YML Feed Generation for Yandex.Market: No Errors, Fast Updates

Recently a store with 30,000 products approached us: their feed was 20 MB, but on upload Yandex gave a price missing error for every second item. It turned out the price was stored in a separate table without an index, and the generator couldn't pull the cost in time. After implementing our solution, errors disappeared, and the feed started updating every 2 hours without downtime. We helped the client avoid fines and save time on manual export.

In this article we'll break down typical problems, generator architecture, and implementation steps. You'll learn how to avoid common mistakes and reduce feed maintenance time. We've prepared a checklist of typical issues to help with self-setup. If you encounter feed errors, contact us — we'll set up the generator in 2 days.

How to Avoid Common Errors When Exporting Products to YML Feed?

Typical errors: missing required fields, incorrect nesting, exceeding description length. Our generator automatically checks each field before writing. If price is missing or zero, the product is excluded from the feed to avoid penalty points. We also check categoryId for parent category existence and currency code validity. All errors are logged with product ID.

Problems We Solve — Product Export to YML Feed

• Incorrect prices and currencies — stores often indicate price without VAT or forget currencyId. Our generator automatically inserts RUR and checks that price is greater than zero.
• Feed too large — a catalog of 50,000 products weighs 25 MB. Without gzip Yandex may not accept. We add on-the-fly compression.
• Frequent updates — if prices change hourly, the feed should update just as often. We configure cron with intervals from 1 hour.

How We Do It

We use PHP and Laravel. For generation, the YmlFeedBuilder class based on XMLWriter. This allows forming the feed without loading the entire DOM into memory.

class YmlFeedBuilder
{
    public function build(string $outputPath): void
    {
        $writer = new XMLWriter();
        $writer->openUri($outputPath);
        $writer->startDocument('1.0', 'UTF-8');
        $writer->writeDtd('yml_catalog', null, 'shops.dtd');

        $writer->startElement('yml_catalog');
        $writer->writeAttribute('date', now()->format('Y-m-d H:i'));

        $this->writeShopInfo($writer);
        $this->writeCurrencies($writer);
        $this->writeCategories($writer);
        $this->writeOffers($writer);

        $writer->endElement();
        $writer->endDocument();
        $writer->flush();
    }

    private function writeOffers(XMLWriter $writer): void
    {
        $writer->startElement('offers');

        Product::with(['category', 'attributes', 'images'])
            ->where('active', true)
            ->where('price', '>', 0)
            ->chunk(500, function ($products) use ($writer) {
                foreach ($products as $product) {
                    $this->writeOffer($writer, $product);
                }
            });

        $writer->endElement();
    }
}

Using chunk() is mandatory — attempting to load 50,000+ products in one query leads to OOM. Our experience with catalogs from 500 to 500,000 products guarantees stable generation. We ensure compliance with the Yandex.Market format. Yandex.Market

Why Background YML Feed Generation Is Critical for Large Catalogs?

For catalogs over 10,000 products, on-the-fly generation is unsuitable — response time exceeds limits. We use a queue or cron task. The feed is written to disk or S3, then becomes available via a direct link. This avoids timeouts and reduces server load.

Performance Comparison

Generation via XMLWriter is 2x faster than via SimpleXML and consumes 30% less memory. This is critical for volumes over 50,000 items.

Offer Types and Their Features

Feed Validation

Before sending to Yandex.Market, the feed should be validated locally. Use the official Yandex.Market validator. Our generator includes built-in validation:

class YmlFeedValidator
{
    public function validate(string $filePath): array
    {
        $errors = [];
        $dom = new DOMDocument();

        if (!$dom->load($filePath)) {
            return ['XML parse error'];
        }

        $offers = $dom->getElementsByTagName('offer');
        foreach ($offers as $offer) {
            $id = $offer->getAttribute('id');

            if (!$offer->getElementsByTagName('price')->length) {
                $errors[] = "Offer {$id}: missing price";
            }
            if (!$offer->getElementsByTagName('url')->length) {
                $errors[] = "Offer {$id}: missing url";
            }
            // Check description length (Yandex truncates after 3000 characters)
            $desc = $offer->getElementsByTagName('description')->item(0);
            if ($desc && mb_strlen($desc->textContent) > 3000) {
                $errors[] = "Offer {$id}: description too long";
            }
        }

        return $errors;
    }
}

Yandex.Market recommends not exceeding 500 MB per feed (per official documentation).

Update Schedule

• Prices and stock — change frequently: recommended regeneration every 1–4 hours.
• Attributes and descriptions — change slowly: once a day is enough.
• New products — immediately or at the next hourly cycle.

Example configuration in Laravel:

$schedule->job(new GenerateYmlFeedJob)->everyFourHours();

Feed Size Optimization

Yandex.Market recommends not exceeding 500 MB per feed. If the catalog is larger, split by categories and register multiple feeds.

Reduce size without quality loss:

• Exclude products without price (WHERE price > 0)
• Exclude inactive categories
• Truncate description to 1000–1500 characters
• Compress feed with gzip — Yandex supports it

Size comparison table:

How to Set Up a YML Feed Generator: Step-by-Step Guide

1. Create a YmlFeedBuilder class based on XMLWriter as shown above.
2. Configure product retrieval with query optimization (use chunk()).
3. Add validation of required fields before writing.
4. Implement update scheduling via cron or queue.
5. Enable gzip compression and check the feed with the official validator.

Implementation Timeline

• Basic generator with default type: 1–2 days
• Adding typed offers (vendor.model): +0.5 day
• Validator + error logging: +0.5 day
• Cron setup + public URL: +0.5 day

Total for a typical project: 2–3 business days. Price is calculated individually — savings on support and fines justify the investment.

What's Included

• YML feed generator tailored to your catalog
• Built-in validator with error report
• Update schedule configuration (cron or queue)
• gzip feed compression
• Documentation on format and maintenance
• Staff training on feed management

Order YML feed setup and forget about errors. Get a consultation for your project — contact us.

Checklist of Typical Errors

• [ ] Inactive products with price 0
• [ ] Missing url or price
• [ ] Description longer than 3000 characters
• [ ] Incorrect category (parentId does not exist)
• [ ] Feed larger than 500 MB

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.