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Sort huge file in java

I've huge file with unique words in each line. Size of file is around 1.6 GB(I've to sort other files after this which are around 15GB). Till now, for smaller files I used Array.sort(). But for this file I get java.lang.OutOfMemoryError: Java heap space. I know the reason for this error. Is there any way instead of writing complete quick sort or merge sort program.
I read that Array.sort() uses Quicksort or Hybrid Sort internally. Is there any procedure like Array.sort() ??
If I have to write a program for sorting, which one should I use? Quicksort or Merge sort. I'm worried about worst case.

Depending on the structure of the data to store, you can do many different things.
In case of well structured data, where you need to sort by one or more specific fields (in which case system tools might not be helpful), you are probably better off using a datastore that allows sorting. MongoDB comes to mind as a good fit for this given that the size doesn't exceed few 100s of GBs. Other NoSQL datastores might also fit the bill nicely, although Mongo's simplicity of use and installation and support for JSON data makes it a really great candidate.
If you really want to go with the java approach, it gets real tricky. This is the kind of questions you ask at job interviews and I would never actually expect anybody to implement code. However, the general solution is merge sort (using random access files is a bad idea because it means insertion sort, i.e., non optimal run time which can be bad given the size of your file).
By merge sort I mean reading one chunk of the file at a time small enough to fit it in memory (so it depends on how much RAM you have), sorting it and then writing it back to a new file on disk. After you read the whole file you can start merging the chunk files two at a time by reading just the head of each and writing (the smaller of the two records) back to a third file. Do that for the 'first generation' of files and then continue with the second one until you end up with one big sorted file. Note that this is basically a bottom up way of implementing merge sort, the academic recursive algorithm being the top down approach.
Note that having intermediate files can be avoided altogether by using a multiway merge algorithm. This is typically based on a heap/priority queue, so the implementation might get slightly more complex but it reduces the number of I/O operations required.
Please also see these links.
Implementing the above in java shouldn't be too difficult with some careful design although it can definitely get tricky. I still highly recommend an out-of-the-box solution like Mongo.

As it turns out, your problem is that your heap cannot accommodate such a large array, so you must forget any solution that implies loading the whole file content in an array (as long as you can't grow your heap).
So you're facing streaming. It's the only (and typical) solution when you have to handle input sources that are larger than your available memory. I would suggest streaming the file content to your program, which should perform the sorting by either outputting to a random access file (trickier) or to a database.

I'd take a different approach.
Given a file, say with a single element per line, I'd read the first n elements. I would repeat this m times, such that the amount of lines in the file is n * m + C with C being left-over lines.
When dealing with Integers, you may wish to use around 100,000 elements per read, with Strings I would use less, maybe around 1,000. It depends on the data type and memory needed per element.
From there, I would sort the n amount of elements and write them to a temporary file with a unique name.
Now, since you have all the files sorted, the smallest elements will be at the start. You can then just iterate over the files until you have processed all the elements, finding the smallest element and printing it to the new final output.
This approach will reduce the amount of RAM needed and instead rely on drive space and will allow you to handle sorting of any file size.

Build the array of record positions inside the file (kind of index), maybe it would fit into memory instead. You need a 8 byte java long per file record. Sort the array, loading records only for comparison and not retaining (use RandomAccessFile). After sorting, write the new final file using index pointers to get the records in the needed order.
This will also work if the records are not all the same size.

Large-scale processing of seralized Integer objects

I have a large data set in the following format:
In total, there are 3687 object files. Each of which contains 2,000,000 records. Each file is 42MB in size.
Each record contains the following:
An id (Integer value)
Value1 (Integer)
Value2 (Integer)
Value3 (Integer)
The content of each file is not sorted or ordered in any way as they are observed during a data collection process.
Ideally, I want to build an index for this data. (Indexed by the id) which would mean the following:
Dividing the set of ids into manageable chunks.
Scanning the files to get data related to the current working set of ids.
Build the index.
Go over the next chunk and repeat 1,2,3.
To me this sounds fine but loading 152GB back and forth is time-consuming and wonder about the best possible approach or even whether Java is actually the right language to use for such a process.
I've 256GB of ram and 32 cores on my machine.
Update:
Let me modify this, putting aside I/O, and assuming the file is in-memory in a byte array.
What would be the fastest possible way to decode a 42MB Object file that have 2,000,000 records and each record contains 4 Integers serialized.

You've made a very poor choice of file format. I would convert the lot from serialized Integers to binary ints written with DataOutputStream.writeInt(), and read them with DataInputStream.readInt(). With buffered streams underneath in both cases. You will save masses of disk space, which will therefore save you I/O time as well, and you also save all the serialization overhead time. And change your collection software to use this format in future. The conversion will take a while, but it only happens once.
Or else use a database as suggested, again with native ints rather than serialized objects.

So, what I would do is just load up each file and store the id into some sort of sorted structure - std::map perhaps [or Java's equivalent, but given that it's probably about 10-20 lines of code to read in the filename and then read the contents of the file into a map, close the file and ask for the next file, I'd probably just write the C++ to do that].
I don't really see what else you can/should do, unless you actually want to load it into a dbms - which I don't think is at all unreasonable of a suggestion.

Hmm.. it seems the better way of doing it is to use some kind of DBMS. Load all your data into database, and you can leverage its indexing, storage and querying facility. Ofcourse this depends on what is your requirement -- and whether or now a DBMS solution suits this

Given that your available memory is > than your dataset and you want very high performance, have you considered Redis? It's well suited to operations on simple data structures and the performance is very fast.
Just be a bit careful about letting java do default serialization when storing values. I've previously run into issues with my primitives getting autoboxed prior to serialization.

Reducing memory footprint while using large XML DOM's in Java

Our application is required to take client data presented in XML format (several files) and parse this into our common XML format (a single file with schema). For this purpose we are using apache's XMLBeans data binding framework. The steps of this process are briefly described below.
First, we take raw java.io.File objects pointing to the client XML files on-disk and load these into a collection. We then iterate over this collection creating a single apache.xmlbeans.XmlObject per file. After all files have been parsed into XmlObjects, we create 4 collections holding the individual objects from the XML documents that we are interested in (to be clear, these are not hand-crafted objects but what I can only describe as 'proxy' objects created by apache's XMLBeans framework). As a final step, we then iterate over these collections to produce our XML document (in memory) and then save this to disk.
For the majority of use cases, this process works fine and can easily run in the JVM when given the '-Xmx1500m' command-line argument. However, issues arise when we are given 'large datasets' by the client. Large in this instance is 123Mb of client XML spread over 7 files. Such datasets result in our in-code collections being populated with almost 40,000 of the aforementioned 'proxy objects'. In these cases the memory usage just goes through the roof. I do not get any outofmemory exceptions the program just hangs until garbage collection occurs, free-ing up a small amount of memory, the program then continues, uses up this new space and the cycle repeats. These parsing sessions currently take 4-5 hours. We are aiming to bring this down to within an hour.
Its important to note that the calculations required to transform client xml into our xml require all of the xml data to cross-reference. Therefore we cannot implement a sequential parsing model or batch this process into smaller blocks.
What I've tried so far
Instead of holding all 123Mb of client xml in memory, on each request for data, load the files, find the data and release the references to these objects. This does seem to reduce the amount of memory consumed during the process but as you can imagine, the amount of time the constant I/O takes removes the benefit of the reduced memory footprint.
I suspected an issue was that we are holding an XmlObject[] for 123Mb worth of XML files as well as the collections of objects taken from these documents (using xpath queries). To remedy, I altered the logic so that instead of querying these collections, the documents were queried directly. The idea here being that at no point does there exist 4 massive Lists with 10's of 1000's of objects in, just the large collection of XmlObjects. This did not seem to make a difference at all and in some cases, increases the memory footprint even more.
Clutching at straws now, I considered that the XmlObject we use to build our xml in-memory before writing to disk was growing too large to maintain alongside all the client data. However, doing some sizeOf queries on this object revealed that at its largest, this object is less than 10Kb. After reading into how XmlBeans manages large DOM objects, it seems to use some form of buffered writer and as a result, is managing this object quite well.
So now I am out of ideas; Can't use SAX approaches instead of memory intensive DOM approaches as we need 100% of the client data in our app at any one time, cannot hold off requesting this data until we absolutely need it as the conversion process requires a lot of looping and the disk I/O time is not worth the saved memory space and I cannot seem to structure our logic in such a way as to reduce the amount of space the internal java collections occupy. Am I out of luck here? Must I just accept that if I want to parse 123Mb worth of xml data into our Xml format that I cannot do it with the 1500m memory allocation? While 123Mb is a large dataset in our domain, I cannot imagine others have never had to do something similar with Gb's of data at a time.
Other information that may be important
I have used JProbe to try and see if that can tell me anything useful. While I am a profiling noob, I ran through their tutorials for memory leaks and thread locks, understood them and there doesn't appear to be any leaks or bottlenecks in our code. After running the application with a large dataset, we quickly see a 'sawblade' type shape on the memory analysis screen (see attached image) with PS Eden space being taken over with a massive green block of PS Old Gen. This leads me to believe that the issue here is simply sheer amount of space taken up by object collections rather than a leak holding onto unused memory.
I am running on a 64-Bit Windows 7 platform but this will need to run on a 32 Bit environment.

The approach I'd take would be make two passes on the files, using SAX in both cases.
The first pass would parse the 'cross-reference' data, needed in the calculations, into custom objects and store them Maps. If the 'cross-reference' data is large then look at using distributed cache (Coherence is the natural fit if you've started with Maps).
The second pass would parse the files, retreive the 'cross-reference' data to perform calculations as needed and then write the output XML using the javax.xml.stream APIs.

Scaling application that reads large XML files

I have an application which reads large set of XML files (multiple around 20-30) periodically, like once every 10 minutes. Now each XML file can be approximated to at least 40-100 MB in size. Once each XML has read, a map is created out of the file, and then the map is passed across a processor chain (10-15), each processor using the data, performing some filter or writing to database, etc.
Now the application is running in 32 bit JVM. No intention on moving to 64 bit JVM right now. The memory foot-print as expected is very high... nearing the threshold of a 32 bit JVM. For now when we receive large files, we serialize the generated map into disk and run through the processor chain maximum of 3-4 map concurrently as if we try to process all the maps at the same time, it would easily go OutOfMemory. Also garbage collection is pretty high.
I have some ideas but wanted to see if there are some options which people have already tried/evaluated. So what are the options here for scaling this kind of application?

Yea, to parrot #aaray and #MeBigFatGuy, you want to use some event based parser for this, the dom4j mentioned, or SAX or StAX.
As a simple example, that 100MB XML is consuming a minimum of 200MB of RAM if you load it wholesale, as each character is immediately expanded to a 16 bit character.
Next, any tag of elements that you're not using is going to consume extra memory (plus all of the other baggage and bookkeeping of the nodes) and it's all wasted. If you're dealing with numbers, converting the raw string to a long will be a net win if the number is larger than 2 digits.
IF (and this is a BIG IF) you are using a lot of a reasonably small set of Strings, you can save some memory by String.intern()'ing them. This is a canonicalization process that makes sure if the string already exists in the jvm, its shared. The downside of this is that it pollutes your permgen (once interned, always interned). PermGen is pretty finite, but on the other hand it's pretty much immune to GC.
Have you considered being able to run the XML through an external XSLT to remove all of the cruft that you don't want to process before it even enters your JVM? There are several standalone, command line XSL processors that you can use to pre-process the files to something perhaps more sane. It really depends on how much of the data that is coming in you're actually using.
By using an event based XML processing model, the XSLT step is pretty much redundant. But the event based models are all basically awful to use, so perhaps using the XSLT step would let you re-use some of your existing DOM logic (assuming that's what you're doing).
The flatter your internal structures, the cheaper they are in terms of memory. You actually have a little bit of an advantage running a 32b vm, since instance pointers are half the size. But still, when you're talking 1000's or millions of nodes, it all adds up, and quickly.

We had a similar problem processing large XML files (around 400Mb). We greatly reduced the memory footprint of the application using this:
http://dom4j.sourceforge.net/dom4j-1.6.1/faq.html#large-doc

You can insert the contents of each XML file into a temporary DB table and each chain link would fetch the data it needs. You will probably lose performance, but gain scalability.

Searching for regex patterns on a 30GB XML dataset. Making use of 16gb of memory

I currently have a Java SAX parser that is extracting some info from a 30GB XML file.
Presently it is:
reading each XML node
storing it into a string object,
running some regexex on the string
storing the results to the database
For several million elements. I'm running this on a computer with 16GB of memory, but the memory is not being fully utilized.
Is there a simple way to dynamically 'buffer' about 10gb worth of data from the input file?
I suspect I could manually take a 'producer' 'consumer' multithreaded version of this (loading the objects on one side, using them and discarding on the other), but damnit, XML is ancient now, are there no efficient libraries to crunch em?

Just to cover the bases, is Java able to use your 16GB? You (obviously) need to be on a 64-bit OS, and you need to run Java with -d64 -XMx10g (or however much memory you want to allocate to it).
It is highly unlikely memory is a limiting factor for what you're doing, so you really shouldn't see it fully utilized. You should be either IO or CPU bound. Most likely, it'll be IO. If it is, IO, make sure you're buffering your streams, and then you're pretty much done; the only thing you can do is buy a faster harddrive.
If you really are CPU-bound, it's possible that you're bottlenecking at regex rather than XML parsing.
See this (which references this)
If your bottleneck is at SAX, you can try other implementations. Off the top of my head, I can think of the following alternatives:
StAX (there are multiple implementations; Woodstox is one of the fastest)
Javolution
Roll your own using JFlex
Roll your own ad hoc, e.g. using regex
For the last two, the more constrained is your XML subset, the more efficient you can make it.
It's very hard to say, but as others mentioned, an XML-native database might be a good alternative for you. I have limited experience with those, but I know that at least Berkeley DB XML supports XPath-based indices.

First, try to find out what's slowing you down.
How much faster is the parser when you parse from memory?
Does using a BufferedInputStream with a large size help?
Is it easy to split up the XML file? In general, shuffling through 30 GiB of any kind of data will take some time, since you have to load it from the hard drive first, so you are always limited by the speed of this. Can you distribute the load to several machines, maybe by using something like Hadoop?

No Java experience, sorry, but maybe you should change the parser? SAX should work sequentially and there should be no need to buffer most of the file ...

SAX is, essentially, "event driven", so the only state you should be holding on to from element to element is state that relevant to that element, rather than the document as a whole. What other state are you maintaining, and why? As each "complete" node (or set of nodes) comes by, you should be discarding them.

I don't really understand what you're trying to do with this huge amount of XML, but I get the impression that
using XML was wrong for the data stored
you are buffering way beyond what you should do (and you are giving up all advantages of SAX parsing by doing so)
Apart from that: XML is not ancient and in massive and active use. What do you think all those interactive web sites are using for their interactive elements?

Are you being slowed down by multiple small commits to your db? Sounds like you would be writing to the db almost all the time from your program and making sure you don't commit too often could improve performance. Possibly also preparing your statements and other standard bulk processing tricks could help
Other than this early comment, we need more info - do you have a profiler handy that can scrape out what makes things run slowly

You can use the Jibx library, and bind your XML "nodes" to objects that represent them. You can even overload an ArrayList, then when x number of objects are added, perform the regexes all at once (presumably using the method on your object that performs this logic) and then save them to the database, before allowing the "add" method to finish once again.
Jibx is hosted on SourceForge: Jibx
To elaborate: you can bind your XML as a "collection" of these specialized String holders. Because you define this as a collection, you must choose what collection type to use. You can then specify your own ArrayList implementation.
Override the add method as follows (forgot the return type, assumed void for example):
public void add(Object o) {
super.add(o);
if(size() > YOUR_DEFINED_THRESHOLD) {
flushObjects();
}
}
YOUR_DEFINED_THRESHOLD
is how many objects you want to store in the arraylist until it has to be flushed out to the database. flushObjects(); is simply the method that will perform this logic. The method will block the addition of objects from the XML file until this process is complete. However, this is ok, the overhead of the database will probably be much greater than file reading and parsing anyways.

I would suggest to first import your massive XML file into a native XML database (such as eXist if you are looking for open source stuff, never tested it myself), and then perform iterative paged queries to process your data small chunks at a time.

You may want to try Stax instead of SAX, I hear it's better for that sort of thing (I haven't used it myself).

If the data in the XML is order independent, can you multi-thread the process to split the file up or run multiple processes starting in different locations in the file? If you're not I/O bound that should help speed it along.