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simple, fast interval searches for nim

This uses a binary search in a sorted list of intervals along with knowledge of the longest interval. It works when the size of the largest interval is smaller than the average distance between intervals. As that ratio of largest-size::mean-distance increases, the performance decreases. On realistic (for my use-case) data, this is 1000 times faster to query results and >5000 times faster to check for presence than a brute-force method.

Lapper also has a special case seek method when we know that the queries will be in order. This method uses a cursor to indicate that start of the last search and does a linear search from that cursor to find matching intervals. This gives an additional 2-fold speedup over the find method.

API docs and examples in nim-doc format are available here

See the Performance section for how large the intervals can be and still get a performance benefit.

To use this, it's simply required that your type have a start(m) int and stop(m) int method to satisfy the concept used by Lapper

You can install this with nimble install lapper.

Example

import lapper
import strutils

# define an appropriate data-type. it must have a `start(m) int` and `stop(m) int` method.
#type myinterval = tuple[start:int, stop:int, val:int]
# if we want to modify the result, then we have to use a ref object type
type myinterval = ref object
  start: int
  stop: int
  val: int

proc start(m: myinterval): int {.inline.} = return m.start
proc stop(m: myinterval): int {.inline.} = return m.stop
proc `$`(m:myinterval): string = return "(start:$#, stop:$#, val:$#)" % [$m.start, $m.stop, $m.val]

# create some fake data
var ivs = new_seq[myinterval]()
for i in countup(0, 100, 10):
  ivs.add(myinterval(start:i, stop:i + 15, val:0))

# make the Lapper "data-structure"
var l = lapify(ivs)
var empty:seq[myinterval]

assert l.find(10, 20, empty)
var notfound = not l.find(200, 300, empty)
assert notfound

var res = new_seq[myinterval]()

# find is the more general case, l.seek gives a speed benefit when consecutive queries are in order.
echo l.find(50, 70, res)
echo res
# @[(start: 40, stop: 55, val:0), (start: 50, stop: 65, val: 0), (start: 60, stop: 75, val: 0), (start: 70, stop: 85, val: 0)]
for r in res:
  r.val += 1

# or we can do a function on each overlapping interval
l.each_seek(50, 60, proc(a:myinterval) = inc(a.val))
# or
l.each_find(50, 60, proc(a:myinterval) = a.val += 10)

discard l.seek(50, 70, res)
echo res
#@[(start:40, stop:55, val:12), (start:50, stop:65, val:12), (start:60, stop:75, val:1)]

Performance

The output of running bench.nim (with -d:release) which generates 200K intervals with positions ranging from 0 to 50 million and max lengths from 10 to 1M is:

max interval size lapper time lapper seek time brute-force time speedup seek speedup each-seek speedup
10 0.06 0.04 387.44 6983.81 9873.11 9681.66
100 0.05 0.04 384.92 7344.32 10412.97 15200.84
1000 0.06 0.05 375.37 6250.23 7942.50 15703.24
10000 0.15 0.14 377.29 2554.61 2702.13 15942.76
100000 0.99 0.99 377.88 383.36 381.37 16241.61
1000000 12.52 12.53 425.61 34.01 33.96 17762.58

Note that this is a worst-case scenario as we could also simulate a case where there are few long intervals instead of many large ones as in this case. Even so, we get a 34X speedup with lapper.

Also note that testing for presence will be even faster than the above comparisons as it returns true as soon as an overlap is found.


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