Breadth-first search (BFS)

This method returns a DataFrame of valid shortest paths from vertices matching fromExpr to vertices matching toExpr. If multiple paths are valid and have the same length, the DataFrame will return one Row for each path. If no paths are valid, the DataFrame will be empty. Note: "Shortest" means globally shortest path. I.e., if the shortest path between two vertices matching fromExpr and toExpr is length 5 (edges) but no path is shorter than 5, then all paths returned by BFS will have length 5.

The returned DataFrame will have the following columns:

• from start vertex of path
• e[i] edge i in the path, indexed from 0
• v[i] intermediate vertex i in the path, indexed from 1
• to end vertex of path Each of these columns is a StructType whose fields are the same as the columns of GraphFrame.vertices or GraphFrame.edges.

For example, suppose we have a graph g. Say the vertices DataFrame of g has columns "id" and "job", and the edges DataFrame of g has columns "src", "dst", and "relation".

// Search from vertex "Joe" to find the closet vertices with attribute job = CEO.
g.bfs(col("id") === "Joe", col("job") === "CEO").run()

If we found a path of 3 edges, each row would have columns:

from | e0 | v1 | e1 | v2 | e2 | to

In the above row, each vertex column (from, v1, v2, to) would have fields "id" and "job" (just like g.vertices). Each edge column (e0, e1, e2) would have fields "src", "dst", and "relation".

If there are ties, then each of the equal paths will be returned as a separate Row.

If one or more vertices match both the from and to conditions, then there is a 0-hop path. The returned DataFrame will have the "from" and "to" columns (as above); however, the "from" and "to" columns will be exactly the same. There will be one row for each vertex in GraphFrame.vertices matching both fromExpr and toExpr.

Parameters:

• fromExpr Spark SQL expression specifying valid starting vertices for the BFS. This condition will be matched against each vertex's id or attributes. To start from a specific vertex, this could be "id = [start vertex id]". To start from multiple valid vertices, this can operate on vertex attributes.
• toExpr Spark SQL expression specifying valid target vertices for the BFS. This condition will be matched against each vertex's id or attributes.
• maxPathLength Limit on the length of paths. If no valid paths of length <= maxPathLength are found, then the BFS is terminated. (default = 10)
• edgeFilter Spark SQL expression specifying edges which may be used in the search. This allows the user to disallow crossing certain edges. Such filters can be applied post-hoc after BFS, run specifying the filter here is more efficient.

Returns:

• DataFrame of valid shortest paths found in the BFS

from | e0 | v1 | e1 | v2 | e2 | to }}} to) would have fields "id" and "job" (just like g.vertices). Each edge column (e0, e1, e2) would have fields "src", "dst", and "relation".

If there are ties, then each of the equal paths will be returned as a separate Row.

If one or more vertices match both the from and to conditions, then there is a 0-hop path. The returned DataFrame will have the "from" and "to" columns (as above); however, the "from" and "to" columns will be exactly the same. There will be one row for each vertex in GraphFrame.vertices matching both fromExpr and toExpr.

Parameters:

• fromExpr Spark SQL expression specifying valid starting vertices for the BFS. This condition will be matched against each vertex's id or attributes. To start from a specific vertex, this could be "id = [start vertex id]". To start from multiple valid vertices, this can operate on vertex attributes.
• toExpr Spark SQL expression specifying valid target vertices for the BFS. This condition will be matched against each vertex's id or attributes.
• maxPathLength Limit on the length of paths. If no valid paths of length <= maxPathLength are found, then the BFS is terminated. (default = 10)
• edgeFilter Spark SQL expression specifying edges which may be used in the search. This allows the user to disallow crossing certain edges. Such filters can be applied post-hoc after BFS, run specifying the filter here is more efficient.

Returns:

• DataFrame of valid shortest paths found in the BFS