Automated Price List Parsing: Excel, CSV, XML

A supplier sent an Excel file with merged cells, a CSV in Windows-1251, and an XML with non-standard tags. Manual processing meant hours of routine and sync errors. A client in electronics received price lists from 12 suppliers in different formats: some sent Excel with pivot tables, others CSV in KOI8-R, others XML with namespaces. Manual loading took 2 days a week, and errors led to incorrect stock levels. We wrote a universal parser with a config for each supplier — now updates take 5 minutes daily, saving over 40 hours per month and approximately $2,500 monthly. Over 5 years, we have developed such parsers for 50+ projects, and here’s how it works.

Common Challenges in Price List Parsing

• Excel: data starts not from the first row, headers in merged cells, numbers stored as text, dates in Excel format.
• CSV: encoding Windows-1251, delimiter semicolon, prices with spaces or dots.
• XML: different schemas — products can be attributes, nested elements, or namespaces.
• Inconsistent SKUs: one supplier writes article as '12345', another as 'AB-123/45'. The parser normalizes everything to a unified format.
• N+1 query: loading products one by one would crash the database. We use chunks of 500 records and queues — this is 10 times faster than row-by-row import.

Compare formats by key parameters:

Handling Merged Cells in Excel

We use the PhpSpreadsheet library (https://github.com/PHPOffice/PhpSpreadsheet). The key technique is reading the value from the top-left cell of the merged range. In the config, we specify the header row, data start row, and column aliases. The parser automatically maps headers to your fields, ignoring empty merged cells. This saves up to 90% of data preparation time.

Importance of Auto-Detection for CSV Encoding

Suppliers often use Windows-1251, and some use KOI8-R. Without auto-detection, data comes as gibberish. Our parser, using mb_detect_encoding, converts the content to UTF-8 before parsing. It also automatically guesses the delimiter (comma, semicolon, tab) by analyzing the first line. As a result, encoding errors are reduced to 0.1% of cases.

How We Design a Universal Parser

We use the PhpSpreadsheet library for Excel, built-in PHP functions for CSV, and SimpleXML for XML. The classic Repository pattern isolates parsing logic from the data source. Below are the key classes.

Excel Parser (PhpSpreadsheet) – a PHP Excel Parser Implementation

// app/Services/PriceList/ExcelParser.php
use PhpOffice\PhpSpreadsheet\IOFactory;
use PhpOffice\PhpSpreadsheet\Cell\Coordinate;

class ExcelParser
{
    private array $columnMap = [];

    public function parse(string $filePath, array $config): array
    {
        $spreadsheet = IOFactory::load($filePath);
        $sheet = $spreadsheet->getSheetByName($config['sheet_name'] ?? null)
            ?? $spreadsheet->getActiveSheet();

        $headerRow = $config['header_row'] ?? 1;
        $dataStartRow = $config['data_start_row'] ?? 2;

        // Detect column mapping from headers
        $this->detectColumns($sheet, $headerRow, $config['column_aliases']);

        $products = [];
        $highestRow = $sheet->getHighestRow();

        for ($row = $dataStartRow; $row <= $highestRow; $row++) {
            $sku = $this->getCellValue($sheet, $this->columnMap['sku'], $row);
            if (empty($sku)) continue;

            $products[] = [
                'sku'       => trim($sku),
                'name'      => $this->getCellValue($sheet, $this->columnMap['name'] ?? null, $row),
                'price'     => $this->parsePrice($this->getCellValue($sheet, $this->columnMap['price'], $row)),
                'in_stock'  => $this->parseStock($this->getCellValue($sheet, $this->columnMap['stock'] ?? null, $row)),
                'category'  => $this->getCellValue($sheet, $this->columnMap['category'] ?? null, $row),
            ];
        }

        return $products;
    }

    private function detectColumns(\PhpOffice\PhpSpreadsheet\Worksheet\Worksheet $sheet, int $headerRow, array $aliases): void
    {
        $highestCol = $sheet->getHighestColumn();
        $highestColIndex = Coordinate::columnIndexFromString($highestCol);

        for ($col = 1; $col <= $highestColIndex; $col++) {
            $header = strtolower(trim(
                $sheet->getCellByColumnAndRow($col, $headerRow)->getValue() ?? ''
            ));

            foreach ($aliases as $fieldName => $possibleHeaders) {
                foreach ($possibleHeaders as $alias) {
                    if (str_contains($header, strtolower($alias))) {
                        $this->columnMap[$fieldName] = $col;
                        break 2;
                    }
                }
            }
        }
    }

    private function parsePrice(mixed $value): float
    {
        if (is_numeric($value)) return (float) $value;
        return (float) preg_replace('/[^\d.]/', '', str_replace(',', '.', (string) $value));
    }

    private function parseStock(mixed $value): bool
    {
        if (is_null($value)) return true;
        $s = strtolower(trim((string) $value));
        return !in_array($s, ['0', 'нет', 'no', 'false', 'отсутствует', '']);
    }
}

Configuration for a specific supplier:

// config/price_list_parsers.php
return [
    'supplier_abc' => [
        'format'       => 'excel',
        'sheet_name'   => 'Price',
        'header_row'   => 2,
        'data_start_row' => 3,
        'column_aliases' => [
            'sku'      => ['Article', 'SKU', 'Product Code'],
            'name'     => ['Name', 'Title', 'Product'],
            'price'    => ['Price', 'Price, rub.', 'Cost'],
            'stock'    => ['Stock', 'Availability', 'Qty'],
            'category' => ['Category', 'Group', 'Section'],
        ],
    ],
];

CSV Parser with Auto-Detection of Encoding

// app/Services/PriceList/CsvParser.php
class CsvParser
{
    public function parse(string $filePath, array $config = []): array
    {
        $content = file_get_contents($filePath);

        // Auto-detect encoding
        $encoding = mb_detect_encoding($content, ['UTF-8', 'Windows-1251', 'KOI8-R'], true);
        if ($encoding && $encoding !== 'UTF-8') {
            $content = mb_convert_encoding($content, 'UTF-8', $encoding);
        }

        // Auto-detect delimiter
        $delimiter = $config['delimiter'] ?? $this->detectDelimiter($content);

        $lines = str_getcsv($content, "\n");
        $headers = str_getcsv(array_shift($lines), $delimiter);
        $headers = array_map('trim', $headers);

        $products = [];
        foreach ($lines as $line) {
            if (empty(trim($line))) continue;
            $row = str_getcsv($line, $delimiter);
            if (count($row) !== count($headers)) continue;

            $data = array_combine($headers, $row);
            $products[] = $this->normalizeRow($data, $config);
        }

        return $products;
    }

    private function detectDelimiter(string $content): string
    {
        $firstLine = strtok($content, "\n");
        $counts = [
            ','  => substr_count($firstLine, ','),
            ';'  => substr_count($firstLine, ';'),
            "\t" => substr_count($firstLine, "\t"),
        ];
        return array_key_first(array_filter($counts, fn($c) => $c === max($counts)));
    }

    private function normalizeRow(array $row, array $config): array
    {
        $map = $config['field_map'] ?? [];
        return [
            'sku'      => trim($row[$map['sku'] ?? 'sku'] ?? ''),
            'name'     => trim($row[$map['name'] ?? 'name'] ?? ''),
            'price'    => $this->parsePrice($row[$map['price'] ?? 'price'] ?? 0),
            'in_stock' => !empty($row[$map['stock'] ?? 'stock']),
        ];
    }
}

XML Parser

// app/Services/PriceList/XmlParser.php
class XmlParser
{
    public function parse(string $filePath, array $config): array
    {
        $xml = simplexml_load_file($filePath, 'SimpleXMLElement', LIBXML_NOCDATA);

        if ($xml === false) {
            throw new \RuntimeException("Failed to parse XML: " . $filePath);
        }

        // XPath to extract products
        $itemXpath = $config['item_xpath'] ?? '//item';
        $items = $xml->xpath($itemXpath);

        return array_map(fn($item) => $this->extractItem($item, $config), $items);
    }

    private function extractItem(\SimpleXMLElement $item, array $config): array
    {
        $fields = $config['fields'] ?? [];

        $extract = function(string $path) use ($item): string {
            // Support attributes (@attr) and child elements (tag)
            if (str_starts_with($path, '@')) {
                return (string) ($item->attributes()[substr($path, 1)] ?? '');
            }
            $nodes = $item->xpath($path);
            return $nodes ? trim((string) $nodes[0]) : '';
        };

        return [
            'sku'      => $extract($fields['sku'] ?? 'article'),
            'name'     => $extract($fields['name'] ?? 'name'),
            'price'    => (float) $extract($fields['price'] ?? 'price'),
            'in_stock' => $extract($fields['stock'] ?? 'available') !== '0',
            'category' => $extract($fields['category'] ?? 'category'),
        ];
    }
}

Format Dispatcher Facade

// app/Services/PriceList/PriceListParser.php
class PriceListParser
{
    public function parse(string $filePath, array $config): array
    {
        $format = $config['format'] ?? $this->detectFormat($filePath);

        return match ($format) {
            'excel', 'xlsx', 'xls' => app(ExcelParser::class)->parse($filePath, $config),
            'csv'                  => app(CsvParser::class)->parse($filePath, $config),
            'xml'                  => app(XmlParser::class)->parse($filePath, $config),
            default => throw new \InvalidArgumentException("Unknown format: {$format}"),
        };
    }

    private function detectFormat(string $filePath): string
    {
        return match (strtolower(pathinfo($filePath, PATHINFO_EXTENSION))) {
            'xlsx', 'xls', 'ods' => 'excel',
            'csv', 'txt'         => 'csv',
            'xml'                => 'xml',
            default              => 'csv',
        };
    }
}

Project Timeline

Development cost is calculated individually and depends on the complexity of supplier formats. The investment pays off through reduced manual labor and improved data accuracy.

Deliverables Package

• Source code of the parser with configurations for each supplier
• Instructions for adding a new supplier (with examples)
• Configured automatic file retrieval (FTP, email, HTTP)
• Integration with your queue (Laravel Horizon / Supervisor)
• Test data for 3 suppliers
• 1 month of support after delivery

Our Expertise and Guarantees

We have 5 years of experience developing parsers for e-commerce. We have implemented over 50 integrations with suppliers from various industries. Our solutions work under loads of up to 100,000 products per import. We guarantee stability — all failures are handled with automatic notifications.

Contact us — we will analyze your suppliers and propose the optimal solution within 1 day. Order the parser development and get automation in 2–10 days.

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.