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  • Search Files (Preferably with index) on Windows 2000 Server

    - by ThinkBohemian
    I have many files on a windows server 2000 machine that is setup to act as a networked disk drive, is there anyway I can index the files and make that index available as a search to more people than just me? Bonus if the index can look inside of documents such as readme.txt? If there is no easy way to do this globaly (for all users) Is there a way I could generate and store an index locally on my computer? If this is the wrong place to ask this question, any advice on community more suited?

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  • Createing a new Index in SQL when current records don't meet that index

    - by Jonathan
    Hey all- I'd like to add an index to a table that already contains data. I know that there a few records currently in the table that are not unique with this new index. Clearly, MySQL won't let me add the index until all of them are. I need a query to identify the rows which currently have the same index. I can then delete or modify these rows as necessary. The new index contains 6 fields. Thanks- Jonathan

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  • High CPU usage by 'svchost.exe' and 'coreServiceShell.exe'

    - by kush.impetus
    I am having a laptop running on Windows 7 Ultimate 32-bit. Since past few days, my laptop is facing a serious problem. Whenever I connect to Internet, either svchost.exe or coreServiceShell.exe or both hog the CPU. The coreServiceShell.exe consumes a lot of RAM also. Going into the details, I found that high CPU usage of svchost.exe is caused by Network Location Awareness service. And the high CPU usage of coreServiceShell.exe is caused by Trend Micro Titanium Internet Security 2012. That kind'a makes me think that Trend Micro may be the root of the problem. After further testing, I found that if I use IE or Firefox to browse the Internet, immediately after connecting to Internet, things are normal. See and But if I use Google Chrome, the coreServiceShell.exe hogs both CPU and RAM. At this point, if I disconnect the Internet, the CPU and RAM usage by coreServiceShell.exe continues to be high till I close the Chrome. Also, when I close the Chrome, while Internet is connected, svchost.exe continues to hog CPU but coreServiceShell.exe leaves the race. That makes think that Chrome is the root of the problem, but again, tracing coreServiceShell.exe takes me back to Trend Micro Internet Security. Stopping the Protection by the Trend Micro Internet Security doesn't help either (I am not able to stop its services though). I have updated the Chrome, but no help. I just can't figure out who is the culprit. I can't do without the Google Chrome (of course, by not using it) because of its immensely useful and indispensable features both during browsing and development. Secondly, I can't uninstall the Trend Micro Internet security Suite since it still has few months before it expires and is proving me reliable protection. What could be the cause of the problem and what can I do to resolve this? Thanks in advance

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  • Where is my CPU usage going?

    - by Josh
    My Ubuntu 10.04 Lucid virtual machine is saying it's at 100% CPU usage... but all I'm running is Thunderbird. According to top, CPU usage should be ~25.9%... How do I interpret this conflicting output from top? top - 13:55:26 up 3:35, 4 users, load average: 3.03, 2.59, 2.48 Tasks: 178 total, 1 running, 177 sleeping, 0 stopped, 0 zombie Cpu(s): 16.0%us, 79.7%sy, 0.0%ni, 0.0%id, 0.0%wa, 1.3%hi, 3.0%si, 0.0%st Mem: 509364k total, 479108k used, 30256k free, 3092k buffers Swap: 2096440k total, 58380k used, 2038060k free, 225116k cached PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 7708 jnet 20 0 480m 109m 17m S 18.4 22.1 21:59.14 thunderbird-bin 4615 jnet 20 0 5488 1268 1040 S 2.3 0.2 5:00.03 nx-rootless-ses 7124 jnet 20 0 56688 27m 4812 S 2.0 5.5 6:35.09 nxagent 6724 nx 20 0 9628 1400 636 S 1.6 0.3 3:26.59 sshd 30106 root 20 0 2544 1236 908 R 0.7 0.2 0:00.33 top 19 root 20 0 0 0 0 S 0.3 0.0 0:22.45 ata/0 38 root 20 0 0 0 0 S 0.3 0.0 0:05.53 scsi_eh_1 345 root 20 0 0 0 0 S 0.3 0.0 0:04.72 kjournald 1719 root 20 0 3260 1192 944 S 0.3 0.2 0:17.36 vmware-guestd 1 root 20 0 2804 1356 940 S 0.0 0.3 0:01.99 init 2 root 20 0 0 0 0 S 0.0 0.0 0:00.01 kthreadd 3 root RT 0 0 0 0 S 0.0 0.0 0:00.00 migration/0 4 root 20 0 0 0 0 S 0.0 0.0 0:00.15 ksoftirqd/0 5 root RT 0 0 0 0 S 0.0 0.0 0:00.00 watchdog/0 ... Specifically I'm referring to the fact that the CPU usage totals show 0% idle time: Cpu(s): 16.0%us, 79.7%sy, 0.0%ni, 0.0%id, 0.0%wa, 1.3%hi, 3.0%si, 0.0%st Yet when adding up the percentages in the %CPU column I get 25.9%, not 100%!

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  • Which non-clustered index should I use?

    - by Junior Mayhé
    Here I am studying nonclustered indexes on SQL Server Management Studio. I've created a table with more than 1 million records. This table has a primary key. CREATE TABLE [dbo].[Customers]( [CustomerId] [int] IDENTITY(1,1) NOT NULL, [CustomerName] [varchar](100) NOT NULL, [Deleted] [bit] NOT NULL, [Active] [bit] NOT NULL, CONSTRAINT [PK_Customers] PRIMARY KEY CLUSTERED ( [CustomerId] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] ) ON [PRIMARY] This is the query I'll be using to see what execution plan is showing: SELECT CustomerName FROM Customers Well, executing this command with no additional non-clustered index, it leads the execution plan to show me: I/O cost = 3.45646 Operator cost = 4.57715 Now I'm trying to see if it's possible to improve performance, so I've created a non-clustered index for this table: 1) First non-clustered index CREATE NONCLUSTERED INDEX [IX_CustomerID_CustomerName] ON [dbo].[Customers] ( [CustomerId] ASC, [CustomerName] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, SORT_IN_TEMPDB = OFF, IGNORE_DUP_KEY = OFF, DROP_EXISTING = OFF, ONLINE = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] GO Executing again the select against Customers table, the execution plan shows me: I/O cost = 2.79942 Operator cost = 3.92001 It seems better. Now I've deleted this just created non-clustered index, in order to create a new one: 2) First non-clustered index CREATE NONCLUSTERED INDEX [IX_CustomerIDIncludeCustomerName] ON [dbo].[Customers] ( [CustomerId] ASC ) INCLUDE ( [CustomerName]) WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, SORT_IN_TEMPDB = OFF, IGNORE_DUP_KEY = OFF, DROP_EXISTING = OFF, ONLINE = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] GO With this new non-clustered index, I've executed the select statement again and the execution plan shows me the same result: I/O cost = 2.79942 Operator cost = 3.92001 So, which non-clustered index should I use? Why the costs are the same on execution plan for I/O and Operator? Am I doing something wrong or this is expected? thank you

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  • Create an index only on certain rows in mysql

    - by dhruvbird
    So, I have this funny requirement of creating an index on a table only on a certain set of rows. This is what my table looks like: USER: userid, friendid, created, blah0, blah1, ..., blahN Now, I'd like to create an index on: (userid, friendid, created) but only on those rows where userid = friendid. The reason being that this index is only going to be used to satisfy queries where the WHERE clause contains "userid = friendid". There will be many rows where this is NOT the case, and I really don't want to waste all that extra space on the index. Another option would be to create a table (query table) which is populated on insert/update of this table and create a trigger to do so, but again I am guessing an index on that table would mean that the data would be stored twice. How does mysql store Primary Keys? I mean is the table ordered on the Primary Key or is it ordered by insert order and the PK is like a normal unique index? I checked up on clustered indexes (http://dev.mysql.com/doc/refman/5.0/en/innodb-index-types.html), but it seems only InnoDB supports them. I am using MyISAM (I mention this because then I could have created a clustered index on these 3 fields in the query table). I am basically looking for something like this: ALTER TABLE USERS ADD INDEX (userid, friendid, created) WHERE userid=friendid

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  • Need help trouble shooting high CPU usage by PHP-fpm

    - by user432506
    There is a problem that is driving me crazy. After the day I tried to fix the CPU usage problem of my VPS, the CPU load has grown from 60% to 150%, and I have no idea what causes the problem. Please help me. I had installed a copy of mediawiki on a Linode 1024. The wiki is running on Niginx + PHP-fpm + MySql. The wiki doesn't have much traffic, only around 4000 requests/day, mostly from Google and Bing bots. It had been using around 60% (of total 400% on the Linode) of the CPU before. I thought it was a bit high, so two day ago, I was trying to fix the problem (not knowing what was waiting for me). I did nothing but added a new empty line to wiki's configure file, which would change the modified time of the configure file, and then all the cached page files would be set invalidated. I had done that before, and that would cause high CPU usage, but normally it would take only hours to let things back to normal again. Not this time, my CPU usage has been around 150% for more than two days. It is php-fpm using most of CPU reassures. Using 100% of three cores is not rare. I hadn't seen that before. There are other sites on the Linode, but it should be the wiki. Because if I offline the wiki, CPU usage will drop back to around 40% soon. The day I also duplicated php-fpm.conf, and opened it, but didn't changed it. I have no idea what I did wrong. I here ask for help to save myself from being crazy!!! It is php-fpm. Is there a way to find out what is it doing? I mean like which scripts are related and what function codes are running? top: top - 06:34:33 up 10 days, 4:23, 2 users, load average: 1.10, 1.24, 1.37 Tasks: 76 total, 4 running, 72 sleeping, 0 stopped, 0 zombie Cpu(s): 61.1%us, 3.1%sy, 0.0%ni, 32.8%id, 2.9%wa, 0.0%hi, 0.0%si, 0.1%st Mem: 1028684k total, 945192k used, 83492k free, 89580k buffers Swap: 524284k total, 18084k used, 506200k free, 530380k cached PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 26721 www-data 20 0 208m 54m 34m R 99 5.4 0:09.07 /opt/php5/sbin/php-fpm --fpm-config /opt/php5/etc/php-fpm.conf 26592 www-data 20 0 207m 45m 26m R 91 4.5 0:12.77 /opt/php5/sbin/php-fpm --fpm-config /opt/php5/etc/php-fpm.conf 26706 www-data 20 0 196m 43m 34m S 47 4.3 0:15.19 /opt/php5/sbin/php-fpm --fpm-config /opt/php5/etc/php-fpm.conf 26583 www-data 20 0 197m 45m 35m S 33 4.5 0:19.08 /opt/php5/sbin/php-fpm --fpm-config /opt/php5/etc/php-fpm.conf 26787 www-data 20 0 206m 36m 18m R 25 3.7 0:00.41 /opt/php5/sbin/php-fpm --fpm-config /opt/php5/etc/php-fpm.conf 26661 www-data 20 0 207m 46m 26m S 13 4.6 0:19.87 /opt/php5/sbin/php-fpm --fpm-config /opt/php5/etc/php-fpm.conf 1971 mysql 20 0 155m 57m 3952 S 8 5.7 383:57.81 /usr/sbin/mysqld 242 root 20 0 0 0 0 S 1 0.0 0:51.36 [kworker/3:1] 5711 root 20 0 139m 95m 580 S 1 9.5 0:41.30 /usr/local/bin/memcached -d -u root -m 128 -p 11211 19463 root 20 0 190m 3984 1284 S 1 0.4 0:02.66 /opt/php5/sbin/php-fpm --fpm-config /opt/php5/etc/php-fpm.conf 29100 www-data 20 0 10928 5540 820 S 1 0.5 4:49.05 nginx: worker process vmstat 30 procs -----------memory---------- ---swap-- -----io---- -system-- ----cpu---- r b swpd free buff cache si so bi bo in cs us sy id wa 0 0 16912 81456 90784 554172 0 0 4 6 3 2 11 1 87 1 0 0 16912 78036 91000 555356 0 0 38 34 1397 375 12 1 87 0 4 0 16912 31776 91528 557508 0 0 78 42 3197 487 45 1 52 1 1 0 16912 83356 91768 558576 0 0 35 56 2608 449 32 1 67 1 1 0 16912 81548 92040 559720 0 0 41 31 1243 432 8 1 91 1 2 0 16912 53056 92332 562744 0 0 105 33 2013 581 17 1 81 1 2 0 16912 73236 92552 564844 0 0 68 36 1968 615 16 1 82 1 0 0 16912 91612 92904 566676 0 0 69 35 1845 692 13 1 85 1 1 0 16912 71248 93180 568428 0 0 58 33 1952 604 15 1 82 1 1 0 16868 55952 93516 572660 1 0 144 42 1801 637 12 1 86 1 2 0 16868 48324 94416 577844 0 0 189 66 2058 702 17 1 80 2 1 0 16928 58644 94592 578184 0 2 160 49 2578 723 25 1 70 3 5 0 16928 22600 94980 580568 0 0 89 32 1496 361 13 0 85 1 0 0 16988 49256 94500 576396 0 2 41 37 1601 426 14 1 85 0 5 0 18084 24336 86032 502748 0 37 83 68 2989 562 42 1 56 0 1 0 18084 123604 86376 506996 0 0 118 41 2201 573 22 1 76 1 2 0 18084 126984 86752 508876 0 0 64 53 1620 490 13 1 85 1 2 0 18084 103104 87148 510768 0 0 71 37 2757 602 33 1 64 1

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  • disparity between `top`'s given CPU % and process CPU usage total

    - by intuited
    I've noticed that there are sometimes (large) differences between the reported total CPU usage and a summation of the per-process CPU utilization given by apps like top and wmtop. As an example: I recently ran a git filter-branch --index-filter on a fairly large repo, with the index-filter command piping git ls-files through a grep filter and into xargs git rm --cached. This took a few minutes to run; while it was going I noticed that both wmtop and top were displaying a high (above 50% on my 2-core machine) total CPU usage, but that neither showed any individual processes which were using a significant amount of CPU time. Are some processes not shown in the process list? What sorts of processes are these, and is there a way to find out how much CPU time they are using?

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  • HIGH CPU usage by PHP on a VPS Magento Server

    - by Anil
    My server running magento is 4gb ram and 4 core cpu. But still i am struggling with the high CPU usage. I only have 10 visitors at any given point of time. I am not sure if the PHP has to take this high % CPU usage. Attached is the TOP result. top - 09:18:32 up 2 days, 15:44, 1 user, load average: 1.16, 2.02, 1.99 Tasks: 179 total, 2 running, 177 sleeping, 0 stopped, 0 zombie Cpu(s): 46.7%us, 3.9%sy, 0.1%ni, 46.9%id, 1.0%wa, 0.0%hi, 0.0%si, 1.4%st Mem: 3919972k total, 3164968k used, 755004k free, 530820k buffers Swap: 1048568k total, 379352k used, 669216k free, 1536388k cached PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 15897 vpsadmin 20 0 431m 168m 54m R 91.7 4.4 2:16.16 php-cgi 12308 vpsadmin 20 0 404m 163m 73m S 29.3 4.3 15:15.90 php-cgi 3644 mysql 20 0 1528m 80m 4944 S 9.8 2.1 1899:58 mysqld 4969 apache 20 0 471m 6228 2824 S 2.0 0.2 0:18.53 httpd 16148 root 20 0 15024 1220 864 R 2.0 0.0 0:00.01 top 1 root 20 0 19364 1064 844 S 0.0 0.0 0:02.50 init

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  • Munin "Disk usage" is too high?

    - by f-aminov
    I've recently installed munin on my VMware client server and saw that the Disk usage shows about 80-90%. Everything else (cpu load, ram, etc.) seems to be running fine. I have only two virtual hosts on my server with 1000 users/day in total, so I don't think that's too much. Here is the graph for the disk usage. Server info: Debian Lenny, CPU 510Mhz, RAM 512MB Is it bad? What could possibly cause this? Thank you for any suggestions.

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  • How to interpret the bash command "usage" syntax?

    - by raoulsson
    How exactly do you have to interpret the output of a commands "usage" output, in bash for example. For example, on my OS X, cp gives me usage: cp [-R [-H | -L | -P]] [-fi | -n] [-apvX] source_file target_file cp [-R [-H | -L | -P]] [-fi | -n] [-apvX] source_file ... target_directory What does the nested options, like -H within -R, indicate? Does upper and lower case have any meaning? When is an argument optional, required? I need to implement a telnet command line against a program of mine and I would like to get this straight.

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  • how to lower CPU usage for Ableton Live 8 in Mac OSX 10.6.8

    - by Travis Dtfsu Crum
    While running Ableton Live 8 after a project gets to a certain size, the audio starts to get a bit choppy and distorted. My levels are fine and is not the cause of the lag and chop. I have samples set to 1024 and the high quality button selected which I need to hear the sound. I always lower these when I'm recording audio with my mic but I need to have these turned up to be able to mix master the song. Anything I can do to lower the CPU usage? It sucks because I can't even use my iZotope Ozone plug-ins because of their CPU usage and they are AMAZING plug-ins that I would love to use.

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  • Html index page and files in that directory

    - by Frank
    On my web site, there is an index page, but if I take out that index page, users will see the files in that directory, for instance my site is : XYZ.com and I have a directory called "My_Dir", so when a user typed in "XYZ.com/My_Dir" he will see the index.html if there is one, but if it's not there, he will see a list of all my files inside "My_Dir", so is it safe to assume that with an index page in any of my sub directories, I can hide all the files in those directories from users, in other words if I have "123.txt, abc.html and index.html" in "My_Dir", users won't be able to see "123.txt, abc.html" because of the existence of "index.html" [ unless of course I mention those two files inside index.html ] ? Frank

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  • MaxTotalSizeInBytes - Blind spots in Usage file and Web Analytics Reports

    - by Gino Abraham
    Originally posted on: http://geekswithblogs.net/GinoAbraham/archive/2013/10/28/maxtotalsizeinbytes---blind-spots-in-usage-file-and-web-analytics.aspx http://blogs.msdn.com/b/sharepoint_strategery/archive/2012/04/16/usage-file-and-web-analytics-reports-with-blind-spots.aspx In my previous post (Troubleshooting SharePoint 2010 Web Analytics), I referenced a problem that can occur when exceeding the daily partition size for the LoggingDB, which generates the ULS message “[Partition] has exceeded the max bytes”. Below, I wanted to provide some additional info on this particular issue and help identify some options if this occurs. As an aside, this post only applies if you are missing portions of Usage data - think blind spots on intermittent days or user activity regularly sparse for the afternoon/evening. If this fits your scenario - read on. But if Usage logs are outright missing, go check out my Troubleshooting post first.  Background on the problem:The LoggingDB database has a default maximum size of ~6GB. However, SharePoint evenly splits this total size into fixed sized logical partitions – and the number of partitions is defined by the number of days to retain Usage data (by default 14 days). In this case, 14 partitions would be created to account for the 14 days of retention. If the retention were halved to 7 days, the LoggingDBwould be split into 7 corresponding partitions at twice the size. In other words, the partition size is generally defined as [max size for DB] / [number of retention days].Going back to the default scenario, the “max size” for the LoggingDB is 6200000000 bytes (~6GB) and the retention period is 14 days. Using our formula, this would be [~6GB] / [14 days], which equates to 444858368 bytes (~425MB) per partition per day. Again, if the retention were halved to 7 days (which halves the number of partitions), the resulting partition size becomes [~6GB] / [7 days], or ~850MB per partition.From my experience, when the partition size for any given day is exceeded, the usage logging for the remainder of the day is essentially thrown away because SharePoint won’t allow any more to be written to that day’s partition. The only clue that this is occurring (beyond truncated usage data) is an error such as the following that gets reported in the ULS:04/08/2012 09:30:04.78    OWSTIMER.EXE (0x1E24)    0x2C98    SharePoint Foundation    Health    i0m6     High    Table RequestUsage_Partition12 has 444858368 bytes that has exceeded the max bytes 444858368It’s also worth noting that the exact bytes reported (e.g. ‘444858368’ above) may slightly vary among farms. For example, you may instead see 445226812, 439123456, or something else in the ballpark. The exact number itself doesn't matter, but this error message intends to indicates that the reporting usage has exceeded the partition size for the given day.What it means:The error itself is easy to miss, which can lead to substantial gaps in the reporting data (your mileage may vary) if not identified. At this point, I can only advise to periodically check the ULS logs for this message. Down the road, I plan to explore if [Developing a Custom Health Rule] could be leveraged to identify the issue (If you've ever built Custom Health Rules, I'd be interested to hear about your experiences). Overcoming this issue also poses a challenge, with workaround options including:Lower the retentionBecause the partition size is generally defined as [max size] / [number of retention days], the first option is to lower the number of days to retain the data – the lower the retention, the lower the divisor and thus a bigger partition. For example, halving the retention from 14 to 7 days would halve the number of partitions, but double the partition size to ~850MB (e.g. [6200000000 bytes] / [7 days] = ~850GB partitions). Lowering it to 2 days would result in two ~3GB partitions… and so on.Recreate the LoggingDB with an increased sizeThe property MaxTotalSizeInBytes is exposed by OM code for the SPUsageDefinition object and can be updated with the example PowerShell snippet below. However, updating this value has no immediate impact because this size only applies when creating a LoggingDB. Therefore, you must create a newLoggingDB for the Usage Service Application. The gotcha: this effectively deletes all prior Usage databecause the Usage Service Application can only have a single LoggingDB.Here is an example snippet to update the "Page Requests" Usage Definition:$def=Get-SPUsageDefinition -Identity "page requests" $def.MaxTotalSizeInBytes=12400000000 $def.update()Create a new Logging database and attach to the Usage Service Application using the following command: Get-spusageapplication | Set-SPUsageApplication -DatabaseServer <dbServer> -DatabaseName <newDBname> Updated (5/10/2012): Once the new database has been created, you can confirm the setting has truly taken by running the following SQL Query (be sure to replace the database name in the following query with the name provided in the PowerShell above)SELECT * FROM [WSS_UsageApplication].[dbo].[Configuration] WITH (nolock) WHERE ConfigName LIKE 'Max Total Bytes - RequestUsage'

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  • Upload File to Windows Azure Blob in Chunks through ASP.NET MVC, JavaScript and HTML5

    - by Shaun
    Originally posted on: http://geekswithblogs.net/shaunxu/archive/2013/07/01/upload-file-to-windows-azure-blob-in-chunks-through-asp.net.aspxMany people are using Windows Azure Blob Storage to store their data in the cloud. Blob storage provides 99.9% availability with easy-to-use API through .NET SDK and HTTP REST. For example, we can store JavaScript files, images, documents in blob storage when we are building an ASP.NET web application on a Web Role in Windows Azure. Or we can store our VHD files in blob and mount it as a hard drive in our cloud service. If you are familiar with Windows Azure, you should know that there are two kinds of blob: page blob and block blob. The page blob is optimized for random read and write, which is very useful when you need to store VHD files. The block blob is optimized for sequential/chunk read and write, which has more common usage. Since we can upload block blob in blocks through BlockBlob.PutBlock, and them commit them as a whole blob with invoking the BlockBlob.PutBlockList, it is very powerful to upload large files, as we can upload blocks in parallel, and provide pause-resume feature. There are many documents, articles and blog posts described on how to upload a block blob. Most of them are focus on the server side, which means when you had received a big file, stream or binaries, how to upload them into blob storage in blocks through .NET SDK.  But the problem is, how can we upload these large files from client side, for example, a browser. This questioned to me when I was working with a Chinese customer to help them build a network disk production on top of azure. The end users upload their files from the web portal, and then the files will be stored in blob storage from the Web Role. My goal is to find the best way to transform the file from client (end user’s machine) to the server (Web Role) through browser. In this post I will demonstrate and describe what I had done, to upload large file in chunks with high speed, and save them as blocks into Windows Azure Blob Storage.   Traditional Upload, Works with Limitation The simplest way to implement this requirement is to create a web page with a form that contains a file input element and a submit button. 1: @using (Html.BeginForm("About", "Index", FormMethod.Post, new { enctype = "multipart/form-data" })) 2: { 3: <input type="file" name="file" /> 4: <input type="submit" value="upload" /> 5: } And then in the backend controller, we retrieve the whole content of this file and upload it in to the blob storage through .NET SDK. We can split the file in blocks and upload them in parallel and commit. The code had been well blogged in the community. 1: [HttpPost] 2: public ActionResult About(HttpPostedFileBase file) 3: { 4: var container = _client.GetContainerReference("test"); 5: container.CreateIfNotExists(); 6: var blob = container.GetBlockBlobReference(file.FileName); 7: var blockDataList = new Dictionary<string, byte[]>(); 8: using (var stream = file.InputStream) 9: { 10: var blockSizeInKB = 1024; 11: var offset = 0; 12: var index = 0; 13: while (offset < stream.Length) 14: { 15: var readLength = Math.Min(1024 * blockSizeInKB, (int)stream.Length - offset); 16: var blockData = new byte[readLength]; 17: offset += stream.Read(blockData, 0, readLength); 18: blockDataList.Add(Convert.ToBase64String(BitConverter.GetBytes(index)), blockData); 19:  20: index++; 21: } 22: } 23:  24: Parallel.ForEach(blockDataList, (bi) => 25: { 26: blob.PutBlock(bi.Key, new MemoryStream(bi.Value), null); 27: }); 28: blob.PutBlockList(blockDataList.Select(b => b.Key).ToArray()); 29:  30: return RedirectToAction("About"); 31: } This works perfect if we selected an image, a music or a small video to upload. But if I selected a large file, let’s say a 6GB HD-movie, after upload for about few minutes the page will be shown as below and the upload will be terminated. In ASP.NET there is a limitation of request length and the maximized request length is defined in the web.config file. It’s a number which less than about 4GB. So if we want to upload a really big file, we cannot simply implement in this way. Also, in Windows Azure, a cloud service network load balancer will terminate the connection if exceed the timeout period. From my test the timeout looks like 2 - 3 minutes. Hence, when we need to upload a large file we cannot just use the basic HTML elements. Besides the limitation mentioned above, the simple HTML file upload cannot provide rich upload experience such as chunk upload, pause and pause-resume. So we need to find a better way to upload large file from the client to the server.   Upload in Chunks through HTML5 and JavaScript In order to break those limitation mentioned above we will try to upload the large file in chunks. This takes some benefit to us such as - No request size limitation: Since we upload in chunks, we can define the request size for each chunks regardless how big the entire file is. - No timeout problem: The size of chunks are controlled by us, which means we should be able to make sure request for each chunk upload will not exceed the timeout period of both ASP.NET and Windows Azure load balancer. It was a big challenge to upload big file in chunks until we have HTML5. There are some new features and improvements introduced in HTML5 and we will use them to implement our solution.   In HTML5, the File interface had been improved with a new method called “slice”. It can be used to read part of the file by specifying the start byte index and the end byte index. For example if the entire file was 1024 bytes, file.slice(512, 768) will read the part of this file from the 512nd byte to 768th byte, and return a new object of interface called "Blob”, which you can treat as an array of bytes. In fact,  a Blob object represents a file-like object of immutable, raw data. The File interface is based on Blob, inheriting blob functionality and expanding it to support files on the user's system. For more information about the Blob please refer here. File and Blob is very useful to implement the chunk upload. We will use File interface to represent the file the user selected from the browser and then use File.slice to read the file in chunks in the size we wanted. For example, if we wanted to upload a 10MB file with 512KB chunks, then we can read it in 512KB blobs by using File.slice in a loop.   Assuming we have a web page as below. User can select a file, an input box to specify the block size in KB and a button to start upload. 1: <div> 2: <input type="file" id="upload_files" name="files[]" /><br /> 3: Block Size: <input type="number" id="block_size" value="512" name="block_size" />KB<br /> 4: <input type="button" id="upload_button_blob" name="upload" value="upload (blob)" /> 5: </div> Then we can have the JavaScript function to upload the file in chunks when user clicked the button. 1: <script type="text/javascript"> 1: 2: $(function () { 3: $("#upload_button_blob").click(function () { 4: }); 5: });</script> Firstly we need to ensure the client browser supports the interfaces we are going to use. Just try to invoke the File, Blob and FormData from the “window” object. If any of them is “undefined” the condition result will be “false” which means your browser doesn’t support these premium feature and it’s time for you to get your browser updated. FormData is another new feature we are going to use in the future. It could generate a temporary form for us. We will use this interface to create a form with chunk and associated metadata when invoked the service through ajax. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: if (window.File && window.Blob && window.FormData) { 4: alert("Your brwoser is awesome, let's rock!"); 5: } 6: else { 7: alert("Oh man plz update to a modern browser before try is cool stuff out."); 8: return; 9: } 10: }); Each browser supports these interfaces by their own implementation and currently the Blob, File and File.slice are supported by Chrome 21, FireFox 13, IE 10, Opera 12 and Safari 5.1 or higher. After that we worked on the files the user selected one by one since in HTML5, user can select multiple files in one file input box. 1: var files = $("#upload_files")[0].files; 2: for (var i = 0; i < files.length; i++) { 3: var file = files[i]; 4: var fileSize = file.size; 5: var fileName = file.name; 6: } Next, we calculated the start index and end index for each chunks based on the size the user specified from the browser. We put them into an array with the file name and the index, which will be used when we upload chunks into Windows Azure Blob Storage as blocks since we need to specify the target blob name and the block index. At the same time we will store the list of all indexes into another variant which will be used to commit blocks into blob in Azure Storage once all chunks had been uploaded successfully. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10:  11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: var blockSizeInKB = $("#block_size").val(); 14: var blockSize = blockSizeInKB * 1024; 15: var blocks = []; 16: var offset = 0; 17: var index = 0; 18: var list = ""; 19: while (offset < fileSize) { 20: var start = offset; 21: var end = Math.min(offset + blockSize, fileSize); 22:  23: blocks.push({ 24: name: fileName, 25: index: index, 26: start: start, 27: end: end 28: }); 29: list += index + ","; 30:  31: offset = end; 32: index++; 33: } 34: } 35: }); Now we have all chunks’ information ready. The next step should be upload them one by one to the server side, and at the server side when received a chunk it will upload as a block into Blob Storage, and finally commit them with the index list through BlockBlobClient.PutBlockList. But since all these invokes are ajax calling, which means not synchronized call. So we need to introduce a new JavaScript library to help us coordinate the asynchronize operation, which named “async.js”. You can download this JavaScript library here, and you can find the document here. I will not explain this library too much in this post. We will put all procedures we want to execute as a function array, and pass into the proper function defined in async.js to let it help us to control the execution sequence, in series or in parallel. Hence we will define an array and put the function for chunk upload into this array. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4:  5: // start to upload each files in chunks 6: var files = $("#upload_files")[0].files; 7: for (var i = 0; i < files.length; i++) { 8: var file = files[i]; 9: var fileSize = file.size; 10: var fileName = file.name; 11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: ... ... 14:  15: // define the function array and push all chunk upload operation into this array 16: blocks.forEach(function (block) { 17: putBlocks.push(function (callback) { 18: }); 19: }); 20: } 21: }); 22: }); As you can see, I used File.slice method to read each chunks based on the start and end byte index we calculated previously, and constructed a temporary HTML form with the file name, chunk index and chunk data through another new feature in HTML5 named FormData. Then post this form to the backend server through jQuery.ajax. This is the key part of our solution. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: blocks.forEach(function (block) { 15: putBlocks.push(function (callback) { 16: // load blob based on the start and end index for each chunks 17: var blob = file.slice(block.start, block.end); 18: // put the file name, index and blob into a temporary from 19: var fd = new FormData(); 20: fd.append("name", block.name); 21: fd.append("index", block.index); 22: fd.append("file", blob); 23: // post the form to backend service (asp.net mvc controller action) 24: $.ajax({ 25: url: "/Home/UploadInFormData", 26: data: fd, 27: processData: false, 28: contentType: "multipart/form-data", 29: type: "POST", 30: success: function (result) { 31: if (!result.success) { 32: alert(result.error); 33: } 34: callback(null, block.index); 35: } 36: }); 37: }); 38: }); 39: } 40: }); Then we will invoke these functions one by one by using the async.js. And once all functions had been executed successfully I invoked another ajax call to the backend service to commit all these chunks (blocks) as the blob in Windows Azure Storage. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.series(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); That’s all in the client side. The outline of our logic would be - Calculate the start and end byte index for each chunks based on the block size. - Defined the functions of reading the chunk form file and upload the content to the backend service through ajax. - Execute the functions defined in previous step with “async.js”. - Commit the chunks by invoking the backend service in Windows Azure Storage finally.   Save Chunks as Blocks into Blob Storage In above we finished the client size JavaScript code. It uploaded the file in chunks to the backend service which we are going to implement in this step. We will use ASP.NET MVC as our backend service, and it will receive the chunks, upload into Windows Azure Bob Storage in blocks, then finally commit as one blob. As in the client side we uploaded chunks by invoking the ajax call to the URL "/Home/UploadInFormData", I created a new action under the Index controller and it only accepts HTTP POST request. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: } 8: catch (Exception e) 9: { 10: error = e.ToString(); 11: } 12:  13: return new JsonResult() 14: { 15: Data = new 16: { 17: success = string.IsNullOrWhiteSpace(error), 18: error = error 19: } 20: }; 21: } Then I retrieved the file name, index and the chunk content from the Request.Form object, which was passed from our client side. And then, used the Windows Azure SDK to create a blob container (in this case we will use the container named “test”.) and create a blob reference with the blob name (same as the file name). Then uploaded the chunk as a block of this blob with the index, since in Blob Storage each block must have an index (ID) associated with so that finally we can put all blocks as one blob by specifying their block ID list. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var index = int.Parse(Request.Form["index"]); 9: var file = Request.Files[0]; 10: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 11:  12: var container = _client.GetContainerReference("test"); 13: container.CreateIfNotExists(); 14: var blob = container.GetBlockBlobReference(name); 15: blob.PutBlock(id, file.InputStream, null); 16: } 17: catch (Exception e) 18: { 19: error = e.ToString(); 20: } 21:  22: return new JsonResult() 23: { 24: Data = new 25: { 26: success = string.IsNullOrWhiteSpace(error), 27: error = error 28: } 29: }; 30: } Next, I created another action to commit the blocks into blob once all chunks had been uploaded. Similarly, I retrieved the blob name from the Request.Form. I also retrieved the chunks ID list, which is the block ID list from the Request.Form in a string format, split them as a list, then invoked the BlockBlob.PutBlockList method. After that our blob will be shown in the container and ready to be download. 1: [HttpPost] 2: public JsonResult Commit() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var list = Request.Form["list"]; 9: var ids = list 10: .Split(',') 11: .Where(id => !string.IsNullOrWhiteSpace(id)) 12: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 13: .ToArray(); 14:  15: var container = _client.GetContainerReference("test"); 16: container.CreateIfNotExists(); 17: var blob = container.GetBlockBlobReference(name); 18: blob.PutBlockList(ids); 19: } 20: catch (Exception e) 21: { 22: error = e.ToString(); 23: } 24:  25: return new JsonResult() 26: { 27: Data = new 28: { 29: success = string.IsNullOrWhiteSpace(error), 30: error = error 31: } 32: }; 33: } Now we finished all code we need. The whole process of uploading would be like this below. Below is the full client side JavaScript code. 1: <script type="text/javascript" src="~/Scripts/async.js"></script> 2: <script type="text/javascript"> 3: $(function () { 4: $("#upload_button_blob").click(function () { 5: // assert the browser support html5 6: if (window.File && window.Blob && window.FormData) { 7: alert("Your brwoser is awesome, let's rock!"); 8: } 9: else { 10: alert("Oh man plz update to a modern browser before try is cool stuff out."); 11: return; 12: } 13:  14: // start to upload each files in chunks 15: var files = $("#upload_files")[0].files; 16: for (var i = 0; i < files.length; i++) { 17: var file = files[i]; 18: var fileSize = file.size; 19: var fileName = file.name; 20:  21: // calculate the start and end byte index for each blocks(chunks) 22: // with the index, file name and index list for future using 23: var blockSizeInKB = $("#block_size").val(); 24: var blockSize = blockSizeInKB * 1024; 25: var blocks = []; 26: var offset = 0; 27: var index = 0; 28: var list = ""; 29: while (offset < fileSize) { 30: var start = offset; 31: var end = Math.min(offset + blockSize, fileSize); 32:  33: blocks.push({ 34: name: fileName, 35: index: index, 36: start: start, 37: end: end 38: }); 39: list += index + ","; 40:  41: offset = end; 42: index++; 43: } 44:  45: // define the function array and push all chunk upload operation into this array 46: var putBlocks = []; 47: blocks.forEach(function (block) { 48: putBlocks.push(function (callback) { 49: // load blob based on the start and end index for each chunks 50: var blob = file.slice(block.start, block.end); 51: // put the file name, index and blob into a temporary from 52: var fd = new FormData(); 53: fd.append("name", block.name); 54: fd.append("index", block.index); 55: fd.append("file", blob); 56: // post the form to backend service (asp.net mvc controller action) 57: $.ajax({ 58: url: "/Home/UploadInFormData", 59: data: fd, 60: processData: false, 61: contentType: "multipart/form-data", 62: type: "POST", 63: success: function (result) { 64: if (!result.success) { 65: alert(result.error); 66: } 67: callback(null, block.index); 68: } 69: }); 70: }); 71: }); 72:  73: // invoke the functions one by one 74: // then invoke the commit ajax call to put blocks into blob in azure storage 75: async.series(putBlocks, function (error, result) { 76: var data = { 77: name: fileName, 78: list: list 79: }; 80: $.post("/Home/Commit", data, function (result) { 81: if (!result.success) { 82: alert(result.error); 83: } 84: else { 85: alert("done!"); 86: } 87: }); 88: }); 89: } 90: }); 91: }); 92: </script> And below is the full ASP.NET MVC controller code. 1: public class HomeController : Controller 2: { 3: private CloudStorageAccount _account; 4: private CloudBlobClient _client; 5:  6: public HomeController() 7: : base() 8: { 9: _account = CloudStorageAccount.Parse(CloudConfigurationManager.GetSetting("DataConnectionString")); 10: _client = _account.CreateCloudBlobClient(); 11: } 12:  13: public ActionResult Index() 14: { 15: ViewBag.Message = "Modify this template to jump-start your ASP.NET MVC application."; 16:  17: return View(); 18: } 19:  20: [HttpPost] 21: public JsonResult UploadInFormData() 22: { 23: var error = string.Empty; 24: try 25: { 26: var name = Request.Form["name"]; 27: var index = int.Parse(Request.Form["index"]); 28: var file = Request.Files[0]; 29: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 30:  31: var container = _client.GetContainerReference("test"); 32: container.CreateIfNotExists(); 33: var blob = container.GetBlockBlobReference(name); 34: blob.PutBlock(id, file.InputStream, null); 35: } 36: catch (Exception e) 37: { 38: error = e.ToString(); 39: } 40:  41: return new JsonResult() 42: { 43: Data = new 44: { 45: success = string.IsNullOrWhiteSpace(error), 46: error = error 47: } 48: }; 49: } 50:  51: [HttpPost] 52: public JsonResult Commit() 53: { 54: var error = string.Empty; 55: try 56: { 57: var name = Request.Form["name"]; 58: var list = Request.Form["list"]; 59: var ids = list 60: .Split(',') 61: .Where(id => !string.IsNullOrWhiteSpace(id)) 62: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 63: .ToArray(); 64:  65: var container = _client.GetContainerReference("test"); 66: container.CreateIfNotExists(); 67: var blob = container.GetBlockBlobReference(name); 68: blob.PutBlockList(ids); 69: } 70: catch (Exception e) 71: { 72: error = e.ToString(); 73: } 74:  75: return new JsonResult() 76: { 77: Data = new 78: { 79: success = string.IsNullOrWhiteSpace(error), 80: error = error 81: } 82: }; 83: } 84: } And if we selected a file from the browser we will see our application will upload chunks in the size we specified to the server through ajax call in background, and then commit all chunks in one blob. Then we can find the blob in our Windows Azure Blob Storage.   Optimized by Parallel Upload In previous example we just uploaded our file in chunks. This solved the problem that ASP.NET MVC request content size limitation as well as the Windows Azure load balancer timeout. But it might introduce the performance problem since we uploaded chunks in sequence. In order to improve the upload performance we could modify our client side code a bit to make the upload operation invoked in parallel. The good news is that, “async.js” library provides the parallel execution function. If you remembered the code we invoke the service to upload chunks, it utilized “async.series” which means all functions will be executed in sequence. Now we will change this code to “async.parallel”. This will invoke all functions in parallel. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallel(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); In this way all chunks will be uploaded to the server side at the same time to maximize the bandwidth usage. This should work if the file was not very large and the chunk size was not very small. But for large file this might introduce another problem that too many ajax calls are sent to the server at the same time. So the best solution should be, upload the chunks in parallel with maximum concurrency limitation. The code below specified the concurrency limitation to 4, which means at the most only 4 ajax calls could be invoked at the same time. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallelLimit(putBlocks, 4, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: });   Summary In this post we discussed how to upload files in chunks to the backend service and then upload them into Windows Azure Blob Storage in blocks. We focused on the frontend side and leverage three new feature introduced in HTML 5 which are - File.slice: Read part of the file by specifying the start and end byte index. - Blob: File-like interface which contains the part of the file content. - FormData: Temporary form element that we can pass the chunk alone with some metadata to the backend service. Then we discussed the performance consideration of chunk uploading. Sequence upload cannot provide maximized upload speed, but the unlimited parallel upload might crash the browser and server if too many chunks. So we finally came up with the solution to upload chunks in parallel with the concurrency limitation. We also demonstrated how to utilize “async.js” JavaScript library to help us control the asynchronize call and the parallel limitation.   Regarding the chunk size and the parallel limitation value there is no “best” value. You need to test vary composition and find out the best one for your particular scenario. It depends on the local bandwidth, client machine cores and the server side (Windows Azure Cloud Service Virtual Machine) cores, memory and bandwidth. Below is one of my performance test result. The client machine was Windows 8 IE 10 with 4 cores. I was using Microsoft Cooperation Network. The web site was hosted on Windows Azure China North data center (in Beijing) with one small web role (1.7GB 1 core CPU, 1.75GB memory with 100Mbps bandwidth). The test cases were - Chunk size: 512KB, 1MB, 2MB, 4MB. - Upload Mode: Sequence, parallel (unlimited), parallel with limit (4 threads, 8 threads). - Chunk Format: base64 string, binaries. - Target file: 100MB. - Each case was tested 3 times. Below is the test result chart. Some thoughts, but not guidance or best practice: - Parallel gets better performance than series. - No significant performance improvement between parallel 4 threads and 8 threads. - Transform with binaries provides better performance than base64. - In all cases, chunk size in 1MB - 2MB gets better performance.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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