# Fixed Job Queue Implementation

Here's the corrected implementation with proper thread synchronization:

```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
            
            # Extract data and remove job while holding the lock
            data = self.jobs[job_id]
            del self.jobs[job_id]
        
        # Process outside the lock to avoid blocking other threads
        result = processor(data)
        
        # Store result while holding the lock
        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 Applied

1. **Added a threading.Lock** - Protects all access to shared data structures (`self.jobs` and `self.results`)

2. **Protected the check-then-act pattern** - The existence check and job removal happen atomically within the lock

3. **Released lock during processing** - The processor function runs outside the lock to prevent blocking other threads from accessing the queue

4. **Protected result storage** - Writing to `self.results` happens within the lock to prevent concurrent writes

5. **Protected read operations** - The `get_result()` method now safely reads from the results dictionary

## Why This Fixes the Race Condition

**Before (Bug):** Multiple threads could simultaneously:
- Read `self.jobs[job_id]`
- Write `self.results[job_id]`
- Delete from `self.jobs[job_id]`

This could cause lost updates, data corruption, or inconsistent state.

**After (Fixed):** All access to shared state is serialized through the lock, ensuring:
- Only one thread can modify `self.jobs` or `self.results` at a time
- The check-then-act pattern is atomic
- No two threads can write to the same result key simultaneously

## Additional Considerations

For production use, you might also want to:
1. Add a `max_jobs` limit to prevent unbounded memory growth
2. Implement job expiration/timeout handling
3. Use `threading.RLock` if the same thread needs to acquire the lock multiple times
4. Consider using `queue.Queue` for a more standard thread-safe job processing pattern