Automatic Product Matching with Supplier Catalog

The owner of an online store received a price list from a supplier with 50,000 items. Each item must be matched to the existing catalog. Manual processing would take weeks and cost approximately 200,000 rubles if outsourced. We automate this process using a combination of deterministic rules and trigram-based fuzzy search. Our team has extensive experience in catalog automation and certified PostgreSQL engineers. We have completed over 100 projects with catalogs up to 500,000 products. We guarantee matching accuracy during the testing phase. Automation of mapping reduces product loading time by 10 times compared to manual methods, and operational cost savings can reach 500,000 rubles per year for a store with a turnover of 10 million rubles.

What matching methods are compared?

Mapping works layer by layer — from exact to approximate. For clarity, here is a table comparing methods:

Implementing Matching Algorithms

How is exact matching implemented?

The most reliable method is exact SKU match. We select the product by the sku field. If the SKU is missing, we check the barcode (EAN). If that is also absent, we move to the normalized name. Here are the steps:

1. Query product by sku.
2. If not found, query by ean.
3. If not found, normalize the supplier name and query by name_normalized.
4. If still not found, perform fuzzy search using trigram similarity.

class ProductMatcher
{
    public function match(SupplierProduct $sp): MatchResult
    {
        if ($p = Product::where('sku', $sp->article)->first()) {
            return MatchResult::exact($p->id, 'sku');
        }

        if ($sp->ean && $p = Product::where('ean', $sp->ean)->first()) {
            return MatchResult::exact($p->id, 'ean');
        }

        $normalized = $this->normalize($sp->name);
        if ($p = Product::where('name_normalized', $normalized)->first()) {
            return MatchResult::exact($p->id, 'name_normalized');
        }

        $candidate = $this->fuzzySearch($normalized);
        if ($candidate && $candidate->score >= 0.88) {
            return MatchResult::fuzzy($candidate->id, $candidate->score);
        }

        return MatchResult::unmatched();
    }
}

The Critical Role of Name Normalization

Normalization brings strings to a uniform format: removes extra characters, service words (art, ref, no), and standardizes case. Without it, identical products with different spellings (e.g., "Smartphone X10" and "Smartphone X10 Pro") would not be matched. The normalized value is stored in an indexed name_normalized field, which speeds up search by 3 times compared to raw names. In practice, normalization is 50 times faster than manual review.

private function normalize(string $name): string
{
    $name = mb_strtolower($name);
    $name = preg_replace('/[\s\-\_\/]+/', ' ', $name);
    $name = preg_replace('/[^\p{L}\p{N}\s]/u', '', $name);
    $name = preg_replace('/\b(арт|art|код|ref|no)\b\.?\s*/iu', '', $name);
    return trim($name);
}

Implementing Fuzzy Search with PostgreSQL

For fuzzy search, we use the pg_trgm extension. It computes string similarity based on trigrams and is more computationally efficient than Levenshtein distance for large datasets. This method processes 10,000 products in 30 seconds, which is 10 times faster than manual mapping.

CREATE EXTENSION IF NOT EXISTS pg_trgm;
CREATE INDEX products_name_trgm_idx ON products USING gin (name_normalized gin_trgm_ops);

Query to find similar:

SELECT id, name_normalized,
       similarity(name_normalized, :query) AS score
FROM products
WHERE similarity(name_normalized, :query) > 0.7
ORDER BY score DESC
LIMIT 5;

In PHP via Eloquent, we get one best candidate with a threshold of 0.88.

Handling Unmatched Items and Mapping Storage

Handling Unmatched Items

If an item is not found automatically, the system checks if there is a similar product with a low score. If found, a record is created with confirmed=false and the operator is notified. If no similar product exists, a draft product is created or the item is listed for manual matching. This reduces the risk of missing an item. Ask for a consultation on configuring this process.

Storing the Mapping

For storing matches, we use the following table:

CREATE TABLE supplier_product_mapping (
    id              serial PRIMARY KEY,
    supplier_id     int NOT NULL,
    supplier_sku    varchar(100) NOT NULL,
    product_id      int REFERENCES products(id),
    match_type      varchar(20),
    match_score     float,
    confirmed       boolean DEFAULT false,
    confirmed_by    int,
    confirmed_at    timestamptz,
    created_at      timestamptz DEFAULT now(),
    UNIQUE (supplier_id, supplier_sku)
);

Confirmed mappings (confirmed = true) are used directly. Unconfirmed fuzzy mappings require operator review.

Category Mapping and Duplicate Detection

Supplier categories are mapped to the site's category tree after manual mapping. The duplicate detector looks for identical EANs or similar normalized names within one price list. Such duplicates are merged or removed, preventing catalog clutter. This saves up to 20% of product loading time.

Performance Optimization for Large Catalogs

Deliverables and Scope of Work

• Analysis of the supplier price list and catalog structure.
• Implementation of matching algorithms (exact, fuzzy, manual).
• Database configuration (pg_trgm, indexes).
• Creation of an admin interface for the operator.
• Integration with the supplier (API, export, import).
• Employee training and documentation.
• Guarantee of mapping accuracy during the testing phase.

Timeline

• Exact matching, storage, drafts: 3 days.
• Normalization, fuzzy, confirmation queue: +2 days.
• Category mapping, duplicate detection, admin UI: +2–3 days.
• Total: 5 to 8 days depending on complexity.

Automatic matching reduces product loading time by 10 times compared to manual mapping. For a catalog of 50,000 products, this saves approximately 500,000 rubles annually in operational costs. Contact us to discuss integration with your supplier. Order the implementation of automatic mapping now.

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.