I want to copy one directory and the two files under it to another shared location of shared storage. Is it possible to combine the three(one directory and two files) as a continuous file writing and decompose it at another side to save the cost? I am limited to c language and Unix/Linux. I am considering to create a structure with the inode info and get the data at receiver.
Thanks!
rsync is what you're looking for. Or tar if you feel like working with the shell on the other side.
The best optimization you can do is to use large buffers for the copy. If that is not enough then restructure your data to be a single file instead of two files in a directory. Next step is to get faster hardware.
There are many file systems in common use for Unix/Linux and you would need to write a custom copy algorithm for each. There is rarely a guarantee of contiguous blocks for even a single file, let alone two. Odds are also good that your block copy routine would bypass and be less efficient than existing file system optimizations.
Reading an entire file into memory before writing it out will give more benefit in terms of minimizing seek times than opening fewer files would, at least for files over a certain size. And not all hardware suffers from seek times.
For some reason, cpio is often preferred over tar for this.
You can, for example, pipe cpio to a ssh session running cpio remotely.
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!
I am trying to modify the ext3 file system. Basically I want to ensure that the inode for a file is saved in the same (or adjacent) block as the file that it stores metadata for. Hopefully this should help disk access performance
I grabbed the kernel source, compiled it, read a bunch about inodes and looked the inode.c file in the fs subdirectory. However, I am just not sure how I can ensure that any new file being created, and the inode for this file, can be saved in the same or adjacent blocks. Any help or pointers to further readings would be appreciated. Thanks!
Interesting idea.
I'm not deeply familiar with ext3, but I can give you some general pointers.
Currently ext3 stores inodes in predetermined places. Each block group has its own inode table, an array of inodes. So when you have an inode number (i.e., as the result of looking up a filename in a directory), you can find the corresponding inode on disk by using the inode number first to select the correct block group and then to index into that block group's inode table.
If you want to put the inodes next to the corresponding file data, you'll need a new scheme for finding an inode on disk. If you're willing to dedicate a block for each inode, then one possible scheme would be to allocate a new block every time you need an inode and then use the block number as the inode number. This might have the benefit that for small files you could store the data in that same block.
To make something like this happen, creating a new file (i.e., allocating an inode) would have to work very differently than in the current ext3 file system. Instead of using a bitmap to find an unused, pre-allocated and pre-initialized inode, you would have to allocate an empty block and initialize it yourself. So, you'll probably want to look at how the file system allocates blocks when it's writing to a file, then mimic that for allocating an inode.
An alternative scheme would be to store the inode inside the directory. So you save an I/O not because the inode is next to its data, but because when you lookup the filename you also read the inode. This was done back in the 90s as an experiment in BSD's FFS file system, and was written up in an excellent USENIX Paper. Those ideas never made it into FFS, or into any other main stream file system that I'm aware of, so it might be interesting to see how they work in ext3.
Regardless of whether you pursue one of these schemes or come up with something of your own, you'll also have to modify mke2fs to initialize the file system on disk in a way that your new file system variant will understand.
Good luck! It sounds like a fun project.
Kudos for getting into file system design!
First, a bit of engineering advice before you get too deep into hacking: make a copy of the ext3 tree and rename the file system to something else. I've found that when introducing experimental changes into a file system, you really don't want it to be used for your main system. Your system should still boot even if you introduce a bug that randomly loses files (it will eventually happen). You'll also need to branch the ext3 userspace tools to work with your new system.
Second, go get a copy of Understanding the Linux Kernel, 3 ed. by Bovet and Cesati. It presents an organized view of kernel subsystems, and I've found its explanations to be worthwhile. It's written for an older kernel (2.6.x for some x < 15; I forget exactly), but it's still accurate in many places. Read through its descriptions of file systems. I believe it covers ext3.
Third, about your actual project, you aren't proposing a simple modification to ext3. That file system has a pretty straightforward way of mapping an inode number to a disk block. You'll need to find a new way of doing this mapping. I would not anticipate any changes to the rest of ext3. Solving this challenge may be one of the key design points of your architecture. Note that keeping around a big array of inode -> disk block maps doesn't solve your problem: it's probably no better than existing ext3.
I have been recently involved in handling the console logs for a server and I was wondering, out of curiosity, that is there a performance issue in writing to a large file as compared to small ones.
For instance is it a good idea to keep the log file size small instead of letting them grow bulky, but I was not able to argue much in favor of either approach.
There might be problems in reading or searching in the file, but right now I am more interested in knowing if writing can be affected in any way.
Looking for an expert advice.
Edit:
The way I thought it was that the OS only has to open a file handle and push the data to the file system. There is little correlation to the file size, since you have to keep on appending the data to the end of the file and whenever a block of data is full, OS will assign another block to the file. As I said earlier, there can be problems in reading and searching because of defragmentation of file blocks, but I could not find much difference while writing.
As a general rule, there should be no practical difference between appending a block to a small file (or writing the first block which is appending to a zero-length file) or appending a block to a large file.
There are special cases (like trying to fault in a triple-indirect block or the initial open having to read all mapping information) which could add additional I/O's. but the steady-state should be the same.
I'd be more worried about the manageability of having huge files: slow to backup, slow to copy, slow to view, etc.
I am not an expert, but I will try to answer anyway.
Larger files may take longer to write on disk and in fact it is not a programming issue. It is file system issue. Perhaps there are file systems, which does not have such issues, but on Windows large files cannot be write down in one piece so fragmenting them will take time (for the simple reason that head will have to move to some other cylinder). Assuming that we are talking about "classic" hard drives...
If you want an advice, I would go for writing down smaller files and rotating them either daily or when they hit some size (or both actually). That is rather common approach I saw in an enterprise-grade products.
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.
I have about 750,000,000 files I need to store on disk. What's more is I need to be able to access these files randomly--any given file at any time--in the shortest time possible. What do I need to do to make accessing these files fastest?
Think of it like a hash table, only the hash keys are the filenames and the associated values are the files' data.
A coworker said to organize them into directories like this: if I want to store a file named "foobar.txt" and it's stored on the D: drive, put the file in "D:\f\o\o\b\a\r.\t\x\t". He couldn't explain why this was a good idea though. Is there anything to this idea?
Any ideas?
The crux of this is finding a file. What's the fastest way to find a file by name to open?
EDIT:
I have no control over the file system upon which this data is stored. It's going to be NTFS or FAT32.
Storing the file data in a database is not an option.
Files are going to be very small--maximum of probably 1 kb.
The drives are going to be solid state.
Data access is virtually random, but I could probably figure out a priority for each file based on how often it is requested. Some files will be accessed much more than others.
Items will constantly be added, and sometimes deleted.
It would be impractical to consolidate multiple files into single files because there's no logical association between files.
I would love to gather some metrics by running tests on this stuff, but that endeavour could become as consuming as the project itself!
EDIT2:
I want to upvote several thorough answers, whether they're spot-on or not, and cannot because of my newbie status. Sorry guys!
This sounds like it's going to be largely a question of filesystem choice. One option to look at might be ZFS, it's designed for high volume applications.
You may also want to consider using a relational database for this sort of thing. 750 million rows is sort of a medium size database, so any robust DBMS (eg. PostgreSQL) would be able to handle it well. You can store arbitrary blobs in the database too, so whatever you were going to store in the files on disk you can just store in the database itself.
Update: Your additional information is certainly helpful. Given a choice between FAT32 and NTFS, then definitely choose NTFS. Don't store too many files in a single directory, 100,000 might be an upper limit to consider (although you will have to experiment, there's no hard and fast rule). Your friend's suggestion of a new directory for every letter is probably too much, you might consider breaking it up on every four letters or something. The best value to choose depends on the shape of your dataset.
The reason breaking up the name is a good idea is that typically the performance of filesystems decreases as the number of files in a directory increases. This depends highly on the filesystem in use, for example FAT32 will be horrible with probably only a few thousand files per directory. You don't want to break up the filenames too much, so you will minimise the number of directory lookups the filesystem will have to do.
That file algorithm will work, but it's not optimal. I would think that using 2 or 3 character "segments" would be better for performance - especially when you start considering doing backups.
For example:
d:\storage\fo\ob\ar\foobar.txt
or
d:\storage\foo\bar\foobar.txt
There are some benefits to using this sort of algorithm:
No database access is necessary.
Files will be spread out across many directories. If you don't spread them out, you'll hit severe performance problems. (I vaguely recall hearing about someone having issues at ~40,000 files in a single folder, but I'm not confident in that number.)
There's no need to search for a file. You can figure out exactly where a file will be from the file name.
Simplicity. You can very easily port this algorithm to just about any language.
There are some down-sides to this too:
Many directories may lead to slow backups. Imagine doing recursive diffs on these directories.
Scalability. What happens when you run out of disk space and need to add more storage?
Your file names cannot contain spaces.
This depends to a large extent on what file system you are going to store the files on. The capabilities of file systems in dealing with large number of files varies widely.
Your coworker is essentially suggesting the use of a Trie data structure. Using such a directory structure would mean that at each directory level there are only a handful of files/directories to choose from; this could help because as the number of files within a directory increases the time to access one of them does too (the actual time difference depends on the file system type.)
That said, I personally wouldn't go that many levels deep -- three to four levels ought to be enough to give the performance benefits -- most levels after that will probably have very entries (assuming your file names don't follow any particular patterns.)
Also, I would store the file itself with its entire name, this will make it easier to traverse this directory structure manually also, if required.
So, I would store foobar.txt as f/o/o/b/foobar.txt
This highly depends on many factors:
What file system are you using?
How large is each file?
What type of drives are you using?
What are the access patterns?
Accessing files purely at random is really expensive in traditional disks. One significant improvement you can get is to use solid state drive.
If you can reason an access pattern, you might be able to leverage locality of reference to place these files.
Another possible way is to use a database system, and store these files in the database to leverage the system's caching mechanism.
Update:
Given your update, is it possbile you consolidate some files? 1k files are not very efficient to store as file systems (fat32, ntfs) have cluster size and each file will use the cluster size anyway even if it is smaller than the cluster size. There is usually a limit on the number of files in each folder, with performance concerns. You can do a simple benchmark by putting as many as 10k files in a folder to see how much performance degrades.
If you are set to use the trie structure, I would suggest survey the distribution of file names and then break them into different folders based on the distribution.
First of all, the file size is very small. Any File System will eat something like at least 4 times more space. I mean any file on disk will occupy 4kb for 1kb file. Especially on SSD disks, the 4kb sector will be the norm.
So you have to group several files into 1 physical file. 1024 file in 1 storage file seems reasonable. To locate the individual files in these storage files you have to use some RDBMS (PostgreSQL was mentioned and it is good but SQLite may be better suited to this) or similar structure to do the mapping.
The directory structure suggested by your friend sounds good but it does not solve the physical storage problem. You may use similar directory structure to store the storage files. It is better to name them by using a numerical system.
If you can, do not let them format as FAT32, at least NTFS or some recent File System of Unix flavor. As total size of the files is not that big, NTFS may be sufficient but ZFS is the better option...
Is there any relation between individual files? As far as access times go, what folders you put things in won't affect much; the physical locations on the disk are what matter.
Why isn't storing the paths in a database table acceptable?
My guess is he is thinking of a Trie data structure to create on disk where the node is a directory.
I'd check out hadoops model.
P
I know this is a few years late, but maybe this can help the next guy..
My suggestion use a SAN, mapped to a Z drive that other servers can map to as well. I wouldn't go with the folder path your friend said to go with, but more with a drive:\clientid\year\month\day\ and if you ingest more than 100k docs a day, then you can add sub folders for hour and even minute if needed. This way, you never have more than 60 sub folders while going all the way down to seconds if required. Store the links in SQL for quick retrieval and reporting. This makes the folder path pretty short for example: Z:\05\2004\02\26\09\55\filename.txt so you don't run into any 256 limitations across the board.
Hope that helps someone. :)