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  • Oracle Solaris 11 ZFS Lab for Openworld 2012

    - by user12626122
    Preface This is the content from the Oracle Openworld 2012 ZFS lab. It was well attended - the feedback was that it was a little short - thats probably because in writing it I bacame very time-concious after the ASM/ACFS on Solaris extravaganza I ran last year which was almost too long for mortal man to finish in the 1 hour session. Enjoy. Table of Contents Exercise Z.1: ZFS Pools Exercise Z.2: ZFS File Systems Exercise Z.3: ZFS Compression Exercise Z.4: ZFS Deduplication Exercise Z.5: ZFS Encryption Exercise Z.6: Solaris 11 Shadow Migration Introduction This set of exercises is designed to briefly demonstrate new features in Solaris 11 ZFS file system: Deduplication, Encryption and Shadow Migration. Also included is the creation of zpools and zfs file systems - the basic building blocks of the technology, and also Compression which is the compliment of Deduplication. The exercises are just introductions - you are referred to the ZFS Adminstration Manual for further information. From Solaris 11 onward the online manual pages consist of zpool(1M) and zfs(1M) with further feature-specific information in zfs_allow(1M), zfs_encrypt(1M) and zfs_share(1M). The lab is easily carried out in a VirtualBox running Solaris 11 with 6 virtual 3 Gb disks to play with. Exercise Z.1: ZFS Pools Task: You have several disks to use for your new file system. Create a new zpool and a file system within it. Lab: You will check the status of existing zpools, create your own pool and expand it. Your Solaris 11 installation already has a root ZFS pool. It contains the root file system. Check this: root@solaris:~# zpool list NAME SIZE ALLOC FREE CAP DEDUP HEALTH ALTROOT rpool 15.9G 6.62G 9.25G 41% 1.00x ONLINE - root@solaris:~# zpool status pool: rpool state: ONLINE scan: none requested config: NAME STATE READ WRITE CKSUM rpool ONLINE 0 0 0 c3t0d0s0 ONLINE 0 0 0 errors: No known data errors Note the disk device the root pool is on - c3t0d0s0 Now you will create your own ZFS pool. First you will check what disks are available: root@solaris:~# echo | format Searching for disks...done AVAILABLE DISK SELECTIONS: 0. c3t0d0 <ATA-VBOX HARDDISK-1.0 cyl 2085 alt 2 hd 255 sec 63> /pci@0,0/pci8086,2829@d/disk@0,0 1. c3t2d0 <ATA-VBOX HARDDISK-1.0 cyl 1534 alt 2 hd 128 sec 32> /pci@0,0/pci8086,2829@d/disk@2,0 2. c3t3d0 <ATA-VBOX HARDDISK-1.0 cyl 1534 alt 2 hd 128 sec 32> /pci@0,0/pci8086,2829@d/disk@3,0 3. c3t4d0 <ATA-VBOX HARDDISK-1.0 cyl 1534 alt 2 hd 128 sec 32> /pci@0,0/pci8086,2829@d/disk@4,0 4. c3t5d0 <ATA-VBOX HARDDISK-1.0 cyl 1534 alt 2 hd 128 sec 32> /pci@0,0/pci8086,2829@d/disk@5,0 5. c3t6d0 <ATA-VBOX HARDDISK-1.0 cyl 1534 alt 2 hd 128 sec 32> /pci@0,0/pci8086,2829@d/disk@6,0 6. c3t7d0 <ATA-VBOX HARDDISK-1.0 cyl 1534 alt 2 hd 128 sec 32> /pci@0,0/pci8086,2829@d/disk@7,0 Specify disk (enter its number): Specify disk (enter its number): The root disk is numbered 0. The others are free for use. Try creating a simple pool and observe the error message: root@solaris:~# zpool create mypool c3t2d0 c3t3d0 'mypool' successfully created, but with no redundancy; failure of one device will cause loss of the pool So destroy that pool and create a mirrored pool instead: root@solaris:~# zpool destroy mypool root@solaris:~# zpool create mypool mirror c3t2d0 c3t3d0 root@solaris:~# zpool status mypool pool: mypool state: ONLINE scan: none requested config: NAME STATE READ WRITE CKSUM mypool ONLINE 0 0 0 mirror-0 ONLINE 0 0 0 c3t2d0 ONLINE 0 0 0 c3t3d0 ONLINE 0 0 0 errors: No known data errors Back to topExercise Z.2: ZFS File Systems Task: You have to create file systems for later exercises. You can see that when a pool is created, a file system of the same name is created: root@solaris:~# zfs list NAME USED AVAIL REFER MOUNTPOINT mypool 86.5K 2.94G 31K /mypool Create your filesystems and mountpoints as follows: root@solaris:~# zfs create -o mountpoint=/data1 mypool/mydata1 The -o option sets the mount point and automatically creates the necessary directory. root@solaris:~# zfs list mypool/mydata1 NAME USED AVAIL REFER MOUNTPOINT mypool/mydata1 31K 2.94G 31K /data1 Back to top Exercise Z.3: ZFS Compression Task:Try out different forms of compression available in ZFS Lab:Create 2nd filesystem with compression, fill both file systems with the same data, observe results You can see from the zfs(1) manual page that there are several types of compression available to you, set with the property=value syntax: compression=on | off | lzjb | gzip | gzip-N | zle Controls the compression algorithm used for this dataset. The lzjb compression algorithm is optimized for performance while providing decent data compression. Setting compression to on uses the lzjb compression algorithm. The gzip compression algorithm uses the same compression as the gzip(1) command. You can specify the gzip level by using the value gzip-N where N is an integer from 1 (fastest) to 9 (best compression ratio). Currently, gzip is equivalent to gzip-6 (which is also the default for gzip(1)). Create a second filesystem with compression turned on. Note how you set and get your values separately: root@solaris:~# zfs create -o mountpoint=/data2 mypool/mydata2 root@solaris:~# zfs set compression=gzip-9 mypool/mydata2 root@solaris:~# zfs get compression mypool/mydata1 NAME PROPERTY VALUE SOURCE mypool/mydata1 compression off default root@solaris:~# zfs get compression mypool/mydata2 NAME PROPERTY VALUE SOURCE mypool/mydata2 compression gzip-9 local Now you can copy the contents of /usr/lib into both your normal and compressing filesystem and observe the results. Don't forget the dot or period (".") in the find(1) command below: root@solaris:~# cd /usr/lib root@solaris:/usr/lib# find . -print | cpio -pdv /data1 root@solaris:/usr/lib# find . -print | cpio -pdv /data2 The copy into the compressing file system takes longer - as it has to perform the compression but the results show the effect: root@solaris:/usr/lib# zfs list NAME USED AVAIL REFER MOUNTPOINT mypool 1.35G 1.59G 31K /mypool mypool/mydata1 1.01G 1.59G 1.01G /data1 mypool/mydata2 341M 1.59G 341M /data2 Note that the available space in the pool is shared amongst the file systems. This behavior can be modified using quotas and reservations which are not covered in this lab but are covered extensively in the ZFS Administrators Guide. Back to top Exercise Z.4: ZFS Deduplication The deduplication property is used to remove redundant data from a ZFS file system. With the property enabled duplicate data blocks are removed synchronously. The result is that only unique data is stored and common componenents are shared. Task:See how to implement deduplication and its effects Lab: You will create a ZFS file system with deduplication turned on and see if it reduces the amount of physical storage needed when we again fill it with a copy of /usr/lib. root@solaris:/usr/lib# zfs destroy mypool/mydata2 root@solaris:/usr/lib# zfs set dedup=on mypool/mydata1 root@solaris:/usr/lib# rm -rf /data1/* root@solaris:/usr/lib# mkdir /data1/2nd-copy root@solaris:/usr/lib# zfs list NAME USED AVAIL REFER MOUNTPOINT mypool 1.02M 2.94G 31K /mypool mypool/mydata1 43K 2.94G 43K /data1 root@solaris:/usr/lib# find . -print | cpio -pd /data1 2142768 blocks root@solaris:/usr/lib# zfs list NAME USED AVAIL REFER MOUNTPOINT mypool 1.02G 1.99G 31K /mypool mypool/mydata1 1.01G 1.99G 1.01G /data1 root@solaris:/usr/lib# find . -print | cpio -pd /data1/2nd-copy 2142768 blocks root@solaris:/usr/lib#zfs list NAME USED AVAIL REFER MOUNTPOINT mypool 1.99G 1.96G 31K /mypool mypool/mydata1 1.98G 1.96G 1.98G /data1 You could go on creating copies for quite a while...but you get the idea. Note that deduplication and compression can be combined: the compression acts on metadata. Deduplication works across file systems in a pool and there is a zpool-wide property dedupratio: root@solaris:/usr/lib# zpool get dedupratio mypool NAME PROPERTY VALUE SOURCE mypool dedupratio 4.30x - Deduplication can also be checked using "zpool list": root@solaris:/usr/lib# zpool list NAME SIZE ALLOC FREE CAP DEDUP HEALTH ALTROOT mypool 2.98G 1001M 2.01G 32% 4.30x ONLINE - rpool 15.9G 6.66G 9.21G 41% 1.00x ONLINE - Before moving on to the next topic, destroy that dataset and free up some space: root@solaris:~# zfs destroy mypool/mydata1 Back to top Exercise Z.5: ZFS Encryption Task: Encrypt sensitive data. Lab: Explore basic ZFS encryption. This lab only covers the basics of ZFS Encryption. In particular it does not cover various aspects of key management. Please see the ZFS Adminastrion Manual and the zfs_encrypt(1M) manual page for more detail on this functionality. Back to top root@solaris:~# zfs create -o encryption=on mypool/data2 Enter passphrase for 'mypool/data2': ******** Enter again: ******** root@solaris:~# Creation of a descendent dataset shows that encryption is inherited from the parent: root@solaris:~# zfs create mypool/data2/data3 root@solaris:~# zfs get -r encryption,keysource,keystatus,checksum mypool/data2 NAME PROPERTY VALUE SOURCE mypool/data2 encryption on local mypool/data2 keysource passphrase,prompt local mypool/data2 keystatus available - mypool/data2 checksum sha256-mac local mypool/data2/data3 encryption on inherited from mypool/data2 mypool/data2/data3 keysource passphrase,prompt inherited from mypool/data2 mypool/data2/data3 keystatus available - mypool/data2/data3 checksum sha256-mac inherited from mypool/data2 You will find the online manual page zfs_encrypt(1M) contains examples. In particular, if time permits during this lab session you may wish to explore the changing of a key using "zfs key -c mypool/data2". Exercise Z.6: Shadow Migration Shadow Migration allows you to migrate data from an old file system to a new file system while simultaneously allowing access and modification to the new file system during the process. You can use Shadow Migration to migrate a local or remote UFS or ZFS file system to a local file system. Task: You wish to migrate data from one file system (UFS, ZFS, VxFS) to ZFS while mainaining access to it. Lab: Create the infrastructure for shadow migration and transfer one file system into another. First create the file system you want to migrate root@solaris:~# zpool create oldstuff c3t4d0 root@solaris:~# zfs create oldstuff/forgotten Then populate it with some files: root@solaris:~# cd /var/adm root@solaris:/var/adm# find . -print | cpio -pdv /oldstuff/forgotten You need the shadow-migration package installed: root@solaris:~# pkg install shadow-migration Packages to install: 1 Create boot environment: No Create backup boot environment: No Services to change: 1 DOWNLOAD PKGS FILES XFER (MB) Completed 1/1 14/14 0.2/0.2 PHASE ACTIONS Install Phase 39/39 PHASE ITEMS Package State Update Phase 1/1 Image State Update Phase 2/2 You then enable the shadowd service: root@solaris:~# svcadm enable shadowd root@solaris:~# svcs shadowd STATE STIME FMRI online 7:16:09 svc:/system/filesystem/shadowd:default Set the filesystem to be migrated to read-only root@solaris:~# zfs set readonly=on oldstuff/forgotten Create a new zfs file system with the shadow property set to the file system to be migrated: root@solaris:~# zfs create -o shadow=file:///oldstuff/forgotten mypool/remembered Use the shadowstat(1M) command to see the progress of the migration: root@solaris:~# shadowstat EST BYTES BYTES ELAPSED DATASET XFRD LEFT ERRORS TIME mypool/remembered 92.5M - - 00:00:59 mypool/remembered 99.1M 302M - 00:01:09 mypool/remembered 109M 260M - 00:01:19 mypool/remembered 133M 304M - 00:01:29 mypool/remembered 149M 339M - 00:01:39 mypool/remembered 156M 86.4M - 00:01:49 mypool/remembered 156M 8E 29 (completed) Note that if you had created /mypool/remembered as encrypted, this would be the preferred method of encrypting existing data. Similarly for compressing or deduplicating existing data. The procedure for migrating a file system over NFS is similar - see the ZFS Administration manual. That concludes this lab session.

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  • SPARC T4-4 Beats 8-CPU IBM POWER7 on TPC-H @3000GB Benchmark

    - by Brian
    Oracle's SPARC T4-4 server delivered a world record TPC-H @3000GB benchmark result for systems with four processors. This result beats eight processor results from IBM (POWER7) and HP (x86). The SPARC T4-4 server also delivered better performance per core than these eight processor systems from IBM and HP. Comparisons below are based upon system to system comparisons, highlighting Oracle's complete software and hardware solution. This database world record result used Oracle's Sun Storage 2540-M2 arrays (rotating disk) connected to a SPARC T4-4 server running Oracle Solaris 11 and Oracle Database 11g Release 2 demonstrating the power of Oracle's integrated hardware and software solution. The SPARC T4-4 server based configuration achieved a TPC-H scale factor 3000 world record for four processor systems of 205,792 QphH@3000GB with price/performance of $4.10/QphH@3000GB. The SPARC T4-4 server with four SPARC T4 processors (total of 32 cores) is 7% faster than the IBM Power 780 server with eight POWER7 processors (total of 32 cores) on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 36% better in price performance compared to the IBM Power 780 server on the TPC-H @3000GB Benchmark. The SPARC T4-4 server is 29% faster than the IBM Power 780 for data loading. The SPARC T4-4 server is up to 3.4 times faster than the IBM Power 780 server for the Refresh Function. The SPARC T4-4 server with four SPARC T4 processors is 27% faster than the HP ProLiant DL980 G7 server with eight x86 processors on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 52% faster than the HP ProLiant DL980 G7 server for data loading. The SPARC T4-4 server is up to 3.2 times faster than the HP ProLiant DL980 G7 for the Refresh Function. The SPARC T4-4 server achieved a peak IO rate from the Oracle database of 17 GB/sec. This rate was independent of the storage used, as demonstrated by the TPC-H @3000TB benchmark which used twelve Sun Storage 2540-M2 arrays (rotating disk) and the TPC-H @1000TB benchmark which used four Sun Storage F5100 Flash Array devices (flash storage). [*] The SPARC T4-4 server showed linear scaling from TPC-H @1000GB to TPC-H @3000GB. This demonstrates that the SPARC T4-4 server can handle the increasingly larger databases required of DSS systems. [*] The SPARC T4-4 server benchmark results demonstrate a complete solution of building Decision Support Systems including data loading, business questions and refreshing data. Each phase usually has a time constraint and the SPARC T4-4 server shows superior performance during each phase. [*] The TPC believes that comparisons of results published with different scale factors are misleading and discourages such comparisons. Performance Landscape The table lists the leading TPC-H @3000GB results for non-clustered systems. TPC-H @3000GB, Non-Clustered Systems System Processor P/C/T – Memory Composite(QphH) $/perf($/QphH) Power(QppH) Throughput(QthH) Database Available SPARC Enterprise M9000 3.0 GHz SPARC64 VII+ 64/256/256 – 1024 GB 386,478.3 $18.19 316,835.8 471,428.6 Oracle 11g R2 09/22/11 SPARC T4-4 3.0 GHz SPARC T4 4/32/256 – 1024 GB 205,792.0 $4.10 190,325.1 222,515.9 Oracle 11g R2 05/31/12 SPARC Enterprise M9000 2.88 GHz SPARC64 VII 32/128/256 – 512 GB 198,907.5 $15.27 182,350.7 216,967.7 Oracle 11g R2 12/09/10 IBM Power 780 4.1 GHz POWER7 8/32/128 – 1024 GB 192,001.1 $6.37 210,368.4 175,237.4 Sybase 15.4 11/30/11 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64/128 – 512 GB 162,601.7 $2.68 185,297.7 142,685.6 SQL Server 2008 10/13/10 P/C/T = Processors, Cores, Threads QphH = the Composite Metric (bigger is better) $/QphH = the Price/Performance metric in USD (smaller is better) QppH = the Power Numerical Quantity QthH = the Throughput Numerical Quantity The following table lists data load times and refresh function times during the power run. TPC-H @3000GB, Non-Clustered Systems Database Load & Database Refresh System Processor Data Loading(h:m:s) T4Advan RF1(sec) T4Advan RF2(sec) T4Advan SPARC T4-4 3.0 GHz SPARC T4 04:08:29 1.0x 67.1 1.0x 39.5 1.0x IBM Power 780 4.1 GHz POWER7 05:51:50 1.5x 147.3 2.2x 133.2 3.4x HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 08:35:17 2.1x 173.0 2.6x 126.3 3.2x Data Loading = database load time RF1 = power test first refresh transaction RF2 = power test second refresh transaction T4 Advan = the ratio of time to T4 time Complete benchmark results found at the TPC benchmark website http://www.tpc.org. Configuration Summary and Results Hardware Configuration: SPARC T4-4 server 4 x SPARC T4 3.0 GHz processors (total of 32 cores, 128 threads) 1024 GB memory 8 x internal SAS (8 x 300 GB) disk drives External Storage: 12 x Sun Storage 2540-M2 array storage, each with 12 x 15K RPM 300 GB drives, 2 controllers, 2 GB cache Software Configuration: Oracle Solaris 11 11/11 Oracle Database 11g Release 2 Enterprise Edition Audited Results: Database Size: 3000 GB (Scale Factor 3000) TPC-H Composite: 205,792.0 QphH@3000GB Price/performance: $4.10/QphH@3000GB Available: 05/31/2012 Total 3 year Cost: $843,656 TPC-H Power: 190,325.1 TPC-H Throughput: 222,515.9 Database Load Time: 4:08:29 Benchmark Description The TPC-H benchmark is a performance benchmark established by the Transaction Processing Council (TPC) to demonstrate Data Warehousing/Decision Support Systems (DSS). TPC-H measurements are produced for customers to evaluate the performance of various DSS systems. These queries and updates are executed against a standard database under controlled conditions. Performance projections and comparisons between different TPC-H Database sizes (100GB, 300GB, 1000GB, 3000GB, 10000GB, 30000GB and 100000GB) are not allowed by the TPC. TPC-H is a data warehousing-oriented, non-industry-specific benchmark that consists of a large number of complex queries typical of decision support applications. It also includes some insert and delete activity that is intended to simulate loading and purging data from a warehouse. TPC-H measures the combined performance of a particular database manager on a specific computer system. The main performance metric reported by TPC-H is called the TPC-H Composite Query-per-Hour Performance Metric (QphH@SF, where SF is the number of GB of raw data, referred to as the scale factor). QphH@SF is intended to summarize the ability of the system to process queries in both single and multiple user modes. The benchmark requires reporting of price/performance, which is the ratio of the total HW/SW cost plus 3 years maintenance to the QphH. A secondary metric is the storage efficiency, which is the ratio of total configured disk space in GB to the scale factor. Key Points and Best Practices Twelve Sun Storage 2540-M2 arrays were used for the benchmark. Each Sun Storage 2540-M2 array contains 12 15K RPM drives and is connected to a single dual port 8Gb FC HBA using 2 ports. Each Sun Storage 2540-M2 array showed 1.5 GB/sec for sequential read operations and showed linear scaling, achieving 18 GB/sec with twelve Sun Storage 2540-M2 arrays. These were stand alone IO tests. The peak IO rate measured from the Oracle database was 17 GB/sec. Oracle Solaris 11 11/11 required very little system tuning. Some vendors try to make the point that storage ratios are of customer concern. However, storage ratio size has more to do with disk layout and the increasing capacities of disks – so this is not an important metric in which to compare systems. The SPARC T4-4 server and Oracle Solaris efficiently managed the system load of over one thousand Oracle Database parallel processes. Six Sun Storage 2540-M2 arrays were mirrored to another six Sun Storage 2540-M2 arrays on which all of the Oracle database files were placed. IO performance was high and balanced across all the arrays. The TPC-H Refresh Function (RF) simulates periodical refresh portion of Data Warehouse by adding new sales and deleting old sales data. Parallel DML (parallel insert and delete in this case) and database log performance are a key for this function and the SPARC T4-4 server outperformed both the IBM POWER7 server and HP ProLiant DL980 G7 server. (See the RF columns above.) See Also Transaction Processing Performance Council (TPC) Home Page Ideas International Benchmark Page SPARC T4-4 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Sun Storage 2540-M2 Array oracle.com OTN Disclosure Statement TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org. SPARC T4-4 205,792.0 QphH@3000GB, $4.10/QphH@3000GB, available 5/31/12, 4 processors, 32 cores, 256 threads; IBM Power 780 QphH@3000GB, 192,001.1 QphH@3000GB, $6.37/QphH@3000GB, available 11/30/11, 8 processors, 32 cores, 128 threads; HP ProLiant DL980 G7 162,601.7 QphH@3000GB, $2.68/QphH@3000GB available 10/13/10, 8 processors, 64 cores, 128 threads.

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  • How to stream H264 Video from camera over FTP?

    - by Jay
    I bought a h264 security camera system last year and set it up to ftp video to my computer. I was able to get the video to play (even though it played a little fast) on Ubuntu 11.04 using mplayer. A few months ago, I did a fresh install of 12.04 and I cannot seem to get the video to play with mplayer, smplayer or VLC. I have the restricted formats video packages installed and when playing with any of the players, all I get is a gray video. When calling mplayer from the command line to play the video with no options, I get a lot of these errors: [h264 @ 0x7f278c61f280]concealing 1320 DC, 1320 AC, 1320 MV errors No pts value from demuxer to use for frame! pts after filters MISSING I'm not a video expert and have been coming up with a lot of dead ends when Googling for this. Could someone offer some advice about how to play these videos? Here is the output of mediainfo for a sample file. mediainfo -f sec-cam01-m-20120921-212454.h264 General Count : 278 Count of stream of this kind : 1 Kind of stream : General Kind of stream : General Stream identifier : 0 Count of video streams : 1 Video_Format_List : AVC Video_Format_WithHint_List : AVC Codecs Video : AVC Complete name : sec-cam01-m-20120921-212454.h264 File name : sec-cam01-m-20120921-212454 File extension : h264 Format : AVC Format : AVC Format/Info : Advanced Video Codec Format/Url : http://developers.videolan.org/x264.html Format/Extensions usually used : avc h264 Commercial name : AVC Internet media type : video/H264 Codec : AVC Codec : AVC Codec/Info : Advanced Video Codec Codec/Url : http://developers.videolan.org/x264.html Codec/Extensions usually used : avc h264 File size : 1097315 File size : 1.05 MiB File size : 1 MiB File size : 1.0 MiB File size : 1.05 MiB File size : 1.046 MiB File last modification date : UTC 2012-09-22 01:27:12 File last modification date (local) : 2012-09-21 21:27:12 Video Count : 205 Count of stream of this kind : 1 Kind of stream : Video Kind of stream : Video Stream identifier : 0 Format : AVC Format/Info : Advanced Video Codec Format/Url : http://developers.videolan.org/x264.html Commercial name : AVC Format profile : [email protected] Format settings : 1 Ref Frames Format settings, CABAC : No Format settings, CABAC : No Format settings, ReFrames : 1 Format settings, ReFrames : 1 frame Format settings, GOP : M=1, N=3 Internet media type : video/H264 Codec : AVC Codec : AVC Codec/Family : AVC Codec/Info : Advanced Video Codec Codec/Url : http://developers.videolan.org/x264.html Codec profile : [email protected] Codec settings : 1 Ref Frames Codec settings, CABAC : No Codec_Settings_RefFrames : 1 Width : 704 Width : 704 pixels Height : 480 Height : 480 pixels Pixel aspect ratio : 1.000 Display aspect ratio : 1.467 Display aspect ratio : 3:2 Standard : NTSC Resolution : 8 Resolution : 8 bits Colorimetry : 4:2:0 Color space : YUV Chroma subsampling : 4:2:0 Bit depth : 8 Bit depth : 8 bits Scan type : Progressive Scan type : Progressive Interlacement : PPF Interlacement : Progressive Edit: Here is a sample video using the same encoding: https://www.dropbox.com/s/l5acwzy8rtqn9xe/sec-cam08-m-20121118-105815.h264 (not the same video as mediainfo output)

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  • Improved Performance on PeopleSoft Combined Benchmark using SPARC T4-4

    - by Brian
    Oracle's SPARC T4-4 server running Oracle's PeopleSoft HCM 9.1 combined online and batch benchmark achieved a world record 18,000 concurrent users experiencing subsecond response time while executing a PeopleSoft Payroll batch job of 500,000 employees in 32.4 minutes. This result was obtained with a SPARC T4-4 server running Oracle Database 11g Release 2, a SPARC T4-4 server running PeopleSoft HCM 9.1 application server and a SPARC T4-2 server running Oracle WebLogic Server in the web tier. The SPARC T4-4 server running the application tier used Oracle Solaris Zones which provide a flexible, scalable and manageable virtualization environment. The average CPU utilization on the SPARC T4-2 server in the web tier was 17%, on the SPARC T4-4 server in the application tier it was 59%, and on the SPARC T4-4 server in the database tier was 47% (online and batch) leaving significant headroom for additional processing across the three tiers. The SPARC T4-4 server used for the database tier hosted Oracle Database 11g Release 2 using Oracle Automatic Storage Management (ASM) for database files management with I/O performance equivalent to raw devices. Performance Landscape Results are presented for the PeopleSoft HRMS Self-Service and Payroll combined benchmark. The new result with 128 streams shows significant improvement in the payroll batch processing time with little impact on the self-service component response time. PeopleSoft HRMS Self-Service and Payroll Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-4 (db) 18,000 0.988 0.539 32.4 128 SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-4 (db) 18,000 0.944 0.503 43.3 64 The following results are for the PeopleSoft HRMS Self-Service benchmark that was previous run. The results are not directly comparable with the combined results because they do not include the payroll component. PeopleSoft HRMS Self-Service 9.1 Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) 2x SPARC T4-2 (db) 18,000 1.048 0.742 N/A N/A The following results are for the PeopleSoft Payroll benchmark that was previous run. The results are not directly comparable with the combined results because they do not include the self-service component. PeopleSoft Payroll (N.A.) 9.1 - 500K Employees (7 Million SQL PayCalc, Unicode) Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-4 (db) N/A N/A N/A 30.84 96 Configuration Summary Application Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 512 GB memory Oracle Solaris 11 11/11 PeopleTools 8.52 PeopleSoft HCM 9.1 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Java Platform, Standard Edition Development Kit 6 Update 32 Database Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 256 GB memory Oracle Solaris 11 11/11 Oracle Database 11g Release 2 PeopleTools 8.52 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Micro Focus Server Express (COBOL v 5.1.00) Web Tier Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory Oracle Solaris 11 11/11 PeopleTools 8.52 Oracle WebLogic Server 10.3.4 Java Platform, Standard Edition Development Kit 6 Update 32 Storage Configuration: 1 x Sun Server X2-4 as a COMSTAR head for data 4 x Intel Xeon X7550, 2.0 GHz 128 GB memory 1 x Sun Storage F5100 Flash Array (80 flash modules) 1 x Sun Storage F5100 Flash Array (40 flash modules) 1 x Sun Fire X4275 as a COMSTAR head for redo logs 12 x 2 TB SAS disks with Niwot Raid controller Benchmark Description This benchmark combines PeopleSoft HCM 9.1 HR Self Service online and PeopleSoft Payroll batch workloads to run on a unified database deployed on Oracle Database 11g Release 2. The PeopleSoft HRSS benchmark kit is a Oracle standard benchmark kit run by all platform vendors to measure the performance. It's an OLTP benchmark where DB SQLs are moderately complex. The results are certified by Oracle and a white paper is published. PeopleSoft HR SS defines a business transaction as a series of HTML pages that guide a user through a particular scenario. Users are defined as corporate Employees, Managers and HR administrators. The benchmark consist of 14 scenarios which emulate users performing typical HCM transactions such as viewing paycheck, promoting and hiring employees, updating employee profile and other typical HCM application transactions. All these transactions are well-defined in the PeopleSoft HR Self-Service 9.1 benchmark kit. This benchmark metric is the weighted average response search/save time for all the transactions. The PeopleSoft 9.1 Payroll (North America) benchmark demonstrates system performance for a range of processing volumes in a specific configuration. This workload represents large batch runs typical of a ERP environment during a mass update. The benchmark measures five application business process run times for a database representing large organization. They are Paysheet Creation, Payroll Calculation, Payroll Confirmation, Print Advice forms, and Create Direct Deposit File. The benchmark metric is the cumulative elapsed time taken to complete the Paysheet Creation, Payroll Calculation and Payroll Confirmation business application processes. The benchmark metrics are taken for each respective benchmark while running simultaneously on the same database back-end. Specifically, the payroll batch processes are started when the online workload reaches steady state (the maximum number of online users) and overlap with online transactions for the duration of the steady state. Key Points and Best Practices Two PeopleSoft Domain sets with 200 application servers each on a SPARC T4-4 server were hosted in 2 separate Oracle Solaris Zones to demonstrate consolidation of multiple application servers, ease of administration and performance tuning. Each Oracle Solaris Zone was bound to a separate processor set, each containing 15 cores (total 120 threads). The default set (1 core from first and third processor socket, total 16 threads) was used for network and disk interrupt handling. This was done to improve performance by reducing memory access latency by using the physical memory closest to the processors and offload I/O interrupt handling to default set threads, freeing up cpu resources for Application Servers threads and balancing application workload across 240 threads. A total of 128 PeopleSoft streams server processes where used on the database node to complete payroll batch job of 500,000 employees in 32.4 minutes. See Also Oracle PeopleSoft Benchmark White Papers oracle.com SPARC T4-2 Server oracle.com OTN SPARC T4-4 Server oracle.com OTN PeopleSoft Enterprise Human Capital Managementoracle.com OTN PeopleSoft Enterprise Human Capital Management (Payroll) oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 8 November 2012.

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  • World Record Oracle E-Business Consolidated Workload on SPARC T4-2

    - by Brian
    Oracle set a World Record for the Oracle E-Business Suite Standard Medium multiple-online module benchmark using Oracle's SPARC T4-2 and SPARC T4-4 servers which ran the application and database. Oracle's SPARC T4 servers demonstrate performance leadership and world-record results on Oracle E-Business Suite Applications R12 OLTP benchmark by publishing the first result using multiple concurrent online application modules with Oracle Database 11g Release 2 running Solaris.   This results shows that a multi-tier configuration of SPARC T4 servers running the Oracle E-Business Suite R12.1.2 application and Oracle Database 11g Release 2 is capable of supporting 4,100 online users with outstanding response-times, executing a mix of complex transactions consolidating 4 Oracle E-Business modules (iProcurement, Order Management, Customer Service and HR Self-Service).   The SPARC T4-2 server in the application tier utilized about 65% and the SPARC T4-4 server in the database tier utilized about 30%, providing significant headroom for additional Oracle E-Business Suite R12.1.2 processing modules, more online users, and future growth.   Oracle E-Business Suite Applications were run in Oracle Solaris Containers on SPARC T4 servers and provides a consolidation platform for multiple E-Business instances.   Performance Landscape Multiple Online Modules (Self-Service, Order-Management, iProcurement, Customer-Service) Medium Configuration System Users AverageResponse Time 90th PercentileResponse Time SPARC T4-2 4,100 2.08 sec 2.52 sec Configuration Summary Application Tier Configuration: 1 x SPARC T4-2 server 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 3 x 300 GB internal disks Oracle Solaris 10 Oracle E-Business Suite 12.1.2 Database Tier Configuration: 1 x SPARC T4-4 server 4 x SPARC T4 processors, 3.0 GHz 256 GB memory 2 x 300 GB internal disks Oracle Solaris 10 Oracle Solaris Containers Oracle Database 11g Release 2 Storage Configuration: 1 x Sun Storage F5100 Flash Array (80 x 24 GB flash modules) Benchmark Description The Oracle R12 E-Business Suite Standard Benchmark combines online transaction execution by simulated users with multiple online concurrent modules to model a typical scenario for a global enterprise. The online component exercises the common UI flows which are most frequently used by a majority of our customers. This benchmark utilized four concurrent flows of OLTP transactions, for Order to Cash, iProcurement, Customer Service and HR Self-Service and measured the response times. The selected flows model simultaneous business activities inclusive of managing customers, services, products and employees. See Also Oracle R12 E-Business Suite Standard Benchmark Results Oracle R12 E-Business Suite Standard Benchmark Overview Oracle R12 E-Business Benchmark Description E-Business Suite Applications R2 (R12.1.2) Online Benchmark - Using Oracle Database 11g on Oracle's SPARC T4-2 and Oracle's SPARC T4-4 Servers oracle.com SPARC T4-2 Server oracle.com OTN SPARC T4-4 Server oracle.com OTN Oracle E-Business Suite oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Oracle E-Business Suite R12 medium multiple-online module benchmark, SPARC T4-2, SPARC T4, 2.85 GHz, 2 chips, 16 cores, 128 threads, 256 GB memory, SPARC T4-4, SPARC T4, 3.0 GHz, 4 chips, 32 cores, 256 threads, 256 GB memory, average response time 2.08 sec, 90th percentile response time 2.52 sec, Oracle Solaris 10, Oracle Solaris Containers, Oracle E-Business Suite 12.1.2, Oracle Database 11g Release 2, Results as of 9/30/2012.

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  • Why would more CPU cores on virtual machine slow compile times?

    - by Sid
    [edit#2] If anyone from VMWare can hit me up with a copy of VMWare Fusion, I'd be more than happy to do the same as a VirtualBox vs VMWare comparison. Somehow I suspect the VMWare hypervisor will be better tuned for hyperthreading (see my answer too) I'm seeing something curious. As I increase the number of cores on my Windows 7 x64 virtual machine, the overall compile time increases instead of decreasing. Compiling is usually very well suited for parallel processing as in the middle part (post dependency mapping) you can simply call a compiler instance on each of your .c/.cpp/.cs/whatever file to build partial objects for the linker to take over. So I would have imagined that compiling would actually scale very well with # of cores. But what I'm seeing is: 8 cores: 1.89 sec 4 cores: 1.33 sec 2 cores: 1.24 sec 1 core: 1.15 sec Is this simply a design artifact due to a particular vendor's hypervisor implementation (type2:virtualbox in my case) or something more pervasive across more VMs to make hypervisor implementations more simpler? With so many factors, I seem to be able to make arguments both for and against this behavior - so if someone knows more about this than me, I'd be curious to read your answer. Thanks Sid [edit:addressing comments] @MartinBeckett: Cold compiles were discarded. @MonsterTruck: Couldn't find an opensource project to compile directly. Would be great but can't screwup my dev env right now. @Mr Lister, @philosodad: Have 8 hw threads, using VirtualBox, so should be 1:1 mapping without emulation @Thorbjorn: I have 6.5GB for the VM and a smallish VS2012 project - it's quite unlikely that I'm swapping in/out trashing the page file. @All: If someone can point to an open source VS2010/VS2012 project, that might be a better community reference than my (proprietary) VS2012 project. Orchard and DNN seem to need environment tweaking to compile in VS2012. I really would like to see if someone with VMWare Fusion also sees this (for VMWare vs VirtualBox compartmentalization) Test details: Hardware: Macbook Pro Retina CPU : Core i7 @ 2.3Ghz (quad core, hyper threaded = 8 cores in windows task manager) Memory : 16 GB Disk : 256GB SSD Host OS: Mac OS X 10.8 VM type: VirtualBox 4.1.18 (type 2 hypervisor) Guest OS: Windows 7 x64 SP1 Compiler: VS2012 compiling a solution with 3 C# Azure projects Compile times measure by VS2012 plugin called 'VSCommands' All tests run 5 times, first 2 runs discarded, last 3 averaged

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  • Seven Accounting Changes for 2010

    - by Theresa Hickman
    I read a very interesting article called Seven Accounting Changes That Will Affect Your 2010 Annual Report from SmartPros that nicely summarized how 2010 annual financial statements will be impacted.  Here’s a Reader’s Digest version of the changes: 1.  Changes to revenue recognition if you sell bundled products with multiple deliverables: Old Rule: You needed to objectively establish the “fair value” of each bundled item. So if you sold a dishwasher plus installation and could not establish the fair value of the installation, you might have to delay recognizing revenue of the dishwasher days or weeks later until it was installed. New Rule (ASU 2009-13): “Objective” proof of each service or good is no longer required; you can simply estimate the selling price of the installation and warranty. So the dishwasher vendor can recognize the dishwasher revenue immediately at the point of sale without waiting a few weeks for the installation. Then they can recognize the estimated value of the installation after it is complete. 2.  Changes to revenue recognition for devices with embedded software: Old Rule: Hardware devices with embedded software, such as the iPhone, had to follow stringent software revrec rules. This forced Apple to recognize iPhone revenues over two years, the period of time that software updates were provided. New Rule (ASU 2009-14): Software revrec rules no longer apply to these devices with embedded software; these devices can now follow ASU 2009-13. This allows vendors, such as Apple, to recognize revenue sooner. 3.  Fair value disclosures: Companies (both public and private) now need to spend extra time gathering, summarizing, and disclosing information about items measured at fair value, such as significant transfers in and out of Level 1(quoted market price), Level 2 (valuation based on observable markets), and Level 3 (valuations based on internal information). 4.  Consolidation of variable interest entities (a.k.a special purpose entities): Consolidation rules for variable interest entities now require a qualitative, not quantitative, analysis to determine the primary beneficiary. Instead of simply looking at the percentage of voting interests, the primary beneficiary could have less than the majority interests as long as it has the power to direct the activities and absorb any losses.  5.  XBRL: Starting in June 2011, all U.S. public companies are required to file financial statements to the SEC using XBRL. Note: Oracle supports XBRL reporting. 6.  Non-GAAP financial disclosures: Companies that report non-GAAP measures of performance, such as EBITDA in SEC filings, have more flexibility.  The new interpretations can be found here: http://www.sec.gov/divisions/corpfin/guidance/nongaapinterp.htm.  7.  Loss contingencies disclosures: Companies should expect additional scrutiny of their loss disclosures, such as those from litigation losses, in their annual financial statements. The SEC wants more disclosures about loss contingencies sooner instead of after the cases are settled.

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  • Bad disk performance on HP DL360 with Smarty Array P400i RAID controller

    - by sarge
    I have a HP DL360 server with 4x 146GB SAS disks and a Smart Array P400i RAID controller with 256MB cache. The disks are in RAID 5 (3 disks + 1 hot spare). The server is running VMware ESX 3i. The disk write performance is really bad. Here are some numbers: ns1:~# hdparm -tT /dev/sda /dev/sda: Timing cached reads: 3364 MB in 2.00 seconds = 1685.69 MB/sec Timing buffered disk reads: 18 MB in 3.79 seconds = 4.75 MB/sec ns1:~# time sh -c "dd if=/dev/zero of=ddfile bs=8k count=125000 && sync" 125000+0 records in 125000+0 records out 1024000000 bytes (1.0 GB) copied, 282.307 s, 3.6 MB/s real 4m52.003s user 0m2.160s sys 3m10.796s Compared to another server those number are terrible: Dell R200, 2x 500GB SATA disks, PERC raid controller (disks are mirrored). web4:~# hdparm -tT /dev/sda /dev/sda: Timing cached reads: 6584 MB in 2.00 seconds = 3297.79 MB/sec Timing buffered disk reads: 316 MB in 3.02 seconds = 104.79 MB/sec web4:~# time sh -c "dd if=/dev/zero of=ddfile bs=8k count=125000 && sync" 125000+0 records in 125000+0 records out 1024000000 bytes (1.0 GB) copied, 35.2919 s, 29.0 MB/s real 0m36.570s user 0m0.476s sys 0m32.298s The server isn't very loaded and the VMware Infrastructure Client performance monitor is showing 550KBps average read and 1208KBps average write for the last 30 minutes (highest write rate: 6.6MBps). This has been a problem from the start. Any ideas?

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  • Different versions of iperf for windows give totally different results

    - by Albert Mata
    Measuring TCP output from a Windows client to Solaris server: WXP SP3 with iperf 1.7.0 -- returns an average around 90Mbit Same client, same server but iperf 2.0.5 for windows -- returns an average of 8.5 Mbit Similar discrepancies have been observed connecting to other servers (W2008, W2003) It's difficult to get to some conclusions when different versions of the same tool provide vastly different results. Example below: C:\tempiperf -v (from iperf.fr) iperf version 2.0.5 (08 Jul 2010) pthreads C:\tempiperf -c solaris10 Client connecting to solaris10, TCP port 5001 TCP window size: 64.0 KByte (default) [ 3] local 10.172.181.159 port 2124 connected with 10.172.180.209 port 5001 [ ID] Interval Transfer Bandwidth [ 3] 0.0-10.2 sec 10.6 MBytes 8.74 Mbits/sec Abysmal perfomance, but now I test from the same host (Windows XP SP3 32bit and 100Mbit) to the same server (Solaris 10/sparc 64bit and 1Gbit running iperf 2.0.5 with default window of 48k) with the old iperf C:\temp1iperf -v iperf version 1.7.0 (13 Mar 2003) win32 threads C:\temp1iperf.exe -c solaris10 -w64k Client connecting to solaris10, TCP port 5001 TCP window size: 64.0 KByte [1208] local 10.172.181.159 port 2128 connected with 10.172.180.209 port 5001 [ ID] Interval Transfer Bandwidth [1208] 0.0-10.0 sec 112 MBytes 94.0 Mbits/sec So one iperf with a 64k window says 8.75Mbit and the old iperf with the same window size says 94.0Mbit. These results are constant through repeated tests. From my testing launching iperf(old) with window size "x" and iperf(new) with window size "x" instead of producing the same or very close results produce totally different results. The only difference I see is the old compiled as win32 threads vs. pthreads but parallelism (-P 10) appears to work in both. Anyone has a clue or can recommend a tool that gives results I can trust?? EDIT: Looking at traces from (old) iperf it sets the TCP Window Scale flag to 3 in the SYN packet, when I run the (new) iperf this is set to 0 in the initial packet. A quick analysis of the window size through the exchange shows the (old) iperf moving back and forth but mostly at 32k while the (new) iperf mostly keeps at 64k. Maybe it will help somebody to connect the dots.

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  • Comparing Nginx+PHP-FPM to Apache-mod_php

    - by Rushi
    I'm running Drupal and trying to figure out the best stack to serve it. Apache + mod_php or Nginx + PHP-FPM I used ApacheBench (ab) and Siege to test both setups and I'm seeing Apache performing better. This surprises me a little bit since I've heard a lot of good things about Nginx + PHP-FPM. My current Nginx setup is something that is a bit out of the box, and the same goes for PHP-FPM What optimizations I can make to speed up the Nginx + PHP-FPM combo over Apache and mo_php ? In my tests using ab, Apache is outperforming Nginx significantly (higher requets/second and finishing tests much faster) I've googled around a bit, but since I've never using Nginx, PHP-FPM or FastCGI, I don't exactly know where to start PHP v5.2.13, Drupal v6, latest PHP-FPM and Nginx compiled from source. Apache v2.0.63 ApacheBench Nginx + PHP-FPM Server Software: nginx/0.7.67 Server Hostname: test2.com Server Port: 80 Concurrency Level: 25 ---> Time taken for tests: 158.510008 seconds Complete requests: 1000 Failed requests: 0 Write errors: 0 ---> Requests per second: 6.31 [#/sec] (mean) Time per request: 3962.750 [ms] (mean) Time per request: 158.510 [ms] (mean, across all concurrent requests) Transfer rate: 181.38 [Kbytes/sec] received ApacheBench Apache using mod_php Server Software: Apache/2.0.63 Server Hostname: test1.com Server Port: 80 Concurrency Level: 25 --> Time taken for tests: 63.556663 seconds Complete requests: 1000 Failed requests: 0 Write errors: 0 --> Requests per second: 15.73 [#/sec] (mean) Time per request: 1588.917 [ms] (mean) Time per request: 63.557 [ms] (mean, across all concurrent requests) Transfer rate: 103.94 [Kbytes/sec] received

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  • RabbitMQ message consumers stop consuming messages

    - by Bruno Thomas
    Hi server fault, Our team is in a spike sprint to choose between ActiveMQ or RabbitMQ. We made 2 little producer/consumer spikes sending an object message with an array of 16 strings, a timestamp, and 2 integers. The spikes are ok on our devs machines (messages are well consumed). Then came the benchs. We first noticed that somtimes, on our machines, when we were sending a lot of messages the consumer was sometimes hanging. It was there, but the messsages were accumulating in the queue. When we went on the bench plateform : cluster of 2 rabbitmq machines 4 cores/3.2Ghz, 4Gb RAM, load balanced by a VIP one to 6 consumers running on the rabbitmq machines, saving the messages in a mysql DB (same type of machine for the DB) 12 producers running on 12 AS machines (tomcat), attacked with jmeter running on another machine. The load is about 600 to 700 http request per second, on the servlets that produces the same load of RabbitMQ messages. We noticed that sometimes, consumers hang (well, they are not blocked, but they dont consume messages anymore). We can see that because each consumer save around 100 msg/sec in database, so when one is stopping consumming, the overall messages saved per seconds in DB fall down with the same ratio (if let say 3 consumers stop, we fall around 600 msg/sec to 300 msg/sec). During that time, the producers are ok, and still produce at the jmeter rate (around 600 msg/sec). The messages are in the queues and taken by the consumers still "alive". We load all the servlets with the producers first, then launch all the consumers one by one, checking if the connexions are ok, then run jmeter. We are sending messages to one direct exchange. All consumers are listening to one persistent queue bounded to the exchange. That point is major for our choice. Have you seen this with rabbitmq, do you have an idea of what is going on ? Thank you for your answers.

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  • Difference in performance: local machine VS amazon medium instance

    - by user644745
    I see a drastic difference in performance matrix when i run it with apache benchmark (ab) in my local machine VS production hosted in amazon medium instance. Same concurrent requests (5) and same total number of requests (111) has been run against both. Amazon has better memory than my local machine. But there are 2 CPUs in my local machine vs 1 CPU in m1.medium. My internet speed is very low at the moment, I am getting Transfer rate as 25.29KBps. How can I improve the performance ? Do not know how to interpret Connect, Processing, Waiting and total in ab output. Here is Localhost: Server Hostname: localhost Server Port: 9999 Document Path: / Document Length: 7631 bytes Concurrency Level: 5 Time taken for tests: 1.424 seconds Complete requests: 111 Failed requests: 102 (Connect: 0, Receive: 0, Length: 102, Exceptions: 0) Write errors: 0 Total transferred: 860808 bytes HTML transferred: 847155 bytes Requests per second: 77.95 [#/sec] (mean) Time per request: 64.148 [ms] (mean) Time per request: 12.830 [ms] (mean, across all concurrent requests) Transfer rate: 590.30 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 0 0 0.5 0 1 Processing: 14 63 99.9 43 562 Waiting: 14 60 96.7 39 560 Total: 14 63 99.9 43 563 And this is production: Document Path: / Document Length: 7783 bytes Concurrency Level: 5 Time taken for tests: 33.883 seconds Complete requests: 111 Failed requests: 0 Write errors: 0 Total transferred: 877566 bytes HTML transferred: 863913 bytes Requests per second: 3.28 [#/sec] (mean) Time per request: 1526.258 [ms] (mean) Time per request: 305.252 [ms] (mean, across all concurrent requests) Transfer rate: 25.29 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 290 297 14.0 293 413 Processing: 897 1178 63.4 1176 1391 Waiting: 296 606 135.6 588 1171 Total: 1191 1475 66.0 1471 1684

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  • Nginx Slower than Apache??

    - by ichilton
    Hi, I've just setup 2x identical Rackspace Cloud instances and am doing some comparisons and benchmarks to compare Apache and Nginx. I'm testing with a 3.4k png file and initially 512MB server instances but have now moved to 1024MB server instances. I'm very surprised to see that whatever I try, Apache seems to consistently outperform Nginx....what am I doing wrong? Nginx: Server Software: nginx/0.8.54 Server Port: 80 Document Length: 3400 bytes Concurrency Level: 100 Time taken for tests: 2.320 seconds Complete requests: 1000 Failed requests: 0 Write errors: 0 Total transferred: 3612000 bytes HTML transferred: 3400000 bytes Requests per second: 431.01 [#/sec] (mean) Time per request: 232.014 [ms] (mean) Time per request: 2.320 [ms] (mean, across all concurrent requests) Transfer rate: 1520.31 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 0 11 15.7 3 120 Processing: 1 35 76.9 20 1674 Waiting: 1 31 73.0 19 1674 Total: 1 46 79.1 21 1693 Percentage of the requests served within a certain time (ms) 50% 21 66% 39 75% 40 80% 40 90% 98 95% 136 98% 269 99% 334 100% 1693 (longest request) And Apache: Server Software: Apache/2.2.16 Server Port: 80 Document Length: 3400 bytes Concurrency Level: 100 Time taken for tests: 1.346 seconds Complete requests: 1000 Failed requests: 0 Write errors: 0 Total transferred: 3647000 bytes HTML transferred: 3400000 bytes Requests per second: 742.90 [#/sec] (mean) Time per request: 134.608 [ms] (mean) Time per request: 1.346 [ms] (mean, across all concurrent requests) Transfer rate: 2645.85 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 0 1 3.7 0 27 Processing: 0 3 6.2 1 29 Waiting: 0 2 5.0 1 29 Total: 1 4 7.0 1 29 Percentage of the requests served within a certain time (ms) 50% 1 66% 1 75% 1 80% 1 90% 17 95% 19 98% 26 99% 27 100% 29 (longest request) I'm currently using worker_processes 4; and worker_connections 1024; but i've tried and benchmarked different values and see the same behaviour on all - I just can't get it to perform as well as Apache and from what i've read previously, i'm shocked about this! Can anyone give any advice? Thanks, Ian

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  • Win 2008 R2 - copying TO disk is very slow, copying FROM is more or less okay

    - by avs099
    I have Windows 2008 R2 SP1 with 4 identical SATA disks (Seagate Barracude 7200) in RAID 5 array. It has 4Gb of memory; all recent updates are installed. Problem: when I copy large file from one folder to another, I get about 10MB/s average speed. When I read this file from network share via 1Gbps connection - I get about 25-30 MB/s. Both numbers seems to be low for me - but specifically I'm very frustrated with low write speed. there is no antivirus, no hyper-v, it's just a fileserver - i when i do my tests nobody else reads/write from it (we have only 4 people in a team, so I'm sure). Not sure if that matters, but there is only 1 logic disk "C" with all available space (1400 GB). I'm not an admin at all, so I have no idea where to look and what other information to provide. I did run performance monitor with "% idle time", "avg bytes read", "avg byte write" - here is the screenshot: I'm not sure why there are such obvious spikes. Any idea? Please let me know if you need me to provide more information - what counters should I check, etc. I'm very eager to get this solved. Thank you. UPDATE: we have another Windows 2008 R2 SP1 server with 2 RAID1 arrays - one is disk C (where windows is installed, another one is disk E). It is running Hyper-V and does not have antivirus. I noticed the following behavior when I copy large file (few GBs): C - C: about 50MB/sec C - E: about 55MB/sec E - E: 8MB/sec!!! E - C: 8MB/sec!!! what could cause this?? E drive is RAID1 array from same Seagate Barracuda 1TB drives..

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  • Copy from CDROM is very slow in Ubuntu

    - by ???
    I'm using the command to copy CDROM image: # dd if=/dev/sr0 of=./maverick.iso But it's very slow, at about 350k bytes/sec. I've searched the google, and try the command # hdparm -vi /dev/sr0 /dev/sr0: HDIO_DRIVE_CMD(identify) failed: Bad address IO_support = 1 (32-bit) readonly = 0 (off) readahead = 256 (on) HDIO_GETGEO failed: Inappropriate ioctl for device Model=DVD-ROM UJDA775, FwRev=DA03, SerialNo= Config={ Fixed Removeable DTR<=5Mbs DTR>10Mbs nonMagnetic } RawCHS=0/0/0, TrkSize=0, SectSize=0, ECCbytes=0 BuffType=unknown, BuffSize=unknown, MaxMultSect=0 (maybe): CurCHS=0/0/0, CurSects=0, LBA=yes, LBAsects=0 IORDY=yes, tPIO={min:180,w/IORDY:120}, tDMA={min:120,rec:120} PIO modes: pio0 pio1 pio2 pio3 pio4 DMA modes: sdma0 sdma1 sdma2 mdma0 mdma1 mdma2 UDMA modes: udma0 udma1 *udma2 AdvancedPM=no Drive conforms to: ATA/ATAPI-5 T13 1321D revision 3: ATA/ATAPI-1,2,3,4,5 * signifies the current active mode Seems like DMA is already on. And a device test gives: # hdparm -t /dev/sr0 /dev/sr0: Timing buffered disk reads: 2 MB in 6.58 seconds = 311.10 kB/sec # sudo hdparm -tT /dev/sr0 /dev/sr0: Timing cached reads: 2 MB in 2.69 seconds = 760.96 kB/sec Timing buffered disk reads: m 4 MB in 5.19 seconds = 789.09 kB/sec The CD-ROM device and disc should be okay because I can copy it very fast in Windows, using UltraISO utility. So I guess there is something not configured right in Ubuntu, is it?

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  • MySQL query, 2 similar servers, 2 minute difference in execution times

    - by mr12086
    I had a similar question on stack overflow, but it seems to be more server/mysql setup related than coding. The queries below all execute instantly on our development server where as they can take upto 2 minutes 20 seconds. The query execution time seems to be affected by home ambiguous the LIKE string's are. If they closely match a country that has few matches it will take less time, and if you use something like 'ge' for germany - it will take longer to execute. But this doesn't always work out like that, at times its quite erratic. Sending data appears to be the culprit but why and what does that mean. Also memory on production looks to be quite low (free memory)? Production: Intel Quad Xeon E3-1220 3.1GHz 4GB DDR3 2x 1TB SATA in RAID1 Network speed 100Mb Ubuntu Development Intel Core i3-2100, 2C/4T, 3.10GHz 500 GB SATA - No RAID 4GB DDR3 UPDATE 2 : mysqltuner output: [prod] -------- General Statistics -------------------------------------------------- [--] Skipped version check for MySQLTuner script [OK] Currently running supported MySQL version 5.1.61-0ubuntu0.10.04.1 [OK] Operating on 64-bit architecture -------- Storage Engine Statistics ------------------------------------------- [--] Status: +Archive -BDB -Federated +InnoDB -ISAM -NDBCluster [--] Data in MyISAM tables: 103M (Tables: 180) [--] Data in InnoDB tables: 491M (Tables: 19) [!!] Total fragmented tables: 38 -------- Security Recommendations ------------------------------------------- [OK] All database users have passwords assigned -------- Performance Metrics ------------------------------------------------- [--] Up for: 77d 4h 6m 1s (53M q [7.968 qps], 14M conn, TX: 87B, RX: 12B) [--] Reads / Writes: 98% / 2% [--] Total buffers: 58.0M global + 2.7M per thread (151 max threads) [OK] Maximum possible memory usage: 463.8M (11% of installed RAM) [OK] Slow queries: 0% (12K/53M) [OK] Highest usage of available connections: 22% (34/151) [OK] Key buffer size / total MyISAM indexes: 16.0M/10.6M [OK] Key buffer hit rate: 98.7% (162M cached / 2M reads) [OK] Query cache efficiency: 20.7% (7M cached / 36M selects) [!!] Query cache prunes per day: 3934 [OK] Sorts requiring temporary tables: 1% (3K temp sorts / 230K sorts) [!!] Joins performed without indexes: 71068 [OK] Temporary tables created on disk: 24% (3M on disk / 13M total) [OK] Thread cache hit rate: 99% (690 created / 14M connections) [!!] Table cache hit rate: 0% (64 open / 85M opened) [OK] Open file limit used: 12% (128/1K) [OK] Table locks acquired immediately: 99% (16M immediate / 16M locks) [!!] InnoDB data size / buffer pool: 491.9M/8.0M -------- Recommendations ----------------------------------------------------- General recommendations: Run OPTIMIZE TABLE to defragment tables for better performance Enable the slow query log to troubleshoot bad queries Adjust your join queries to always utilize indexes Increase table_cache gradually to avoid file descriptor limits Variables to adjust: query_cache_size (> 16M) join_buffer_size (> 128.0K, or always use indexes with joins) table_cache (> 64) innodb_buffer_pool_size (>= 491M) [dev] -------- General Statistics -------------------------------------------------- [--] Skipped version check for MySQLTuner script [OK] Currently running supported MySQL version 5.1.62-0ubuntu0.11.10.1 [!!] Switch to 64-bit OS - MySQL cannot currently use all of your RAM -------- Storage Engine Statistics ------------------------------------------- [--] Status: +Archive -BDB -Federated +InnoDB -ISAM -NDBCluster [--] Data in MyISAM tables: 185M (Tables: 632) [--] Data in InnoDB tables: 967M (Tables: 38) [!!] Total fragmented tables: 73 -------- Security Recommendations ------------------------------------------- [OK] All database users have passwords assigned -------- Performance Metrics ------------------------------------------------- [--] Up for: 1d 2h 26m 9s (5K q [0.058 qps], 1K conn, TX: 4M, RX: 1M) [--] Reads / Writes: 99% / 1% [--] Total buffers: 58.0M global + 2.7M per thread (151 max threads) [OK] Maximum possible memory usage: 463.8M (11% of installed RAM) [OK] Slow queries: 0% (0/5K) [OK] Highest usage of available connections: 1% (2/151) [OK] Key buffer size / total MyISAM indexes: 16.0M/18.6M [OK] Key buffer hit rate: 99.9% (60K cached / 36 reads) [OK] Query cache efficiency: 44.5% (1K cached / 2K selects) [OK] Query cache prunes per day: 0 [OK] Sorts requiring temporary tables: 0% (0 temp sorts / 44 sorts) [OK] Temporary tables created on disk: 24% (162 on disk / 666 total) [OK] Thread cache hit rate: 99% (2 created / 1K connections) [!!] Table cache hit rate: 1% (64 open / 4K opened) [OK] Open file limit used: 8% (88/1K) [OK] Table locks acquired immediately: 100% (1K immediate / 1K locks) [!!] InnoDB data size / buffer pool: 967.7M/8.0M -------- Recommendations ----------------------------------------------------- General recommendations: Run OPTIMIZE TABLE to defragment tables for better performance Enable the slow query log to troubleshoot bad queries Increase table_cache gradually to avoid file descriptor limits Variables to adjust: table_cache (> 64) innodb_buffer_pool_size (>= 967M) UPDATE 1: When testing the queries listed here there is usually no more than one other query taking place, and usually none. Because production is actually handling apache requests that development gets very few of as it's only myself and 1 other who accesses it - could the 4GB of RAM be getting exhausted by using the single machine for both apache and mysql server? Production: sudo hdparm -tT /dev/sda /dev/sda: Timing cached reads: 24872 MB in 2.00 seconds = 12450.72 MB/sec Timing buffered disk reads: 368 MB in 3.00 seconds = 122.49 MB/sec sudo hdparm -tT /dev/sdb /dev/sdb: Timing cached reads: 24786 MB in 2.00 seconds = 12407.22 MB/sec Timing buffered disk reads: 350 MB in 3.00 seconds = 116.53 MB/sec Server version(mysql + ubuntu versions): 5.1.61-0ubuntu0.10.04.1 Development: sudo hdparm -tT /dev/sda /dev/sda: Timing cached reads: 10632 MB in 2.00 seconds = 5319.40 MB/sec Timing buffered disk reads: 400 MB in 3.01 seconds = 132.85 MB/sec Server version(mysql + ubuntu versions): 5.1.62-0ubuntu0.11.10.1 ORIGINAL DATA : This query is NOT the query in question but is related so ill post it. SELECT f.form_question_has_answer_id FROM form_question_has_answer f INNER JOIN project_company_has_user p ON f.form_question_has_answer_user_id = p.project_company_has_user_user_id INNER JOIN company c ON p.project_company_has_user_company_id = c.company_id INNER JOIN project p2 ON p.project_company_has_user_project_id = p2.project_id INNER JOIN user u ON p.project_company_has_user_user_id = u.user_id INNER JOIN form f2 ON p.project_company_has_user_project_id = f2.form_project_id WHERE (f2.form_template_name = 'custom' AND p.project_company_has_user_garbage_collection = 0 AND p.project_company_has_user_project_id = '29') AND (LCASE(c.company_country) LIKE '%ge%' OR LCASE(c.company_country) LIKE '%abcde%') AND f.form_question_has_answer_form_id = '174' And the explain plan for the above query is, run on both dev and production produce the same plan. +----+-------------+-------+--------+----------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------+---------+----------------------------------------------------+------+-------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+-------+--------+----------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------+---------+----------------------------------------------------+------+-------------+ | 1 | SIMPLE | p2 | const | PRIMARY | PRIMARY | 4 | const | 1 | Using index | | 1 | SIMPLE | f | ref | form_question_has_answer_form_id,form_question_has_answer_user_id | form_question_has_answer_form_id | 4 | const | 796 | Using where | | 1 | SIMPLE | u | eq_ref | PRIMARY | PRIMARY | 4 | new_klarents.f.form_question_has_answer_user_id | 1 | Using index | | 1 | SIMPLE | p | ref | project_company_has_user_unique_key,project_company_has_user_user_id,project_company_has_user_company_id,project_company_has_user_project_id | project_company_has_user_user_id | 4 | new_klarents.f.form_question_has_answer_user_id | 1 | Using where | | 1 | SIMPLE | f2 | ref | form_project_id | form_project_id | 4 | const | 15 | Using where | | 1 | SIMPLE | c | eq_ref | PRIMARY | PRIMARY | 4 | new_klarents.p.project_company_has_user_company_id | 1 | Using where | +----+-------------+-------+--------+----------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------+---------+----------------------------------------------------+------+-------------+ This query takes 2 minutes ~20 seconds to execute. The query that is ACTUALLY being run on the server is this one: SELECT COUNT(*) AS num_results FROM (SELECT f.form_question_has_answer_id FROM form_question_has_answer f INNER JOIN project_company_has_user p ON f.form_question_has_answer_user_id = p.project_company_has_user_user_id INNER JOIN company c ON p.project_company_has_user_company_id = c.company_id INNER JOIN project p2 ON p.project_company_has_user_project_id = p2.project_id INNER JOIN user u ON p.project_company_has_user_user_id = u.user_id INNER JOIN form f2 ON p.project_company_has_user_project_id = f2.form_project_id WHERE (f2.form_template_name = 'custom' AND p.project_company_has_user_garbage_collection = 0 AND p.project_company_has_user_project_id = '29') AND (LCASE(c.company_country) LIKE '%ge%' OR LCASE(c.company_country) LIKE '%abcde%') AND f.form_question_has_answer_form_id = '174' GROUP BY f.form_question_has_answer_id;) dctrn_count_query; With explain plans (again same on dev and production): +----+-------------+-------+--------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------+---------+----------------------------------------------------+------+------------------------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+-------+--------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------+---------+----------------------------------------------------+------+------------------------------+ | 1 | PRIMARY | NULL | NULL | NULL | NULL | NULL | NULL | NULL | Select tables optimized away | | 2 | DERIVED | p2 | const | PRIMARY | PRIMARY | 4 | | 1 | Using index | | 2 | DERIVED | f | ref | form_question_has_answer_form_id,form_question_has_answer_user_id | form_question_has_answer_form_id | 4 | | 797 | Using where | | 2 | DERIVED | p | ref | project_company_has_user_unique_key,project_company_has_user_user_id,project_company_has_user_company_id,project_company_has_user_project_id,project_company_has_user_garbage_collection | project_company_has_user_user_id | 4 | new_klarents.f.form_question_has_answer_user_id | 1 | Using where | | 2 | DERIVED | f2 | ref | form_project_id | form_project_id | 4 | | 15 | Using where | | 2 | DERIVED | c | eq_ref | PRIMARY | PRIMARY | 4 | new_klarents.p.project_company_has_user_company_id | 1 | Using where | | 2 | DERIVED | u | eq_ref | PRIMARY | PRIMARY | 4 | new_klarents.p.project_company_has_user_user_id | 1 | Using where; Using index | +----+-------------+-------+--------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------+---------+----------------------------------------------------+------+------------------------------+ On the production server the information I have is as follows. Upon execution: +-------------+ | num_results | +-------------+ | 3 | +-------------+ 1 row in set (2 min 14.28 sec) Show profile: +--------------------------------+------------+ | Status | Duration | +--------------------------------+------------+ | starting | 0.000016 | | checking query cache for query | 0.000057 | | Opening tables | 0.004388 | | System lock | 0.000003 | | Table lock | 0.000036 | | init | 0.000030 | | optimizing | 0.000016 | | statistics | 0.000111 | | preparing | 0.000022 | | executing | 0.000004 | | Sorting result | 0.000002 | | Sending data | 136.213836 | | end | 0.000007 | | query end | 0.000002 | | freeing items | 0.004273 | | storing result in query cache | 0.000010 | | logging slow query | 0.000001 | | logging slow query | 0.000002 | | cleaning up | 0.000002 | +--------------------------------+------------+ On development the results are as follows. +-------------+ | num_results | +-------------+ | 3 | +-------------+ 1 row in set (0.08 sec) Again the profile for this query: +--------------------------------+----------+ | Status | Duration | +--------------------------------+----------+ | starting | 0.000022 | | checking query cache for query | 0.000148 | | Opening tables | 0.000025 | | System lock | 0.000008 | | Table lock | 0.000101 | | optimizing | 0.000035 | | statistics | 0.001019 | | preparing | 0.000047 | | executing | 0.000008 | | Sorting result | 0.000005 | | Sending data | 0.086565 | | init | 0.000015 | | optimizing | 0.000006 | | executing | 0.000020 | | end | 0.000004 | | query end | 0.000004 | | freeing items | 0.000028 | | storing result in query cache | 0.000005 | | removing tmp table | 0.000008 | | closing tables | 0.000008 | | logging slow query | 0.000002 | | cleaning up | 0.000005 | +--------------------------------+----------+ If i remove user and/or project innerjoins the query is reduced to 30s. Last bit of information I have: Mysqlserver and Apache are on the same box, there is only one box for production. Production output from top: before & after. top - 15:43:25 up 78 days, 12:11, 4 users, load average: 1.42, 0.99, 0.78 Tasks: 162 total, 2 running, 160 sleeping, 0 stopped, 0 zombie Cpu(s): 0.1%us, 50.4%sy, 0.0%ni, 49.5%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st Mem: 4037868k total, 3772580k used, 265288k free, 243704k buffers Swap: 3905528k total, 265384k used, 3640144k free, 1207944k cached top - 15:44:31 up 78 days, 12:13, 4 users, load average: 1.94, 1.23, 0.87 Tasks: 160 total, 2 running, 157 sleeping, 0 stopped, 1 zombie Cpu(s): 0.2%us, 50.6%sy, 0.0%ni, 49.3%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st Mem: 4037868k total, 3834300k used, 203568k free, 243736k buffers Swap: 3905528k total, 265384k used, 3640144k free, 1207804k cached But this isn't a good representation of production's normal status so here is a grab of it from today outside of executing the queries. top - 11:04:58 up 79 days, 7:33, 4 users, load average: 0.39, 0.58, 0.76 Tasks: 156 total, 1 running, 155 sleeping, 0 stopped, 0 zombie Cpu(s): 3.3%us, 2.8%sy, 0.0%ni, 93.9%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st Mem: 4037868k total, 3676136k used, 361732k free, 271480k buffers Swap: 3905528k total, 268736k used, 3636792k free, 1063432k cached Development: This one doesn't change during or after. top - 15:47:07 up 110 days, 22:11, 7 users, load average: 0.17, 0.07, 0.06 Tasks: 210 total, 2 running, 208 sleeping, 0 stopped, 0 zombie Cpu(s): 0.1%us, 0.2%sy, 0.0%ni, 99.7%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st Mem: 4111972k total, 1821100k used, 2290872k free, 238860k buffers Swap: 4183036k total, 66472k used, 4116564k free, 921072k cached

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  • AWS elastic load balancer basic issues

    - by Jones
    I have an array of EC2 t1.micro instances behind a load balancer and each node can manage ~100 concurrent users before it starts to get wonky. i would THINK if i have 2 such instances it would allow my network to manage 200 concurrent users... apparently not. When i really slam the server (blitz.io) with a full 275 concurrents, it behaves the same as if there is just one node. it goes from 400ms response time to 1.6 seconds (which for a single t1.micro is expected, but not 6). So the question is, am i simply not doing something right or is ELB effectively worthless? Anyone have some wisdom on this? AB logs: Loadbalancer (3x m1.medium) Document Path: /ping/index.html Document Length: 185 bytes Concurrency Level: 100 Time taken for tests: 11.668 seconds Complete requests: 50000 Failed requests: 0 Write errors: 0 Non-2xx responses: 50001 Total transferred: 19850397 bytes HTML transferred: 9250185 bytes Requests per second: 4285.10 [#/sec] (mean) Time per request: 23.337 [ms] (mean) Time per request: 0.233 [ms] (mean, across all concurrent requests) Transfer rate: 1661.35 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 1 2 4.3 2 63 Processing: 2 21 15.1 19 302 Waiting: 2 21 15.0 19 261 Total: 3 23 15.7 21 304 Single instance (1x m1.medium direct connection) Document Path: /ping/index.html Document Length: 185 bytes Concurrency Level: 100 Time taken for tests: 9.597 seconds Complete requests: 50000 Failed requests: 0 Write errors: 0 Non-2xx responses: 50001 Total transferred: 19850397 bytes HTML transferred: 9250185 bytes Requests per second: 5210.19 [#/sec] (mean) Time per request: 19.193 [ms] (mean) Time per request: 0.192 [ms] (mean, across all concurrent requests) Transfer rate: 2020.01 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 1 9 128.9 3 3010 Processing: 1 10 8.7 9 141 Waiting: 1 9 8.7 8 140 Total: 2 19 129.0 12 3020

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  • HP DL185 - very slow disk read speed

    - by fistameeny
    Hi, I have a HP DL185 G6 Server (12 disk model) with the following spec: Quad Core Xeon 2.27GHz 6GB RAM HP P212 RAID controller with battery backup 2 x 128GB 15K SAS 3.5" (RAID-1 for the operating system) 4 x 750GB 7.5K SAS 3.5" (RAID-5 for the data, 2TB usable space) The operating system is Ubuntu Server 9.10. Both drives have been formatted as EXT4. We are finding that read speed of the RAID-5 array is poor. Disk test results below: sudo hdparm -tT /dev/cciss/c0d1p1 /dev/cciss/c0d1p1: Timing cached reads: 15284 MB in 2.00 seconds = 7650.18 MB/sec Timing buffered disk reads: 74 MB in 3.02 seconds = 24.53 MB/sec For info, the RAID-1 array performs as follows: sudo hdparm -tT /dev/cciss/c0d0p1 /dev/cciss/c0d0p1: Timing cached reads: 15652 MB in 2.00 seconds = 7834.26 MB/sec Timing buffered disk reads: 492 MB in 3.01 seconds = 163.46 MB/sec We thought this was because with no battery, read/write cache is disabled. We have bought and installed the battery backup and have used the HP bootable CD to change the cache settings to 50% read / 50% write and check cache is enabled on the drives and the controller. Is there something I'm missing?

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  • Possible disk IO issue

    - by Tim Meers
    I've been trying to really figure out what my IOPS are on my DB server array and see if it's just too much. The array is four 72.6gb 15k rpm drives in RAID 5. To calculate IOPS for RAID 5 the following formula is used: (reads + (4 * Writes)) / Number of disks = total IOPS. The formula is from MSDN. I also want to calculate the Avg Queue Length but I'm not sure where they are getting the formula from, but i think it reads on that page as avg que length/number of disks = actual queue. To populate that formula I used the perfmon to gather the needed information. I came up with this, under normal production load: (873.982 + (4 * 28.999)) / 4 = 247.495. Also the disk queue lengh of 14.454/4 = 3.614. So to the question, am I wrong in thinking this array has a very high disk IO? Edit I got the chance to review it again this morning under normal/high load. This time with even bigger numbers and IOPS in excess of 600 for about 5 minutes then it died down again. But I also took a look at the Avg sec/Transfer, %Disk Time, and %Idle Time. These number were taken when the reads/writes per sec were only 332.997/17.999 respectively. %Disk Time: 219.436 %Idle Time: 0.300 Avg Disk Queue Length: 2.194 Avg Disk sec/Transfer: 0.006 Pages/sec: 2927.802 % Processor Time: 21.877 Edit (again) Looks like I have that issue solved. Thanks for the help. Also for a pretty slick parser I found this: http://pal.codeplex.com/ It works pretty well for breaking down the data into something usable.

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  • Decrease in disk performance after partitioning and encryption, is this much of a drop normal?

    - by Biohazard
    I have a server that I only have remote access to. Earlier in the week I repartitioned the 2 disk raid as follows: Filesystem Size Used Avail Use% Mounted on /dev/mapper/sda1_crypt 363G 1.8G 343G 1% / tmpfs 2.0G 0 2.0G 0% /lib/init/rw udev 2.0G 140K 2.0G 1% /dev tmpfs 2.0G 0 2.0G 0% /dev/shm /dev/sda5 461M 26M 412M 6% /boot /dev/sda7 179G 8.6G 162G 6% /data The raid consists of 2 x 300gb SAS 15k disks. Prior to the changes I made, it was being used as a single unencrypted root parition and hdparm -t /dev/sda was giving readings around 240mb/s, which I still get if I do it now: /dev/sda: Timing buffered disk reads: 730 MB in 3.00 seconds = 243.06 MB/sec Since the repartition and encryption, I get the following on the separate partitions: Unencrypted /dev/sda7: /dev/sda7: Timing buffered disk reads: 540 MB in 3.00 seconds = 179.78 MB/sec Unencrypted /dev/sda5: /dev/sda5: Timing buffered disk reads: 476 MB in 2.55 seconds = 186.86 MB/sec Encrypted /dev/mapper/sda1_crypt: /dev/mapper/sda1_crypt: Timing buffered disk reads: 150 MB in 3.03 seconds = 49.54 MB/sec I expected a drop in performance on the encrypted partition, but not that much, but I didn't expect I would get a drop in performance on the other partitions at all. The other hardware in the server is: 2 x Quad Core Intel(R) Xeon(R) CPU E5405 @ 2.00GHz and 4gb RAM $ cat /proc/scsi/scsi Attached devices: Host: scsi0 Channel: 00 Id: 32 Lun: 00 Vendor: DP Model: BACKPLANE Rev: 1.05 Type: Enclosure ANSI SCSI revision: 05 Host: scsi0 Channel: 02 Id: 00 Lun: 00 Vendor: DELL Model: PERC 6/i Rev: 1.11 Type: Direct-Access ANSI SCSI revision: 05 Host: scsi1 Channel: 00 Id: 00 Lun: 00 Vendor: HL-DT-ST Model: CD-ROM GCR-8240N Rev: 1.10 Type: CD-ROM ANSI SCSI revision: 05 I'm guessing this means the server has a PERC 6/i RAID controller? The encryption was done with default settings during debian 6 installation. I can't recall the exact specifics and am not sure how I go about finding them? Thanks

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  • Why is my new PC so slow at startup?

    - by rumtscho
    Bought a new PC this weekend, and it works really good. Only I have one big problem: startup time. Its BIOS needs 62 sec to load, then from Grub start to pw entering screen it's another 26 sec. I think this is a lot, because my old PC needs 34 sec for BIOS and another 8 sec to pw screen. After I enter the pw, the desktop is usable with practically no delay on both. The new PC is a core i7-930, running a Lucid Lynx 64 bit from a Intel Postville SSD (no internal HDs). The old PC is a Pentium 4 celeron (forgot the clock speed) running a Lucid Lynx 32 bit from an ATA 100 hard drive. Neither PC is overclocked. The new one has boot sequence 1.DVD ROM, 2.SSD (connected over SATA in AHCI mode), 3. removable drive. The old one boots from 1. DVD ROM, 2. HDD, 3. Floppy. Neither has a second OS installed. The new one has less software installed than the old one (I think), but the boot time difference was noticeable even before I made any installs. As far as I know, just the SSD should be enough to make a noticeable difference in boot time. I thought that having a good mainboard on the new PC as opposed to the basic office model on the old one would also mean a faster loading BIOS. If these assumptions are right, I guess I must have misconfigured something in the BIOS of the new PC. How should I configure it for a fast boot? It has an ASUS P6X58D board with an AMI BIOS, if you need the BIOS revision number I could post that too.

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  • Problems with SQL Server 2008 - "The client was unable to reuse a session with SPID 62, which had ..

    - by GrZeCh
    Hello, I'm having problems with my SQL Server 2008 installation (10.0.2531.0 - SP1 installed). It works as a database server for small hosting environment (about 500 sites). I'm getting errors like this: The client was unable to reuse a session with SPID 62, which had been reset for connection pooling. The failure ID is 29. This error may have been caused by an earlier operation failing. Check the error logs for failed operations immediately before this error message. in Windows event log and when I run this: SELECT * FROM sys.dm_os_performance_counters WHERE object_name = 'SQLServer:General Statistics' I see that one of counters looks a little odd: Logins/sec 429 Connection Reset/sec 163459 Logouts/sec 399 User Connections 30 Logical Connections 33 any ideas how to check what is causing this problem?

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  • Vertex 2 SSD is running faster than my Vertex 3 SSD?

    - by Kairan
    I used Acronis Disk Director to do a direct clone of my C:\ windows 7 x64 drive from my Vertex 2 to my new Vertex 3 SSD (Just to show the drive software winstall everything is identical.) I ran a performance test on Windows using the Windows Experience Index. The rating I am receiving when booting on the Vertex 2 is 7.5 While I am getting only a rating for the Vertex 3 of 6.9 My understanding is that the read/write speeds of the Vertex 2 is only up to 250MB/sec while the Vertex 3 is up to 500MB/sec. Copying a single file (3GB in size) from the Vertex 3 to itself was getting speed of approx 70-80MB/sec This speed is no better (maybe worse) than what I got from the Vertex 2 I am connected via the SATA 3 port on the motherboard, using an SATA 3 cable Is this issue caused by the drive cloning? Do I have a bad SSD?

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  • RAID Array performance on an HP Proliant ML350 G5 Smart Array E200i

    - by Nate Pinchot
    We have a client who is complaining about performance of an application which utilizes an MS SQL database. They do not believe the performance issues are the fault of the application itself. The Smart Array E200i RAID controller has 128MB cache and we have the cache set to 75% read/25% write. The disk array set to enable write caching. Recently we ran a disk performance test using SQLIO based on this guide. We used a 10 GB file for the test found that the average sequential read rate was ~60 MB/sec (megabytes/sec) and the average random read rate was ~30 MB/sec. Are these numbers on par for what the server should be performing? Better than on par? Horrible? Amazing?

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  • MySQL 5.5.9 Query Cache not working.

    - by thepearson
    I am running MySQL 5.5.9 x86_64 RPM as downloaded from mysql.com. Running on CentOS 5.5 Xen DomU. I have enabled the Query_cache however MySQL NEVER uses it. All of my tables are InnoDB. Why is the Qcache never hit? Here are my settings. mysql> SELECT VERSION(); +-----------+ | VERSION() | +-----------+ | 5.5.9 | +-----------+ 1 row in set (0.00 sec) mysql> SHOW VARIABLES LIKE '%query_cache%'; +------------------------------+-----------+ | Variable_name | Value | +------------------------------+-----------+ | have_query_cache | YES | | query_cache_limit | 2097152 | | query_cache_min_res_unit | 4096 | | query_cache_size | 536870912 | | query_cache_type | ON | | query_cache_wlock_invalidate | OFF | +------------------------------+-----------+ 6 rows in set (0.00 sec) mysql> show status like 'Qcache%'; +-------------------------+-----------+ | Variable_name | Value | +-------------------------+-----------+ | Qcache_free_blocks | 1 | | Qcache_free_memory | 536852824 | | Qcache_hits | 0 | | Qcache_inserts | 0 | | Qcache_lowmem_prunes | 0 | | Qcache_not_cached | 7665775 | | Qcache_queries_in_cache | 0 | | Qcache_total_blocks | 1 | +-------------------------+-----------+ 8 rows in set (0.00 sec)

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