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In performance-sensitive code, avoid doing expensive operations repeatedly inside loops (repeated flattening/sorting, repeated filesystem attribute resolution, repeated regex/compiler/formatter construction, repeated list+search scans). Also ensure your tree/hierarchy logic uses consistent keys for both ordering and relationship detection.

Apply these practices:

• Hoist/cache heavy objects used per call (e.g., formatters, regex).
• Don’t re-build global intermediate structures inside inner loops (e.g., avoid flatMap+sort per node); restructure the algorithm to compute parent candidates once or maintain incremental state.
• Prefer bulk APIs over “list+search per item” patterns.
• Request needed filesystem attributes in bulk; resolve symlink targets only when necessary.
• Avoid allocation-heavy functional chains in hot paths; prefer lazy/nested loops that can stop early.
• Pre-size collections using the final workload size (after deduping/filtering).

Example (hoist cached regex):

extension GraphBuilder {
    private static let argSubRegex: NSRegularExpression = {
        try! NSRegularExpression(pattern: #"\$\{([A-Za-z_][A-Za-z0-9_]*)\}"#)
    }()

    func substituteArgs(_ input: String, inFromContext: Bool) -> String {
        // use Self.argSubRegex here (don’t compile per call)
        // ...
        return input
    }
}

Example (avoid reserveCapacity mismatch):

let mounts = mounts.dedupe()
var result: [Filesystem] = []
result.reserveCapacity(mounts.count)

If a change introduces a new loop over many elements, re-check for inner-loop allocations, repeated sorting/flattening, repeated filesystem operations, and repeated scans/caches not being reused.

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