When implementing algorithms, ensure they handle all edge cases correctly and robustly. Code should gracefully manage exceptional conditions rather than producing incorrect results or panicking.
Key practices:
Test boundary conditions: Verify your algorithm works correctly with empty collections, maximum/minimum values, and other edge cases.
Handle hash functions comprehensively: Include all relevant state in hash functions to prevent unintended collisions.
// Suboptimal: Missing important fields
impl Hash for ImportTask {
fn hash<H: Hasher>(&self, state: &mut H) {
self.key_range.hash(state);
self.path.hash(state);
}
}
// Better: Includes all relevant fields
impl Hash for ImportTask {
fn hash<H: Hasher>(&self, state: &mut H) {
self.shard_identity.hash(state); // Include this for robustness
self.key_range.hash(state);
self.path.hash(state);
}
}
// Problematic: Assumes perfect precision
if total_percentage == 100.0 {
// ...
}
// Better: Use appropriate epsilon or handle the last case separately
let last_variant = variants.last().unwrap();
if mapped_user_id <= total_percentage / 100.0 {
return Some(last_variant.key.clone());
}
// Incorrect: Complex condition with a logic error
if !(plan_hash == progress.import_plan_hash || progress.jobs != plan.jobs.len()) {
anyhow::bail!("Import plan does not match storcon metadata");
}
// Correct: Simplified, clear conditions
if plan_hash != progress.import_plan_hash {
anyhow::bail!("Import plan hash does not match storcon metadata hash");
}
if progress.jobs != plan.jobs.len() {
anyhow::bail!("Import plan job length does not match storcon metadata");
}
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