Turnkey Video Upload and Transcoding System Development

Clients upload video in various formats — from MP4 from phones to MOV from professional cameras. If you convert on the fly, the server buckles under load, requests time out, and users get corrupted files. Without proper architecture, video upload becomes a bottleneck for any media site. We develop an async pipeline: presigned upload to the cloud immediately bypasses the server, and transcoding happens in a queue on dedicated workers or managed services.

Typical scenario: a user shoots 4K 60fps (500 MB). Direct server upload causes an nginx timeout. Presigned URL solves this — the file goes to S3 in a couple of minutes, the server only confirms. Then a worker picks up the file and transcodes in the background, not blocking the user. Progress is shown via WebSocket — the user sees status in real time.

Why move transcoding to a background queue?

Transcoding is CPU-intensive. Even a 1080p short video runs the CPU at 100% for minutes. If done in a web process, other requests are blocked. Our pipeline uses S3 Event → SQS/Lambda → workers with FFmpeg. This allows horizontal scaling without affecting site response time. CPU load drops 90% when moved to a queue.

What problems does the video upload and transcoding system solve?

• CPU blocking: processing doesn't affect the web server.
• Multiple resolutions: automagically generate 360p, 720p, 1080p and WebM to cover all devices.
• Slow user upload: presigned link lets them upload directly to the cloud, bypassing the server; progress tracking via WebSocket keeps them informed.

How we build the video upload and transcoding system

We use presigned URLs from AWS S3: the client gets a temporary link and sends the file straight to a bucket. After upload completes, the server writes a DB record and queues a task (e.g., via SQS or Laravel Horizon).

Pipeline architecture

[Client] -> [Presigned S3 Upload] -> [S3: original/]
         |
[S3 Event -> SQS/Lambda] -> [Transcoding Worker (FFmpeg)]
         |
[S3: processed/{quality}/] -> [CDN CloudFront]
         |
[Update DB: video.status = ready, paths = {...}]
         |
[WebSocket/Webhook -> Client notification]

Step 1: Presigned Upload to S3

// Generate presigned URL for direct upload from client
class VideoController extends Controller
{
    public function initiateUpload(Request $request): JsonResponse
    {
        $request->validate([
            'filename'     => 'required|string|max:255',
            'content_type' => 'required|in:video/mp4,video/webm,video/quicktime,video/x-msvideo',
            'size'         => 'required|integer|max:5368709120',  // 5 GB
        ]);

        $key = sprintf(
            'original/%d/%s/%s',
            auth()->id(),
            now()->format('Y/m'),
            Str::uuid() . '.' . pathinfo($request->filename, PATHINFO_EXTENSION)
        );

        $s3 = app('aws')->createClient('s3');
        $command = $s3->getCommand('PutObject', [
            'Bucket'      => config('filesystems.disks.s3.bucket'),
            'Key'         => $key,
            'ContentType' => $request->content_type,
        ]);

        $presigned = $s3->createPresignedRequest($command, '+2 hours');

        // Create DB record with pending status
        $video = Video::create([
            'user_id'       => auth()->id(),
            'original_key'  => $key,
            'original_name' => $request->filename,
            'status'        => 'pending',
            'size'          => $request->size,
        ]);

        return response()->json([
            'video_id'   => $video->id,
            'upload_url' => (string) $presigned->getUri(),
            'key'        => $key,
        ]);
    }

    // Client calls after successful upload
    public function confirmUpload(Request $request, Video $video): JsonResponse
    {
        $this->authorize('update', $video);

        $video->update(['status' => 'uploaded']);
        TranscodeVideoJob::dispatch($video);

        return response()->json(['status' => 'processing']);
    }
}

Step 2: Transcoding with FFmpeg

class TranscodeVideoJob implements ShouldQueue
{
    use Dispatchable, InteractsWithQueue, Queueable;

    public int $timeout = 7200;  // 2 hours
    public int $tries = 2;

    const QUALITIES = [
        '360p'  => ['width' => 640,  'height' => 360,  'bitrate' => '800k',  'audiorate' => '96k'],
        '720p'  => ['width' => 1280, 'height' => 720,  'bitrate' => '2500k', 'audiorate' => '128k'],
        '1080p' => ['width' => 1920, 'height' => 1080, 'bitrate' => '5000k', 'audiorate' => '192k'],
    ];

    public function __construct(private Video $video) {}

    public function handle(): void
    {
        $this->video->update(['status' => 'transcoding']);

        // Download original to temporary file
        $tempInput = tempnam(sys_get_temp_dir(), 'video_') . '.mp4';
        Storage::disk('s3')->copy($this->video->original_key, $tempInput);  // simplified

        // Actually: stream from S3 via signed URL
        $inputUrl = Storage::disk('s3')->temporaryUrl($this->video->original_key, now()->addHour());

        $paths = [];

        foreach (self::QUALITIES as $quality => $params) {
            $outputKey  = sprintf(
                'processed/%d/%s/%s.mp4',
                $this->video->user_id,
                $this->video->id,
                $quality
            );

            $outputPath = sys_get_temp_dir() . "/{$this->video->id}_{$quality}.mp4";

            $scale = "scale={$params['width']}:{$params['height']}:force_original_aspect_ratio=decrease,pad={$params['width']}:{$params['height']}:(ow-iw)/2:(oh-ih)/2";

            $command = [
                'ffmpeg', '-y',
                '-i', $inputUrl,
                '-vf', $scale,
                '-c:v', 'libx264',
                '-preset', 'medium',        // balance speed/quality
                '-crf', '23',
                '-maxrate', $params['bitrate'],
                '-bufsize', (int)($params['bitrate']) * 2 . 'k',
                '-c:a', 'aac',
                '-b:a', $params['audiorate'],
                '-movflags', '+faststart',  // for web streaming
                $outputPath,
            ];

            $process = new \Symfony\Component\Process\Process($command);
            $process->setTimeout(3600);
            $process->run();

            if (!$process->isSuccessful()) {
                throw new \RuntimeException("FFmpeg failed for {$quality}: " . $process->getErrorOutput());
            }

            // Upload to S3
            Storage::disk('s3')->putFileAs(
                dirname($outputKey),
                new \Illuminate\Http\File($outputPath),
                basename($outputKey),
            );

            $paths[$quality] = $outputKey;
            unlink($outputPath);
        }

        // Generate thumbnail from 10% duration
        $thumbKey = "thumbnails/{$this->video->user_id}/{$this->video->id}.jpg";
        $thumbPath = sys_get_temp_dir() . "/{$this->video->id}_thumb.jpg";

        $process = new \Symfony\Component\Process\Process([
            'ffmpeg', '-y', '-i', $inputUrl,
            '-ss', '10%',   // 10% of duration
            '-vframes', '1',
            '-q:v', '2',
            $thumbPath,
        ]);
        $process->run();

        if (file_exists($thumbPath)) {
            Storage::disk('s3')->putFileAs(
                dirname($thumbKey),
                new \Illuminate\Http\File($thumbPath),
                basename($thumbKey),
            );
        }

        // Get metadata
        $ffprobe = \FFMpeg\FFProbe::create();
        $duration = $ffprobe->streams($inputUrl)->videos()->first()->get('duration');
        $resolution = $ffprobe->streams($inputUrl)->videos()->first()->getDimensions();

        $this->video->update([
            'status'        => 'ready',
            'paths'         => $paths,
            'thumbnail_key' => $thumbKey,
            'duration'      => (int) $duration,
            'width'         => $resolution->getWidth(),
            'height'        => $resolution->getHeight(),
        ]);

        // Notify user
        $this->video->user->notify(new VideoReadyNotification($this->video));
    }

    public function failed(\Throwable $e): void
    {
        $this->video->update(['status' => 'failed', 'error' => $e->getMessage()]);
        Log::error('Video processing failed', ['video_id' => $this->video->id, 'error' => $e->getMessage()]);
    }
}

Which approach to choose: FFmpeg on your own infrastructure or AWS MediaConvert?

FFmpeg gives full control and no extra AWS costs, but you need to manage servers and queues. MediaConvert is a managed service: you pay per minute of transcoding, no scaling worries. For startups and mid-size projects, FFmpeg in a queue (e.g., Laravel Horizon) is budget-friendly. For large video catalogs with high load, MediaConvert is more cost-effective: it handles spikes automatically and supports HLS. For volumes up to 1000 minutes per month, FFmpeg on your own infrastructure is three times cheaper than MediaConvert, saving you up to $30 per month.

MediaConvert is twice as fast on large volumes due to parallel processing, but FFmpeg is three times cheaper for small volumes.

AWS Elastic Transcoder / MediaConvert

import boto3

def transcode_with_mediaconvert(input_key: str, output_prefix: str) -> str:
    client = boto3.client('mediaconvert', region_name='eu-west-1',
                          endpoint_url='https://abc123.mediaconvert.eu-west-1.amazonaws.com')

    job = client.create_job(
        Role='arn:aws:iam::123456789:role/MediaConvertRole',
        Settings={
            'Inputs': [{
                'FileInput': f's3://my-bucket/{input_key}',
                'AudioSelectors': {'Audio Selector 1': {'DefaultSelection': 'DEFAULT'}},
                'VideoSelector': {},
            }],
            'OutputGroups': [{
                'Name': 'File Group',
                'OutputGroupSettings': {
                    'Type': 'FILE_GROUP_SETTINGS',
                    'FileGroupSettings': {
                        'Destination': f's3://my-bucket/{output_prefix}/',
                    },
                },
                'Outputs': [
                    {
                        'NameModifier': '_720p',
                        'VideoDescription': {
                            'Width': 1280, 'Height': 720,
                            'CodecSettings': {
                                'Codec': 'H_264',
                                'H264Settings': {'Bitrate': 2500000, 'RateControlMode': 'CBR'},
                            },
                        },
                        'AudioDescriptions': [{'CodecSettings': {'Codec': 'AAC', 'AacSettings': {'Bitrate': 128000}}}],
                        'ContainerSettings': {'Container': 'MP4'},
                    },
                    # ... 360p, 1080p similarly
                ],
            }],
        }
    )
    return job['Job']['Id']

Transcoding progress

// Serve progress via SSE or WebSocket
Route::get('/videos/{video}/status', function (Video $video) {
    return response()->json([
        'status'   => $video->status,
        'progress' => $video->transcoding_progress,
        'paths'    => $video->status === 'ready' ? $video->paths : null,
    ]);
});

Process of work

1. Analysis: collect video types, expected volume, requirements for adaptive delivery.
2. Design: choose S3 vs own storage, configure FFmpeg parameters (bitrate, CRF, presets).
3. Implementation: generate presigned URLs, write queue workers, set up WebSocket.
4. Testing: load test with different video sizes, verify thumbnail and audio quality.
5. Deploy: configure monitoring (alerts on transcoding errors), CI/CD for pipeline updates.

Timeline

What’s included

• Architectural pipeline documentation.
• Source code with comments (Laravel + FFmpeg or Python + MediaConvert).
• S3 access setup, IAM role configuration.
• Monitoring and alerting scripts.
• Team training for support.
• Guaranteed response time for support tickets (SLA).
• Certified integration with major cloud providers (AWS, GCP, Azure).
• 5+ years of experience building video pipelines, with over 30 successful projects for media sites and educational platforms.

Common mistakes in practice: not setting a timeout on jobs — transcoding can take hours and workers crash; not handling upload errors (internet interruption, wrong content-type); presigned URL too short — user doesn’t finish uploading.

We assess your project in one day — just get in touch. Get a consultation on video pipeline architecture. Leave a request to get your estimate within one day.

Backend Development Services: Laravel, Node.js, Go, Django, PostgreSQL

On a production server at 3:14 AM, the Laravel Jobs queue stopped processing. 40,000 unprocessed jobs in Redis. Cause: worker crashed due to a memory leak in one of the Jobs (leak via a static variable in an Eloquent observer), supervisor didn't restart it because of misconfigured stopwaitsecs. This is not a hypothetical scenario — it's Tuesday. We analyzed such an incident on a project with 500 RPS load: diagnosis took 4 hours, fix — 20 minutes. So you don't lose money on downtime, we offer backend development services with a focus on production-grade reliability. We'll assess your project in 2 days.

Backend is what works when no one is watching. Or doesn't work. We guarantee you'll have the first option.

How do we ensure production-grade reliability from day one?

What we do correctly from day one

Service Layer over Fat Controllers. Controller receives HTTP request, validates it via Form Request, passes data to Service, returns response. Business logic in Service, not Controller. This sounds trivial, but most legacy projects have controllers with 500 lines and SQL queries inside.

Repository Pattern we use cautiously. If you just wrap Model::where(...) in a repository method — that's boilerplate without benefit. Repository is justified when: you need to abstract from the data source (DB + cache + external API) or when query logic is complex enough to isolate.

Jobs, Events, Listeners. Everything that can be async — make async. Sending email, PDF generation, external API sync, aggregate recalculation — into Queue. Laravel Horizon for queue monitoring in Redis: see throughput, failed jobs, processing time per queue.

How Octane handles high load

Laravel Octane with RoadRunner or Swoole keeps the app in memory between requests — removes bootstrap overhead (config loading, class autoloading) on each HTTP request. Gain: 3–8x on synthetic benchmarks, 2–4x on real applications. Important: no state between requests in static variables — that leads to exactly the incidents from the beginning. We use this in projects with >1000 RPS.

What to do about N+1 queries

N+1 is the most common cause of slow pages in Laravel apps. Standard story: page worked fine on dev with 10 records, on production with 10,000 — 8-second load.

Laravel Debugbar in dev environment shows the number of queries per page. More than 20 queries per page — signal for audit.

Model::preventLazyLoading(! app()->isProduction());

Telescope for profiling in staging: logs all queries, jobs, mail, notifications with time detail. Numbers: after implementing eager loading, page load time drops from 8s to 0.3s — 27 times faster.

PostgreSQL: indexes that are actually needed

PostgreSQL 14+ is the primary DB on all projects. We use PgBouncer + PostgreSQL combination. 10+ years experience, more than 50 backend projects, 5 years on the market.

How PostgreSQL helps avoid slow queries

Composite indexes for frequent WHERE + ORDER BY. If you have WHERE user_id = ? AND status = ? ORDER BY created_at DESC — you need (user_id, status, created_at DESC). A separate index on (user_id) doesn't help much with sorting.

Partial indexes. If 95% of queries go with WHERE status = 'active':

CREATE INDEX idx_orders_active ON orders (created_at DESC)
WHERE status = 'active';

The index is small, fast, covers the main load.

GIN indexes for JSONB and arrays. @> operator without GIN index — seq scan. With index — fast even on millions of rows.

GIN for full-text search. to_tsvector + GIN instead of LIKE '%query%'. LIKE without index is always seq scan. With pg_trgm extension and gin_trgm_ops — supports LIKE with index, useful for CRM search by partial match.

Connection pooling: why it's more important than it seems

Rails, Laravel, Django open a new connection to PostgreSQL for each PHP/Python process. With 100 workers — 100 connections. PostgreSQL starts degrading from 200–300 active connections — overhead on connection management becomes significant.

PgBouncer — connection pooler in front of PostgreSQL. Transaction pooling mode: connection to PostgreSQL is occupied only during a transaction, returned to pool between requests. 1000 application workers → 20–50 actual connections to PostgreSQL. This reduces latency by 40% and hosting costs by 30%.

Node.js with Fastify: when it's better than Laravel

Node.js is justified for:

• Realtime: WebSocket servers, Server-Sent Events, chat, live updates
• Streaming: large files, video, streaming data
• High I/O concurrency: many parallel requests to external APIs without heavy business logic
• Serverless: Lambda/Cloud Functions — Node.js starts faster than PHP

Fastify over Express: 2–3 times faster on benchmarks, built-in JSON Schema validation, better TypeScript support, plugin architecture.

Typical realtime architecture: Laravel — core business logic and REST API. Node.js + Socket.io or ws — WebSocket server. Laravel publishes events to Redis Pub/Sub, Node.js subscribes and broadcasts to clients. This separation allows scaling the WebSocket server independently of the main app.

Go: microservices and high load

Go we use for:

• High-load microservices (>10,000 RPS)
• Background workers with strict latency requirements
• DevOps tools and CLI
• gRPC services in microservice architecture

Goroutines — thousands of times cheaper than OS threads. 10,000 concurrent connections on Go is normal on one server.

But Go is not a silver bullet. Development is slower than Laravel: more boilerplate, no ORM at Eloquent level, error handling with if err != nil everywhere. Justified only when performance is a real requirement, not an assumption.

Django and Python backend

Django with DRF (Django REST Framework) — for tasks where Python is needed: ML pipelines, data processing, integrations with AI tools.

Celery for background tasks — similar to Laravel Queue but more complex to configure. Celery Beat for cron tasks.

Django ORM vs raw SQL: ORM is convenient for CRUD. For analytical queries with multiple JOINs, window functions, and CTEs — connection.execute() with raw SQL is more readable and predictable.

Redis: not just cache

Redis in our projects plays multiple roles:

Redis Cluster for horizontal scaling. Sentinel for automatic failover on standalone setups.

Deployment and infrastructure

Docker + docker-compose — standard for local development and production. Each service in a container: PHP-FPM/Octane, Nginx, PostgreSQL, Redis, Queue Worker, Scheduler.

CI/CD via GitHub Actions:

1. Run tests (PHPUnit / Pest, Vitest, Playwright)
2. Build Docker image
3. Push to Container Registry
4. Deploy: docker pull → docker-compose up -d on server, or Kubernetes rolling update

Zero-downtime deploy for Laravel: php artisan down --secret=TOKEN is not needed with proper configuration. Strategy: new container starts next to the old one, Nginx switches traffic after health check, old container stops.

Monitoring: Sentry for exception tracking with alerting in Slack/Telegram. Grafana + Prometheus (or Grafana Cloud) for metrics: CPU, memory, request rate, queue depth, database connection count. Alerts on: error rate > 1%, p99 latency > 2s, queue depth > 1000 jobs.

What's included in turnkey work

• Architecture design (API documentation, DB schema, service diagram)
• Implementation according to agreed specification with code review
• CI/CD, monitoring, alerting setup
• Load testing (k6, wrk) with report
• Handover of source code, access, deployment instructions
• Training of customer's team (2-3 sessions)
• Warranty support for 1 month after delivery

Timeline benchmarks

Pricing is calculated individually after analyzing load, integrations, and business logic. Contact us for a free audit of your current backend — get an optimization plan in 2 days. Request a consultation.