I have to read binary data into char-arrays from large (2GB) binary files in a C++ program. When reading the files for the first time from my SSD, reading takes about 6.4 seconds per file. But when running the same code again or even after running a different dummy-program, which does almost the same before, the next readings take only about 1.4 seconds per file. The Windows Task Manager even shows much less disk-activity on the second, third, fourth… run. So, my guess is Window’s File Caching is sparing me from waiting for data from the SSD, when filling the arrays another time.
Is there any clean option to read the files into file cache before the customer runs the software? Any better option than just already loading the files with fread in advance? And how can I make sure, the data remains in the File Cache until I need it?
Or am I totally wrong with my File Cache assumption? Is there another (better) explanation for these different loading times?
Educated guess here:
You most likely are right with your file cache assumption.
Can you pre load files before the user runs the software?
Not directly. How would your program be supposed to know that it is going to be run in the next few minutes?
So you probably need a helper mechanism or tricks.
The options I see here are:
Indexing mechanisms to provide a faster and better aimed access to your data. This is helpful if you only need small chunks of information from these data at once.
Attempt to parallelize the loading of the data, so even if it does not really get faster, the user has the impression it does because he can start working already with the data he has, while the rest is fetched in the background.
Have a helper tool starting up with the OS and pre-fetching everything, so you already have it in memory when required. Caution: This has serious implications since you reserve either a large chunk of RAM or even SSD-cache (depending on implementation) for your tool from the start. Only consider doing this if the alternative is the apocalypse…
You can also try to combine the first two options. The key to a faster data availability is to figure out what to read in which order instead of trying to load everything at once en-bloc. Divide and Conquer.
Without further details on the problem it is impossible to provide more specific solutions though.
Related
I have an input file in my application that contains a vast amount of information. Reading over it sequentially, and at only a single file offset at a time is not sufficient for my application's usage. Ideally, I'd like to have two threads, that have separate and distinct ifstreams reading from two unique file offsets of the same file. I can't just start one ifstream up, and then make a copy of it using its copy constructor (since its uncopyable). So, how do I handle this?
Immediately I can think of two ways,
Construct a new ifstream for the second thread, open it on the same file.
Share a single instance of an open ifstream across both threads (using for instance boost::shared_ptr<>). Seek to the appropriate file offset that current thread is currently interested in, when the thread gets a time slice.
Is one of these two methods preferred?
Is there a third (or fourth) option that I have not yet thought of?
Obviously I am ultimately limited by the hard drive having to spin back and forth, but what I am interested in taking advantage of (if possible), is some OS level disk caching at both file offsets simultaneously.
Thanks.
Two std::ifstream instances will probably be the best option here. Modern HDDs are optimized for a large queue of I/O requests, so reading from two std::ifstream instances concurrently should give quite nice performance.
If you have a single std::ifstream you'll have to worry about synchronizing access to it, plus it might defeat the operating system's automatic sequential access read-ahead caching, resulting in poorer performance.
Between the two, I would prefer the second. Having two openings of the same file might cause an inconsistent view between the files, depending on the underlying OS.
For a third option, pass a reference or raw pointer into the other thread. So long as the semantics are that one thread "owns" the istream, the raw pointer or reference are fine.
Finally note that on the vast majority of hardware, the disk is the bottleneck, not CPU, when loading large files. Using two threads will make this worse because you're turning a sequential file access into a random access. Typical hard disks can do maybe 100MB/s sequentially, but top out at 3 or 4 MB/s random access.
Other option:
Memory-map the file, create as many memory istream objects as you want. (istrstream is good for this, istringstream is not).
It really depends on your system. A modern system will generally read
ahead; seeking within the file is likely to inhibit this, so should
definitly be avoided.
It might be worth experimenting how read-ahead works on your system:
open the file, then read the first half of it sequentially, and see how
long that takes. Then open it, seek to the middle, and read the second
half sequentially. (On some systems I've seen in the past, a simple
seek, at any time, will turn off read-ahead.) Finally, open it, then
read every other record; this will simulate two threads using the same
file descriptor. (For all of these tests, use fixed length records, and
open in binary mode. Also take whatever steps are necessary to ensure
that any data from the file is purged from the OS's cache before
starting the test—under Unix, copying a file of 10 or 20 Gigabytes
to /dev/null is usually sufficient for this.
That will give you some ideas, but to be really certain, the best
solution would be to test the real cases. I'd be surprised if sharing a
single ifstream (and thus a single file descriptor), and constantly
seeking, won, but you never know.
I'd also recommend system specific solutions like mmap, but if you've
got that much data, there's a good chance you won't be able to map it
all in one go anyway. (You can still use mmap, mapping sections of it
at a time, but it becomes a lot more complicated.)
Finally, would it be possible to get the data already cut up into
smaller files? That might be the fastest solution of all. (Ideally,
this would be done where the data is generated or imported into the
system.)
My vote would be a single reader, which hands the data to multiple worker threads.
If your file is on a single disk, then multiple readers will kill your read performance. Yes, your kernel may have some fantastic caching or queuing capabilities, but it is going to be spending more time seeking than reading data.
My C++ program has to read information about 256 images just one time. The information is simple: the path and some floats per image.
I don't need any kind of concurrent access. Also, I don't care about writing, deleting or updating the information and I don't have to do any kind of complex query. This is my pipeline:
Read information about one image.
Store that information on a object.
Do some calculation with the information.
Delete the object.
Next image.
I can use 256 files (every image has the same information), 1 file with all the information or a PostgreSQL databases. What will be faster?
Your question 'which will be faster' is tricky as performance is dependent on so many different factors, including OS, whether the database or file system are on the same machines as your application, the size of the images etc. I would guess that you could find some combinations that would make any of your options faster if you try hard enough.
Having said that, if everything is running on the same machine, then a file based approach would seem intuitively to be faster than a database, just because a database generally provides more functionality, and hence does more work (not just serving requests but in the background also) so has to use more of your computing power.
Similarly, it seems intuitive that a single file will be more efficient than multiple files as it saves the opening (and closing if necessary) operations associated with multiple files. But, again, giving an absolute answer is hard as opening and closing multiple files may be a common use case that certain OS's have optimised, hence making it as fast (or even faster) than a just using a single file.
If performance is very important for your solution, it is hard to avoid having to do some comparative testing with your target deployment systems.
My application continuously calculates strings and outputs them into a file. This is being run for almost an entire day. But writing to the file is slowing my application. Is there a way I can improve the speed ? Also I want to extend the application so that I can send the results to an another system after some particular amount of time.
Thanks & Regards,
Mousey
There are several things that may or may not help you, depending on your scenario:
Consider using asynchronous I/O, for instance by using Boost.Asio. This way your application does not have to wait for expensive I/O-operations to finish. However, you will have to buffer your generated data in memory, so make sure there is enough available.
Consider buffering your strings to a certain size, and then write them to disk (or the network) in big batches. Few big writes are usually faster than many small ones.
If you want to make it really good C++, meaning STL-comliant, make your algorithm a template-function that takes and output-iterator as argument. This way you can easily have it write to files, the network, memory or the console by providing appropriate iterators.
How if you write the results to a socket, instead of file. Another program Y, will read the socket, open a file, write on it and close it, and after the specified time will transfer the results to another system.
I mean the process of file handling is handled by other program. Original program X just sends the output to the socket. It does not concern it self with flushing the file stream.
Also I want to extend the application
so that I can send the results to an
another system after some particular
amount of time.
If you just want to transfer the file to other system, then I think a simple script will be enough for that.
Use more than one file for the logging. Say, after your file reaches size of 1 MB, change its name to something contains the date and the time and start to write to a new one, named as the original file name.
then you have:
results.txt
results2010-1-2-1-12-30.txt (January 2 2010, 1:12:30)
and so on.
You can buffer the result of different computations in memory and only write to the file when buffer is full. For example, your can design your application in such a way that, it computes result for 100 calculations and writes all those 100 results at once in a file. Then computes another 100 and so on.
Writing file is obviously slow, but you can buffered data and initiate the separate thread for writhing on file. This can improve speed of your application.
Secondly you can use ftp for transfer files to other system.
I think there are some red herrings here.
On an older computer system, I would recommend caching the strings and doing a small number of large writes instead of a large number of small writes. On modern systems, the default disk-caching is more than adequate and doing additional buffering is unlikely to help.
I presume that you aren't disabling caching or opening the file for every write.
It is possible that there is some issue with writing very large files, but that would not be my first guess.
How big is the output file when you finish?
What causes you to think that the file is the bottleneck? Do you have profiling data?
Is it possible that there is a memory leak?
Any code or statistics you can post would help in the diagnosis.
I have a 1GB binary file on another system.
Requirement: ftp/download and convert binary to CSV on main system.
The converted file will be magnitudes larger ~ 8GB
What is the most common way of doing something similar to this?
Should this be a two step independent process, download - then convert?
Should I download small chunks at a time and convert while downloading?
I don't know the most efficient way to do this...also what should I be cautions of with files this size?
Any advice is appreciated.
Thank You.
(Visual Studio C++)
I would write a program that converts the binary format and outputs to CSV format. This program would read from stdin and write to stdout.
Then I would call
wget URL_of_remote_binary_file --output-document=- | my_converter_program > output_file.csv
That way you can start converting immediately (without downloading the entire files) and your program doesn't deal with networking. You can also run the program on the remote side, assuming it's portable enough.
Without knowing any specifics, I would go with a binary ftp download and then post-process with a separate conversion program. This would break the process into two distinct and unrelated parts which would aid in building and debugging the overall system. No need to reinvent an FTP system and lots of potential to optimize the post-processing.
To avoid too much traffic I would in a first step compress and transfer the file. The conversion process, if something goes wrong or want another output can be redone locally without refetching the data.
The only precaution is not to load the whole stuff in memory and then convert but do it chunk-wise like you said. You can prevent some unpleasant effects for users of your program by creating/pre-allocating a huge file of the max expected size. This to avoid running out of disk space during the conversion phase. Also some filesystems do not like files bigger than 2GB or 4GB, that would also be caught by the pre-allocation trick.
It depends on your data and your requirements. What performance requirements do you have? Do you need to finish such as task in X amount of time (where speed is critical), or is this something that will just be done periodically (in which case speed is not essential)?
That said, you will certainly get a cleaner implementation if you separate the work out into two tasks - a downloader and a converter. That way each component can be simple and just focus on the task at hand. All things being equal, I recommend this approach.
Otherwise if you try to download/convert at the same time you may get into situations where your downloader has data ready, but the converter needs more data before it can proceed. Again, there is no reason why your code cannot handle this, but it will make the implementation more complicated and that much more difficult to debug / test / validate.
It's usually better to do it as separate processes with no interdependency. If your requirements change in the future you can reuse the pieces, or use them for other projects.
Here are even more guesses about your requirements and possible solutions:
Concerned about file integrity? Implement something that includes integrity checks such as sequence numbers, size fields and checksums/hashes, and just enough transaction semantics so that the system knows whether a transfer completed or didn't.
Are uploads happening on slow/congested links, and may be interrrupted? Implement a protocol that allows the transfer to resume after interruption.
Are uploads recurring, with much of the data unchanged? Implement something amenable to incremental update, so you upload only the differences.
I have created an application that does the following:
Make some calculations, write calculated data to a file - repeat for 500,000 times (over all, write 500,000 files one after the other) - repeat 2 more times (over all, 1.5 mil files were written).
Read data from a file, make some intense calculations with the data from the file - repeat for 1,500,000 iterations (iterate over all the files written in step 1.)
Repeat step 2 for 200 iterations.
Each file is ~212k, so over all i have ~300Gb of data. It looks like the entire process takes ~40 days on a Core 2 Duo CPU with 2.8 Ghz.
My problem is (as you can probably guess) is the time it takes to complete the entire process. All the calculations are serial (each calculation is dependent on the one before), so i can't parallel this process to different CPUs or PCs. I'm trying to think how to make the process more efficient and I'm pretty sure the most of the overhead goes to file system access (duh...). Every time i access a file i open a handle to it and then close it once i finish reading the data.
One of my ideas to improve the run time was to use one big file of 300Gb (or several big files of 50Gb each), and then I would only use one open file handle and simply seek to each relevant data and read it, but I'm not what is the overhead of opening and closing file handles. can someone shed some light on this?
Another idea i had was to try and group the files to bigger ~100Mb files and then i would read 100Mb each time instead of many 212k reads, but this is much more complicated to implement than the idea above.
Anyway, if anyone can give me some advice on this or have any idea how to improve the run time i would appreciate it!
Thanks.
Profiler update:
I ran a profiler on the process, it looks like the calculations take 62% of runtime and the file read takes 34%. Meaning that even if i miraculously cut file i/o costs by a factor of 34, I'm still left with 24 days, which is quite an improvement, but still a long time :)
Opening a file handle isn't probable to be the bottleneck; actual disk IO is. If you can parallelize disk access (by e.g. using multiple disks, faster disks, a RAM disk, ...) you may benefit way more. Also, be sure to have IO not block the application: read from disk, and process while waiting for IO. E.g. with a reader and a processor thread.
Another thing: if the next step depends on the current calculation, why go through the effort of saving it to disk? Maybe with another view on the process' dependencies you can rework the data flow and get rid of a lot of IO.
Oh yes, and measure it :)
Each file is ~212k, so over all i have
~300Gb of data. It looks like the
entire process takes ~40 days ...a ll the
calculations are serial (each
calculation is dependent on the one
before), so i can't parallel this
process to different CPUs or PCs. ... pretty
sure the most of the overhead goes to
file system access ... Every
time i access a file i open a handle
to it and then close it once i finish
reading the data.
Writing data 300GB of data serially might take 40 minutes, only a tiny fraction of 40 days. Disk write performance shouldn't be an issue here.
Your idea of opening the file only once is spot-on. Probably closing the file after every operation is causing your processing to block until the disk has completely written out all the data, negating the benefits of disk caching.
My bet is the fastest implementation of this application will use a memory-mapped file, all modern operating systems have this capability. It can end up being the simplest code, too. You'll need a 64-bit processor and operating system, you should not need 300GB of RAM. Map the whole file into address space at one time and just read and write your data with pointers.
From your brief explaination it sounds like xtofl suggestion of threads is the correct way to go. I would recommend you profile your application first though to ensure that the time is divided between IO an cpu.
Then I would consider three threads joined by two queues.
Thread 1 reads files and loads them into ram, then places data/pointers in the queue. If the queue goes over a certain size the thread sleeps, if it goes below a certain size if starts again.
Thread 2 reads the data off the queue and does the calculations then writes the data to the second queue
Thread 3 reads the second queue and writes the data to disk
You could consider merging thread 1 and 3, this might reduce contention on the disk as your app would only do one disk op at a time.
Also how does the operating system handle all the files? Are they all in one directory? What is performance like when you browse the directory (gui filemanager/dir/ls)? If this performance is bad you might be working outside your file systems comfort zone. Although you could only change this on unix, some file systems are optimised for different types of file usage, eg large files, lots of small files etc. You could also consider splitting the files across different directories.
Before making any changes it might be useful to run a profiler trace to figure out where most of the time is spent to make sure you actually optimize the real problem.
What about using SQLite? I think you can get away with a single table.
Using memory mapped files should be investigated as it will reduce the number of system calls.