I'm writing a "file sharing hosting" and I want to rename all the files when uploading to a unique name and somehow keep track of the names on the database. Since I don't want two or more files having same name (which is surely impossible), I'm looking for an algorithm which based on key or something generates random names for me.
Moreover, I don't want to generate a name and search the database to see if the file already exists. I want to make sure 100% or 99% that the generated filename has never been created earlier by my application.
Any idea how I can write such application?
You could produce a hash based on the file contents itself. There are two good reasons to do this:
Allows you to never store the same file twice - for example, if you have two copies of a music file which are identical in content you could check to see if you have already stored that file, and just store it once.
You separate meta-data (file name is just meta data) from the blob. So you would have a storage system which is indexed by the hash of the file contents, and you then associate the file meta-data with that hash lookup code.
The risk of finding two files that compute the same hash that aren't indeed the same contents, depending on the size of the hash would be low, and you can effectively mitigate that by perhaps hashing the file in chunks (which could then lead to some interesting storage optimisation scenarios :P).
GUIDs are one way. You're basically guaranteed to not get any repeats (if you have a proper random generator).
You could also append with the time since epoch.
The best solution have already been mentioned. I just want to add some thoughts.
The simplest solution is to have a counter and increment on every new file. This works quite well as long as only one thread creates new files. If multiple threads, processes or even systems add new files, things get a bit more complicated. You must coordinate the creation of new ids with locking or any similar synchronisation method. You could also assign id ranges to every proceses to reduce the synchronisation work, or extend the file id by a unique process id.
A better solution might be to use GUIDs in this scenario and do not have to care about synchronisation between processes.
Finally, you can at some random data to every identifier to make them harder to guess if this is a requirement.
Also coommon is storing files in a directory structure where the location of a file depends on its name. File abcdef1234.xyz might be stored as /ab/cd/ef/1234.xyz. This avoids directories with a huge number of files. I am not really aware why this is done - may be file system limitations, performance issues - but it is quite common. I do not know if similar things are common if the files are stored directly in the database.
The best way is to simply use a counter. The first file is 1, the next is 2, another is 3, and so on...
But, it seems you want random. To quickly do this, you could make sure that your random number is greater than the last file created. You can cache the last file and then just offset your random number with its last name.
file = last_file + random(1 through 10)
Related
If I were to give you a file. You can read the file but you can't change it or copy it. Then I take the file, rename it, move it to a new location. How could you identify that file? (Fairly reliably)
I'm looking if I have a database of media files for a program and the user alters the location/name of file, could I find the file by searching a directory and looking for something.
I have done exactly this, it's not hard.
I take a 256-bit hash (I forget which routine I used off the top of my head) of the file and the filesize and write it to a table. If they match the files match. (And I think tracking the size is more paranoia than necessity.) To speed things up I also fold that hash to a 32-bit value. If the 32-bit values match then I check all the data.
For the sake of performance I persist the last 10 million files I have examined. The 32-bit values go in one file which is read in it's entirety, when a main record needs to be examined I pull in a "page" (I forget exactly how big) of them which is padded to align it with the disk.
I am using PStore to store the results of some computer simulations. Unfortunately, when the file becomes too large (more than 2GB from what I can see) I am not able to write the file to disk anymore and I receive the following error;
Errno::EINVAL: Invalid argument - <filename>
I am aware that this is probably a limitation of IO but I was wondering whether there is a workaround. For example, to read large JSON files, I would first split the file and then read it in parts. Probably the definitive solution should be to switch to a proper database in the backend, but because of some limitations of the specific Ruby (Sketchup) I am using this is not always possible.
I am going to assume that your data has a field that could be used as a crude key.
Therefore I would suggest that instead of dumping data into one huge file, you could put your data into different files/buckets.
For example, if your data has a name field, you could take the first 1-4 chars of the name, create a file with those chars like rojj-datafile.pstore and add the entry there. Any records with a name starting 'rojj' go in that file.
A more structured version is to take the first char as a directory, then put the file inside that, like r/rojj-datafile.pstore.
Obviously your mechanism for reading/writing will have to take this new file structure into account, and it will undoubtedly end up slower to process the data into the pstores.
My Mac app keeps a collection of objects (with Core Data), each of which has a cover image, and to which I assign a UUID upon creation. I had originally been storing the cover images as a field in my Core Data store, but recently started storing them on disk in the file system, instead.
Initially, I'm storing the covers in a flat directory, using the UUID to name the file, as below. This gives me O(1) fetching, as I know exactly where to look.
...
/.../Covers/3B723A52-C228-4C5F-A71C-3169EBA33677.jpg
/.../Covers/6BEC2FC4-B9DA-4E28-8A58-387BC6FF8E06.jpg
...
I've looked at the way other applications handle this task, though, and noticed a multi-level scheme, as below (for instance). This could still be implemented in O(1) time.
...
/.../Covers/A/B/3B723A52-C228-4C5F-A71C-3169EBA33677.jpg
/.../Covers/C/D/6BEC2FC4-B9DA-4E28-8A58-387BC6FF8E06.jpg
...
What might be the reason to do it this way? Does OS X limit the number of files in a directory? Is it in some way faster to retrieve them from disk? It would make the code used to calculate the file's name more complicated, so I want to find out if there is a good reason to do it that way.
On certain file systems (and I beleive HFS+ too), having too many files in the same directory will cause performance issues.
I used to work in an ISP where they would break up the home directories (they had 90k+ of them) Using a multi-directory scheme. You can partition your directories by using, say, the first two characters of the UUID, then the second two, eg:
/.../Covers/3B/72/3B723A52-C228-4C5F-A71C-3169EBA33677.jpg
/.../Covers/6B/EC/6BEC2FC4-B9DA-4E28-8A58-387BC6FF8E06.jpg
That way you don't need to calculate any extra characters or codes, just use the ones you have already to break it up. Since your UUIDs will be different every time, this should suffice.
The main reason is that in the latter way, as you've mentioned, disk retrieval is faster because your directory is smaller (so the FS will lookup in a smaller table for a file to exists).
As others mentioned, on some file systems it takes longer for the OS to open the file, because one directory with many files is longer to read than a couple of short directories.
However, you should perform measurements on your particular file system and for your particular usage scenario. I did this for NTFS on Windows XP and was surprised to discover that flat directory was performing better in all kinds of tests, than hierarchical structure.
I'm rendering millions of tiles which will be displayed as an overlay on Google Maps. The files are created by GMapCreator from the Centre for Advanced Spatial Analysis at University College London. The application renders files in to a single folder at a time, in some cases I need to create about 4.2 million tiles. Im running it on Windows XP using an NTFS filesystem, the disk is 500GB and was formatted using the default operating system options.
I'm finding the rendering of tiles gets slower and slower as the number of rendered tiles increases. I have also seen that if I try to look at the folders in Windows Explorer or using the Command line then the whole machine effectively locks up for a number of minutes before it recovers enough to do something again.
I've been splitting the input shapefiles into smaller pieces, running on different machines and so on, but the issue is still causing me considerable pain. I wondered if the cluster size on my disk might be hindering the thing or whether I should look at using another file system altogether. Does anyone have any ideas how I might be able to overcome this issue?
Thanks,
Barry.
Update:
Thanks to everyone for the suggestions. The eventual solution involved writing piece of code which monitored the GMapCreator output folder, moving files into a directory heirarchy based upon their filenames; so a file named abcdefg.gif would be moved into \a\b\c\d\e\f\g.gif. Running this at the same time as GMapCreator overcame the filesystem performance problems. The hint about the generation of DOS 8.3 filenames was also very useful - as noted below I was amazed how much of a difference this made. Cheers :-)
There are several things you could/should do
Disable automatic NTFS short file name generation (google it)
Or restrict file names to use 8.3 pattern (e.g. i0000001.jpg, ...)
In any case try making the first six characters of the filename as unique/different as possible
If you use the same folder over and (say adding file, removing file, readding files, ...)
Use contig to keep the index file of the directory as less fragmented as possible (check this for explanation)
Especially when removing many files consider using the folder remove trick to reduce the direcotry index file size
As already posted consider splitting up the files in multiple directories.
.e.g. instead of
directory/abc.jpg
directory/acc.jpg
directory/acd.jpg
directory/adc.jpg
directory/aec.jpg
use
directory/b/c/abc.jpg
directory/c/c/acc.jpg
directory/c/d/acd.jpg
directory/d/c/adc.jpg
directory/e/c/aec.jpg
You could try an SSD....
http://www.crucial.com/promo/index.aspx?prog=ssd
Use more folders and limit the number of entries in any given folder. The time to enumerate the number of entries in a directory goes up (exponentially? I'm not sure about that) with the number of entries, and if you have millions of small files in the same directory, even doing something like dir folder_with_millions_of_files can take minutes. Switching to another FS or OS will not solve the problem---Linux has the same behavior, last time I checked.
Find a way to group the images into subfolders of no more than a few hundred files each. Make the directory tree as deep as it needs to be in order to support this.
The solution is most likely to restrict the number of files per directory.
I had a very similar problem with financial data held in ~200,000 flat files. We solved it by storing the files in directories based on their name. e.g.
gbp97m.xls
was stored in
g/b/p97m.xls
This works fine provided your files are named appropriately (we had a spread of characters to work with). So the resulting tree of directories and files wasn't optimal in terms of distribution, but it worked well enough to reduced each directory to 100s of files and free the disk bottleneck.
One solution is to implement haystacks. This is what Facebook does for photos, as the meta-data and random-reads required to fetch a file is quite high, and offers no value for a data store.
Haystack presents a generic HTTP-based object store containing needles that map to stored opaque objects. Storing photos as needles in the haystack eliminates the metadata overhead by aggregating hundreds of thousands of images in a single haystack store file. This keeps the metadata overhead very small and allows us to store each needle’s location in the store file in an in-memory index. This allows retrieval of an image’s data in a minimal number of I/O operations, eliminating all unnecessary metadata overhead.
Previously, I asked the question.
The problem is the demands of our file structure are very high.
For instance, we're trying to create a container with up to 4500 files and 500mb data.
The file structure of this container consists of
SQLite DB (under 1mb)
Text based xml-like file
Images inside a dynamic folder structure that make up the rest of the 4,500ish files
After the initial creation the images files are read only with the exception of deletion.
The small db is used regularly when the container is accessed.
Tar, Zip and the likes are all too slow (even with 0 compression). Slow is subjective I know, but to untar a container of this size is over 20 seconds.
Any thoughts?
As you seem to be doing arbitrary file system operations on your container (say, creation, deletion of new files in the container, overwriting existing files, appending), I think you should go for some kind of file system. Allocate a large file, then create a file system structure in it.
There are several options for the file system available: for both Berkeley UFS and Linux ext2/ext3, there are user-mode libraries available. It might also be possible that you find a FAT implementation somewhere. Make sure you understand the structure of the file system, and pick one that allows for extending - I know that ext2 is fairly easy to extend (by another block group), and FAT is difficult to extend (need to append to the FAT).
Alternatively, you can put a virtual disk format yet below the file system, allowing arbitrary remapping of blocks. Then "free" blocks of the file system don't need to appear on disk, and you can allocate the virtual disk much larger than the real container file will be.
Three things.
1) What Timothy Walters said is right on, I'll go in to more detail.
2) 4500 files and 500Mb of data is simply a lot of data and disk writes. If you're operating on the entire dataset, it's going to be slow. Just I/O truth.
3) As others have mentioned, there's no detail on the use case.
If we assume a read only, random access scenario, then what Timothy says is pretty much dead on, and implementation is straightforward.
In a nutshell, here is what you do.
You concatenate all of the files in to a single blob. While you are concatenating them, you track their filename, the file length, and the offset that the file starts within the blob. You write that information out in to a block of data, sorted by name. We'll call this the Table of Contents, or TOC block.
Next, then, you concatenate the two files together. In the simple case, you have the TOC block first, then the data block.
When you wish to get data from this format, search the TOC for the file name, grab the offset from the begining of the data block, add in the TOC block size, and read FILE_LENGTH bytes of data. Simple.
If you want to be clever, you can put the TOC at the END of the blob file. Then, append at the very end, the offset to the start of the TOC. Then you lseek to the end of the file, back up 4 or 8 bytes (depending on your number size), take THAT value and lseek even farther back to the start of your TOC. Then you're back to square one. You do this so you don't have to rebuild the archive twice at the beginning.
If you lay out your TOC in blocks (say 1K byte in size), then you can easily perform a binary search on the TOC. Simply fill each block with the File information entries, and when you run out of room, write a marker, pad with zeroes and advance to the next block. To do the binary search, you already know the size of the TOC, start in the middle, read the first file name, and go from there. Soon, you'll find the block, and then you read in the block and scan it for the file. This makes it efficient for reading without having the entire TOC in RAM. The other benefit is that the blocking requires less disk activity than a chained scheme like TAR (where you have to crawl the archive to find something).
I suggest you pad the files to block sizes as well, disks like work with regular sized blocks of data, this isn't difficult either.
Updating this without rebuilding the entire thing is difficult. If you want an updatable container system, then you may as well look in to some of the simpler file system designs, because that's what you're really looking for in that case.
As for portability, I suggest you store your binary numbers in network order, as most standard libraries have routines to handle those details for you.
Working on the assumption that you're only going to need read-only access to the files why not just merge them all together and have a second "index" file (or an index in the header) that tells you the file name, start position and length. All you need to do is seek to the start point and read the correct number of bytes. The method will vary depending on your language but it's pretty straight forward in most of them.
The hardest part then becomes creating your data file + index, and even that is pretty basic!
An ISO disk image might do the trick. It should be able to hold that many files easily, and is supported by many pieces of software on all the major operating systems.
First, thank-you for expanding your question, it helps a lot in providing better answers.
Given that you're going to need a SQLite database anyway, have you looked at the performance of putting it all into the database? My experience is based around SQL Server 2000/2005/2008 so I'm not positive of the capabilities of SQLite but I'm sure it's going to be a pretty fast option for looking up records and getting the data, while still allowing for delete and/or update options.
Usually I would not recommend to put files inside the database, but given that the total size of all images is around 500MB for 4500 images you're looking at a little over 100K per image right? If you're using a dynamic path to store the images then in a slightly more normalized database you could have a "ImagePaths" table that maps each path to an ID, then you can look for images with that PathID and load the data from the BLOB column as needed.
The XML file(s) could also be in the SQLite database, which gives you a single 'data file' for your app that can move between Windows and OSX without issue. You can simply rely on your SQLite engine to provide the performance and compatability you need.
How you optimize it depends on your usage, for example if you're frequently needing to get all images at a certain path then having a PathID (as an integer for performance) would be fast, but if you're showing all images that start with "A" and simply show the path as a property then an index on the ImageName column would be of more use.
I am a little concerned though that this sounds like premature optimization, as you really need to find a solution that works 'fast enough', abstract the mechanics of it so your application (or both apps if you have both Mac and PC versions) use a simple repository or similar and then you can change the storage/retrieval method at will without any implication to your application.
Check Solid File System - it seems to be what you need.