DDevelopment & Design

How to create a native Java App

Recently, I stumbled upon JCGO, an interesting project, which translates Java 1.4 code into C.
In this article, I show how to create a native Windows app out of a small Java app.

The Java app I will use is NetCat (https://github.com/rafalrusin/netcat). You can download precompiled executable, netcat.exe, from https://github.com/rafalrusin/netcat/downloads.

So the first step is to download all dependencies. I will use MinGW, MinGW GCC, jcgo-lib-1_14.tar.gz, jcgo-src-1_14.tar.bz2, classpath-0.93 (http://ftp.gnu.org/gnu/classpath/classpath-0.93.tar.gz) and Java sources for the app with dependent libraries: https://github.com/rafalrusin/netcat, commons cli 1.2 (http://commons.apache.org/cli/download_cli.cgi). You need to put all this in the same directory, so it’ll have structure like this:

auxbin
classpath-0.93
commons-cli-1.2-src
dlls
goclsp
src
jcgo
jcgo.exe
jcgo.jar
libs
miscsrc
netcat
out
rflg_out
stdpaths.in

Then, you need to run Java to C translator by using command:

jcgo.exe -sourcepath netcat/src -sourcepath commons-cli-1.2-src/src/java netcat.NetCat @stdpaths.in -d out

Initializing...

Analysis pass...

Output pass...

Writing class tables...
Creating main file...
Parsed: 293 java files (2699 KiB). Analyzed: 3067 methods.
Produced: 640 c/h files (3769 KiB).
Contains: 1490 java methods, 4119 normal and 288 indirect calls.
Done conversion in 1 seconds. Total heap size: 36572 KiB.
 
Next step is to compile it into final executable. Following command does this:
 
gcc -DJCGO_INET -DJCGO_NOFP -DJCGO_WIN32 -DJCGO_THREADS -I src/include/ -I src/include/boehmgc/ -I src/native/ out/Main.c -o netcat.exe libs/x86/mingw/libgcmt.a -lws2_32
 
I used some switches, which are suitable for this particular app. For example, by default JCGO doesn’t use multithreading or networking. This has to be enabled explicitly. 
 
And that’s it. Now you can try out the app by calling google.com, like this:
 
$ netcat.exe google.com -p 80
Connecting to google.com port 80
GET
HTTP/1.0 302 Found
Location: http://www.google.pl/
Cache-Control: private
Content-Type: text/html; charset=UTF-8
Set-Cookie: PREF=ID=2f3085ac38771e98:FF=0:TM=1345885031:LM=1345885031:S=8A-IkreMgCogMsey; expires=Mon, 25-Aug-2014 08:57:11 GMT; path=/; domain=.google.com
Set-Cookie: NID=63=O_QZ4bDrzYNiiE0DY8RT-34c_pGt_OZagP3gzrzqCAx_Xo2kO7s9zVrUOx7FVz4TyAEY7Wx9UhglYZSX9UHSdzT7c9mUKzfkJFp5lk5FyfiMIcKITLhgSX4__3QwEYBS; expires=Sun
, 24-Feb-2013 08:57:11 GMT; path=/; domain=.google.com; HttpOnly
P3P: CP="This is not a P3P policy! See http://www.google.com/support/accounts/bin/answer.py?hl=en&answer=151657 for more info."
Date: Sat, 25 Aug 2012 08:57:11 GMT
Server: gws
Content-Length: 218
X-XSS-Protection: 1; mode=block
X-Frame-Options: SAMEORIGIN


<HTML><HEAD><meta http-equiv="content-type" content="text/html;charset=utf-8">
<TITLE>302 Moved</TITLE></HEAD><BODY>
<H1>302 Moved</H1>
The document has moved
<A HREF="http://www.google.pl/">here</A>.
</BODY></HTML>

I like the approach of translating Java code into C, because compared to other tools, which generate C++ code, this is more suitable for embedded devices. For example it is possible to generate code for iOS, because Objective C is a superset of C.

One feature I would like to see though is to be able to use reference counting instead of full gc. This is because one of the advantages of C over Java is that it doesn’t have GC hangs. So then the programmer would have to make sure there’s no cycles in orphaned object structure.

Update: Ivan Maidansky, an author of JCGO, has put some interesting comments regarding this article. In particular, he is aware of some apps in Apple Store, which do this kind of translation. Also, reference counting is discouraged due to multithreading issues. These comments can be found here: https://github.com/ivmai/JCGO/issues/2

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DDevelopment & Design

Recently at storm-users

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()

  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()

  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.

DDevelopment & Design

JCE keystore and untrusted sites

Recently at work I was in need of connecting to a web service exposed via HTTPS. I've been doing this from inside Servicemix 3.3.1, which may seem a bit inhibiting, but that was a requirement. Nevertheless I've been trying my luck with the included ser...Recently at work I was in need of connecting to a web service exposed via HTTPS. I've been doing this from inside Servicemix 3.3.1, which may seem a bit inhibiting, but that was a requirement. Nevertheless I've been trying my luck with the included ser...