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We can implement a pipeline by constructing a single, complex operation that combines the operations that constitute the pipeline Although this approach may be feasible for various frequently occurring situations, it is desirable in general to reuse the code for individual operations in the construction of a pipeline Therefore, each operation in the pipeline is modeled as a separate process or thread within the system, which takes a stream of tuples from its pipelined inputs, and generates a stream of tuples for its output For each pair of adjacent operations in the pipeline, the system creates a buffer to hold tuples being passed from one operation to the next In the example of Figure 1310, all three operations can be placed in a pipeline, which passes the results of the selection to the join as they are generated In turn, it passes the results of the join to the projection as they are generated The memory requirements are low, since results of an operation are not stored for long However, as a result of pipelining, the inputs to the operations are not available all at once for processing Pipelines can be executed in either of two ways: 1 Demand driven 2 Producer driven In a demand-driven pipeline, the system makes repeated requests for tuples from the operation at the top of the pipeline Each time that an operation receives a request for tuples, it computes the next tuple (or tuples) to be returned, and then returns that tuple If the inputs of the operation are not pipelined, the next tuple(s) to be returned can be computed from the input relations, while the system keeps track of what has been returned so far If it has some pipelined inputs, the operation also makes requests for tuples from its pipelined inputs Using the tuples received from its pipelined inputs, the operation computes tuples for its output, and passes them up to its parent In a producer-driven pipeline, operations do not wait for requests to produce tuples, but instead generate the tuples eagerly Each operation at the bottom of a pipeline continually generates output tuples, and puts them in its output buffer, until the buffer is full An operation at any other level of a pipeline generates output tuples when it gets input tuples from lower down in the pipeline, until its output buffer is full Once the operation uses a tuple from a pipelined input, it removes the tuple from its input buffer In either case, once the output buffer is full, the operation waits until its parent operation removes tuples from the buffer, so that the buffer has space for more tuples At this point, the operation generates more tuples, until the buffer is full again The operation repeats this process until all the output tuples have been generated It is necessary for the system to switch between operations only when an output buffer is full, or an input buffer is empty and more input tuples are needed to generate any more output tuples In a parallel-processing system, operations in a pipeline may be run concurrently on distinct processors (see 20) Using producer-driven pipelining can be thought of as pushing data up an operation tree from below, whereas using demand-driven pipelining can be thought of as.

The McGraw Hill Companies, 2001

8/13

pulling data up an operation tree from the top Whereas tuples are generated eagerly in producer-driven pipelining, they are generated lazily, on demand, in demanddriven pipelining Each operation in a demand-driven pipeline can be implemented as an iterator, which provides the following functions: open(), next(), and close() After a call to open(), each call to next() returns the next output tuple of the operation The implementation of the operation in turn calls open() and next() on its inputs, to get its input tuples when required The function close() tells an iterator that no more tuples are required The iterator maintains the state of its execution in between calls, so that successive next() requests receive successive result tuples For example, for an iterator implementing the select operation using linear search, the open() operation starts a le scan, and the iterator s state records the point to which the le has been scanned When the next() function is called, the le scan continues from after the previous point; when the next tuple satisfying the selection is found by scanning the le, the tuple is returned after storing the point where it was found in the iterator state A merge join iterator s open() operation would open its inputs, and if they are not already sorted, it would also sort the inputs On calls to next(), it would return the next pair of matching tuples The state information would consist of up to where each input had been scanned Details of the implementation of iterators are left for you to complete in Exercise 1312 Demand-driven pipelining is used more commonly than producer-driven pipelining, because it is easier to implement

CHAPTER 15: