Here comes a straight-forward question about random access when it comes to file systems using FAT.
I have seen different explanations of FAT with different kinds of pictures/animations showing different things. I don't understand how random access is possible without going through the file once. I thought of some kind of table that listed all the blocks that belong to a certain file, but it looks like the FAT is only mapping to the next block, meaning you still have to go through the FAT until you find the End-Of-File, then save these indexes in an array, and only then would you be able to perform random access.
My question is if what I wrote above is true. Is the whole random access only possible after first looking through the table to find all the blocks?
The File Allocation Table, FAT, used by DOS is a variation of linked allocation, where all the links are stored in a separate table at the beginning of the disk. The benefit of this approach is that the FAT table can be cached in memory, greatly improving random access speeds.
So it can be cached which makes it faster.
Ref: Abraham Silberschatz, Greg Gagne, and Peter Baer Galvin, "Operating System Concepts, Ninth Edition ", Chapter 12
I think it only reduce the cost of random access compared with normal linked access, since only it only traverse the link of each file. Thus, it says that random access can be optimised by FAT.
Related
I'm writing a program that needs to read and write lots of data in random order, and since I don't want to use hundreds of small files, I'm trying to develop a sort of virtual file system that writes to one large file that keeps track of where the "files" are in the "disk" file.
Thus, I've been trying to find detailed information about file system implementations, but theres one thing that never seems to be explained in a way I can understand: How does the file system track free/deleted sectors for new file creation? FAT, for example, has an index of all sectors at the beginning that seems to be the only place that holds this information, but searching the index for a new area of free space in a linear, O(n) fashion seems like it would be rather inefficient, especially if there are no deleted sectors and you have to insert something at the end of the list. Am I missing something, or is this is how file systems really detect unused sectors for writing? Thanks!
The answer depends on the overall file system architecture. It can be a linear list of free pages, or the free space can be counted in the same way as other files (eg. linked lists).
Practically developing an efficient file system is quite serious task for a side task that you have. So it makes sense to use some already created virtual file system, such as the one CodeBase offers or our Solid File System.
I found a helpful PDF explaining how free space is mapped in linux file systems. This is more along the lines of what I was looking for.
http://www.kernel.org/doc/ols/2010/ols2010-pages-121-132.pdf
it's just like a link list: each file may be seperated into many partitions and at the end of the partition each partition it's refering to begining of the next the same goes for the free speaces. think of free space as one large file that contains every byte which is not inside another file!
at the moment i am trying to write a unreal amount of data out to files,
basically i generate a new struct of data and write it out to file untill the file becomes 1gb big and this occurs for 6 files of 1gb each, the structs are small. 8 bytes long with two 2 variables id and amount
when i generate my data, the structs are created and written to file in the order of amount.
but i need the data to sorted by id.
remember there is 6gb's of data , how could i sort these structs by there id value and then written to file?
or should i write to file first, and then sort each individual file ,and how would i bring all this data together into one file?
i am kind of stuck , because i would like to hold it in an array , but obviously this amount of data is too big.
i need a good way to sort alot of data? (6gb)
I haven't found a question with a really basic answer on this, so here goes.
If you're on a 64 bit machine, by the way, you should seriously consider writing all the data into a file, memory mapping the file, and just use whatever array sort you like. Quicksort is pretty cache-friendly: it won't thrash badly. The assignment is probably designed to stop you doing this, but might be a bit out of date ;-)
Failing that, you need some kind of external sort. There are other ways to do it, but I think merge sort is probably the simplest. Before you start merging:
work out how much data you can fit into memory (or, again, mmap it). If you're on a PC then 1GB seems like a fair assumption, but it may be a few times more or less.
load this much data (so one of your 6 files, in the example)
quicksort it (since you tagged "quicksort", I guess you know how to do that), or any other sort of your choice.
write it back to disk (if you didn't mmap).
This leaves you with 6 1GB files, each of which individually is sorted. At this point you can either work up gradually, or go for the whole lot in one go. With 6 chunks, going for the whole lot is fine, in what is called a "6-way merge":
open a file for writing
open your 6 files for reading, and read a few million records out of each
examine the 6 records at the start of each of the 6 buffers. One of theses 6 must be the smallest of all. Write this to the output, and move forward one step through that buffer.
as you reach the end of each buffer, refill it from the correct file.
There's some optimization you can do regarding how you work out which of your 6 possibilities is the smallest, but the big performance difference will be to make sure you use large enough read and write buffers.
Obviously there's nothing special about the merge being 6-way. If you'd rather stick to a 2-way merge, which is easier to code, then of course you can. It will take 5 2-way merges to merge 6 files.
I would recommend this tool, it is a light weight database that runs in memory and takes up very little memory. It will hold your information and you can query it to retrieve your information.
http://www.sqlite.org/features.html
I suggest you don't.
If you are to hold such amount of data, why not using a dedicated database format that can have lots of different indexes and a powerful request engine.
But if you still want to use your old fashioned fixed-endian struct, then i would suggest breaking your data into smaller files, sort each one, and merge them. A good merge algorithm runs in nlog(q). Be also sure to pick the right algorithm for your files.
The easiest way (in development time) to do this is to write out the data to separate files according to their ID. You don't have to have a 1 to 1 match between the number of files and the number of IDs (in case there are a lot of IDs), but if you choose a prefix of the ID (so if the key for one particular record is 987 it might go in the 9 file while the record with key 456 would go in the 4 file) you won't have to worry about locating all of the keys across all of the files because sorting each file by itself would result and then looking at the files in their order (by their names) would give you sorted results.
If that is not possible or easy the you need to do an external sort of some type. Since the data is still spread across several files this is a bit of a pain. The easiest thing (by development time) is to first sort each individual file independently and then merge them together into a new set of files sorted by ID. Look up merge sort if you don't know what I'm talking about. At this step you are pretty much starting in the middle of merge sort.
As far as sorting the contents of a file which is too large to fit into RAM you can either use merge sort directly on the file or use replacement selection sort to sort the file in place. This involves making several passes over the file while using some RAM (the more the better) to hold a priority queue (a binary heap) and a set of records that are not possibly of any use in this run (their keys suggest that they should be earlier in the file than the current run position, so you're just holding on to them until the next run).
Searching for replacement selection sort or tournament sort will yield better explanations.
First, sort each file individually. Either load the whole thing into memory, or (better) mmap it, and use the qsort function.
Then, write your own merge sort that takes N FILE * inputs (i.e. N=6 in your case) and outputs to N new files, switching to the next one whenever one fills up.
Check out external sort. Find any of the external mergesort libraries out there and modify them to suit your need.
Well - since the actual assignment is to keep encoded data and later just compare it with decoded-data, I would also say - use a database and just create an hash index on the ID column.
But regarding sort of such hugh number, another very important thing is to do it in parallel. There are many ways to do it. Steve Jessop mentioned a sort-merge approach, it is really easy to sort the first 6 chunks in parallel, the only question is how much cpu cores andd memory you have on your machine. (It is rare to find a computer with only 1 core today and also not so rare to have 4GB memory).
Maybe you could use mmap and use it as a huge array which you could sort with qsort. I'm not sure what the implications would be. Would it grow to much in memory?
I have a text file which contains some info on extents about all the files in the file system, like below
C:\Program Files\abcd.txt
12345 100
23456 200
C:\Program Files\bcde.txt
56789 50
26746 300
...
Now i have another binary which tries to find out about extents for all the files.
Now currently i am using linear search to find extent info for the files in the above mentioned text file. This is a time consuming process. Is there a better way of coding this ? Like Implementing any good data structure like BTree. If B+ Tree is used what is the key, branch factor i need to use ?
Use a database.
The key points in implementing a tree in a file are to have fixed record lengths and to use file offsets instead of pointers.
Use a database. Hmmm, SQL Lite.
Another point to consider with files is that reading in chunks of data is faster than reading individual items (regardless of whether or not the hard disk has a cache or the OS has a cache). I implemented a B+Tree, which uses pages as it's nodes.
Use a database. Databases have already been written and tested.
A more efficient design is to keep the initial node in memory. This reduces the number of fetches from the file. If your program has the space, keeping the first couple of levels in memory may also speed up execution.
Use a database.
I gave up writing a B-Tree implementation for my application because I wanted to concentrate on the other functionality of the program. I later learned that in the real world (the world where programs need to be finished on a schedule) that time should be spent on the 'core' of the application rather than accessories that have already been written and tested (a.k.a. off-the-shelf).
It depends on how do you want to search your file. I assume that you want to look up your info given a file name. Then a hash table or a Trie would be a good data structure to use.
The B-tree is possible but not the most convenient choice given that your keys are strings.
I have an application (currently written in Python as we iron out the specifics but eventually it will be written in C) that makes use of individual records stored in plain text files. We can't use a database and new records will need to be manually added regularly.
My question is this: would it be faster to have a single file (500k-1Mb) and have my application open, loop through, find and close a file OR would it be faster to have the records separated and named using some appropriate convention so that the application could simply loop over filenames to find the data it needs?
I know my question is quite general so direction to any good articles on the topic are as appreciated as much as suggestions.
Thanks very much in advance for your time,
Dan
Essentially your second approach is an index - it's just that you're building your index in the filesystem itself. There's nothing inherently wrong with this, and as long as you arrange things so that you don't get too many files in the one directory, it will be plenty fast.
You can achieve the "don't put too many files in the one directory" goal by using multiple levels of directories - for example, the record with key FOOBAR might be stored in data/F/FO/FOOBAR rather than just data/FOOBAR.
Alternatively, you can make the single-large-file perform as well by building an index file, that contains a (sorted) list of key-offset pairs. Where the directories-as-index approach falls down is when you want to search on key different from the one you used to create the filenames - if you've used an index file, then you can just create a second index for this situation.
You may want to reconsider the "we can't use a database" restriction, since you are effectively just building your own database anyway.
Reading a directory is in general more costly than reading a file. But if you can find the file you want without reading the directory (i.e. not "loop over filenames" but "construct a file name") due to your naming convention, it may be benefical to split your database.
Given your data is 1 MB, I would even consider to store it entirely in memory.
To give you some clue about your question, I'd consider that having one single big file means that your application is doing the management of the lines. Having multiple small files is relying an the system and the filesystem to manage the data. The latter can be quite slow though, because it involves system calls for all your operations.
Opening File and Closing file in C Would take much time
i.e. you have 500 files 2 KB each... and if you process it 1000 Additonal Operation would be added to your application (500 Opening file and 500 Closing)... while only having 1 file with 1 MB of size would save you that 1000 additional operation...(That is purely my personal Opinion...)
Generally it's better to have multiple small files. Keeps memory usage low and performance is much better when searching through it.
But it depends on the amount of operations you'll need, because filesystem calls are much more expensive when compared to memory storage for instance.
This all depends on your file system, block size and memory cache among others.
As usual, measure and find out if this is a real problem since premature optimization should be avoided. It may be that using one file vs many small files does not matter much for performance in practice and that the choice should be based on clarity and maintainability instead.
(What I can say for certain is that you should not resort to linear file search, use a naming convention to pinpoint the file in O(1) time instead).
The general trade off is that having one big file can be more difficult to update but having lots of little files is fiddly. My suggestion would be that if you use multiple files and you end up having a lot it can get very slow traversing a directory with a million files in it. If possible break the files into some sort of grouping so they can be put into separate directories and "keyed". I have an application that requires the creation of lots of little pdf documents for all user users of the system. If we put this in one directory it would be a nightmare but having a directory per user id makes it much more manageable.
Why can't you use a DB, I'm curious? I respect your preference, but just want to make sure it's for the right reason.
Not all DBs require a server to connect to or complex deployment. SQLite, for instance, can be easily embedded in your application. Python already has it built-in, and it's very easy to connect with C code (SQLite itself is written in C and its primary API is for C). SQLite manages a feature-complete DB in a single file on the disk, where you can create multiple tables and use all the other nice features of a DB.
A problem I was working on recently got me to wishing that I could lop off the front of a file. Kind of like a “truncate at front,” if you will. Truncating a file at the back end is a common operation–something we do without even thinking much about it. But lopping off the front of a file? Sounds ridiculous at first, but only because we’ve been trained to think that it’s impossible. But a lop operation could be useful in some situations.
A simple example (certainly not the only or necessarily the best example) is a FIFO queue. You’re adding new items to the end of the file and pulling items out of the file from the front. The file grows over time and there’s a huge empty space at the front. With current file systems, there are several ways around this problem:
As each item is removed, copy the
remaining items up to replace it, and
truncate the file. Although it works,
this solution is very expensive
time-wise.
Monitor the size of the empty space at
the front, and when it reaches a
particular size or percentage of the
entire file size, move everything up
and truncate the file. This is much
more efficient than the previous
solution, but still costs time when
items are moved in the file.
Implement a circular queue in the
file, adding new items to the hole at
the front of the file as items are
removed. This can be quite efficient,
especially if you don’t mind the
possibility of things getting out of
order in the queue. If you do care
about order, there’s the potential of
having to move items around. But in
general, a circular queue is pretty
easy to implement and manages disk
space well.
But if there was a lop operation, removing an item from the queue would be as easy as updating the beginning-of-file marker. As easy, in fact, as truncating a file. Why, then, is there no such operation?
I understand a bit about file systems implementation, and don't see any particular reason this would be difficult. It looks to me like all it would require is another word (dword, perhaps?) per allocation entry to say where the file starts within the block. With 1 terabyte drives under $100 US, it seems like a pretty small price to pay for such functionality.
What other tasks would be made easier if you could lop off the front of a file as efficiently as you can truncate at the end?
Can you think of any technical reason this function couldn't be added to a modern file system? Other, non-technical reasons?
On file systems that support sparse files "punching" a hole and removing data at an arbitrary file position is very easy. The operating system just has to mark the corresponding blocks as "not allocated". Removing data from the beginning of a file is just a special case of this operation. The main thing that is required is a system call that will implement such an operation: ftruncate2(int fd, off_t offset, size_t count).
On Linux systems this is actually implemented with the fallocate system call by specifying the FALLOC_FL_PUNCH_HOLE flag to zero-out a range and the FALLOC_FL_COLLAPSE_RANGE flag to completely remove the data in that range. Note that there are restrictions on what ranges can be specified and that not all filesystems support these operations.
Truncate files at front seems not too hard to implement at system level.
But there are issues.
The first one is at programming level. When opening file in random access the current paradigm is to use offset from the beginning of the file to point out different places in the file. If we truncate at beginning of file (or perform insertion or removal from the middle of the file) that is not any more a stable property. (While appendind or truncating from the end is not a problem).
In other words truncating the beginning would change the only reference point and that is bad.
At a system level uses exist as you pointed out, but are quite rare. I believe most uses of files are of the write once read many kind, so even truncate is not a critical feature and we could probably do without it (well some things would become more difficult, but nothing would become impossible).
If we want more complex accesses (and there are indeed needs) we open files in random mode and add some internal data structure. Theses informations can also be shared between several files. This leads us to the last issue I see, probably the most important.
In a sense when we using random access files with some internal structure... we are still using files but we are not any more using files paradigm. Typical such cases are the databases where we want to perform insertion or removal of records without caring at all about their physical place. Databases can use files as low level implementation but for optimisation purposes some database editors choose to completely bypass filesystem (think about Oracle partitions).
I see no technical reason why we couldn't do everything that is currently done in an operating system with files using a database as data storage layer. I even heard that NTFS has many common points with databases in it's internals. An operating system can (and probably will in some not so far future) use another paradigm than files one.
Summarily i believe that's not a technical problem at all, just a change of paradigm and that removing the beginning is definitely not part of the current "files paradigm", but not a big and useful enough change to compell changing anything at all.
NTFS can do something like this with it's sparse file support but it's generaly not that useful.
I think there's a bit of a chicken-and-egg problem in there: because filesystems have not supported this kind of behavior efficiently, people haven't written programs to use it, and because people haven't written programs to use it, there's little incentive for filesystems to support it.
You could always write your own filesystem to do this, or maybe modify an existing one (although filesystems used "in the wild" are probably pretty complicated, you might have an easier time starting from scratch). If people find it useful enough it might catch on ;-)
Actually there are record base file systems - IBM have one and I believe DEC VMS also had this facility. I seem to remember both allowed (allow? I guess they are still around) deleting and inserting at random positions in a file.
There is also a unix command called head -- so you could do this via:
head -n1000 file > file_truncated
may can achieve this goal in two steps
long fileLength; //file total length
long reserveLength; //reserve length until the file ending
int fd; //file open for read & write
sendfile(fd, fd, fileLength-reserveLength, reserveLength);
ftruncate(fd, reserveLength);