Michael J. Swart

August 23, 2011

ACID Properties By Example (And Counterexample) Part Four: Durable

Filed under: SQLServerPedia Syndication,Technical Articles — Tags: , , , — Michael J. Swart @ 9:00 am
ACID Properties By (Counter) Example

The last ACID property is D, Durability. Again, Haerder and Reuter describe Durability:

“Once a transaction has been completed and has committed its results to the database, the system must guarantee that these results survive any subsequent malfunctions.”

What does this mean exactly? That’s a tall order for a database system! I mean any malfunction whatsoever? I’m pretty sure our database systems are designed to survive a power failure but I don’t expect that they could survive something as severe as the heat death of the universe.

Actually databases don’t have to go that far. When designing a database system, only two kinds of malfunctions are considered: media failure and system failure.

Media Failures

For media failure (e.g. a faulty hard drive) databases are recovered by using backups and transaction logs. And this leads directly to three bits of super-common DBA advice:

  • Take backups regularly.
  • Keep your transaction logs and your main database files on different hard drives.
  • When dealing with a disk failures, step one is backing up the tail of the log

System Failures

System failures (e.g. system crashes, power outages etc…) have to be handled too.

SQL Server does it this way. When SQL Server is processing transactions, it will first write changes to a transaction log and then write the associated changes to the database file. Always always in that order (There’s a bit more too it, but that’s the main part). It’s called the Write-Ahead Transaction Log.

But when there’s a system malfunction, a few things need to be cleaned up the next time the server restarts (to maintain atomicity and consistency). There may be transactions that were interrupted and not yet committed. And some transactions may not have their changes written to disk, or sometimes not written completely to disk. How do you recover from stuff like that?

Well the database recovers from a failure like that during a startup process called (unsurprisingly) “recovery”. It can look at these half-performed transactions and it can roll them back using the info in the logs. Or alternatively it can roll-forward and replay committed transactions that haven’t made it to disk if the conditions are right and there’s enough info in the transaction log to do so. (Further Information at MCM Prep Video: Log File Internals and Maintenance)

So What Does This Mean To You?

If an ACID database system like SQL Server reports that your transaction has committed successfully then because it’s durable, your transaction is truly persisted: You don’t have to worry about buffer flushes or power failures “losing” your work.


So what is interestingly durable? Durability in database systems usually means that something is redundant so that if one thing is lost, the transaction is not lost. So I give a list here of things that are too redundant:

  • The Hydra‘s heads (Greek Mythology)
  • Enchanted Brooms from the Sorcerer’s Apprentice.
  • Autofac (An interesting short story by Philip K. Dick which I finished reading last night).


I have two examples and they both come from the career of Richard Harris (best known to my family as the first Dumbledore). Did you know he was a one-hit wonder? He had a hit single in the seventies called MacArthur Park. If you’ve never heard the song, skip this article and experience the utter madness that is¬†MacArthur Park. You won’t regret it.

Back to the example. The singer of MacArthur Park would like to have his cake. Unfortunately, it’s been left out in the rain (malfunction). But that’s okay right? He could always get out the recipe (transaction log) and make a new one right? Wrong! He’ll never have that recipe again (durability fail). Had he persisted that recipe, the poor sucker would still have his cake.

Bonus Richard Harris Counterexample

You may remember he played Emperor Marcus Aurelius in the movie Gladiator. (Spoiler alert!) In that movie, he plans to make Maximus his heir instead of his son Commodus. He first tells his plans to Maximus (who is reluctant to rule Rome) and then he tells Commodus who did not take the news well at all. In fact he murdered his father after hearing it! ¬†The Emperor’s plans never make it to the public and so Commodus becomes Emperor.

You see, his plans to make Maximus his heir was not durable! Had the Emperor told a bunch of other people first, then his intended heir Maximus would have ruled Rome as he wanted (Not to mention it would have removed the motive for his murder).

That’s The Series

So that’s it. I had fun with it. It gave me a chance to “geek out”. And even though blog post series are a nice way of treating a topic in depth, I still found myself struggling to keep each article to blog-post length. There’s just so much to learn here. I guarantee I learned more writing this series than a reader would reading it ūüėČ

Tell me what you think!

August 10, 2011

ACID Properties By Example (And Counterexample) Part Three: Isolation

Filed under: SQLServerPedia Syndication,Technical Articles — Tags: , , , — Michael J. Swart @ 12:00 pm

ACID Properties By (Counter) Example

So the third ACID property of database transactions is I for Isolation. This is the only ACID property that deals with behaviour of a transaction with respect to other concurrent transactions (all the other properties describe the behaviour of single transactions). Haerder and Reuter describe it as:

Isolation: Events within a transaction must be hidden from other transactions running concurrently.

It’s not super-rigorous, but I think of it like this: No looking at works-in-progress

Back to the drawing board.

(Actually, I don’t always believe in that advice, but it helps the cartoon)

So there are different kinds of database isolation. Even with the the guideline: no looking at other transactions in progress. And now these levels of isolation are well defined. I wrote a series on those earlier, the different levels are READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ and SERIALIZABLE. By the way READ UNCOMMITTED is the only isolation level here that is not really isolated, more on that later.

Isolation in SQL Server

SQL Server supports all of these isolation levels. It enforces this isolation using various¬†locks on data (fascinating stuff actually), processes will wait to maintain isolation. In contrast, Oracle supports only SERIALIZABLE and a kind of READ COMMITTED that is closer in behaviour to SQL Server’s SNAPSHOT isolation. No matter how it’s implemented,¬†READ COMMITTED is the default isolation level in both SQL Server and Oracle.

Unisolated Transactions:

So it is possible for other transactions to see the effects of a transaction in-flight (i.e. as it’s happening, before it’s committed). This is done with NOLOCK hints or with the READ UNCOMMITTED isolation level. In fact, I learned recently that when using NOLOCK hints, you not only can see the effects of an in-flight transaction, but you can see the effects of an in-flight statement. This is an Isolation failure¬†and it boils down to this: SQL Server transactions are atomic, but when using NOLOCK, it might not seem that way. So take care.


Today’s example and counterexample both come from the newspapers headlines of Chicago.

For the example – a fictional example – I explain a situation that’s all about not making assumptions. It’s all about being cautious and not committing to a decision while the jury’s still out. This immediately brought to mind a scene from the movie Chicago¬†[spoiler alert!] :

The movie (and play) is about a court case. The main character Roxie is on trial for murder. It’s a sensational trial and the papers are eager to publish the results of the trial. The papers are so eager in fact that the papers have printed out two editions of their newspapers. One headline read “She’s Innocent” the other headline read “She’s Guilty”. But those two stacks of papers are just sitting there in the van. The man in the newspaper van waits for a signal from the courthouse.¬†Once he got the proper signal, he cracked open the innocent¬†edition and gave them to a paper boy to hand out.

It’s about not acting on information while the jury is still out. The jury is isolated from the world and no one can act on what the jury has to say until they’ve committed to a verdict.


Our counter-example comes from non-fiction. In reality, the assumptions we make tend to be correct. Our assumptions are only interesting when they turn out to be incorrect. This counter-example comes from the most incorrect newspaper headline I can think of:

“Dewey Defeats Truman”

Click through for Wikipedia’s page on cool piece of newspaper history (Chicago newspaper history). It’s a great example of what can go wrong when we act on tentative (uncommitted) information. The Chicago Tribune published the wrong presidential candidate as the winner.

But the really really cautious reporters would report¬†neither¬†candidate as the winner. They’d be waiting at the Electoral College¬†convention. They’d be keen on seeing how that turns out.

August 3, 2011

ACID Properties By Example (And Counterexample) Part Two: Consistent

Filed under: SQLServerPedia Syndication,Technical Articles — Tags: , , , — Michael J. Swart @ 12:00 pm

ACID Properties By (Counter) Example

Dr. Jim Gray developed many of the properties that transactions have in well-designed databases. Later, Haerder and Reuter took these properties and used them to coin the acronym ACID. At that time, they defined consistency this way:

A transaction reaching its normal end, thereby committing its results, preserves the consistency of the database. In other words, each successful transaction by definition commits only legal results.

Essentially, consistency means that database systems have to enforce business rules defined for their databases.
But it’s interesting. The word consistency (applied to database systems) aren’t always used the same way! For example, in Brewer’s CAP theorem the C, standing for consistency is defined as “All clients have the same view of the data.” (Really computer scientists?? That’s the word you decide to overload with different meanings?). So if you ever hear someone say eventually consistent. They’re using the consistency term from CAP, not the consistency term from ACID.

I guess “C” means something different for everyone.

Consistency in SQL Server

In my own words, consistency means that any defined checks and constraints are satisfied after any transaction:

  • Columns only store values of a particular type (int columns store only ints, etc…)
  • Primary keys and unique keys are unique
  • Check constraints are satisfied
  • Foreign key constraints are satisfied

Constraints Are Enforced One Record At A Time
Some things you might notice about these constraints. They can all be checked and validated by looking at a single row. Check constraints enforce rules based only on the columns of a single row. One exception is where these constraints might perform a singleton lookup in an index to look for the existence of a row (for enforcing foreign keys and primary keys).
Multi-line constraints are not supported directly because it would be impractical to efficiently enforce consistency. For example, it’s not possible to create a constraint on an EMPLOYEE table that would enforce the rule that the sum of employee salaries must not exceed a specific amount.

Consistency Enforced After Each Statement
Another interesting thing about SQL Server is that while ACID only requires the DBMS to enforce consistency after a complete transaction, SQL Server will go further and enforce consistency after every single statement inside a transaction. It might be nice to insert rows into several tables in any order you wish. But if these rows reference each other with foreign keys, you still have to be careful about the order you do the inserting, transaction or no transaction.

Handling Inconsistencies
When SQL Server finds inconsistencies. It handles it in one of a few ways.

  • If a foreign key is defined properly, a change to one row can cascade to other other rows.
  • If a value of a particular datatype is inserted into a column which is defined to hold a different datatype, SQL Server may sometimes implicitly convert the value to the target datatype.
  • Most often, SQL Server gives up and throws an error, rolling back all effects of that statement.

Inconsistent Data Any Way
It also turns out that it’s very easy to work around these constraints! (Besides the all-too-common method of not defining constraints in the first place). Primary keys, Unique constraints and datatype validation are always enforced, no getting around them. But you can get around foreign keys and check constraints by

  • using WITH NOCHECK when creating a foreign key or a check constraint. You’re basically saying, enforce any new or changing data, but don’t bother looking at any existing data. These constraints will then be marked as not trusted
  • using the BULK INSERT statement (or other similar bulk operations) without CHECK_CONSTRAINTS. In this case foreign keys and check constraints are ignored and marked as not trusted.


I’m taking the following example not from I.T., but from the world of medical labs.

When processing medical tests (at least in my part of the world), there’s a whole set of rules that medical professionals have to follow. Doctors and their staff have to fill in a requisition properly. The specimen collection centre has do verify that information, take samples and pass everything on to a lab. The lab that performs the tests, ensures that everything is valid before performing the test and sending back results to the doctor.

Just like a database transaction, the hope is that everything goes smoothly. All patient information is entered properly. Patients and lab techs have followed all appropriate instructions.

Fixing Inconsistent Data: It sometimes happens that information is entered incorrectly or missing (like insurance info, or the date and time of the test). In these cases, often the lab might call back for corrections before continuing with the test. This is similar to the case when SQL Server recognizes that a statement will not leave the database in a consistent state. In some cases, SQL Server can try to do something about it. For example it can do an implicit conversion of a datatype, or it can cascade a delete/update.

Giving Up And Rolling Back: But sometimes a medical test can’t be saved. For example, sometimes a sample arrives clotted when it should have arrived unclotted (or vice versa). In these cases, meaningful results aren’t possible and the whole test has to be rejected to be performed again correctly. SQL Server will do this whenever it’s necessary to maintain consistent data. It will raise an error and the entire statement or transaction is undone (to be corrected and performed again).


Well, this counterexample comes from the world of cheesy Science Fiction. Normally we want our databases to store only consistent and legal data. Any illegal data should be rejected right away. ¬† What we don’t want is for our databases to get hung up on some crazy inconsistent data.

But if you’re Captain Kirk and you need to deal with a rogue computer or robot that’s acting up. What do you do? Simple, confuse it with inconsistent information! Those robots won’t know what hit them.

This bit of dialog comes straight from an episode of Star Trek called “I, Mudd” (I’m not even making this up, Google it!)

Kirk: Norman, Everything Harry tells you is a lie, remember that, everything Harry tells you is a lie.
Harry Mudd: Listen to this carefully Norman: I am lying.
[Norman the android starts beeping, his light starts flashing and his ears start smoking]
Norman: You say you are lying but if everything you say is a lie then you are telling the truth but you cannot tell the truth because everything you say is a lie but you lie, you tell the truth but you cannot for you lie … Illogical! Illogical!
[more of the same, more smoke and kaboom]

Honest-to-God smoke from the ears! It’s so classic it gets parodied a lot. (Here’s one of my favourites, a comic from Cyanide and Happiness).

I’m grateful that our databases don’t choke on inconsistent data. They just throw an error and tell clients “Here, you deal with it!”.

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