Solve Graph - Multiple Routing (Seattle)¶

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

Prerequisites
- Python API Installation
- Data File
Script Detail
Download & Run

Prerequisites¶

The prerequisites for running the multiple routing solve graph example are listed below:

Kinetica (v. 7.0 or later)
Graph server enabled
Python API
Solve graph script
Seattle road network CSV file

Python API Installation¶

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

For development on the Kinetica server:
- Kinetica RPM
For development not on the Kinetica server:
- Git
- PyPI

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:

/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¶

In the desired directory, run the following but be sure to replace <kinetica-version> with the name of the installed Kinetica version, e.g., v7.0:
```
git clone -b release/<kinetica-version> --single-branch https://github.com/kineticadb/kinetica-api-python.git
```
Change directory into the newly downloaded repository:
```
cd kinetica-api-python
```
In the root directory of the unzipped repository, install the Kinetica API:
```
sudo python setup.py install
```
Test the installation (Python 2.7 (or greater) is necessary for running the API example):
```
python examples/example.py
```

PyPI¶

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

Install the API:
```
pip install gpudb --upgrade
```
Test the installation:
```
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 current directory. This can be updated to point to a valid path on the host where the file will be located, or the script can be run with the data file in the current directory.

CSV = "road_weights.csv"

Script Detail¶

This example is going to demonstrate solving for the shortest possible route between destination points and a source point located in a Seattle road network.

Constants¶

Several constants are defined at the beginning of the script:

HOST / PORT -- host and port values for the database
OPTION_NO_ERROR -- reference to a /clear/table option for ease of use and repeatability
TABLE_SRN -- the name of the table into which the Seattle road network dataset is loaded
GRAPH_S -- the Seattle road network graph
GRAPH_S_MRSOLVED -- the solved Seattle road network graph using the MULTIPLE_ROUTING solver type

HOST = "127.0.0.1"
PORT = "9191"

OPTION_NO_ERROR = {"no_error_if_not_exists": "true"}

TABLE_SRN = "seattle_road_network"

GRAPH_S = TABLE_SRN + "_graph"
GRAPH_S_MRSOLVED = GRAPH_S + "_multiple_routing_solved"

Graph Creation¶

One graph is used for the multiple routing solve graph example utilized in the script: seattle_road_network_graph, a graph based on the Seattle road network dataset (the CSV file mentioned in Prerequisites).

The seattle_road_network_graph 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)

print("Creating {}".format(GRAPH_S))
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"
    }
)

Multiple Routing¶

Before the seattle_road_network_graph graph is solved, the source node and destination nodes are defined.

source_node = "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 graph is solved with the solve results being exported to the response:

solve_s_mrgraph_response = kinetica.solve_graph(
    graph_name=GRAPH_S,
    solver_type="MULTIPLE_ROUTING",
    source_nodes=[source_node],
    destination_nodes=destination_nodes,
    solution_table=GRAPH_S_MRSOLVED,
    options={"export_solve_results": "true"}
)["result_per_destination_node"][0]

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

Cost (in minutes) for source node ID POINT(-122.1792501 47.2113606) to visit destination node IDs
['POINT(-122.2221 47.5707)', 'POINT(-122.541017 47.809121)', 'POINT(-122.520440 47.624725)', 'POINT(-122.467915 47.427280)']: 240.35945638

The solution output to WMS:

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_multi_route.py and road_weights.csv files are in the same directory (assuming the locations were not changed in the solve_graph_seattle_multi_route.py script); then switch to that directory and do the following:

If on the Kinetica host:

/opt/gpudb/bin/gpudb_python solve_graph_seattle_multi_route.py

If running after using PyPI or GitHub to install the Python API:
```
python solve_graph_seattle_multi_route.py
```

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