Automate Product Import via Supplier API

Imagine your online store sells 10,000 products, and supplier changes prices twice a day. Manual Excel upload takes hours and leads to outdated prices and stock errors. Direct integration via supplier API solves this — data updates automatically without human involvement. Over 10 years, we have implemented more than 50 such integrations, from simple REST links to multi-supplier systems with OAuth 2.0 and SOAP. Our experience confirms that the right architecture ensures stability and data accuracy. For instance, for an auto parts store with 500,000 SKUs, we set up incremental synchronization with 5 suppliers, each with its own API. Result: prices and stock are accurate with a delay of no more than 15 minutes.

The challenge is that supplier APIs vary greatly. Authentication formats, response structures, pagination models — all are unique. Without proper architecture, integration turns into chaos. We use proven patterns that simplify adding new suppliers and ensure stability.

How to choose the API type for a supplier?

Determining the type is the first step. We always start by analyzing the supplier's documentation: if there is a REST API specification, half the work is done.

Basic client with retry and rate limiting

class SupplierApiClient
{
    private \GuzzleHttp\Client $http;
    private RateLimiter        $rateLimiter;

    public function __construct(
        private SupplierApiConfig $config,
    ) {
        $this->http = new \GuzzleHttp\Client([
            'base_uri' => $config->baseUrl,
            'timeout'  => 30,
            'handler'  => $this->buildHandlerStack(),
        ]);
    }

    private function buildHandlerStack(): \GuzzleHttp\HandlerStack
    {
        $stack = \GuzzleHttp\HandlerStack::create();
        $stack->push(\GuzzleHttp\Middleware::retry(
            function (int $retries, $request, $response, $exception) {
                if ($retries >= 3) return false;
                if ($exception instanceof \GuzzleHttp\Exception\ConnectException) return true;
                if ($response && $response->getStatusCode() >= 500) return true;
                return false;
            },
            fn(int $retries) => 1000 * (2 ** $retries)
        ));
        return $stack;
    }

    public function get(string $path, array $params = []): array
    {
        $this->rateLimiter->throttle($this->config->id, $this->config->rateLimit);

        $response = $this->http->get($path, [
            'query'   => $params,
            'headers' => $this->buildHeaders(),
        ]);

        return json_decode($response->getBody(), true);
    }

    private function buildHeaders(): array
    {
        return match ($this->config->authType) {
            'bearer' => ['Authorization' => 'Bearer ' . $this->config->token],
            'api_key' => ['X-API-Key' => $this->config->apiKey],
            'basic'   => ['Authorization' => 'Basic ' . base64_encode(
                $this->config->login . ':' . $this->config->password
            )],
            default => [],
        };
    }
}

Exponential backoff (1s, 2s, 4s) reduces load on the supplier's server and increases success probability during transient failures. Rate limiting prevents blocking due to exceeding request limits.

Why is data normalization important?

Each supplier has its own JSON field for name, price, SKU. Without normalization, the code becomes messy — checks and extractions everywhere. We use a fieldMap with dot-notation, stored in the database as JSON. Adding a new supplier is just an entry in the table, with no code changes.

class SupplierResponseNormalizer
{
    private array $fieldMap;

    public function normalize(array $raw): array
    {
        return [
            'sku'         => $this->extract($raw, $this->fieldMap['sku']),
            'name'        => $this->extract($raw, $this->fieldMap['name']),
            'price'       => (float) $this->extract($raw, $this->fieldMap['price']),
            'qty'         => (int)   $this->extract($raw, $this->fieldMap['qty']),
            'description' => $this->extract($raw, $this->fieldMap['description']),
            'images'      => $this->extractImages($raw),
        ];
    }

    private function extract(array $data, string $path): mixed
    {
        return data_get($data, $path);
    }
}

When is incremental synchronization needed?

Incremental synchronization is indispensable when data volume is large or update frequency is high. It requests only changes since the last update, using the updated_after parameter. The time of the last successful sync is stored in the database. This reduces data transfer volume many times over — in one project with 500,000 SKUs, API load dropped by 90%.

Pagination and method comparison

Cursor pagination is up to 10 times more efficient than offset for large datasets under frequent changes because it uses a unique identifier for the last record. Offset is simple but inefficient when data shifts. For large volumes, we recommend cursor or scroll.

OAuth 2.0 authorization

Many suppliers require OAuth 2.0 client credentials. The token is cached until expiration — eliminating extra requests.

class OAuth2TokenProvider
{
    private ?string $accessToken  = null;
    private ?int    $expiresAt    = null;

    public function getToken(): string
    {
        if ($this->accessToken && time() < ($this->expiresAt - 60)) {
            return $this->accessToken;
        }

        $response = Http::asForm()->post($this->tokenUrl, [
            'grant_type'    => 'client_credentials',
            'client_id'     => $this->clientId,
            'client_secret' => $this->clientSecret,
            'scope'         => 'products:read stocks:read',
        ]);

        $data               = $response->json();
        $this->accessToken  = $data['access_token'];
        $this->expiresAt    = time() + $data['expires_in'];

        return $this->accessToken;
    }
}

SOAP client for 1C-compatible suppliers

For integration with 1C-based systems, we use SOAP. WSDL documentation describes methods and data structures.

$client = new \SoapClient($this->wsdlUrl, [
    'login'         => $this->login,
    'password'      => $this->password,
    'encoding'      => 'UTF-8',
    'soap_version'  => SOAP_1_2,
    'cache_wsdl'    => WSDL_CACHE_DISK,
]);

$result = $client->GetProductList([
    'DateFrom'   => $since->format('Y-m-d\TH:i:s'),
    'Categories' => $this->categoryFilter,
]);

foreach ($result->Products->Product as $product) {
    yield (array) $product;
}

Typical issues and solutions

What’s included in the work

• Analysis of supplier API documentation (OpenAPI, WSDL, Postman collections).
• Development of client with retry, rate limiting, authentication (OAuth 2.0, API-key, Basic).
• Implementation of pagination (offset, cursor, scroll).
• Normalization of fields to a unified format (sku, name, price, qty).
• Configuration of incremental synchronization by updated_after.
• Stability testing under network failures and timeouts.
• Integration documentation (data schema, configuration, instructions for adding a new supplier).
• Training of your team (1–2 hour workshop).
• One month of support after launch (bug fixes, tuning).

Implementation timelines and cost

We deliver turnkey. Approximate timelines and cost:

• One REST supplier with offset pagination and normalization — from 2 days, cost starting at $2,000.
• Adding OAuth 2.0, cursor pagination, and incremental sync — +1 day, +$1,000.
• Multi-supplier with configurable settings, SOAP, rate limiting — +2 days, +$2,000.

Timelines are approximate — an accurate estimate is given after analyzing the supplier's documentation. With 10+ years of experience, we guarantee a smooth integration. Request a preliminary assessment of your project — we will calculate the timeline and cost individually. Contact us, and we will prepare a detailed proposal. Get a consultation — we will evaluate your project within one business day.

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.