I am writing an application to read from a list of files, line by line and do some processing. I want to use as little RAM as I can.
I came across this question https://stackoverflow.com/a/41741702/3531263
Where the poster is saying readString uses more RAM than readLine and they have posted some code.
What I don't understand is how one uses more RAM? Because ultimately, the way their code is written, they are still writing an entire line to their buffer. So would that not mean if they had just used readString, it would have been the same thing?
the way their code is written, they are still writing an entire line to their buffer
Their code, yes. Your code might not need the whole line to be in memory at the same time. For example, your program is filtering a log file by request id, which is in the beginning of the line. It doesn't need to read the whole line which may be a few megabytes or more, only to reject it due to wrong request id. But with ReadString you don't have the luxury of choice.
I 'gree with Sergio. Also, have a look at the current implementation in the standard library. ReadLine calls ReadSlice('\n') once, then runs through a few branches to make sure the appropriate sentinel values or errors are returned with the converted data. On the other hand, ReadBytes and ReadString both loop over repeated calls to ReadSlice(delim), so it follows that they would necessarily be copying at least as much data into memory as ReadLine, and potentially much more if the delimiter wasn't found in the first call.
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.
New to this forum - looks great!
I have some Processing code that periodically reads data wirelessly from remote devices and writes that data as bytes to a file, e.g. data.dat. I want to write an Objective C program on my Mac Mini using Xcode to read this file, parse the data, and act on the data if data values indicate a problem. My question is: can my two different programs access the same file asynchronously without a problem? If this is a problem can you suggest a technique that will allow these operations?
Thanks,
Kevin H.
Multiple processes can read from the same file at a time without any problem. A process can also read from a file while another writes without problem, although you'll have to take care to ensure that you read in any new data that was written. Multiple processes should not write to the same file at at the same time, though. The OS will let you do it, but the ordering of data will be undefined, and you'll like overwrite data—in general, you're gonna have a bad time if you do that. So you should take care to ensure that only one process writes to a file at a time.
The simplest way to protect a file so that only one process can write to it at a time is with the C function flock(), although that function is admittedly a bit rudimentary and may or may not suit your use case.
I have a custom file type that is implemented in sections with a header at the shows the offset and length of each section within the file.
Currently, whenever I want to interact with the file, I must either load and parse the entire thing up front, or else pick only the sections that I need and load just them.
What I would like to do is to achieve a hybrid approach where each of the sections is loaded on-demand.
It seems however that doing this has a lot of potential downsides in terms of leaving filesystem handles open for longer than I would like and the additional code complexity that I would incur.
Are there any standard patterns for this sort of thing? It seems that my options are to:
Just load the entire file and stop grousing about the cycles/memory wasted
Load the entire file into memory as raw bytes and then satisfy any requests for unloaded sections from the memory buffer rather than disk. This saves me the cost of parsing the unneeded sections and requires less memory (since the disk representation is much more compact than the object model around it), but still means that I waste memory for sections that I never end up loading.
Load whatever sections I need right away and close the file but hold onto the source location of the file. Then if another section is requested, re-open the file and load the data. In this case I could get strange results if the underlying file is changed.
Same as the above but leave a file handle open (perhaps allowing read sharing).
Load the file using Memory-Mapped IO and leave a view on the file open.
Any thoughts
If possible, MMAP-ing the whole file is usually the easiest thing to do if you have a random-access pattern. This way you just delegate the loading/unloading issue to the OS and you have 1 & 2 for free.
If you have very special access patterns, you can even use something like fadvise() (I don't the exact Win32 equivalent) to tell the OS your access intend.
If your file is more than 2GB and you can either go the 64bits way or to mmap() the file on demand.
If the file is relatively small, mmap-ing the entire file is good enough. If the file is large, you could leave a mmap view open, and just move it around the file and resize it to view each section when needed.
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.