I'm planning on writing a simple text viewer, which I'd expect to be able to deal with very large sized files. I was thinking of using Tie::File for this, and kind of paginate the lines. Is this loading the lines lazily, or all of them at once?
It won't load the whole file. From the documentation:
The file is not loaded into memory, so this will work even for gigantic files.
As far as I can see from its source code it stores only used lines in memory. And yes, it loads data only when needed.
You can limit amount of used memory with memory parameter.
It also tracks offsets of all lines in the file to optimize disk access.
Related
I'm trying to understanding something a bit better about mmap. I recently read this portion of this accepted answer in the related stackoverflow question (quoted below):mmap and memory usage
Let's say you read a 100MB chunk of data, and according to the initial
1MB of header data, the information that you want is located at offset
75MB, so you don't need anything between 1~74.9MB! You have read it
for nothing but to make your code simpler. With mmap, you will only
read the data you have actually accessed (rounded 4kb, or the OS page
size, which is mostly 4kb), so it would only read the first and the
75th MB.
I understand most of the benefits of mmap (no need for context-switches, no need to swap contents out, etc), but I don't quite understand this offset. If we don't mmap and we need information at the 75th MB offset, can't we do that with standard POSIX file I/O calls without having to use mmap? Why does mmap exactly help here?
Of course you could. You can always open a file and read just the portions you need.
mmap() can be convenient when you don't want to write said code or you need sparse access to the contents and don't want to have to write a bunch of caching logic.
With mmap(), you're "mapping" the entire contest of the file to offsets in memory. Most implementation of mmap() do this lazily, so each ~4K block of the file is read on-demand, as you access those memory locations.
All you have to do is access the data in your file like it was a huge array of chars (i.e. int* someInt = &map[750000000]; return *someInt;), and let the OS worry about what portions of the file have been read, when to read the file, how much, writing the dirty data blocks back to the file, and purging the memory to free up RAM.
I have large amounts of data stored as nested cells in .mat files. My biggest problem right now is the load times for accessing these files, but I'm wondering if the underlying problem is that I came up with an inefficient way for storing the data and I should restructure it to be smaller.
The full file consists of a cell aray:
Hemi{1,h} where there are 52 versions of h
.{n,p} where there are 85 versions of n and up to ~100 versions of p
.Variable where there are 10 variables, each with ~2500 values
This full file ate up all my memory, so I saved it in parts, aka:
Hemi1.mat=Hemi{1,1}
Hemi2.mat=Hemi{1,2}
etc.
The next step for this application is to load each file, determine which part of it is an appropriate solution (I need Hemi{1,h}.{n,p}.Var1, Hemi{1,h}.{n,p}.Var2, and Hemi{1,h}.{n,p}.Var3 for this, but I still need to keep track of the other Variables), save the solution, then close the file and move to the next one.
Is there a faster way to load these files?
Is the problem less my dataset and more how I've chosen to store it? Is there a better alternative?
That is quite a lot of data. I have a few suggestions that you could look into. The first is to see if you can change the datatypes to something like Categorical Objects. They are way more memory efficient. Also if you are storing strings as your final data this can be quite heavy storage wise.
Second you could look into HDF5 file storage. I hear it is a nice way to store structured data.
You could finally try to convert your {n,p} arrays into Table structures. I am not sure if this is better for memory, but tables are nice to work with and it may help you out. (Depending on your version of Matlab you may not have tables :P).
I hope this helps!
-Kyle
Basically, I am caching a bunch of files in memory. The problem is, if I get too many files cached into memory I can run out of memory.
Is there any sort of java memory structure that will automatically page part of itself to disk?
For example, I would like to set a 2 mb limit the size of the files in memory. After that limit I would like some of the files to be written to disk.
Is there any library that does this sort of thing?
Grae
"files in memory". Conventionally, in memory data is stored in some data structure like a HashMap or whatever, and referred to as a 'file' when its written to disc. You could code a data storage class which did this programmatically. I don't know of any library which did this. It would be pretty useful. In effect you would be implementing virtual memory.
This link will might help you :
http://docs.oracle.com/javase/7/docs/api/java/nio/channels/FileChannel.html
EhCache is general purpose caching library for Java. One of the option is to have disk-backed cache, which overflows to file system. Seems to be exactly what you need.
I'm trying to determine the best way to store large numbers of small .mat files, around 9000 objects with sizes ranging from 2k to 100k, for a total of around half a gig.
The typical use case is that I only need to pull a small number (say 10) of the files from disk at a time.
What I've tried:
Method 1: If I save each file individually, I get performance problems (very slow save times and system sluggishness for some time after) as Windows 7 has difficulty handling so may files in a folder (And I think my SSD is having a rough time of it, too). However, the end result is fine, I can load what I need very quickly. This is using '-v6' save.
Method 2: If I save all of the files in one .mat file and then load just the variables I need, access is very slow (loading takes around three quarters of the time it takes to load the whole file, with small variation depending on the ordering of the save). This is using '-v6' save, too.
I know I could split the files up into many folders but it seems like such a nasty hack (and won't fix the SSD's dislike of writing many small files), is there a better way?
Edit:
The objects are consist mainly of a numeric matrix of double data and an accompanying vector of uint32 identifiers, plus a bunch of small identifying properties (char and numeric).
Five ideas to consider:
Try storing in an HDF5 object - take a look at http://www.mathworks.com/help/techdoc/ref/hdf5.html - you may find that this solves all of your problems. It will also be compatible with many other systems (e.g. Python, Java, R).
A variation on your method #2 is to store them in one or more files, but to turn off compression.
Different datatypes: It may also be the case that you have some objects that compress or decompress inexplicably poorly. I have had such issues with either cell arrays or struct arrays. I eventually found a way around it, but it's been awhile & I can't remember how to reproduce this particular problem. The solution was to use a different data structure.
#SB proposed a database. If all else fails, try that. I don't like building external dependencies and additional interfaces, but it should work (the primary problem is that if the DB starts to groan or corrupts your data, then you're back at square 1). For this purpose consider SQLite, which doesn't require a separate server/client framework. There is an interface available on Matlab Central: http://www.mathworks.com/matlabcentral/linkexchange/links/1549-matlab-sqlite
(New) Considering that the objects are less than 1GB, it may be easier to just copy the entire set to a RAM disk and then access through that. Just remember to copy from the RAM disk if anything is saved (or wrap save to save objects in two places).
Update: The OP has mentioned custom objects. There are two methods to consider for serializing these:
Two serialization program from Matlab Central: http://www.mathworks.com/matlabcentral/fileexchange/29457 - which was inspired by: http://www.mathworks.com/matlabcentral/fileexchange/12063-serialize
Google's Protocol Buffers. Take a look here: http://code.google.com/p/protobuf-matlab/
Try storing them as blobs in a database.
I would also try the multiple folders method as well - it might perform better than you think. It might also help with organization of the files if that's something you need.
The solution I have come up with is to save object arrays of around 100 of the objects each. These files tend to be 5-6 meg so loading is not prohibitive and access is just a matter of loading the right array(s) and then subsetting them to the desired entry(ies). This compromise avoids writing too many small files, still allows for fast access of single objects and avoids any extra database or serialization overhead.
Primarily this seems to be a technique used by games, where they have all the sounds in one file, textures in another etc. With these files commonly reaching the GB size.
What is the reason behind doing this over maintaining it all in subdirectories as small files - one per texture which many small games use this, with the monolithic system being favoured by larger companies?
Is there some file system overhead with lots of small files?
Are they trying to protect their property - although most just seem to be a compressed file with a new extension?
The reasons we use an "archive" system like this where I work (a game development company):
lookup speed: We rarely need to iterate over files in a directory; we're far more often looking them up directly by name. By using a custom "file allocation table" that is essentially just a sequence of hash( normalized_filename ) -> [ offset, size ], we can look up files very quickly. We can also keep this index in RAM, potentially interleave it with other index tables, etc.
(When we do need to iterate, we can either easily iterate over all files in a .arc, or we can store a list of filenames, a list of hash-of-filenames, or just a list of [ offset, size ] pairs somewhere -- maybe even as a file in the archive. This is usually faster than directory-traversal on a FS.)
metadata: It's easy for us to tuck in any file metadata we want. For example, a single bit in the "size" field indicates whether the file is compressed or not (if it is, it has a header with more details about how to decompress it). We can even vary compression on pieces of a file if we know enough about the structure of the file ahead of time (we do this for sprite archives).
size: One of the devices we use has a "file size must be a multiple of X" requirement, where X is large compared to some of our files. For example, some of our lua scripts end up being just a few hundred bytes when compiled; taking extra overhead per .luc file adds up quickly.
alignment: on the other hand, sometimes we want to waste space. To take advantage of faster streaming (e.g. background DMA) from the filesystem, some of our files do want to obey certain alignment/size requirements. We can take care of that right in the tool, and the align/size we're shooting for doesn't necessarily have to line up with the underlying FS, allowing us to waste space only where we need it.
But those are the mundane reasons. The more fun stuff:
Each .arc registers in a list, and attempts to open a file know to look in the arcs. We search already-in-RAM archives first, then archives on the device FS, then the actual device FS. This gives us a ton of flexibility:
dynamic additions to the filesystem: at any time we can stream a new file or archive to the machine in question (over the network or the like) and have it appear as part of the "logical" filesystem; this is great when the actual FS resides in ROM or on a CD, and allows us to iterate much more quickly than we could otherwise.
(Doom's .wad system is a sort of example of the above, which allows modders to more easily override assets and scripts built into the game.)
possibility of no underlying fs: It's possible to use bin2obj to embed an entire arc directly in the executable (.rodata) at link time, at which point you don't ever need to look at the device FS -- we do this for certain small demo builds and the like. We can also send levels across the network or savegame-sneakernet this way. =)
organization and load/unload: since we can load and unload and override virtual "pieces" of our filesystem at any time, we can do some performance tricks with having the number of files in the FS be very small at any given time. We can additionally specify that an entire archive be loaded into memory, index table and data; our file load code is smart enough to know that if the file is already in memory, it doesn't need to do anything to read it other than move a pointer around. Some of the higher level code can actually detect that the file is in ram and just ask for the probably-already-looks-like-a-struct pointer directly.
portability: we only need to figure out how to get a few files on each new device we use, and then the remainder of the FS code is more or less the same. =) We do change the tool output a bit occasionally (for alignment reasons), but most of the processing remains the same.
de-duplication: with smarter archives, such as our sprite archives, we can (and do) de-duplicate data. If "jump" animation's fifth frame and "kick"'s third frame are the same, we can pull apart the file and only store one copy of that frame. We can do the same for whole files.
We ported a PC game to a system with much slower FS access recently. We didn't change the data format, and it turns out iterating through a dir on the raw device FS to load a hundred small XML files was absolutely killing our load times. The solution we used was to take each dir, make it into its own subdir.arc, and stick it in the master game.arc compressed. When the dir was needed (something like opendir was called) we decompressed the entire subdir.arc into RAM, added it to the filesystem, then iterated through it super-quickly.
It's the ability to throw something like this together in a few hours, and to ease the pain of porting across systems, that makes stuff like this worthwhile.
File systems do have an overhead. Usually, a file takes disk space rounded up to some power of 2 (e.g. up to 4 KB), so many small files would waste space. Some modern file systems try to mitigate that, but AFAIK it's not widespread yet. Additionally, file systems are often quite slow when accessing multiple files. E.g. it is usually considerably faster to copy one 400 MB file than 4000 100 KB files.
File systems come in handy when you have to modify files, because they handle changing file sizes much better than any simple home-grown solution. However, that's certainly not the case for constant game data.
On Apple systems, the most common way is to use, as you suggest, directories. They are called Bundles, and are in the Finder represented as just one file, but if you explore them more, they're actually directories. This makes writing code and conserving memory when loading individual items out of this bundle very easy. :-) Also, this makes taking incremental backups of gigantic databases easy, as for instance your iPhoto database is just a bundle, so you just backup changed and new files
On Windows, however, I believe this is much harder to do, it will look like a directory "no matter what" (I'm sure smart people have found a solution that will make Explorer see certain directories as a single file, but it's not common).
From a games developer point of view, you're not dealing with so small files that disk space overhead is something you're very much concerned with, so I doubt #doublep's suggestion, since it makes for such a hassle, but it makes it much easier with a single file if users are to copy an entire game over somewhere, then it's easy to check if the entire set is correct.
And, of course, it's harder to read for people that shouldn't have access to it. But it's also harder to modify, which means harder to patch, and harder to write extensions. Someone that uses extensions a lot, prefers the directory structure: The Sims.
Were I the games developer, I'd love to go for individual files. Then again, I'd be using bundles as I'd be writing for the Mac ;-)
Cheers
Nik
I can think of multiple reasons.
As doublep suggested, files occupy more space on the disc than they require. So an archive saves space. 10k files (of any size) should save you 20MB when packed into an archive. Not exactly a large amount of space nowadays, but still.
The other reason I can think of is disc fragmentation. I suspect a heavily fragmented disc will perform worse when accessing thousands of separate files on a fragmented space. But I'm no expert in this field, so I'd appreciate if someone more experienced verified this.
Finally, I think this may also have something to do with restricting access to separate game files. You can have a bunch of Lua scripts exposed, mess with them and break something. Or you could have the outro cinematic/sound/text/whatever exposed and get spoiled by accessing it. I do that myself as well: I encrypt images with a multipass XOR key, pack text files and config variables into a monolithic file (zipped for extra security) and only leave music freely accessible. This way, the game's secrets will remain undiscovered for a bit longer :).
Or there may be another reason I never thought about :D.
As you know games, especially with larger companies try to squeeze as much performance as they can. One technique is to have all the data in one large file and just DMA it to memory (think of it as a memcpy from CD to RAM). Since all the files are in one large one there will be no disk seeks and you can have a large number of files (which may cause large amount of seeks) all loaded quicky because of the technique.