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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

How to Read Kafka Logs Quickly

In Kafka, the log file's function is to store entries. Here, you can find entries for the producer's incoming messages. You can call these topics. And, topics are divided into partitions.


How to Read Logs in Kafka

IN THIS PAGE

  1. Kafka Logs
  2. How Producer Messages Store
  3. Benefits of Kafka Logs
  4. How to check Logs in Kafka
How to Read Kafka Logs Quickly

1. Kafka Logs

  • The mechanism underlying Kafka is the log. Most software engineers are familiar with this. It tracks what an application is doing. 
  • If you have performance issues or errors in your application, the first place to check is the application logs. But it is a different sort of log. 
  • In the context of Kafka (or any other distributed system), a log is "an append-only, totally ordered sequence of records - ordered by time.

Kafka Basics [Video]





2. How Producer Messages Store

  • The producer writes the messages to Broker, and the records are stored in a log file. The records are stored as 0,1,2,3 and so on.
  • Each record will have one unique id.

4. Benefits of Kafka Logs

  • Logs are a simple data abstraction with powerful implications. If you have records in order with time, resolving conflicts, or determining which update to apply to different machines becomes straightforward.
  • Topics in Kafka are logs that are segregated by topic name. You could almost think of topics as labeled logs. If the log is replicated among a cluster of machines, and a single machine goes down, it’s easy to bring that server back up: just replay the log file. 
  • The ability to recover from failure is precisely the role of a distributed commit log.

5. How to Read Logs in Kafka

# The directory under which to store log files 

$  log.dir=/tmp/kafka8-logs 

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