I'm using Seafile (on docker) to sync some files to a Synology nas and it is all working correctly. I've created an external folder that is pointed to /shared folder in the container.
I think I already know the answer, but are the files synced to the server stored 'normally' somewhere? i.e. If I sync a folder called 'photos' and it has 'a.jpg' in it, will I be able to find that file on the seafile server?
The reason for the question is I would like to backup the original files that are sync'd, rather than having to backup the seafile DB, etc.
(I am aware that syncthing does what I want, so I may choose to use that instead, just want to confirm my understanding)
Thanks
TLDR;
No you won't find your a.jpg file on the server. Your files are going to be turned into blocks of bytes.
To understand
If you take a look at this part of the documentation of data model
FS
There are two types of FS objects, SeafDir Object and Seafile Object. SeafDir Object represents a directory, and Seafile Object represents a file.
Block
A file is further divided into blocks with variable lengths. We use Content Defined Chunking algorithm to divide file into blocks. A clear overview of this algorithm can be found at http://pdos.csail.mit.edu/papers/lbfs:sosp01/lbfs.pdf. On average, a block's size is around 1MB.
So backing up files will won't be as easy as making a raw copy of the seafile drive. As mentioned by #JensV you may still achieve something along those lines using the seafile drive client.
I am looking for a way to read data from netcdf format files stored within a tar file without extracting the file first. The reason for this is we have thousands of such data file of significant file size each, and extracting them would require significant disk space and time.
Is there a way I can achieve this using Matlab or other ways? some online topics discuss reading text file within tar file without extracting using linux, but not netcdf file.
I see there may be ways to do this on a unix/Linux machine, but is there a way to do the same in a Windows operating system?
I reached out to Matlab support and they gave me a solution that reduced the tar extraction time significantly.
Solution: Instead of using Matlab “untar” command, use direct system command as : system(‘tar xzvf filename.tar.gz *.nc’).
This reduced extraction time for a file from 13 minute to 8 seconds.
Lets say I have two files with two different sha1 hashes. Is there a way to change the hash of one file to the hash from the other file so they both have the same hash value?
The two files have different values in it.
Yes.
Either:
Change the content of the file so the two files are identical or
Find another set of data (you'll probably have to brute force search this so it would take a lot of computing power) which happens to hash to the same value
You can't change the hash without changing the content of the file. The hash is just what you get after doing some calculations on the content of the file.
how can I set one md5 hash to multiple .ps files?
I know how to change md5 hash, but no idea how to set it to value which I would like to have.
To create two files with the same md5 hash you can have a look here https://natmchugh.blogspot.co.za/2014/10/how-i-made-two-php-files-with-same-md5.html?m=1 . The tricky part may be getting them to remain as valid PS files. In the example the author takes advantage of Pups language to add the differing text in such a way the syntax remains correct. Perhaps in a PS text section. Also have a look at https://natmchugh.blogspot.co.za/2015/05/how-to-make-two-binaries-with-same-md5.html?m=1
However usually you do not set the md5 hash but generate it from a file. This allows the recipient of the file to confirm it has not been corrupted by generating the md5 hash on their end and comparing with the one you gave them. If you are looking to generate one md5 hash for multiple files you could
Compress the files and generate the md5 hash from that file
Generate multiple md5 hashes and join them using an algorithm that always ensures the same value is produced from the hashes each time. For example concatenate the hashes or create an md5 hash of all the md5 hashes. The downside is whoever will use the hashes will also need to know the algorithm.
Have a look at Combine MD5 hashes of multiple files
A product that I am working on collects several thousand readings a day and stores them as 64k binary files on a NTFS partition (Windows XP). After a year in production there is over 300000 files in a single directory and the number keeps growing. This has made accessing the parent/ancestor directories from windows explorer very time consuming.
I have tried turning off the indexing service but that made no difference. I have also contemplated moving the file content into a database/zip files/tarballs but it is beneficial for us to access the files individually; basically, the files are still needed for research purposes and the researchers are not willing to deal with anything else.
Is there a way to optimize NTFS or Windows so that it can work with all these small files?
NTFS actually will perform fine with many more than 10,000 files in a directory as long as you tell it to stop creating alternative file names compatible with 16 bit Windows platforms. By default NTFS automatically creates an '8 dot 3' file name for every file that is created. This becomes a problem when there are many files in a directory because Windows looks at the files in the directory to make sure the name they are creating isn't already in use. You can disable '8 dot 3' naming by setting the NtfsDisable8dot3NameCreation registry value to 1. The value is found in the HKEY_LOCAL_MACHINE\System\CurrentControlSet\Control\FileSystem registry path. It is safe to make this change as '8 dot 3' name files are only required by programs written for very old versions of Windows.
A reboot is required before this setting will take effect.
NTFS performance severely degrades after 10,000 files in a directory. What you do is create an additional level in the directory hierarchy, with each subdirectory having 10,000 files.
For what it's worth, this is the approach that the SVN folks took in version 1.5. They used 1,000 files as the default threshold.
The performance issue is being caused by the huge amount of files in a single directory: once you eliminate that, you should be fine. This isn't a NTFS-specific problem: in fact, it's commonly encountered with user home/mail files on large UNIX systems.
One obvious way to resolve this issue, is moving the files to folders with a name based on the file name. Assuming all your files have file names of similar length, e.g. ABCDEFGHI.db, ABCEFGHIJ.db, etc, create a directory structure like this:
ABC\
DEF\
ABCDEFGHI.db
EFG\
ABCEFGHIJ.db
Using this structure, you can quickly locate a file based on its name. If the file names have variable lengths, pick a maximum length, and prepend zeroes (or any other character) in order to determine the directory the file belongs in.
I have seen vast improvements in the past from splitting the files up into a nested hierarchy of directories by, e.g., first then second letter of filename; then each directory does not contain an excessive number of files. Manipulating the whole database is still slow, however.
I have run into this problem lots of times in the past. We tried storing by date, zipping files below the date so you don't have lots of small files, etc. All of them were bandaids to the real problem of storing the data as lots of small files on NTFS.
You can go to ZFS or some other file system that handles small files better, but still stop and ask if you NEED to store the small files.
In our case we eventually went to a system were all of the small files for a certain date were appended in a TAR type of fashion with simple delimiters to parse them. The disk files went from 1.2 million to under a few thousand. They actually loaded faster because NTFS can't handle the small files very well, and the drive was better able to cache a 1MB file anyway. In our case the access and parse time to find the right part of the file was minimal compared to the actual storage and maintenance of stored files.
You could try using something like Solid File System.
This gives you a virtual file system that applications can mount as if it were a physical disk. Your application sees lots of small files, but just one file sits on your hard drive.
http://www.eldos.com/solfsdrv/
If you can calculate names of files, you might be able to sort them into folders by date, so that each folder only have files for a particular date. You might also want to create month and year hierarchies.
Also, could you move files older than say, a year, to a different (but still accessible) location?
Finally, and again, this requires you to be able to calculate names, you'll find that directly accessing a file is much faster than trying to open it via explorer. For example, saying
notepad.exe "P:\ath\to\your\filen.ame"
from the command line should actually be pretty quick, assuming you know the path of the file you need without having to get a directory listing.
One common trick is to simply create a handful of subdirectories and divvy up the files.
For instance, Doxygen, an automated code documentation program which can produce tons of html pages, has an option for creating a two-level deep directory hierarchy. The files are then evenly distributed across the bottom directories.
Aside from placing the files in sub-directories..
Personally, I would develop an application that keeps the interface to that folder the same, ie all files are displayed as being individual files. Then in the application background actually takes these files and combine them into a larger files(and since the sizes are always 64k getting the data you need should be relatively easy) To get rid of the mess you have.
So you can still make it easy for them to access the files they want, but also lets you have more control how everything is structured.
Having hundreds of thousands of files in a single directory will indeed cripple NTFS, and there is not really much you can do about that. You should reconsider storing the data in a more practical format, like one big tarball or in a database.
If you really need a separate file for each reading, you should sort them into several sub directories instead of having all of them in the same directory. You can do this by creating a hierarchy of directories and put the files in different ones depending on the file name. This way you can still store and load your files knowing just the file name.
The method we use is to take the last few letters of the file name, reversing them, and creating one letter directories from that. Consider the following files for example:
1.xml
24.xml
12331.xml
2304252.xml
you can sort them into directories like so:
data/1.xml
data/24.xml
data/1/3/3/12331.xml
data/2/5/2/4/0/2304252.xml
This scheme will ensure that you will never have more than 100 files in each directory.
Consider pushing them to another server that uses a filesystem friendlier to massive quantities of small files (Solaris w/ZFS for example)?
If there are any meaningful, categorical, aspects of the data you could nest them in a directory tree. I believe the slowdown is due to the number of files in one directory, not the sheer number of files itself.
The most obvious, general grouping is by date, and gives you a three-tiered nesting structure (year, month, day) with a relatively safe bound on the number of files in each leaf directory (1-3k).
Even if you are able to improve the filesystem/file browser performance, it sounds like this is a problem you will run into in another 2 years, or 3 years... just looking at a list of 0.3-1mil files is going to incur a cost, so it may be better in the long-term to find ways to only look at smaller subsets of the files.
Using tools like 'find' (under cygwin, or mingw) can make the presence of the subdirectory tree a non-issue when browsing files.
Rename the folder each day with a time stamp.
If the application is saving the files into c:\Readings, then set up a scheduled task to rename Reading at midnight and create a new empty folder.
Then you will get one folder for each day, each containing several thousand files.
You can extend the method further to group by month. For example, C:\Reading become c:\Archive\September\22.
You have to be careful with your timing to ensure you are not trying to rename the folder while the product is saving to it.
To create a folder structure that will scale to a large unknown number of files, I like the following system:
Split the filename into fixed length pieces, and then create nested folders for each piece except the last.
The advantage of this system is that the depth of the folder structure only grows as deep as the length of the filename. So if your files are automatically generated in a numeric sequence, the structure is only is deep is it needs to be.
12.jpg -> 12.jpg
123.jpg -> 12\123.jpg
123456.jpg -> 12\34\123456.jpg
This approach does mean that folders contain files and sub-folders, but I think it's a reasonable trade off.
And here's a beautiful PowerShell one-liner to get you going!
$s = '123456'
-join (( $s -replace '(..)(?!$)', '$1\' -replace '[^\\]*$','' ), $s )