Graph Solvers with REST - Kinetica Docs

The following guide provides step-by-step instructions to get started with using the Graphs & Solvers in Kinetica. This guide demonstrates some key graph concepts as well as how to create and solve a graph using the Kinetica REST API.

Prerequisites

D.C. Shape data file
Access to GAdmin
Access to a REST client or a terminal

Data File

The tutorial makes use of the dc_shape dataset, which can be ingested from the data file mentioned above. To ingest the file using GAdmin:

Navigate to GAdmin and log in (http://localhost:8080/)
In the top menu, click Data —> Import
In the top right corner, click Advanced CSV Import
Click Select File and select the data file from your local directory
Leave the default options and values for the rest of Step 1
Under Step 2, change Schema to graph
Under Step 3: Confirm, click Import CSV

The file will be validated and records will be inserted.

Using the REST API

As mentioned above, running the following examples requires access to a REST client or a terminal. If opting to run the tutorial via a terminal, the tutorial script and cURL package are required. See Download & Run for more information.

Key Information and Concepts

After the data file has been ingested into Kinetica, you should learn about the dataset and how it relates to the Graph Solvers.

Data

The dc_shape dataset is a HERE dataset and is analogous to most road network datasets you could find in that it includes columns for the type of road, the average speed, the direction of the road, a WKT linestring for its geographic location, a unique ID integer for the road, and more. The graph used in the example is created with two columns from the dc_shape dataset:

shape — a WKT linestring composed of points that make up various streets, roads, highways, alleyways, and footpaths throughout Washington, D.C.
direction — an integer column that conveys information about the directionality of the road, with forward meaning the direction in which the way is drawn in OSM and backward being the opposite direction:
- 0 — a forward one-way road
- 1 — a two-way road
- 2 — a backward one-way road

You’ll notice later that the shape column is also part of an inline calculation for distance as weight during graph creation using the ST_LENGTH and ST_NPOINTS geospatial functions.

Graph Concepts

A graph typically comprises nodes, edges, weights, and restrictions, but only requires edges and weights. The graph created in this tutorial only uses edges and weights. In this particular example, edges are logically mapped to sections of roadways and footpaths throughout the Washington, D.C., area. Each edge corresponds to a consecutive pair of points from each of the source LINESTRINGs, so a LINESTRING containing n points will have n-1 edges. Because the source graph is not created with nodes, implicit nodes are assigned at either end of each edge after graph creation. For example, link ID 18350083 is a part of the Ward Circle roundabout, which itself is part of Massachusetts Avenue Northwest near American University. Selecting link ID 18350083 from the dc_shape table reveals the following WKT linestring (the end of the linestring was removed for clarity):

LINESTRING (-77.08544159 38.93787003, -77.08544159 38.93793869, -77.08545685 38.9380188, -77.08548737 38.9381218, ...)

As noted above, each consecutive pair of coordinates will correspond to an edge in the graph, e.g.:

Edge A - LINESTRING(-77.08544159 38.93787003, -77.08544159 38.93793869)
Edge B - LINESTRING(-77.08544159 38.93793869, -77.08545685 38.9380188)
Edge C - LINESTRING(-77.08545685 38.9380188, -77.08548737 38.9381218)

After graph creation (and assuming the graph table was created), you can view each edge’s details, including its respective WKT linestring, using WMS and clicking an edge:

Each coordinate pair composing an edge is an implicit node:

../img/graph_concepts/edge_wms_nodes.png

Weights in this graph, as mentioned previously, are an abstract cost (distance, time, etc.) for traveling any roadway or footpath in the Washington, D.C., area. Weights are particularly important when solving a graph. There are two solver types presented in the tutorial below:

shortest_path — Find the shortest path between two points on a graph
multiple_routing — Find the quickest route between a source point and many destination points; also known as traveling salesman

Because there are no explicit nodes in the tutorial graph, source and destination point(s) must be selected from any of the edges comprising the Washington, D.C., road network. Otherwise, the graph will not be able to traverse to the point and will result in a null solution.

Tutorial via REST Client

This tutorial can be run via cURL to create and solve the graph, but any REST client will work. In the following examples, the JSON block presented is passed as a text file to cURL’s --data parameter, and the name of the endpoint is appended to the database connection URL. A call to the /create/graph endpoint looks like:

REST Create Graph Call

curl -sS --header "Content-Type: application/json" \
  --user ${USERNAME}:${PASSWORD} \
  --data @create_graph.json ${HOST_URL}/create/graph

See the REST tab of Connecting via API for how to construct the HOST_URL parameter above. The response will be a JSON block containing three primary fields:

status - OK or ERROR, depending on the success of the call
message - error message, if any
data_str - an escaped-quotes JSON block containing the response data for the request

Create Graph

One graph is used for both solve graph examples later in the tutorial: dc_shape_graph, a graph based on the aforementioned dc_shape dataset. The dc_shape_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 derived from WKT LINESTRINGs in the shape column of the dc_shape table (WKTLINE). Each road segment’s directionality is derived from the direction column of the dc_shape table (DIRECTION). Each road segment’s weight is represented as its distance, which is calculated as the length of the entire shape column’s LINESTRING (in meters) divided by the number of points in the LINESTRING minus 1 (WEIGHT_VALUESPECIFIED).
It has no explicitly defined weights, as those were defined as part of the edge configuration above
It has no inherent restrictions for any of the nodes or edges in the graph
It utilizes the following options:
- It will be replaced with this instance of the graph if a graph of the same name exists (recreate)
- If nodes are within 0.00001 degrees (1 meter) of each other, they will be merged together (merge_tolerance)
- The resulting graph’s information will be placed into a table (graph_table) and an EDGE_WKTLINE column will be included so the graph can be visualized

To create the graph:

Call the /create/graph endpoint with the following JSON parameter block:

Create Graph Endpoint Call JSON Payload

{
  "graph_name": "graph_rest.dc_shape_graph",
  "directed_graph": true,
  "nodes": [],
  "edges": [
    "graph.dc_shape.shape AS WKTLINE",
    "graph.dc_shape.direction AS DIRECTION",
    "ST_LENGTH(graph.dc_shape.shape,1)/(ST_NPOINTS(graph.dc_shape.shape)-1) AS WEIGHT_VALUESPECIFIED"
  ],
  "weights": [],
  "restrictions": [],
  "options": {
    "merge_tolerance": "0.00001",
    "recreate": "true",
    "graph_table": "graph_rest.dc_shape_graph_table"
  }
}

The response should contain the following JSON block:

Create Graph Endpoint Response

{
    "edges_ids": [],
    "info": {
        "status": "OK"
    },
    "num_edges": 491457,
    "num_nodes": 446491,
    "result": true
}

Solve the Graph (Shortest Path)

The following scenario has been designed to illustrate a shortest path solution: You work near the White House, and after you get off work, you’d like to attend a baseball game at the nearby ballpark. What’s the shortest path you could take to get to the ballpark?

The source and destination points were selected from endpoints of edges within the graph. The easiest way to review the points that make up an edge is using the WMS tool on the graph table (dc_shape_graph_table).

	Source	Destination
WKT	`POINT(-77.03511810000001 38.89876175)`	`POINT(-77.00585175000001 38.87462997)`
Location Description	The corner of Madison Place Northwest and Pennsylvania Avenue Northwest	The corner of N Street Southeast and Southeast 1st street
WMS Location

To generate the solution:

Call the /solve/graph endpoint with the following JSON parameter block:

Solve Graph Endpoint Call (Shortest Path) JSON Payload

{
  "graph_name": "graph_rest.dc_shape_graph",
  "weights_on_edges": [],
  "restrictions": [],
  "solver_type": "shortest_path",
  "source_nodes": ["POINT(-77.03511810000001 38.89876175)"],
  "destination_nodes": ["POINT(-77.00585175000001 38.87462997)"],
  "solution_table": "graph_rest.dc_shape_graph_solved_shortest_path",
  "options": {}
}

Because the tutorial graph was created using WKT linestrings, the /solve/graph call provides the source and destination points as WKT points.

The response should contain the following JSON block:

Solve Graph Endpoint Response (Shortest Path)

{
    "info": {
        "status": "OK"
    },
    "result": true,
    "result_per_destination_node": []
}

The solution can be viewed in GAdmin by:
1. Click Data —> Table
2. Click on the graph_rest schema
3. Click on Map in the dc_shape_graph_solved_shortest_path table’s row
4. Click Feature at the bottom left of the image

Solve the Graph (Multiple Routing)

The following scenario has been designed to illustrate a multiple routing solution: You’re currently taking the subway to Union Station to visit Washington D.C. for the day. You would like to see some of the most iconic monuments and buildings in Washington, D.C. before returning back to Union Station. Starting from Union Station, what’s the quickest route between each stop before returning back to Union Station?

	Source	Destination	Destination	Destination	Destination
WKT	`POINT(-77.00576019 38.89677811)`	`POINT(-77.03517151 38.8898201)`	`POINT(-77.03626251 38.88068008)`	`POINT(-77.04974365 38.89020157)`	`POINT(-77.01207733 38.89072037)`
Location Description	Union Station	Near the Washington Monument	Near the Jefferson Memorial	Near the Lincoln Memorial	Near Capitol Hill
WMS Location

To generate the solution:

Call the /solve/graph endpoint with the following JSON parameter block:

Solve Graph Endpoint Call (Multiple Routing) JSON Payload

{
  "graph_name": "graph_rest.dc_shape_graph",
  "weights_on_edges": [],
  "restrictions": [],
  "solver_type": "multiple_routing",
  "source_nodes": ["POINT(-77.00576019 38.89677811)"],
  "destination_nodes": [
    "POINT(-77.03517151 38.8898201)",
    "POINT(-77.03626251 38.88068008)",
    "POINT(-77.04974365 38.89020157)",
    "POINT(-77.01207733 38.89072037)"
  ],
  "solution_table": "graph_rest.dc_shape_graph_solved_multiple_routing",
  "options": {}
}

Because the tutorial graph was created using WKT linestrings, the /solve/graph call provides the source and destination points as WKT points.

The response should contain the following JSON block:

Solve Graph Endpoint Response (Multiple Routing)

{
    "info": {
        "status": "OK"
    },
    "result": true,
    "result_per_destination_node": []
}

The solution can be viewed in GAdmin by:
1. Click Data —> Table
2. Click on the graph_rest schema
3. Click on Map in the dc_shape_graph_solved_multiple_routing table’s row
4. Click Feature at the bottom left of the image

Download & Run

Included below is the complete tutorial containing all the above requests and the necessary JSON files:

To run the complete sample, ensure the graphs_rest.sh and the JSON files are in the same directory; then switch to that directory and do the following:

Run Example

./graphs_rest.sh <kinetica_url> <username> <password>

​Prerequisites

​Data File

​Using the REST API

​Key Information and Concepts

​Data

​Graph Concepts

​Tutorial via REST Client

​Create Graph

​Solve the Graph (Shortest Path)

​Solve the Graph (Multiple Routing)

​Download & Run

Prerequisites

Data File

Using the REST API

Key Information and Concepts

Data

Graph Concepts

Tutorial via REST Client

Create Graph

Solve the Graph (Shortest Path)

Solve the Graph (Multiple Routing)

Download & Run