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README.md

CircleCI LICENSE VERSION

:ddrt (README)

A Dynamic, Distributed R-Tree (DDRT) library written in Elixir. The 'dynamic' part of the title refers to the fact that this implementation is optimized for a high volume of update operations. Put another way, this is an R-tree best suited for use with spatial data in constant movement. The 'distributed' part refers to the fact that this library is designed to maintain a spatial index (rtree) across a cluster of distributed elixir nodes.

The library uses @derekkraan's MerkleMap and CRDT implementations to ensure reliable, "eventually consistent" distributed behavior.

The complete documentation is available on hexdocs. You can find the hex package here.

Getting Started

Installation

The package can be installed by adding ddrt to your list of dependencies in mix.exs:

def deps do
  [
    {:ddrt, "~> 0.2.1"}
  ]
end

Starting DDRT Processes

Start up a DDRT process with default values

DDRT.start_link([
  name: DDRT
  width: 6,
  verbose: false,
  seed: 0
])

Or add it to your supervision tree:

Supervisor.start_link([
  {DDRT, [
  	name: DDRT,
  	width: 6,
  	verbose: false,
  	seed: 0
  ]}
], [name: MySupervisor])

Otherwise if you're just looking to use the standalone R-tree functionality on a single machine (not a cluster of machines), you would instead use the DDRT.DynamicRtree module:

DDRT.DynamicRtree.start_link([name: DynamicRtree])

Note: all configuration parameters and public API methods are exactly the same between the DDRT and DDRT.DynamicRtree modules.

Configuration

Available configuration parameters are:

  • name: The name of the DDRT process. Defaults to DDRT
  • width: The max number of children a node may have. Defaults to 6
  • verbose: allows Logger to report console logs. (Also decreases performance). Defaults to false.
  • seed: Sets the seed value for the pseudo-random number generator which generates the unique IDs for each node in the tree. This is a deterministic process; so the same seed value will guarantee the same pseudo-random unique IDs being generated for your tree in the same order each time. Defaults to 0

Replicating your R-Tree in a cluster

First it's important to understand that distributed networking capabilities come built-in with Erlang. To get Elixir processes communicating amongst themselves over a network in general, we first have to use that fundamental Erlang networking magic to make all of the running Erlang Virtual Machines "aware" of eachother's existence on the network. In Elixir, these concepts are expressed in the Node module. One can use Node.connect/2 to make two Erlang VM nodes aware of eachother, and then Elixir processes are able to send messages to eachother on those nodes.

Connecting up the Erlang VMs in your cluster is outside of the scope of this package. There are already other libraries in Elixir designed to do exactly this. Possibly the best example is bitwalker/libcluster.

A very simple libcluster configuration for quick and easy development might look like:

## config.exs ##

use Mix.Config
config :libcluster,
topologies: [
 example: [
   strategy: Cluster.Strategy.Epmd,
   config: [hosts: [:"a@localhost", :"b@localhost"]],
 ]
]

Then you would have to pass in those same node names to iex when you start your application, like:

eduardo@ddrt $ iex --name a@localhost -S mix
iex(a@localhost)1>

eduardo@ddrt $ iex --name b@localhost -S mix
iex(b@localhost)1>

Finally, after starting DDRT on each node you would use DDRT.set_members/2 to begin communication between DDRT processes like this:

# on node A:
{:ok, _pid} = DDRT.start_link([name: DDRT])
DDRT.set_members(DDRT, [{DDRT, :b@localhost}])

# on node B:
{:ok, _pid} = DDRT.start_link([name: DDRT])
DDRT.set_members(DDRT, [{DDRT, :a@localhost}])

From here, everything done on either tree will be reflected in the other tree.

Note: it's important that you have the same configuration parameters for each DDRT process running on each connected node in your cluster.

Usage

Starts a local DDRT named :peter

iex> DDRT.start_link([name: :peter])
{:ok, #PID<0.214.0>}

Insert "Griffin" into the :peter DDRT

iex> DDRT.insert({"Griffin", [{4,5}, {6,7}]}, :peter)
{:ok,
  %{
    43143342109176739 => {["Griffin"], nil, [{4, 5}, {6, 7}]},
    :root => 43143342109176739,
    :ticket => [19125803434255161 | 82545666616502197],
    "Griffin" => {:leaf, 43143342109176739, [{4, 5}, {6, 7}]}
}}

Insert "Parker" on into the :peter DDRT

iex> DDRT.insert({"Parker",[{10,11},{16,17}]},:peter)
{:ok,
  %{
    43143342109176739 => {["Parker", "Griffin"], nil, [{4, 11}, {6, 17}]},
    :root => 43143342109176739,
    :ticket => [19125803434255161 | 82545666616502197],
    "Griffin" => {:leaf, 43143342109176739, [{4, 5}, {6, 7}]},
    "Parker" => {:leaf, 43143342109176739, [{10, 11}, {16, 17}]}
}}

Query which leaves in the :peter R-tree overlap with box [{0,7},{4,8}]

iex> DDRT.query([{0,7},{4,8}],:peter)
{:ok, ["Griffin"]}

Updates "Griffin" bounding box in the :peter R-tree

iex> DDRT.update("Griffin", [{-6,-5},{11,12}], :peter)
{:ok,
  %{
    43143342109176739 => {["Parker", "Griffin"], nil, [{-6, 11}, {6, 17}]},
    :root => 43143342109176739,
    :ticket => [19125803434255161 | 82545666616502197],
    "Griffin" => {:leaf, 43143342109176739, [{-6, -5}, {11, 12}]},
    "Parker" => {:leaf, 43143342109176739, [{10, 11}, {16, 17}]}
}}

Repeat the last query again. (This time "Griffin" is no longer within the query bounding box.)

 iex> DDRT.query([{0,7},{4,8}], :peter)
 {:ok, []}

Now lets delete both "Griffin" and "Parker" keys from the tree.

iex> DDRT.delete(["Griffin","Parker"], :peter)
{:ok,
  %{
    43143342109176739 => {[], nil, [{0, 0}, {0, 0}]},
    :root => 43143342109176739,
    :ticket => [19125803434255161 | 82545666616502197]
}}

Bounding-Box Format

[{x_min,x_max}, {y_min,y_max}]

Example:                               & & & & & y_max & & & & &
  A unit at pos x: 10, y: -12 ,        &                       &
  with x_size: 1 and y_size: 2         &                       &
  would be represented with            &          pos          &
  the following bounding box         x_min       (x,y)       x_max
  [{9.5,10.5},{-13,-11}]               &                       &
                                       &                       &
                                       &                       &
                                       & & & & & y_min & & & & &

Standalone (non-distributed) R-tree mode

If you're only interested in using an R-tree on a single machine in Elixir, you should be using the DDRT.DynamicRtree module. This module is optimized to run on a single machine, and as such the r-tree is significantly faster without the distribution overhead.

The DDRT.DynamicRtree module shares the same API and initialization options as the main DDRT module.

Benchmarks

Operating System: macOS
CPU Information: Intel(R) Core(TM) i5-5257U CPU @ 2.70GHz
Number of Available Cores: 4
Available memory: 8 GB
Elixir 1.9.0
Erlang 22.0.7

Delete

Benchmark suite executing with the following configuration:
warmup: 2 s
time: 5 s
memory time: 0 ns
parallel: 1
inputs: delete all leaves of tree [1000]
Estimated total run time: 28 s

##### With input delete all leaves of tree [1000] #####
Name                       ips        average  deviation         median         99th %
map bulk                175.20        5.71 ms     ±9.18%        5.60 ms        9.47 ms
merklemap bulk           80.27       12.46 ms    ±21.27%       11.74 ms       25.37 ms
map 1 by 1                4.68      213.68 ms     ±3.12%      213.24 ms      227.16 ms
merklemap 1 by 1          1.55      643.75 ms    ±14.80%      616.84 ms      878.20 ms

Comparison: 
map bulk                175.20
merklemap bulk           80.27 - 2.18x slower +6.75 ms
map 1 by 1                4.68 - 37.44x slower +207.97 ms
merklemap 1 by 1          1.55 - 112.79x slower +638.04 ms

Update

Benchmark suite executing with the following configuration:
warmup: 2 s
time: 10 s
memory time: 0 ns
parallel: 1
inputs: all leaves of tree [1000], all leaves of tree [100000]
Estimated total run time: 48 s

##### With input all leaves of tree [1000] #####
Name                ips        average  deviation         median         99th %
map              133.88        7.47 ms    ±22.82%        6.92 ms       14.83 ms
merklemap         65.74       15.21 ms    ±21.93%       14.18 ms       26.42 ms

Comparison: 
map              133.88
merklemap         65.74 - 2.04x slower +7.74 ms

##### With input all leaves of tree [100000] #####
Name                ips        average  deviation         median         99th %
map                0.68         1.46 s    ±15.84%         1.47 s         1.82 s
merklemap          0.33         3.01 s     ±8.23%         3.09 s         3.21 s

Comparison: 
map                0.68
merklemap          0.33 - 2.06x slower +1.55 s

Query

Benchmark suite executing with the following configuration:
warmup: 2 s
time: 5 s
memory time: 0 ns
parallel: 1
inputs: 100x100 box query, 10x10 box query, 1x1 box query, world box query
Estimated total run time: 56 s

##### With input 100x100 box query #####
Name                ips        average  deviation         median         99th %
merklemap        299.97        3.33 ms    ±28.87%        3.03 ms        6.92 ms
map              268.51        3.72 ms    ±36.46%        3.35 ms        8.57 ms

Comparison: 
merklemap        299.97
map              268.51 - 1.12x slower +0.39 ms

##### With input 10x10 box query #####
Name                ips        average  deviation         median         99th %
map              1.50 K      667.16 μs    ±37.04%         594 μs     1557.56 μs
merklemap        1.01 K      992.92 μs    ±48.86%         883 μs     2418.52 μs

Comparison: 
map              1.50 K
merklemap        1.01 K - 1.49x slower +325.76 μs

##### With input 1x1 box query #####
Name                ips        average  deviation         median         99th %
map              2.01 K      498.54 μs    ±39.28%         430 μs        1257 μs
merklemap        1.51 K      660.89 μs    ±45.08%         603 μs     1551.25 μs

Comparison: 
map              2.01 K
merklemap        1.51 K - 1.33x slower +162.34 μs

##### With input world box query #####
Name                ips        average  deviation         median         99th %
map              156.18        6.40 ms    ±18.51%        5.99 ms       10.70 ms
merklemap        152.11        6.57 ms    ±26.12%        5.92 ms       13.93 ms

Comparison: 
map              156.18
merklemap        152.11 - 1.03x slower +0.171 ms

Insert

Benchmark suite executing with the following configuration:
warmup: 2 s
time: 5 s
memory time: 0 ns
parallel: 1
inputs: 1000 leaves
Estimated total run time: 28 s

##### With input 1000 leaves #####
Name                       ips        average  deviation         median         99th %
map bulk                305.53        3.27 ms    ±39.39%        2.79 ms        7.96 ms
merklemap bulk          190.61        5.25 ms    ±62.06%        4.37 ms       17.65 ms
map 1 by 1               66.73       14.99 ms     ±4.63%       14.78 ms       19.11 ms
merklemap 1 by 1         23.00       43.48 ms    ±23.79%       39.24 ms       81.16 ms

Comparison: 
map bulk                305.53
merklemap bulk          190.61 - 1.60x slower +1.97 ms
map 1 by 1               66.73 - 4.58x slower +11.71 ms
merklemap 1 by 1         23.00 - 13.28x slower +40.21 ms
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