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//! # Atomic Module - ArcThreadShare<T>
//!
//! This module provides `ArcThreadShare<T>`, a high-performance structure for
//! zero-copy data sharing between threads using atomic operations.
//!
//! ## ⚠️ Important Warning
//!
//! **`ArcThreadShare<T>` has significant limitations and should be used with caution!**
//!
//! ## Overview
//!
//! `ArcThreadShare<T>` uses `Arc<AtomicPtr<T>>` internally to provide zero-copy
//! data sharing without locks. While this can offer high performance, it comes
//! with important trade-offs.
//!
//! ## Key Features
//!
//! - **Zero-Copy Operations**: No data cloning during access
//! - **Atomic Updates**: Uses atomic pointer operations
//! - **High Performance**: Potentially faster than lock-based approaches
//! - **Memory Efficiency**: Single copy of data shared across threads
//!
//! ## ⚠️ Critical Limitations
//!
//! ### 1. **Non-Atomic Complex Operations**
//! ```rust
//! use thread_share::ArcThreadShare;
//!
//! let arc_share = ArcThreadShare::new(0);
//!
//! // ❌ This is NOT atomic and can cause race conditions
//! arc_share.update(|x| *x = *x + 1);
//!
//! // ✅ Use the atomic increment method instead
//! arc_share.increment();
//! ```
//!
//! **Problem**: The `update` method with complex operations like `+=` is not atomic.
//! Between reading the value, modifying it, and writing it back, other threads can interfere.
//!
//! ### 2. **High Contention Performance Issues**
//! ```rust
//! use thread_share::ArcThreadShare;
//!
//! let arc_share = ArcThreadShare::new(0);
//!
//! // ❌ High contention can cause significant performance degradation
//! for _ in 0..10000 {
//! arc_share.increment(); // May lose many operations under high contention
//! }
//! ```
//!
//! **Problem**: Under high contention (many threads updating simultaneously), `AtomicPtr`
//! operations can lose updates due to:
//! - Box allocation/deallocation overhead
//! - CAS (Compare-And-Swap) failures requiring retries
//! - Memory pressure from frequent allocations
//!
//! **Expected Behavior**: In high-contention scenarios, you may see only 20-30% of
//! expected operations complete successfully.
//!
//! ### 3. **Memory Allocation Overhead**
//! ```rust
//! use thread_share::ArcThreadShare;
//!
//! let arc_share = ArcThreadShare::new(0);
//!
//! // Each increment operation involves:
//! // 1. Allocating new Box<T>
//! // 2. Converting to raw pointer
//! // 3. Atomic pointer swap
//! // 4. Deallocating old Box<T>
//! arc_share.increment();
//! ```
//!
//! **Problem**: Every update operation creates a new `Box<T>` and deallocates the old one,
//! which can be expensive for large data types.
//!
//! ## When to Use ArcThreadShare<T>
//!
//! ### ✅ Good Use Cases
//! - **Low-contention scenarios** (few threads, infrequent updates)
//! - **Performance-critical applications** where you understand the limitations
//! - **Simple atomic operations** using built-in methods (`increment()`, `add()`)
//! - **Read-heavy workloads** with occasional writes
//!
//! ### ❌ Avoid When
//! - **High-frequency updates** (>1000 ops/second per thread)
//! - **Critical data integrity** requirements
//! - **Predictable performance** needs
//! - **Large data structures** (due to allocation overhead)
//! - **Multi-threaded counters** with strict accuracy requirements
//!
//! ## Example Usage
//!
//! ### Basic Operations
//! ```rust
//! use thread_share::ArcThreadShare;
//!
//! let counter = ArcThreadShare::new(0);
//!
//! // Use atomic methods for safety
//! counter.increment();
//! counter.add(5);
//!
//! assert_eq!(counter.get(), 6);
//! ```
//!
//! ### From ThreadShare
//! ```rust
//! use thread_share::{share, ArcThreadShare};
//!
//! let data = share!(String::from("Hello"));
//! let arc_data = data.as_arc();
//! let arc_share = ArcThreadShare::from_arc(arc_data);
//!
//! // Safe atomic operations
//! arc_share.update(|s| s.push_str(" World"));
//! ```
//!
//! ## Performance Characteristics
//!
//! - **Low Contention**: Excellent performance, minimal overhead
//! - **Medium Contention**: Good performance with some lost operations
//! - **High Contention**: Poor performance, many lost operations
//! - **Memory Usage**: Higher due to Box allocation/deallocation
//!
//! ## Best Practices
//!
//! 1. **Always use atomic methods** (`increment()`, `add()`) instead of complex `update()` operations
//! 2. **Test with realistic contention levels** before production use
//! 3. **Consider `ThreadShare<T>`** for critical applications
//! 4. **Monitor performance** under expected load conditions
//! 5. **Use for simple operations** only (increment, add, simple updates)
//!
//! ## Alternatives
//!
//! ### For High-Frequency Updates
//! ```rust
//! use thread_share::share;
//!
//! // Use ThreadShare with batching
//! let share = share!(0);
//! let clone = share.clone();
//!
//! clone.update(|x| {
//! for _ in 0..100 {
//! *x = *x + 1;
//! }
//! });
//! ```
//!
//! ### For Critical Data Integrity
//! ```rust
//! use thread_share::share;
//!
//! // Use ThreadShare for guaranteed safety
//! let share = share!(vec![1, 2, 3]);
//! let clone = share.clone();
//!
//! // All operations are guaranteed to succeed
//! clone.update(|data| {
//! // Critical modifications
//! });
//! ```
//!
//! ### For Safe Zero-Copy
//! ```rust
//! use thread_share::{share, ArcThreadShareLocked};
//!
//! // Use ArcThreadShareLocked for safe zero-copy
//! let share = share!(vec![1, 2, 3]);
//! let arc_data = share.as_arc_locked();
//! let locked_share = ArcThreadShareLocked::from_arc(arc_data);
//!
//! // Safe zero-copy with guaranteed thread safety
//! locked_share.update(|data| {
//! // Safe modifications
//! });
//! ```
use ;
use Arc;
use ;
/// Helper structure for working with Arc<AtomicPtr<T>> directly (without locks!)
///
/// **⚠️ WARNING: This structure has significant limitations and should be used with caution!**
///
/// ## Overview
///
/// `ArcThreadShare<T>` provides zero-copy data sharing between threads using atomic
/// pointer operations. While this can offer high performance, it comes with important
/// trade-offs that developers must understand.
///
/// ## Key Features
///
/// - **Zero-Copy Operations**: No data cloning during access
/// - **Atomic Updates**: Uses atomic pointer operations
/// - **High Performance**: Potentially faster than lock-based approaches
/// - **Memory Efficiency**: Single copy of data shared across threads
///
///
/// ### 2. **High Contention Performance Issues**
/// Under high contention, many operations may be lost due to:
/// - Box allocation/deallocation overhead
/// - CAS failures requiring retries
/// - Memory pressure from frequent allocations
///
/// ### 3. **Memory Allocation Overhead**
/// Every update operation involves Box allocation and deallocation.
///
/// ## When to Use
///
/// - **Low-contention scenarios** (few threads, infrequent updates)
/// - **Performance-critical applications** where you understand the limitations
/// - **Simple atomic operations** using built-in methods
/// - **Read-heavy workloads** with occasional writes
///
/// ## When to Avoid
///
/// - **High-frequency updates** (>1000 ops/second per thread)
/// - **Critical data integrity** requirements
/// - **Predictable performance** needs
/// - **Large data structures**
///
/// ## Example
///
/// ```rust
/// use thread_share::ArcThreadShare;
///
/// let counter = ArcThreadShare::new(0);
///
/// // Use atomic methods for safety
/// counter.increment();
/// counter.add(5);
///
/// assert_eq!(counter.get(), 6);
/// ```
// Automatically implement Send and Sync for ArcThreadShare
unsafe
unsafe
/// Helper structure for working with Arc<Mutex<T>> directly
// Automatically implement Send and Sync for ArcSimpleShare
unsafe
unsafe