# Fixed Job Queue Implementation

The race condition in the original code occurs because multiple threads can simultaneously access and modify the shared `jobs` and `results` dictionaries without synchronization. Here's the corrected implementation using proper locking:

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        self.lock = threading.Lock()

    def add_job(self, job_id, data):
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        with self.lock:
            if job_id not in self.jobs:
                return False

            # Acquire data and remove job from queue atomically
            data = self.jobs[job_id]
            del self.jobs[job_id]
        
        # Process outside the lock to avoid blocking other operations
        result = processor(data)

        with self.lock:
            self.results[job_id] = result
        
        return True

    def get_result(self, job_id):
        with self.lock:
            return self.results.get(job_id)
```

## Key Fixes

1. **Added a threading Lock** (`self.lock`) to protect shared state access.

2. **Protected all dictionary operations** with `with self.lock:` context managers to ensure atomic access.

3. **Removed the job from the queue during processing** while holding the lock. This prevents other threads from attempting to process the same job simultaneously.

4. **Processed the job outside the lock** to avoid blocking other threads while the processor runs (improves performance).

## Why This Works

- **Atomic job removal**: By deleting the job from `self.jobs` while holding the lock, we ensure only one thread can process each job.
- **No lost updates**: All writes to `self.results` are protected by the lock.
- **Safe concurrent access**: Multiple threads can call `add_job`, `process_job`, and `get_result` concurrently without corrupting shared state.
- **Better performance**: The processor runs outside the lock, so other threads aren't blocked during long-running computations.

## Alternative: Using RLock

If you need the same thread to acquire the lock multiple times (e.g., for nested method calls), you can use `threading.RLock()` instead of `threading.Lock()`.