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Setting up two or more separate threads of execution in Python is quite straightforward The complexity arises when we want separate threads to share data Imagine that we have two threads sharing a list One thread might start iterating over the list using for x in L and then somewhere in the middle another thread might delete some items in the list At best this will lead to obscure crashes, at worst to incorrect results One common solution is to use some kind of locking mechanism For example, one thread might acquire a lock and then start iterating over the list; any other thread will then be blocked by the lock In fact, things are not quite as clean as this The relationship between a lock and the data it is locking exists purely in our imagination If one thread acquires a lock and a second thread tries to acquire the same lock, the second thread will be blocked until the rst releases the lock By putting access to shared data within the scope of acquired locks we can ensure that the shared data is accessed by only one thread at a time, even though the protection is indirect

One problem with locking is the risk of deadlock Suppose thread #1 acquires lock A so that it can access shared data a and then within the scope of lock A tries to acquire lock B so that it can access shared data b but it cannot acquire lock B because meanwhile, thread #2 has acquired lock B so that it can access b, and is itself now trying to acquire lock A so that it can access a So thread #1 holds lock A and is trying to acquire lock B, while thread #2 holds lock B and is trying to acquire lock A As a result, both threads are blocked, so the program is deadlocked, as Figure 102 illustrates

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Constraints are great, and along with and primary and foreign keys, they go a long way to enforce a lot of business rules on your data But what if you need to implement something a little more advanced, and constraints and keys just aren t getting the job done That s where triggers come into play Using triggers, you can write custom T-SQL code to run after something has happened to a table Triggers can be set up to run after an INSERT, UPDATE, or DELETE or even instead of one of these actions Really, the sky is the limit when you use triggers; you can do almost anything, including canceling the statement that fired the trigger Keep in mind, however, that triggers fire as part of the transaction that started them, and they fire each time the action occurs This means that if you insert 100,000 rows into a table, an insert trigger would fire 100,000

while( !condition) {wait();} do_something;

thread #1 thread #2

.

Although it is easy to visualize this particular deadlock, in practice deadlocks can be dif cult to spot because they are not always so obvious Some threading libraries are able to help with warnings about potential deadlocks, but it requires human care and attention to avoid them One simple yet effective way to avoid deadlocks is to have a policy that de nes the order in which locks should be acquired For example, if we had the policy that lock A must always be acquired before lock B, and we wanted to acquire lock B, the policy requires us to rst acquire lock A This would ensure that the deadlock described here would not occur since both threads would begin by trying to acquire A and the rst one that did would then go on to lock B unless someone forgets to follow the policy Another problem with locking is that if multiple threads are waiting to acquire a lock, they are blocked and are not doing any useful work We can mitigate this to a small extent with subtle changes to our coding style to minimize the amount of work we do within the context of a lock Every Python program has at least one thread, the main thread To create multiple threads we must import the threading module and use that to create as many additional threads as we want There are two ways to create threads: We can call threadingThread() and pass it a callable object, or we can subclass the threadingThread class both approaches are shown in this chapter Subclassing is the most exible approach and is quite straightforward Subclasses can reimplement __init__() (in which case they must call the base class implementation), and they must reimplement run() it is in this method that the thread s work is done The run() method must never be called by our code threads are started by calling the start() method and that will call run().

Second, we examine the other operations on the shared queue and add a notifyAll to any operations that might establish condition The result is an instance of the basic idiom for using wait, described in more detail in the Java appendix, Appendix C Thus, two major changes are made in moving to the code in Fig 538 First, we replace the code

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