I needed to store a large quantities of images so took the following measurements:

Format Time Size Sample
ppm0.6485401769488 Browser does not support PPM
tiff1.0112161769600 Browser does not support TIFF

Gif is the clear loser - it takes a long time to process but still looks terrible.
For space use jpeg, speed ppm.

Google’s new WebP format looks promising.

I was concerned about what blind spots I might have with the way I run my business. For example I am Australian and Australian’s are usually very informal, even in a professional setting - was my communication with international clients too informal?

To try and address these concerns I developed a feedback survey with Google Docs, which I have been (politely) requesting my clients to complete at the end of a job. The results have been helpful, and it also seems to have impressed some clients that I wanted their feedback. Wish I had thought of this earlier!

I have been asked a few times why I chose to reinvent the wheel when libraries such as Scrapy and lxml already exist.

I am aware of these libraries and have used them in the past with good results. However my current work involves building relatively simple web scraping scripts that I want to run without hassle on the clients machine. This rules out installing full frameworks such as Scrapy or compiling C based libraries such as lxml - I need a pure Python solution. This also gives me the flexibility to run the script on Google App Engine.

To scrape webpages there are generally two stages: parse the HTML and then select the relevant nodes.
The most well known Python HTML parser seems to be BeautifulSoup, however I find it slow, difficult to use (compared to XPath), often parses HTML inaccurately, and significantly - the original author has lost interest in further developing it. So I would not recommend using it - instead go with html5lib.

To select HTML content I use XPath. Is there a decent pure Python XPath solution? I didn’t find one 6 months ago when I needed it so developed this simple version that covers my typical use cases. I would deprecate this in future if a decent solution does come along, but for now I am happy with my pure Python infrastructure.

When I started freelancing I created accounts on every freelance site I could find (oDesk, guru, scriptlance, etc) to get as much work as possible. However I found I got almost all work from just one source - Elance. How is Elance different?

With most freelancing sites you create an account and immediately start bidding on jobs. There is no cost to bidding so people bid on many projects even if they don’t have the skill or time to complete it. This is obviously frustrating for clients who waste a lot of time sifting through bids.

On the other hand Elance has a high barrier to entry: you have to pass a test to show you understand their system, then receive a phone call to confirm your identity, and when established pay money for each job you bid on. Often I see jobs on Elance with no bids because it requires obscure experience - people weren’t willing to waste their money bidding for a job they can’t do. This barrier serves to weed out less serious freelancers so that the average bid is of higher quality.

From my experience the clients are different on Elance too. On most freelancing sites the client is trying to get the job done for the smallest amount of money possible and are often willing to spend their time sifting through dozens of proposals, hoping to get lucky. Elance seems to attract clients who consider their time valuable and are willing to pay a premium for good service.
Often clients contact me directly through Elance because I am native English and want to avoid potential communication or cultural problems. One client even requested me to double my bid because “we are not cheap!”

After a year of freelancing I now get the majority of work directly through my website, but still get a decent percentage of clients through Elance.

My advice for new freelancers - focus on building your Elance profile and don’t waste your time with the others. (Though do let me know if you have had good experience elsewhere.)

Regarding the title of this blog “All your data are belong to us” - I realized not everyone gets the reference. See this wikipedia article for an explanation.

When crawling large websites I store the HTML in a local cache so if I need to rescrape the website later I can load the webpages quickly and avoid extra load on their website server. This is often necessary when a client realizes they require additional features included in the scraped output.

I built the pdict library to manage my cache. Pdict provides a dictionary like interface but stores the data in a sqlite database on disk rather than in memory. All data is automatically compressed (using zlib) before writing and decompressed after reading. Both zlib and sqlite3 come builtin with Python (2.5+) so there are no external dependencies.

Here is some example usage of pdict:

>>> from webscraping.pdict import PersistentDict  
>>> cache = PersistentDict(CACHE_FILE)  
>>> cache[url1] = html1  
>>> cache[url2] = html2  
>>> url1 in cache  
>>> cache[url1]  
>>> cache.keys()  
[url1, url2]  
>>> del cache[url1]  
>>> url1 in cache