Clean code, the SQL: Part 2: Electric Boogaloo

After the previous discussion about nested views, encapsulation & abstraction, I’d like to write about duplication specifically, and the distinction between actual lines of code being duplicated, versus functional duplication. Because the latter is not OK, but the former is generally acceptable when it’s boilerplate code, or done, again, in the name of performance and efficiency.

boilerplate with alphabet and stuff
letters and numbers and symbols!


So, to expand on last week’s “Encapsulation & Abstraction” segment.  The conversation with one of my favorite developers went something like this.


While I agree that there’s some over-reliance on nested views, the reason they get implemented a lot is because there’s a particular problem they seem to easily solve: how to encapsulate business-data rules without violating DRY.

Let’s say we have a biz-rule for a “core segment” of data.  For simplicity’s sake, let’s call that rule “Widget A consists of a Widget record and a WidgetSupplement record joined by WidgetID, where Widget.WidgetType is ‘foo’.”  So it seems obvious to create a view WidgetFooComplete, which pulls in the data from both tables and applies the type condition.  This creates a sort of “atomic building block” of data, which can be consumed by apps & data-access methods repeatedly & consistently.

Now, most downstream apps will use that WidgetFooComplete data in its entirety (or nearly).  But let’s say there’s a hot new app that needs some more data about the Widgets, and it needs to go out to the WidgetMoarProperties table.  The natural inclination is to incorporate our existing “building block” view, WidgetFooComplete, into a new view for this app & its dependencies, and call it WidgetFooMoarComplete.

But what’s the alternative?  If we re-create the JOIN/WHERE conditions on the base-tables in this new view, it violates DRY and makes possible future refactoring difficult if that biz-rule changes.

Admittedly, most modern data-access technologies make it easier to create these “building blocks” of joined data entities.  And sometimes those biz-rules belong in the app’s lower layers, but this can lead to writing lots of little disparate queries/db-calls for what should have been one atomic operation.  That can be a maintenance headache, as could dozens (hundreds) of tailored stored-procs for every data-access scenario.

So it seems like nested views can have their place, but “deep” nesting is usually troublesome.  And to prevent the “slippery slope” effect, we have to practice diligence.


That’s pretty spot-on.  DBAs tend to criticize them (nested views) as a practice in general because of the tendency to over-use and over-rely on them, and because of that slippery slope, where “a little” use turns into “a lot”, and leads to troubleshooting headaches.  And generalizations are just that.

To take some examples in-hand: simple entity relationships, especially when biz-critical, should be A) obvious, and B) documented.  Unified views can serve this purpose, but should only be used where appropriate — i.e. to load an object that gets passed around/up the app stack.  They’re great “atomic building blocks” when you actually need the entire block of data.  But when you don’t — say in a stored-proc that’s doing some data flow operation and only needs a small subset of that data block — it’s probably better to get the relationship logic from the view and copy-paste it (but hopefully not all of it!), while omitting the stuff that’s not needed.

The main reason for this is usually index tuning.  If we’ve crafted some indexes to meet certain query patterns in certain troublesome procs, we want those procs to use those indexes, not just do a full table scan because they’re using a nested-view which does select * .

When we get to more complex business rules, we need to up our diligence game and be more mindful of dependency checking when planning for a rule change.  Proc comment-headers can be helpful here, as can tools that search thru SQL object meta-data and code-bases to produce dependency chains.

The main point is, duplication tends to be OK when it’s not functional duplication, i.e. when the SQL code is more-or-less similar in some places but it’s not exactly the same because the purpose (responsibility) of that module/stored-proc is not the same.

You’re right in that the “31-flavors of tailored procs for data-access” is a big maintenance headache, and sometimes that trumps even the performance concerns.  Again it’s about balance — we have to be mindful of both the biz-rule-maintenance concerns and the performance concerns.


I figured.  Sometimes I see DBAs criticize developers’ work without seeming to understand that it doesn’t always come from sloppiness or laziness (although sometimes it does!).  Often, we’re trying to thread that needle of performance vs. maintainability.  In Dev-land, “lazy” is good in the sense of aiming for simplified logic, for ease of both maintenance and understanding.  Painstakingly tailoring each data-access call (stored-proc), while good for performance, is kinda opposite of that.  But, admittedly, we do fall back on SELECT * all too easily.

Mostly, we try to avoid code duplication because it leads to heavier maintenance overhead.  When some modules may perform similar operations, functionally, they will often re-use the same “core” logic, which we in turn encapsulate into its own ‘thing’.  But in SQL modules, as you say, that’s not always performant, so it’s definitely a tightrope-walk.

The “Clean Code” school of thought says, if it’s obvious, it’s “self-documenting”.  I don’t always agree with it, but it comes from maintenance concerns again.  We don’t like situations where someone tweaks the code but doesn’t update the comments, and you end up with misleading comments.  Unfortunately, it does come down to diligence again, and even “good” developers will easily fall back to rarely including comments just to avoid this situation.  Of course, another potential pitfall of supposedly self-documenting code is, what’s “obvious” to one person isn’t necessarily so to everyone else!

(We both enjoy writing, can you tell?)  =P

So basically we agreed to “moderation in all things” and exchanged Buddha statues and sang Kum-Bay-Yah.  I enjoyed the exchange because it really got us both thinking more deeply about topics and areas of our business/app environment where things were in better/worse shape than others.

yay collaboration!

To conclude this part.  You will continue to see DBAs rant and rail against nested views and other “sins against SQL”, but:  Developers, don’t take it personally — we’re just trying to eek the most performance-per-$3k-core-license out of our precious servers, and spend less time chasing the white rabbit down the nested-views-hole.  And DBAs, go easy on your Devs — they still outnumber you, and they can whip out a complete web-app using the hottest JavaScript framework and a cloud-of-the-month service, faster than you can tune a server.  Everybody’s valuable, and everybody works toward the same goal: solving the business’s problems thru technology.

Moving on…

Part 3: Misusing & Abusing Datatypes

Because I’m getting long-winded again, let’s wrap up with a final “Clean SQL Code” topic that’s short & sweet.

Well, not really.  There are entire presentations dedicated to this topic.  But I’ll try to keep it condensed.

A date is not a datetime is not a time​ is not a time interval.  Okay?  For the third time, stop interchanging them!  Yes I know, SQL Server is a bit behind some other RDBMS platforms when it comes to this stuff.  Sorry, I don’t work for Microsoft.  I just deal with their tech.

Deep breaths…

More to the point, know your data.  Understand that there can be consequences to repeatedly casting types, or losing precision during conversion, sometimes exponentially so.  Yes I know, we all love loosely-typed (sometimes stringly typed) languages like JS & Python.  Those are wonderful tools for certain jobs/problems.  Again, be mindful and know your flows.

flow with the chart yo
I’m not sure what’s more disturbing.. the fact that this was the first image search result for “flow meme”, or the fact that it’s actually quite appropriate.

Thanks for reading, as always!

Stupid Defaults

Every big enterprise product has them.  I just happen to pick on SQL because it’s my area of expertise.

This week’s topic was triggered by an amazing quote from Adam Machanic (b|t) in the sqlcommunity Slack group, where he was trying to help someone troubleshoot a slow linked-server query:

That default, I consider to have been checked into the SQL Server codebase directly by Satan.

I imagine he looked something like this… Satan, not Adam.  =D

He’s referring, in this case, to the default option of remote proc transaction promotion, or “Enable Promotion of Distributed Transactions for RPC” in the GUI, which is set to True by default.  Admittedly, linked-servers are a dicey area of the technology and not everybody needs them, but when you do need to deal with them, there are some caveats to consider and a few potholes to avoid.

I won’t go into gory details, but the mile-high gist of it is that you should probably change a couple of the default linked-server settings when you’re going from MSSQL to MSSQL (which most of the time, you are): rpc and rpc out both to true, and the above dude to false.  The reasons behind that are subtle, but it boils down to usage patterns:  If you’re purely running basic SELECT statements against the remote server, you can leave this stuff alone; but if you want to do cool stuff like exec MyLinkedServer.RemoteDB.sys.sp_executesql '--some awesome dynamic-sql'​, you’ll want to change these.  (That last bit comes in especially handy if you’re building a dynamic query string, then running it against the remote server to bring in the results to a #temptable to further massage/mangle/munge said data.)

Even though you probably really shouldn’t be doing that in the database (that’s what web server farms are for!).

stack of colorful rings with arrow pointing up
push it up the stack!

So, what are some other “stupid defaults” in SQL Server?

Every big enterprise product has them.  I just happen to pick on SQL because it’s my area of expertise.  And it’s not even just “defaults”; there are some options which shouldn’t even be a thing — they should be completely and irrevocably in love with Edward removed from the product.  Yet, because the RDBMS tech space is infamously conservative and slow-to-change (the accepted euphemism is “mature“), these options and defaults have remained in the product despite our best attempts to convince MS that they’re heinous.

1. Parallelism settings (server/instance level)

Your servers have many-core CPUs, right?  And you want SQL to utilize those cores to the best of its ability, distributing the many users’ workloads fairly amongst them, yes?  Damn right, you paid $3k or more per core in freaking licensing costs!  “OK”, says SQL Server, “I’ll use all available CPUs for any query with a ‘cost’ over ‘5’“.  (To give context here, in case you’re not aware, ‘5’ is a LOW number; most OLTP workload queries are in the double to triple digits).  “But wait!”, you protest, “I have more than 1 user, obviously, and I don’t want their horrible queries bringing all CPUs to their knees and forcing the 50 other user queries to wait their turn!”

well... tough shit
-SQL Server

Hardly seems fair, no?  But those are the defaults, have been for over a decade, despite how much hardware has evolved since.  Just for completeness, I am talking about the following 2 options, which you can view by right-clicking on the instance in SSMS, or by running sys.sp_configure.  They are max degree of parallelism and cost threshold for parallelism, respectively (# cores to use, and how expensive the query should be to “go parallel” — as opposed to serial/single-threaded, which is perfectly fine for those itty-bitty-teeny-tiny queries that are so quick that 1 CPU core is more than enough to handle them).  We commonly abbreviate the former as maxDOP; the latter is less commonly abbreviated, but because I’m a sucker for acronyms, I like to call it CTFP, or maybe costFP if I’m feeling verbose.

Now obviously you can, and should, change these settings.  “To what?” you ask.  Well, the answer, as always my friend, is “It Depends ©®™“… a perennial favorite of DBAs and consultants alike.  But don’t ask me — there are plenty of people much smarter than I with blog posts on the topic.  If you put a gun to my head, for CTFP, I’d say “pick your favorite number between 50 and 100, start there, and test to see if it makes your server CPUs happy or sad”.  And for maxDOP I’d say “divide your # of CPU cores by 2 (if you have 8 or fewer) or 4 (if you have 16 or more)”.

And if you have somewhere between 9 and 15 CPU cores, don’t ask me, because you’re running some.. interesting hardware.  Figure it out yourself, and get that gun out of my face!

OK, I know 12 cores is probably not an unreasonable config, so.. extrapolating my logic above, divide by.. 3?  Yeah let’s go with that.

squirrel with "ship it" flag
ship it good

2. Auto Close (database option)

It’s at the top of the list in the GUI under Database Properties -> Options.  Yet nobody knows why you would ever enable it.  I once heard a community member theorize that it might have been put into the product back when it was considered a viable “local persistence option for Windows CE/Mobile apps” and it would help w/ resource management on said mobile device by freeing up resources when the DB wasn’t in-use.  Well, we all know how well that product line did in the market (hint: poorly).  There are so many better options for localized data stores in mobile dev, MS isn’t even a blip in the conversation.  (Again, talking local data persistence.)

If we’re talking cloud, MS is a big part of that conversation — Azure is amazing, and a solid competitor to AWS & GCP.

Anyway, if you ever find a SQL DB with the auto_close option enabled, find the person responsible, and slap them with a trout.

stick figure chasing another with purple trout
I will find you, and I will trout-slap you…

3. Server Max Memory

Last one for today.  This is something that’s not completely heinous, but could use a LOT more built-in intelligence during the installation process so that DBAs & SysAdmins didn’t need to think about it so much.  SQL Server will, by default, sets its max-memory to some-odd-billion-MBs (technically it’s the max value of a 32-bit int, which the more geeky among you have probably memorized), which is of course some-odd-million-GBs, which is more than even the most bleeding-edge servers have to date.  Which is fine in theory — you paid a crap-ton of money for this system, it might as well use up all the RAM that it can to perform to its potential, right?

all your ram are belong to us
in AD 2101…

Right.  Until you realize that “Oh wait, it’s running inside an OS” (whether that’s Windows or Linux, thanks to 2016 & 2017 product versions) — that that OS needs some RAM too, to keep itself running!  (Cue the Linux zealots with their “it’s so much more lightweight than Windoze, haha!!1” — yeah, well you still need some memory space, don’a ya?)

Here’s what I’d like to see, in my ideal world.  During SQL Server installation, it would detect how much RAM is on the system, subtract about 10% or 4-8 GB, leave that for the OS, and use the resulting number as its limit.  Boom, done, nobody has to think about configuring it and checking off another checkbox on their setup checklist.

But noooo… The vaunted MSSQL engineers can built all sorts of amazing things into the product like QueryStore, Adaptive Query Processing, and The Artist Formerly Known as Hekaton, but heaven forbid we get a little more intelligence in the installer.  It got a lot  better with 2016 when it let you configure tempDB reasonably correctly (multiple files, different locations, etc), but there’s still a LOT that could use some lurv.

two kitties cuddling
because KITTEHS!!

Do you have a favorite “stupid default” or “horrible setting” related to SQL Server or any other technology that you work with?  Share in the comments!

Clean Code, the SQL

as a developer, DBA, or hybrid “DbDev”, you’re often tasked with writing or improving the stored procedures which house that complex logic.  And that’s my topic today: being clean about your SQL code.

Get it? It’s just too punny! … Ok I’m done.

The Coding Blocks guys did a series of episodes about the perennial favorite Clean Code book.  If you haven’t subscribed to their podcast…

do it do it now -Arnold
What are you waiting for?!?!

And it’s a great book, no doubt. But those guidelines for application code are not 100% directly applicable to database code.

wait... what?

Let’s back up a second. Why? That sounds about counter-intuitive, no?  Ok, more context. See, the traditional (“legacy”?) app consists of about 3 layers, the bottom one being the database. And it’s usually relational, and is usually responsible for far more than simple data access and persistence.  Read: complex business rules and process logic.  Data flow, not just getters and setters.

So that means, as a developer, DBA, or hybrid “DbDev”, you’re often tasked with writing or improving the stored procedures which house that complex logic.  And that’s my topic today: being clean about your SQL code.

Part 1: Comments

There’s a fairly famous quote from the book about comments:

Comments are always failures.

He’s using hyperbole, but for a purpose.  While his views on comments may be extreme, most programmers tend to realize the core essence of that chapter, which is that comments only serve to express something in plain English that the code has failed to express clearly enough to be easily and immediately understood.

With SQL scripts, and in particular with stored-procedures, I’m taking a somewhat opposite stance:

Comments are always appreciated, even if they’re potentially outdated or inaccurate.

There are two types of comments in SQL, the --inline and the /* block */.  Different people have their preferred flavors of block — sometimes it’s just several lines prefaced with the double-dash --.  And that’s fine, whatever floats your comment-boat.

hms commentus comment-boat
I made my own!! (most copied from an example at

In particular, I always encourage a comment block at the top of ever stored-proc & other user-defined programmable objects (function, types, etc).  Just a small example for illustration:

Location: Server.Database
Author: NateTheDBA
Created: 2012-12-21
Description: Gets users who have not logged in since the given date.
Consumers: MyCoolAppName, MyReportServer
2015-05-15, Nate: removed archive (never used after archive-date)
2017-06-07, Nate: fixed formatting for blog post
    @SinceDate datetime2
    --some clever stuff goes here...

“But wait”, you say, “what about source control?”  Yes, all your programmable objects (and even, arguably, your reference data) should be in source control.  There are tool-vendors aplenty to help you with that.  But guess what?  Budgets.  Time & effort.  Oh, did I mention, legacy legacy legacy?  Yes, dear reader, the average business has years (decades) of organically evolved relational databases and processes.  Are you the guy or gal to swoop in on your unicorn and seamlessly convert their entire data tier infrastructure to a beautiful DevOps pipeline with shiny rainbows and kittens for all?  No?  Okay then.  Baby-steps.

Not that I’m bitter or anything…

Yes, my procs are in source control.  It’s called “daily automated script-out-objects-to-files which are then committed to SVN”.  It’s not built-in to SSMS.  Which means that I, or another DBA, or a potential consultant, or a Dev who gets enlisted to help improve a proc that runs for hours when it should only take minutes, would be inconvenienced by the extra trip to a separate tool/system to fetch some change-history just for context.  And really, that’s all this is for — CONTEXT.  We like to know what it is we’re working on when we start to work on it, without having to traverse a change-tree or go bug 3 other people who “might” have touched it last.  I’m not asking for a detailed log of every single time someone touched the thing; just give me the overview, the milestones and significant changes to functionality/features/scope so that I have a jump-off point for troubleshooting/testing/reasoning about it.

“But wait”, you say again, “shouldn’t your name be a sufficient description of what the proc does?”  Sure, in theory.  Until you have dependencies which need that name to stay the same even after an update or logic-change.  Like reports.  Or data-connected Excel workbooks.  Which are used daily by managers, who will come yelling at you if their worksheets suddenly stop functioning.

end rant

Back to comments in general.  The reason they’re helpful (besides documentation-headers for objects) is that they provide context and explain intent.  Half the time, my job as a DBA is improving or fixing someone else’s code.  Therefore, I want to see, in plain English, what it is they’re trying to accomplish, notes about attempts and failures, and the like.  Yes, I could have a discussion with them.  And I will.  But if I’m working on it asynchronously and they’ve moved on to something else, or our hours are different, I want those little nuggets of context and intent to be right there in the script, because that’s where I’m working!

What about queries that get passed-down from the app to the DB?  ORMs don’t support pre-pending a comment to their data calls, do they?  I wish.  Maybe some do, I haven’t researched it, but I know for sure that LINQ doesn’t.  But then again, when I’m using a query-capture tool (like DMVs, Profiler, X-events, or a vendor monitoring tool), ORM queries are so painfully obvious in comparison to hand-crafted SQL that I can usually spot them from a mile away, and go bother the app-devs for some context & conversation.  If you’re one of the poor unfortunate souls who still passes ad-hoc generated SQL statements down thru ODBC to your DB, then sure, a little comment won’t hurt anybody.

you poor unfortunate soul
it’s sad, but true…

So do your DBAs a favor, comment your SQL code, at least in terms of programmable database objects and ad-hoc scripts.  I promise, it’ll make them hate you less.  It might even make you love yourself more, because 3 months down the road when you revisit that proc, and you need to remember what it was for and why you did it that way, you’ll see it right there in your very own writing!  (OK, typing.)

Part 2: SRP, Encapsulation, and Abstraction

A bit of paraphrase of one of the book’s key points:

A reusable module (function, method) should do one thing, and do it well.

Also, the DRY principle:

Don’t repeat yourself.

When building SQL modules, we’re usually concerned with performance and accuracy, over abstraction and composability.  Therefore, repeating oneself is not necessarily a bad thing, when done for the right reasons.  Diligence is a big factor here — if there’s a non-trivial relationship between some entities that you’re repeating in several places, and you know that it could become a maintenance headache if that relationship’s definition has to change later, do as much as possible to mitigate the risk of dependency/consistency-loss.  This can be documentation, comments, and/or building that relationship into a view.

It’s important.

The latter brings up an interesting topic, one which I had a lively discussion about with a colleague recently (he’s a developer, and a dang good one) — nested views.  Because inevitably, the encapsulation of those relationships & business-rules into things like views or ITVF’s can and will lead to nesting those objects into other objects.  And troubleshooting many-level-nested views is a particularly frustrating exercise; in fact they’re what some DBAs call one of the “deadly sins of SQL“.  But there are perfectly valid reasons and uses for them, sometimes, and I really enjoyed the discussion thread we had on it, so I’ll have to expand on that in another post.

Anyway, I’m already getting long-winded and well over 1k words, so I’ll wrap it up for now, and continue this topic next week.

Thanks for reading, stay tuned!

Reports for Auditors

This is how code quality dies…

Look, I get it, audits and auditors are a necessary evil.  But the sheer volume of crappy code, hack-y scripts, man-hours, and alcohol, dedicated to meeting the often arbitrary, sometimes ridiculous, and possibly even downright obscene requirements, of said auditors, is staggering.  There are entire product suites dedicate to help you “report on” or “meet/pass” audits of various kinds.  And thank god for those.  Because without them, we’d all be slightly more insane than we already are.

I’m not crazy; my mother had me tested.  And my rubber duck agrees.

Here’s an example.

In one of our main applications, at the database level (SQL), we’ve implemented poor-man’s data-change-tracking via table triggers.  In a past audit, there was a “flag” on an unauthorized data change.  As a result, an “exception report” was created, which basically produces a daily CSV dump of those changes and emails it to a few managers.  Now, because the tracking table includes “who” as well as “when” & “what”, and there are certain privileged accounts that can be ignored for auditing, and because there are dozens of application users whose app-controlled changes are also logged in said tracking table (and need to be ignored), the “report” writer at the time decided it was a wonderful (read: terrible) idea to hard-code a list of usernames into the query that produces this data-dump.

That is horrible.  Never do that!

using hard-coded values is bad
Mr. Garrison knows…

This query, the job that scheduled it, the data that it produced, and the emails that it sent, were subsequently filed away and silently forgotten about.  Until we did some environment cleanup and discovered “Oh look, here’s a thing that’s not doing anything” (because the hard-coded list of users was now woefully out of date), and the same managers who received the reports for years past, gave the OK to disable the job!

What could possibly go wrong?

Then the next audit season comes around.  And guess who comes running back to the DBA having changed his mind?  Why yes, that very same manager!

i dont always say i told you so
Stay shortsighted, my friends…

So I tell him what we need to do to fix it — remove the hard-coded list, replace it with an AD group or at least a look-up table that can be periodically updated with “current” users in the desired departments/teams.  We do that, and the CSV dump goes from zero to huge.  As in, too huge for email.  So we try to pare it down.  We get it to a manageable size.  Then we get ready to deploy the changes and confirm the desired email recipients.

  • Boss: “Oh it doesn’t really matter, we just ignore the emails anyway… it’s just to satisfy an audit requirement.”
  • Me: “So why did we even bother fixing it?”
  • Boss: “Well, it was broken!”
  • Me: “…”

I’d like the last hour of my life back please.

It works, I sw…

Here’s another fun one.

The ERP system has a rickety tack-on audit-trail system involving some SQL Agent Jobs and an obscenely large data repository for storing all the changes that the ERP system users make every hour of every day.  Thankfully, somebody along the way was prescient enough to partition those storage tables using a partitioned view scheme.  But apparently there were NOT smart enough to realize that the clustered index needs to be the datetime column.  Yes, it was in the primary key, but that PK was not clustered (nor should it have been, since the actual unique keys were GUIDs, but this is a prime example of making the clustering key different and separate from the PK.

Obviously enough, in a date-driven partitioned view scheme, that date does need to be involved in the PK also.  But again, the best clustered index is just that date, because A) it’s the partition divider, and B) it’s always your queries’ main filter-predicate (which is a fancy way of saying “the thing in your JOIN/WHERE clauses”).

Once again, audit season comes around, and someone needs those reports driven by these audit-trail repos.  And guess what?  They’re slow as molasses.

you don't say
Nicholas Cage is apparently quite the flexible meme fodder..

So we get to spend another few hours applying the correct clustered indexes, waiting for them to rebuild, testing the queries for the report, and finally running the report itself.  Oh and don’t forget the down-time of the jobs, and having to “catch up” with all the changes that were made in the ERP system while we were working on all this.

Is it 5 o’clock yet?

In conclusion.

Audits suck.  But please, stop producing crappy code in response.  It just makes the next guy/gal that comes after you even more frustrated than he/she already is with the whole process.  Practice the “boy scout principle” — leave things at least a little better than you found them.  And if you’re forced to produce a ridiculous “exception report” that nobody will ever read, spend as little time on it as possible while still making it less heinous than the last terribly designed monstrosity.

oh my look its beer-o-clock
That clock might be slightly ahead… but we’re gonna go with it.

Dates, Date-pickers, and the Devil

When a date range, or time period, is specified in SQL, it’s easiest, clearest, and most concise, to use a “greater-than-or-equal-to Period-Start, and less-than Next-Period-Start” logic. Mathematically speaking, we are defining the range as closed on the left, open on the right.

This is a bit rant-y, but… indulge me.  I’ve been writing/refactoring a lot of old reporting queries.  And, like most reports, they deal with dates and datetimes — as parameters, boundaries, or where/join predicates.  I also got way too intense with a recent SSC post (Sql Server Central), which fueled the fire even more.

I’m so cute and ANGRY!

SQL Server is very good at handling temporal datatypes and calculations against them.  We’ve got functions like dateadd, datediff, dateparts, datatypes datetime2 and datetimeoffset, datetime, etc.  It supports all sorts of format conversions if you need to display them in various ways.

..even though that should be left to the presentation layer!

Here’s the issue.  Well, there are several issues, but we only have time for a few.

Here’s the first problem

Report users don’t understand the “end of a time period” problem.  I don’t have a good name for it; others might call it the “Day plus one” problem or the “Less than date” problem.  What do I mean by this?  Well, let’s back up a bit, to DBA Commandment #6, “Thou shalt not use between with datetimes.”  In order to first understand the issue, we have to understand why this is a commandment.

When a date range, or time period, is specified in SQL, it’s easiest, clearest, and most concise, to specify it like so: @TheDate >= @StartOfPeriod and @TheDate < @StartOfNextPeriod.  Mathematically speaking, we’re defining the range as “closed on the left, open on the right”.  In other words, Min <= X < Max.

The reason we do this with datetimes is found right there in the name of the datatype — it has (or can have) a time component!

There are probably more than 10, but it’s a good starting point…

Let’s talk examples

Say you’d like to report on the month of March 2017.  How do you determine if your data-points (stored as datetime or, hopefully, datetime2) are within that period, that month?  Well sure, you could write where month(MyDateColumn) = 3 and year(myDateColumn) = 2017 

NO.  That is horrible, don’t do that.

It’s not SARGable and renders your index on that column useless.  (You do have an index on it, don’t you? No? Make one!)  Okay, let’s stick with something SARGable.  How about MyDateColumn between '20170301' and '2017-03-31T23:59:55.999'?  (You did read this post about using culture-neutral datetime literals right?)  But wait!  If your data is a datetime, it’s not actually that precise — your literal gets rounded up to 20170401 and you’re now including dates from April 1st (at midnight)!

Oh that’ll never happen… until it does.

Second problem

Many developers and report-writers assume that the values in their data will never be within the typical “1 second before midnight” or “1/300th of a second before midnight” escape window of your “3/31/2017 23:59:59.997” bounding value.  But can you guarantee that?  Didn’t think so.  Worse, if you use the .999 fraction as given in the 2nd example, you’re either “more” or “less” correct, and nobody can actually tell you which way that pendulum swings because it depends on the statistical likelihood of your data having actual literal “midnight” values vs. realistic (millisecond-y, aka “continuous”) values.  Sure, if you’re storing just a date, these things become a lot less complicated and more predictable.

But then why aren’t you storing it as an actual date, not a datetime!?

So what’s the right answer?

As I said, “greater than or equal to  ‘Start’, and less than ‘End'”, where ‘End’ is the day after the end of the period, at midnight (no later!).  Hence, MyDateColumn >= '20170301' and MyDateColumn < '20170401'.  Simple, yes?

keep calm and keep it simple

But wait, there’s more!

I mentioned “date-pickers” in the title.  When it comes to UX, date-pickers are a sore subject, and rightly so — it’s difficult to truly “get it right”.  On a “desktop-ish” device (i.e. something with a keyboard), it may be easiest on the user to give them a simple text-box which can handle various formats and interpret them intelligently — this is what SSRS does.  But on mobile devices, you often see those “spinner” controls, which is a pain in the arse when you have to select, say, your birth date and the “Year” spinner starts at 2017.  #StopIt

I mean, I’m not that old, but spinning thru a few decades is still slower than just typing 4 digits on my keyboard — especially if your input-box is smart enough to flip my keyboard into “numeric only” mode.

Another seemingly popular date-picker UX is the “calendar control”.  Oh gawd.  It’s horrible!  Clicking thru pages and pages of months to find and click (tap?) on an itty bitty day box, only to realize “Oh crap, that was the wrong year… ok let me go back.. click, click, tap..” ad-nauseum.

#StopIt again

The point here is, use the type of date-picker that’s right for the context.  If it’s meant to be a date within a few days/weeks of today, past/future — OK, spinner or calendar is probably fine.  If it’s a birth date or something that could reasonably be several years in the past or future, just give me a damn box.  (Heck, I’ll take a series of 3 boxes, M/D/Y or Y/M/D, as long as they’re labeled and don’t break when I omit the leading-zero from a single-digit month #!)  If there’s extra pre-validation logic that “blocks out” certain dates (think bill-payer calendars or Disneyland annual-pass blackout-days), that probably needs to be a calendar too.

..just make sure it’s responsive on a mobile device.

And in all cases, pass that “ending date” to your SQL queries in a consistent, logical, sensible manner.  For reporting, where the smallest increment of a period is 1 day, that probably means automagically “adding 1 day” to their given end-date, because the end-user tends to think in those terms.  I.e. if I say “show me my bank activity from 1/1/2017 to 1/31/2017”, I really mean “through the end of the month“, i.e. the end of the day of 1/31.  So your query is going to end up wanting the end-date parameter to be 2/1/2017, because it’s using the correct & consistent “greater than or equal to start, and less than start-of-next” logic.

The 3 C’s

Final thoughts

I know it’s not easy to explain to business folks, and it’s not easy to implement correctly.  But it’s important.  The >= & < logic is clear, concise, and can be used consistently regardless of underlying datatype.  You just need to adjust your presentation layer (whether that’s SSRS parameters or a .NET date-picker) to convey their intent to the user, whether that’s “show/enter the last day of the month, but translate to the next day to feed to the query/proc.”, or “make them enter the next-day (day after the end of the month/period) and understand the ‘less than’ logic.”  I’m more inclined to the first, but it depends on your audience.

Thanks for reading, and happy date-ing!

DBA Holy Wars

On a lighter note than usual, I thought it was time I weighed in on some of the long standing “programmer holy wars”, but with a little DBA-twist (like a twist of lime, only less delicious).  Like any good holy war, this will be full of posturing, pontificating, and political correctness.  And I probably won’t even commit to a particular side on some issues.  But hey, isn’t that the point?

Battle 1: Tabs vs. Spaces

Text editors and IDEs have long been mature enough to handle “smart tabs” and preference-based tab size.  However, you will occasionally have to copy-paste code into a non-code-oriented environment, such as an email or a document, where of course the tab size is based on inches rather than spaces in a monospace font.  I will admit in those rare instances, tabs are annoying.  But what is more annoying is the inconsistency you can get when spaces are used incorrectly, especially in the midst of lines in a sad attempt to do some kind of vertical alignment.  Plus, if you happen to have a different spacing-size preference than the original code author, you’re now battling that visual discrepancy as you read & maintain said code.

So I prefer tabs.  But I won’t fight my team on it if everybody else prefers spaces — that’s what those settings in the editor/IDE are there for!  I will happily conform with the best of them.  A quick Google says I’m in the minority anyway — which I’m OK with.

Battle 2: The Case for Casing

The original, if somewhat dated.
Certain languages (COBOL, SQL) have a historical bent toward ALLCAPS for their keywords and language constructs.  Some argue that this is archaic, outmoded, etc.  I don’t mind it, working primarily with SQL, but in almost all other languages (C#, Python, JavaScript), I think it makes sense to follow the established conventions, and modern conventions never favor caps.  As I transitioned from C# to SQL, I actually wrote my scripts and stored-procs primarily in lower case for the longest time.  And then I came into an environment where RedGate’s SQL Prompt was in heavy use, and since its default “auto-format” settings are in-line with the SQL language “standard” (however old and dated it may be), it started YELLING all the keywords at me.. and like most people, I just accepted it, eventually letting it become my own “default” style.  (SQL Prompt is a fantastic tool, don’t get me wrong.  I absolutely love it, but its default formatting settings never agreed with me — then again, nor do anybody else’s, as we already discussed!)

But that’s not really what this battle is usually about.  Most often, it’s about your names, i.e. the identifiers for objects/methods/variables/procedures/APIs/etc. that your team and your developers have to come up with on a constant basis.  And usually it comes down to camelCase, TitleCase (which are often incorrectly used interchangeably!  and is apparently better known as PascalCase, which I just learned today, or possibly re-learned after several years), or lower_case_with_underscores (which, in another learning moment, I discovered is named snake_case!  How cool is that?).  Rarely, if ever, do people argue for ALLCAPS in these areas — it just feels.. obnoxious.

Yelling doesn’t always get you what you want…
As with any programmer-y topic, you can dive down the rabbit-hole and dissect layer upon layer of nuance in this battle until you’ve lost all semblance of productivity.  Because casing is, in some languages, important; while in others it’s simply convention-based, dependent on the abstraction level or family of things you’re talking about.  For example, C# Class names are TitleCase, and so typically are Methods, while object instances are usually camelCasepublic members can be TitleCase or camelCase, and private members can be _underscore_led, or whatever flavors for each that your boiler-plate/template system prefers.  Scoped variableNames are most often camel’d as well, while global constants are typically CAPS_WITH_UNDERSCORES.  And god help you if you ask a team of more than 3 people what their dependency packages’ names should look like.

Shamelessly borrowed from Adam Prescott’s blog, which you should definitely go read.
So in this battle, I have to play Switzerland.  I’m not vehemently opposed to any particular flavor of casing, finding it best to work within the conventions of the language and tool-set at hand.

Side-battle: Spacing in Names

That said, I can’t stand names/identifiers with actual white space in them, but that’s a somewhat different battle.  Most languages don’t even allow that, but most RDBMSs will happily accept your ridiculous My Cool Database and its resident Silly Tables and Happy Column 1/2/etc. as long as you properly “quote” them (surround them with [square-brackets] or `backticks`, depending on the SQL flavor).  If you submit that kind of nonsense to me, I will find you, and I will slap you with a large trout.

Particularly offensive names may warrant a double trout slap.

Battle 3: ORM vs Stored-Procs (vs Linq?)

This is that little twist-of-DBA as promised.  I recently read an interesting post related to this topic, and essentially the point was this: Developers have “won” (won what? I thought were all on the same side!), the ORM is here to stay, and as DBAs/DBDevs, we (you/I) need to build up our understanding of them so that we A) know them even better than our devs, and B) can troubleshoot performance issues with them.

I think there’s some truth to that, and some necessary context as well.  Ideally, yes, I would be an ORM expert on whatever 1 or 2 specific frameworks my colleagues are using (Entity Framework, most likely), and any time there was a potential performance challenge with a app-to-database call, I’d be able to parachute-in and sprinkle some magic dust and make it all better.  But I’m also the one DBA (out of approx. 1.3 total), serving 4 teams of 3-6 devs each, so in the immortal words of meme-dom:

Ain’t nobody got time for that!

because sometimes old-fashioned things are funny too…
Now I’m not making excuses.  All I’m saying is, the burden of understanding is on more than just one team member or job-role.  If your dev team is adapting an ORM, said devs need to learn how it works too — at least enough to help with basic performance troubleshooting.  Even if it’s just the ability to extract, from a debug session, the actual T-SQL code that’s being sent to the server, and give me a sample query to analyze for performance bottlenecks.

Let’s step back a bit.  It’s all about using the right tool for the job, yes?  ORMs are meant for basic CRuD operations and simple data access patterns, right?  So why try to build complex business logic into them?  Because, like it not, teams do build complex business logic into the data layer — despite our protests and soapbox sermons to not do it.  And because the vast majority of applications we’re dealing with are not greenfield.  Furthermore, ORMs tend to work best when the data model is well-defined, or the database is modeled well (well-modeled?).  And again, we don’t all get to work with unicorns in utopia.

Put it this way: If you want an efficient, performant module of data-layer business-logic against your SQL database, it’s likely going to be a stored procedure carefully crafted by a DBA/DBDev.  Could you achieve the same results from the app layer, using Linq and/or some mix of ORM and code?  Probably.  Do you have the time and patience to do so?  Maybe not.

If I don’t survive this… tell my wife, “hello”.
So once again, I’m Switzerland.  Well, preferably a more pragmatic version — what country would that be?  Norway?  Anyway.  Use the methodology that’s the best compromise between “right tool for the job”, “optimized developer productivity”, and “easiest to troubleshoot”.  It’s a tough call, but that’s why we get paid.

Until next time!

An Open Letter To Management

We need to talk about this stuff – candidly, openly, broadly, deeply.

More accurately, to legacy enterprise management.

Let’s say the following directive comes down from on-high: “Hey, our CEO wants us to provide better financial metrics reports and a dashboard that management can see to show real-time stats about the company.”


I mean… Sure!  Yay, digital transformation, modernization, mobile friendly, all that good stuff!!

So, I have some thoughts on this, because I’ve seen the current state of things in small-medium enterprise, and am anxious to help improve that state to provide better value to the business.  To misquote Dennis Miller, I don’t mean to on a rant here, but…

First topic: Reality Check

It starts at the top, with a couple realizations:

  1. Data is ever-growing.
    1. We need to get smarter about managing its growth, including archiving/retention schemes, data warehousing, etc.
    2. This involves compliance regulations and operational resources.
      1. We need to ensure compliance with biz standards and data shelf-life.
      2. We need to automate as much as possible to avoid over-burdening our human resources (and to some extent our servers too).

For example, you can’t expect the same response-time for a query into 10-year-old financial data as you do for 1-year-old data.

  1. Traditional SSRS (SQL Server Reporting Services) is an operational time-sink.
    1. We spend way too much time assigning access, creating redundant “on demand” reports, and making seldom-used email subscriptions.
    2. We’re probably running on an old version, say 2008R2
      1. Vast improvements have come to the MS Data/BI platforms in the last decade and we need to take advantage of them.
      2. It’s not mobile-friendly at all; it’s not even modern-browser friendly, as some of its UX elements are still explicitly functional in Internet Explorer
    3. We tacked-on some 3rd-party application to attempt to bring some data-warehouse functionality into the environment, but only 1 person “knows it intimately” and is comfortable developing new reports with it.
  2. Our ERP system, in its current state/version, is a tangled mess, to the eyes of a DBA & query-writer/report-writer.
    1. We’ve bolted-on so much customization and special-configuration that it’s not suitable for stock/canned reports from the vendor, even if we upgraded to a version of the app that had a decent reporting engine.
    2. We can’t even decide on very basic things like “What is a ‘unit‘ of production?”, or “What are the different areas/groupings we break-out for revenue metrics?”

Ok sure, maybe we can agree on what those groupings are, but we can’t even get a consensus on what we call them!

Second topic: Single Source of Truth

We need to agree on a standard, documented, official set of business rules that answer such questions as “how do we measure revenue?”, “what are our different sub-orgs/departments/groupings for how we report on revenue?”, “what is ‘production output’ and how do we measure it?”, “how do we calculate bonuses for this group of employees?”, etc. More than that, we need to agree on naming things – we need a common, consistent nomenclature and understanding of what it means when someone says “N# Units”, “Department X” or “Order Aging” or “Membership Level” or “Bonus Type Y”.

And even more than that, we need to map those concepts to concrete, documented rule-sets that are manifested in the data somehow (from the simplest example, a “look-up table” or “reference table”, to the complex examples like a “data mart” or “analysis cube” or “ETL process”). This concept is sometimes called a “data dictionary”, which kinda belies its complexity, because it’s really more of a “data encyclopedia” – it needs to document what, how, why, & when.

What our concepts/terms/data-points mean, how they’re used, why they’re useful, & when they should be used.

Third topic: KISS and KPI’s

Management reports need to be simple. Yes, there are power-users who want the detail, and there are auditors who in fact require the detail. But your average C-level (or even P/VP-level) exec doesn’t care about that stuff – they want very simple answers to deceivingly simple (i.e. can be very complex under-the-hood) questions, like “How much money did we make this quarter for department X?”, or “What kind of productivity bonus do I give to group Y?”. But that’s just the beginning – that’s descriptive analytics. What they really want, but are sometimes too afraid to ask, are more powerful questions, like “How much money can I expect to make in market Z or state XX?”, “What are our expected new loyalty program memberships, and how much will they profit us?” — predictive analytics.  (And we’re not even going to touch prescriptive analytics yet, because you’re not ready for that.)

KISS means we need to try our best to hide the nitty-gritty details and “under the hood” logic/calculations from the end-user or report audience. But, that means fully knowing and understanding those details and rules and logic flows so that we can implement them!

KPI is Key Performance Metric. That’s the golden nugget, the one piece of information that the manager/report-viewer ultimately is after, the thing that makes them go “Got it! That’s the answer I was looking for!”, so they can make their business-decision and move on with their day. These aren’t necessarily just single numbers (like an overall revenue figure); they can be pie-charts, bar-graphs, a clear & concise grid, or whatever makes the most sense for the business-problem/business-decision at hand.

This all sounds fantastic, right? So what’s the catch?

Fourth topic: Time & Effort

Time is money, which is resources, which is people, learning, training, developing, implementing, testing, validating… rinse, repeat.  You don’t put that all on the shoulders of a lone DBA; that life-cycle touches many different disciplines and team members – managers, business users, accounting folks, marketing people, analysts, developers, testers, operational leads, and yes, of course, all of IT infrastructure (helpdesk, engineering, DBA).  And you don’t just buy a box off the shelf at your local software retailer and say “look, we’re gonna implement Tableu!”, wave a magical IT wand, and call it day.

Now we, as technologists, are more than willing to learn and educate ourselves, but…

There needs to be a matching dedication from the business to that effort, and to the platform(s) that is/are chosen.

That means, in concrete terms, a few things:

  1. Training budget & resources
    • Conferences, courses thru online training providers, cross-team collaboration.
  2. Product & technology investment
    • Upgrades, net-new products, whatever is needed.
  3. Time allowances & agreements
    • Dedicated scheduling where the “daily grind” operations take a back-seat and we can focus on the new stuff.
  4. Support from SME’s
    • The ability to call-out to a qualified expert when critical questions or roadblocks arise.
    • Can be contractors, consultants, service-providers, or platform-providers. The point is, you only use them if you need them, so you keep the cost relatively low.

That’s if you’re dedicated to in-house team/ability build-up. If you want to outsource, you have a different set of challenges:

  1. Contractors are expensive!
    1. Their requirements are exceedingly rigid.
    2. They’re likely to scoff (yes, even outright laugh) at the quagmire of data & logic & rules that we’ve created and/or want to build into our “magical reporting stack”.
  2. They’ll still require that same product/tech investment.
    1. No contractor is going to accept your old legacy SSRS instance as a baseline for building a modern, responsive, effect reporting system. The first thing they’ll say is “upgrade that, & come back to us.”
    2. Likewise for your legacy ERP system – sure, it’s a little less obsolete, and there are probably plenty of shops running it & developing on it, but good luck getting new-hire contractors to embrace it; at best, they’ll begrudge it; at worst, they’ll charge exorbitant fees for having to work on such an old platform.
  3. Technical debt is their worst enemy.
    1. Like it or not, like most decades-old enterprises, we have technical debt up the wazoo.
    2. Contractors won’t work in a debt-heavy environment; they’ll insist you “fix the debt” and come back to them in a few months/years when it’s all happy & pretty & green.

Technical debt is our enemy, too, but at least we “own” it – i.e. we’re aware of it and we have ideas on how to fix it, if/when we ever get the time.

It’s like our city roads: at least we know where the potholes are, and how to avoid them.

Executive Summary

My point, from this rambling and probably way too lengthy post, is this: We need to talk about this stuff. Yes, Mr. Manager, I know you already said that. Let me embellish:

We need to talk about this stuff, candidly, openly, broadly, deeply, cross-functionally (made-up phrase #2), even company-wide.

Because, while the end-goal is deceptively simple (“We want report dashboards!”), the underlying systems are complex, with lots of moving parts, requiring lots of knowledge (both domain/biz and tech), and lots of management (compliance, governance, automation, visibility/monitoring).

It’s not just a technology challenge. It’s a people challenge. It’s a cultural challenge. It’s an organizational challenge.

It’s a challenge that, when faced, met, and overcome, can lead to spectacular growth and success for all involved!

(And that’s my attempt to end this rant on a positive note. Enjoy!)

PS: No, I’m not happy about WordPress’s inability to understand the ‘style’ attribute of a simple <ol> tag, but I tried… so apologies if the outlines are not intuitive because each level is just another set of numbers, instead of Word-style outlining like 1.. a.. i.. etc.  Grr arg!