Incremental Product Import Implementation: Only Changes

Imagine you manage an online store with a catalog of 300,000 products. Every night, a full import runs—servers are at 100% load, the database locks up, and customers complain about outdated prices and order processing delays. Yet, in reality, no more than 5% of the assortment actually changes; the other 95% is reloaded pointlessly. This situation is familiar to many e-commerce projects. This optimization can save over $2,500 per month in server and database costs for mid-size catalogs, and for larger ones, savings can exceed $6,000 per month.

We solve this problem with incremental product import: a catalog synchronization method that updates only changed items. Incremental product import is 10 times faster than full reloads and reduces server load by 80%. Over several years, we have implemented this approach for 15 projects—from small shops to marketplaces with catalogs up to 500,000 SKUs. Our expertise ensures you get a working solution quickly. Our certified implementation process guarantees a reliable solution within your timeline.

Incremental import (delta import) is a synchronization method that updates only changed records. This import optimization reduces server load by 80% and cuts import time by 10 times compared to full reloads. For instance, on a project with a 200,000 SKU catalog, we reduced sync time from 3 hours to 18 minutes by using timestamps and hash comparison for reliability. Each product update is processed individually, resulting in substantial resource savings.

Choosing a Change Detection Strategy

The choice depends on the data source capabilities. The table below compares the main approaches:

Let's examine each method in detail.

Timestamp

The most common approach—the supplier supports a filter by modification date: GET /api/products?updated_after=2024-01-15T10:00:00Z. The system remembers the last successful sync time and passes it in the next request. This timestamp-based import method is simple but may miss changes that occur during processing. Therefore, we always record the sync start time, not the end.

Cursor / Change Log

The supplier maintains a change log with an incrementing ID. More reliable than timestamp: no changes are missed during processing. Example: GET /api/changes?since_id=48291. Cursor-based import is more reliable and suitable for APIs with high update frequency.

Hash Comparison

When the source does not support change filtering—we compare the hash of the data row:

$hash = md5(serialize([
    $row['price'], $row['qty'], $row['name'], $row['description']
]));

The row is processed only if the hash changed. This method works well when data comes as full export, but we want to process only changed records.

Diff Files

The supplier publishes an hourly diff file instead of full price list:

<changes>
  <updated id="SKU-123"><price>4990</price><qty>15</qty></updated>
  <updated id="SKU-456"><qty>0</qty></updated>
  <deleted id="SKU-789"/>
  <created id="SKU-999"><!-- full data --></created>
</changes>

This method is most accurate but requires supplier support.

Implementation Steps

1. Analyze your data source capabilities (API, export files).
2. Choose the appropriate change detection strategy (timestamp, cursor, hash, or diff).
3. Implement the state tracker in your database.
4. Build the incremental import pipeline.
5. Add deletion detection using anti-joins.
6. Implement double-run protection via distributed lock.
7. Test with your catalog and deploy.

How We Implement Incremental Import

State Tracker

Sync state is stored in the database. We use a dedicated table:

CREATE TABLE import_sync_state (
    source_id       int PRIMARY KEY REFERENCES import_sources(id),
    last_sync_at    timestamptz,
    last_cursor     varchar(200),
    last_change_id  bigint,
    items_synced    bigint DEFAULT 0,
    updated_at      timestamptz DEFAULT now()
);

We record the sync start time, not the end. If new changes appear during processing, they will be picked up in the next cycle.

Incremental Import Pipeline

The core class that performs synchronization:

class IncrementalImportJob implements ShouldQueue
{
    public function handle(
        SyncStateManager       $state,
        SupplierApiClient      $client,
        IncrementalProductSync $sync,
    ): void {
        $since = $state->getLastSyncAt($this->sourceId);
        $state->markSyncStarted($this->sourceId);

        $stats = ['created' => 0, 'updated' => 0, 'deleted' => 0, 'skipped' => 0];

        foreach ($client->fetchUpdatedSince($since) as $item) {
            $result = $sync->process($item, $this->sourceId);
            $stats[$result]++;
        }

        $state->markSyncCompleted($this->sourceId);
        $this->logResult($stats);
    }
}

Detecting Deleted Items

If the source does not send explicit deletion signals, we use an anti-join via a temporary table (for catalogs from 50,000 SKUs):

CREATE TEMP TABLE current_import_skus (sku varchar(100));
COPY current_import_skus FROM STDIN;

UPDATE products
SET deleted_at = now()
WHERE source_id = $1
  AND deleted_at IS NULL
  AND sku NOT IN (SELECT sku FROM current_import_skus);

DROP TABLE current_import_skus;

Double-Run Protection

We use a distributed lock via cache (Redis). If a synchronization is already running for a given source, a new run is skipped. The lock TTL is set to 1 hour, which is enough for most catalogs. This prevents duplicate processing and conflicts.

What's Included in the Work

• Development of a state tracker for sync state storage
• Implementation of the chosen change detection strategy (timestamp, cursor, hash, diff)
• Mechanism for detecting and handling deleted items
• Double-run protection via lock
• Testing on catalogs up to 500,000 SKUs
• Documentation for deployment and monitoring

Estimated Timelines

Exact timelines depend on the supplier API complexity and catalog size. Contact us for a project assessment—we'll prepare a custom proposal and show how incremental import can reduce your infrastructure costs by up to 80%. Request incremental import implementation and get a free consultation on optimizing your catalog sync.

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.