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NEW QUESTION: 1
You designed a standard toolbar to accompany a number of forms so the code behind the buttons is written as generically as possible.
One of the features of this toolbar is that the buttons are synchronized with the state of the form. To achieve this functionality, you wrote generic procedures that are placed in an attached library and are called from various triggers.
For example, if the form is in Enter-Query mode, the procedure set_enter_query_mode will disable the Enter Query button, change the icon for the Exit button, and modify its Tooltip to read "Cancel Query" rather than "Exit".
What must you code to support this approach?
A. Form-level Key triggers that call the generic procedures and item-level When-Button-
Pressed triggers that call the DO_KEY() built-in. For
example, Key-Entqry has the following code:
set_enter_query_mode;
The When-Button-Pressed trigger on the Enter Query button has the following code:
DO_KEY('ENTER_QUERY');
B. Form-level When-Button-Pressed triggers for each button to call the generic procedures.
For example, a form-level trigger on the Enter Query
button has the following code:
set_enter_query_mode;
C. Item-level Key triggers for each button as well as item-level When-Button-Pressed triggers for each button to call the generic procedures. For example, the When-Button-Pressed trigger on the Enter Query button has the following code:
set_enter_query_mode;
The Key-Entqry trigger on the Enter-Query button has the following code:
set_enter_query_mode;
D. Block-level Key triggers that call the generic procedures and item-level When-Button-
Pressed triggers that call the EXECUTE_TRIGGER()
built-in and pass the name of the button as a parameter. For example, the Key-Entqry trigger has the following code:
set_enter_query_mode;
The When-Button-Pressed trigger on the Enter Query button has the following code:
EXECUTE_TRIGGER('ENTER_QUERY');
Answer: A
NEW QUESTION: 2
A. Option D
B. Option B
C. Option A
D. Option C
Answer: C
NEW QUESTION: 3
What types of algorithms are difficult to express in MapReduce v1 (MRv1)?
A. Relational operations on large amounts of structured and semi-structured data.
B. Algorithms that require applying the same mathematical function to large numbers of individual binary records.
C. Algorithms that require global, sharing states.
D. Text analysis algorithms on large collections of unstructured text (e.g, Web crawls).
E. Large-scale graph algorithms that require one-step link traversal.
Answer: C
Explanation:
See 3) below. Limitations of Mapreduce - where not to use Mapreduce
While very powerful and applicable to a wide variety of problems, MapReduce is not the answer to every problem. Here are some problems I found where MapReudce is not suited and some papers that address the limitations of MapReuce.
1.Computation depends on previously computed values If the computation of a value depends on previously computed values, then MapReduce cannot be used. One good example is the Fibonacci series where each value is summation of the previous two values. i.e., f(k+2) = f(k+1) + f(k). Also, if the data set is small enough to be computed on a
single machine, then it is better to do it as a single reduce(map(data)) operation rather than going through the entire map reduce process.
2.Full-text indexing or ad hoc searching The index generated in the Map step is one dimensional, and the Reduce step must not generate a large amount of data or there will be a serious performance degradation. For example, CouchDB's MapReduce may not be a good fit for full-text indexing or ad hoc searching. This is a problem better suited for a tool such as Lucene.
3.Algorithms depend on shared global state Solutions to many interesting problems in text processing do not require global synchronization. As a result, they can be expressed naturally in MapReduce, since map and reduce tasks run independently and in isolation. However, there are many examples of algorithms that depend crucially on the existence of shared global state during processing, making them difficult to implement in MapReduce (since the single opportunity for global synchronization in MapReduce is the barrier between the map and reduce phases of processing)
Reference: Limitations of Mapreduce - where not to use Mapreduce
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