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    3.2.1 • Public • Published


    k-dimensional B tree backed to a chunk store

    This code is based on the original kdb tree paper and the algorithm description in "Data Structures and Algorithms in C++, 4th edition".

    For an in-memory version of this algorithm, look at the kdb-tree package.


    var kdbtree = require('kdb-tree-store')
    var fdstore = require('fd-chunk-store')
    var tmpdir = require('os').tmpdir()
    var path = require('path')
    var file = path.join(tmpdir, 'kdb-tree-' + Math.random())
    var n = 5000
    var kdb = kdbtree({
      types: [ 'float32', 'float32', 'float32', 'uint32' ],
      store: fdstore(1024, file)
    var pending = n
    for (var i = 0; i < n; i++) (function () {
      var x = Math.random() * 200 - 100
      var y = Math.random() * 200 - 100
      var z = Math.random() * 200 - 100
      var loc = Math.floor(Math.random() * 1000)
      kdb.insert([x,y,z], loc, function (err) {
        if (--pending === 0) check()
    function check () {
      kdb.query([[-100,0],[0,5],[-50,-40]], function (err, pts) {


    var kdbtree = require('kdb-tree-store')

    var kdb = kdbtree(opts)

    Create a new kdb tree instance kdb given opts:

    • opts.types - array of data types for each dimension plus the payload type at the end
    • opts.store - chunk store instance
    • opts.available - next free chunk index to use, set if loading a previously saved file with data from 'available' events

    kdb.query(q, opts={}, cb)

    Query for results with q, an array of [min,max] arrays for each dimension. The results are given as an array of points in cb(err, results). Each element in results has a point and value property.

    • opts.depth - add depth information to each matching point when true in a depth property (default: false)
    • opts.index - add [chunkIndex,pointIndex] pairs to each matching point when true in an index property (default: false)

    var stream = kdb.queryStream(q, opts={})

    Return a readable stream of query results from the query q.

    kdb.insert(pt, value, cb)

    Insert value at a point pt.

    kdb.remove(q, opts={}, cb)

    kdb.remove(opts, cb)

    Remove all the points in a query q, modified by these options:

    • opts.value - only remove points that value this value
    • opts.filter(pt) - only remove points where this function returns true. Points have point and value properties. Precedence over opts.value.
    • opts.index - remove exactly one item by its [chunkIndex,pointIndex]. Highest precedence.

    kdb.on('available', function (n) {})

    Index n of the next available chunk to use.

    Save n and pass as opts.available to future kdb instances that load from the same file.

    data types

    These data types are provided under string aliases:

    • float (float32)
    • double (float64)
    • uint8
    • uint16
    • uint32
    • int8
    • int16
    • int32
    • buffer[BYTES] - ex: buffer[10] for 10 bytes

    Otherwise, a data type must be an object with these properties:

    • t.read(buf, offset)
    • t.write(buf, value, offset)
    • t.size (in bytes)
    • t.min
    • t.max
    • t.cmp.eq(a, b)
    • t.cmp.lt(a, b)
    • t.cmp.lte(a, b)
    • t.cmp.gt(a, b)
    • t.cmp.gte(a, b)

    The combined size of all the types in a chunk must be below the chunkLength of the opts.store given in the kdbtree() constructor.

    32-bit floating point error

    Javascript Numbers are IEEE-754 floating-point values (54-bits). If you choose to use the float/float32 data type, be aware that rounding errors can silently occur, making kdb.remove or kdb.query operations at specific coordinates fail.

    One workaround is to quantize the values you insert so they are consistent with what kdb-tree-store will write for that data type, e.g.

    function insert2d (x, y, value) {
      x = quant(x, kdb.types[0])
      y = quant(y, kdb.types[1])
      kdb.insert([x, y], value)
    function quant (v, type) {
      var buf = new Buffer(type.size)
      type.write(buf, v, 0)
      return type.read(buf, 0)


    The kdb tree paper describes the resulting tree as balanced, but this module does not yet generate very balanaced trees in practice. Some help on this part would be great!

    The splitting plane is not yet chosen very well, looking only at the median of the presently overfull point page along the depth modulo dimension axis.

    Here is a histogram of depths (right column) for 15000 points under the current implementation:

    $ node example/depth.js 15000 | uniq -c
       2876 2
       2487 4
       2825 5
        274 6
       1204 7
       1990 8
       1223 9
       1092 10
        338 11
        242 13
        124 14
        208 15
        117 17


    npm install kdb-tree-store




    npm i kdb-tree-store

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