Preventing Inconsistent Reads in Java

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Preventing Inconsistent Reads
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Consider this situation Martin edits the Customer class, which makes calls on the Order class Meanwhile David edits the Order class and changes the interface David compiles and checks in; Martin then compiles and checks in Now the shared code is broken because Martin didn't realize that the Order class was altered underneath him Some source code control systems will spot this inconsistent read, but others require some kind of manual discipline to enforce consistency, such as updating your files from the trunk before you check in
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In essence this is the inconsistent read problem, and it's often easy to miss because most people tend to focus on lost updates as the essential problem in concurrency Pessimistic locks have a well-worn way of dealing with this problem through read and write locks To read data you need a read (or shared) lock; to write data you need a write (or exclusive) lock Many people can have read locks on the data at one time, but if anyone has a read lock nobody can get a write lock Conversely, once somebody has a write lock, then nobody else can have any lock With this system you can avoid inconsistent reads with pessimistic locks
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Optimistic locks usually base their conflict detection on some kind of version marker on the data This can be a timestamp or a sequential counter To detect lost updates the system checks the version marker of your update with the version marker of the shared data If they're the same, the system allows the update and updates the version marker
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Detecting an inconsistent read is essentially similar: In this case every bit of data that was read also needs to have its version marker compared with the shared data Any differences indicate a conflict
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Controlling access to every bit of data that's read often causes unnecessary problems due to conflicts or waits on data that doesn't actually matter that much You can reduce this burden by separating out data you've used from data you've merely read With a pick list of products it doesn't matter if a new product appears in it after you start your changes But a list of charges that you're summarizing for a bill may be more important The difficulty is that this requires some careful analysis of what it's used for A zip code in a customer's address may seem innocuous, but, if a tax calculation is based on where somebody lives, that address has to be controlled for concurrency As you can see, figuring out what you need to control and what you don't is an involved exercise whichever form of concurrency control you use
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Another way to deal with inconsistent read problems is to use Temporal Reads These prefix each read of data with some kind of timestamp or immutable label, and the database returns the data as it was according to that time or label Very few databases have anything like this, but developers often come across this in source code control systems The problem is that the data source needs to provide a full temporal history of changes, which
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takes time and space to process This is reasonable for source code but both more difficult and more expensive for databases You may need to provide this capability for specific areas of your domain logic: see [Snodgrass] and [Fowler TP] for ideas on how to do that
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Deadlocks
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A particular problem with pessimistic techniques is deadlock Say Martin starts editing the Customer file and David starts editing the Order file David realizes that to complete his task he needs to edit the Customer file too, but Martin has a lock on it so he has to wait Then Martin realizes he has to edit the Order file, which David has locked They are now deadlocked neither can make progress until the other completes Described like this, deadlocks sound easy to prevent, but they can occur with many people involved in a complex chain, and that makes them more tricky
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There are various techniques you can use to deal with deadlocks One is to have software that can detect a deadlock when it occurs In this case you pick a victim, who has to throw away his work and his locks so the others can make progress Deadlock detection is very difficult and causes pain for victims A similar approach is to give every lock a time limit Once you hit that limit you lose your locks and your work essentially becoming a victim Timeouts are easier to implement than a deadlock detection mechanism, but if anyone holds locks for a while some people will be victimized when there actually is no deadlock present
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Timeouts and detection deal with a deadlock when it occurs, other approaches try to stop deadlocks occurring at all Deadlocks essentially occur when people who already have locks try to get more (or to upgrade from read to write locks) Thus, one way of preventing them is to force people to acquire all their locks at once at the beginning of their work and then prevent them gaining more
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You can force an order on how everybody gets locks An example might be to always get locks on files in alphabetical order This way, once David had a lock on the Order file, he can't try to get a lock on the Customer file because it's earlier in the sequence At that point he essentially becomes a victim
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You can also make it so that, if Martin tries to acquire a lock and David already has one, Martin automatically becomes a victim It's a drastic technique, but it's simple to implement And in many cases such a scheme works just fine
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If you're very conservative, you can use multiple schemes For example, you force everyone to get all their locks at the beginning, but add a timeout in case something goes wrong That may seem like using a belt and braces, but such conservatism is often wise with deadlocks because they are pesky things that are easy to get wrong
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It's very easy to think you have a deadlock-proof scheme and then find some chain of events you didn't consider As a result we prefer very simple and conservative schemes for enterprise application development They may cause unnecessary victims, but that's usually much better than the consequences of missing a deadlock scenario
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