Single-source Shortest Path - Platform For AI - Alibaba Cloud Documentation Center

The single-source shortest path is used to calculate the shortest paths from a specific source node to other nodes in a graph. The shortest path is calculated by using the Dijkstra algorithm. The single-source shortest path is suitable for graphs with non-negative edge weights. It is widely applied in areas such as network routing, traffic planning, and geographic information systems.

Configure the component

Method 1: Configure the component on the pipeline page

On the pipeline details page in Machine Learning Designer, add the Single-source Shortest Path component to the pipeline and configure the parameters described in the following table.

Tab	Parameter	Description
Fields Setting	Source Vertex Column	The start vertex column in the edge table.
	Target Vertex Column	The end vertex column in the edge table.
	Edge Weight Column	The edge weight column in the edge table.
Parameters Setting	Initial Node ID	The start vertex that is used to calculate the shortest path.
Tuning	Number of Workers	The number of vertices for parallel job execution. The degree of parallelism and framework communication costs increase with the value of this parameter.
Tuning	Worker Memory (MB)	The maximum size of memory that a single job can use. Unit: MB. Default value: 4096. If the size of used memory exceeds the value of this parameter, the `OutOfMemory` error is reported.

Method 2: Configure the component by using PAI commands

Configure the component parameters by using PAI commands. You can use the SQL Script component to call PAI commands. For more information, see Scenario 4: Execute PAI commands within the SQL script component.

PAI -name SSSP
    -project algo_public
    -DinputEdgeTableName=SSSP_func_test_edge
    -DfromVertexCol=flow_out_id
    -DtoVertexCol=flow_in_id
    -DoutputTableName=SSSP_func_test_result
    -DhasEdgeWeight=true
    -DedgeWeightCol=edge_weight
    -DstartVertex=a;

Parameter	Required	Default value	Description
inputEdgeTableName	Yes	No default value	The name of the input edge table.
inputEdgeTablePartitions	No	Full table	The partitions in the input edge table.
fromVertexCol	Yes	No default value	The start vertex column in the input edge table.
toVertexCol	Yes	No default value	The end vertex column in the input edge table.
outputTableName	Yes	No default value	The name of the output table.
outputTablePartitions	No	No default value	The partitions in the output table.
lifecycle	No	No default value	The lifecycle of the output table.
workerNum	No	No default value	The number of vertices for parallel job execution. The degree of parallelism and framework communication costs increase with the value of this parameter.
workerMem	No	4096	The maximum size of memory that a single job can use. Unit: MB. Default value: 4096. If the size of used memory exceeds the value of this parameter, the `OutOfMemory` error is reported.
splitSize	No	64	The data split size. Unit: MB.
startVertex	Yes	No default value	The ID of the start vertex.
hasEdgeWeight	No	false	Specifies whether the edges in the input edge table have weights.
edgeWeightCol	No	No default value	The edge weight column in the input edge table.

Example

On the pipeline details page, add a SQL Script component to the pipeline and click the component. On the Parameters Setting tab, clear Use Script Mode and Whether the system adds a create table statement, and enter the following SQL statements in the SQL Script editor:

drop table if exists SSSP_func_test_edge;
create table SSSP_func_test_edge as
select
    flow_out_id,flow_in_id,edge_weight
from
(
    select "a" as flow_out_id,"b" as flow_in_id,1.0 as edge_weight
    union all
    select "b" as flow_out_id,"c" as flow_in_id,2.0 as edge_weight
    union all
    select "c" as flow_out_id,"d" as flow_in_id,1.0 as edge_weight
    union all
    select "b" as flow_out_id,"e" as flow_in_id,2.0 as edge_weight
    union all
    select "e" as flow_out_id,"d" as flow_in_id,1.0 as edge_weight
    union all
    select "c" as flow_out_id,"e" as flow_in_id,1.0 as edge_weight
    union all
    select "f" as flow_out_id,"g" as flow_in_id,3.0 as edge_weight
    union all
    select "a" as flow_out_id,"d" as flow_in_id,4.0 as edge_weight
) tmp;

Data structure

Add a SQL Script component to the pipeline and click the component. On the Parameters Setting tab, clear Use Script Mode and Whether the system adds a create table statement, and enter the following SQL statements in the SQL Script editor. Connect this component with the component added in Step 1.
```
drop table if exists ${o1};
PAI -name SSSP
    -project algo_public
    -DinputEdgeTableName=SSSP_func_test_edge
    -DfromVertexCol=flow_out_id
    -DtoVertexCol=flow_in_id
    -DoutputTableName=${o1}
    -DhasEdgeWeight=true
    -DedgeWeightCol=edge_weight
    -DstartVertex=a;
```
In the upper-left corner of the canvas, click to run the pipeline.

After the pipeline is run, click the SQL Script component added in Step 2, and choose View Data > SQL Script Output to view the training results.

| start_node | dest_node | distance | distance_cnt |
| ---------- | --------- | -------- | ------------ |
| a          | a         | 0.0      | 0            |
| a          | b         | 1.0      | 1            |
| a          | c         | 3.0      | 1            |
| a          | d         | 4.0      | 3            |
| a          | e         | 3.0      | 1            |