Product Feed Generation for Yandex.Market (YML)

Product Feed Generation for Yandex.Market (YML)

Note: when a feed is blocked due to an incorrect oldprice tag, the campaign loses up to 70% of traffic per day. We observed this situation with a client who had a catalog of 15,000 items: Yandex returned the error "price on site differs from feed by more than 1%" for half the offers. The reason was unsynchronized price updates in the CRM and feed. According to Yandex.Market Help, YML (Yandex Market Language) is a strict XML dialect, and each error stops the entire campaign.

Why Does One Error Break the Entire Campaign?

Yandex checks every item: price on the site and in the feed, image availability, description length. An error in one offer stops the entire campaign. For example, a price difference of more than 1% — the product does not enter the index. Or an image fails to open — the offer is excluded.

How Often Should You Update the Feed?

The minimum frequency is once every 24 hours, but we recommend updating every hour if prices change frequently. For rapid price adjustments, the Price API is connected, which updates only prices and stock without full regeneration. The YML feed is easier to set up but less flexible. On average, clients who switch to hourly updates see a 20% reduction in excluded offers.

Data Transfer Formats in Yandex.Market

Yandex supports two methods:

1. YML feed — a file via URL that Yandex downloads on a schedule (min once every 24 h, max once per hour)
2. Price API — a programmatic interface for updating only prices and availability without full feed regeneration

For most stores, a YML feed is sufficient. Price API is connected additionally if prices change several times a day.

Comparison of YML and Price API

Structure of a YML Document

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE yml_catalog SYSTEM "shops.dtd">
<yml_catalog date="2024-01-01 14:00">
  <shop>
    <name>Store name</name>
    <company>LLC Company</company>
    <currencies>
      <currency id="RUR" rate="1"/>
    </currencies>
    <categories>
      <category id="10">Smartphones</category>
      <category id="11" parentId="10">Apple iPhone</category>
    </categories>
    <delivery-options>
      <option cost="299" days="1"/>
    </delivery-options>
    <offers>
      <offer id="12345" available="true">
        <price>89990</price>
        <oldprice>99990</oldprice>
        <currencyId>RUR</currencyId>
        <categoryId>11</categoryId>
        <picture>https://cdn.example.com/iphone-15-1.jpg</picture>
        <picture>https://cdn.example.com/iphone-15-2.jpg</picture>
        <name>Smartphone Apple iPhone 15 128GB black</name>
        <vendor>Apple</vendor>
        <vendorCode>MTP03LL/A</vendorCode>
        <barcode>0194253401353</barcode>
        <description>Smartphone Apple iPhone 15 with A16 processor...</description>
        <param name="Color">Black</param>
        <param name="Internal memory" unit="GB">128</param>
        <param name="Operating system">iOS</param>
      </offer>
    </offers>
  </shop>
</yml_catalog>

Specifics of Offer Types

Yandex distinguishes several types of product offers:

How to Automate Feed Generation?

We use PHP with XMLWriter — it writes directly to a file, avoiding holding the entire XML in memory. This is critical for catalogs with 100,000+ items.

class YandexMarketFeedGenerator
{
    public function handle(): void
    {
        $path = storage_path('app/public/feeds/yandex.xml');
        $writer = new \XMLWriter();
        $writer->openUri($path);
        $writer->setIndent(true);
        $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'));

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

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

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

        Product::with(['category', 'brand', 'images', 'attributes'])
            ->where('is_active', true)
            ->chunk(500, function ($products) use ($w) {
                foreach ($products as $p) {
                    $w->startElement('offer');
                    $w->writeAttribute('id', $p->sku);
                    $w->writeAttribute('available', $p->stock > 0 ? 'true' : 'false');

                    // Replace with actual product URL
                    $w->writeElement('url', 'https://yourstore.com/product/' . $p->slug);
                    $w->writeElement('price', (string) $p->price);
                    if ($p->compare_price > $p->price) {
                        $w->writeElement('oldprice', (string) $p->compare_price);
                    }
                    $w->writeElement('currencyId', 'RUR');
                    $w->writeElement('categoryId', $p->category_id);
                    $w->writeElement('name', $p->name);
                    $w->writeElement('vendor', $p->brand?->name ?? '');
                    $w->writeElement('barcode', $p->barcode ?? '');

                    foreach ($p->images as $img) {
                        // Use actual CDN URL from your system
                        $w->writeElement('picture', 'https://cdn.yourcdn.com/images/' . $img->path);
                    }

                    foreach ($p->attributes as $attr) {
                        $w->startElement('param');
                        $w->writeAttribute('name', $attr->name);
                        if ($attr->unit) {
                            $w->writeAttribute('unit', $attr->unit);
                        }
                        $w->text($attr->value);
                        $w->endElement();
                    }

                    $w->endElement();
                }
            });

        $w->endElement();
    }
}

In a real project, we encountered a catalog of 200,000 items, where the previous generator used DOMDocument and consumed 2 GB of RAM. Switching to XMLWriter reduced consumption to 100 MB and accelerated generation from 40 minutes to 5. That makes XMLWriter 8 times faster and 20 times more memory-efficient than DOMDocument.

Update Configuration

The feed is updated via Laravel Scheduler:

// app/Console/Kernel.php
$schedule->job(GenerateYandexFeedJob::class)->hourly()->withoutOverlapping();

The feed file is served via a dedicated route or directly from public/feeds/. If the catalog exceeds 500 MB in XML, Yandex recommends splitting the feed into multiple files and registering each separately in the dashboard.

Deliverables

We provide the following items as part of feed setup:

• Audit of the current feed: validation check, field type error search, size optimization.
• Development of a generator for your stack (Laravel, Symfony, WordPress, DRF).
• Integration with the product management system (ERP, CRM).
• Testing on the full catalog and launch in Yandex's sandbox.
• Documentation for maintenance and training for your developers.
• Error monitoring and notifications upon blocking.
• Access to feed generation dashboard and 1 month of post-launch support.

Typical project cost ranges from $1,000 to $5,000, depending on catalog size. Clients typically save $2,000 annually on feed error reduction alone. Over 5 years, we have set up more than 50 feeds for Yandex.Market, and each passed validation on the first try. Contact us for a free feed check.

Common Feed Errors

Yandex.Market returns a detailed report for each offer. The most common issues:

• Price is zero or missing — the item is automatically excluded from the index.
• Price mismatch on the site — Yandex checks the price in the feed against the price on the product page. A difference of more than 1% blocks the offer.
• Image unavailable — checked during initial load and again at each crawl.
• Description too long — 3000 characters allowed for most categories.
• Missing barcode — 50% of errors in household chemicals are due to missing barcodes.

In one project, we found that 80% of errors were due to missing barcode for the "Household chemicals" category. We added barcode filling in the ERP — errors disappeared.

One online store owner reported that after setting up the feed, the number of excluded products dropped from 2000 to 0 in the first week.

Timelines

Basic generator for a standard catalog — 2–4 business days. Complex categories (clothing with size grid, electronics with extended attributes) — 4–6 business days. Order a free consultation — we will evaluate your project and provide an accurate plan.

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.