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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par
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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.
Healthy file checking ides

 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 -move block-names
  • It will move corrupted blocks to /lost directory
  • Other options we can use with fsck:
    • files
    • blocks
    • locations

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