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Write large PDFs with Java sequentially

I am looking for a Java library that let's you write large PDFs sequentially with a minimum amount of memory. Most of the libraries I had a look at has to build up the document in memory first before you can actually write it.
The problem I have to deal with are OutOfMemoryErrors. It would be great if I could flush the writer programmatically whenever needed e.g. for each page.
Does anyone have any recommendations? I need something with a license along the lines of the LGPL (so not the GPL or the Affero GPL that iText uses).

You can do that with iText. It supports writing to OutputStreams.

The free version of Docmosis has a fairly open license so it might suit you. It uses a template-approach which is different from building from code. Docmosis processes all documents in a stream-based fashion since it's intended for singificant parallel use and for large documents. It also allows you to offload the most CPU intensive part processing to another server. Hope that helps.

I actually, had same issue as you do, a friend help me out, but he did in C# and using an api called GhostScriptSharp, you should check for it.
I can't give you a copy of the code, since its copyrighted, but i'm sure it would help you out, since the tool i think is builded on Java.

jPod can swap indirect objects and supports incremental writing.
This is still not optimal as you need an "increment" on each flush, but better than nothing...
EDIT
Öhhh - this is one of the famous examples of self describing code :-) Your're right, theres not much of a tutorial or that - but the Javadoc is quite good.
jPod writes incremental by default. See "CosDocument.setWriteModeHint" to set to full mode.
The example "CreateDoc" and "AppendPage" are simple examples of how to add pages. You may do the same and call "save" every 10 or 100 pages. This should "soften" all references to pages in memory and if not held by some other references of yours, the can be garbage collected.
THere's still the question how you fill the pages? THere are examples dealing with content streams, too (DrawText,..). BUT jPod is not like iText, jasper or whatever. There's only PDF model abstractions. You have no "Layouter" or "Renderer" that creates page content from text, html or something like that. How do you do this?

Using Python or Java, what would be the best way to create charts?

I've been searching and found jFreeChart, Python Google Chart and matplotlib. Searching here I also found CairoPlot. I've heard I might be able to use OpenOffice to do it too. Is the API easy to use? Or would it be simpler to stick to one of those libraries?
I have more experience with Java, but I've read most of Dive Into Python 3 and done some mockup programs in Python for simple things. I'm probably gonna have to spend more time doing it in Python, though I'm willing to do it as long as it isn't anything mindblowing. I want to automate some tests to put into a thesis, so I'm more worried about the end product.
So far I'm thinking of using matplotlib simply because it's the only one that's had any recent updates, which leads me to assume there might be more documentation due to continued support. I've used jFreeChart in the past too for some testing, and it was ok. But I was hoping to find something better, or to have more documentation/examples to use. Last time I didn't customize the graphics appearance as I wanted - say, change the background in a line plot - due to the lack of examples/documentation.

I recommend you to use matplotlib, it has high quality backends and a lot of graphical representations, you'll have the whole control over your plots and Python is a very handy and easy language to automatize tests, very practical for what you're willing to do. Matplotlib has also a large community that can help you and a lot of documentation/examples; just remember that matplotlib was not ported to Python 3.x yet, I don't know if this is important for you.
What I absolutely don't recommend is CairoPlot, it is not maintained anymore and is a toy project.

Google's Visualization API is fantastic - and much cleaner if you're working in a web environment, since you just output some text JS with your HTML, don't have to call back and render an image.
JFree also has Eastwood which is a reimplementation of Google Charts API, if you don't want to send your data to Google, or need SSL, though I don't think it's quite current, it's a good subset.

RFC: What's a good approach to remotely edit very large binary files?

I have a number of rather large binary files (fixed length records, the layout of which is described in another –textual– file). Data files can get as big as 6 GB. Layout files (cobol copybooks) are small in size, usually less than 5 KB.
All data files are concentrated in a GNU/Linux server (although they were generated in a mainframe).
I need to provide the testers with the means to edit those binary files. There is a free product called RecordEdit (http://record-editor.sourceforge.net/), but it has two severe drawbacks:
It forces the testers to download
the huge files through SFTP, only to
upload them once again every time a slight
change has been made. Very
inefficient.
It loads the entire
file into working memory, rendering
it useless for all but the relatively small
data files.
What I have in mind is a client/server architecture based in Java:
The server would be running a permanent
process, listening for
edition-oriented requests coming from
the client. Such requests would
include stuff like
return the list of available files
lock certain file for edition
modify this data in that record
return the n-th page of records
and so on…
The client could take any form
(RCP-based in a desktop –which is my first candidate-, ncurses in the same server, a middle web
application…) as long as it is able to
send requests to the server.
I've been exploring NIO (because of its buffers) and MINA (because of protocol transparency) in order to implement the scheme. However, before any further advancement of this endeavor, I would like to collect your expert opinions.
Is mine a reasonable way to frame the problem?
Is it feasible to do it using the language and frameworks I'm thinking of? Is it convenient?
Do you know of any patterns, blue prints, success cases or open projects that resemble or have to do with what I'm trying to do?

As I see it, the tricky thing here is decoding the files on the server. Once you've written that, it should be pretty easy.
I would suggest that, whatever the thing you use client-side is, it should basically upload a 'diff' of the person's changes.
Might it make sense to make something that acts like a database (or use an existing database) for this data? Or is there just too much of it?
Depending on how many people need to do this, the quick-and-dirty solution is to run the program via X forwarding -- that eliminates a number of the issues.. as long as that server has quite a lot of RAM free.

Is mine a reasonable way to frame the problem?
IMO, yes.
Is it feasible to do it using the language and frameworks I'm thinking of?
I think so. But there are other alternatives. For example:
Put the records into a database, and access by a key consisting of a filename + a record number. Could be a full RDBMS, or a more lightweight solution.
Implement as a RESTful web service with a UI implemented in HTML + javascript.
Implement using a scalable distributed file-system.
Also, from your description there doesn't seem to be a pressing need to use a highly scalable / transport independent layer ... unless you need to support hundreds of simultaneous users.
Is it convenient?
Convenient for who? If you are talking about you the developer, it depends if you are already familiar with those frameworks.

Have you considered using a distributed file system like OpenAFS? That should be able to handle very large files. Then you can write a client-side app for editing the files as if they are local.

Using SIFT for Augmented Reality

I've come across MANY AR libraries/SDKs/APIs, all of them are marker-based, until I found this video, from the description and the comments, it looks like he's using SIFT to detect the object and follow it around.
I need to do that for Android, so I'm gonna need a full implementation of SIFT in pure Java.
I'm willing to do that but I need to know how SIFT is used for augmented reality first.
I could make use of any information you give.

In my opinion, trying to implement SIFT for a portable device is madness. SIFT is an image feature extraction algorithm, which includes complex math and certainly requires a lot of computing power. SIFT is also patented.
Still, if you indeed want to go forth with this task, you should do quite some research at first. You need to check things like:
Any variants of SIFT that enhance performance, including different algorithms all around
I would recommend looking into SURF which is very robust and much more faster (but still one of those scary algorithms)
Android NDK (I'll explain later)
Lots and lots of publications
Why Android NDK? Because you'll probably have a much more significant performance gain by implementing the algorithm in a C library that's being used by your Java application.
Before starting anything, make sure you do that research cause it would be a pity to realize halfway that the image feature extraction algorithms are just too much for an Android phone. It's a serious endeavor in itself implementing such an algorithm that provides good results and runs in an acceptable amount of time, let alone using it to create an AR application.
As in how you would use that for AR, I guess that the descriptor you get from running the algorithm on an image would have to be matched against with data saved in a central database. Then the results can be displayed to the user. The features of an image gathered from SURF are supposed to describe it such as that it can be then identified using those. I'm not really experienced on doing that but there's always resources on the web. You'd probably wanna start with generic stuff such as Object Recognition.
Best of luck :)

I have tried SURF for 330Mhz Symbian mobile and it was still too slow even with all optimizations and lookup tables. And SIFT should be even more slow. Everyone using FAST for mobiles now. Anyway feature extraction is not a biggest problem. Correspondence and clearing false positive in it is more difficult.
FAST link
http://svr-www.eng.cam.ac.uk/~er258/work/fast.html

If I where you, I'd look into how (and why) the SIFT feature works (as was said, its wikipedia-page offers a good cochise explanation, and for more details check the science paper (which is linked to at wikipedia)), and then build your own variant that suits your taste; i.e. has the optimal balance between performance and cpu-load, needed for your application.
For instance, I think Gaussian smoothing might be replaced by some faster way of smoothing.
Also, when you build your own variant, you don't have anything to do with patents (there already are lots of variants, like GLOH).

I would recommend you to start by looking at the features already implemented in the OpenCV library, which include SURF, MSER and others:
http://opencv.willowgarage.com/documentation/cpp/feature_detection.html
This might be enough for your application and are faster than SIFT. And as mentioned above, SIFT is patented.
Also, start by making performance tests in your mobile platform, just by extracting the features at every frame, this way you'll have an idea which ones can run real-time or not.

Have you tried OpenCV's FAST implementation in the Android port? I've tested it out and it runs blazingly fast.
You can also compute reduced histogram descriptors around the detected FAST keypoints. I've heard of 3x3 rather than standard 4x4 of SIFT. That has a decent chance of working in real time if you optimize it heavily with NEON instructions. Otherwise, I'd recommend something fast and simple like sum of squared or absolute differences for a patch around the keypoints which are very fast.
SIFT is not a panacea. For real time video applications, it's usually overkill.

As always, Wikipedia is a good place to start from : http://en.wikipedia.org/wiki/Scale-invariant_feature_transform, but note that SIFT is patented.

Python, PyTables, Java - tying all together

Question in nutshell
What is the best way to get Python and Java to play nice with each other?
More detailed explanation
I have a somewhat complicated situation. I'll try my best to explain both in pictures and words. Here's the current system architecture:
We have an agent-based modeling simulation written in Java. It has options of either writing locally to CSV files, or remotely via a connection to a Java server to an HDF5 file. Each simulation run spits out over a gigabyte of data, and we run the simulation dozens of times. We need to be able to aggregate over multiple runs of the same scenario (with different random seeds) in order to see some trends (e.g. min, max, median, mean). As you can imagine, trying to move around all these CSV files is a nightmare; there are multiple files produced per run, and like I said some of them are enormous. That's the reason we've been trying to move towards an HDF5 solution, where all the data for a study is stored in one place, rather than scattered across dozens of plain text files. Furthermore, since it is a binary file format, it should be able to get significant space savings as compared to uncompressed CSVS.
As the diagram shows, the current post-processing we do of the raw output data from simulation also takes place in Java, and reads in the CSV files produced by local output. This post-processing module uses JFreeChart to create some charts and graphs related to the simulation.
The Problem
As I alluded to earlier, the CSVs are really untenable and are not scaling well as we generate more and more data from simulation. Furthermore, the post-processing code is doing more than it should have to do, essentially performing the work of a very, very poor man's relational database (making joins across 'tables' (csv files) based on foreign keys (the unique agent IDs). It is also difficult in this system to visualize the data in other ways (e.g. Prefuse, Processing, JMonkeyEngine getting some subset of the raw data to play with in MatLab or SPSS).
Solution?
My group decided we really need a way of filtering and querying the data we have, as well as performing cross table joins. Given this is a write-once, read-many situation, we really don't need the overhead of a real relational database; instead we just need some way to put a nicer front end on the HDF5 files. I found a few papers about this, such as one describing how to use [XQuery as the query language on HDF5 files][3], but the paper describes having to write a compiler to convert from XQuery/XPath into the native HDF5 calls, way beyond our needs.
Enter [PyTables][4]. It seems to do exactly what we need (provides two different ways of querying data, either through Python list comprehension or through [in-kernel (C level) searches][5].
The proposed architecture I envision is this:
What I'm not really sure how to do is to link together the python code that will be written for querying, with the Java code that serves up the HDF5 files, and the Java code that does the post processing of the data. Obviously I will want to rewrite much of the post-processing code that is implicitly doing queries and instead let the excellent PyTables do this much more elegantly.
Java/Python options
A simple google search turns up a few options for [communicating between Java and Python][7], but I am so new to the topic that I'm looking for some actual expertise and criticism of the proposed architecture. It seems like the Python process should be running on same machine as the Datahose so that the large .h5 files do not have to be transferred over the network, but rather the much smaller, filtered views of it would be transmitted to the clients. [Pyro][8] seems to be an interesting choice - does anyone have experience with that?

This is an epic question, and there are lots of considerations. Since you didn't mention any specific performance or architectural constraints, I'll try and offer the best well-rounded suggestions.
The initial plan of using PyTables as an intermediary layer between your other elements and the datafiles seems solid. However, one design constraint that wasn't mentioned is one of the most critical of all data processing: Which of these data processing tasks can be done in batch processing style and which data processing tasks are more of a live stream.
This differentiation between "we know exactly our input and output and can just do the processing" (batch) and "we know our input and what needs to be available for something else to ask" (live) makes all the difference to an architectural question. Looking at your diagram, there are several relationships that imply the different processing styles.
Additionally, on your diagram you have components of different types all using the same symbols. It makes it a little bit difficult to analyze the expected performance and efficiency.
Another contraint that's significant is your IT infrastructure. Do you have high speed network available storage? If you do, intermediary files become a brilliant, simple, and fast way of sharing data between the elements of your infrastructure for all batch processing needs. You mentioned running your PyTables-using-application on the same server that's running the Java simulation. However, that means that server will experience load for both writing and reading the data. (That is to say, the simulation environment could be affected by the needs of unrelated software when they query the data.)
To answer your questions directly:
PyTables looks like a nice match.
There are many ways for Python and Java to communicate, but consider a language agnostic communication method so these components can be changed later if necessarily. This is just as simple as finding libraries that support both Java and Python and trying them. The API you choose to implement with whatever library should be the same anyway. (XML-RPC would be fine for prototyping, as it's in the standard library, Google's Protocol Buffers or Facebook's Thrift make good production choices. But don't underestimate how great and simple just "writing things to intermediary files" can be if data is predictable and batchable.
To help with the design process more and flesh out your needs:
It's easy to look at a small piece of the puzzle, make some reasonable assumptions, and jump into solution evaluation. But it's even better to look at the problem holistically with a clear understanding of your constraints. May I suggest this process:
Create two diagrams of your current architecture, physical and logical.
On the physical diagram, create boxes for each physical server and diagram the physical connections between each.
Be certain to label the resources available to each server and the type and resources available to each connection.
Include physical hardware that isn't involved in your current setup if it might be useful. (If you have a SAN available, but aren't using it, include it in case the solution might want to.)
On the logical diagram, create boxes for every application that is running in your current architecture.
Include relevant libraries as boxes inside the application boxes. (This is important, because your future solution diagram currently has PyTables as a box, but it's just a library and can't do anything on it's own.)
Draw on disk resources (like the HDF5 and CSV files) as cylinders.
Connect the applications with arrows to other applications and resources as necessary. Always draw the arrow from the "actor" to the "target". So if an app writes and HDF5 file, they arrow goes from the app to the file. If an app reads a CSV file, the arrow goes from the app to the file.
Every arrow must be labeled with the communication mechanism. Unlabeled arrows show a relationship, but they don't show what relationship and so they won't help you make decisions or communicate constraints.
Once you've got these diagrams done, make a few copies of them, and then right on top of them start to do data-flow doodles. With a copy of the diagram for each "end point" application that needs your original data, start at the simulation and end at the end point with a pretty much solid flowing arrow. Any time your data arrow flows across a communication/protocol arrow, make notes of how the data changes (if any).
At this point, if you and your team all agree on what's on paper, then you've explained your current architecture in a manner that should be easily communicable to anyone. (Not just helpers here on stackoverflow, but also to bosses and project managers and other purse holders.)
To start planning your solution, look at your dataflow diagrams and work your way backwards from endpoint to startpoint and create a nested list that contains every app and intermediary format on the way back to the start. Then, list requirements for every application. Be sure to feature:
What data formats or methods can this application use to communicate.
What data does it actually want. (Is this always the same or does it change on a whim depending on other requirements?)
How often does it need it.
Approximately how much resources does the application need.
What does the application do now that it doesn't do that well.
What can this application do now that would help, but it isn't doing.
If you do a good job with this list, you can see how this will help define what protocols and solutions you choose. You look at the situations where the data crosses a communication line, and you compare the requirements list for both sides of the communication.
You've already described one particular situation where you have quite a bit of java post-processing code that is doing "joins" on tables of data in CSV files, thats a "do now but doesn't do that well". So you look at the other side of that communication to see if the other side can do that thing well. At this point, the other side is the CSV file and before that, the simulation, so no, there's nothing that can do that better in the current architecture.
So you've proposed a new Python application that uses the PyTables library to make that process better. Sounds good so far! But in your next diagram, you added a bunch of other things that talk to "PyTables". Now we've extended past the understanding of the group here at StackOverflow, because we don't know the requirements of those other applications. But if you make the requirements list like mentioned above, you'll know exactly what to consider. Maybe your Python application using PyTables to provide querying on the HDF5 files can support all of these applications. Maybe it will only support one or two of them. Maybe it will provide live querying to the post-processor, but periodically write intermediary files for the other applications. We can't tell, but with planning, you can.
Some final guidelines:
Keep things simple! The enemy here is complexity. The more complex your solution, the more difficult the solution to implement and the more likely it is to fail. Use the least number operations, use the least complex operations. Sometimes just one application to handle the queries for all the other parts of your architecture is the simplest. Sometimes an application to handle "live" queries and a separate application to handle "batch requests" is better.
Keep things simple! It's a big deal! Don't write anything that can already be done for you. (This is why intermediary files can be so great, the OS handles all the difficult parts.) Also, you mention that a relational database is too much overhead, but consider that a relational database also comes with a very expressive and well-known query language, the network communication protocol that goes with it, and you don't have to develop anything to use it! Whatever solution you come up with has to be better than using the off-the-shelf solution that's going to work, for certain, very well, or it's not the best solution.
Refer to your physical layer documentation frequently so you understand the resource use of your considerations. A slow network link or putting too much on one server can both rule out otherwise good solutions.
Save those docs. Whatever you decide, the documentation you generated in the process is valuable. Wiki-them or file them away so you can whip them out again when the topic come s up.
And the answer to the direct question, "How to get Python and Java to play nice together?" is simply "use a language agnostic communication method." The truth of the matter is that Python and Java are both not important to your describe problem-set. What's important is the data that's flowing through it. Anything that can easily and effectively share data is going to be just fine.

Do not make this more complex than it needs to be.
Your Java process can -- simply -- spawn a separate subprocess to run your PyTables queries. Let the Operating System do what OS's do best.
Your Java application can simply fork a process which has the necessary parameters as command-line options. Then your Java can move on to the next thing while Python runs in the background.
This has HUGE advantages in terms of concurrent performance. Your Python "backend" runs concurrently with your Java simulation "front end".

You could try Jython, a Python interpreter for the JVM which can import Java classes.
Jython project homepage
Unfortunately, that's all I know on the subject.

Not sure if this is good etiquette. I couldn't fit all my comments into a normal comment, and the post has no activity for 8 months.
Just wanted to see how this was going for you? We have a very very very similar situation where I work - only the simulation is written in C and the storage format is binary files. Every time a boss wants a different summary we have to make/modify handwritten code to do summaries. Our binary files are about 10 GB in size and there is one of these for every year of the simulation, so as you can imagine, things get hairy when we want to run it with different seeds and such.
I've just discovered pyTables and had a similar idea to yours. I was hoping to change our storage format to hdf5 and then run our summary reports/queries using pytables. Part of this involves joining tables from each year. Have you had much luck doing these types of "joins" using pytables?