Design a URL Shortener ( TinyURL ) System

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    Problem: Design a service like TinyURL, a URL shortening service, a web service that provides short aliases for redirection of long URLs.

    Solution: If you don't know about TinyURL, just check it. Basically we need a one to one mapping to get shorten URL which can retrieve original URL later. This will involve saving such data into database.
    We should check the following things:

    • What's the traffic volume / length of the shortened URL?
    • What's the mapping function?
    • Single machine or multiple machines?

    Traffic: Let's assume we want to serve more than 1000 billion URLs. If we can use 62 characters [A-Z, a-z, 0-9] for the short URLs having length n, then we can have total 62^n URLs. So, we should keep our URLs as short as possible given that it should fulfill the requirement. For our requirement, we should use n=7 i.e the length of short URLs will be 7 and we can serve 62^7 ~= 3500 billion URLs.

    Basic solution:
    To make things easier, we can assume the alias is something like<alias_hash> and alias_hash is a fixed length string.
    To begin with, let’s store all the mappings in a single database. A straightforward approach is using alias_hash as the ID of each mapping, which can be generated as a random string of length 7.

    Therefore, we can first just store <ID, URL>. When a user inputs a long URL “”, the system creates a random 7-character string like “abcd123” as ID and inserts entry <“abcd123”, “”> into the database.

    In the run time, when someone visits, we look up by ID “abcd123” and redirect to the corresponding URL “”.

    Problem with this solution:
    We can't generate unique hash values for the given long URL. In hashing, there may be collisions (2 long urls map to same short url) and we need a unique short url for every long url so that we can access long url back but hash is one way function.

    Better Solution:

    One of the most simple but also effective one, is to have a database table set up this way:

    Table Tiny_Url(
    Original_url : varchar,
    Short_url : varchar
    Then the auto-incremental primary key ID is used to do the conversion: (ID, 10) <==> (short_url, BASE). Whenever you insert a new original_url, the query can return the new inserted ID, and use it to derive the short_url, save this short_url and send it to cilent.

    Code for methods (that are used to convert ID to short_url and short_url to ID):

    string idToShortURL(long int n)
        // Map to store 62 possible characters
        char map[] = "abcdefghijklmnopqrstuvwxyzABCDEF"
        string shorturl;
        // Convert given integer id to a base 62 number
        while (n)
            n = n/62;
        // Reverse shortURL to complete base conversion
        reverse(shorturl.begin(), shorturl.end());
        return shorturl;
    // Function to get integer ID back from a short url
    long int shortURLtoID(string shortURL)
        long int id = 0; // initialize result
        // A simple base conversion logic
        for (int i=0; i < shortURL.length(); i++)
            if ('a' <= shortURL[i] && shortURL[i] <= 'z')
              id = id*62 + shortURL[i] - 'a';
            if ('A' <= shortURL[i] && shortURL[i] <= 'Z')
              id = id*62 + shortURL[i] - 'A' + 26;
            if ('0' <= shortURL[i] && shortURL[i] <= '9')
              id = id*62 + shortURL[i] - '0' + 52;
        return id;

    Multiple machines:

    If we are dealing with massive data of our service, distributed storage can increase our capacity. The idea is simple, get a hash code from original URL and go to corresponding machine then use the same process as a single machine. For routing to the correct node in cluster, Consistent Hashing is commonly used.

    Following is the pseudo code for example,

    Get shortened URL

    • hash original URL string to 2 digits as hashed value hash_val

    • use hash_val to locate machine on the ring

    • insert original URL into the database and use getShortURL function to get shortened URL short_url

    • Combine hash_val and short_url as our final_short_url (length=8) and return to the user

    Retrieve original from short URL

    • get first two chars in final_short_url as hash_val

    • use hash_val to locate the machine

    • find the row in the table by rest of 6 chars in final_short_url as short_url

    • return original_url to the user

    Other factors:

    One thing I’d like to further discuss here is that by using GUID (Globally Unique Identifier) as the entry ID, what would be pros/cons versus incremental ID in this problem?

    If you dig into the insert/query process, you will notice that using random string as IDs may sacrifice performance a little bit. More specifically, when you already have millions of records, insertion can be costly. Since IDs are not sequential, so every time a new record is inserted, the database needs to go look at the correct page for this ID. However, when using incremental IDs, insertion can be much easier – just go to the last page.

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    @shashipk11 Very nice write up.

    One thing to note is that when you set up a db for auto increment and if you make it distributed - then if you have to add a node or remove a node and rebalance the data, there will ID collision since each node has auto increment.

    db1-123, 456, 789
    db2-123, 456, 789
    Even though content is differnet (original and short url), all ids in nodes will be same.
    You're doing correct routing but ID has to be unique. If you want to rebalance data, add or delete node, you get id collisions.

    To fix this - you can use two (or more) db just to create ids - ID_db1 creates odd id, ID_db2 creates even number ids. (basically just return id if (id % (1 for ID_db1 or 2 for ID_db2) == 0) else auto_increment). So even if one db goes down, we still proceed and fix and resume the process.

    And now this id can be used in whatever nodes you wish by directly apply consistent hash on this id. no collision problem.

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    @wavy Thanks a lot for the suggestions.

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    Some of my thoughts:

    1. I won't worry about the write performance too much. Reading is more frequent, also more important.
    2. For the multi machine solution, it's possible that one of them goes down and the system should keep working. So you will need multiple machines per partition.
    3. Caching can be used.

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    @gaofy0612 Thanks for sharing your thoughts.
    I agree with you.

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