Programming

Solving problems with startups

Thursday, November 27th, 2014

Interesting insider Q&A with Paul Sutter, co-founder of Quantcast. Via Hacker News:

Q: What methodical process did you follow for your startup? Did you first test the market using tactics similar to the lean startup approach?

A: Basically, make a list of known problems that you’re well suited to solving, rank them by criteria, fail a lot, bang your head against the wall, and eventually things start to stick.

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Web interest in Apache Storm, Kafka, Spark in the Python community

Thursday, November 27th, 2014

Apache Storm, Kafka, and Spark are gaining a lot of momentum in the data analysis and processing communities. I was curious whether the interest in using these technologies with Python, in particular, is growing. Based on these Google Trends reports, it seems like it is.

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Clojonic: Pythonic Clojure

Sunday, November 2nd, 2014

In June 2012, I promised myself that I’d learn Clojure “as a mind expander”. As a long-time Python programmer who has been using Python full-time in my work at Parse.ly, I wanted to explore. I wrote then:

I don’t know whether Clojure programs will be better or worse than equivalent Python programs. But I know they will be different.

It took me awhile, but in January of this year, I started teaching myself the language.

Rich Hickey, and the “Cult of Personality”

My approach was to first learn the underpinnings of the language from books and online videos. If you embark on this for Clojure, you will inevitably run into the copious publicly-available material from the language’s creator, Rich Hickey.

In stark contrast to Guido van Rossum in the Python community, Rich Hickey is undeniably not just the Clojure language’s creator, but also a kind of spokesperson for a functional programming renaissance. Guido van Rossum generally lays low and lets the Python language and community speak for itself, and tries to avoid controversy. To him, Python is just a popular tool he happened to create, and it doesn’t represent any major paradigm shift in programming. It’s a positive evolutionary improvement supported by a great open source ecosystem and community. To Hickey, however, “traditional” programming languages — but especially popular ones with an object-oriented focus, such as Java and C++ — are just plain wrong. He proposes Clojure as an antidote of sorts.

You can get the gist of this from his motivating videos, such as Hammock-Driven Development, Are We There Yet?, and Simple Made Easy. For a thorough overview of Clojure as a language, you can also get a walkthrough by Hickey, given to a room full of Java developers, in Clojure for Java Programmers Part I and Part II.

Here is a summary of the viewpoint. Most languages are missing some important attributes that can help us tackle the most complex issues in programming projects:

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“So, you work in IT?”

Monday, August 18th, 2014

For many years, IT as a field was dominated by people who could not write code.

This is because computer technology was mystifying and befuddling to most people that anyone who knew merely how to use computers with any level of comfort could demand a tax from those who didn’t.

During that same period (late 90s and early 2000′s), programming itself was being commoditized by offshore outsourcing, so the same IT people were positioning themselves for management positions. This is how MIS (Management of Information Systems) became a popular career path among the IT elite, and why when I was in college in 2002-2006, Comp Sci enrollment was at a major low.

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Python annotations and type-checking

Saturday, August 16th, 2014

In 2010, the Python core team wrote PEP 3107, which introduced function annotations for Python 3.x.

Nearly 4 years ago, I wrote this response to the PEP, but I published it to a discussion site that ended up becoming defunct (Clusterify). I saw that recently, interest in function annotations for type-checking was revived by GvR, and thought I might resurrect this discussion.

Background

There is a huge flaw with the creation of Python annotations, IMO. Lack of composability.

The problem only arises when you consider that at some point in the future, there may be more than one use case for function annotations (as the PEP suggests). For example, let’s say that in my code, I use function annotations both for documentation and for optional run-time type checking. If I have a framework that expects all the annotations on my function definition to be docstrings, and another framework that expects all the annotations to be classes, how do I annotate my function with both documentation and type checks?

This amounts to lack of a standard for layering function annotations. Is this really a problem?

It’s true that some standard for this could organically form in the community. For example, one could imagine tuples being used for this. If an annotation expression is a tuple, then every framework should iterate through the items of the tuple until they find an item of the matching type. However, this won’t always work: what if two frameworks are both expecting strings, or two frameworks are both expecting classes, with different semantics?

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Joel Spolsky’s business operating system

Thursday, July 24th, 2014

Joel Spolsky wrote his first blog post in a year today, announcing Trello, Inc., a spin-off company for the successful project management product Fog Creek Software developed, Trello.

Trello has announced a $10M+ venture financing round and they are going to expand the product and team. This was a bit of a surprise to me, because Spolsky had always been critical of the VC-funded tech startup industry on his blog over the years.

But in the blog post, he explains, he has really been critical of the kind of company that this industry typically breeds. So, he has made his life’s work creating companies with a different “operating system” altogether.

highNotes

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5 years ago, I was bored

Friday, July 4th, 2014

I wrote this to a friend five years ago, a few weeks after I had quit my job to embark on the crazy ride that has been Parse.ly’s founding story.

You said to me, “I am glad that you left because you sounded unhappy there.”

But you know, I wasn’t exactly unhappy.

I was just bored.

I’m eager to work on my own stuff. I had a good work environment and I learned a lot. I was making money, had flexibility about hours and work from home, and was respected on my team.

But I had a couple of realizations. First, I didn’t see a future for myself in financial firms. I just don’t like their core business enough; in fact, I think their core business is somewhat superfluous and that financial firms should be way, way smaller than they are. They should make less money, have less power, etc.

Second, my specific project had this split personality. On the one hand, it wanted to be this cutting edge framework to really empower application developers throughout the company. On the other, it was a lost project — lots of code, lots of ideas, but no solid product and no real customer.

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Disable Google Hangout’s auto-mute on typing

Wednesday, July 2nd, 2014

Damnit, Google. Sometimes, you make product improvements that are awesome. Other times, you make “improvements” that are downright depressing regressions.

In an effort to stop the annoying sensation that happens when you are on a Google Hangout video conference and you hear nothing but your colleague’s “tap-tap-tap” on their loud programmer keyboards, Google added a feature to the software that automatically detects when someone is typing and auto-mutes them.

This is a nice idea, but what about when talking while typing is what you actually want to do? In this case, Google provides no recourse. And indeed, recently I gave a walkthrough to my team of a new code project, but constantly cut out because as I was showcasing ideas in code (and even simply navigating code with my keyboard using vim), Google would constantly mute me and make me cut out. Damnit, Google! You suck!

Well, Internet users unite! We have a working fix for this “feature”.

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streamparse: Python + Apache Storm for real-time stream processing

Sunday, May 4th, 2014

Parse.ly released streamparse today, which lets you run Python code against real-time streams of data by integrating with Apache Storm.

We released it for our talk, “Real-time streams & logs with Apache Kafka and Storm” at PyData Silicon Valley 2014.

An initial release (0.0.5) was made. It includes a command-line tool, sparse, with the ability to set up and run local Storm-friendly Python projects.

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The Log: a building block for large-scale data systems

Monday, December 16th, 2013

A software engineer at LinkedIn has written a monster of a blog post about “The Log”, a building block for large-scale data systems. The concepts in this post are near and dear to my heart due to my work on precisely these kinds of problems at Parse.ly.

What is “a log”?

The log is similar to the list of all credits and debits and bank processes; a table is all the current account balances. If you have a log of changes, you can apply these changes in order to create the table capturing the current state. This table will record the latest state for each key (as of a particular log time). There is a sense in which the log is the more fundamental data structure: in addition to creating the original table you can also transform it to create all kinds of derived tables.

At Parse.ly, we just adopted Kafka widely in our backend to address just these use cases for data integration and real-time/historical analysis for the large-scale web analytics use case. Prior, we were using ZeroMQ, which is good, but Kafka is better for this use case.

We have always had a log-centric infrastructure, not born out of any understanding of theory, but simply of requirements. We knew that as a data analysis company, we needed to keep data as raw as possible in order to do derived analysis, and we knew that we needed to harden our data collection services and make it easy to prototype data aggregates atop them.

I also recently read Nathan Marz’s book (creator of Apache Storm), which proposes a similar “log-centric” architecture, though Marz calls it a “master dataset” and uses the fanciful term, “Lambda Architecture”. In his case, he describes that atop a “timestamped set of facts” (essentially, a log) you can build any historical / real-time aggregates of your data via dedicated “batch” and “speed” layers. There is a lot of overlap of thinking in that book and in this article.

full-stack

LinkedIn’s log-centric stack, visualized.

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