B木 - naoyaのはてなダイアリー
http://d.hatena.ne.jp/naoya/20090412/btree

ハードディスクのような遅い記憶装置にはB-treeが、SSDのような速い記憶装置にはSuffix Arrayが適しているという論。

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Ternary trees are the fastest way to search for data strings, at least in hardcore programming terms, but how exactly do they work?

A team member thought we should add an index on a 90 million row table to improve performance. The field on which he wanted to create this index had only four possible values. To which I replied that an index on a low cardinality field wasn't really going to help anything. My boss then asked me why wouldn't it help? I sputtered around for a response but ended up telling him that I'd get back to him with a reasonable explanation.

Imported from http://twitter.com/newsycombinator/status/2645303258 How b-tree database indexes work and how to tell if they are efficient http://bit.ly/dd6mf

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What finger trees do is give me a way of representing a list that has both the convenience of the traditional cons list, and the search efficiency of the array based method. The basic idea of the finger tree is amazingly simple. It's a balanced tree where you store all of the data in the leaves. The internal nodes are just a structure on which you can hang annotations, which you can use for optimizing search operations on the tree.

"The basic idea of the finger tree is amazingly simple. It's a balanced tree where you store all of the data in the leaves. The internal nodes are just a structure on which you can hang annotations, which you can use for optimizing search operations on the tree. What makes the finger tree so elegant is the way that some very smart people have generalized the idea of annotations to make finger trees into a single, easily customizable structure that's useful for so many different purposes: you customize the annotations that you're going to store in the internal nodes according to the main use of your tree." A commentator says regarding the article however, "Ørjan Johanse is right. You described a monoid-annotated-binary-tree, which is not enough to be a finger tree."

"A very powerful application of monoids are 2-3 finger trees, first described by Ralf Hinze and Ross Patterson. Basically, they allow you to write fast implementations for pretty much every abstract data type mentioned in Okasaki's book on purely functional data structures. For example, you can do sequences, priority queues, search trees and priority search queues. Moreover, any fancy and custom data structures like interval trees or something for stock trading are likely to be implementable in this framework as well. How can one tree be useful for so many different data structures? The answer: monoids! Namely, the finger tree works with elements that are related to a monoid, and all the different data structures mentioned above arise by different choices for this monoid. Let me explain how this monoid magic works."

A presentation about modelling trees relationally and storing them in an SQL database.

Storing tree structures in a bi-dimensional table has always been problematic. The simplest tree models are usually quit

trees in database

how to store tree structure on RDB

階層構造

Best Javascript Tree Menu Widgets

MifJS.Tree

The name says everything

hich implements the J.Q.Walker II layout algorit

calendar, date picker, scrolling tabs, checkbox tree

As the inventor of the Judy algorithm I've been asked repeatedly, "What makes Judy so fast?" The answer is not simple, but finally I can share all of the details.

A complex (to implement) but efficient scalable data-structure that obtains very high performance by minimising the number of cache-line fills required.

As the inventor of the Judy algorithm I've been asked repeatedly, "What makes Judy so fast?" The answer is not simple, but finally I can share all of the details.

A complex (to implement) but efficient scalable data-structure that obtains very high performance by minimising the number of cache-line fills required.