# Fixed JobQueue Implementation

The race condition occurs because multiple threads can simultaneously read, write, and delete from the shared `jobs` and `results` dictionaries. Here's the complete fixed 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
            
            # Copy data before releasing lock to avoid blocking during processing
            data = self.jobs[job_id]
            del self.jobs[job_id]
        
        # Process outside the lock to allow concurrent job processing
        result = processor(data)
        
        # Store result under lock for thread safety
        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 Lock**: `self._lock = threading.Lock()` protects all shared state access.

2. **Protected `add_job()`**: Ensures only one thread can add jobs at a time.

3. **Protected `process_job()`**: 
   - Checks and removes the job from `self.jobs` under lock
   - Processes the job **outside** the lock (allows concurrent processing)
   - Writes the result back under lock (prevents race condition on `self.results`)

4. **Protected `get_result()`**: Ensures thread-safe read access to results.

## Why This Works:

- **Atomic Operations**: Reading, writing, and deleting from shared dictionaries are now atomic.
- **Performance**: CPU-intensive processing happens outside the lock, allowing other jobs to be queued or retrieved concurrently.
- **No Lost Updates**: Only one thread can write to `self.results[job_id]` at a time.
- **No Dangling Deletes**: The job is removed from `self.jobs` atomically before processing begins.

## Alternative (Simpler but Less Performant):

If the processor is fast, you can keep everything under one lock:

```python
def process_job(self, job_id, processor):
    with self._lock:
        if job_id not in self.jobs:
            return False
        
        data = self.jobs[job_id]
        result = processor(data)
        self.results[job_id] = result
        del self.jobs[job_id]
    
    return True
```

This is simpler but blocks all other operations while processing. The first solution is generally better for performance.