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Figure 10-7. Arduino, shield, LCD, and buttons all mounted inside weatherproof case The only hardware assembly left to do now is to connect the Hall-effect flow sensor. As shown on the circuit diagram in Figure 10-2, the sensor needs to be connected to ground, +5V, and to the end of the 1K resistor fitted previously to digital I/O line 2. You can either fit a line plug to the cable and mount a socket in the case, or just pass the cable through a hole in the box, tie a knot in it to prevent it from pulling back out, and solder it directly to the prototyping shield as shown in Figure 10-8.

The Java 5 concurrent utilities include support for deferred execution in the form of the ExecutorService class. This class makes it clear that posting code is different from waiting for it: ExecutorService service = Executors.newSingleThreadExecutor(); // scheduling is quite different Future< > task = service.submit(someRunnable); // compared to waiting task.get(1000, TimeUnit.MILLISECONDS); This builds on the expected knowledge that waiting is something that can cause deadlocks and that developers should do so with care.

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Just as the fields on the Entry model logically broke down into groups according to how they would be used, the model you ll use to represent links a class called Link will need fields for several different purposes: Core fields representing the link: A title, a description and, of course, the URL to link to. Metadata: This includes the date the link was posted and the user who posted it as well as whether to allow comments for the link. Categorization: You ll accomplish this with tags. Integration with an external link-posting service: In this case, del.icio.us. Let s begin with the basic core fields for the model (as with the Category and Entry models, this code goes in coltrane/models.py). Just as before, you ll build it up incrementally (so don t run syncdb yet): class Link(models.Model): title = models.CharField(max_length=250) description = models.TextField(blank=True) description_html = models.TextField(blank=True) url = models.URLField(unique=True) There s one new field type here: URLField. As the name suggests, it s meant to store a URL. In the database, it will simply be a VARCHAR column like most other text-based field types, but in automatically generated forms (like the ones displayed by the admin interface), additional validation will be performed for this field:

Figure 10-8. Assembled unit with sensor connected With the sensor connections in place make sure the prototyping shield is firmly mounted, fit the lid, and move on to the software.

However, it s difficult to completely eliminate waiting. That would turn the whole system into a kind of message-based program. Various Java Messaging Servers prove that this kind of program is possible. However, it s difficult to adopt this style with programs that are not messagebased, or with libraries. You can defer as much execution as possible. However, there comes a time when it s necessary to wait. The only way to prevent deadlocks while waiting is to allow only time-out waits: to allow a thread to block for a certain amount of time only. This breaks the third deadlock-creation condition where a thread can wait for a condition indefinitely. It prevents deadlocks as well, unless someone puts the wait into a while loop, of course.

Like almost all Hall-effect devices, water flow-rate sensors output a series of pulses at a rate that varies proportionally with the parameter being measured. All devices that output pulses need a scaling factor to convert the frequency into a meaningful value. For example, a car wheel rotation sensor might output one, two, four, or five pulses per rotation, but that information is useless on its own: you also need to know the circumference of the wheel so you can multiply the pulse count by the circumference to determine the distance traveled. The sensor we used outputs approximately 4.5 pulses per second per liter of flow per minute. That sounds odd because we're using values measured in pulses per second to represent liters per minute. Consider the following examples: At 1 liter per minute, the sensor will output 4.5 pulses per second. At 5 liters per minute, the sensor will output 22.5 pulses per second. At 10 liters per minute, the sensor will output 45 pulses per second. At 20 liters per minute, the sensor will output 90 pulses per second.

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