daveslab - Developer IT

How to burn .dvdmedia files to DVD on Mac OS X?

- by daveslab

Hi folks, I have a .dvdmedia file that I got from RipIt on Mac OS 10.6. How can I burn it back into a dvd or at least into an ISO file? Thanks!

2 GB of memory in 1 GB system is a problem?

Hi folks, I just installed 2 1 Gig sticks into my friend's machine, thinking that it would take all the 2 GBs. Unfortunately, according to Dell's website, it says the maximum amount of memory accessible to the machine is arbitrarily set to 1 GB! The system indeed reports having 1 GB of memory accessible to it, but I'm worried that having 2 GB in there might break something. Are my fears reasonable? Should I buy two 512 MB sticks instead? Thanks for any help!

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What is an MQ and why do I want to use it?

- by daveslab

Hi folks, On my team at work, we use the IBM MQ technology a lot for cross-application communication. I've seen lately on Hacker News and other places about other MQ technologies like RabbitMQ. I have a basic understanding of what it is (a commonly checked area to put and get messages), but what I want to know what exactly is it good at? How will I know where I want to use it and when? Why not just stick with more rudimentary forms of interprocess messaging?

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Efficient and accurate way to compact and compare Python lists?

- by daveslab

Hi folks, I'm trying to a somewhat sophisticated diff between individual rows in two CSV files. I need to ensure that a row from one file does not appear in the other file, but I am given no guarantee of the order of the rows in either file. As a starting point, I've been trying to compare the hashes of the string representations of the rows (i.e. Python lists). For example: import csv hashes = [] for row in csv.reader(open('old.csv','rb')): hashes.append( hash(str(row)) ) for row in csv.reader(open('new.csv','rb')): if hash(str(row)) not in hashes: print 'Not found' But this is failing miserably. I am constrained by artificially imposed memory limits that I cannot change, and thusly I went with the hashes instead of storing and comparing the lists directly. Some of the files I am comparing can be hundreds of megabytes in size. Any ideas for a way to accurately compress Python lists so that they can be compared in terms of simple equality to other lists? I.e. a hashing system that actually works? Bonus points: why didn't the above method work?

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How to find foreign-key dependencies pointing to one record in Oracle?

- by daveslab

Hi folks, I have a very large Oracle database, with many many tables and millions of rows. I need to delete one of them, but want to make sure that dropping it will not break any other dependent rows that point to it as a foreign key record. Is there a way to get a list of all the other records, or at least table schemas, that point to this row? I know that I could just try to delete it myself, and catch the exception, but I won't be running the script myself and need it to run clean the first time through. I have the tools SQL Developer from Oracle, and PL/SQL Developer from AllRoundAutomations at my disposal. Thanks in advance!

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How to trim all columns in all rows in all tables of type string?

- by daveslab

Hi folks, In Oracle 10g, is there a way to do the following in PL/SQL? for each table in database for each row in table for each column in row if column is of type 'varchar2' column = trim(column) Thanks!

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Implementing "select distinct ... from ..." over a list of Python dictionaries

- by daveslab

Hi folks, Here is my problem: I have a list of Python dictionaries of identical form, that are meant to represent the rows of a table in a database, something like this: [ {'ID': 1, 'NAME': 'Joe', 'CLASS': '8th', ... }, {'ID': 1, 'NAME': 'Joe', 'CLASS': '11th', ... }, ...] I have already written a function to get the unique values for a particular field in this list of dictionaries, which was trivial. That function implements something like: select distinct NAME from ... However, I want to be able to get the list of multiple unique fields, similar to: select distinct NAME, CLASS from ... Which I am finding to be non-trivial. Is there an algorithm or Python included function to help me with this quandry? Before you suggest loading the CSV files into a SQLite table or something similar, that is not an option for the environment I'm in, and trust me, that was my first thought.

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