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There are tools to just say, Give me your social security number, give me your address and your mother s maiden name, and we send you a physical piece of paper and you sign it and send it back to us By the time that s all accomplished, you are a very safe user But by then you are also not a user, because for every step you have to take, the dropoff rate is probably 30 percent If you take ten steps, and each time you lose one-third of the users, you ll have no users by the time you re done with the fourth step The point is, the startups didn t realize there was this risk We didn t really realize there was this risk component either when we started But we were just lucky enough .

to give you an idea of what is possible. You could write an entire book on C++ interop. For more information, you may want to consult Expert Visual C++/CLI by Marcus Heege (Apress, 2007).

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from sqlalchemy import schema, types metadata = schemaMetaData() page_table = schemaTable('page', metadata, schemaColumn('id', typesInteger, primary_key=True), schemaColumn('name', typesUnicode(255), default=u''), schemaColumn('title', typesUnicode(255), default=u'Untitled Page'), schemaColumn('content', typesText(), default=u''), ) Here you ve created a metadata object from schemaMetaData, which will hold all the information about the tables, columns, types, foreign keys, indexes, and sequences that make up the database structure You ll see more about how these are used later in the chapter You ve then created a schemaTable object to describe the page table and passed it the metadata object This is so that the table object can add information about the table to the metadata object SQLAlchemy is then able to access the table information via the metadata object Add the following to the end of the metadata_testpy file too: for t in metadata.

Maybe I should be thankful for that happy year of boredom when I was expecting Windows and digging into stuff, figuring out what fraud was all about But one way or the other whatever caused that we were smart enough to realize that fraud was a huge issue very quickly, and then were successful enough combating it while the startup competitors of ours did not and got buried very quickly I remember all these companies announcing that they were going out of business and they expected PayPal to go out of business soon too, because the fraud numbers were so staggering that they could not see anyone handling this sort of thing There was one company I think it was eMoneyMail that shut down the company at a conference basically saying that the Internet is not a safe place to conduct transactions They had 25 percent fraud.

So for every $400 changing hands in the system, $100 was stolen And it was all coming out of their pocket They said, We lost a ton of money, and they just quit Then, people like Citibank and other large financial institutions that also competed with us that understood the fraud thing very well they knew from many years of practice that this was going to become a big problem didn t really approach it with the same happy abandon that we did We started with this, Fraud is going to kill us What can we do to save ourselves They started from, We have no fraud How can we build this and not let any more fraud in Which is the wrong position to start because you are limiting your users, and new users learning about a new system really don t want to be restricted..

sorted_tables print "Table name: ", tname print "t is page_table: ", t is page_table If you run this example, you will see the following output: $ python metadata_testpy Table name: page t is page_table: True As you can see, the metadata object contains information about the tables, and the table object assigned to t in this example is the same as the page_table object Each of the columns that makes up the tables has its own type SQLAlchemy supports the following built-in types: String Unicode Text/UnicodeText Numeric Float Datetime/Date/Time Interval Binary Boolean In addition to the types listed here, there is a PickleType that is based on SQLAlchemy s Binary type PickleType uses Python s pickledumps() to pickle objects being saved to the database, and it uses pickleloads() to unpickle objects being retrieved.

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