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15 Python Tips : How to Write Code Effectively

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 Here are some Python tips to keep in mind that will help you write clean, efficient, and bug-free code.     Python Tips for Effective Coding 1. Code Readability and PEP 8  Always aim for clean and readable code by following PEP 8 guidelines.  Use meaningful variable names, avoid excessively long lines (stick to 79 characters), and organize imports properly. 2. Use List Comprehensions List comprehensions are concise and often faster than regular for-loops. Example: squares = [x**2 for x in range(10)] instead of creating an empty list and appending each square value. 3. Take Advantage of Python’s Built-in Libraries  Libraries like itertools, collections, math, and datetime provide powerful functions and data structures that can simplify your code.   For example, collections.Counter can quickly count elements in a list, and itertools.chain can flatten nested lists. 4. Use enumerate Instead of Range     When you need both the index ...
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Here is Hadoop MapReduce DataFlow Tutorial

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Here are the six stages of MapReduce. The MapReduce is critical for your data processing needs. Traditionally, the whole file needs to read once then divided manually, but it is not convenient. With that respect, Hadoop provides the facility to read files (ignoring their size) line-for-line by using offset and key-value.

Explained the dataflow in Hadoop MapReducer

MapReduce dataflow Quick Tutorial


1. Dataflow Diagram



How a Mapreduce process in Hadoop divides input and processes it, you will learn in this post.


2. MapReduce Stages


MapReduce receives input and processes it. Here are the six stages of processing. It is helpful for your interviews and project.


MapReduce Stage-1


Take the file as input for processing purposes. Any file will consist of a group of lines. These lines containing key-value pairs of data. The whole file can be read out with this method.

MapReduce Stage-2


In the next step, the file will be in "splitting" mode. This mode will divide the file into key, value pair of data. This time key will be offset and data will be a valuable part of the program. Each line will be read individually so there is no need to split data manually.

MapReduce Stage-3


The further step is to process the value of each line with an associate from counting numbers. Each individual that is separated from a space counted with the number and that number is written with each key. This is the logic of "mapping" that programmers need to write.

MapReduce Stage-4


After that shuffling is performed and with this, each key gets associated with the group of numbers that are involved in the mapping section. Now scenario becomes key with string and value will be a list of numbers. This will go as input to the reducer.

MapReduce Stage-5


In the reducer phase, whole numbers are counted and each key associated with final counting is the sum of all numbers which leads to the final result.

MapReduce Stage-6


Output of the reducer phase will lead to the final result. This final result will have counting of individual word count. This is independent of the size of the file used for processing.


Keep Reading
  1. Big Data and Hadoop: Learn by Example

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