# FFmpeg Real-World Scenarios & Recipes

Production scenarios with complete, tested solutions.

---

## Scenario 1: Web Video Platform

**Requirements:**
- Accept user-uploaded videos (up to 2GB)
- Generate thumbnail at specific timestamp
- Create adaptive bitrate variants (720p, 480p, 360p)
- Generate GIF preview
- Track progress

### Solution

```python
import asyncio
from pathlib import Path
from typing import Optional, Callable
import subprocess
import json

class WebVideoPlatform:
    """Complete video processing pipeline."""

    def __init__(self, output_dir: str = "./videos"):
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(exist_ok=True)

    async def validate_upload(self, video_path: str) -> dict:
        """Validate uploaded video."""
        info = self._get_video_info(video_path)

        if not info:
            raise ValueError("Invalid video")

        if info['duration'] > 3600:  # 1 hour max
            raise ValueError("Video too long")

        if Path(video_path).stat().st_size / (1024**3) > 2:
            raise ValueError("File too large")

        return info

    def _get_video_info(self, video_path: str) -> Optional[dict]:
        """Get video metadata."""
        cmd = [
            'ffprobe', '-v', 'error',
            '-select_streams', 'v:0',
            '-show_entries', 'stream=width,height,r_frame_rate,duration',
            '-of', 'json',
            video_path
        ]

        try:
            result = subprocess.run(cmd, capture_output=True, text=True, check=True)
            data = json.loads(result.stdout)

            if not data['streams']:
                return None

            stream = data['streams'][0]
            fps_str = stream.get('r_frame_rate', '30/1')
            fps = float(fps_str.split('/')[0]) / float(fps_str.split('/')[1])

            return {
                'width': stream['width'],
                'height': stream['height'],
                'fps': fps,
                'duration': float(stream['duration'])
            }
        except:
            return None

    async def generate_thumbnail(
        self,
        video_path: str,
        output_name: str,
        timestamp: str = "5"
    ) -> Path:
        """Generate thumbnail at timestamp."""

        output_path = self.output_dir / f"{output_name}_thumb.jpg"

        cmd = [
            'ffmpeg', '-ss', timestamp, '-i', video_path,
            '-frames:v', '1', '-q:v', '2', '-y',
            str(output_path)
        ]

        proc = await asyncio.create_subprocess_exec(*cmd)
        await proc.wait()

        return output_path

    async def generate_preview_gif(
        self,
        video_path: str,
        output_name: str,
        duration: float
    ) -> Path:
        """Generate animated GIF preview."""

        output_path = self.output_dir / f"{output_name}_preview.gif"
        palette_path = self.output_dir / f"{output_name}_palette.png"

        # Determine clip duration (max 10 seconds)
        clip_duration = min(10, duration)

        # Pass 1: Generate palette
        cmd1 = [
            'ffmpeg', '-i', video_path,
            '-t', str(clip_duration),
            '-vf', 'fps=10,scale=480:-1,palettegen',
            str(palette_path),
            '-y'
        ]

        proc = await asyncio.create_subprocess_exec(*cmd1)
        await proc.wait()

        # Pass 2: Create GIF
        cmd2 = [
            'ffmpeg', '-i', video_path,
            '-i', str(palette_path),
            '-t', str(clip_duration),
            '-lavfi', 'fps=10,scale=480:-1[x];[x][1:v]paletteuse',
            str(output_path),
            '-y'
        ]

        proc = await asyncio.create_subprocess_exec(*cmd2)
        await proc.wait()

        palette_path.unlink(missing_ok=True)
        return output_path

    async def generate_adaptive_variants(
        self,
        video_path: str,
        output_name: str,
        on_progress: Optional[Callable[[str, float], None]] = None
    ) -> dict:
        """Generate multiple quality variants."""

        variants = {
            'hd': {'width': 1280, 'height': 720, 'bitrate': '5000k'},
            'sd': {'width': 854, 'height': 480, 'bitrate': '2500k'},
            'mobile': {'width': 640, 'height': 360, 'bitrate': '1000k'},
        }

        outputs = {}

        for variant_name, config in variants.items():
            output_path = self.output_dir / f"{output_name}_{variant_name}.mp4"

            cmd = [
                'ffmpeg', '-i', video_path,
                '-c:v', 'libx264',
                '-preset', 'fast',
                '-b:v', config['bitrate'],
                '-maxrate', config['bitrate'],
                '-bufsize', config['bitrate'],
                '-s', f"{config['width']}x{config['height']}",
                '-c:a', 'aac',
                '-b:a', '128k',
                '-y',
                str(output_path)
            ]

            proc = await asyncio.create_subprocess_exec(*cmd)
            await proc.wait()

            if on_progress:
                on_progress(variant_name, 100.0)

            outputs[variant_name] = str(output_path)

        return outputs

    async def process_upload(
        self,
        video_path: str,
        video_id: str,
        on_progress: Optional[Callable[[str, float], None]] = None
    ) -> dict:
        """Complete upload processing pipeline."""

        # 1. Validate
        info = await self.validate_upload(video_path)
        print(f"Valid video: {info}")

        # 2. Generate thumbnail
        if on_progress:
            on_progress("thumbnail", 0)

        thumb = await self.generate_thumbnail(video_path, video_id, "5")

        if on_progress:
            on_progress("thumbnail", 100)

        # 3. Generate GIF preview
        if on_progress:
            on_progress("gif", 0)

        gif = await self.generate_preview_gif(video_path, video_id, info['duration'])

        if on_progress:
            on_progress("gif", 100)

        # 4. Generate variants
        if on_progress:
            on_progress("variants", 0)

        variants = await self.generate_adaptive_variants(video_path, video_id, on_progress)

        if on_progress:
            on_progress("variants", 100)

        return {
            'metadata': info,
            'thumbnail': str(thumb),
            'preview_gif': str(gif),
            'variants': variants
        }

# Usage
async def main():
    def on_progress(task: str, progress: float):
        print(f"{task}: {progress:.0f}%")

    platform = WebVideoPlatform()

    result = await platform.process_upload(
        "user_video.mp4",
        "video_123",
        on_progress=on_progress
    )

    print("Processing complete!")
    print(json.dumps(result, indent=2))

# asyncio.run(main())
```

---

## Scenario 2: Content Moderation

**Requirements:**
- Extract keyframes for moderation review
- Fast processing (minimize API latency)
- Generate storyboard for quick scanning
- Flag scenes with cuts/transitions

### Solution

```python
import subprocess
import json
from pathlib import Path
from typing import List

class ContentModerationPipeline:
    """Fast keyframe extraction for moderation."""

    def __init__(self, cache_dir: str = "./moderation"):
        self.cache_dir = Path(cache_dir)
        self.cache_dir.mkdir(exist_ok=True)

    def extract_keyframes(self, video_path: str) -> List[Path]:
        """Extract I-frames (fastest method)."""

        output_pattern = str(self.cache_dir / f"keyframe_%04d.jpg")

        cmd = [
            'ffmpeg', '-i', video_path,
            '-vf', "select='eq(pict_type,I)',scale=320:-1",
            '-q:v', '2',
            output_pattern,
            '-y'
        ]

        subprocess.run(cmd, capture_output=True, check=True)

        # Collect output files
        keyframes = sorted(self.cache_dir.glob("keyframe_*.jpg"))
        return keyframes

    def generate_storyboard(
        self,
        video_path: str,
        rows: int = 10,
        cols: int = 10
    ) -> Path:
        """Generate storyboard grid of frames."""

        output_file = self.cache_dir / "storyboard.png"

        # Extract frames at regular intervals
        cmd = [
            'ffmpeg', '-i', video_path,
            '-vf', f"scale=160:90,fps=1/10,tile={cols}x{rows}",
            '-frames:v', '1',
            '-q:v', '3',
            str(output_file),
            '-y'
        ]

        subprocess.run(cmd, capture_output=True, check=True)
        return output_file

    def detect_scene_changes(
        self,
        video_path: str,
        threshold: float = 0.4
    ) -> List[dict]:
        """Detect scene changes and return timestamps."""

        output_file = self.cache_dir / "scenes.txt"

        cmd = [
            'ffmpeg', '-i', video_path,
            '-vf', f"select='gt(scene,{threshold})',metadata=print:file={output_file}",
            '-f', 'null',
            '-'
        ]

        subprocess.run(cmd, capture_output=True, check=True)

        # Parse output
        scenes = []
        if output_file.exists():
            with open(output_file) as f:
                for line in f:
                    if 'Parsed_select' in line:
                        # Extract timestamp
                        parts = line.split()
                        for part in parts:
                            if part.startswith('pts_time:'):
                                timestamp = float(part.split(':')[1])
                                scenes.append({
                                    'timestamp': timestamp,
                                    'frame_number': len(scenes)
                                })

        return scenes

    def moderate(self, video_path: str) -> dict:
        """Run complete moderation pipeline."""

        print("Extracting keyframes...")
        keyframes = self.extract_keyframes(video_path)

        print("Generating storyboard...")
        storyboard = self.generate_storyboard(video_path)

        print("Detecting scene changes...")
        scenes = self.detect_scene_changes(video_path)

        return {
            'keyframe_count': len(keyframes),
            'keyframes': [str(f) for f in keyframes],
            'storyboard': str(storyboard),
            'scene_changes': scenes,
            'total_scenes': len(scenes)
        }

# Usage
pipeline = ContentModerationPipeline()
result = pipeline.moderate("suspicious_video.mp4")

print(f"Keyframes extracted: {result['keyframe_count']}")
print(f"Scene changes detected: {result['total_scenes']}")
print(f"Storyboard: {result['storyboard']}")

# Send keyframes to ML moderation API
for kf in result['keyframes'][:5]:  # First 5 keyframes
    # moderation_api.scan_image(kf)
    pass
```

---

## Scenario 3: Batch Video Processing Pipeline

**Requirements:**
- Process hundreds of videos daily
- Minimize CPU/memory usage
- Graceful error handling
- Progress tracking
- Resume on failure

### Solution

```python
import asyncio
import sqlite3
from dataclasses import dataclass
from enum import Enum
from pathlib import Path
from typing import Optional, List
import subprocess

class ProcessingStatus(Enum):
    PENDING = "pending"
    PROCESSING = "processing"
    COMPLETED = "completed"
    FAILED = "failed"

@dataclass
class ProcessingJob:
    id: str
    input_path: str
    output_path: str
    status: ProcessingStatus
    error: Optional[str] = None

class BatchProcessingDB:
    """Track processing jobs in SQLite."""

    def __init__(self, db_path: str = "processing.db"):
        self.db_path = db_path
        self._init_db()

    def _init_db(self):
        """Initialize database schema."""
        with sqlite3.connect(self.db_path) as conn:
            conn.execute("""
                CREATE TABLE IF NOT EXISTS jobs (
                    id TEXT PRIMARY KEY,
                    input_path TEXT,
                    output_path TEXT,
                    status TEXT,
                    error TEXT,
                    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
                )
            """)
            conn.commit()

    def add_job(self, job_id: str, input_path: str, output_path: str):
        """Add new job."""
        with sqlite3.connect(self.db_path) as conn:
            conn.execute(
                "INSERT OR REPLACE INTO jobs (id, input_path, output_path, status) VALUES (?, ?, ?, ?)",
                (job_id, input_path, output_path, ProcessingStatus.PENDING.value)
            )
            conn.commit()

    def get_pending_jobs(self, limit: int = 10) -> List[ProcessingJob]:
        """Get jobs waiting to be processed."""
        with sqlite3.connect(self.db_path) as conn:
            rows = conn.execute(
                "SELECT id, input_path, output_path, status FROM jobs WHERE status = ? LIMIT ?",
                (ProcessingStatus.PENDING.value, limit)
            ).fetchall()

            return [
                ProcessingJob(
                    id=row[0],
                    input_path=row[1],
                    output_path=row[2],
                    status=ProcessingStatus(row[3])
                )
                for row in rows
            ]

    def update_status(self, job_id: str, status: ProcessingStatus, error: Optional[str] = None):
        """Update job status."""
        with sqlite3.connect(self.db_path) as conn:
            conn.execute(
                "UPDATE jobs SET status = ?, error = ? WHERE id = ?",
                (status.value, error, job_id)
            )
            conn.commit()

class BatchVideoProcessor:
    """Production-grade batch processing."""

    def __init__(
        self,
        max_parallel_jobs: int = 4,
        max_cores_per_job: int = 2
    ):
        self.max_parallel_jobs = max_parallel_jobs
        self.max_cores_per_job = max_cores_per_job
        self.db = BatchProcessingDB()
        self.active_jobs = 0

    async def process_job(self, job: ProcessingJob) -> bool:
        """Process single job."""

        self.db.update_status(job.id, ProcessingStatus.PROCESSING)

        try:
            # Validate input
            if not Path(job.input_path).exists():
                raise FileNotFoundError(f"Input not found: {job.input_path}")

            # Build command with thread limiting
            cmd = [
                'ffmpeg', '-i', job.input_path,
                '-c:v', 'libx264',
                '-crf', '23',
                '-preset', 'fast',
                '-threads', str(self.max_cores_per_job),
                '-c:a', 'aac',
                '-y',
                job.output_path
            ]

            # Execute with timeout
            proc = await asyncio.create_subprocess_exec(*cmd)
            returncode = await asyncio.wait_for(proc.wait(), timeout=3600)

            if returncode != 0:
                raise RuntimeError(f"FFmpeg failed with code {returncode}")

            self.db.update_status(job.id, ProcessingStatus.COMPLETED)
            print(f"✓ Completed: {job.id}")
            return True

        except asyncio.TimeoutError:
            self.db.update_status(job.id, ProcessingStatus.FAILED, "Timeout")
            print(f"✗ Timeout: {job.id}")
            return False

        except Exception as e:
            self.db.update_status(job.id, ProcessingStatus.FAILED, str(e))
            print(f"✗ Failed: {job.id} - {e}")
            return False

    async def process_queue(self):
        """Process queue indefinitely."""

        while True:
            # Get pending jobs
            pending = self.db.get_pending_jobs(limit=self.max_parallel_jobs)

            if not pending:
                print("No pending jobs. Waiting...")
                await asyncio.sleep(10)
                continue

            # Process in parallel
            tasks = [self.process_job(job) for job in pending]
            results = await asyncio.gather(*tasks)

            completed = sum(results)
            print(f"Batch complete: {completed}/{len(pending)} succeeded")

            await asyncio.sleep(5)  # Brief pause between batches

# Usage
async def main():
    processor = BatchVideoProcessor(max_parallel_jobs=4)

    # Add some jobs
    for i in range(10):
        processor.db.add_job(
            f"job_{i:03d}",
            f"input_{i}.mp4",
            f"output_{i}.mp4"
        )

    # Start processing
    await processor.process_queue()

# asyncio.run(main())
```

---

## Scenario 4: Real-Time Stream Processing

**Requirements:**
- Monitor incoming stream segments
- Re-encode to multiple bitrates
- Generate scene thumbnails
- Minimal latency

### Solution

```python
import asyncio
import subprocess
from pathlib import Path
from typing import Callable, Optional
from datetime import datetime

class StreamProcessor:
    """Process incoming video segments."""

    def __init__(self, watch_dir: str, output_dir: str):
        self.watch_dir = Path(watch_dir)
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(exist_ok=True)
        self.processed_files = set()

    async def process_segment(
        self,
        segment_path: str,
        segment_id: str,
        on_complete: Optional[Callable[[str], None]] = None
    ) -> dict:
        """Process single stream segment."""

        results = {
            'segment_id': segment_id,
            'timestamp': datetime.now().isoformat(),
            'variants': {},
            'thumbnail': None
        }

        # Extract timestamp from segment
        cmd_thumb = [
            'ffmpeg', '-ss', '0', '-i', segment_path,
            '-frames:v', '1', '-q:v', '2',
            str(self.output_dir / f"{segment_id}_thumb.jpg"),
            '-y'
        ]

        proc = await asyncio.create_subprocess_exec(*cmd_thumb)
        await proc.wait()
        results['thumbnail'] = f"{segment_id}_thumb.jpg"

        # Transcode to variants (in parallel)
        variants = {
            '720p': '-vf scale=1280:720 -b:v 5000k',
            '480p': '-vf scale=854:480 -b:v 2500k',
            '360p': '-vf scale=640:360 -b:v 1000k',
        }

        tasks = []
        for variant_name, filters in variants.items():
            output_path = str(self.output_dir / f"{segment_id}_{variant_name}.mp4")

            cmd = [
                'ffmpeg', '-i', segment_path,
                '-c:v', 'libx264',
                '-preset', 'ultrafast',  # Minimize latency
                '-b:v', filters.split()[-1],
                '-vf', filters.split()[1],
                '-c:a', 'aac',
                '-b:a', '128k',
                '-y',
                output_path
            ]

            tasks.append(self._run_encode(cmd, variant_name, results))

        await asyncio.gather(*tasks)

        if on_complete:
            on_complete(segment_id)

        return results

    async def _run_encode(self, cmd: list, variant: str, results: dict) -> None:
        """Run encoding command."""
        proc = await asyncio.create_subprocess_exec(*cmd)
        returncode = await proc.wait()
        results['variants'][variant] = 'success' if returncode == 0 else 'failed'

    async def watch_and_process(self):
        """Watch directory for new segments."""

        while True:
            # Find new files
            mp4_files = list(self.watch_dir.glob("*.mp4"))

            for video_file in mp4_files:
                if video_file.name not in self.processed_files:
                    segment_id = video_file.stem

                    print(f"Processing: {segment_id}")

                    result = await self.process_segment(
                        str(video_file),
                        segment_id
                    )

                    print(f"Completed: {segment_id}")
                    print(f"  Variants: {result['variants']}")
                    print(f"  Thumbnail: {result['thumbnail']}")

                    self.processed_files.add(video_file.name)

            await asyncio.sleep(2)  # Check every 2 seconds

# Usage
async def main():
    processor = StreamProcessor(
        watch_dir="./segments",
        output_dir="./encoded"
    )

    await processor.watch_and_process()

# asyncio.run(main())
```

---

## Scenario 5: Social Media Auto-Generation

**Requirements:**
- Generate platform-specific variants
- Create optimized GIFs
- Aspect ratio handling
- Metadata embedding

### Solution

```python
import subprocess
from pathlib import Path
from typing import Dict

class SocialMediaAutomation:
    """Generate platform-specific video variants."""

    # Platform-specific requirements
    PLATFORMS = {
        'twitter': {
            'resolution': '1280x720',
            'duration': 30,  # Max 30 seconds for GIF
            'gif_fps': 8,
            'gif_scale': 480,
            'description': 'Twitter - max 30s'
        },
        'tiktok': {
            'resolution': '1080x1920',  # Vertical
            'fps': 30,
            'codec': 'libx264',
            'crf': 23,
            'description': 'TikTok - vertical, 1080x1920'
        },
        'instagram': {
            'resolution': '1080x1080',  # Square
            'fps': 30,
            'codec': 'libx265',
            'crf': 24,
            'description': 'Instagram - square, 1080x1080'
        },
        'youtube': {
            'resolution': '1920x1080',
            'fps': 30,
            'codec': 'libx264',
            'crf': 23,
            'description': 'YouTube - 1080p'
        },
        'facebook': {
            'resolution': '1280x720',
            'fps': 30,
            'codec': 'libx264',
            'crf': 23,
            'description': 'Facebook - 720p'
        }
    }

    def __init__(self, output_dir: str = "./social_media"):
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(exist_ok=True)

    def create_twitter_gif(
        self,
        input_path: str,
        output_name: str,
        max_duration: int = 30
    ) -> Path:
        """Create optimized GIF for Twitter."""

        output_path = self.output_dir / f"{output_name}_twitter.gif"
        palette_path = self.output_dir / f"{output_name}_twitter_palette.png"

        # Pass 1: Palette
        cmd1 = [
            'ffmpeg', '-i', input_path,
            '-t', str(max_duration),
            '-vf', 'fps=8,scale=480:-1,palettegen',
            str(palette_path),
            '-y'
        ]

        subprocess.run(cmd1, check=True, capture_output=True)

        # Pass 2: GIF
        cmd2 = [
            'ffmpeg', '-i', input_path,
            '-i', str(palette_path),
            '-t', str(max_duration),
            '-lavfi', 'fps=8,scale=480:-1[x];[x][1:v]paletteuse',
            str(output_path),
            '-y'
        ]

        subprocess.run(cmd2, check=True, capture_output=True)
        palette_path.unlink()

        return output_path

    def create_tiktok_vertical(
        self,
        input_path: str,
        output_name: str
    ) -> Path:
        """Create vertical video for TikTok."""

        output_path = self.output_dir / f"{output_name}_tiktok.mp4"

        # Pad or crop to 9:16 aspect ratio (1080x1920)
        cmd = [
            'ffmpeg', '-i', input_path,
            '-vf', 'scale=1080:1920:force_original_aspect_ratio=decrease,pad=1080:1920:(ow-iw)/2:(oh-ih)/2:color=black',
            '-c:v', 'libx264',
            '-preset', 'fast',
            '-crf', '23',
            '-c:a', 'aac',
            '-y',
            str(output_path)
        ]

        subprocess.run(cmd, check=True, capture_output=True)
        return output_path

    def create_instagram_square(
        self,
        input_path: str,
        output_name: str
    ) -> Path:
        """Create square video for Instagram."""

        output_path = self.output_dir / f"{output_name}_instagram.mp4"

        # Pad to 1:1 aspect ratio (1080x1080)
        cmd = [
            'ffmpeg', '-i', input_path,
            '-vf', 'scale=1080:1080:force_original_aspect_ratio=decrease,pad=1080:1080:(ow-iw)/2:(oh-ih)/2:color=white',
            '-c:v', 'libx265',
            '-preset', 'fast',
            '-crf', '24',
            '-c:a', 'aac',
            '-y',
            str(output_path)
        ]

        subprocess.run(cmd, check=True, capture_output=True)
        return output_path

    def create_youtube_hd(
        self,
        input_path: str,
        output_name: str
    ) -> Path:
        """Create HD video for YouTube."""

        output_path = self.output_dir / f"{output_name}_youtube.mp4"

        cmd = [
            'ffmpeg', '-i', input_path,
            '-c:v', 'libx264',
            '-preset', 'medium',
            '-crf', '23',
            '-s', '1920x1080',
            '-c:a', 'aac',
            '-b:a', '192k',
            '-movflags', '+faststart',
            '-y',
            str(output_path)
        ]

        subprocess.run(cmd, check=True, capture_output=True)
        return output_path

    def create_facebook_hd(
        self,
        input_path: str,
        output_name: str
    ) -> Path:
        """Create video for Facebook."""

        output_path = self.output_dir / f"{output_name}_facebook.mp4"

        cmd = [
            'ffmpeg', '-i', input_path,
            '-c:v', 'libx264',
            '-preset', 'fast',
            '-crf', '23',
            '-s', '1280x720',
            '-c:a', 'aac',
            '-b:a', '128k',
            '-y',
            str(output_path)
        ]

        subprocess.run(cmd, check=True, capture_output=True)
        return output_path

    def generate_all_variants(
        self,
        input_path: str,
        video_name: str
    ) -> Dict[str, Path]:
        """Generate all platform variants."""

        outputs = {}

        print("Generating Twitter GIF...")
        outputs['twitter_gif'] = self.create_twitter_gif(input_path, video_name)

        print("Generating TikTok vertical...")
        outputs['tiktok'] = self.create_tiktok_vertical(input_path, video_name)

        print("Generating Instagram square...")
        outputs['instagram'] = self.create_instagram_square(input_path, video_name)

        print("Generating YouTube HD...")
        outputs['youtube'] = self.create_youtube_hd(input_path, video_name)

        print("Generating Facebook...")
        outputs['facebook'] = self.create_facebook_hd(input_path, video_name)

        return outputs

# Usage
automation = SocialMediaAutomation()
variants = automation.generate_all_variants("original.mp4", "my_video")

for platform, path in variants.items():
    size_mb = path.stat().st_size / (1024**2)
    print(f"{platform:15} {size_mb:8.1f} MB  {path.name}")
```

---

## Summary of Scenarios

| Scenario | Key Techniques | Tools |
|----------|----------------| -----|
| Web Platform | Adaptive bitrate, thumbnails, GIF, validation | ffprobe, parallel processing |
| Content Moderation | Keyframe extraction, scene detection, storyboard | Select filter, scdet |
| Batch Processing | Queue management, SQLite tracking, parallelism | Database, asyncio |
| Stream Processing | Real-time re-encoding, minimal latency | ultrafast preset |
| Social Media | Platform-specific dimensions, aspect ratios | Filter_complex, padding |

Each scenario can be adapted for your specific use case by adjusting parameters and adding domain-specific logic.

