Competitor Price Scraper Development for Monitoring

We develop competitor price monitoring systems that solve a specific task: knowing when and by how much a competitor changed a price, before customers notice. Without monitoring automation, you risk missing a price drop on a key product. Manual checking of tens of thousands of items is unrealistic, and a one-time audit gives only a snapshot. Over our work, we have launched more than 30 such projects for e-commerce—from small stores to large marketplaces. Our experience guarantees stable operation of the scraper even when site structure changes. If you need a reliable tool, contact us—we will evaluate your project and offer the optimal solution.

Monitoring System Architecture

The key difference from a one-time scraper is product prioritization. Popular items should be checked every hour, the long tail of the catalog once a day. This reduces load on the source and speeds up reaction to important changes. The task queue is distributed by priority, allowing processing up to 10,000 products per minute without overload.

[Scheduler]
  ├── High priority queue (top products, every hour)
  └── Low priority queue  (the rest, once a day)
        ↓
[Fetcher] → [Parser] → [Change Detector] → [Alert Engine]
                              ↓
                       [price_history table]

The Change Detector compares the new price with the last record in history. If changed—record in price_history and event in alert queue. If unchanged—only update last_checked_at to avoid bloating history. This approach allows storing up to 5 years of history with minimal data volume.

Why JSON-LD Is the Best Source for Price Parsing?

Prices on websites are represented differently. In HTML—CSS selector .product-price or attribute data-price. In Schema.org—reliable, does not break during redesign. Via XHR API—intercepting network requests with Playwright. Dynamically via JS after loading—needs headless browser. JSON-LD is the most stable source: many SEO-optimized stores add microdata for search bots. Error rate when parsing via HTML can reach 10%, while via JSON-LD it's less than 0.5%.

import * as cheerio from 'cheerio';

interface PriceData {
  price: number;
  priceSale?: number;
  currency: string;
  inStock: boolean;
}

function extractPriceFromJsonLd(html: string): PriceData | null {
  const $ = cheerio.load(html);

  for (const scriptEl of $('script[type="application/ld+json"]').toArray()) {
    try {
      const data = JSON.parse($(scriptEl).html() ?? '{}');
      const product = data['@type'] === 'Product' ? data :
        (Array.isArray(data['@graph'])
          ? data['@graph'].find((n: { '@type': string }) => n['@type'] === 'Product')
          : null);

      if (product?.offers) {
        const offer = Array.isArray(product.offers) ? product.offers[0] : product.offers;
        return {
          price: parseFloat(offer.price),
          currency: offer.priceCurrency ?? 'RUB',
          inStock: offer.availability?.includes('InStock') ?? true,
        };
      }
    } catch { continue; }
  }

  return null;
}

Handling price formats in text: "1 299,00 ₽", "$12.99", "€ 9,90"—normalization via regex. Store as DECIMAL(10,2) with separate currency field. Track three levels: price without discount (price_original), price with discount (price_sale), loyalty card price (often a third hidden price).

Change Detector Mechanism

CREATE TABLE monitored_products (
  id              SERIAL PRIMARY KEY,
  source          VARCHAR(100) NOT NULL,
  external_id     VARCHAR(255) NOT NULL,
  title           TEXT,
  url             TEXT NOT NULL,
  priority        SMALLINT DEFAULT 5,  -- 1=highest, 10=lowest
  check_interval  INT DEFAULT 360,     -- minutes
  last_checked_at TIMESTAMPTZ,
  UNIQUE(source, external_id)
);

CREATE TABLE price_history (
  id             BIGSERIAL PRIMARY KEY,
  product_id     INT REFERENCES monitored_products(id),
  price          DECIMAL(10,2),
  price_original DECIMAL(10,2),
  in_stock       BOOLEAN,
  currency       VARCHAR(3) DEFAULT 'RUB',
  recorded_at    TIMESTAMPTZ DEFAULT NOW()
);

CREATE INDEX ON price_history(product_id, recorded_at DESC);

-- Fast access to current price without JOIN with history
ALTER TABLE monitored_products
  ADD COLUMN current_price DECIMAL(10,2),
  ADD COLUMN current_in_stock BOOLEAN;

async function processNewPrice(
  productId: number,
  newPrice: number,
  newInStock: boolean
): Promise<{ changed: boolean; delta?: number }> {
  const product = await db.monitoredProducts.findById(productId);

  const priceChanged = product.currentPrice !== newPrice;
  const stockChanged = product.currentInStock !== newInStock;

  if (!priceChanged && !stockChanged) {
    // Only update check time
    await db.monitoredProducts.update(productId, { lastCheckedAt: new Date() });
    return { changed: false };
  }

  // Record in history
  await db.priceHistory.create({
    productId,
    price: newPrice,
    inStock: newInStock,
    recordedAt: new Date(),
  });

  // Update current values
  await db.monitoredProducts.update(productId, {
    currentPrice: newPrice,
    currentInStock: newInStock,
    lastCheckedAt: new Date(),
  });

  const delta = product.currentPrice
    ? ((newPrice - product.currentPrice) / product.currentPrice) * 100
    : 0;

  return { changed: true, delta };
}

Configuring Alerts and Thresholds

Configurable trigger rules:

• Price dropped more than X% (e.g., 5% or 10%)
• Price fell below your price for a similar product
• Product appeared or disappeared from stock
• Price changed for N+ competitors simultaneously (sign of market shift)
• Price reached historical minimum over the last 90 days

async function checkAlertRules(productId: number, delta: number): Promise<void> {
  const rules = await db.alertRules.findAll({ productId, active: true });

  for (const rule of rules) {
    const triggered =
      (rule.type === 'price_drop_percent' && delta < -rule.threshold) ||
      (rule.type === 'below_my_price' && await isPriceBelowMyPrice(productId)) ||
      (rule.type === 'out_of_stock' && newInStock === false);

    if (triggered) {
      await sendAlert(rule, productId, delta);
    }
  }
}

Delivery: Telegram bot (instant via Bot API), email digest (once a day), webhook to price management system (for automatic reaction). Thresholds can be changed in real-time via dashboard.

What's Inside the Analytics Dashboard?

Minimum necessary screens:

Monitoring Table — all tracked products with current competitor price, your price, percentage difference, and trend (up/down arrow).

Price Chart — competitor price vs your price over selected period. Recharts LineChart with two lines and change markers.

Alert Feed — last 50 changes with filtering by source and change type.

Quick dashboard implementation — Metabase connected to PostgreSQL. Custom React interface with Recharts is needed if the dashboard is embedded into an existing assortment management system.

Development Process

1. Analytics — study target sites, determine product priorities, agree on alerts and dashboard.
2. Design — design scraper architecture, database, alert rules.
3. Implementation — write code, configure queues, integrations.
4. Testing — run on real data, check accuracy and stability.
5. Deployment and support — deploy on your server or cloud, hand over documentation, train.

What's Included

• Scraper source code with open documentation
• Dashboard and API access
• Operation manual
• 3-month stability guarantee
• Support for site structure changes (up to 5 adaptations per month)

Timelines and Scale

For 100k+ products with a year of history, ClickHouse instead of PostgreSQL for storing price_history: analytical queries (aggregation over period, finding minimum) work an order of magnitude faster on large volumes. PostgreSQL remains for operational data and configuration.

Contact us for a consultation—we will evaluate your project and offer the optimal solution. Get a reliable price scraper turnkey with a stability guarantee.

Backend Development Services: Laravel, Node.js, Go, Django, PostgreSQL

On a production server at 3:14 AM, the Laravel Jobs queue stopped processing. 40,000 unprocessed jobs in Redis. Cause: worker crashed due to a memory leak in one of the Jobs (leak via a static variable in an Eloquent observer), supervisor didn't restart it because of misconfigured stopwaitsecs. This is not a hypothetical scenario — it's Tuesday. We analyzed such an incident on a project with 500 RPS load: diagnosis took 4 hours, fix — 20 minutes. So you don't lose money on downtime, we offer backend development services with a focus on production-grade reliability. We'll assess your project in 2 days.

Backend is what works when no one is watching. Or doesn't work. We guarantee you'll have the first option.

How do we ensure production-grade reliability from day one?

What we do correctly from day one

Service Layer over Fat Controllers. Controller receives HTTP request, validates it via Form Request, passes data to Service, returns response. Business logic in Service, not Controller. This sounds trivial, but most legacy projects have controllers with 500 lines and SQL queries inside.

Repository Pattern we use cautiously. If you just wrap Model::where(...) in a repository method — that's boilerplate without benefit. Repository is justified when: you need to abstract from the data source (DB + cache + external API) or when query logic is complex enough to isolate.

Jobs, Events, Listeners. Everything that can be async — make async. Sending email, PDF generation, external API sync, aggregate recalculation — into Queue. Laravel Horizon for queue monitoring in Redis: see throughput, failed jobs, processing time per queue.

How Octane handles high load

Laravel Octane with RoadRunner or Swoole keeps the app in memory between requests — removes bootstrap overhead (config loading, class autoloading) on each HTTP request. Gain: 3–8x on synthetic benchmarks, 2–4x on real applications. Important: no state between requests in static variables — that leads to exactly the incidents from the beginning. We use this in projects with >1000 RPS.

What to do about N+1 queries

N+1 is the most common cause of slow pages in Laravel apps. Standard story: page worked fine on dev with 10 records, on production with 10,000 — 8-second load.

Laravel Debugbar in dev environment shows the number of queries per page. More than 20 queries per page — signal for audit.

Model::preventLazyLoading(! app()->isProduction());

Telescope for profiling in staging: logs all queries, jobs, mail, notifications with time detail. Numbers: after implementing eager loading, page load time drops from 8s to 0.3s — 27 times faster.

PostgreSQL: indexes that are actually needed

PostgreSQL 14+ is the primary DB on all projects. We use PgBouncer + PostgreSQL combination. 10+ years experience, more than 50 backend projects, 5 years on the market.

How PostgreSQL helps avoid slow queries

Composite indexes for frequent WHERE + ORDER BY. If you have WHERE user_id = ? AND status = ? ORDER BY created_at DESC — you need (user_id, status, created_at DESC). A separate index on (user_id) doesn't help much with sorting.

Partial indexes. If 95% of queries go with WHERE status = 'active':

CREATE INDEX idx_orders_active ON orders (created_at DESC)
WHERE status = 'active';

The index is small, fast, covers the main load.

GIN indexes for JSONB and arrays. @> operator without GIN index — seq scan. With index — fast even on millions of rows.

GIN for full-text search. to_tsvector + GIN instead of LIKE '%query%'. LIKE without index is always seq scan. With pg_trgm extension and gin_trgm_ops — supports LIKE with index, useful for CRM search by partial match.

Connection pooling: why it's more important than it seems

Rails, Laravel, Django open a new connection to PostgreSQL for each PHP/Python process. With 100 workers — 100 connections. PostgreSQL starts degrading from 200–300 active connections — overhead on connection management becomes significant.

PgBouncer — connection pooler in front of PostgreSQL. Transaction pooling mode: connection to PostgreSQL is occupied only during a transaction, returned to pool between requests. 1000 application workers → 20–50 actual connections to PostgreSQL. This reduces latency by 40% and hosting costs by 30%.

Node.js with Fastify: when it's better than Laravel

Node.js is justified for:

• Realtime: WebSocket servers, Server-Sent Events, chat, live updates
• Streaming: large files, video, streaming data
• High I/O concurrency: many parallel requests to external APIs without heavy business logic
• Serverless: Lambda/Cloud Functions — Node.js starts faster than PHP

Fastify over Express: 2–3 times faster on benchmarks, built-in JSON Schema validation, better TypeScript support, plugin architecture.

Typical realtime architecture: Laravel — core business logic and REST API. Node.js + Socket.io or ws — WebSocket server. Laravel publishes events to Redis Pub/Sub, Node.js subscribes and broadcasts to clients. This separation allows scaling the WebSocket server independently of the main app.

Go: microservices and high load

Go we use for:

• High-load microservices (>10,000 RPS)
• Background workers with strict latency requirements
• DevOps tools and CLI
• gRPC services in microservice architecture

Goroutines — thousands of times cheaper than OS threads. 10,000 concurrent connections on Go is normal on one server.

But Go is not a silver bullet. Development is slower than Laravel: more boilerplate, no ORM at Eloquent level, error handling with if err != nil everywhere. Justified only when performance is a real requirement, not an assumption.

Django and Python backend

Django with DRF (Django REST Framework) — for tasks where Python is needed: ML pipelines, data processing, integrations with AI tools.

Celery for background tasks — similar to Laravel Queue but more complex to configure. Celery Beat for cron tasks.

Django ORM vs raw SQL: ORM is convenient for CRUD. For analytical queries with multiple JOINs, window functions, and CTEs — connection.execute() with raw SQL is more readable and predictable.

Redis: not just cache

Redis in our projects plays multiple roles:

Redis Cluster for horizontal scaling. Sentinel for automatic failover on standalone setups.

Deployment and infrastructure

Docker + docker-compose — standard for local development and production. Each service in a container: PHP-FPM/Octane, Nginx, PostgreSQL, Redis, Queue Worker, Scheduler.

CI/CD via GitHub Actions:

1. Run tests (PHPUnit / Pest, Vitest, Playwright)
2. Build Docker image
3. Push to Container Registry
4. Deploy: docker pull → docker-compose up -d on server, or Kubernetes rolling update

Zero-downtime deploy for Laravel: php artisan down --secret=TOKEN is not needed with proper configuration. Strategy: new container starts next to the old one, Nginx switches traffic after health check, old container stops.

Monitoring: Sentry for exception tracking with alerting in Slack/Telegram. Grafana + Prometheus (or Grafana Cloud) for metrics: CPU, memory, request rate, queue depth, database connection count. Alerts on: error rate > 1%, p99 latency > 2s, queue depth > 1000 jobs.

What's included in turnkey work

• Architecture design (API documentation, DB schema, service diagram)
• Implementation according to agreed specification with code review
• CI/CD, monitoring, alerting setup
• Load testing (k6, wrk) with report
• Handover of source code, access, deployment instructions
• Training of customer's team (2-3 sessions)
• Warranty support for 1 month after delivery

Timeline benchmarks

Pricing is calculated individually after analyzing load, integrations, and business logic. Contact us for a free audit of your current backend — get an optimization plan in 2 days. Request a consultation.