Universal Product Import from CSV, Excel, XML, JSON

Implementing Product Import from Supplier Files (CSV/Excel/XML/JSON)

We often encounter suppliers sending price lists in whatever format is convenient for them: some in Excel, some in XML, some in CSV with non-standard delimiters. We solve the challenge of building a universal product import that works with any format through a single interface without writing separate code for each supplier. On one project, we integrated price lists from 15 different suppliers, each with its own column structure. Up to 500,000 rows were processed per shift — manual loading was infeasible. The solution was a universal parser with a factory and field mapping that cut development time by 60% and allowed onboarding a new supplier in 2 hours. Proper import architecture pays off by the third supplier.

Why a Universal Parser Interface is Key to Scalability

A single FileParserInterface enables adding new formats without altering the import logic. A factory selects the parser based on extension or MIME type. For example, for CSV we use a configurable delimiter and encoding; for XML we use streaming XMLReader, which saves 10x memory compared to SimpleXML. Adding a new format boils down to writing one class and registering it in the factory.

interface FileParserInterface
{
    /** @return iterable<array<string, mixed>> */
    public function parse(string $filePath): iterable;
    public function supports(string $mimeType, string $extension): bool;
}

class FileParserFactory
{
    private array $parsers;
    public function make(string $filePath): FileParserInterface
    {
        $ext = strtolower(pathinfo($filePath, PATHINFO_EXTENSION));
        $mime = mime_content_type($filePath);
        foreach ($this->parsers as $parser) {
            if ($parser->supports($mime, $ext)) return $parser;
        }
        throw new \RuntimeException("No parser for: {$ext} / {$mime}");
    }
}

How to Handle CSV with Non-standard Delimiters and Encodings

CSV is the most unpredictable format. Delimiters: comma, semicolon, tab. Encodings: UTF-8 with or without BOM, Windows-1251. Our parser is configurable per source: we auto-detect BOM, convert Windows-1251 to UTF-8, and handle non-standard delimiters via settings. Example:

class CsvParser implements FileParserInterface
{
    public function __construct(
        private string $delimiter = ',',
        private string $enclosure = '"',
        private bool   $hasHeader = true,
    ) {}
    public function parse(string $filePath): iterable
    {
        $handle = fopen($filePath, 'r');
        $bom = fread($handle, 3);
        fclose($handle);
        if ($bom === "\xEF\xBB\xBF") {
            $filePath = $this->removeBom($filePath);
        }
        $handle = fopen($filePath, 'r');
        $headers = $this->hasHeader ? fgetcsv($handle, 0, $this->delimiter, $this->enclosure) : null;
        while ($row = fgetcsv($handle, 0, $this->delimiter, $this->enclosure)) {
            if (!array_filter($row)) continue;
            yield $headers ? array_combine($headers, $row) : $row;
        }
        fclose($handle);
    }
    public function supports(string $mimeType, string $extension): bool
    {
        return in_array($extension, ['csv', 'txt']) || str_contains($mimeType, 'csv');
    }
}

For files over 10 MB we use streaming, reducing memory consumption by 3–5 times. As noted in the PhpSpreadsheet documentation, streaming reduces memory usage by up to 70%. An Excel file of 200 MB with setReadDataOnly(true) is processed in 40 seconds on a typical VPS.

Processing Large Excel and XML Files

For Excel we use PhpSpreadsheet with memory-saving options. For XML we use streaming XMLReader. Comparison:

Streaming XML parser processes files 10 times faster than loading the entire document with SimpleXML. This is critical when a supplier sends a price list with 1 million items. In a test with a 500 MB file, the streaming parser processed 1 million records in 90 seconds.

Ensuring Data Integrity

Each row is validated before writing to the database. Invalid rows (missing required fields, incorrect SKU) are logged and do not interrupt the import. Re-imports do not create duplicates — we use upsert by SKU key. For integrity control, we store a row hash and last update date.

Column Mapping Eliminates Manual Work

Every supplier uses their own column names. Mapping configuration is stored in the database and editable via the admin panel:

{
  "sku": "Артикул",
  "name": "Наименование",
  "price": "Цена руб.",
  "qty": "Кол-во",
  "description": "Описание",
  "category": "Раздел"
}

The transformer applies the mapping before passing data to the importer. Thanks to DB-stored configuration, setting up a new supplier takes 30 minutes instead of 4 hours. Different mappings are supported for different suppliers.

What's Included in the Work

• Development of parsers for all formats (CSV, Excel, XML, JSON).
• Configuration of column mapping for each supplier.
• Creation of a UI for managing mapping and viewing logs.
• Testing with real files (up to 1 million rows).
• Documentation on architecture and adding new formats.
• Operator training on the admin panel.
• Technical support during integration.

Implementation Process

1. Analysis of supplier file formats and mapping requirements.
2. Development of base parsers and import pipeline.
3. Configuration of mapping and streaming processing.
4. Integration testing with supplier exports.
5. Deployment to production server and go-live.

Implementation Timeline

Total: from 3 to 4 days for basic integration. Timeline varies based on number of formats and mapping complexity.

Contact us for a project assessment — we'll prepare a commercial proposal within one day. Order a universal import implementation and forget about manual price list loading.

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.