I came across FileBackedOutputStream class from Google Guava library and was wondering if it's suitable to be used as a kind of a buffer: once every day, a process in my webapp generates tens of thousands of lines (each containing about 100characters) which are then uploaded to a file on an FTP server. I was thinking of using a FileBackedOutputStream object to first write all these strings to and then give access to them to my FTP client by using FileBackedOutputStream.getSupplier().getInput(), which returns an InputStream. Would this be a correct use case for FileBackedOutputStream?
Yes, I think that would be an acceptable use case for FileBackedOutputStream. However, I think FileBackedOutputStream is best when you're using it with data that may vary in size considerably... for small amounts of data that can fit in memory without a problem you want to just buffer them in memory but for large amounts of data that might give you an OutOfMemoryError if you try to read it all in to memory, you want to switch to buffering to a file. This is where FileBackedOutputStream really shines I think. I've used it for buffering uploaded files that I need to do several things with.
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We are emulating a p2p network in java. So we divide the file into chunks (with checksums) so that the individual chunks can be recompiled into the original file once we have all the parts. What is the best way to store the individual parts while they are being downloaded?
I was thinking of just storing each chunk as a separate file...but if there are 20000 chunks, it would create as many files. is this the best way?
Thanks
Either keep chunks in memory or in files. There is no much to discuss here about. Findd the perfect ratio between chunks count and the actual size of it, to suit your needs.
Files sounds more reasonable as data would not be totally lost in case of application crash and continue of download would be possible.
I would write to memory until you reach some threshold, at which point you dump your memory to disk, and keep reading into memory. When the file transfer completes, you can take what is currently stored in memory, and concatenate it with what may have been stored on disk.
I need to do some performance tests for my program and I need to evaluate the worst IO access time to some data stored in files, so I think that the best way to evaluate this is to randomly store these data into different HD sectors in order to avoid contiguous data access and caching improvements. I think that the only way to do this is using some low-level OS commands, like dd in UNIX where you can specify the sector where you write the data, but if I'm not mistaken, this is an insecure method. Someone know a good alternative to do this?
PS: Any solution for any OS will work, the only requirement is that I have to do the tests over different data size, accessing the data through a JAVA program.
I think that the only way to do this is using some low-level OS commands
No... RandomAccessFile has .seek():
final RandomAccessFile f = new RandomAccessFile(someFile, "rw");
f.seek(someRandomLong);
f.write(...);
Now, it is of course up to you to ensure that writes don't collide with one another.
Another solution is to map the file in memory and set the buffer's position to some random position before writing.
I'm trying to transfer a stream of strings from my C++ program to my Java program in an efficient manner but I'm not sure how to do this. Can anyone post up links/explain the basic idea about how do implement this?
I was thinking of writing my data into a text file and then reading the text file from my Java program but I'm not sure that this will be fast enough. I need it so that a single string can be transferred in 16ms so that we can get around 60 data strings to the C++ program in a second.
Text files can easily be written to and read from upwards with 60 strings worth of content in merely a few milliseconds.
Some alternatives, if you find that you are running into timing troubles anyway:
Use socket programming. http://beej.us/guide/bgnet/output/html/multipage/index.html.
Sockets should easily be fast enough.
There are other alternatives, such as the tibco messaging service, which will be an order of magnitude faster than what you need: http://www.tibco.com/
Another alternative would be to use a mysql table to pass the data, and potentially just set an environment variable in order to indicate the table should be queried for the most recent entries.
Or I suppose you could just use an environment variable itself to convey all of the info -- 60 strings isn't very much.
The first two options are the more respectable solutions though.
Serialization:
protobuf or s11n
Pretty much any way you do this will be this fast. A file is likely to be the slowest and it could be around 10ms total!. A Socket will be similar if you have to create a new connection as well (its the connect, not the data which will take most time) Using a socket has the advantage of the sender and receiver knowing how much data has been produced. If you use a file instead, you need another way to say, the file is complete now, you should read it. e.g. a socket ;)
If the C++ and Java are in the same process, you can use a ByteBuffer to wrap a C array and import into Java in around 1 micro-second.
I have a file of size 2GB which has student records in it. I need to find students based on certain attributes in each record and create a new file with results. The order of the filtered students should be same as in the original file. What's the efficient & fastest way of doing this using Java IO API and threads without having memory issues? The maxheap size for JVM is set to 512MB.
What kind of file? Text-based, like CSV?
The easiest way would be to do something like grep does: Read the file line by line, parse the line, check your filter criterion, if matched, output a result line, then go to the next line, until the file is done. This is very memory efficient, as you only have the current line (or a buffer a little larger) loaded at the same time. Your process needs to read through the whole file just once.
I do not think multiple threads are going to help much. It would make things much more complicated, and since the process seems to be I/O bound anyway, trying to read the same file with multiple threads probably does not improve throughput.
If you find that you need to do this often, and going through the file each time is too slow, you need to build some kind of index. The easiest way to do that would be to import the file into a DB (can be an embedded DB like SQLite or HSQL) first.
I wouldn't overcomplicate this until you find that the boringly simple way doesn't work for what you need. Essentially you just need to:
open input stream to 2GB file, remembering to buffer (e.g. by wrapping with BufferedInputStream)
open output stream to filtered file you're going to create
read first record from input stream, look at whatever attribute to decide if you "need" it; if you do, write it to output file
repeat for remaining records
On one of my test systems with extremely modest hardware, BufferedInputStream around a FileInputStream out of the box read about 500 MB in 25 seconds, i.e. probably under 2 minutes to process your 2GB file, and the default buffer size is basically as good as it gets (see the BufferedInputStream timings I made for more details). I imagine with state of the art hardware it's quite possible the time would be halved.
Whether you need to go to a lot of effort to reduce the 2/3 minutes or just go for a wee while you're waiting for it to run is a decision that you'll have to make depending on your requirements. I think the database option won't buy you much unless you need to do a lot of different processing runs on the same set of data (and there are other solutions to this that don't automatically mean database).
2GB for a file is huge, you SHOULD go for a db.
If you really want to use Java I/O API, then try out this: Handling large data files efficiently with Java and this: Tuning Java I/O Performance
I think you should use memory mapped files.This will help you to map the bigger file to a
smaller memory.This will act like virtual memory and as far as performance is concerned mapped files are the faster than stream write/read.
So I have a "large" number of "very large" ASCII files of numerical data (gigabytes altogether), and my program will need to process the entirety of it sequentially at least once.
Any advice on storing/loading the data? I've thought of converting the files to binary to make them smaller and for faster loading.
Should I load everything into memory all at once?
If not, is opening what's a good way of loading the data partially?
What are some Java-relevant efficiency tips?
So then what if the processing requires jumping around in the data for multiple files and multiple buffers? Is constant opening and closing of binary files going to become expensive?
I'm a big fan of 'memory mapped i/o', aka 'direct byte buffers'. In Java they are called Mapped Byte Buffers are are part of java.nio. (Basically, this mechanism uses the OS's virtual memory paging system to 'map' your files and present them programmatically as byte buffers. The OS will manage moving the bytes to/from disk and memory auto-magically and very quickly.
I suggest this approach because a) it works for me, and b) it will let you focus on your algorithm and let the JVM, OS and hardware deal with the performance optimization. All to frequently, they know what is best more so than us lowly programmers. ;)
How would you use MBBs in your context? Just create an MBB for each of your files and read them as you see fit. You will only need to store your results. .
BTW: How much data are you dealing with, in GB? If it is more than 3-4GB, then this won't work for you on a 32-bit machine as the MBB implementation is defendant on the addressable memory space by the platform architecture. A 64-bit machine & OS will take you to 1TB or 128TB of mappable data.
If you are thinking about performance, then know Kirk Pepperdine (a somewhat famous Java performance guru.) He is involved with a website, www.JavaPerformanceTuning.com, that has some more MBB details: NIO Performance Tips and other Java performance related things.
You might want to have a look at the entries in the Wide Finder Project (do a google search for "wide finder" java).
The Wide finder involves reading over lots of lines in log files, so look at the Java implementations and see what worked and didn't work there.
You could convert to binary, but then you have 1+ something copies of the data, if you need to keep the original around.
It may be practical to build some kind of index on top of your original ascii data, so that if you need to go through the data again you can do it faster in subsequent times.
To answer your questions in order:
Should I load everything into memory all at once?
Not if don't have to. for some files, you may be able to, but if you're just processing sequentially, just do some kind of buffered read through the things one by one, storing whatever you need along the way.
If not, is opening what's a good way of loading the data partially?
BufferedReaders/etc is simplest, although you could look deeper into FileChannel/etc to use memorymapped I/O to go through windows of the data at a time.
What are some Java-relevant efficiency tips?
That really depends on what you're doing with the data itself!
Without any additional insight into what kind of processing is going on, here are some general thoughts from when I have done similar work.
Write a prototype of your application (maybe even "one to throw away") that performs some arbitrary operation on your data set. See how fast it goes. If the simplest, most naive thing you can think of is acceptably fast, no worries!
If the naive approach does not work, consider pre-processing the data so that subsequent runs will run in an acceptable length of time. You mention having to "jump around" in the data set quite a bit. Is there any way to pre-process that out? Or, one pre-processing step can be to generate even more data - index data - that provides byte-accurate location information about critical, necessary sections of your data set. Then, your main processing run can utilize this information to jump straight to the necessary data.
So, to summarize, my approach would be to try something simple right now and see what the performance looks like. Maybe it will be fine. Otherwise, look into processing the data in multiple steps, saving the most expensive operations for infrequent pre-processing.
Don't "load everything into memory". Just perform file accesses and let the operating system's disk page cache decide when you get to actually pull things directly out of memory.
This depends a lot on the data in the file. Big mainframes have been doing sequential data processing for a long time but they don't normally use random access for the data. They just pull it in a line at a time and process that much before continuing.
For random access it is often best to build objects with caching wrappers which know where in the file the data they need to construct is. When needed they read that data in and construct themselves. This way when memory is tight you can just start killing stuff off without worrying too much about not being able to get it back later.
You really haven't given us enough info to help you. Do you need to load each file in its entiretly in order to process it? Or can you process it line by line?
Loading an entire file at a time is likely to result in poor performance even for files that aren't terribly large. Your best bet is to define a buffer size that works for you and read/process the data a buffer at a time.
I've found Informatica to be an exceptionally useful data processing tool. The good news is that the more recent versions even allow Java transformations. If you're dealing with terabytes of data, it might be time to pony up for the best-of-breed ETL tools.
I'm assuming you want to do something with the results of the processing here, like store it somewhere.
If your numerical data is regularly sampled and you need to do random access consider to store them in a quadtree.
I recommend strongly leveraging Regular Expressions and looking into the "new" IO nio package for faster input. Then it should go as quickly as you can realistically expect Gigabytes of data to go.
If at all possible, get the data into a database. Then you can leverage all the indexing, caching, memory pinning, and other functionality available to you there.
If you need to access the data more than once, load it into a database. Most databases have some sort of bulk loading utility. If the data can all fit in memory, and you don't need to keep it around or access it that often, you can probably write something simple in Perl or your favorite scripting language.