What datastructure is the fastest to find the best matching prefix?











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Context: I'm working on an analyzer for useragent strings (Yauaa) and as part of this analysis I want to make an educated guess what brand of the device should be reported. I have an implementation that I need to rewrite to be a lot more efficient.



Because I do not want to have a complete list of all devices I want to do the detection based on the prefix of the model.



So I have a dataset with prefixes and the brand that is associated:




  • "GT-" --> "Samsung"

  • "LLD-" --> "Huawei"


And then I want to do a .get("GT-1234124") which should result in "Samsung" because that is the "longest matching prefix".



I had a look at the Trie structure but that seems to be for the opposite situation. What I understand is that you start with a set of values and you can efficiently get all the values that starts with the provided prefix.



If I were to implement this from scratch I would use a tree similar to the Trie but walk around it differently. What I'm looking for is a datastructure that does what I need as fast as possible.



What datastructure do you recommend for this usecase?



Is there an existing (proven) implementation I can use?






data-structures binary-search-tree prefix prefix-tree







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    down vote

    favorite












    Context: I'm working on an analyzer for useragent strings (Yauaa) and as part of this analysis I want to make an educated guess what brand of the device should be reported. I have an implementation that I need to rewrite to be a lot more efficient.



    Because I do not want to have a complete list of all devices I want to do the detection based on the prefix of the model.



    So I have a dataset with prefixes and the brand that is associated:




    • "GT-" --> "Samsung"

    • "LLD-" --> "Huawei"


    And then I want to do a .get("GT-1234124") which should result in "Samsung" because that is the "longest matching prefix".



    I had a look at the Trie structure but that seems to be for the opposite situation. What I understand is that you start with a set of values and you can efficiently get all the values that starts with the provided prefix.



    If I were to implement this from scratch I would use a tree similar to the Trie but walk around it differently. What I'm looking for is a datastructure that does what I need as fast as possible.



    What datastructure do you recommend for this usecase?



    Is there an existing (proven) implementation I can use?






    data-structures binary-search-tree prefix prefix-tree







    share|improve this question
























      up vote
      0
      down vote

      favorite









      up vote
      0
      down vote

      favorite











      Context: I'm working on an analyzer for useragent strings (Yauaa) and as part of this analysis I want to make an educated guess what brand of the device should be reported. I have an implementation that I need to rewrite to be a lot more efficient.



      Because I do not want to have a complete list of all devices I want to do the detection based on the prefix of the model.



      So I have a dataset with prefixes and the brand that is associated:




      • "GT-" --> "Samsung"

      • "LLD-" --> "Huawei"


      And then I want to do a .get("GT-1234124") which should result in "Samsung" because that is the "longest matching prefix".



      I had a look at the Trie structure but that seems to be for the opposite situation. What I understand is that you start with a set of values and you can efficiently get all the values that starts with the provided prefix.



      If I were to implement this from scratch I would use a tree similar to the Trie but walk around it differently. What I'm looking for is a datastructure that does what I need as fast as possible.



      What datastructure do you recommend for this usecase?



      Is there an existing (proven) implementation I can use?






      data-structures binary-search-tree prefix prefix-tree







      share|improve this question













      Context: I'm working on an analyzer for useragent strings (Yauaa) and as part of this analysis I want to make an educated guess what brand of the device should be reported. I have an implementation that I need to rewrite to be a lot more efficient.



      Because I do not want to have a complete list of all devices I want to do the detection based on the prefix of the model.



      So I have a dataset with prefixes and the brand that is associated:




      • "GT-" --> "Samsung"

      • "LLD-" --> "Huawei"


      And then I want to do a .get("GT-1234124") which should result in "Samsung" because that is the "longest matching prefix".



      I had a look at the Trie structure but that seems to be for the opposite situation. What I understand is that you start with a set of values and you can efficiently get all the values that starts with the provided prefix.



      If I were to implement this from scratch I would use a tree similar to the Trie but walk around it differently. What I'm looking for is a datastructure that does what I need as fast as possible.



      What datastructure do you recommend for this usecase?



      Is there an existing (proven) implementation I can use?






      data-structures binary-search-tree prefix prefix-tree




      data-structures binary-search-tree prefix prefix-tree






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked 3 hours ago









      Niels Basjes

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      6,42273852





























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