# Fixed Job Queue Implementation

The race condition occurs because multiple threads can access and modify the shared dictionaries (`self.jobs` and `self.results`) simultaneously. 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

            data = self.jobs[job_id]
            # Process the job
            result = processor(data)
            # Store result
            self.results[job_id] = result
            # Remove job from queue
            del self.jobs[job_id]
        
        return True

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

## Key Fixes:

1. **Added a `threading.Lock()`** in `__init__` to protect shared state
2. **Wrapped all shared state access** with `with self.lock:` context manager
3. **Protected all dictionary operations** (`jobs` and `results`) from concurrent access
4. **Ensured atomic operations** for reading, writing, and deleting from shared dictionaries

## Performance Note:

In a production system with slow processors, you might want to release the lock during the processor call to avoid blocking other threads:

```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]
        del self.jobs[job_id]
    
    # Process outside the lock
    result = processor(data)
    
    with self.lock:
        self.results[job_id] = result
    
    return True
```

The basic fix above maintains simplicity while ensuring thread safety for the shared state.