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.
I am relatively new to Tableau and may have encountered some data loss .. but let us proceed to see if there might be some means to salvage the data.
Upon re-loading a workbook after a couple of weeks of non use we can see reference to an Extract load attempt from a temporary (OS/X) location:
Now I had not realized that the Extract were not being saved with the .twb itself - and even less that it were in a transient disk location.
So .. is the data gone? Secondarily - did I miss some step that would nudge me to save the extract to a non-volatile/non temporary location on disk?
Your file location shows you are accessing file from temporary location and you are loading your workbook after a couple of week.
So may be your OS deleted your files.
Try to save your files in safe location rather then accessing from temporary location.
i want to process the multiple files using talend and one after another and the size of the files are large and while processing one file if another file comes into that directory it has to process that file also.
is there any possible way to do this could you please suggest guys?
You can use tFileList component, which will iterate all the files in a given directory.
You can check the component functionality here
Simple concept would be,
When there is a file in a directory say Folder1, move that file to another location say Folder2.
After processing file in Folder2 again, check Folder1, that is any new files arrived.
If arrived, then again move that file to Folder2 and process it.
If there is no new file, end the job.
A great way to do this in Talend is to setup a file watcher job which is simple to do. Talend provides the tWaitForFile Component which will watch a directory for files. You can configure the max iterations in which it will look for the file and time between polls/scans. Since you said you are loading large files, to avoid DB concurrency issues give enough time between scans to account for this.
In my example below I am watching a directory for new files, scanning every 60 seconds over an 8 hour period. You would want to schedule the job in either the TAC or whatever scheduling tool you use. In my example I simply join to a tJavaRow and display the information about the file that was found.
you can see the output from my tJavaRow here which shows the file info:
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 )