A Human Readable Predicate with ObjectLab Kit

Recently, I have been working with Predicates.

I'm modelling some stuff for a bank but I needed to present updates and changes to the handwritten rules regularly. I don't know about you but wouldn't be nice if we could print the Predicate and not getting something like:

net.objectlab.kit.util.excel.ExcelWorkbookTest$$Lambda$1/868693306@13221655

Therefore, here comes the PrintablePredicate class in the next release of ObjectLab Kit.

See PrintablePredicate.java

The idea is to create an implementation of a Predicate and allow you to give it a name and the values it is comparing against.  We also support AND, OR and NEGATE as per a Predicate.

Let's imagine a small model, a financial Instrument 'Asset' and some basic Predicates.  Imagine that we create a predicate that detects instruments that are either Bonds or Commodities but that they should also be Active.

Using PrintablePredicate, I can combine 2 predicates and when I print the predicate (in an Excel spreadsheet that I generate automatically, more on that later) I can see a nice string "AssetClass in (Bond, Commodities) AND Active".  Here is the code for it:



This tiny class will come with ObjectLab Kit 1.4.1. Enjoy!

Flatpack 4.0.2 released! 50% speed improvement

Dear All

It is with great pleasure to announce that, after a while one must admit, we have released a new version of FlatPack, v 4.02.

See FlatPack Website for more information.

The libraries are available on Maven Central and still only required JDK 1.8.

The release jumped directly to Java8 as we make use of some cool stream functionalities and Autocloseable features.  We have also spent a little bit of time on performance and improved the overall parsing results by about 50%! 

Enjoy!

How to efficiently add BigDecimals

Anyone who deals with monetary values knows that double/float won't cut the mustard and if you deal with prices and FX rates, then BigDecimal is the only real option.

This comes with a lot of potential issues, BigDecimal methods do not handle null very well (i.e. not at all) and sometimes a bug crops up because BigDecimal returns new instances.


So the ObjectLabKit Util package will help, but here is a question for you... what is an efficient way to sum a list of BigDecimal coming from a Class.

Assume that we have a list of 500 Test instances and that we need to sum the Test.value and that value could be null.

We shall run the test 1,000 times.

Option 1: Use Total in a for loop

Option 2: Use Total with java8 forEach

Option 3: Use Total and java8 map()

Option 4: Use Java8 map and reduce

Option 5: Use Java8 map, reduce and accumulator

Option 6: Use Java8 and home-made Collector

Option 7: Use Java8 and ObjectLabKit Calculator

Option 8: Use Java8 and Parallel Stream

So what are the results?

On my 2012 MacBook Pro for a list of 500 Test instances.

Algo	Average (ms)	Min (ms)	Max (ms)
Use Total in a for loop	0.1	0	4
Use Total with java8 forEach	0.1	0	40
Use Total and java8 map()	0.1	0	6
Use Java8 map and reduce	0	0	2
Use Java8 map, reduce and accumulator	0	0	2
Use Java8 and home-made Collector	0.1	0	6
Use Java8 and ObjectLabKit Calculator	0	0	2
Use Java8 and Parallel Stream	0.1	0	10

First of all, the value generated is the same for every algo, so no bug there it seems.

The results are quite similar except for the Max value, implying a greater deviation in the results. I've used JAmon for measuring min/max and average time.

Surprisingly, it seems that forEach has at least 1 execution at 40ms, which is way above the rest. Otherwise using the ObjectLabKit Calculator seems a good compromise between having to write the reduce correctly (! watch out if the BigDecimal on the right is null!) and using the raw map/reduce. 

The Parallel Stream is not as efficient, as it takes some time to coordinate the tasks and split the list. let's see if it gets any different with more data. 

On my 2012 MacBook Pro (QuadCore) for a list of 50,000 Test instances and the parallelStream is then becoming the most efficient.

Algo	Average (ms)	Min (ms)	Max (ms)
Use Total in a for loop	1	0	20
Use Total with java8 forEach	1.1	0	48
Use Total and java8 map()	2.1	1	40
Use Java8 map and reduce	1.2	1	9
Use Java8 map, reduce and accumulator	1.2	1	10
Use Java8 and home-made Collector	1.4	1	12
Use Java8 and ObjectLabKit Calculator	1.2	1	11
Use Java8 and Parallel Stream	0.6	0	17

So it looks like, when using single thread, that the RAW use of stream.map and reduce is the most efficient but one has to remember how to write it:

  final BigDecimal reduce = list.stream()

        .map(Test::getValue)

        .reduce(BigDecimal.ZERO, 

                (a, b) -> b != null ? a.add(b) : a);

Using the parallelStream (when suitable) reduces the average to 0.5ms but the max is 18ms

  final BigDecimal reduce = list.parallelStream()

        .map(Test::getValue)

        .reduce(BigDecimal.ZERO, 

                (a, b) -> b != null ? a.add(b) : a);

Full code available here at GitHub Gist

ObjectLab Kit 1.3.0 released

We are delighted to announce the release of ObjectLab Kit 1.3.0.

See http://objectlabkit.sf.net

The release is available on Maven Central or Under files in SourceForge.

The Source Code lives at GitHub: http://github.com/appendium/objectlabkit

Feel free to fork and contribute!

This release fixes a couple of bugs but also:

1. includes a JDK8 module using java.time.LocalDate; it is the only module requiring JDK8
2. includes the first official release of ObjectLab Utils a small library with

• Some caches with expiring/timeout but unlike Guava or EHCache, the cache can be refreshed in its entirety in one go; this is suitable only if you can hold the entire dataset in memory; on the plus side, you would hit the generator/DB only once.
• Lots of small utilities for BigDecimal, Integer, Boolean and Collection, mainly to deal with nulls.  And if you deal with BigDecimal, Total, Average and WeightedAverage classes will be very useful.
• ConsoleMenu a way to create user menus for a console/command line application.

Enjoy!

Benoit & the team.

FlatPack 3.4.0 released

Dear All

It is with great pleasure to announce that, after a while one must admit, we have released a new version of FlatPack, v 3.4.0

See FlatPack Website for more information.

Or the change log

The libraries are available on Maven Central and still only required JDK 1.5.

Note that the next release will jump directly to Java8 as we will make use of some cool stream functionalities and Autocloseable features (jdk7).

here is an example of what is coming with 4.0:


Enjoy!

Sonar and BlackDuck.com for ObjectLab Kit.

A big thank you to Simon from SonarSource.org to include ObjectLab Kit in their demo site.

Check this out: http://nemo.sonarqube.org/dashboard/index/250253

But we're going to make it better... reach a higher % of compliance...

Also thanks to ohloh.net

https://www.openhub.net/p/objectlabkit

If you are using it... vote for it!

And, yepee, I am ranked 2,400 or something out of 315,000...

https://www.openhub.net/accounts/benoitx

Whatever that means.

Enjoy!

FlatPack 3.2.0 is released!

FlatPack on SourceForge: a Java (1.4+) flat file parser that handles CSV, fixed length and custom delimiters. The formats are configured in XML, it is fast and released under Apache license 2.0.

http://flatpack.sf.net

Changes in this version include:

New Features:

o Added a getBigDecimal method on DataSet.

Fixed bugs:

o Fixed SF Bug 1869636. The parameters for the XML Map and data file were reversed in the BuffReaderDelimParser.
o Stopped the fixed width parser from removing leading spaces in a data element. Added the ParserUtils.rTrim() method.
o Added check for duplicate column names when using file header for column names.
o Applied patch from Dirk Olmes to prevent duplicate column names in the XML
mapping. IllegalArgumentException is now thrown if a duplicate column name exists in the map. Thanks Dirk...
o doParse() on DBFixedLengthParser was returning a null and was never getting a DataSet returned

Enjoy!