Shortest Path with Python

A Shortest Path graph solver example with the Python API

The following is a complete example, using the Python API, of solving a graph created with Seattle road network data for a shortest path problem via the /solve/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts.

Prerequisites

The prerequisites for running the shortest path solve graph example are listed below:

Python API Installation

The native Kinetica Python API is accessible through the following means:

  • For development on the Kinetica server:
  • For development not on the Kinetica server:

Kinetica RPM

In default Kinetica installations, the native Python API is located in the /opt/gpudb/api/python directory. The /opt/gpudb/bin/gpudb_python wrapper script is provided, which sets the execution environment appropriately.

Test the installation:

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/opt/gpudb/bin/gpudb_python /opt/gpudb/api/python/examples/example.py

Important

When developing on the Kinetica server, use /opt/gpudb/bin/gpudb_python to run Python programs and /opt/gpudb/bin/gpudb_pip to install dependent libraries.

Git

  1. In the desired directory, run the following but be sure to replace <kinetica-version> with the name of the installed Kinetica version, e.g., v7.1:

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    git clone -b release/<kinetica-version> --single-branch https://github.com/kineticadb/kinetica-api-python.git
    
  2. Change directory into the newly downloaded repository:

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    cd kinetica-api-python
    
  3. In the root directory of the unzipped repository, install the Kinetica API:

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    sudo python setup.py install
    
  4. Test the installation (Python 2.7 (or greater) is necessary for running the API example):

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    python examples/example.py
    

PyPI

The Python package manager, pip, is required to install the API from PyPI.

  1. Install the API:

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    pip install gpudb --upgrade
    
  2. Test the installation:

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    python -c "import gpudb;print('Import Successful')"
    

    If Import Successful is displayed, the API has been installed as is ready for use.

Data File

The example script makes reference to a road_weights.csv data file in the /tmp/data directory. This can be updated to point to a valid path on the host where the file will be located.

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CSV = "/tmp/data/road_weights.csv"

Script Detail

This example is going to demonstrate solving (both individual and batch solve) for the shortest path between source points and several destination points located within a road network in Seattle.

Constants

Several constants are defined at the beginning of the script:

  • SCHEMA -- the name of the schema in which the tables supporting the graph creation and match operations will be created

    Important

    The schema is created during the table setup portion of the script because the schema must exist prior to creating the tables that will later support the graph creation and match operations.

  • TABLE_SRN -- the name of the table into which the Seattle road network dataset is loaded

  • GRAPH_S -- the Seattle road network graph

  • TABLE_GRAPH_S_SPSOLVED / TABLE_GRAPH_S_SPSOLVED2 / TABLE_GRAPH_S_SPSOLVED3 -- the Seattle road network graph shortest path solution tables

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SCHEMA = "tutorial_graph"
TABLE_SRN = SCHEMA + ".seattle_road_network"

GRAPH_S = "seattle_road_network_graph"
TABLE_GRAPH_S_SPSOLVED = SCHEMA + "." + GRAPH_S + "_shortest_path_solved"
TABLE_GRAPH_S_SPSOLVED2 = SCHEMA + "." + GRAPH_S + "_shortest_path_solved2"
TABLE_GRAPH_S_SPSOLVED3 = SCHEMA + "." + GRAPH_S + "_shortest_path_solved3"

Graph Creation

One graph is used for the shortest path solve graph example utilized in the script: seattle_road_network_graph, a graph based on the road_weights dataset (the CSV file mentioned in Prerequisites).

The GRAPH_S graph is created with the following characteristics:

  • It is directed because the roads in the graph have directionality (one-way and two-way roads)
  • It has no explicitly defined nodes because the example relies on implicit nodes attached to the defined edges
  • The edges are represented using WKT LINESTRINGs in the WKTLINE column of the seattle_road_network table (EDGE_WKTLINE). The road segments' directionality is derived from the TwoWay column of the seattle_road_network table (EDGE_DIRECTION).
  • The weights are represented using the time taken to travel the segment found in the time column of the seattle_road_network table (WEIGHTS_VALUESPECIFIED). The weights are matched to the edges using the same WKTLINE column as edges (WEIGHTS_EDGE_WKTLINE) and the same TwoWay column as the edge direction (WEIGHTS_EDGE_DIRECTION).
  • It has no inherent restrictions for any of the nodes or edges in the graph
  • It will be replaced with this instance of the graph if a graph of the same name exists (recreate)
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create_s_graph_response = kinetica.create_graph(
    graph_name=GRAPH_S,
    directed_graph=True,
    nodes=[],
    edges=[
        TABLE_SRN + ".WKTLINE AS EDGE_WKTLINE",
        TABLE_SRN + ".TwoWay AS EDGE_DIRECTION"
    ],
    weights=[
        TABLE_SRN + ".WKTLINE AS WEIGHTS_EDGE_WKTLINE",
        TABLE_SRN + ".TwoWay AS WEIGHTS_EDGE_DIRECTION",
        TABLE_SRN + ".time AS WEIGHTS_VALUESPECIFIED"
    ],
    restrictions=[],
    options={
        "recreate": "true",
        "enable_graph_draw": "true",
        "graph_table": "seattle_graph_debug"
    }
)

Shortest Path

Single Source to Single Destination

The first example illustrates a simple shortest path solve from a single source node to a single destination node. First, the source node and destination node are defined.

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source_nodes = ["POINT(-122.1792501 47.2113606)"]
destination_nodes = ["POINT(-122.2221 47.5707)"]

Next, the seattle_road_network_graph graph is solved with the solve results being exported to the response:

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solve_s_spgraph_response = kinetica.solve_graph(
    graph_name=GRAPH_S,
    solver_type="SHORTEST_PATH",
    source_nodes=source_nodes,
    destination_nodes=destination_nodes,
    solution_table=TABLE_GRAPH_S_SPSOLVED,
    options={"export_solve_results": "true"}
)["result_per_destination_node"]

The cost for the source node to visit the destination node is represented as time in minutes:

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Cost per destination node when source node = ['POINT(-122.1792501 47.2113606)']: 
+--------------------------+---------------------+
| Destination Node         |   Cost (in minutes) |
+==========================+=====================+
| POINT(-122.2221 47.5707) |             40.6214 |
+--------------------------+---------------------+

The solution output to WMS:

../img/seattle_sp_solved.png

Single Source to Many Destinations

The second example illustrates a shortest path solve from a single source node to many destination nodes. First, the source node and destination nodes are defined. When one source node and many destination nodes are provided, the graph solver will calculate a shortest path solve for each destination node.

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source_nodes = ["POINT(-122.1792501 47.2113606)"]
destination_nodes = [
    "POINT(-122.2221 47.5707)", 
    "POINT(-122.541017 47.809121)",
    "POINT(-122.520440 47.624725)", 
    "POINT(-122.467915 47.427280)"
]

Next, the seattle_road_network_graph graph is solved with the solve results being exported to the response

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solve_s_spgraph_response2 = kinetica.solve_graph(
    graph_name=GRAPH_S,
    solver_type="SHORTEST_PATH",
    source_nodes=source_nodes,
    destination_nodes=destination_nodes,
    solution_table=TABLE_GRAPH_S_SPSOLVED2,
    options={"export_solve_results": "true"}
)["result_per_destination_node"]

The cost for the source node to visit each of the destination nodes separately is represented as time in minutes:

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Cost per destination node when source node = ['POINT(-122.1792501 47.2113606)']: 
+------------------------------+---------------------+
| Destination Node             |   Cost (in minutes) |
+==============================+=====================+
| POINT(-122.2221 47.5707)     |             40.6214 |
+------------------------------+---------------------+
| POINT(-122.541017 47.809121) |             90.2934 |
+------------------------------+---------------------+
| POINT(-122.520440 47.624725) |             79.7342 |
+------------------------------+---------------------+
| POINT(-122.467915 47.427280) |             69.0357 |
+------------------------------+---------------------+

The solutions output to WMS:

../img/seattle_sp_solved2.png

Many Sources to Many Destinations

The third example illustrates a shortest path solve from a many source nodes to many destination nodes. First, the source node and destination nodes are defined. If many source node and many destination nodes are provided, the graph solver will pair the source and destination node by list index and calculate a shortest path solve for each pair. For this example, there are two starting points (POINT(-122.1792501 47.2113606) and POINT(-122.375180125237 47.8122103214264)) and paths will be calculated from the first source to two different destinations and from the second source to two other destinations.

Important

Calculations from multiple unique sources are faster and more efficient than calculations with one unique source, but results may differ slightly between multiple unique source calculations and single unique source calculations (less than ~1% variance).

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source_nodes = [
    "POINT(-122.1792501 47.2113606)",
    "POINT(-122.1792501 47.2113606)",
    "POINT(-122.375180125237 47.8122103214264)",
    "POINT(-122.375180125237 47.8122103214264)"
]
destination_nodes = [
    "POINT(-122.2221 47.5707)",
    "POINT(-122.541017 47.809121)",
    "POINT(-122.520440 47.624725)",
    "POINT(-122.467915 47.427280)"
]

Next, the seattle_road_network_graph graph is solved with the solve results being exported to the response:

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solve_s_spgraph_response3 = kinetica.solve_graph(
    graph_name=GRAPH_S,
    solver_type="SHORTEST_PATH",
    source_nodes=source_nodes,
    destination_nodes=destination_nodes,
    solution_table=TABLE_GRAPH_S_SPSOLVED3,
    options={"export_solve_results": "true"}
)["result_per_destination_node"]

The cost for the source node to visit each of the destination nodes separately is represented as time in minutes:

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Cost per destination node: 
+-------------------------------------------+------------------------------+---------------------+
| Source Node                               | Destination Node             |   Cost (in minutes) |
+===========================================+==============================+=====================+
| POINT(-122.1792501 47.2113606)            | POINT(-122.2221 47.5707)     |             40.6723 |
+-------------------------------------------+------------------------------+---------------------+
| POINT(-122.1792501 47.2113606)            | POINT(-122.541017 47.809121) |             90.2636 |
+-------------------------------------------+------------------------------+---------------------+
| POINT(-122.375180125237 47.8122103214264) | POINT(-122.520440 47.624725) |             51.1153 |
+-------------------------------------------+------------------------------+---------------------+
| POINT(-122.375180125237 47.8122103214264) | POINT(-122.467915 47.427280) |             59.9621 |
+-------------------------------------------+------------------------------+---------------------+

The solutions output to WMS:

../img/seattle_sp_solved3.png

Download & Run

Included below is a complete example containing all the above requests, the data files, and output.

To run the complete sample, ensure the solve_graph_seattle_shortest_path.py script is in the current directory, and the road_weights.csv file is in the directory defined in that script; then do the following:

  • If on the Kinetica host:

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    /opt/gpudb/bin/gpudb_python solve_graph_seattle_shortest_path.py [--username <username> --password <password>]
    
  • If running after using PyPI or GitHub to install the Python API:

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    python solve_graph_seattle_shortest_path.py [--host <target_host_ip>] [--username <username> --password <password>]