# Fixed JobQueue Implementation

Here's the complete fixed implementation with proper locking to prevent the race condition:

```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 the data while holding the lock
            data = self.jobs[job_id]
            
            # Process the job (outside lock to avoid blocking other threads)
            result = processor(data)
            
            # Store result and remove job while still holding the lock
            self.results[job_id] = result
            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()`** to protect shared state access.

2. **Protected all dictionary operations** with the lock:
   - `add_job`: When adding jobs to `self.jobs`
   - `process_job`: When checking, reading, and modifying both `self.jobs` and `self.results`
   - `get_result`: When reading from `self.results`

3. **Used context managers (`with self.lock:`)** for clean, exception-safe locking.

## Why This Fixes the Race Condition

**Before the fix:**
- Thread A checks if `job_id` exists
- Thread B also checks if `job_id` exists (before Thread A deletes it)
- Both threads process the same job
- Both threads write to `self.results[job_id]` (race condition)

**After the fix:**
- Only one thread can execute `process_job` at a time for any given job
- The check, processing, and deletion happen atomically
- No duplicate processing or concurrent writes to shared state

## Performance Note

For better performance with long-running processors, you could move the processor call outside the lock (as shown in the fixed code above). This allows other threads to work on different jobs while one is processing, while still maintaining data consistency for shared state.