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Given a graph and a source vertex, the single source shortest path (SSSP) algorithm finds a path from the source vertex to every other vertex in the graph, such that the sum of the weights of the path edges is minimized.

SSSP

graph_sssp( vertex_table,
            vertex_id,
            edge_table,
            edge_args,
            source_vertex,
            out_table,
            grouping_cols
          )

Arguments

vertex_table

TEXT. Name of the table containing the vertex data for the graph. Must contain the column specified in the 'vertex_id' parameter below.

vertex_id

TEXT, default = 'id'. Name of the column in 'vertex_table' containing vertex ids. The vertex ids can be of type INTEGER or BIGINT with no duplicates. They do not need to be contiguous.

edge_table

TEXT. Name of the table containing the edge data. The edge table must contain columns for source vertex, destination vertex and edge weight. Column naming convention is described below in the 'edge_args' parameter.

edge_args

TEXT. A comma-delimited string containing multiple named arguments of the form "name=value". The following parameters are supported for this string argument:

src (INTEGER or BIGINT): Name of the column containing the source vertex ids in the edge table. Default column name is 'src'.
dest (INTEGER or BIGINT): Name of the column containing the destination vertex ids in the edge table. Default column name is 'dest'.
weight (FLOAT8): Name of the column containing the edge weights in the edge table. Default column name is 'weight'.

source_vertex

INTEGER or BIGINT. The source vertex id for the algorithm to start. This vertex id must exist in the 'vertex_id' column of 'vertex_table'.

out_table

TEXT. Name of the table to store the result of SSSP. It contains a row for every vertex of every group and have the following columns (in addition to the grouping columns):

vertex_id : The id for the destination. Will use the input parameter 'vertex_id' for column naming.
weight : The total weight of the shortest path from the source vertex to this particular vertex. Will use the input parameter 'weight' for column naming.
parent : The parent of this vertex in the shortest path from source. Will use 'parent' for column naming.

A summary table named <out_table>_summary is also created. This is an internal table that keeps a record of the input parameters and is used by the path function described below.

grouping_cols (optional)

TEXT, default = NULL. List of columns used to group the input into discrete subgraphs. These columns must exist in the edge table. When this value is null, no grouping is used and a single SSSP result is generated.

Path Retrieval

The path retrieval function returns the shortest path from the source vertex to a specified desination vertex.

graph_sssp_get_path( sssp_table,
                     dest_vertex,
                     path_table
                    )

Arguments

sssp_table

TEXT. Name of the table that contains the SSSP output.

dest_vertex

INTEGER or BIGINT. The vertex that will be the destination of the desired path.

path_table

TEXT. Name of the output table that contains the path. It contains a row for every group and has the following columns:

grouping_cols : The grouping columns given in the creation of the SSSP table. If there are no grouping columns, these columns will not exist and the table will have a single row.
path (ARRAY) : The shortest path from the source vertex (as specified in the SSSP execution) to the destination vertex.

Examples

Create vertex and edge tables to represent the graph:

DROP TABLE IF EXISTS vertex, edge;
CREATE TABLE vertex(
        id INTEGER
        );
CREATE TABLE edge(
        src INTEGER,
        dest INTEGER,
        weight FLOAT8
        );
INSERT INTO vertex VALUES
(0),
(1),
(2),
(3),
(4),
(5),
(6),
(7);
INSERT INTO edge VALUES
(0, 1, 1.0),
(0, 2, 1.0),
(0, 4, 10.0),
(1, 2, 2.0),
(1, 3, 10.0),
(2, 3, 1.0),
(2, 5, 1.0),
(2, 6, 3.0),
(3, 0, 1.0),
(4, 0, -2.0),
(5, 6, 1.0),
(6, 7, 1.0);

Calculate the shortest paths from vertex 0:

DROP TABLE IF EXISTS out, out_summary;
SELECT madlib.graph_sssp(
                         'vertex',      -- Vertex table
                         NULL,          -- Vertix id column (NULL means use default naming)
                         'edge',        -- Edge table
                         NULL,          -- Edge arguments (NULL means use default naming)
                         0,             -- Source vertex for path calculation
                         'out');        -- Output table of shortest paths
SELECT * FROM out ORDER BY id;

 id | weight | parent
----+--------+--------
  0 |      0 |      0
  1 |      1 |      0
  2 |      1 |      0
  3 |      2 |      2
  4 |     10 |      0
  5 |      2 |      2
  6 |      3 |      5
  7 |      4 |      6
(8 rows)

Get the shortest path to vertex 5:

DROP TABLE IF EXISTS out_path;
SELECT madlib.graph_sssp_get_path('out',5,'out_path');
SELECT * FROM out_path;

  path
---------
 {0,2,5}

Now let's do a similar example except using different column names in the tables (i.e., not the defaults). Create the vertex and edge tables:

DROP TABLE IF EXISTS vertex_alt, edge_alt;
CREATE TABLE vertex_alt AS SELECT id AS v_id FROM vertex;
CREATE TABLE edge_alt AS SELECT src AS e_src, dest, weight AS e_weight FROM edge;

Get the shortest path from vertex 1:

DROP TABLE IF EXISTS out_alt, out_alt_summary;
SELECT madlib.graph_sssp(
                         'vertex_alt',                  -- Vertex table
                         'v_id',                        -- Vertex id column (NULL means use default naming)
                         'edge_alt',                    -- Edge table
                         'src=e_src, weight=e_weight',  -- Edge arguments (NULL means use default naming)
                         1,                             -- Source vertex for path calculation
                         'out_alt');                    -- Output table of shortest paths
SELECT * FROM out_alt ORDER BY v_id;

 v_id | e_weight | parent
------+----------+--------
    0 |        4 |      3
    1 |        0 |      1
    2 |        2 |      1
    3 |        3 |      2
    4 |       14 |      0
    5 |        3 |      2
    6 |        4 |      5
    7 |        5 |      6
(8 rows)

Create a graph with 2 groups:

DROP TABLE IF EXISTS edge_gr;
CREATE TABLE edge_gr AS
(
  SELECT *, 0 AS grp FROM edge
  UNION
  SELECT *, 1 AS grp FROM edge WHERE src < 6 AND dest < 6
);
INSERT INTO edge_gr VALUES
(4,5,-20,1);

Find SSSP for all groups

DROP TABLE IF EXISTS out_gr, out_gr_summary;
SELECT madlib.graph_sssp(
                         'vertex',      -- Vertex table
                         NULL,          -- Vertex id column (NULL means use default naming)
                         'edge_gr',     -- Edge table
                         NULL,          -- Edge arguments (NULL means use default naming)
                         0,             -- Source vertex for path calculation
                         'out_gr',      -- Output table of shortest paths
                         'grp'          -- Grouping columns
);
SELECT * FROM out_gr ORDER BY grp,id;

 grp | id | weight | parent
-----+----+--------+--------
   0 |  0 |      0 |      0
   0 |  1 |      1 |      0
   0 |  2 |      1 |      0
   0 |  3 |      2 |      2
   0 |  4 |     10 |      0
   0 |  5 |      2 |      2
   0 |  6 |      3 |      5
   0 |  7 |      4 |      6
   1 |  0 |      0 |      0
   1 |  1 |      1 |      0
   1 |  2 |      1 |      0
   1 |  3 |      2 |      2
   1 |  4 |     10 |      0
   1 |  5 |    -10 |      4

Find the path to vertex 5 in every group

DROP TABLE IF EXISTS out_gr_path;
SELECT madlib.graph_sssp_get_path('out_gr',5,'out_gr_path');
SELECT * FROM out_gr_path ORDER BY grp;

 grp |  path
-----+---------
   0 | {0,2,5}
   1 | {0,4,5}

Notes

On a Greenplum cluster, the edge table should be distributed by the source vertex id column for better performance.
The Bellman-Ford algorithm [1] is used to implement SSSP. This algorithm allows negative edges but not negative cycles. In the case of graphs with negative cycles, an error will be given and no output table will be generated.
Also see the Grail project [2] for more background on graph analytics processing in relational databases.

Literature

[1] Bellman–Ford algorithm. https://en.wikipedia.org/wiki/Bellman%E2%80%93Ford_algorithm

[2] The case against specialized graph analytics engines, J. Fan, G. Soosai Raj, and J. M. Patel. CIDR 2015. http://cidrdb.org/cidr2015/Papers/CIDR15_Paper20.pdf