Guido Kollerie's Blog

Oracle and Python on OS X Mountain Lion

2012-12-06T07:00:00+01:00

I had been running OS X Snow Leopard, released on August 28th, 2009, relatively happily for many years and never saw the need to upgrade to newer incarnations of Apple's operating system. With Snow Leopard Apple focussed on under the hood changes and not so much on end user features. A laudable effort as many small and not so small things just did not work correctly; better make what's already there work than introduce more broken features.

That all changed with OS X Lion, released on July 20th, 2011. Lion was not particularly well received. It was buggy and presented Apple's first forays into dumbing OS X down to iOS levels. OS X Mountain Lion continued that effort but supposedly did solve many of Lion's issues.

The other day I was handed a relatively new MacBook Pro to be my main development machine at work. It ran Lion and might have run Snow Leopard, but I opted for installing Mountain Lion. One can only postpone the inevitable for so long.

Although some people strongly prefer HomeBrew, I am generally happy with MacPorts. It works just fine. Though it does require you to have installed Apple's Xcode Developer Tools and Apple's Command Line Developer Tools.

With MacPorts installing Python, and some basic tools, is a simple matter of:

$ sudo port install python26 py26-distribute py26-pip py26-virtualenv

Yes I know, I'm stuck at version 2.6 for deployment reasons out of my control.

For a particular project I needed to connect to an Oracle database. In order to install the Python interface to Oracle, cx_Oracle, in my virtualenv I needed to install the Oracle Instant Client first. Running on a 64 bit processor (Intel Core i7) and a 64 bit operating system with a 64 bit version of Python installed as demonstrated by:

$ python -c 'import struct;print( 8 * struct.calcsize("P"))'
64

I, logically, choose the 64 bit version of the Oracle Instant Client. Big mistake!

Even though cx_Oracle installed just fine. Executing code that tries to connect to the Oracle database consistently resulted in a segmentation fault 11 error message.

Under Snow Leopard, that I had running in 64 bit mode as well, this worked without any issues. Though in Mountain Lion and supposedly in Lion the Oracle Instant Client cannot be run in 64 bit mode. Fortunately I don't have strict requirements to run this particular application in 64 bit. So the solution is simple; use the 32 bit Oracle Instant Client and run Python in 32 bit mode.

The latter turned out to not so obvious. How do I tell MacPorts to build me a 32 bit version of Python? And how do I tell pip to build me 32 bit library?

Building 32 bit ports using MacPorts

Contrary to the Apple provided binary of the Python interpreter, the binaries as built by MacPorts do not contain both a 32 bit and a 64 bit version. MacPorts builds a 64 bits version if it is in a 64 bit environment. To tell it we want it to built a 32 bit version we need to edit macports.conf

$ sudo mvim /opt/local/etc/macports/macports.conf

And uncomment the line:

#build_arch        i386

From this moment on MacPorts will build 32 bit binaries exclusively.

Building 32 bit libraries using pip

Without any extra information, pip too, builds 64 bit libraries for those libraries that need C extensions to be built. To tell it we want a 32 bit library instead we run pip as follows:

$ ARCHFLAGS="-arch i386" pip install cx-Oracle

To make this the default we can add the variable ARCHFLAGS="-arch i386" to our shell environment by adding it to our shell startup scripts.

Now we can connect to Oracle without any issues.

Python Users Netherlands Meeting

2012-09-22T15:46:00+02:00

Last night I attended the Python Users Netherlands (PUN) Meeting hosted by Nelen & Schuurmans in Utrecht. This must have been the third or fourth time I attended a PUN Meeting. Each time I'm struck by the small size of the Python community in The Netherlands. I do realize not every Python developer in The Netherlands attends every or even any PUN meeting. But still, it is an excellent way to keep in touch with fellow Python developers and get a feel for what Python developers in The Netherlands are working on.

If you are a Python developer looking for work there is even more reason to attend. This time two companies formally announced they were looking for Python developers to join their development teams. Even more people in the audience raised their hands when asked whether their company was looking for Python developers as well. I heard similar sounds when I attended PyGrunn earlier this year. Even in these challenging times there seems plenty of opportunity for good Python developers.

The 30 min Talks

On the agenda the two half hour talks both mentioned Pyramid. I have played with Pyramid once before and came away very impressed. So I was hoping to see more details on how people were using Pyramid in production. The two talks were a little light on specific Pyramid details, but did gave a very good overview of the products being built on top of it.

Wichert Akkerman's talk on How 2Style4You uses Pyramid (and more Python) intriguingly showed the complexities of developing i18n and l10n applications. Something most Dutch developers normally don't have to take into account.

Wichert was also kind enough to lend me his laptop for my 5 min lightning talk as mine had broken done earlier that day.

Marcel van den Elst seemed to have a lot of fun developing with MongoDB. His talk on MongoEngine + Relational + Privileges (on Pyramid!) showed the open source tools his company had built on top of MongoDB to implement their progressive planning tool.

The 5 min Lightning Talks

I started off the lightning talks with my 5 min presentation on Disco. Let me tell you that 5 minutes to present something is really, really short. I should have prepared myself a little better because I ran out of time as soon as I had started.

Disco

Anyway, back to Disco. A couple of months ago I was researching the means to process (OCR'ing, shape detection in diagrams, cross referencing, etc) a fairly large number of documents (60K - 100K). Processing that many documents on a single machine took a number of weeks. How could we speed that up?

One of the possible solutions I came across was Disco. Disco is a MapReduce implementation written in Erlang (core) and Python (tools and the MapReduce jobs). Working for a C# development shop at the time Disco was not immediately applicable. It did, however, remain in the back of my head to return to one day. The 5 min lightning talks seemed like a good excuse to play with it a bit more.

Installation of Disco was fairly simple. However I did have to patch one file (lib/disco/comm.py) to get chunking in its ddfs tool working. When I later realized a fix for this issue had been available in Disco's repository for over six months as a pull request I prematurely drew the conclusion that Disco was not actively developed.

Today, while writing this blog post, I took another look at Disco's project page at Github and noticed the steady stream of commits. Hence it is, contrary to what I said at the PUN meeting, definitely actively developed.

So, what makes Disco so interesting that I wanted to bring it to the attention of other Python developers? An example shows that best:

from disco.core import Job, result_iterator

def map(line, params):
    for word in line.split():
        yield word, 1

def reduce(iter, params):
    from disco.util import kvgroup
    for word, counts in kvgroup(sorted(iter)):
        yield word, sum(counts)

if __name__ == '__main__':
    job = Job().run(
        input=["http://discoproject.org/media/text/chekhov.txt"],
        map=map,
        reduce=reduce)
    for word, count in result_iterator(job.wait(show=True)):
        print word, count

That's plain and simple Python code! Furthermore it's a complete Disco MapReduce job. As you can see it only takes two functions, map and reduce, without a lot of boiler plate code to implement a MapReduce job. Now compare that to writing a simple Apache Hadoop client

Something slightly more complicated, an inner_join operation on arbitrarily large datasets still looks simple. I think it is a testimony to good design if problems can be expressed easily and succinctly in a framework.

You might wonder whether Disco actually scales. After all, MapReduce problems crave to be distributed over as many nodes as you can dedicate to it. Well, Nokia Research Center in Palo Alto runs Disco on an 800 node cluster. That should give you an idea of its scalability.

According to the What is Disco page, Disco's main features are:

Proven to scale to hundreds of CPUs and tens of thousands of simultaneous tasks.
Used to process datasets in the scale of tens of terabytes.
Extremely simple to use: A typical tasks consists of two functions written in Python and two calls to the Disco API.
Tasks can be specified in any other language as well, by implementing the Disco worker protocol.
Input data can be in any format, even binary data such as images. The data can be located on any source that is accessible by HTTP or it can distributed to local disks.
Fault-tolerant: Server crashes don’t interrupt jobs. New servers can be added to the system on the fly.
Flexible: In addition to the core map and reduce functions, a combiner function, a partition function and an input reader can be provided by the user.
Easy to integrate to larger applications using the standard Disco module and the Web APIs.
Comes with a built-in distributed storage system (Disco Distributed Filesystem).

5 minutes to intrigue you, I hope it worked.

The other 5 min Lightning Talks

The other talks covered a wide range of topics:

(Really) naive data mining, Joël Cox
"Requests" library for easy json api access + testing dikes, Reinout van Rees
Python for those little throwaway scripts (that you end up not throwing away), Tikitu de Jager
Shell pearls (not to be confused with Perl shells), Remco Wendt
A script for running shell commands from the OS X command line but executed in Virtual Machines, Reinout van Rees

All in all an evening well spent!

Converting a Tumblr blog to a rstblog

2011-06-21T00:00:00+02:00

I have been writing a private (password protected) blog for family and friends on Tumblr for almost half a year and it suddenly freaked me out that Tumblr had all my carefully written blog posts. What if they lost it all, what if they went out of business, etc? Shortly before I freaked out I had discovered rstblog, a static blog generator that generates a blog from reStructuredText formatted text files. Could I somehow get all my posts out of Tumblr, convert them to reStructuredText and generate a rstblog? Turns out I could, though it did involve a fair bit of manual editing and writing a Python script.

Getting my posts out of Tumblr

Tumblr has a backup tool that was released as a beta on Dec 16th, 2009. It has not been updated since and never got bumped to release status. It works just fine though. However the fact that it has not been updated in 1.5 years seems seems to suggest that they do not consider it a high priority for users to be able to backup their data. That means you will either have to trust Tumblr or get your data out while you still can.

Tumblr's backup tool does not work with password protected blogs (nor does their search feature, but that's something entirely different). Hence I had to temporarily turn off the password protection, run the backup and re-enable the password protection. The backup tool writes all blog posts, images and other relevant data into the following directory structure:

$ ls -la
total 32
drwxr-xr-x    9 gkoller  staff   306 May 31 14:15 .
drwx------   15 gkoller  staff   510 Jun 17 22:16 ..
drwxr-xr-x    6 gkoller  staff   204 May 31 12:50 archive.noindex
-rw-r--r--    1 gkoller  staff  5616 May 31 12:50 avatar.png
drwxr-xr-x   17 gkoller  staff   578 May 31 12:50 images
-rw-r--r--    1 gkoller  staff  1138 May 31 12:50 index.html
drwxr-xr-x  216 gkoller  staff  7344 Jun  8 11:41 posts
-rw-r--r--    1 gkoller  staff   498 May 31 12:50 style.css
drwxr-xr-x    3 gkoller  staff   102 May 31 14:15 theme

The posts directory contains all the posts. The images directory contains all the images. Both directories are flat; they have no further hierarchy.

Converting HTML to reStructuredText

Each post in the posts directory is a simple HTML file. For rstblog to be able to generate a complete static blog these posts need to be converted to reStructuredText. Initially I thought I had to write a script to do this conversion for me, but as it turns out, there is a tool that already does this: Pandoc. Converting (most) HTML files to reStructuredText files was as simple as:

for f in `ls -1 *.html`
do
    echo processing ${f}
    pandoc -f html -t rst ${f} -o ${f%%html}rst
done

Unfortunately Pandoc is not perfect. Converting HTML tables to their reStructuredText equivalents was a no-go; Pandoc simple hang. For those posts with tables I took an intermediate route via Markdown (also supported by Pandoc); HTML -> Markdown -> reStructuredText. The remaining posts generally converted reasonably well. I say reasonably, as posts with images with alt text's generated incorrect reStructuredText equivalent markup where the :alt: marker and the corresponding text were on separate lines instead of on the same line. This required a fair bit of manual editing to fix.

rstblog Metadata and Directory Structure

rstblog requires more than just a bunch of reStructuredText files. First, each post needs to have some metadata in YAML at the top; this includes, among other things, tags. Furthermore rstblog requires the posts to be in a specific directory hierarchy where year, month and day each are a directory. Last, all the images needed to be copied to subdirectory of rstblog's static directory and the references to them from the reStructuredText files adjusted. Doing all of this manually for 100+ posts would have been a major pain. Hence I wrote a small Python 3 script to do exactly that.

It is run by executing:

python converter.py -tumblr_path ~/tmp/tumblr_backup -rstblog_path ~/tmp/rstblog

This assumes that ~/tmp/rstblog contains a rudimentary rstblog setup.

The source code for the script (mind the dependency on pytz):

import glob
import sys
import argparse
from xml.etree.ElementTree import fromstring
import locale
import os
import re
from datetime import datetime
import pytz
import shutil

parser = argparse.ArgumentParser(
    description='Convert Tumblr posts backup into rstblog posts.')
parser.add_argument('-tumblr_path', required=True,
                    help='The root directory of the Tumblr backup.')
parser.add_argument('-rstblog_path', required=True,
                    help='The root directory of the rstblog blog.')
args = parser.parse_args(sys.argv[1:])


def verify_path(path, path_argname, path_name, path_sig):
    format_params = {'path': path,
                     'path_argname': path_argname,
                     'path_name': path_name}
    if os.path.isdir(path):
        missing = path_sig - set(os.listdir(path))
        if missing:
            format_params['missing'] = ','.join(missing)
            return "{path_argname} does not look like to be a {path_name} directory. It's missing '{missing}'".format(
                **format_params)
    else:
        return '{path_argname} does not refer to a directory.'.format(
            **format_params)
    return None

# set of files or directories that are typical for the type of directories
# we're dealing with. This allows us to perform a few sanity checks before we
# start reading and writing files.
rstblog_sig = {'config.yml', 'static', '_templates'}
tumblr_sig = {'post', 'index.html', 'archive.noindex'}

verify_path(args.tumblr_path, '-tumblr_path', 'Tumblr Backup', tumblr_sig)
verify_path(args.rstblog_path, '-rstblog_path', 'rstblog', rstblog_sig)

# create image dir in rstblog_dir.

def get_local_timezone():
    locale.setlocale(locale.LC_ALL, "")
    # ('nl_NL', 'ISO8859-1') -> NL
    # works even with explicit char encoding present, eg nl_NL.UTF-8
    country = locale.getlocale()[0][3:5]
    timezone_names = pytz.country_timezones[country]
    if len(timezone_names) > 1:
        print(
            "Multiple timezones {} found for country '{}', picking the first one '{}'".format(
                timezone_names, country,
                timezone_names[0]))
    tz = pytz.timezone(timezone_names[0])
    return tz

local_tz = get_local_timezone()

tumblr_img_dir = os.path.join(args.tumblr_path, 'images')
tumblr_posts_glob = os.path.join(args.tumblr_path, 'posts/*.html')
for tumblr_post in sorted(glob.iglob(tumblr_posts_glob)):
    rst_post_src = os.path.splitext(tumblr_post)[0] + '.rst'
    with open(tumblr_post) as tp:
        contents = tp.read()
        tags = []
        # The Tumblr backup stores the tags in XML document contained in an
        # HTML comment. Before we can use an XML parser to extract the tags
        # we need to extract the comment from the HTML.
        m = re.search(r'<!--\s*BEGIN TUMBLR XML\s*(.*)\s*END TUMBLR XML\s*-->',
                      contents, re.DOTALL)
        if m and len(m.groups()) > 0:
            comment = m.group(1)
            post_elem = fromstring(comment)

            # strptime ignores timezones even if %Z is specified. This is
            # because it relies on time.strptime which does not handle
            # timezones. Hence we need to take care of it ourselves. We expect
            # the string dates to be in the following format:
            # 2011-05-13 07:32:00 GMT
            # BTW given the attribute name one could argue the date will
            # always be in GMT. However it is not that much work to actually
            # parse the date including timezone information. They violated the
            # SPOT rule, so there must be a reason for it (eg other timezones
            # possible?)
            date_str, tz_str = post_elem.get('date-gmt').rsplit(' ', 1)
            date = datetime.strptime(date_str, '%Y-%m-%d %H:%M:%S')
            date = date.replace(tzinfo=pytz.timezone(tz_str))
            local_date = local_tz.normalize(date.astimezone(local_tz))

            # clean up the tags. Apparently it is possible for Tumblr tags
            # to contain ','. rstblog can't deal with them.
            tags = [tag.text.strip().replace(',', '') for tag in
                    post_elem.findall('tag') if tag.text.strip() != '']
            print(tags)

            date_components = ['{:02d}'.format(i) for i in
                (local_date.year, local_date.month, local_date.day)]
            rst_post_dir = os.path.join(args.rstblog_path, *date_components)
            os.makedirs(rst_post_dir, 0o744, True)

            day_order_glob = os.path.join(rst_post_dir, '*.rst')
            day_order = len(glob.glob(day_order_glob)) + 1
            shutil.copy(rst_post_src, rst_post_dir)
            rst_post_dst = os.path.join(rst_post_dir,
                                        os.path.basename(rst_post_src))

            p = re.compile(r'\.\./images/(\w+\.\w+)')
            with open(rst_post_dst, 'r+') as rpd:
                lines = rpd.readlines()
                header = []
                header.append('public: yes\n')
                header.append('tags: [{}]\n'.format(', '.join(tags)))
                header.append('day-order: {:d}\n'.format(day_order))
                header.append('\n')
                lines = header + lines[1:]

                for i, line in enumerate(lines):
                    m = p.search(line)
                    if m:
                        image_fn = m.group(1)
                        shutil.copy(os.path.join(tumblr_img_dir, image_fn),
                                    rst_post_dir)
                        lines[i] = p.sub(r'/static/images/\1', line)

                rpd.seek(0, 0)
                rpd.truncate()
                rpd.writelines(lines)

rstblog

2011-05-03T00:00:00+02:00

For quite some time I have wanted to self-host my blog. However I did not feel like administering a dynamic system, such as Wordpress, that is in constant need of patches and upgrades. Hence a static blog generator was an obvious choice. As a Python developer I have a slight preference for a system written in Python. Blogofile is such a system and seems to be gaining some traction within the Python community lately. For good reason: it is well documented, actively developed and easy to work with. However, when I found out Armin Ronacher wrote an even simpler system I knew I had found what I was looking for: rstblog

rstblog is a very simple script that allows you to write your posts in reStructuredText. It is however lacking in documentation. Fortunately Morten Siebuhr wrote a blog post on using rstblog that should get you up and running in no-time. At least, it did for me.

Setting up PostgreSQL on OS X for development

2010-01-02T00:00:00+01:00

Currently I’m working on my third web based project that uses PostgreSQL as its backend. Two of these projects were/are being developed under OS X. Installing PostgreSQL under OS X is a breeze when one uses MacPorts. However I have seen more than one developer being confused about the steps that should follow the installation and the post installation instructions as printed out by the PostgreSQL port.

The installation instructions can be compressed into three steps:

Install/update MacPorts
Execute: sudo port install postgresql83 postgresql83-server
Follow post-install instructions as printed out by above command

Trying to connect to PostgreSQL

Now that the PostgreSQL is installed you might be tempted to connect to it by starting the PostgreSQL interactive terminal. This is what will happen (gkoller is the user I’m currently logged in as under OS X):

gkoller@Kinchenna $ psql
psql: FATAL: database "gkoller" does not exist

So by default it looks for a database named identically to the currently logged in user. Should we want to connect to a different database we should specify the database’s name after the ‘psql’ command. E.g.:

psql hgh

I have chosen the name hgh as it is the (abbreviated) name of my latest project. The above command will fail with a similar message as the first command. So let’s create the ‘hgh’ database:

gkoller@Kinchenna $ createdb hgh
createdb: database creation failed: ERROR: role "gkoller" does not exist

Again the error message is clear. The currently logged in user does not have access to (does not have a role with the same name defined in) PostgreSQL. Simply executing a createuser gkoller will not help as we don’t have enough privileges to do that. More importantly nor does root. So a ‘sudo createuser gkoller’ does not work either. And this is what stumps most developers that try to get PostgreSQL up and running for web development on OS X

Granting privileges to the currently logged in user

When PostgreSQL was installed it was configured with one superuser, namely postgres. Hence adding new users with superuser privileges should be done as user postgres:

sudo su postgres -c 'createuser -P --superuser gkoller'

Now the currently logged in user has PostgreSQL superuser privileges. This means we don’t have to use sudo and su to executed PostgreSQL commands to create databases, roles, and other users.

Creating a database and user for project HGH

Now that I am a super user it is easy to create additional users and databases. For my HGH project I want a separate database and user. I’ll name them both hgh.

This is how:

gkoller@Kinchenna $ createuser hgh -P
Enter password for new role: <password>
Enter it again: <password>
Shall the new role be a superuser? (y/n) n
Shall the new role be allowed to create databases? (y/n) y
Shall the new role be allowed to create more new roles? (y/n) n

gkoller@Kinchenna $ createdb -E utf8 -O hgh -W -U hgh hgh
Password: <password>

Conclusion

After PostgreSQL installation and post-installation you should create a new superuser named after your OS X login account. This allows for access to PostgreSQL commands without the need to use sudo and su.

This is achieved by executing the following command:

sudo su postgres -c 'createuser -P --superuser <your_username>'

Where <your_username> should be replaced with the username of your OS X account (short) name.

Devoxx ’08: The Good & The Bad

2008-12-14T00:00:00+01:00

This year I attended Devoxx; the largest Java conference in Europe. I had high expectations as I previously had heard many good things about JavaPolis (Devoxx’ former name). However after three days of attending presentations I returned home with mixed feelings. First the good.

The Good

A conference of this size is bound to give you a good feeling of what is going on in the Java world. It might be my pick of the presentations but I got the feeling that nothing shockingly new was happening (except for arguably JavaFX, but more on that later). Instead the focus seemed to be on consolidating that what is working for the Java developers and addressing real issues Java developers are dealing with.

For instance in Thursday’s keynote Joshua Bloch talked about enums and generics and how to use them effectively. A good thing as I have seen many situations where developers should have used enums, but opted for a more conventional solution instead. As for generics, if things get more complicated then ArrayList<String> I have to slow down and be really careful about what I do. Any tips on using generics effectively is always welcome.

A good presentation was John Ferguson Smart’s presentation on “Behavior Driven Development in Java with easyb”. Again nothing shockingly new, but it did clearly point out problems many developers have with Test Driven Development; what to test? By focussing on the required behavior that needs to be implemented and using a testing framework that allows this to be expressed easily, writing meaningful tests should be a lot easier for many developers.

There was a strong focus on running other languages then Java on the JVM. Bill Venners presented on the “Feel of Scala” though I didn’t attend that one as it had been scheduled at the same time as the presentation on easyb. Nor did I attend Charles Nutter’s and Thomas Enebo’s presentation on JRuby. However I did attend Jim Baker’s and Tobias Ivarsson’s presentation on Jython and Brian Goetz’ “Towards a Dynamic VM”. Both were great.

I was thrilled to hear Jython is alive and kicking. With all the focus on JRuby, Scala, and more recently Clojure in blogosphere, one might have gotten the impression that Jython was all but dead. Instead a release that’s compatible with Python 2.5 is imminent and they demonstrated Django running on Jython.

Dynamic languages on the JVM are already quite fast compared to their relatives written in C. However with the enhancements planned for the JVM this should improve significantly in the future. Brian Goetz’ presentation was fairly technical and as such one of the better ones. It made me well aware of all the work the JVM has to do in running dynamic languages. Knowing this made me appreciate the current speed of the dynamic language on the JVM even more.

Another thing worth mentioning was the organization of the conference. They did a great job. From registration, sending my badge to my home address, lunch, coffee, snacks, drinks, announcements. Everything was organized very well allowing me to focus on what I had came for, attending presentations and talking to people.

The Bad

Even though there were a fair number of good presentations some were really awful. Most notably IBM’s keynote presentation on Java and RFID. IBM is a large multinational. Devoxx is a huge conference with 3300 attendees. You would have figured they would have made an effort in delivering an interesting keynote. Looking at the result they did not. It was embarrassingly amateurish and clumsy.

They same holds for many other presentations. Some had potentially interesting content, but due to the way it was presented it was difficult to follow, or outright incomprehensible. I do realize it is easy for me to criticize people sitting behind my keyboard and writing a blog entry. After all I did not have to stand in front of a huge room filled with anything between 60 to 1000 people. But even so, more presenters should have done significantly better for a conference of this size.

And last JavaFX. Not necessarily bad as the presentations on it that I did attend were well executed and interesting. However I failed to see JavaFX’ relevance for enterprise Java developers. I might be wrong, but I would expect most of the attendees to fall into this class of developers. Sun however seemed to target a different class of developers. Nice graphics, animations, sound and video is all great. However I as an enterprise Java developer am more interested in how I can construct forms, the kind of controls that are available, communication with back end systems, etc. None of that was addressed. A pity and a missed opportunity for Sun.

Implementing Java’s equals method

2008-06-16T00:00:00+02:00

There’s a lot of incorrect information on the web and even in published books on how to implement Java’s equals method. Ivan Memruk in his blog post The equals/instanceof pitfall describes this issue nicely. There is however a small error in his post; what he calls the reflexive property is actually the symmetric property of the equals method.

Reflexive is: a=a, whereas symmetric is: if a=b then also b=a. It’s this property, among others, that should hold for Java’s equals method. By using instanceof in equals() the symmetric property doesn’t hold. Instead use the getClass method. A good template for Java’s equals method would be:

public boolean equals(Object o) {
    if (this == o)
        return true;
    if (o == null || getClass() != o.getClass())
        return false;


   // your customized check for equality goes here
}

Another good article on implementing equals() is Manish Hatwalne’s Equals and Hash Code