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Python map() and lambda() Use Cases and Examples

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 In Python, map() and lambda functions are often used together for functional programming. Here are some examples to illustrate how they work. Python map and lambda top use cases 1. Using map() with lambda The map() function applies a given function to all items in an iterable (like a list) and returns a map object (which can be converted to a list). Example: Doubling Numbers numbers = [ 1 , 2 , 3 , 4 , 5 ] doubled = list ( map ( lambda x: x * 2 , numbers)) print (doubled) # Output: [2, 4, 6, 8, 10] 2. Using map() to Convert Data Types Example: Converting Strings to Integers string_numbers = [ "1" , "2" , "3" , "4" , "5" ] integers = list ( map ( lambda x: int (x), string_numbers)) print (integers) # Output: [1, 2, 3, 4, 5] 3. Using map() with Multiple Iterables You can also use map() with more than one iterable. The lambda function can take multiple arguments. Example: Adding Two Lists Element-wise list1 = [ 1 , 2 , 3 ]

Hadoop: How to find which file is healthy

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Hadoop provides file system health check utility which is called "fsck". Basically, it checks the health of all the files under a path It also checks the health of all the files under the '/'(root). BIN/HADOOP fsck / - It checks the health of all the files BIN/HADOOP fsck /test/ - It checks the health of files under the path By default fsck utility cannot do anything for under replicated blocks and over replicated blocks. Hadoop itself heal the blocks.   How to find which file is healthy It prints out dot for each healthy file It will print a message for each file, if it is not healthy, also for under replicated blocks, over replicated blocks, mis-replicated blocks, and corrupted blocks. By default fsck utility cannot do anything for under replicated blocks and over replicated blocks. Hadoop itself heal the blocks. How to delete corrupted blocks BIN/HADOOP fsck -delete block-names It will delete all corrupted blocks BIN/HADOOP fsck -m