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How to Create a Symmetric Array in Python: A Fun Logic Exercise

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 Here's a Python program that says to write a Symmetric array transformation. A top interview question. Symmetric Array Transformation Problem: Write a Python function that transforms a given array into a symmetric array by mirroring it around its center. For example: Input: [1, 2, 3] Output: [1, 2, 3, 2, 1] Hints: Use slicing for the reverse part. Concatenate the original array with its mirrored part. Example def symmetric_array(arr):     """     Transforms the input array into a symmetric array by mirroring it around its center.     Parameters:     arr (list): The input array.     Returns:     list: The symmetric array.     """     # Mirror the array by concatenating the original with its reverse (excluding the last element to avoid duplication)     return arr + arr[-2::-1] # Example usage input_array = [1, 2, 3] symmetric_result = symmetric_array(input_array) print("Input Array:", input_arr...
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Exclusive Apache Kafka Top Features

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Here are the top features of Kafka. It works on the principle of publishing messages. It routes real-time information to consumers far faster. Also, it connects heterogeneous applications by sending messages among them. Here the prime component (a.k.a message router) is a broker. The top features you can read here.


Kafka features


The exclusive Kafka features

The message broker provides seamless integration, but there are two collateral objectives: the first is to not block the producers and the second is to not let the producers know who the final consumers are.

Apache Kafka is a real-time publish-subscribe solution messaging system: open source, distributed, partitioned, replicated, commit-log based with a publish-subscribe schema. Its main characteristics are as follows:

1. Distributed. Cluster


Centric design that supports the distribution of the messages over the cluster members, maintaining the semantics. So you can grow the cluster horizontally without downtime.

2. Multiclient.


Easy integration with different clients from different platforms: Java, .NET, PHP, Ruby, Python, etc.

3. Persistent.


You cannot afford any data lost. Kafka is designed with efficient O(1), so data structures provide constant time performance no matter the data size.

4. Real time.


The messages produced are immediately seen by consumer threads; these are the basis of the systems called complex event processing (CEP).

5. Very high throughput.


As we mentioned, all the technologies in the stack are designed to work in commodity hardware. Kafka can handle hundreds of read and write operations per second from a large number of clients.


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