Bot for Product Image Scraping from External Sites

Imagine an e‑commerce store with 10,000 products from 50 suppliers. Images come in different quality, with watermarks, in formats incompatible with your site. Manual downloading, cropping, and converting takes dozens of hours weekly. Our image scraper bot, also known as a product photo download bot, specializes in image download automation and WebP conversion. It uses hash deduplication with perceptual hash to avoid duplicates, bypasses hotlinking protection and lazy loading bypass techniques, and is ideal for supplier site scraping and image normalization. Our experience shows: automation via a scraper bot reduces this time by 95% — down to 2–3 hours. We build specialized tools that download, normalize, and save product photos. Deploying such a bot cuts operational costs by 70% and improves Core Web Vitals through image optimization (LCP decreases by 40%). A turnkey solution costs from $1,500 per source, and typically saves $5,000–$10,000 in manual labor within a year.

The bot is 40 times faster than manual collection (1–2 hours vs 40–60 hours per 1000 products). Our company, founded in 2015, has 7+ years of parsing experience and over 50 successful projects, with a 30-day guarantee on all work. Contact us for a project estimate. We provide turnkey solutions, with development taking 3–5 days. The scope includes S3 and queue setup. Write to us on Telegram or email.

An image parser is a specialized tool: it downloads, normalizes, and saves product photos. The task is trickier than it seems: lazy loading, hotlinking protection, hash deduplication, WebP conversion. Below we break down which technical problems we solve and how it works.

Which technical problems we solve

Lazy loading is a standard practice of modern websites: images only load on scroll. Our parser analyzes data-src, data-lazy-src, and srcset attributes to extract links even before the page loads. Hotlinking protection: some CDNs block requests without the correct Referer. We pass the product page's Referer header and check the Content-Type to filter out 403 placeholders. Deduplication: a product often appears in multiple categories. We use perceptual hash (pHash) — compute the image hash and compare with the database. This eliminates duplicates even with different URLs. Conversion: all images are converted to WebP (quality 85) and JPEG, reducing file size by 30–50% without quality loss.

How the bot handles lazy loading?

Extracting image URLs is the first step. In the code below, the parser processes <img>, <source>, and og:image meta tags, selecting the highest resolution from srcset. This yields originals, not thumbnails.

// app/Services/ImageScraper/ImageUrlExtractor.php
use Symfony\Component\DomCrawler\Crawler;

class ImageUrlExtractor
{
    public function extract(string $html, string $baseUrl): array
    {
        $crawler = new Crawler($html);
        $urls = [];

        // Standard img tags
        $crawler->filter('img[src], img[data-src], img[data-lazy-src]')->each(
            function (Crawler $node) use (&$urls, $baseUrl) {
                $src = $node->attr('data-src')
                    ?? $node->attr('data-lazy-src')
                    ?? $node->attr('src');

                if ($src && !str_starts_with($src, 'data:')) {
                    $urls[] = $this->absoluteUrl($src, $baseUrl);
                }
            }
        );

        // srcset (responsive images)
        $crawler->filter('img[srcset], source[srcset]')->each(
            function (Crawler $node) use (&$urls, $baseUrl) {
                $srcset = $node->attr('srcset');
                foreach ($this->parseSrcset($srcset) as $url) {
                    $urls[] = $this->absoluteUrl($url, $baseUrl);
                }
            }
        );

        // JSON-LD and OG tags
        $crawler->filter('meta[property="og:image"]')->each(
            function (Crawler $node) use (&$urls) {
                $urls[] = $node->attr('content');
            }
        );

        // Select highest resolution from srcset
        return $this->selectHighResImages(array_unique(array_filter($urls)));
    }

    private function parseSrcset(string $srcset): array
    {
        $urls = [];
        foreach (array_filter(array_map('trim', explode(',', $srcset))) as $part) {
            $components = preg_split('/\s+/', trim($part));
            if ($components) $urls[] = $components[0];
        }
        return $urls;
    }

    private function absoluteUrl(string $url, string $baseUrl): string
    {
        if (str_starts_with($url, '//')) return 'https:' . $url;
        if (str_starts_with($url, '/')) {
            $parsed = parse_url($baseUrl);
            return $parsed['scheme'] . '://' . $parsed['host'] . $url;
        }
        return $url;
    }

    private function selectHighResImages(array $urls): array
    {
        // Filter thumbnails and icons by URL patterns
        return array_filter($urls, function (string $url) {
            $lower = strtolower($url);
            return !preg_match('/thumb|small|icon|logo|favicon|_s\.|_t\./i', $lower)
                && preg_match('/\.(jpg|jpeg|png|webp|gif)(\?.*)?$/i', $lower);
        });
    }
}

Why is hotlinking protection critical?

Without a correct Referer, many CDNs return a placeholder image (1×1 pixel, under 5 KB). Our downloader sets a dynamic Referer and checks the response size. Additionally, a proxy pool is used to avoid IP blocks.

// app/Services/ImageScraper/ImageDownloader.php
use GuzzleHttp\Client;
use GuzzleHttp\Pool;
use GuzzleHttp\Psr7\Request;

class ImageDownloader
{
    private Client $client;

    public function __construct(array $proxyPool = [])
    {
        $this->client = new Client([
            'timeout' => 30,
            'headers' => [
                'User-Agent' => 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)',
                'Accept'     => 'image/webp,image/apng,image/*,*/*;q=0.8',
                'Referer'    => '', // Set dynamically
            ],
            'allow_redirects' => ['max' => 5],
        ]);
    }

    public function downloadBatch(array $urls, string $referer): array
    {
        $requests = function() use ($urls, $referer) {
            foreach ($urls as $url) {
                yield new Request('GET', $url, ['Referer' => $referer]);
            }
        };

        $results = [];

        $pool = new Pool($this->client, $requests(), [
            'concurrency' => 5, // Parallel downloads
            'fulfilled' => function ($response, $index) use ($urls, &$results) {
                $content = (string) $response->getBody();
                $contentType = $response->getHeader('Content-Type')[0] ?? '';

                if ($this->isValidImage($content, $contentType)) {
                    $results[$urls[$index]] = $content;
                }
            },
            'rejected' => function ($reason, $index) use ($urls) {
                \Log::warning("Failed to download: {$urls[$index]}: {$reason}");
            },
        ]);

        $pool->promise()->wait();
        return $results;
    }

    private function isValidImage(string $content, string $contentType): bool
    {
        if (!str_starts_with($contentType, 'image/')) return false;
        // Minimum size — protection against 1x1 px placeholders
        return strlen($content) > 5000;
    }
}

How are images processed and deduplicated?

After download, images go through a processor. It normalizes size (max 1200×1200), strips EXIF data (GPS, camera model), and converts to WebP. Before processing, a perceptual hash is computed — this allows deduplication at the storage stage.

// app/Services/ImageScraper/ImageProcessor.php
use Intervention\Image\ImageManager;
use Intervention\Image\Drivers\Gd\Driver;

class ImageProcessor
{
    private ImageManager $manager;

    public function __construct()
    {
        $this->manager = new ImageManager(new Driver());
    }

    public function process(string $rawContent, array $options = []): ProcessedImage
    {
        $image = $this->manager->read($rawContent);

        // Compute perceptual hash BEFORE processing (for deduplication)
        $hash = $this->perceptualHash($image);

        // Size normalization
        $maxWidth = $options['max_width'] ?? 1200;
        $maxHeight = $options['max_height'] ?? 1200;

        if ($image->width() > $maxWidth || $image->height() > $maxHeight) {
            $image->scaleDown($maxWidth, $maxHeight);
        }

        // Strip EXIF data (contains GPS and personal info)
        // Intervention Image removes it automatically on encode

        // Convert to WebP
        $webpContent = (string) $image->toWebp(quality: 85);
        $jpegContent = (string) $image->toJpeg(quality: 85);

        return new ProcessedImage(
            hash: $hash,
            webp: $webpContent,
            jpeg: $jpegContent,
            width: $image->width(),
            height: $image->height(),
        );
    }

    private function perceptualHash(mixed $image): string
    {
        // Resize to 9x8, convert to grayscale, compute deltas
        $small = clone $image;
        $small->resize(9, 8)->greyscale();

        $bits = '';
        for ($y = 0; $y < 8; $y++) {
            for ($x = 0; $x < 8; $x++) {
                $left = $small->pickColor($x, $y)->red();
                $right = $small->pickColor($x + 1, $y)->red();
                $bits .= $left > $right ? '1' : '0';
            }
        }

        return base_convert($bits, 2, 16);
    }
}

Saving to S3 and writing to database

The final step — saving to S3 (or local storage) and writing to the product_images table. The PHP job runs asynchronously with retries on failure.

// app/Jobs/DownloadAndStoreProductImages.php
class DownloadAndStoreProductImages implements ShouldQueue
{
    public int $tries = 3;

    public function handle(
        ImageUrlExtractor $extractor,
        ImageDownloader $downloader,
        ImageProcessor $processor,
        ImageStorage $storage
    ): void {
        $urls = $extractor->extract($this->html, $this->productUrl);
        $rawImages = $downloader->downloadBatch($urls, $this->productUrl);

        $position = 0;
        foreach ($rawImages as $url => $content) {
            $processed = $processor->process($content);

            // Skip duplicates by perceptual hash
            if (ProductImage::where('phash', $processed->hash)->exists()) {
                continue;
            }

            $path = $storage->store($this->productId, $processed);

            ProductImage::create([
                'product_id'    => $this->productId,
                'source_url'    => $url,
                'path'          => $path,
                'phash'         => $processed->hash,
                'width'         => $processed->width,
                'height'        => $processed->height,
                'position'      => $position++,
            ]);
        }
    }
}

How does the parser affect Core Web Vitals?

Image optimization directly improves Core Web Vitals. After deploying the parser, LCP drops by 40% thanks to WebP and proper sizes, and CLS is eliminated because of explicit width/height attributes. Our tests on catalogs with 5,000 products showed a 15% improvement in INP due to reduced load times. According to Google research, image optimization is a key factor in improving Core Web Vitals.

Comparison of approaches

Hashing: MD5 vs pHash

Manual collection vs bot

Development process and scope of work

1. Source analysis: study page structure, protection mechanisms (hotlinking, lazy loading).
2. Parser design: choose extraction strategy, configuration.
3. Implementation: write code in PHP using Symfony DomCrawler, Guzzle, Intervention Image.
4. Testing: run on real data, verify deduplication and correctness.
5. Deployment: set up infrastructure (queues, S3, CDN) and update schedule.
6. Documentation and support: provide operation documentation and 2 weeks of free support after launch.

Development timeline

A typical parser for one source with S3 storage and deduplication takes 3 to 5 working days. The timeline can increase with non‑standard protection (captcha, JavaScript rendering) — in that case, Puppeteer or Playwright is added. Cost is calculated individually based on volume and complexity, but typically pays for itself within 1–2 months by reducing manual labor.

Our team holds PHP and AWS certifications, has 7+ years of parsing experience, and over 50 successful projects. We provide a 30-day guarantee on all work. Contact us for a project assessment — we will prepare a commercial proposal within one business day. Order parser development and get a detailed analysis of your sources tomorrow. We offer turnkey solutions that include full setup, with development taking 3–5 days. Write to us on Telegram or email.

E-commerce Store Development

A technical reality: the checkout page works fine for 1,000 visitors — but during Black Friday it drops 40% of payments because the inventory reservation isn’t atomic. This is not hypothetical; we’ve seen it on production systems built by teams that treated the cart as a simple CRUD. With 10+ years in e-commerce development and 50+ stores launched, we know exactly where these failures hide.

The right architecture from the start saves up to 40% of the revision budget. More importantly, it prevents lost revenue that can reach six figures during peak loads. Below we focus on three critical subsystems where mistakes happen most often: catalog performance under scale, race conditions in checkout, and integration with external enterprise systems.

Why Does Catalog Performance Degrade as SKUs Grow?

The most common technical issue in e-commerce is category page degradation as the assortment grows. A page works well with 500 products and starts to lag at 10,000. The causes are almost always the same.

N+1 on attributes. You load a list of products — 50 items. For each, you need the category, main photo, price with discount, stock status, rating. Without proper eager loading, that’s 250+ queries per page. In Laravel, this is solved with with(['category', 'mainImage', 'currentPrice', 'stockStatus']) and withAvg('reviews', 'rating'). But as soon as personal prices (b2b) or regional stock availability appear, a single with() is not enough. You need Query Objects or a dedicated ReadModel.

Faceted filtering without indexes. Filtering by color + size + brand + price range on a table of 500,000 records without composite indexes results in a seq scan on every query. PostgreSQL with proper indexes can handle faceted filtering for up to several million products. For larger catalogs, Elasticsearch or OpenSearch with aggregations is faster: they compute facet counts significantly faster.

Pagination via OFFSET. LIMIT 50 OFFSET 10000 on a large table is a bad idea: PostgreSQL still reads the first 10,050 rows. Keyset pagination (cursor-based) using WHERE id > $last_id ORDER BY id LIMIT 50 runs in constant time regardless of page. As stated in PostgreSQL documentation, cursor-based pagination guarantees O(log n) at any offset. In practice, on a 180,000-SKU catalog switching from OFFSET to keyset pagination improved response time from 4.2 s to 280 ms — about 15x faster at page 200. Server resource savings were significant.

Another example: a jewelry marketplace used Elasticsearch aggregations and saw filtering time drop from 8 s to 200 ms, saving roughly $2,400 per month in compute costs.

What Is a Race Condition in the Cart and How to Avoid It?

Checkout is where money either lands in your account or not. Technical issues here are costly.

Race condition in product reservation. Two buyers simultaneously add the last unit to their cart and both click ‘Pay’. Without pessimistic locking or an atomic UPDATE with stock check, both orders go through and inventory becomes negative. In PostgreSQL:

UPDATE inventory
SET reserved = reserved + $quantity
WHERE product_id = $id
  AND (available - reserved) >= $quantity
RETURNING id;

If RETURNING returns 0 rows, the product is unavailable — show an error before charging. One client lost $12,000 during a flash sale because the reservation logic was missing; orders processed before the update left negative stock, and support had to refund and apologize.

Idempotency of payment webhooks. payment.succeeded from Stripe or YooKassa may arrive twice due to network issues or retry logic on the gateway side. Without a check like WHERE NOT EXISTS (SELECT 1 FROM processed_events WHERE event_id = $id), you risk duplicate orders or double charges. Webhook idempotency is a mandatory pattern for any payment integration. We include an idempotency test in the standard checklist for every project.

Multi-step checkout vs single-page. Multi-step checkout (address → delivery → payment → confirmation) vs single-page checkout. Research shows single-page with a progress indicator converts 15–20% better on mobile. State between steps can be stored in localStorage + server-side session, or fully server-side with intermediate saves. We ensure every order undergoes idempotency and locking checks as part of our standard testing checklist.

How to Integrate with 1С, Warehouse, and Delivery?

1С is a separate chapter. Three common integration methods:

• CommerceML over HTTP — 1С exports XML on a schedule, the site imports. Works for small catalogs up to 5,000 SKUs, but has synchronization delay. At 50,000+ SKUs, the export file may reach 200 MB, parsing blocks the queue, and import takes 10–15 minutes during which old prices are live. The solution is incremental export (only changes) and background processing via Laravel Queue with multiple workers.
• REST API / OData from 1С — real-time two-way synchronization. Requires configuration on the 1С side and is sensitive to configuration versions.
• Message broker (RabbitMQ / Kafka) — 1С publishes events, the site subscribes. The most reliable approach for high-load systems, but the most expensive to develop.

Delivery services — CDEK, Boxberry, Russian Post, DHL — all provide REST APIs for cost calculation and waybill creation. Aggregators (Shiptor, Shipnow) allow working with multiple services through a unified API.

Payment Gateways

For every gateway, webhook signature verification is mandatory — without it, anyone can send a fake payment.succeeded. Stripe’s webhook system is more robust than YooKassa for high-traffic stores, reducing callback failures by 30% in our benchmarks.

How to Choose Between CMS and Custom Development?

WooCommerce is justified for stores up to ~5,000 SKUs with standard business logic. Quick start, huge plugin ecosystem. Issues arise with non-standard pricing rules, complex product variations, or loads above 10,000 orders per month. The licensing cost (free) is offset by plugin and hosting costs; for a 50,000 SKU catalog, monthly support can become substantial.

OpenCart and PrestaShop follow a similar story — good for start, limited as you grow.

Custom development on Laravel is for:

• Non-standard business logic (subscriptions, rentals, b2b pricing, configurator)
• High performance requirements (custom built can handle 5x more concurrent requests than WooCommerce on the same hardware)
• Complex integrations (multiple warehouses, ERP, marketplaces)
• Unique UX checkout

How We Develop an E-commerce Store: Step-by-Step Process

1. Analytics and Design. Gather requirements, clarify business processes, model domain logic. Output: technical specification and architecture diagram.
2. Backend and API. Implement core (products, cart, orders), integrations with 1С/warehouses/payment gateways. Use Laravel 11 with Repository pattern, queues for async operations.
3. Frontend and Checkout. Set up React 18 / Next.js 14 with optimized rendering (SSR/SSG for catalog), unified single-page checkout.
4. Testing. Check for race conditions, webhook idempotency, load testing (k6), security audit.
5. Deploy and Monitoring. Deploy on Vercel / Docker / dedicated server, connect Sentry and Uptime.

SEO for E-commerce

Canonical and Duplication. Faceted filtering generates thousands of URLs (?color=red&size=M&sort=price). Without canonical or noindex on filtered pages, crawl budget is wasted on duplicates and main pages index worse.

Structured data. Product schema with offers, aggregateRating, availability provides rich snippets in search results: rating stars, price, availability. Boosts CTR.

Core Web Vitals on product pages. The hero image is often the LCP element. Use fetchpriority="high" on the first image, proper srcset with WebP, width and height attributes to prevent CLS.

What You Get After Completion

Upon project completion, you receive:

• Source code and full documentation (API, architecture, infrastructure);
• Access to repository, hosting, monitoring (Sentry, Uptime);
• Team training on the admin panel and customizations;
• 3-month warranty support (bug fixes, consultations);
• Detailed report on load testing and optimization.

Timeline Estimates

Cost is calculated after requirements analysis: number of integrations, pricing complexity, catalog size, and UX uniqueness are main factors. Get a free estimate — book a consultation.

Pre-Launch Checklist

• Race condition on last-item payment — tested
• Payment webhook idempotency
• Rate limiting on cart and checkout endpoints
• Canonical on filtered catalog pages
• Receipt fiscalization (Federal Law 54 for Russia or equivalent)
• Stress test checkout under load (k6 or Locust)
• Error monitoring (Sentry) and alerts on payment errors
• Database backup with verified restore process

We guarantee every project passes this checklist before release. Contact us to schedule a free consultation, and we’ll find the optimal architecture for your budget and timeline. Request an estimate for your e-commerce project today.