldedip4k031 kernel: [87976.524983] [ 869] 0 869 1158 213 3 0 0 getty Oct 25 07:28:34 nldedip4k031 kernel: [87976.524986] [ 873] 0 873 1158 214 6 0 0 getty Oct 25 07:28:34 nldedip4k031 kernel: [87976.524988] [ 911] 0 911 1158 215 3 0 0 getty Oct 25 07:28:34 nldedip4k031 kernel: [87976.524990] [ 912] 0 912 1158 214 2 0 0 getty Oct 25 07:28:34 nldedip4k031 kernel: [87976.524992] [ 914] 0 914 1158 213 1 0 0 getty Oct 25 07:28:34 nldedip4k031 kernel: [87976.524995] [ 916] 0 916 618 86 1 0 0 atd Oct 25 07:28:34 nldedip4k031 kernel: [87976.524997] [ 917] 0 917 655 226 3 0 0 cron Oct 25 07:28:34 nldedip4k031 kernel: [87976.524999] [ 948] 0 948 902 159 5 0 0 irqbalance Oct 25 07:28:34 nldedip4k031 kernel: [87976.525002] [ 993] 0 993 1145 363 3 0 0 master Oct 25 07:28:34 nldedip4k031 kernel: [87976.525004] [ 1002] 104 1002 1162 333 1 0 0 qmgr Oct 25 07:28:34 nldedip4k031 kernel: [87976.525007] [ 1016] 0 1016 730 149 2 0 0 mdadm Oct 25 07:28:34 nldedip4k031 kernel: [87976.525009] [ 1057] 0 1057 6066 2160 3 0 0 /usr/sbin/apach Oct 25 07:28:34 nldedip4k031 kernel: [87976.525012] [ 1086] 0 1086 1158 213 3 0 0 getty Oct 25 07:28:34 nldedip4k031 kernel: [87976.525014] [ 1088] 33 1088 6191 1517 0 0 0 /usr/sbin/apach Oct 25 07:28:34 nldedip4k031 kernel: [87976.525017] [ 1089] 33 1089 6191 1451 1 0 0 /usr/sbin/apach Oct 25 07:28:34 nldedip4k031 kernel: [87976.525019] [ 1090] 33 1090 6175 1451 1 0 0 /usr/sbin/apach Oct 25 07:28:34 nldedip4k031 kernel: [87976.525021] [ 1091] 33 1091 6191 1451 1 0 0 /usr/sbin/apach Oct 25 07:28:34 nldedip4k031 kernel: [87976.525024] [ 1092] 33 1092 6191 1451 0 0 0 /usr/sbin/apach Oct 25 07:28:34 nldedip4k031 kernel: [87976.525026] [ 1109] 33 1109 6191 1517 0 0 0 /usr/sbin/apach Oct 25 07:28:34 nldedip4k031 kernel: [87976.525029] [ 1151] 33 1151 6191 1451 1 0 0 /usr/sbin/apach Oct 25 07:28:34 nldedip4k031 kernel: [87976.525031] [ 1201] 104 1201 1803 652 1 0 0 tlsmgr Oct 25 07:28:34 nldedip4k031 kernel: [87976.525033] [ 2475] 0 2475 2435 812 0 0 0 sshd Oct 25 07:28:34 nldedip4k031 kernel: [87976.525036] [ 2494] 0 2494 1745 839 1 0 0 bash Oct 25 07:28:34 nldedip4k031 kernel: [87976.525038] [ 2573] 0 2573 3394 1689 3 0 0 sshd Oct 25 07:28:34 nldedip4k031 kernel: [87976.525040] [ 2589] 0 2589 5014 457 3 0 0 rsync Oct 25 07:28:34 nldedip4k031 kernel: [87976.525043] [ 2590] 0 2590 7970 522 1 0 0 rsync Oct 25 07:28:34 nldedip4k031 kernel: [87976.525045] [ 2652] 104 2652 1150 326 5 0 0 pickup Oct 25 07:28:34 nldedip4k031 kernel: [87976.525048] [ 2847] 0 2847 709 89 0 0 0 upstart-udev-br Oct 25 07:28:34 nldedip4k031 kernel: [87976.525050] Out of memory: Kill process 764 (upstart-socket-) score 1 or sacrifice child Oct 25 07:28:34 nldedip4k031 kernel: [87976.525484] Killed process 764 (upstart-socket-) total-vm:2848kB, anon-rss:204kB, file-rss:208kB Oct 25 07:28:34 nldedip4k031 kernel: [87976.526161] init: upstart-socket-bridge main process (764) killed by KILL signal Oct 25 07:28:34 nldedip4k031 kernel: [87976.526180] init: upstart-socket-bridge main process ended, respawning Oct 25 07:28:44 nldedip4k031 kernel: [87986.439671] irqbalance invoked oom-killer: gfp_mask=0x80d0, order=0, oom_adj=0, oom_score_adj=0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439674] irqbalance cpuset=/ mems_allowed=0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439676] Pid: 948, comm: irqbalance Not tainted 3.2.0-55-generic-pae #85-Ubuntu Oct 25 07:28:44 nldedip4k031 kernel: [87986.439678] Call Trace: Oct 25 07:28:44 nldedip4k031 kernel: [87986.439684] [] dump_header.isra.6+0x85/0xc0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439686] [] oom_kill_process+0x5c/0x80 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439688] [] out_of_memory+0xc5/0x1c0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439691] [] __alloc_pages_nodemask+0x72c/0x740 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439694] [] __get_free_pages+0x1c/0x30 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439696] [] get_zeroed_page+0x12/0x20 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439699] [] fill_read_buffer.isra.8+0xaa/0xd0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439702] [] sysfs_read_file+0x7d/0x90 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439704] [] vfs_read+0x8c/0x160 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439707] [] ? fill_read_buffer.isra.8+0xd0/0xd0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439709] [] sys_read+0x3d/0x70 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439712] [] sysenter_do_call+0x12/0x28 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439714] Mem-Info: Oct 25 07:28:44 nldedip4k031 kernel: [87986.439714] DMA per-cpu: Oct 25 07:28:44 nldedip4k031 kernel: [87986.439716] CPU 0: hi: 0, btch: 1 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439717] CPU 1: hi: 0, btch: 1 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439718] CPU 2: hi: 0, btch: 1 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439719] CPU 3: hi: 0, btch: 1 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439720] CPU 4: hi: 0, btch: 1 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439721] CPU 5: hi: 0, btch: 1 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439722] CPU 6: hi: 0, btch: 1 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439723] CPU 7: hi: 0, btch: 1 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439724] Normal per-cpu: Oct 25 07:28:44 nldedip4k031 kernel: [87986.439725] CPU 0: hi: 186, btch: 31 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439726] CPU 1: hi: 186, btch: 31 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439727] CPU 2: hi: 186, btch: 31 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439728] CPU 3: hi: 186, btch: 31 usd: 0 Oct 25 07:28:44 nldedip4k031 kernel: [87986.439729] CPU 4: hi: 186, btch: 31 usd: 0 Oct 25 07:33:48 nldedip4k031 kernel: imklog 5.8.6, log source = /proc/kmsg started. Oct 25 07:33:48 nldedip4k031 rsyslogd: [origin software="rsyslogd" swVersion="5.8.6" x-pid="2880" x-info="http://www.rsyslog.com"] start Oct 25 07:33:48 nldedip4k031 rsyslogd: rsyslogd's groupid changed to 103 Oct 25 07:33:48 nldedip4k031 rsyslogd: rsyslogd's userid changed to 101 Oct 25 07:33:48 nldedip4k031 rsyslogd-2039: Could not open output pipe '/dev/xconsole' [try http://www.rsyslog.com/e/2039 ]

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• Why do we get a sudden spike in response times?

  - by Christian Hagelid

  We have an API that is implemented using ServiceStack which is hosted in IIS. While performing load testing of the API we discovered that the response times are good but that they deteriorate rapidly as soon as we hit about 3,500 concurrent users per server. We have two servers and when hitting them with 7,000 users the average response times sit below 500ms for all endpoints. The boxes are behind a load balancer so we get 3,500 concurrents per server. However as soon as we increase the number of total concurrent users we see a significant increase in response times. Increasing the concurrent users to 5,000 per server gives us an average response time per endpoint of around 7 seconds. The memory and CPU on the servers are quite low, both while the response times are good and when after they deteriorate. At peak with 10,000 concurrent users the CPU averages just below 50% and the RAM sits around 3-4 GB out of 16. This leaves us thinking that we are hitting some kind of limit somewhere. The below screenshot shows some key counters in perfmon during a load test with a total of 10,000 concurrent users. The highlighted counter is requests/second. To the right of the screenshot you can see the requests per second graph becoming really erratic. This is the main indicator for slow response times. As soon as we see this pattern we notice slow response times in the load test. How do we go about troubleshooting this performance issue? We are trying to identify if this is a coding issue or a configuration issue. Are there any settings in web.config or IIS that could explain this behaviour? The application pool is running .NET v4.0 and the IIS version is 7.5. The only change we have made from the default settings is to update the application pool Queue Length value from 1,000 to 5,000. We have also added the following config settings to the Aspnet.config file: <system.web> <applicationPool maxConcurrentRequestsPerCPU="5000" maxConcurrentThreadsPerCPU="0" requestQueueLimit="5000" /> </system.web> More details: The purpose of the API is to combine data from various external sources and return as JSON. It is currently using an InMemory cache implementation to cache individual external calls at the data layer. The first request to a resource will fetch all data required and any subsequent requests for the same resource will get results from the cache. We have a 'cache runner' that is implemented as a background process that updates the information in the cache at certain set intervals. We have added locking around the code that fetches data from the external resources. We have also implemented the services to fetch the data from the external sources in an asynchronous fashion so that the endpoint should only be as slow as the slowest external call (unless we have data in the cache of course). This is done using the System.Threading.Tasks.Task class. Could we be hitting a limitation in terms of number of threads available to the process?

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• Does this prove a network bandwidth bottleneck?

  - by Yuji Tomita

  I've incorrectly assumed that my internal AB testing means my server can handle 1k concurrency @3k hits per second. My theory at at the moment is that the network is the bottleneck. The server can't send enough data fast enough. External testing from blitz.io at 1k concurrency shows my hits/s capping off at 180, with pages taking longer and longer to respond as the server is only able to return 180 per second. I've served a blank file from nginx and benched it: it scales 1:1 with concurrency. Now to rule out IO / memcached bottlenecks (nginx normally pulls from memcached), I serve up a static version of the cached page from the filesystem. The results are very similar to my original test; I'm capped at around 180 RPS. Splitting the HTML page in half gives me double the RPS, so it's definitely limited by the size of the page. If I internally ApacheBench from the local server, I get consistent results of around 4k RPS on both the Full Page and the Half Page, at high transfer rates. Transfer rate: 62586.14 [Kbytes/sec] received If I AB from an external server, I get around 180RPS - same as the blitz.io results. How do I know it's not intentional throttling? If I benchmark from multiple external servers, all results become poor which leads me to believe the problem is in MY servers outbound traffic, not a download speed issue with my benchmarking servers / blitz.io. So I'm back to my conclusion that my server can't send data fast enough. Am I right? Are there other ways to interpret this data? Is the solution/optimization to set up multiple servers + load balancing that can each serve 180 hits per second? I'm quite new to server optimization, so I'd appreciate any confirmation interpreting this data. Outbound traffic Here's more information about the outbound bandwidth: The network graph shows a maximum output of 16 Mb/s: 16 megabits per second. Doesn't sound like much at all. Due to a suggestion about throttling, I looked into this and found that linode has a 50mbps cap (which I'm not even close to hitting, apparently). I had it raised to 100mbps. Since linode caps my traffic, and I'm not even hitting it, does this mean that my server should indeed be capable of outputting up to 100mbps but is limited by some other internal bottleneck? I just don't understand how networks at this large of a scale work; can they literally send data as fast as they can read from the HDD? Is the network pipe that big? In conclusion 1: Based on the above, I'm thinking I can definitely raise my 180RPS by adding an nginx load balancer on top of a multi nginx server setup at exactly 180RPS per server behind the LB. 2: If linode has a 50/100mbit limit that I'm not hitting at all, there must be something I can do to hit that limit with my single server setup. If I can read / transmit data fast enough locally, and linode even bothers to have a 50mbit/100mbit cap, there must be an internal bottleneck that's not allowing me to hit those caps that I'm not sure how to detect. Correct? I realize the question is huge and vague now, but I'm not sure how to condense it. Any input is appreciated on any conclusion I've made.

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• Log transport and aggregation at scale

  - by markdrayton

  How're you analysing log files from UNIX/Linux machines? We run several hundred servers which all generate their own log files, either directly or through syslog. I'm looking for a decent solution to aggregate these and pick out important events. This problem breaks down into 3 components: 1) Message transport The classic way is to use syslog to log messages to a remote host. This works fine for applications that log into syslog but less useful for apps that write to a local file. Solutions for this might include having the application log into a FIFO connected to a program to send the message using syslog, or by writing something that will grep the local files and send the output to the central syslog host. However, if we go to the trouble of writing tools to get messages into syslog would we be better replacing the whole lot with something like Facebook's Scribe which offers more flexibility and reliability than syslog? 2) Message aggregation Log entries seem to fall into one of two types: per-host and per-service. Per-host messages are those which occur on one machine; think disk failures or suspicious logins. Per-service messages occur on most or all of the hosts running a service. For instance, we want to know when Apache finds an SSI error but we don't want the same error from 100 machines. In all cases we only want to see one of each type of message: we don't want 10 messages saying the same disk has failed, and we don't want a message each time a broken SSI is hit. One approach to solving this is to aggregate multiple messages of the same type into one on each host, send the messages to a central server and then aggregate messages of the same kind into one overall event. SER can do this but it's awkward to use. Even after a couple of days of fiddling I had only rudimentary aggregations working and had to constantly look up the logic SER uses to correlate events. It's powerful but tricky stuff: I need something which my colleagues can pick up and use in the shortest possible time. SER rules don't meet that requirement. 3) Generating alerts How do we tell our admins when something interesting happens? Mail the group inbox? Inject into Nagios? So, how're you solving this problem? I don't expect an answer on a plate; I can work out the details myself but some high-level discussion on what is surely a common problem would be great. At the moment we're using a mishmash of cron jobs, syslog and who knows what else to find events. This isn't extensible, maintainable or flexible and as such we miss a lot of stuff we shouldn't. Updated: we're already using Nagios for monitoring which is great for detected down hosts/testing services/etc but less useful for scraping log files. I know there are log plugins for Nagios but I'm interested in something more scalable and hierarchical than per-host alerts.

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• What I don&rsquo;t like about WIF&rsquo;s Claims-based Authorization

  - by Your DisplayName here!

  In my last post I wrote about what I like about WIF’s proposed approach to authorization – I also said that I definitely would build upon that infrastructure for my own systems. But implementing such a system is a little harder as it could be. Here’s why (and that’s purely my perspective): First of all WIF’s authorization comes in two “modes” Per-request authorization. When an ASP.NET/WCF request comes in, the registered authorization manager gets called. For SOAP the SOAP action gets passed in. For HTTP requests (ASP.NET, WCF REST) the URL and verb. Imperative authorization This happens when you explicitly call the claims authorization API from within your code. There you have full control over the values for action and resource. In ASP.NET per-request authorization is optional (depends on if you have added the ClaimsAuthorizationHttpModule). In WCF you always get the per-request checks as soon as you register the authorization manager in configuration. I personally prefer the imperative authorization because first of all I don’t believe in URL based authorization. Especially in the times of MVC and routing tables, URLs can be easily changed – but then you also have to adjust your authorization logic every time. Also – you typically need more knowledge than a simple “if user x is allowed to invoke operation x”. One problem I have is, both the per-request calls as well as the standard WIF imperative authorization APIs wrap actions and resources in the same claim type. This makes it hard to distinguish between the two authorization modes in your authorization manager. But you typically need that feature to structure your authorization policy evaluation in a clean way. The second problem (which is somehow related to the first one) is the standard API for interacting with the claims authorization manager. The API comes as an attribute (ClaimsPrincipalPermissionAttribute) as well as a class to use programmatically (ClaimsPrincipalPermission). Both only allow to pass in simple strings (which results in the wrapping with standard claim types mentioned earlier). Both throw a SecurityException when the check fails. The attribute is a code access permission attribute (like PrincipalPermission). That means it will always be invoked regardless how you call the code. This may be exactly what you want, or not. In a unit testing situation (like an MVC controller) you typically want to test the logic in the function – not the security check. The good news is, the WIF API is flexible enough that you can build your own infrastructure around their core. For my own projects I implemented the following extensions: A way to invoke the registered claims authorization manager with more overloads, e.g. with different claim types or a complete AuthorizationContext. A new CAS attribute (with the same calling semantics as the built-in one) with custom claim types. A MVC authorization attribute with custom claim types. A way to use branching – as opposed to catching a SecurityException. I will post the code for these various extensions here – so stay tuned.

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• Oracle’s New Memory-Optimized x86 Servers: Getting the Most Out of Oracle Database In-Memory

  - by Josh Rosen, x86 Product Manager-Oracle

  With the launch of Oracle Database In-Memory, it is now possible to perform real-time analytics operations on your business data as it exists at that moment – in the DRAM of the server – and immediately return completely current and consistent data. The Oracle Database In-Memory option dramatically accelerates the performance of analytics queries by storing data in a highly optimized columnar in-memory format.  This is a truly exciting advance in database technology.As Larry Ellison mentioned in his recent webcast about Oracle Database In-Memory, queries run 100 times faster simply by throwing a switch.  But in order to get the most from the Oracle Database In-Memory option, the underlying server must also be memory-optimized. This week Oracle announced new 4-socket and 8-socket x86 servers, the Sun Server X4-4 and Sun Server X4-8, both of which have been designed specifically for Oracle Database In-Memory.  These new servers use the fastest Intel® Xeon® E7 v2 processors and each subsystem has been designed to be the best for Oracle Database, from the memory, I/O and flash technologies right down to the system firmware.Amongst these subsystems, one of the most important aspects we have optimized with the Sun Server X4-4 and Sun Server X4-8 are their memory subsystems.  The new In-Memory option makes it possible to select which parts of the database should be memory optimized.  You can choose to put a single column or table in memory or, if you can, put the whole database in memory.  The more, the better.  With 3 TB and 6 TB total memory capacity on the Sun Server X4-4 and Sun Server X4-8, respectively, you can memory-optimize more, if not your entire database.   Sun Server X4-8 CMOD with 24 DIMM slots per socket (up to 192 DIMM slots per server) But memory capacity is not the only important factor in selecting the best server platform for Oracle Database In-Memory.  As you put more of your database in memory, a critical performance metric known as memory bandwidth comes into play.  The total memory bandwidth for the server will dictate the rate in which data can be stored and retrieved from memory.  In order to achieve real-time analysis of your data using Oracle Database In-Memory, even under heavy load, the server must be able to handle extreme memory workloads.  With that in mind, the Sun Server X4-8 was designed with the maximum possible memory bandwidth, providing over a terabyte per second of total memory bandwidth.  Likewise, the Sun Server X4-4 also provides extreme memory bandwidth in an even more compact form factor with over half a terabyte per second, providing customers with scalability and choice depending on the size of the database.Beyond the memory subsystem, Oracle’s Sun Server X4-4 and Sun Server X4-8 systems provide other key technologies that enable Oracle Database to run at its best.  The Sun Server X4-4 allows for up 4.8 TB of internal, write-optimized PCIe flash while the Sun Server X4-8 allows for up to 6.4 TB of PCIe flash.  This enables dramatic acceleration of data inserts and updates to Oracle Database.  And with the new elastic computing capability of Oracle’s new x86 servers, server performance can be adapted to your specific Oracle Database workload to ensure that every last bit of processing power is utilized.Because Oracle designs and tests its x86 servers specifically for Oracle workloads, we provide the highest possible performance and reliability when running Oracle Database.  To learn more about Sun Server X4-4 and Sun Server X4-8, you can find more details including data sheets and white papers here. Josh Rosen is a Principal Product Manager for Oracle’s x86 servers, focusing on Oracle’s operating systems and software.  He previously spent more than a decade as a developer and architect of system management software. Josh has worked on system management for many of Oracle's hardware products ranging from the earliest blade systems to the latest Oracle x86 servers. 

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• XNA Health Bar continually decreasing

  - by Craig

  As per the Health bar tutorial on ... http://www.xnadevelopment.com/tutorials/notsohealthy/NotSoHealthy.shtml I have set up the above, how do I make it decrease by 1 health per second? I want to create a mini survival game, and this is an important factor. Where am i going wrong? I want it to visibly decrease every second. using System; using System.Collections.Generic; using System.Linq; using Microsoft.Xna.Framework; using Microsoft.Xna.Framework.Audio; using Microsoft.Xna.Framework.Content; using Microsoft.Xna.Framework.GamerServices; using Microsoft.Xna.Framework.Graphics; using Microsoft.Xna.Framework.Input; using Microsoft.Xna.Framework.Media; namespace Health { /// <summary> /// This is the main type for your game /// </summary> public class Game1 : Microsoft.Xna.Framework.Game { GraphicsDeviceManager graphics; SpriteBatch spriteBatch; Texture2D healthBar; int currentHealth = 100; float seconds; public Game1() { graphics = new GraphicsDeviceManager(this); Content.RootDirectory = "Content"; } /// <summary> /// Allows the game to perform any initialization it needs to before starting to run. /// This is where it can query for any required services and load any non-graphic /// related content. Calling base.Initialize will enumerate through any components /// and initialize them as well. /// </summary> protected override void Initialize() { // TODO: Add your initialization logic here base.Initialize(); } /// <summary> /// LoadContent will be called once per game and is the place to load /// all of your content. /// </summary> protected override void LoadContent() { // Create a new SpriteBatch, which can be used to draw textures. spriteBatch = new SpriteBatch(GraphicsDevice); healthBar = Content.Load<Texture2D>("HealthBar"); // TODO: use this.Content to load your game content here } /// <summary> /// UnloadContent will be called once per game and is the place to unload /// all content. /// </summary> protected override void UnloadContent() { // TODO: Unload any non ContentManager content here } /// <summary> /// Allows the game to run logic such as updating the world, /// checking for collisions, gathering input, and playing audio. /// </summary> /// <param name="gameTime">Provides a snapshot of timing values.</param> protected override void Update(GameTime gameTime) { // Allows the game to exit if (GamePad.GetState(PlayerIndex.One).Buttons.Back == ButtonState.Pressed) this.Exit(); // TODO: Add your update logic here currentHealth = (int)MathHelper.Clamp(currentHealth, 0, 100); seconds += (float)gameTime.ElapsedGameTime.TotalSeconds; if (seconds >= 1) { currentHealth -= 1; } seconds = 0; base.Update(gameTime); } /// <summary> /// This is called when the game should draw itself. /// </summary> /// <param name="gameTime">Provides a snapshot of timing values.</param> protected override void Draw(GameTime gameTime) { GraphicsDevice.Clear(Color.CornflowerBlue); spriteBatch.Begin(); spriteBatch.Draw(healthBar, new Rectangle(this.Window.ClientBounds.Width / 2 - healthBar.Width / 2, 30, healthBar.Width, 44), new Rectangle(0, 45, healthBar.Width, 44), Color.Gray); spriteBatch.Draw(healthBar, new Rectangle(this.Window.ClientBounds.Width / 2 - healthBar.Width / 2, 30, (int)(healthBar.Width * ((double)currentHealth / 100)), 44), new Rectangle(0, 45, healthBar.Width, 44), Color.Red); spriteBatch.Draw(healthBar, new Rectangle(this.Window.ClientBounds.Width / 2 - healthBar.Width / 2, 30, healthBar.Width, 44), new Rectangle(0, 0, healthBar.Width, 44), Color.White); spriteBatch.End(); base.Draw(gameTime); } } } Cheers!

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• Ext3 fs: Block bitmap for group 1 not in group (block 0). is fs dead?

  - by ip

  Hi, My company has a server with one big partition with Mysql database and php files. Now this partition seems to be corrupted, as reported from kernel messages when I tried to mount it manually: [329862.817837] EXT3-fs error (device loop1): ext3_check_descriptors: Block bitmap for group 1 not in group (block 0)! [329862.817846] EXT3-fs: group descriptors corrupted! I've tried to recovery it running tools from a PLD livecd. These are the tools I have tested: - e2retrieve - testdisk - photorec - dd_rescue/dd_rhelp - ddrescue - fsck.ext2 - e2salvage without any success. dumpe2fs 1.41.3 (12-Oct-2008) Filesystem volume name: /dev/sda3 Last mounted on: <not available> Filesystem UUID: dd51610b-6de0-4392-a6f3-67160dbc0343 Filesystem magic number: 0xEF53 Filesystem revision #: 1 (dynamic) Filesystem features: has_journal filetype sparse_super Default mount options: (none) Filesystem state: not clean with errors Errors behavior: Continue Filesystem OS type: Linux Inode count: 9502720 Block count: 18987570 Reserved block count: 949378 Free blocks: 11555345 Free inodes: 11858398 First block: 0 Block size: 4096 Fragment size: 4096 Blocks per group: 32768 Fragments per group: 32768 Inodes per group: 16384 Inode blocks per group: 512 Last mount time: Wed Mar 24 09:31:03 2010 Last write time: Mon Apr 12 11:46:32 2010 Mount count: 10 Maximum mount count: 30 Last checked: Thu Jan 1 01:00:00 1970 Check interval: 0 (<none>) Reserved blocks uid: 0 (user root) Reserved blocks gid: 0 (group root) First inode: 11 Inode size: 128 Journal inode: 8 Journal backup: inode blocks dumpe2fs: A block group is missing an inode table while reading journal inode There's any other tools I have to test before considering these disk definitely unrecoverable? Many thanks, ip

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• Pros & Cons of Google App Engine

  - by Rishi

  Pros & Cons of Google App Engine [An Updated List 21st Aug 09] Help me Compile a List of all the Advantages & Disadvantages of Building an Application on the Google App Engine Pros: 1) No Need to buy Servers or Server Space (no maintenance). 2) Makes solving the problem of scaling much easier. Cons: 1) Locked into Google App Engine ?? 2)Developers have read-only access to the filesystem on App Engine. 3)App Engine can only execute code called from an HTTP request (except for scheduled background tasks). 4)Users may upload arbitrary Python modules, but only if they are pure-Python; C and Pyrex modules are not supported. 5)App Engine limits the maximum rows returned from an entity get to 1000 rows per Datastore call. 6)Java applications may only use a subset (The JRE Class White List) of the classes from the JRE standard edition. 7)Java applications cannot create new threads. Known Issues!! http://code.google.com/p/googleappengine/issues/list Hard limits Apps per developer - 10 Time per request - 30 sec Files per app - 3,000 HTTP response size - 10 MB Datastore item size - 1 MB Application code size - 150 MB Pro or Con? App Engine's infrastructure removes many of the system administration and development challenges of building applications to scale to millions of hits. Google handles deploying code to a cluster, monitoring, failover, and launching application instances as necessary. While other services let users install and configure nearly any *NIX compatible software, App Engine requires developers to use Python or Java as the programming language and a limited set of APIs. Current APIs allow storing and retrieving data from a BigTable non-relational database; making HTTP requests; sending e-mail; manipulating images; and caching. Most existing Web applications can't run on App Engine without modification, because they require a relational database.

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• What is best practice (and implications) for packaging projects into JAR's?

  - by user245510

  What is considered best practice deciding how to define the set of JAR's for a project (for example a Swing GUI)? There are many possible groupings: JAR per layer (presentation, business, data) JAR per (significant?) GUI panel. For significant system, this results in a large number of JAR's, but the JAR's are (should be) more re-usable - fine-grained granularity JAR per "project" (in the sense of an IDE project); "common.jar", "resources.jar", "gui.jar", etc I am an experienced developer; I know the mechanics of creating JAR's, I'm just looking for wisdom on best-practice. Personally, I like the idea of a JAR per component (e.g. a panel), as I am mad-keen on encapsulation, and the holy-grail of re-use accross projects. I am concerned, however, that on a practical, performance level, the JVM would struggle class loading over dozens, maybe hundreds of small JAR's. Each JAR would contain; the GUI panel code, necessary resources (i.e. not centralised) so each panel can stand alone. Does anyone have wisdom to share?

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• Cassandra random read speed

  - by Jody Powlette

  We're still evaluating Cassandra for our data store. As a very simple test, I inserted a value for 4 columns into the Keyspace1/Standard1 column family on my local machine amounting to about 100 bytes of data. Then I read it back as fast as I could by row key. I can read it back at 160,000/second. Great. Then I put in a million similar records all with keys in the form of X.Y where X in (1..10) and Y in (1..100,000) and I queried for a random record. Performance fell to 26,000 queries per second. This is still well above the number of queries we need to support (about 1,500/sec) Finally I put ten million records in from 1.1 up through 10.1000000 and randomly queried for one of the 10 million records. Performance is abysmal at 60 queries per second and my disk is thrashing around like crazy. I also verified that if I ask for a subset of the data, say the 1,000 records between 3,000,000 and 3,001,000, it returns slowly at first and then as they cache, it speeds right up to 20,000 queries per second and my disk stops going crazy. I've read all over that people are storing billions of records in Cassandra and fetching them at 5-6k per second, but I can't get anywhere near that with only 10mil records. Any idea what I'm doing wrong? Is there some setting I need to change from the defaults? I'm on an overclocked Core i7 box with 6gigs of ram so I don't think it's the machine. Here's my code to fetch records which I'm spawning into 8 threads to ask for one value from one column via row key: ColumnPath cp = new ColumnPath(); cp.Column_family = "Standard1"; cp.Column = utf8Encoding.GetBytes("site"); string key = (1+sRand.Next(9)) + "." + (1+sRand.Next(1000000)); ColumnOrSuperColumn logline = client.get("Keyspace1", key, cp, ConsistencyLevel.ONE); Thanks for any insights

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• How to setup Lucene search for a B2B web app?

  - by Bill Paetzke

  Given: 5000 databases (spread out over 5 servers) 1 database per client (so you can infer there are 1000 clients) 2 to 2000 users per client (let's say avg is 100 users per client) Clients (databases) come and go every day (let's assume most remain for at least one year) Let's stay agnostic of language or sql brand, since Lucene (and Solr) have a breadth of support The Question: How would you setup Lucene search so that each client can only search within its database? How would you setup the index(es)? Would you need to add a filter to all search queries? If a client cancelled, how would you delete their (part of the) index? (this may be trivial--not sure yet) Possible Solutions: Make an index for each client (database) Pro: Search is faster (than one-index-for-all method). Indices are relative to the size of the client's data. Con: I'm not sure what this entails, nor do I know if this is beyond Lucene's scope. Have a single, gigantic index with a database_name field. Always include database_name as a filter. Pro: Not sure. Maybe good for tech support or billing dept to search all databases for info. Con: Search is slower (than index-per-client method). Flawed security if query filter removed. For Example: Joel Spolsky said in Podcast #11 that his hosted web app product, FogBugz On-Demand, uses Lucene. He has thousands of on-demand clients. And each client gets their own database. His situation is quite similar to mine. Although, he didn't elaborate on the setup (particularly indices); hence, the need for this question. One last thing: I would also accept an answer that uses Solr (the extension of Lucene). Perhaps it's better suited for this problem. Not sure.

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• Displaying timecode using NSTimer and NSDateFormatter

  - by Chris B

  Hi. I am very close to completing my first iphone app and it has been a joy. I am trying to add running timecode using the current time via an NSTimer displaying the current time (NSDate) on a UILabel. NSDate is working fine for me, showing hour, minute, second, milliseconds. But instead of milliseconds, I need to show 24 frames per second. The problem is that I need the frames per second to be synced 100% with the hour, minute and second, so I can't add the frames in a separate timer. I tried that and had it working but the frame timer was not running in sync with the date timer. Can anyone help me out with this? Is there a way to customize NSDateFormatter so that I can have a date timer formatted with 24 frames per second? Right now I'm limited to formatting just hours, minutes, seconds, and milliseconds. Here's the code I'm using right now -(void)runTimer { // This starts the timer which fires the displayCount method every 0.01 seconds runTimer = [NSTimer scheduledTimerWithTimeInterval: .01 target: self selector: @selector(displayCount) userInfo: nil repeats: YES]; } //This formats the timer using the current date and sets text on UILabels - (void)displayCount; { NSDateFormatter *formatter = [[[NSDateFormatter alloc] init] autorelease]; NSDate *date = [NSDate date]; // This will produce a time that looks like "12:15:07:75" using 4 separate labels // I could also have this on just one label but for now they are separated // This sets the Hour Label and formats it in hours [formatter setDateFormat:@"HH"]; [timecodeHourLabel setText:[formatter stringFromDate:date]]; // This sets the Minute Label and formats it in minutes [formatter setDateFormat:@"mm"]; [timecodeMinuteLabel setText:[formatter stringFromDate:date]]; // This sets the Second Label and formats it in seconds [formatter setDateFormat:@"ss"]; [timecodeSecondLabel setText:[formatter stringFromDate:date]]; //This sets the Frame Label and formats it in milliseconds //I need this to be 24 frames per second [formatter setDateFormat:@"SS"]; [timecodeFrameLabel setText:[formatter stringFromDate:date]]; }

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• How to allow users to define financial formulas in a C# app

  - by Peter Morris

  I need to allow my users to be able to define formulas which will calculate values based on data. For example //Example 1 return GetMonetaryAmountFromDatabase("Amount due") * 1.2; //Example 2 return GetMonetaryAmountFromDatabase("Amount due") * GetFactorFromDatabase("Discount"); I will need to allow / * + - operations, also to assign local variables and execute IF statements, like so var amountDue = GetMonetaryAmountFromDatabase("Amount due"); if (amountDue > 100000) return amountDue * 0.75; if (amountDue > 50000) return amountDue * 0.9; return amountDue; The scenario is complicated because I have the following structure.. Customer (a few hundred) Configuration (about 10 per customer) Item (about 10,000 per customer configuration) So I will perform a 3 level loop. At each "Configuration" level I will start a DB transaction and compile the forumlas, each "Item" will use the same transaction + compiled formulas (there are about 20 formulas per configuration, each item will use all of them). This further complicates things because I can't just use the compiler services as it would result in continued memory usage growth. I can't use a new AppDomain per each "Configuration" loop level because some of the references I need to pass cannot be marshalled. Any suggestions?

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• How can i get rid of 'ORA-01489: result of string concatenation is too long' in this query?

  - by core_pro

  this query gets the dominating sets in a network. so for example given a network A<----->B B<----->C B<----->D C<----->E D<----->C D<----->E F<----->E it returns B,E B,F A,E but it doesn't work for large data because i'm using string methods in my result. i have been trying to remove the string methods and return a view or something but to no avail With t as (select 'A' as per1, 'B' as per2 from dual union all select 'B','C' from dual union all select 'B','D' from dual union all select 'C','B' from dual union all select 'C','E' from dual union all select 'D','C' from dual union all select 'D','E' from dual union all select 'E','C' from dual union all select 'E','D' from dual union all select 'F','E' from dual) ,t2 as (select distinct least(per1, per2) as per1, greatest(per1, per2) as per2 from t union select distinct greatest(per1, per2) as per1, least(per1, per2) as per1 from t) ,t3 as (select per1, per2, row_number() over (partition by per1 order by per2) as rn from t2) ,people as (select per, row_number() over (order by per) rn from (select distinct per1 as per from t union select distinct per2 from t) ) ,comb as (select sys_connect_by_path(per,',')||',' as p from people connect by rn > prior rn ) ,find as (select p, per2, count(*) over (partition by p) as cnt from ( select distinct comb.p, t3.per2 from comb, t3 where instr(comb.p, ','||t3.per1||',') > 0 or instr(comb.p, ','||t3.per2||',') > 0 ) ) ,rnk as (select p, rank() over (order by length(p)) as rnk from find where cnt = (select count(*) from people) order by rnk ) select distinct trim(',' from p) as p from rnk where rnk.rnk = 1`

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• Using Node.js as an accelerator for WCF REST services

  - by Elton Stoneman

  Node.js is a server-side JavaScript platform "for easily building fast, scalable network applications". It's built on Google's V8 JavaScript engine and uses an (almost) entirely async event-driven processing model, running in a single thread. If you're new to Node and your reaction is "why would I want to run JavaScript on the server side?", this is the headline answer: in 150 lines of JavaScript you can build a Node.js app which works as an accelerator for WCF REST services*. It can double your messages-per-second throughput, halve your CPU workload and use one-fifth of the memory footprint, compared to the WCF services direct.   Well, it can if: 1) your WCF services are first-class HTTP citizens, honouring client cache ETag headers in request and response; 2) your services do a reasonable amount of work to build a response; 3) your data is read more often than it's written. In one of my projects I have a set of REST services in WCF which deal with data that only gets updated weekly, but which can be read hundreds of times an hour. The services issue ETags and will return a 304 if the client sends a request with the current ETag, which means in the most common scenario the client uses its local cached copy. But when the weekly update happens, then all the client caches are invalidated and they all need the same new data. Then the service will get hundreds of requests with old ETags, and they go through the full service stack to build the same response for each, taking up threads and processing time. Part of that processing means going off to a database on a separate cloud, which introduces more latency and downtime potential.   We can use ASP.NET output caching with WCF to solve the repeated processing problem, but the server will still be thread-bound on incoming requests, and to get the current ETags reliably needs a database call per request. The accelerator solves that by running as a proxy - all client calls come into the proxy, and the proxy routes calls to the underlying REST service. We could use Node as a straight passthrough proxy and expect some benefit, as the server would be less thread-bound, but we would still have one WCF and one database call per proxy call. But add some smart caching logic to the proxy, and share ETags between Node and WCF (so the proxy doesn't even need to call the servcie to get the current ETag), and the underlying service will only be invoked when data has changed, and then only once - all subsequent client requests will be served from the proxy cache.   I've built this as a sample up on GitHub: NodeWcfAccelerator on sixeyed.codegallery. Here's how the architecture looks:     The code is very simple. The Node proxy runs on port 8010 and all client requests target the proxy. If the client request has an ETag header then the proxy looks up the ETag in the tag cache to see if it is current - the sample uses memcached to share ETags between .NET and Node. If the ETag from the client matches the current server tag, the proxy sends a 304 response with an empty body to the client, telling it to use its own cached version of the data. If the ETag from the client is stale, the proxy looks for a local cached version of the response, checking for a file named after the current ETag. If that file exists, its contents are returned to the client as the body in a 200 response, which includes the current ETag in the header. If the proxy does not have a local cached file for the service response, it calls the service, and writes the WCF response to the local cache file, and to the body of a 200 response for the client. So the WCF service is only troubled if both client and proxy have stale (or no) caches.   The only (vaguely) clever bit in the sample is using the ETag cache, so the proxy can serve cached requests without any communication with the underlying service, which it does completely generically, so the proxy has no notion of what it is serving or what the services it proxies are doing. The relative path from the URL is used as the lookup key, so there's no shared key-generation logic between .NET and Node, and when WCF stores a tag it also stores the "read" URL against the ETag so it can be used for a reverse lookup, e.g:   Key Value /WcfSampleService/PersonService.svc/rest/fetch/3 "28cd4796-76b8-451b-adfd-75cb50a50fa6" "28cd4796-76b8-451b-adfd-75cb50a50fa6" /WcfSampleService/PersonService.svc/rest/fetch/3    In Node we read the cache using the incoming URL path as the key and we know that "28cd4796-76b8-451b-adfd-75cb50a50fa6" is the current ETag; we look for a local cached response in /caches/28cd4796-76b8-451b-adfd-75cb50a50fa6.body (and the corresponding .header file which contains the original service response headers, so the proxy response is exactly the same as the underlying service). When the data is updated, we need to invalidate the ETag cache – which is why we need the reverse lookup in the cache. In the WCF update service, we don't need to know the URL of the related read service - we fetch the entity from the database, do a reverse lookup on the tag cache using the old ETag to get the read URL, update the new ETag against the URL, store the new reverse lookup and delete the old one.   Running Apache Bench against the two endpoints gives the headline performance comparison. Making 1000 requests with concurrency of 100, and not sending any ETag headers in the requests, with the Node proxy I get 102 requests handled per second, average response time of 975 milliseconds with 90% of responses served within 850 milliseconds; going direct to WCF with the same parameters, I get 53 requests handled per second, mean response time of 1853 milliseconds, with 90% of response served within 3260 milliseconds. Informally monitoring server usage during the tests, Node maxed at 20% CPU and 20Mb memory; IIS maxed at 60% CPU and 100Mb memory.   Note that the sample WCF service does a database read and sleeps for 250 milliseconds to simulate a moderate processing load, so this is *not* a baseline Node-vs-WCF comparison, but for similar scenarios where the  service call is expensive but applicable to numerous clients for a long timespan, the performance boost from the accelerator is considerable.     * - actually, the accelerator will work nicely for any HTTP request, where the URL (path + querystring) uniquely identifies a resource. In the sample, there is an assumption that the ETag is a GUID wrapped in double-quotes (e.g. "28cd4796-76b8-451b-adfd-75cb50a50fa6") – which is the default for WCF services. I use that assumption to name the cache files uniquely, but it is a trivial change to adapt to other ETag formats.

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• Developing a Cost Model for Cloud Applications

  - by BuckWoody

  Note - please pay attention to the date of this post. As much as I attempt to make the information below accurate, the nature of distributed computing means that components, units and pricing will change over time. The definitive costs for Microsoft Windows Azure and SQL Azure are located here, and are more accurate than anything you will see in this post: http://www.microsoft.com/windowsazure/offers/  When writing software that is run on a Platform-as-a-Service (PaaS) offering like Windows Azure / SQL Azure, one of the questions you must answer is how much the system will cost. I will not discuss the comparisons between on-premise costs (which are nigh impossible to calculate accurately) versus cloud costs, but instead focus on creating a general model for estimating costs for a given application. You should be aware that there are (at this writing) two billing mechanisms for Windows and SQL Azure: “Pay-as-you-go” or consumption, and “Subscription” or commitment. Conceptually, you can consider the former a pay-as-you-go cell phone plan, where you pay by the unit used (at a slightly higher rate) and the latter as a standard cell phone plan where you commit to a contract and thus pay lower rates. In this post I’ll stick with the pay-as-you-go mechanism for simplicity, which should be the maximum cost you would pay. From there you may be able to get a lower cost if you use the other mechanism. In any case, the model you create should hold. Developing a good cost model is essential. As a developer or architect, you’ll most certainly be asked how much something will cost, and you need to have a reliable way to estimate that. Businesses and Organizations have been used to paying for servers, software licenses, and other infrastructure as an up-front cost, and power, people to the systems and so on as an ongoing (and sometimes not factored) cost. When presented with a new paradigm like distributed computing, they may not understand the true cost/value proposition, and that’s where the architect and developer can guide the conversation to make a choice based on features of the application versus the true costs. The two big buckets of use-types for these applications are customer-based and steady-state. In the customer-based use type, each successful use of the program results in a sale or income for your organization. Perhaps you’ve written an application that provides the spot-price of foo, and your customer pays for the use of that application. In that case, once you’ve estimated your cost for a successful traversal of the application, you can build that into the price you charge the user. It’s a standard restaurant model, where the price of the meal is determined by the cost of making it, plus any profit you can make. In the second use-type, the application will be used by a more-or-less constant number of processes or users and no direct revenue is attached to the system. A typical example is a customer-tracking system used by the employees within your company. In this case, the cost model is often created “in reverse” - meaning that you pilot the application, monitor the use (and costs) and that cost is held steady. This is where the comparison with an on-premise system becomes necessary, even though it is more difficult to estimate those on-premise true costs. For instance, do you know exactly how much cost the air conditioning is because you have a team of system administrators? This may sound trivial, but that, along with the insurance for the building, the wiring, and every other part of the system is in fact a cost to the business. There are three primary methods that I’ve been successful with in estimating the cost. None are perfect, all are demand-driven. The general process is to lay out a matrix of: components units cost per unit and then multiply that times the usage of the system, based on which components you use in the program. That sounds a bit simplistic, but using those metrics in a calculation becomes more detailed. In all of the methods that follow, you need to know your application. The components for a PaaS include computing instances, storage, transactions, bandwidth and in the case of SQL Azure, database size. In most cases, architects start with the first model and progress through the other methods to gain accuracy. Simple Estimation The simplest way to calculate costs is to architect the application (even UML or on-paper, no coding involved) and then estimate which of the components you’ll use, and how much of each will be used. Microsoft provides two tools to do this - one is a simple slider-application located here: http://www.microsoft.com/windowsazure/pricing-calculator/  The other is a tool you download to create an “Return on Investment” (ROI) spreadsheet, which has the advantage of leading you through various questions to estimate what you plan to use, located here: https://roianalyst.alinean.com/msft/AutoLogin.do?d=176318219048082115  You can also just create a spreadsheet yourself with a structure like this: Program Element Azure Component Unit of Measure Cost Per Unit Estimated Use of Component Total Cost Per Component Cumulative Cost               Of course, the consideration with this model is that it is difficult to predict a system that is not running or hasn’t even been developed. Which brings us to the next model type. Measure and Project A more accurate model is to actually write the code for the application, using the Software Development Kit (SDK) which can run entirely disconnected from Azure. The code should be instrumented to estimate the use of the application components, logging to a local file on the development system. A series of unit and integration tests should be run, which will create load on the test system. You can use standard development concepts to track this usage, and even use Windows Performance Monitor counters. The best place to start with this method is to use the Windows Azure Diagnostics subsystem in your code, which you can read more about here: http://blogs.msdn.com/b/sumitm/archive/2009/11/18/introducing-windows-azure-diagnostics.aspx This set of API’s greatly simplifies tracking the application, and in fact you can use this information for more than just a cost model. After you have the tracking logs, you can plug the numbers into ay of the tools above, which should give a representative cost or in some cases a unit cost. The consideration with this model is that the SDK fabric is not a one-to-one comparison with performance on the actual Windows Azure fabric. Those differences are usually smaller, but they do need to be considered. Also, you may not be able to accurately predict the load on the system, which might lead to an architectural change, which changes the model. This leads us to the next, most accurate method for a cost model. Sample and Estimate Using standard statistical and other predictive math, once the application is deployed you will get a bill each month from Microsoft for your Azure usage. The bill is quite detailed, and you can export the data from it to do analysis, and using methods like regression and so on project out into the future what the costs will be. I normally advise that the architect also extrapolate a unit cost from those metrics as well. This is the information that should be reported back to the executives that pay the bills: the past cost, future projected costs, and unit cost “per click” or “per transaction”, as your case warrants. The challenge here is in the model itself - statistical methods are not foolproof, and the larger the sample (in this case I recommend the entire population, not a smaller sample) is key. References and Tools Articles: http://blogs.msdn.com/b/patrick_butler_monterde/archive/2010/02/10/windows-azure-billing-overview.aspx http://technet.microsoft.com/en-us/magazine/gg213848.aspx http://blog.codingoutloud.com/2011/06/05/azure-faq-how-much-will-it-cost-me-to-run-my-application-on-windows-azure/ http://blogs.msdn.com/b/johnalioto/archive/2010/08/25/10054193.aspx http://geekswithblogs.net/iupdateable/archive/2010/02/08/qampa-how-can-i-calculate-the-tco-and-roi-when.aspx   Other Tools: http://cloud-assessment.com/ http://communities.quest.com/community/cloud_tools

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• Evaluating Solutions to Manage Product Compliance? Don't Wait Much Longer

  - by Kerrie Foy

  Depending on severity, product compliance issues can cause all sorts of problems from run-away budgets to business closures. But effective policies and safeguards can create a strong foundation for innovation, productivity, market penetration and competitive advantage. If you’ve been putting off a systematic approach to product compliance, it is time to reconsider that decision, or indecision. Why now?  No matter what industry, companies face a litany of worldwide and regional regulations that require proof of product compliance and environmental friendliness for market access.  For example, Restriction of Hazardous Substances (RoHS) is a regulation that restricts the use of six dangerous materials used in the manufacture of electronic and electrical equipment.  ROHS was originally adopted by the European Union in 2003 for implementation in 2006, and it has evolved over time through various regional versions for North America, China, Japan, Korea, Norway and Turkey.  In addition, the RoHS directive allowed for material exemptions used in Medical Devices, but that exemption ends in 2014.   Additional regulations worth watching are the Battery Directive, Waste Electrical and Electronic Equipment (WEEE), and Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) directives.  Additional evolving regulations are coming from governing bodies like the Food and Drug Administration (FDA) and the International Organization for Standardization (ISO). Corporate sustainability initiatives are also gaining urgency and influencing product design. In a survey of 405 corporations in the Global 500 by Carbon Disclosure Project, co-written by PwC (CDP Global 500 Climate Change Report 2012 entitled Business Resilience in an Uncertain, Resource-Constrained World), 48% of the respondents indicated they saw potential to create new products and business services as a response to climate change. Just 21% reported a dedicated budget for the research. However, the report goes on to explain that those few companies are winning over new customers and driving additional profits by exploiting their abilities to adapt to environmental needs. The article cites Dell as an example – Dell has invested in research to develop new products designed to reduce its customers’ emissions by more than 10 million metric tons of CO2e per year. This reduction in emissions should save Dell’s customers over $1billion per year as a result! Over time we expect to see many additional companies prove that eco-design provides marketplace benefits through differentiation and direct customer value. How do you meet compliance requirements and also successfully invest in eco-friendly designs? No doubt companies struggle to answer this question. After all, the journey to get there may involve transforming business models, go-to-market strategies, supply networks, quality assurance policies and compliance processes per the rapidly evolving global and regional directives. There may be limited executive focus on the initiative, inability to quantify noncompliance, or not enough resources to justify investment. To make things even more difficult to address, compliance responsibility can be a passionate topic within an organization, making the prospect of change on an enterprise scale problematic and time-consuming. Without a single source of truth for product data and without proper processes in place, ensuring product compliance burgeons into a crushing task that is cost-prohibitive and overwhelming to an organization. With all the overhead, certain markets or demographics become simply inaccessible. Therefore, the risk to consumer goodwill and satisfaction, revenue, business continuity, and market potential is too great not to solve the compliance challenge. Companies are beginning to adapt and even thrive in today’s highly regulated and transparent environment by implementing systematic approaches to product compliance that are more than functional bandages but revenue-generating engines. Consider partnering with Oracle to help you address your compliance needs. Many of the world’s most innovative leaders and pioneers are leveraging Oracle’s Agile Product Lifecycle Management (PLM) portfolio of enterprise applications to manage the product value chain, centralize product data, automate processes, and launch more eco-friendly products to market faster.   Particularly, the Agile Product Governance & Compliance (PG&C) solution provides out-of-the-box functionality to integrate actionable regulatory information into the enterprise product record from the ideation to the disposal/recycling phase. Agile PG&C makes it possible to efficiently manage compliance per corporate green initiatives as well as regional and global directives. Options are critical, but so is ease-of-use. Anyone who’s grappled with compliance policy knows legal interpretation plays a major role in determining how an organization responds to regulation. Agile PG&C gives you the freedom to configure product compliance per your needs, while maintaining rigorous control over the product record in an easy-to-use interface that facilitates adoption efforts. It allows you to assign regulations as specifications for a part or BOM roll-up. Each specification has a threshold value that alerts you to a non-compliance issue if the threshold value is exceeded. Set however many regulations as specifications you need to make sure a product can be sold in your target countries. Another option is to implement like one of our leading consumer electronics customers and define your own “catch-all” specification to ensure compliance in all markets. You can give your suppliers secure access to enter their component data or integrate a third party’s data. With Agile PG&C you are able to design compliance earlier into your products to reduce cost and improve quality downstream when stakes are higher. Agile PG&C is a comprehensive solution that makes product compliance more reliable and efficient. Throughout product lifecycles, use the solution to support full material disclosures, efficiently manage declarations with your suppliers, feed compliance data into a corrective action if a product must be changed, and swiftly satisfy audits by showing all due diligence tracked in one solution. Given the compounding regulation and consumer focus on urgent environmental issues, now is the time to act. Implementing an enterprise, systematic approach to product compliance is a competitive investment. From the start, Agile Product Governance & Compliance enables companies to confidently design for compliance and sustainability, reduce the cost of compliance, minimize the risk of business interruption, deliver responsible products, and inspire new innovation.  Don’t wait any longer! To find out more about Agile Product Governance & Compliance download the data sheet, contact your sales representative, or call Oracle at 1-800-633-0738. Many thanks to Shane Goodwin, Senior Manager, Oracle Agile PLM Product Management, for contributions to this article. 

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• Question regarding filesystems true or false?

  - by Avon

  Hello all, though I'm familiar with stackoverflow , and loving it , i've actually got a couple of questions myself about something other then programming. Here are my question Is it true that in FAT filesystems the maximum number of files per filesystem equals the number of entries in the FAT table. And is it also true that in indexed filesystems the maximum number of files per filesystem equals the number of indexblocks – 1. I'm reading some stuff and am trying to get a good understanding of it.

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• Log every IP connecting on a system with iptables

  - by Alexandre Nizoux

  Title says it all. How can I, with iptables, log all IP connecting to a server? As a little detail, I'd like to have only ONE entry in the log PER DAY PER IP. Thanks :)

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• One Database vs. Multiple Databases

  - by Ricardo

  I need to design a system which represents multiple "projects", one per client in SQL Server , something similar to StackExchange... same data model, different sites (one per customer). Each project has the same data model, but is independent of all others. My inclination is to use one database to store all projects. What is your recommendation?

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• How many domains can you configure on a Sun M5000 system?

  - by Andre Miller

  We have a few Sun M5000 servers with the following configuration: Each system has 2 system boards each containing 2 x 2.5Ghz quad core processors Each system board has 16GB of RAM Each system has 4 x 300GB disks I would like to know how many hardware domains can I configure per system? Do I need one system board per domain (implying a total of 2 domains), or can I create 4 domains, each with one cpu each?

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• Tracking down rogue disk usage

  - by Amadan

  I found several other questions regarding the theory behind my problem (e.g. this, this), but I don't know how to apply the answers to my machine. # du -hsx / 11000283 / # df -kT / Filesystem Type 1K-blocks Used Available Use% Mounted on /dev/mapper/csisv13-root ext4 516032952 361387456 128432532 74% / There is a big difference between 11G (du) and 345G (df). Where are the remaining 334G? It's not in deleted files. There was only one, it was short, and I truncated it just in case. This is what remains: # lsof -a +L1 / COMMAND PID USER FD TYPE DEVICE SIZE/OFF NLINK NODE NAME zabbix_ag 4902 zabbix 1w REG 252,0 0 0 28836028 /var/log/zabbix-agent/zabbix_agentd.log.1 (deleted) zabbix_ag 4902 zabbix 2w REG 252,0 0 0 28836028 /var/log/zabbix-agent/zabbix_agentd.log.1 (deleted) zabbix_ag 4906 zabbix 1w REG 252,0 0 0 28836028 /var/log/zabbix-agent/zabbix_agentd.log.1 (deleted) zabbix_ag 4906 zabbix 2w REG 252,0 0 0 28836028 /var/log/zabbix-agent/zabbix_agentd.log.1 (deleted) zabbix_ag 4907 zabbix 1w REG 252,0 0 0 28836028 /var/log/zabbix-agent/zabbix_agentd.log.1 (deleted) zabbix_ag 4907 zabbix 2w REG 252,0 0 0 28836028 /var/log/zabbix-agent/zabbix_agentd.log.1 (deleted) zabbix_ag 4908 zabbix 1w REG 252,0 0 0 28836028 /var/log/zabbix-agent/zabbix_agentd.log.1 (deleted) zabbix_ag 4908 zabbix 2w REG 252,0 0 0 28836028 /var/log/zabbix-agent/zabbix_agentd.log.1 (deleted) zabbix_ag 4909 zabbix 1w REG 252,0 0 0 28836028 /var/log/zabbix-agent/zabbix_agentd.log.1 (deleted) zabbix_ag 4909 zabbix 2w REG 252,0 0 0 28836028 /var/log/zabbix-agent/zabbix_agentd.log.1 (deleted) zabbix_ag 4910 zabbix 1w REG 252,0 0 0 28836028 /var/log/zabbix-agent/zabbix_agentd.log.1 (deleted) zabbix_ag 4910 zabbix 2w REG 252,0 0 0 28836028 /var/log/zabbix-agent/zabbix_agentd.log.1 (deleted) I rebooted to see if fsck does anything. But, from /var/log/boot.log, it seems there are no issues: /dev/mapper/server-root: clean, 3936097/32768000 files, 125368568/131064832 blocks Thinking maybe someone overzealously reserved root space, I checked the master record: # tune2fs -l /dev/mapper/server-root tune2fs 1.42 (29-Nov-2011) Filesystem volume name: <none> Last mounted on: / Filesystem UUID: 86430ade-cea7-46ce-979c-41769a41ecbe Filesystem magic number: 0xEF53 Filesystem revision #: 1 (dynamic) Filesystem features: has_journal ext_attr resize_inode dir_index filetype needs_recovery extent flex_bg sparse_super large_file huge_file uninit_bg dir_nlink extra_isize Filesystem flags: signed_directory_hash Default mount options: user_xattr acl Filesystem state: clean Errors behavior: Continue Filesystem OS type: Linux Inode count: 32768000 Block count: 131064832 Reserved block count: 6553241 Free blocks: 5696264 Free inodes: 28831903 First block: 0 Block size: 4096 Fragment size: 4096 Reserved GDT blocks: 992 Blocks per group: 32768 Fragments per group: 32768 Inodes per group: 8192 Inode blocks per group: 512 Flex block group size: 16 Filesystem created: Fri Feb 1 13:44:04 2013 Last mount time: Tue Aug 19 16:56:13 2014 Last write time: Fri Feb 1 13:51:28 2013 Mount count: 9 Maximum mount count: -1 Last checked: Fri Feb 1 13:44:04 2013 Check interval: 0 (<none>) Lifetime writes: 1215 GB Reserved blocks uid: 0 (user root) Reserved blocks gid: 0 (group root) First inode: 11 Inode size: 256 Required extra isize: 28 Desired extra isize: 28 Journal inode: 8 First orphan inode: 28836028 Default directory hash: half_md4 Directory Hash Seed: bca55ff5-f530-48d1-8347-25c004f66d43 Journal backup: inode blocks The system is: # uname -a Linux server 3.2.0-67-generic #101-Ubuntu SMP Tue Jul 15 17:46:11 UTC 2014 x86_64 x86_64 x86_64 GNU/Linux # cat /etc/lsb-release DISTRIB_ID=Ubuntu DISTRIB_RELEASE=12.04 DISTRIB_CODENAME=precise DISTRIB_DESCRIPTION="Ubuntu 12.04.2 LTS" Does anyone have any tips on what exactly to do to find and hopefully reclaim the missing space?

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