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implicitly synchronized when entering and leaving a synchronized block, and, in Java 2 15, when locking and unlocking locks Also, variables marked volatile are implicitly synchronized with respect to memory This is discussed in more detail in the Implementation Mechanisms design space Task scheduling Consider whether the explicitly managed data dependencies addressed by this pattern affect task scheduling A key goal in deciding how to schedule tasks is good load balance; in addition to the considerations described in the Algorithm Structure pattern being used, one should also take into account that tasks might be suspended waiting for access to shared data It makes sense to try to assign tasks in a way that minimizes such waiting, or to assign multiple tasks to each UE in the hope that there will always be one task per UE that is not waiting for access to shared data Examples

In both cases the problem is that we are passing a string when a bytes, bytearray, or similar object is expected We can either perform the conversion in this case by passing infoencode("utf8") or work back to nd the source of the problem and x it there Python 30 introduced support for exception chaining this means that an exception that is raised in response to another exception can contain the details of the original exception When a chained exception goes uncaught the traceback includes not just the uncaught exception, but also the exception that caused it (providing it was chained) The approach to debugging chained exceptions is almost the same as before: We start at the end and work backward until we nd the problem in our own code However, rather than doing this just for the last exception, we might then repeat the process for each chained exception above it, until we get to the problem s true origin We can take advantage of exception chaining in our own code for example, if we want to use a custom exception class but still want the underlying problem to be visible

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class InvalidDataError(Exception): pass def process(data): try: i = int(data) except ValueError as err: raise InvalidDataError("Invalid data received") from err

The shared queue is a commonly used ADT and an excellent example of the Shared Data pattern The Shared Queue pattern discusses concurrency control protocols and the techniques used to achieve highly efficient shared queue programs

Here, if the int() conversion fails, a ValueError is raised and caught We then raise our custom exception, but with from err, which creates a chained

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exception, our own, plus the one in err If the InvalidDataError exception is raised and not caught, the resulting traceback will look something like this:

Consider the GAFORT program from the SPEC OMP2001 benchmark suite [ADE 01 + ] GAFORT is a small Fortran program (around 1,500 lines) that implements a genetic algorithm for nonlinear optimization The calculations are predominantly integer arithmetic, and the program's performance is dominated by the cost of moving large arrays of data through the memory subsystem The details of the genetic algorithm are not important for this discussion We are going to focus on a single loop within GAFORT Pseudocode for the sequential version of this loop, based on the discussion of GAFORT in [EM], is shown in Fig 534 This loop shuffles the population of chromosomes and consumes on the order of 36 percent of the runtime in a typical GAFORT job [AE03]

Traceback (most recent call last): File "applicationpy", line 249, in process i = int(data) ValueError: invalid literal for int() with base 10: '175 ' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "applicationpy", line 288, in <module> print(process(line)) File "applicationpy", line 283, in process raise InvalidDataError("Invalid data received") from err __main__InvalidDataError: Invalid data received

At the bottom our custom exception and text explain what the problem is, with the lines above them showing where the exception was raised (line 283), and where it was caused (line 288) But we can also go back further, into the chained exception which gives more details about the speci c error, and which shows the line that triggered the exception (249) For a detailed rationale and further information about chained exceptions, see PEP 3134

The Orders submodel contains the most complicated set of entities in the entire model That s fitting, because the database is centered on customer orders via the Mountain View Music Web site We have a few decisions to make when building the physical model for these entities First, let s look at the entities we are dealing with; they are shown next and in Figure 97

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