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Avoid unnecessary memory allocations in performance-critical code paths. These allocations not only consume memory but also trigger expensive CPU operations that can significantly impact system performance.

Key optimization techniques:

1. Use efficient data structures:
   • Box large enum variants to reduce memory footprint ```rust // Before enum Schedule { Cron(OwnedScheduleIterator), // 288 bytes Every(Duration), // 16 bytes }

   // After enum Schedule { Cron(Box<OwnedScheduleIterator>), // 16 bytes Every(Duration), // 16 bytes } ```

   • Prefer HashMap over BTreeMap for lookup-heavy operations
   • Consider Arc<str> or &str over String when appropriate
2. Optimize container operations:
   • Use contains() instead of iter().any() for membership checks ```rust // Less efficient if paths_without_authz.iter().any(|disabled_authz_path| *disabled_authz_path == path) { // … }

   // More efficient if paths_without_authz.contains(&path) { // … } ```

   • Consider how you handle capacity in vectors (clear vs. take)
   • Use iterators for lazy evaluation rather than materializing full collections
3. Avoid string overhead:
   • Use to_string() instead of format!() for simple conversions ```rust // Less efficient let partition_key = data_types::PartitionKey::from(format!(“{}”, parquet_file.chunk_time));

   // More efficient let partition_key = data_types::PartitionKey::from(parquet_file.chunk_time.to_string()); ```

   • Avoid rebuilding schemas or data structures repeatedly in hot paths

Remember that even small allocations can have a significant impact when they occur frequently in critical code paths, particularly in high-throughput systems handling many requests or processing large datasets.