# 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. This can lead to data corruption or inconsistent state.

## Solution

Use a `threading.Lock` to protect all access to shared data structures. The lock should be held during critical sections but released during the actual job processing to avoid blocking other threads.

```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):
        # Acquire lock to safely check and read job
        with self.lock:
            if job_id not in self.jobs:
                return False

            data = self.jobs[job_id]
        
        # Process outside the lock to avoid blocking other threads
        # (processor may take time, so we don't want to hold the lock)
        result = processor(data)
        
        # Re-acquire lock to safely update results and remove job
        with self.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 `self.lock = threading.Lock()`** in `__init__` to create a lock object.
2. **Protected all shared data access** with `with self.lock:` context managers.
3. **Released lock during processing** to avoid blocking other threads while the processor function runs.
4. **Re-acquired lock for write operations** (updating results and deleting jobs).

## Why This Works

- **Thread Safety**: Only one thread can modify the dictionaries at a time
- **Performance**: Lock is released during the actual processing, allowing other jobs to be processed concurrently
- **Atomic Operations**: Check-then-read and write-delete operations are now atomic with respect to other threads

## Usage Example

```python
queue = JobQueue()

# Add jobs
queue.add_job("job1", "data1")
queue.add_job("job2", "data2")

# Process jobs (can be called from multiple threads)
queue.process_job("job1", lambda x: x.upper())
queue.process_job("job2", lambda x: x.lower())

# Get results
result1 = queue.get_result("job1")
result2 = queue.get_result("job2")
```

This implementation ensures thread-safe job processing while maintaining good performance by not holding the lock during the actual job computation.