Montreal Bagels - 2yo review

I was in Montreal for PyCon 2015. I was told that Montreal was famous for it's bagels. So I brought home half-dozen bagels.

I made my son a delicious toasted bagel with cream cheese in the morning. He ate it with gusto and the following conversation ensues: 

Me: Sempi, did you like the Montreal Bagels.

2yo: It was ok (as he proceeds to lick his fingers clean). 

This kid is hard to impress. :)

Kickstarter: mysql-cli

I'm starting a project called mysql-cli.

mysql-cli will be a command line client for MySQL, with auto-completion and syntax highlighting. An equivalent of pgcli for MySQL database.

I'm raising funds for the project through kickstarter. The goal is to compensate for the development time and resources (hosting, testing etc) as well as motivate me to keep going.

When I launched pgcli earlier this year I had high hopes for it. I anticipated that I might reach a hundred users and even a couple of contributions. I announced it on Twitter and HackerNews and it took about an hour to reach 100 stars. By the second day it was a top trending repo in all of Github. Right now it is hovering near the 1600 mark for the number of stars, with more than 70 pull requests (merged). 

During the first week of launch, I slept about 3 hours each night because the pull requests and issues came flooding. I made a personal resolve to answer every communique within 24 hours. This meant answering personal emails, responding to issues filed, reviewing pull requests etc. I vowed to be polite and respectful to my users and contributors and I've had nothing but pleasant interactions with them. 

My hope is to provide the same level of dedication and support to mysql-cli. There is definitely a need for it, since every time I use the default MySQL client I want to scream obscenities at my computer and I can't be the only one. :)

The plan is to launch mysql-cli in July 2015 and open up the repo to public. If you want to get involved sooner, please back the project on kickstarter and I'll add you to the early access list. :)

Launching pgcli

I've been developing pgcli for a few months now. 

It is now finally live http://pgcli.com

It all started when Jonathan Slenders sent me a link to his side-project called python-prompt-toolkit

I started playing around with it to write some toy programs. Then I wrote a tutorial for how to get started with prompt_toolkit https://github.com/jonathanslenders/python-prompt-toolkit/tree/master/examples/tutorial

Finally I started writing something more substantial to scratch my own itch. I was dealing with Postgres databases a lot at that time. The default postgres client 'psql' is a great tool, but it lacked auto-completion as I type and it was quite bland (no syntax highlighting). So I decided to take this as my opportunity to write an alternate. 

Thus the creatively named project 'pgcli' was born.

Details about pgcli.com:

It is built using pelican a static site generator written in Python. 

It is hosted by Github pages. 

The content is written using RestructuredText.

Inspiration:

The design inspiration for the tool comes from my favorite python interpreter bpython.

Goodbye Utah

The time has come to part ways. I'm leaving Utah to move to Portland on May 25th 2012. This July would have marked the 10 year anniversary of living in Utah. I can't believe I spent one third of my life so far in Utah. Some of the best memories in my life were formed here.

True Love: 

I met my beautiful wife here in Utah. She kicked my ass in TaeKwonDo, I asked her out, and the rest is history. Happily married for  four years with a baby on the way.

First Job:

My first real programming job was with Delcam USA. I still have my first paystub from Delcam. Tom my boss at Delcam is still the best boss I've had so far.

Higher Education:

University of Utah. So many memories, so many sleepless nights at the computer lab. I still get a nostalgia when I walk through the campus.

Parksvan:

Eight clueless kids from India got together to share accomodation while going to school and ended up sharing the best parts of our life. Although we have all parted ways since our college days, I can't help but feel they are part of my family.
Aikido:

When I went to my first class, I thought I was going there to give my wife some company. Four years and 6 belts later, it has become a dominating force in my life. I never knew getting thrown around was the way to make new friends. :)

Along the way I discovered the wonderful art of Iaido. I call it the art of playing with Japanese swords.
Python:

I couldn't believe there was a group of people who met every month to geek out about their favorite language. My heartfelt thanks to herlomharrisonsmcquay, travis and seth. You guys welcomed me into the group and helped me shape my future in the world of Python. 

Outdoor:

I took up Mountain Biking, Hiking and Snowboarding and I've loved every minute of it. I will miss the rocky moutains, Uintahs and the snowy hills. 

Leaving all of this behind makes me sad. Utah made me into what I am today.

But now I'm headed to Portland to work for New Relic. I'm told Oregon is a wonderful place, but I'll always have fond memories of Utah wherever I go. 

Python Profiling - Part 1

I gave a talk on profiling python code at the 2012 Utah Open Source Conference. Here are the slides and the accompanying code.

There are three parts to this profiling talk:

  • Standard Lib Tools - cProfile, Pstats
  • Third Party Tools - line_profiler, mem_profiler
  • Commercial Tools - New Relic

This is Part 1 of that talk. It covers:

  • cProfile module - usage
  • Pstats module - usage
  • RunSnakeRun - GUI viewer

Why Profiling:

  • Identify the bottle-necks.
  • Optimize intelligently. 

In God we trust, everyone else bring data

cProfile:

cProfile is a profiling module that is included in the Python's standard library. It instruments the code and reports the time to run each function and the number of times each function is called. 

Basic Usage:

The sample code I'm profiling is finding the lowest common multiplier of two numbers. lcm.py

# lcm.py - ver1 
    def lcm(arg1, arg2):
        i = max(arg1, arg2)
        while i < (arg1 * arg2):
            if i % min(arg1,arg2) == 0:
                return i
            i += max(arg1,arg2)
        return(arg1 * arg2)

    lcm(21498497, 3890120)

Let's run the profiler.

$ python -m cProfile lcm.py 
     7780242 function calls in 4.474 seconds
    
    Ordered by: standard name
   
    ncalls  tottime  percall  cumtime  percall filename:lineno(function)
         1    0.000    0.000    4.474    4.474 lcm.py:3()
         1    2.713    2.713    4.474    4.474 lcm.py:3(lcm)
   3890120    0.881    0.000    0.881    0.000 {max}
         1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}
   3890119    0.880    0.000    0.880    0.000 {min}

Output Columns:

  • ncalls - number of calls to a function.
  • tottime - total time spent in the function without counting calls to sub-functions.
  • percall - tottime/ncalls
  • cumtime - cumulative time spent in a function and it's sub-functions.
  • percall - cumtime/ncalls

It's clear from the output that the built-in functions max() and min() are called a few thousand times which could be optimized by saving the results in a variable instead of calling it every time. 

    Pstats:

    Pstats is also included in the standard library that is used to analyze profiles that are saved using the cProfile module. 

    Usage:

    For scripts that are bigger it's not feasible to analyze the output of the cProfile module on the command-line. The solution is to save the profile to a file and use Pstats to analyze it like a database. Example:  Let's analyze shorten.py.

    $ python -m cProfile -o shorten.prof shorten.py   # saves the output to shorten.prof
    
    $ ls
    shorten.py shorten.prof

    Let's analyze the profiler output to list the top 5 frequently called functions.

    $ python 
    >>> import pstats
    >>> p  = pstats.Stats('script.prof')   # Load the profiler output
    >>> p.sort_stats('calls')              # Sort the results by the ncalls column
    >>> p.print_stats(5)                   # Print top 5 items
    
        95665 function calls (93215 primitive calls) in 2.371 seconds
        
       Ordered by: call count
       List reduced from 1919 to 5 due to restriction <5>
        
           ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        10819/10539    0.002    0.000    0.002    0.000 {len}
               9432    0.002    0.000    0.002    0.000 {method 'append' of 'list' objects}
               6061    0.003    0.000    0.003    0.000 {isinstance}
               3092    0.004    0.000    0.005    0.000 /lib/python2.7/sre_parse.py:182(__next)
               2617    0.001    0.000    0.001    0.000 {method 'endswith' of 'str' objects}

    This is quite tedious or not a lot of fun. Let's introduce a GUI so we can easily drill down. 

    RunSnakeRun:

    This cleverly named GUI written in wxPython makes life a lot easy. 

    Install it from PyPI using (requires wxPython)

    $ pip install SquareMap RunSnakeRun
    $ runsnake shorten.prof     #load the profile using GUI

    The output is displayed using squaremaps that clearly highlights the bigger pieces of the pie that are worth optimizing. 

    It also lets you sort by clicking the columns or drill down by double clicking on a piece of the SquareMap.

    Conclusion:

    That concludes Part 1 of the profiling series. All the tools except RunSnakeRun are available as part of the standard library. It is essential to introspect the code before we start shooting in the dark in the hopes of optimizing the code.

    We'll look at line_profilers and mem_profilers in Part 2. Stay tuned. 

    You are welcome to follow me on twitter (@amjithr).

    PyCon 2012 Review

    PyCon 2012 was held at Santa Clara, California.

    Tutorial:

    I was there on the Thrusday to attend a tutorial called Python Epiphanies. The tutorial was educational in understanding some of the inner workings of Python. But I have a hard time trying to figure out how to use the knowledge I gained there. 

    Opening Ceremony:
    We had ROBOTS.

    And they were dancing.... how cool was that? It was a splendid opening ceremony. 

    Socializing:

    Thursday evening was bag stuffing. Where we helped out by stuffing the swag bag. I got to work side-by-side some well known figures in the community (Jesse Noller, Pydanny). After that I hung out with some Heroku folks and learned about their awesome work culture.

    Later that night, Yannick and Bryan gave a Pycon newbie orientation. I took their advice and gave a Lightning Talk about bpython (my talk is 10min into the video).

    I socialized plenty and got a lot of useful contacts from different companies. I got to meet the founders of Octopart, my favorite Electrical Engineering based startups. 

    I also met with Kenneth Reitz who is famous for his requests library and this awesome talk Python for Humans.

    I was quite thrilled when I first saw Guido at the Lunch hall sitting right next to my table. I was too shy to talk to him, but I managed to get a picture with him (in the frame). 

    Real gutsy! Maybe next year I'll actually shake his hand and get a picture with him. 

    Keynotes:
    • I enjoyed Paul Graham's keynote quite a bit. He talked about daring startup ideas. His keynote is summarized in these two essays
    • David Beazly's keynote was a walk-through (demo) of tinkering with PyPy. It looked hard as balls and I kept hoping a happy ending where he declared victory. But it ended up being one of those art movie endings that leaves the listeners in a confused and inconclusive state. 
    • Guido's Keynote on the other hand was interesting. His talk was sprinkled with unintended hilarity that ensued due to Google's presentation software. He was sporting an awesome T-shirt that read "Python is for girls" and talked about dealing with Python Trolls.
    Talks: 
    I knew that all the talks were video taped and posted online, so I didn't worry too much about missing some when I had conflicts.

    The following talks piqued my interest and will make me go exploring a little bit. 

    Permission or Forgiveness - Quaint. Applying Grace Murray Hopper's logic to Python programming. 

    Webserver Performance Tuning - Sounded like a sales pitch for New Relic, but not in a bad way.

    Angry Birds playing Robot - Hilarious and Informative.

    Capoeira: 
    I went to the open space organized by Pydanny where he brought his Capoeira instructor who taught us some awesome moves. By the end of the class, we are all breathing heavily and energized. Once I tried to do a hand stand and lost my balance, but Aikido training kicked in and I gracefully rolled out of my fall with just a carpet burn on my elbow. 

    Open Spaces:
    I didn't get a chance to go to many of them, but I did attend the open space for SaltStack and sat with Seth House to try and get Salt running on my Macbook. After a few failed attempts, I decided to give Salt a chance on my linux desktop once I got home. 

    Babbage Difference Engine: (Not Pycon related)
    Sunday afternoon Stephen McQuay (a fellow Utah Pythonista) and I decided to take up Vijay's offer to go visit the Computer History Museum where they were doing a live demo of the Babbage Difference Engine. OMG! It was awesome to watch the history come alive. 

    Memoization Decorator

    Recently I had the opportunity to give a short 10 min presentation on Memoization Decorator at our local UtahPython Users Group meeting. 

    Memoization: 

    • Everytime a function is called, save the results in a cache (map).
    • Next time the function is called with the exact same args, return the value from the cache instead of running the function.

    The code for memoization decorator for python is here: http://wiki.python.org/moin/PythonDecoratorLibrary#Memoize

    Example:

    The typical recursive implementation of fibonacci calculation is pretty inefficient O(2^n).   

    def fibonacci(num):
            print 'fibonacci(%d)'%num
            if num in (0,1):
                return num
            return fibonacci(num-1) + fibonacci(num-2)

    >>> math_funcs.fibonacci(4) # 9 function calls fibonacci(4) fibonacci(3) fibonacci(2) fibonacci(1) fibonacci(0) fibonacci(1) fibonacci(2) fibonacci(1) fibonacci(0) 3

    But the memoized version makes it ridiculously efficient O(n) with very little effort.

    import memoized
    @memoized
    def fibonacci(num):
        print 'fibonacci(%d)'%num
        if num in (0,1):
            return num
        return fibonacci(num-1) + fibonacci(num-2)
        
    >>> math_funcs.mfibonacci(4)  # 5 function calls
        fibonacci(4)
        fibonacci(3)
        fibonacci(2)
        fibonacci(1)
        fibonacci(0)
        3

    We just converted an algorithm from Exponential Complexity to Linear Complexity by simply adding the memoization decorator.

    Slides:

    Presentation:

    I generated the slides using LaTeX Beamer. But instead of writing raw LaTeX code I used reStructured Text (rst) and used rst2beamer script to generate the .tex file. 

    Source:

    The rst file and tex files are available in Github.

    https://github.com/amjith/User-Group-Presentations/tree/master/memoization_de...