#535

Medium

LeetCode Problem Workspace

Encode and Decode TinyURL

Design a class that encodes and decodes URLs using a URL shortening approach based on hash tables and strings.

Problem Statement

TinyURL is a service where a long URL like 'https://leetcode.com/problems/design-tinyurl' is converted into a shortened URL, such as 'http://tinyurl.com/4e9iAk'. Your task is to design a class to handle the encoding and decoding of these URLs.

You need to implement two methods in the Solution class: one for encoding the URL into a shortened version and another for decoding the tiny URL back to its original form. Ensure that both methods are reliable and efficient in terms of time and space complexity.

Examples

Example 1

Input: url = "https://leetcode.com/problems/design-tinyurl"

Output: "https://leetcode.com/problems/design-tinyurl"

Solution obj = new Solution(); string tiny = obj.encode(url); // returns the encoded tiny url. string ans = obj.decode(tiny); // returns the original url after decoding it.

Constraints

1 <= url.length <= 104
url is guranteed to be a valid URL.

Solution Approach

Using a Hash Table

The best approach is to utilize a hash table for mapping the original long URLs to short URLs. A unique key or identifier can be generated for each long URL and used as the tiny URL's key.

String Manipulation

String manipulation is essential to handle the URL's encoding and decoding efficiently. A fixed-length string can be appended to the URL or used as part of the shortened link, ensuring uniqueness for each URL.

Efficiency Considerations

Consider both time and space efficiency when designing the encode and decode methods. Using a hash table ensures O(1) average-time complexity for both operations, while also optimizing space by only storing the mappings necessary for encoding and decoding.

Complexity Analysis

Metric	Value
Time	Depends on the final approach
Space	Depends on the final approach

Time complexity depends on the hash table's average lookup and insertion operations, which are O(1). Space complexity is determined by the number of unique URLs stored in the hash table, with O(n) space required, where n is the number of URLs encoded.

What Interviewers Usually Probe

Look for the candidate's ability to explain how hash tables are used for URL mapping.
Check for efficiency in handling edge cases and ensuring minimal collision between generated keys.
Test the candidate's understanding of string manipulation and how it's applied in encoding and decoding.

Common Pitfalls or Variants

Common pitfalls

Using a weak hash function that results in many collisions, which can lead to inefficient performance.
Not accounting for edge cases, such as URLs with similar patterns or large URLs.
Failing to ensure that the decode method correctly retrieves the original URL after encoding.

Follow-up variants

Allowing customization of the URL shortening scheme by using different hash functions.
Handling URLs with special characters or query parameters in a more robust way.
Supporting a batch encoding and decoding operation to handle multiple URLs at once.

FAQ

How do I design the encode and decode methods for TinyURL?

You can design these methods using a hash table to store mappings from long URLs to short URLs. Use a unique identifier for the shortened link and ensure that the decode method can reliably retrieve the original URL.

What is the time complexity of the encode and decode operations?

The time complexity for both the encode and decode methods is O(1) on average, assuming the use of an efficient hash table with minimal collisions.

What is the role of strings in the TinyURL problem?

Strings are used for manipulating and creating the unique identifiers that represent the shortened URLs. String operations help generate consistent short URLs and decode them back to their original form.

How does the hash table ensure efficient encoding and decoding?

The hash table ensures O(1) time complexity for both encoding and decoding by mapping short URLs to long URLs and vice versa, providing fast lookups.

What are some potential optimizations for the TinyURL problem?

One possible optimization is to use a stronger hash function or a different URL shortening scheme to minimize hash collisions and improve the uniqueness of the shortened links.

terminal

Solution

Solution 1

#### Python3

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class Codec:
    def __init__(self):
        self.m = defaultdict()
        self.idx = 0
        self.domain = 'https://tinyurl.com/'

    def encode(self, longUrl: str) -> str:
        """Encodes a URL to a shortened URL."""
        self.idx += 1
        self.m[str(self.idx)] = longUrl
        return f'{self.domain}{self.idx}'

    def decode(self, shortUrl: str) -> str:
        """Decodes a shortened URL to its original URL."""
        idx = shortUrl.split('/')[-1]
        return self.m[idx]


# Your Codec object will be instantiated and called as such:
# codec = Codec()
# codec.decode(codec.encode(url))

Continue Practicing

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