Price Aggregator Development for Price Comparison — Turnkey

You visit a website, type "Samsung Galaxy S24", and it shows prices from 20 stores, sorts by cheapest, and plots a price history graph. We build such price aggregators—platforms that automatically collect prices, match products, and show users the best offers. Technically, this is a complex task: parsing heterogeneous sources (YML, API, HTML), data normalization, fuzzy matching, and continuous updates. Each step is full of pitfalls: from rate limiting to differences in product names. With over 10 years of experience, we have built fault-tolerant collection and matching systems processing up to 1 million products per day. A matching error—and users see incorrect prices or duplicates. Web scraping without control—blocks and penalties. We ensure data stability and accuracy through thoughtful architecture, helping our clients save up to 40% of their budget by eliminating manual checks.

How to organize data collection from different sources?

Data Sources

Product and price data comes in three ways:

• Price lists and feeds — the store provides a YML, XML, or CSV file with the current assortment. The most reliable source: structured data, official partnership, no risk of bans. Yandex.Market YML is the de facto standard for the Russian-language market.
• Partner APIs — some stores provide REST APIs. Documentation is often weak, request limits are strict. Per official Yandex documentation, partner links require prior approval.
• Web scraping — for stores without feeds. High risk: CAPTCHA, rate limiting, layout changes, IP blocking. Requires constant maintenance.

At the start of an aggregator, it is better to work only with feeds and APIs—they are more stable. We selectively add scraping for key sources.

Data Collector Architecture

Scheduler (Celery Beat / Laravel Scheduler)
  ↓ every N hours
FeedFetcher workers (one per source)
  ↓
RawData storage (S3 or local FS)
  ↓
Parser workers (XML/CSV/JSON → normalized objects)
  ↓
Normalizer (unit conversion, text cleanup)
  ↓
Matcher (match against products in DB)
  ↓
PriceHistory (record in timeseries)
  ↓
ElasticsearchIndexer (update index)

Task queue: Celery + Redis for Python stack, Laravel Horizon + Redis for PHP stack. Each feed is processed independently; an error in one source does not block others.

Parsing Yandex.Market YML

YML is an XML with a strict schema. Critical fields:

<offer id="12345" available="true">
  <url>https://shop.example.com/product/12345</url>
  <price>4990</price>
  <currencyId>RUB</currencyId>
  <categoryId>14</categoryId>
  <name>Samsung Galaxy A55 128GB</name>
  <vendor>Samsung</vendor>
  <model>Galaxy A55</model>
  <barcode>8806095076783</barcode>
  <param name="Color">Blue</param>
  <param name="Memory">128 GB</param>
</offer>

The barcode (barcode) is the best key for matching. GTIN/EAN is unique for each product variation. If barcodes are present for most suppliers, matching becomes trivial. According to Yandex.Market documentation, barcode is a recommended element for precise matching.

Why is product matching the key stage?

This is the most complex part of the aggregator. The task: determine that Samsung Galaxy A55 128GB Blue from store A and Smartphone Samsung Galaxy A55 (SM-A556B) 128 Gb Blue from store B are the same product.

Deterministic Methods

• GTIN/EAN matching: if both products have a barcode—unambiguous match.
• Manufacturer part number (MPN): SM-A556B is unique within the brand.
• URL canonicalization: some stores include GTIN in the URL.

Fuzzy Matching

from rapidfuzz import fuzz

def match_score(title_a: str, title_b: str, brand_a: str, brand_b: str) -> float:
    if brand_a.lower() != brand_b.lower():
        return 0.0
    title_similarity = fuzz.token_sort_ratio(title_a, title_b)
    return title_similarity / 100

Match threshold: 0.85+ considered automatic match, 0.65–0.85 sent for manual review, below—new product.

ML Approach

Product name embeddings (sentence-transformers, ruBERT) + cosine similarity. Significantly more accurate than fuzzy, especially for different phrasings of the same product. The model is trained on historically confirmed matches.

Storage structure:

canonical_products (id, gtin, mpn, brand, name, category_id, attrs JSONB)
source_offers (id, source_id, external_id, canonical_product_id, price, url, in_stock, updated_at)
match_candidates (offer_id, canonical_id, score, status)  -- pending | approved | rejected

How are prices updated and history stored?

Price History

The core value of an aggregator is not only the current price but also the change history. Each price change is recorded, not overwritten.

price_history (
  id BIGSERIAL,
  source_offer_id BIGINT,
  price NUMERIC(12,2),
  in_stock BOOLEAN,
  recorded_at TIMESTAMPTZ DEFAULT NOW()
)

For timeseries storage we use TimescaleDB—a PostgreSQL extension that partitions the table by time. Alternatives: InfluxDB or ClickHouse for high loads (up to 10,000 inserts per second).

A price history chart is a standard component on the product page. We use Chart.js or Recharts, aggregating data by day: SELECT date_trunc('day', recorded_at), min(price) FROM price_history WHERE ....

Update Frequency

When a price changes—page cache invalidation and recalculation of minimum price in the index.

How to set up data collection in 4 steps

1. Connect sources: obtain YML feeds from stores or set up API access. One source = one config.
2. Configure parsing: for feeds—use ready YML/XML parser; for APIs—write an adapter per documentation.
3. Run matching: define rule set—GTIN priority, then fuzzy, then ML. Start with manual validation.
4. Monitor and update: set up Celery or Horizon scheduler, add alerts for source failures.

What's included in aggregator development

As a result, you receive:

• Documentation for source integration (format descriptions, examples).
• Deployed infrastructure (Docker, CI/CD, monitoring).
• Admin panel for editing matching and viewing statistics.
• Training of the client's team on system operation.
• Technical support for 3 months after launch.
• Guarantee on matching correctness and timely price updates.

Want to discuss your project? Contact us for a free estimate. Get an engineer's consultation with no obligations.

Our experience

Over 10 years on the market, 40+ implemented projects. We don't just write code—we design architecture that doesn't fall under load and scales easily. We'll evaluate your project for free. Get in touch—we'll propose architecture, timelines, and turnkey pricing.

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.