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colmmacc's profile
Colm MacCárthaigh
Colm MacCárthaigh
Colm MacCárthaigh
@colmmacc

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Colm MacCárthaigh

@colmmacc

AWS, Apache, Crypto, Irish Music, Haiku, Photography

Seattle
notesfromthesound.com
Joined April 2008

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    1. Cindy Sridharan‏ @copyconstruct 16 Jun 2019
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      I ❤️ data structures. I especially love learning about real world use cases of data structures. What’s your favorite data structure (bonus if it’s something you’ve coded in real life to solve a fun problem). 😀

      69 replies 75 retweets 465 likes
    2. Colm MacCárthaigh‏ @colmmacc 16 Jun 2019
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      Replying to @copyconstruct

      Probably a level-compressed suffix trie. It's so versatile. Implemented it a few times for CIDR matching in routing or ACL tables. Then used it for string matching - often faster than a hash table! Reverse a domain and you can use one for efficient SSL or DNS wildcards.

      4 replies 4 retweets 68 likes
      Colm MacCárthaigh‏ @colmmacc 16 Jun 2019
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      Replying to @colmmacc @copyconstruct

      I use it sometimes to show that hashes are not O(1) but are O(N) in the space of the input, whereas a trie can be O(log N) ... ala @hashbreaker. I consider "hashes are O(1)" the CS equivalent of the widely wrong "This is how wings generate lift" meme.

      8:02 PM - 16 Jun 2019
      • 2 Retweets
      • 27 Likes
      • Brett Evans Miguel Morales Jesse Davidson Dan Bentley Sam Donnelley Chris David Murray Matt "MSG superfan" Olson Cindy Sridharan
      5 replies 2 retweets 27 likes
        1. New conversation
        2. Noj Vek‏ @nojvek 16 Jun 2019
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          Replying to @colmmacc @copyconstruct @hashbreaker

          Uhmmm, why are hash lookups O(N)?

          1 reply 0 retweets 0 likes
        3. Colm MacCárthaigh‏ @colmmacc 16 Jun 2019
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          Replying to @nojvek @copyconstruct @hashbreaker

          In the space of the input, it's because hash("foobar") takes twice as long as hash("foo"). A trie is often more efficient, in that same space, and overall .. . which defies expectations driven by inaccurate over-simplification.

          1 reply 1 retweet 6 likes
        4. 1 more reply
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        2. Marc Brooker‏ @MarcJBrooker 16 Jun 2019
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          Replying to @colmmacc @copyconstruct @hashbreaker

          Hash tables are probabilisticly balanced trees, stored semi-flattened.

          2 replies 1 retweet 11 likes
        3. Colm MacCárthaigh‏ @colmmacc 16 Jun 2019
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          Replying to @MarcJBrooker @copyconstruct @hashbreaker

          This is the best take.

          1 reply 0 retweets 1 like
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        1. Niall Murphy‏ @niallm 16 Jun 2019
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          Replying to @colmmacc @copyconstruct @hashbreaker

          Wrote some Patricia Trie stuff for an IP-address oriented use case a while back, and was very pleasantly surprised at how easy it was.

          0 replies 0 retweets 4 likes
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        2. Colm MacCárthaigh‏ @colmmacc 16 Jun 2019
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          Replying to @iversion @copyconstruct @hashbreaker

          You can pack a trie like a b-tree for cache efficiency, and pipeline pre-fetching at least has a shot with a tree access. Hashes have no cache locality or predictability But you're right, and it depends on the data set and the problem.

          0 replies 0 retweets 1 like
        3. End of conversation
        1. Chris‏ @CPetersen_CS 16 Jun 2019
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          Replying to @colmmacc @copyconstruct @hashbreaker

          Data structures and algorithmic analysis? This is the kind of conversation that keeps me on twitter...

          0 replies 0 retweets 0 likes
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