Classical $kd$-Tree


  • split attributes at different levels are different
    • in the example: first split by $Y$, then by $X$, then by $Y$...
  • interior nodes of the tree contain
    • attribute on which we divided the space
    • a dividing value
    • left and right pointers (only 2!)
  • leaves are blocks with records



  • similar to binary search trees
  • but need to use only attribute specified in a interior node of the tree


  • look up the block
  • if there's room - put it there
  • not - split into 2
    • we divide by the most appropriate attribute
  • and create a new interior node that points to the split halves


Good for See Multi-Dimensional Indexes#Typical Queries

  • Point Queries (just lookup)
  • Partial Match
    • suppose we specified only $Y$
    • then if it's not $Y$ follow both left and right pointers
    • if it's $Y$ then use the dividing value in the node to decide whether to go left or right

Reasonable Support

  • Nearest-Neighbor

Adaptation to Secondary Storage

The described algorithm is for Main Memory, not for disk

2 ways to adapt

  • Multi-Way Branches
    • like in B-Tree: $n$ keys, $n + 1$ pointers
    • hard to merge to keep it balanced
  • Several Nodes Per Block
    • Keep 2 children per block as described
    • but store several nodes per one block
    • to minimize I/O it's better to put in the same block records that are likely to be accessed together
    • For example, a node and it's descendants

See also