Laptopistan is getting crowded

This is purely anecdotal, of course, but on the occasions when I've ventured out from my home office to get some fresh air or avoid being pestered by my dog, I've seen more and more people working remotely. Your average Starbucks is completely overrun with laptop-facing contractors, remote workers, and students these days, as are most other public areas with seating and free WiFi. Getting a table all to yourself is becoming rare, and forget about finding an open power outlet!

This says to me that there is a serious hole in the co-working space market. Assuming these Starbucks campers order an average of a $4 latte per day (some get black coffee, which is cheaper, but some add a muffin), and the cost of a dedicated desk at Affinity Lab is on the order of $900, that works out to a price differential of $800/mo. Surely there must be a point somewhere in the middle for which one could add certain beneficial services while keeping the cost enough to attract customers.

Starbucks, after all, does not exactly inspire loyalty - the seating is functional, but rarely comfortable; there are never enough outlets; the coffee is okay but somewhat expensive; the WiFi is often slow; the environment is noisier than a worker would prefer; and the venues themselves hardly encourage hard work. If I could pay $200/mo for a similarly casual but better-outfitted space in which I could work, chat, and drink coffee, I would jump at the chance. I've been trying to check out DCIOLab, but they haven't emailed me back.

Makes me think there's a definite business opportunity here, but certainly a risky one. Timing is important - is NOW the inflection point in density of remote workers, or a year from now? You can quickly go broke renting a commercial space for a year with not enough customers. Is there enough consistency in the office support requirements of the proposed customers? A good coffee pot or two (or a Chemex or Aeropress, maybe) is definitely necessary, but a copier? Fax machine? Phones? A conference room?

These are ways to differentiate a workspace from a Starbucks, but I think they may raise the cost too much and get too little use. I don't need any of those things besides the coffee-making apparatus. In fact, all I want is coffee, power, internet, good light, and preferably high ceilings. Could one provide those things, pack people in as tightly as they do at Starbucks, and make money charging $200/mo?

Query plan troubleshooting

I was troubleshooting a medium-complexity query problem this morning in which the query execution within an app was orders of magnitude slower than the execution with the same parameters within Management Studio. I knew this could be due to different cached plans, but couldn't remember why.

Fortunately, Erland Sommerskog had written a detailed and helpful article explaining why this happens: http://www.sommarskog.se/query-plan-mysteries.html

And indeed, the app was using ODBC and therefore the results of sys.dm_exec_plan_attributes showed ARITHABORT OFF, while my instance of Management Studio had it set to ON, therefore generating a separate cached plan. I'm going to update my settings in SSMS to set this off by default, especially since it appears that it has no effect with ANSI_WARNINGS set to on, other than to foil attempts to troubleshoot slow procs.

Refreshing Intellisense in SQL Management Studio 2008

If you're like me, you create and drop a lot of tables while developing, and you may be getting annoyed with SQL Server Management Studio because it fills in the names of old tables while you're typing unless you hit Escape. Turns out that refreshing the Intellisense cache is as easy as

Ctrl-Shift-R

If your DATE comes through pyodbc as a unicode, it's probably the driver

File this under "should have been obvious." I had a test script break today because I was trying to do date math on an object that wound up being a unicode instead of a date. I had gotten the variable value from a pyodbc query against a SQL 2008 database table with a DATE column. I knew that DATETIME columns came in as datetime.datetime objects, but the DATE column came in as a unicode, which seemed strange.

Turns out it's the ODBC driver. Demo:

In [78]: cnxn = pyodbc.connect("DSN=local;")

In [79]: crsr = cnxn.execute("select cast('2012-01-01' as date) as bar")

In [80]: r = crsr.fetchone()

In [81]: type(r.bar)
Out[81]: unicode

Then, when switching to SQLNCLI10:

In [96]: cnxn = pyodbc.connect("Driver={SQL Server Native Client 10.0};Server=localhost;Trusted_Connection=Yes;")

In [97]: crsr = cnxn.execute("select cast('2012-01-01' as date) as bar")

In [98]: r = crsr.fetchone()

In [99]: type(r.bar)
Out[99]: datetime.date

Faster, thinner, lighter

It's almost time to retire my trusty old Dell E4300. The warranty just expired, which means that if Dell has optimized their average component MTBF vs warranty span correctly, the laptop will implode shortly. I replaced the original hard drive with a 2nd-gen Sandforce SSD a year ago, and it's made a big difference, but I'm ready for a little more speed. And, with the advent of the "Ultrabook" form factor, for even less weight and size. I've decided I want one.

At CES this year, there were plenty of ultrabooks on display - Engadget's roundup has a few, more on Anandtech). And most of them seemed to get at least some part of the formula right, but I don't think any of them have quite grabbed me yet. Maybe I'm being picky, but I've decided that my top priorities are:

- 1600 x 900 resolution

- Thunderbolt port (for docking)

- decent battery life (5 hrs+)

- light weight

I'm sick of my current 1280 x 800 screen - the drop in my productivity from 2 big 1920 x 1080 screens down to the tiny laptop screen is painfully apparent. 1366 x 768 is arguably worse, since I really need those vertical lines of resolution. And although I move around a lot with a laptop, which is why I want a good battery and light weight, I also use it as my primary workstation, so I need to be able to dock it and use my big monitors. In fact, a laptop that would support 3 screens instead of 2 like my current Dell would be preferable. Few of the ultrabooks seem to have this capability, but at least a Thunderbolt port would make it theoretically possible.

Other requirements are pretty normal. The number of USB ports matters, but not hugely, since most peripherals I use at home while connected to a dock. I do use my SD card reader pretty often, so it would be nice to keep that. A decent keyboard, preferably backlit, would be useful.

Requirements in mind, I took a look at the current crop of ultrabooks just demoed at CES, and... was totally disappointed. Nothing had all the features I wanted. The closest ones were:

Samsung Series 9

- no Thunderbolt but both HDMI and DisplayPort connections

- 1600 x 900 resolution (on what I hear is a great screen)

- light weight (2.5 lbs)

Sony Vaio Z

- Thunderbolt, sorta, via a proprietary connector as usual (thanks a lot, Sony)

- 1600 x 900

- light weight

- ungodly price ($1900+)

Here's my spreadsheet comparing the current crop of ultrabooks and their cousins using my completely biased and proprietary scoring system. It's a definite work in progress and the scoring may change without warning. If something comes along that scores above a 3, I'll probably buy it, but right now, none of these are worth the money.

More fun with SSIS type conversions

Just finished painfully debugging a strange SSIS issue about which I could find no documentation anywhere on the web, so I'm noting it here.

I had changed a type within a large unwieldy data flow from DT_R4 (float) to DT_R8 (double), since the smaller data type was causing some weirdness around the least significant figures on some option prices. This seemed to work in testing, but I got a cryptic error about a conditional operation failing during a derived column transformation when I deployed it to Production. I opened the package, added an error redirection for the component and a data viewer, and sure enough, it was failing on a transformation that utilized some of the columns I had modified.

I couldn't find any reason why the conditional would fail, since it was faithfully spitting out BOOLs, but the resulting transformation split the float into Integer and Decimal by casting the value into a WSTR, 20 and then finding the decimal point. After trying a dozen other things, I tried adjusting the size of the WSTR cast to a WSTR, 40. That worked.

Note that the values that were failing would not even come close to 40 characters (ex: 110.68), but apparently the possibility of running into an overflow breaks something in the SSIS runtime.

So, a word to the wise: if casting from float (DT_R4 or DT_R8) to Unicode string (WSTR), make sure you leave enough room to cast any possible value.

pyodbc isn't playing well with multiprocessing

Wrote my first Python script yesterday that incorporates the multiprocessing module in order to parallelize some CPU-intensive calculations that were going extremely slowly when run in the database. Mission accomplished there, and it was remarkably easy in Python, but then I hit a brick wall when trying to load everything back to the database.

As it turns out, the calculation time is dominated by the database IO (~25 mil rows in and out), so I went about trying to optimize that as best I could. The reads are coming off an SSD, so they're fine, but the writes are UPDATEs, and I didn't want to issue them one at a time and incur all the connection and transaction overhead for each one. Instead, I created a staging table as a heap and batched up INSERTs of my data in a specified batch size (1000 to start) using SQL 2008 row constructors. Then the main table gets updated from the staging table right at the end, so only one giant transaction and UPDATE statement is necessary.

This strategy appeared to work fine when single-threading, but then I figured I'd throw in multiprocessing there, too, since I already had the module imported and had crafted the functions around it. Didn't work.

The error message was 42S02, aka Invalid object specified - in other words, the INSERT can't find the staging table, even though I'm creating it within the Python script, during a single-threaded section before I Pool.map() out different calculated shards to be inserted. SQL Server can obviously accept just about as many threads as you want to throw at it, so that's not the problem. The table exists, which I confirmed by adding a breakpoint (well, a set_trace()) and looking at it through Management Studio. It's all using integrated authentication, and I'm a sysadmin on the server, so there can't be any problems with schema, although I threw in some "dbo." references just to make sure. I even tried to trace the SQLNCLI driver operations, but haven't managed to get usable output yet.

So the only thing I can come up with so far is an incompatibility between pyodbc and multiprocessing. If I get more time to investigate, I'll update the post, but for now I'm just going to have to switch the DB section back to single-threaded. Boo.