Shortest Path with Python
A Shortest Path graph solver example with the Python API
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.
The prerequisites for running the shortest path solve graph example are listed below:
The native Kinetica Python API is accessible through the following means:
The Python package manager, pip, is required to install the API from PyPI.
Install the API:


Test the installation:


If Import Successful is displayed, the API has been installed as is ready for use.
In the desired directory, run the following, but be sure to replace <kineticaversion> with the name of the installed Kinetica version, e.g., v7.1:


Change directory into the newly downloaded repository:


In the root directory of the unzipped repository, install the Kinetica API:


Test the installation (Python 2.7 (or greater) is necessary for running the API example):


The example script references the road_weights.csv
data file,
mentioned in the Prerequisites, in the current local directory, by default.
This directory can specified as a parameter when running the example script.
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.
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


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:


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.


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


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


The solution output to WMS:
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.


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


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


The solutions output to WMS:
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).


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


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


The solutions output to WMS:
Included below is a complete example containing all the above requests, the data files, and output.
To run the complete sample, ensure that:
solve_graph_seattle_shortest_path.py
script is in the
current directoryroad_weights.csv
file is in the current directory or use
the data_dir parameter to specify the local directory containing itThen, run the following:

