When implementing search or traversal algorithms, use short-circuit evaluation strategically to avoid unnecessary computation while ensuring correctness. Short-circuiting is beneficial when the first match is sufficient, but can lead to incorrect results when complete information is needed.
When implementing search or traversal algorithms, use short-circuit evaluation strategically to avoid unnecessary computation while ensuring correctness. Short-circuiting is beneficial when the first match is sufficient, but can lead to incorrect results when complete information is needed.
For example, when implementing a search function:
// Good: Use short-circuit when first match is sufficient
fn find_python_installation(installations: &[Installation]) -> Option<&Installation> {
installations.iter().find(|installation|
request.matches_installation(installation))
}
// Good: Use closure to make short-circuit logic clearer
let (db, actual_rev, maybe_task) = || -> Result<(GitDatabase, GitOid, Option<usize>)> {
// Short-circuit early if we already have the commit
if let (Some(rev), Some(db)) = (self.git.precise(), &maybe_db) {
if db.contains(rev) {
debug!("Using existing Git source `{}`", self.git.repository());
return Ok((maybe_db.unwrap(), rev, None));
}
}
// Continue with fetching otherwise...
}();
// Bad: Using short-circuit when complete information is needed
// @any isn't sorted by version during discovery because we short-circuit
// when we find an interpreter that meets the request
// @latest needs to scan ALL Python interpreters to select the latest version
Consider the full requirements before deciding on a traversal strategy. When implementing algorithms that search or process collections:
.find()) when the first match is sufficient.filter() + .max_by_key()) when you need to examine all elementsEnter the URL of a public GitHub repository