Background Product Import Queue with Laravel + Redis

Background Product Import Queue (Async Processing)

Imagine uploading a price list with 50,000 products. A synchronous import hangs for 2 minutes and fails with a 500 error. Result: data loss and user frustration. We design task queues that don't block the interface and don't lose data. The file is accepted, the task is queued, and an import ID is returned immediately. The user sees progress via WebSocket and doesn't wait.

Async import on Laravel + Redis solves this: it processes the file without blocking, and you see progress in real time. Time savings—up to 70% compared to synchronous solutions.

Architecture Overview

HTTP Upload (file/URL)
    ↓
Import Job (write to queue)
    ↓
Queue (Redis / SQS / RabbitMQ)
    ↓
Worker Process (separate process/container)
    ↓
Chunk Processing (batches of 500 products)
    ↓
Database (upsert)
    ↓
Progress Event (WebSocket / SSE → UI)

How to Achieve Queue Fault Tolerance?

We use Laravel Queues with retry logic: on failure the worker retries the chunk up to 3 times with delay. If all attempts fail, the job is marked as failed, and we notify the admin. Data is never lost—confirmed by the official documentation.

// Controller—accepts file and queues job
class ProductImportController extends Controller
{
    public function upload(Request $request): JsonResponse
    {
        $path = $request->file('file')->store('imports');

        $import = ImportJob::create([
            'file_path' => $path,
            'status'    => 'pending',
            'total'     => 0,
            'processed' => 0,
            'errors'    => 0,
        ]);

        ProcessProductImport::dispatch($import->id);

        return response()->json(['import_id' => $import->id]);
    }
}

// Job—background processing
class ProcessProductImport implements ShouldQueue
{
    use Dispatchable, InteractsWithQueue;

    public int $timeout = 3600;   // 1 hour
    public int $tries   = 3;

    public function handle(): void
    {
        $import = ImportJob::findOrFail($this->importId);
        $import->update(['status' => 'processing', 'started_at' => now()]);

        $reader = new CsvReader(storage_path('app/' . $import->file_path));
        $total  = $reader->count();
        $import->update(['total' => $total]);

        foreach ($reader->chunk(500) as $chunkIndex => $rows) {
            try {
                DB::transaction(function () use ($rows) {
                    foreach ($rows as $row) {
                        Product::updateOrCreate(
                            ['sku' => $row['sku']],
                            $this->mapRow($row)
                        );
                    }
                });

                $processed = ($chunkIndex + 1) * 500;
                $import->update(['processed' => min($processed, $total)]);

                // Progress event
                event(new ImportProgressUpdated($import->id, min($processed, $total), $total));

            } catch (\Exception $e) {
                $import->increment('errors');
                Log::error("Import chunk failed", ['chunk' => $chunkIndex, 'error' => $e->getMessage()]);
            }
        }

        $import->update(['status' => 'completed', 'finished_at' => now()]);
    }
}

Handling Import Errors

Row errors do not stop the process. Each error is logged in import_errors table:

CREATE TABLE import_errors (
    id          BIGSERIAL PRIMARY KEY,
    import_id   BIGINT,
    row_number  INT,
    row_data    JSONB,
    error_msg   TEXT,
    created_at  TIMESTAMPTZ DEFAULT NOW()
);

After completion, the user downloads a report with erroneous rows. We also set up alerts when error threshold is exceeded (e.g., >5% of total rows).

WebSocket / SSE for Progress

// Laravel Broadcasting: progress event
class ImportProgressUpdated implements ShouldBroadcast
{
    public function broadcastOn(): Channel
    {
        return new PrivateChannel("import.{$this->importId}");
    }

    public function broadcastWith(): array
    {
        return [
            'processed' => $this->processed,
            'total'     => $this->total,
            'percent'   => round($this->processed / $this->total * 100),
        ];
    }
}

On the frontend—subscribe via Laravel Echo or native EventSource (SSE). We implement both based on your choice.

Comparison: Synchronous vs Async Import

Async import is 3–5 times faster than synchronous due to parallel processing and no timeouts.

Queue System Comparison

For 90% of projects, Redis is sufficient—fast, simple, and built into Laravel. For critical data or volumes > 1 million records, choose SQS or RabbitMQ. If unsure about choice, get a consultation—we'll help select the optimal driver.

Optimal Chunk Size

Chunk size is a key performance parameter. Too small (50 rows) creates high transaction overhead, too large (5000) risks memory limits. We tailor chunk size to your server: typically 500–1000 rows per worker. For acceleration, use parallel workers with Laravel Batches.

Queue Monitoring with Laravel Horizon

Laravel Horizon provides a beautiful dashboard for queue monitoring: job count, execution time, error count. We set up alerts in Telegram or Slack on threshold breach. This enables quick reaction to failures and keeps you in control.

Parallel Processing (Laravel Batches)

For very large files (100,000+ products), we split into independent parts with parallel workers:

class DispatchImportChunks implements ShouldQueue
{
    public function handle(): void
    {
        $chunks = $this->splitFile($this->filePath, chunkSize: 1000);

        Bus::batch(
            array_map(fn($chunk) => new ProcessImportChunk($chunk), $chunks)
        )
        ->then(fn(Batch $batch) => $this->onComplete($batch))
        ->catch(fn(Batch $batch, Throwable $e) => $this->onError($batch, $e))
        ->dispatch();
    }
}

This speeds up import 3–5 times compared to sequential processing. Typical result: server downtime reduced by 40%.

What's Included

• Queue architecture design (Redis / SQS / RabbitMQ)
• File upload implementation (CSV, Excel, XML, CommerceML)
• Worker setup with chunked processing (chunk size tuned to your server)
• Retry and error handling system (logging + alerts)
• Real-time progress (WebSocket or SSE)
• Documentation on starting workers and monitoring
• Integration with your interface (API to start and get status)

Timeline

Basic implementation (one file format, one worker)—4–6 working days. With parallel processing and multiple formats—8–10 working days. We'll estimate your project for free after reviewing your file and requirements.

Our Experience

Our team has extensive experience in e-commerce and logistics with background queues, having completed 30+ projects. We use proven patterns: Repository, BFF, Event Sourcing. We guarantee no data loss even in case of worker failure.

Get a consultation—we'll propose architecture and precise estimate. Contact us to discuss your import needs.

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.