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Top 10 Advance Java Interview questions?

Top 10 Advance Java Interview questions?   What are the differences between abstract classes and interfaces in Java? What is the difference between ArrayList and LinkedList in Java? What is the purpose of the finalize() method in Java? What is polymorphism in Java and how is it achieved? What are the different types of inner classes in Java? What is the difference between static and non-static methods in Java? What are the different types of exceptions in Java and how do they differ? What is the difference between checked and unchecked exceptions in Java? How does Java handle multithreading and synchronization? What are the different types of JDBC drivers in Java and how do they differ?
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Why Not Python?

Why Not Python?

While Python is an excellent environment for building computationally-intensive scientific applications and building most kinds of general-purpose systems, there are a number of uses for which Python may be less suitable. As Python is an interpreted programming language, in general, most Python code will run substantially slower than code written in a compiled language like Java or C++. As programmer time is typically more valuable than CPU time, many are happy to make this tradeoff. However, in an application with very low latency requirements (for example, a high-frequency trading system), the time spent programming in a lower-level, lower-productivity language like C++ to achieve the maximum possible performance might be time well spent. Python is not an ideal language for highly concurrent, multithreaded applications, particularly applications with many CPU-bound threads. The reason for this is that it has what is known as the global interpreter lock (GIL), a mechanism that prevents the interpreter from executing more than one Python bytecode instruction at a time. The technical reasons for why the GIL exists are beyond the scope of this book, but as of this writing, it does not seem likely that the GIL will disappear anytime soon. While it is true that in many big data processing applications, a cluster of computers may be required to process a data set in a reasonable amount of time, there are still situations where a single-process, multithreaded system is desirable. This is not to say that Python cannot execute truly multithreaded, parallel code; that code just cannot be executed in a single Python process. As an example, the Cython project features easy integration with OpenMP, a C framework for parallel computing, in order to parallelize loops and thus significantly speed up numerical algorithms.

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