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Pruning for Efficient Searches

Pruning allows you to create conditions that reduce the amount of data examined during a search, improving query performance and reducing overhead. You can apply pruning to vertices, edges, paths, and any variables defined earlier in the query.

The following examples are based on the traversal graph. If you create this example graph, then you can run any of the queries listed in this page.

Pruning with theTruth Edge Condition​

FOR v, e, p IN 1..5 OUTBOUND 'circles/A' GRAPH 'traversalGraph'
    PRUNE e.theTruth == true
    RETURN { vertices: p.vertices[*]._key, edges: p.edges[*].label }

In this query, the search continues until an edge with theTruth == true is found. The path containing this edge is returned, and the search doesn't continue beyond this edge. All returned paths either have no edge with theTruth == true, or the last edge on the path has theTruth == true.

Pruning with Vertex Key Condition​

FOR v, e, p IN 1..5 OUTBOUND 'circles/A' GRAPH 'traversalGraph'
    PRUNE v._key == 'G'
    FILTER v._key == 'G'
    RETURN { vertices: p.vertices[*]._key, edges: p.edges[*].label }

This query searches for all paths from the source circles/A to the vertex circles/G. The PRUNE statement stops the search as soon as G is found and doesn't go beyond G or loop back to it. The FILTER statement removes all paths that don't end in G, including shorter ones that haven't been eliminated by PRUNE. As a result, all paths from A to G are returned.

Pruning Upon Reaching a Certain Collection​

FOR v, e, p IN 1..5 OUTBOUND 'circles/A' GRAPH 'traversalGraph'
    PRUNE IS_SAME_COLLECTION('circles', v)
    RETURN { vertices: p.vertices[*]._key, edges: p.edges[*].label }

In this query, pruning occurs as soon as a vertex in the 'circles' collection is reached. This helps to focus the search on a specific collection, making it more efficient.