Data Crunching
On the positive side, there is a lot of good stuff in this book. I would even go so far as to recommend it to everyone who writes code to extract or manipulate data, particularly those less experienced. Greg Wilson should be praised for taking the idea of data crunching seriously and for systematically dealing with its patterns and pitfalls. A lot of important work gets done every day with one-off programs and behind the scenes scripts and Wilson is right that the techniques that go into this sort of coding are different, but just as important, as those that go into full-blown application development.
The strength of this book is that it offers useful approaches and patterns for dealing with a variety of common programming situations and types of data, while also pointing out their common traps and pitfalls. Wilson starts with techniques for crunching text data, moves on to the use of regular expressions, XML, binary data, and SQL databases before concluding with a special section on "horseshoe nails," various little techniques which just might save help save the day. Quite often he uses examples in both Python, which he calls an "agile" language and Java, a "sturdy" language. The basic advice offered is sound, if not shocking -- keep things simple, test as you develop, don't duplicate code, use existing scripts and utilities when possible, and so on. The combination of such sound advice with a wealth of practical examples is makes for a very effective handbook, particularly for someone new to data crunching.
So is Data Crunching a good book? Definitely. Should you read it if you regularly do routine data manipulation and extraction? Absolutely. And yet...
And yet there are number of things that just aren't quite right. The text and binary sections are the best, while I would say that the XML and SQL sections are the weakest, partly because those topics are too broad to cover in a single slim chapter. If you already have an idea of how you might want to use XML or how to extract data from a SQL database, you're likely find something handy in those chapters. On the other hand, if you're unfamiliar with them, this book probably doesn't have enough detail to get you writing useful code. I should say it doesn't have enough detail to get you writing useful code knowing what you're doing. And data crunching without knowing what you're doing is a bad idea. Trust me on that one.
I have another problem with the section on SQL. Several of the slicker SQL recipes rely on nested queries (page 147-151). MySQL, clearly a very popular SQL database, has nested queries only in its latest versions, so many, if not the majority, of MySQL installations do not yet have that capability. Yet the text carries on as if nested queries were universal, without so much as parenthetical mention that some things might not work on all SQL implementations. It seems to me that this is exactly the sort of pitfall a book like this should inform the reader of.
There are also several coding examples that bother me. Since I tend to both learn and teach by paying close attention to examples, I get uncomfortable with examples that seem to suggest something other than what they should.
For instance, the very first pieces of sample code (pages 9-10) in the text chapter are Python and Java programs to reverse the order of lines in a text file. I don't have a problem with the exercise itself, I've often assigned it to beginning programmers. However, this book is about quick and reliable solutions to common data handling problems, not leading people through basic programming exercises. Ironically, the very same chapter discusses the advantages of using the Unix command-line and its wealth of little tools. So wouldn't it be reasonable to expect at least a brief note or example showing that the REALLY easy way to solve the problem is with a single line: $ tac filename > filename2? Yet tac is not even in the list of "Useful Commands" on page 24. If reversing lines is just a programming example, it shouldn't be the lead example in a book like this, and if it is important, then you should mention that the problem has already been solved.
In the same vein, Wilson spends a fair amount of time in the text chapter illustrating code to parse command-line parameters, before admitting that libraries for the task abound in most languages. Granted, being able to snag a parameter or two off of the command-line without using a library can sometimes be handy; but implementing a more involved command-line parser is a problem that has already been abundantly solved.
Similarly, one of the examples in the chapter on regular expressions uses a regular expression to check to see if a string contains a valid IP address (pages 65-66). After showing how to use a regular expression to scan a dotted quad of digits, the text then admits that using a regular expression alone would lead to too much complexity, since it's hard to use a regular expression to check to see if a 1 to 3 digit number is less than 255 (or 127, which is what he uses in his code). So the example on page 66 ends up compiling and matching a regular expression like this:
pat = re.compile("(\\d{1,3})\\.(\\d{1,3})\\.(\\d{1,3})\\ .(\\d{1,3})")
. . .
m = pat.match(text)
for g in m.groups():
. . .
when a Python coder would more naturally just use:
quads = text.split('.')
for number in quads:
Sure, it's a good example of how to extract matched items, but the implication is that using a regular expression is the best way to extract extract numbers separated by dots, when in fact the Python has a simpler, easier and more reliable way to deal with it. Again a quick mention of the "easy" way to solve the problem would have been appropriate.
These kinds of issues are what keeps Data Crunching from being a great book. In spite of them, it is still a very good and useful book and Mark Wilson has done a good job with a topic all too often ignored. The general idea is great, and the principles, problems and solutions are well-explained and relevant. If data crunching is something you do, I would certainly recommend that you read this book, but with a somewhat critical eye.
You can purchase Data Crunching: Solve Everyday Problems Using Java, Python, and more. from bn.com. Slashdot welcomes readers' book reviews -- to see your own review here, read the book review guidelines, then visit the submission page.
sounds like even though this book gives a good idea of how to do certian things, there's a lot of little, yet very useful tips and tricks that it leaves out
Don't berate the author for his examples using nested SQL when a paragraph later you call him out for not using "tac" because you assumed it is universal.
Like nested queries, tac, isn't standard across all unix platforms.
--- I do not moderate.
rooooar
If a book uses nested queries and some rdbms doesn't -- the problem lies with the rdbms. I've never used mysql and I've avoided the flames about it not being a real database.... but come on. That is weak.
It's hard to believe that's how Micronians are made. Why don't we see it right now by having you both kiss one another?
-- Thou hast strayed far from the path of the Avatar.
Shouldn't be too hard if we can use ereg() or similar. How about checking for 0-255 like so: "([1-9][0-9]{0,1}|1[0-9][0-9]|2[0-4][0-9]|25[0-5]| 0)", then it's just a matter of checking for those between dots?
Dude, I like to start controversy as much as the next Tr...errr...guy, but you need to give some evidence.
Your oversimplification of his solution for validating ip addresses is a fine example of a poor review by someone who thinks he knows more than the author.
Try passing in a string such as "I.like puppies!!!". A regex like the one the author provided will easily reject this, so there's no need to worry about checking for numericness, or any other strange characters at all. The regex in fact filters out EVERYthing so that all that has to be done is to check the actual numeric values for the right value range. I would not like to see the remainder of the alternate example (I'm sure it wouldn't be simple)
I'm all for KISS but there is definitely is such a thing as too simple.
MySQL, clearly a very popular SQL database, has nested queries only in its latest versions, so many, if not the majority, of MySQL installations do not yet have that capability. Yet the text carries on as if nested queries were universal, without so much as parenthetical mention that some things might not work on all SQL implementations.
The fact that MySQL sucks is not a limitation of the book, as far as I'm concerned. Stumbling across the bits of SQL that some particular version of MySQL does not support (e.g. UNIONs, inline views, etc etc) is just one of the great treats in life.
That's right: I'm gumby dammit.
Link contains redirect to kaleidojewel's referral account. Don't encourage his spamming by rewarding him with payoffs.
In the end, it is a matter of style, but just invoking text.split and trusting user input is... naive?!
You know I had that same problem with my Operating Systems class. That text by Tannenbaum goes through countless examples of what makes a good system, and then at the end he FINALLY admits that there is something called Unix that I can just go and install. What a waste learning all of those concepts!
(okay, so it's 1-255, but you get the idea, and slashcode broke the string so I'll blame any further problems on that, just like I would have posted this 5 seconds following the parent if it weren't for the b0rken time limit... you'd think it would be possible to adapt this so that users who are good citizens get to wait a shorter period, or not at all? Maybe that's patented :-\)
"It's been 7 minutes since you last successfully posted a comment"
Oh, I'm not giving up. I can wait a week if that's what it takes.
It's not fair to criticize the book because you use a tarted up text file instead of something like postgres or oracle or db2 or any number of other rdbms's that managed to support subqueries and foreign keys within 30 years of their invnetion.
"The plural of anecdote is not data." -- Roger Brinner
That's all you need for the perfect Data crunching machine.
Especially when the new Mactels come out !!!!
>I've never used mysql and I've avoided the flames about it not being a real database.... but come on. That is weak.
13.1.8. Subquery Syntax
HTH. HAND.
Your two last posts, combined with your high Slashdot ID and the general trollishness of your comments lead me to think you were born in 1992. Did I guess right?
1992 called, they want their spermatozoid-turned-spotty-teenager back...
"A door is what a dog is perpetually on the wrong side of" - Ogden Nash
"read this book, but with a somewhat critical eye." Blindingly obvious but good advice.
Sounds like you're having a little too much fun with your database...
the pragprog books only have IMHO one problem:
the price/page ratio is not right
I don't fault the author for not mentioning tac. It is part of the GNU textutils package, and although it might be standard on every Linux distro, it's most likely not in ANY enterprise Unix. I just checked my Sun boxes and it's not installed there, except for the ones that I've installed GNU textutils on.
I really wish a lot of Open Source developers would stop assuming that all of us have every GNU utility ever invented on our system. I can't tell you how difficult it is to get the average GNU autoconf program to compile correctly on Solaris or any flavor of enterprise Unix, simply because most authors assume they're writing platform-independent code, without realizing that GNU's M4 is different from System V M4. Also, differences between lex, flex, tar, and GNU tar abound. Please, for the love of god, don't assume that the tools you know and love on your Linux box at home are available or even installable on enterprise kit at work. Most company policies prevent the installation of these type of tools.
"When the president does it, that means it's not illegal." - Richard M. Nixon
Fuck Slashdot.
I have another problem with the section on SQL. Several of the slicker SQL recipes rely on nested queries (page 147-151). MySQL, clearly a very popular SQL database, has nested queries only in its latest versions, so many, if not the majority, of MySQL installations do not yet have that capability. Yet the text carries on as if nested queries were universal, without so much as parenthetical mention that some things might not work on all SQL implementations. It seems to me that this is exactly the sort of pitfall a book like this should inform the reader of.
Nested queries are *basic* database functionality. This is just one of many reasons why those of us who are experienced DBAs and database developers do not consider MySQL a database. The fact that there are lots of people trying to use it as such is irrelevant. The author didn't mention that the book is also missing a section of spreadsheets. Why not? Lots of people use spreadsheets as a database!
I don't respond to AC's.
It's interesting the way that's written, because it tells me that you didn't like the book in the first sentence. If getting people to read the entire review was an issue, which is not the case here, then that would have been moved to the last paragraph.
1983 actually... There's the 60% exchange rate on Canadian Asshats.
This rating is Unfair ( ) ( ) Fair (*) Funny
Sigh... If only. Modding would be so much more fun.
You might want to compare this book to "Data Munging With Perl" by David Cross.
/ 1229238&tid=145&tid=6
See the Slashdot Review:
http://books.slashdot.org/article.pl?sid=01/04/26
Near the beginning of the post, in the green box, we have:
author | Greg Wilson
And yet, in the final paragraph we see:
In spite of them, it is still a very good and useful book and Mark Wilson has done a good job with a topic all too often ignored.
What's going on?
SIGSEGV caught, terminating
wait... not that kind of sig.
Given that MySQL is the best fit for some types of data crunching applications, the earlier comment about assuming nested queries has merit.
My requirements arise in a research setting, so perhaps they're less common. Companies like wal-mart can afford big iron on which to do their data mining. Smaller data crunching tasks don't make the same kind of performance demands on their RDBMS. Of course, one thing to consider is that the standard RDBMS model isn't all that well suited to huge-scale data-mining in general, so there may be no silver bullet here for any of us to get religious about yet.
Are there any better books about data crunching? I found at least Data Munging with Perl by David Cross. BTW: check out DataConv for a survey of data conversion tools, many of them GPLed and often unix-based.
Might as well use yacc/bison to generate a LALR parser while you're being stupid about it.