Competitor Catalog Parser Development — Prices, Inventory, Alerts

You launched an online store, competitors' prices change daily, and manual monitoring eats up hours of your managers' time. Without automated collection, you lose profit: you fail to react to a competitor's price drop or miss new products in their assortment. A competitor catalog parser is a tool that daily collects current prices, availability, and characteristics into your database. No more manually checking sites: the system automatically crawls the catalog, records changes, and sends alerts. Our experience — over 10 years in developing such solutions, dozens of successful end-to-end projects.

Why manual collection is ineffective?

Manual monitoring of three competitors with 500 products each takes 2–3 hours per day. Errors, omissions, outdated data. An automated parser solves these problems: collects data in minutes, works 24/7, never gets tired. Time savings — up to 90% compared to manual collection. Pays for itself in 2–3 months.

Site analysis before development

Before writing code — analysis of the target site:

• Catalog URL structure: pagination via ?page=N, infinite scroll, or tree navigation by categories
• Rendering: static HTML (fast and simple) or data loaded via XHR/fetch (needs interception or headless)
• Protection: Cloudflare, rate limiting, authorization
• Data update frequency on the site — how quickly new products appear and prices change

Typical minimum field set: SKU / article, title, price (regular + sale), availability, category, product page URL, scraping date. For some niches, important fields include: rating, number of reviews, weight/dimensions, brand.

Technical implementation

For static sites — httpx + parsel (or Cheerio for Node.js). Async requests, connection pool of 10–20 workers, delay of 1–3 seconds between requests to the same domain.

import httpx
import asyncio
import random
from parsel import Selector

UA_POOL = [
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
    'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36',
]

async def fetch_page(session: httpx.AsyncClient, url: str) -> str:
    headers = {
        'User-Agent': random.choice(UA_POOL),
        'Accept-Language': 'ru-RU,ru;q=0.9',
    }
    resp = await session.get(url, headers=headers, timeout=15)
    resp.raise_for_status()
    return resp.text

async def parse_catalog_page(html: str, base_url: str) -> list[dict]:
    sel = Selector(html)
    products = []

    for item in sel.css('.product-card'):
        price_raw = item.css('.price::text').get('').strip()
        price = int(''.join(c for c in price_raw if c.isdigit())) if price_raw else None

        products.append({
            'title': item.css('.product-title::text').get('').strip(),
            'price': price,
            'sku': item.attrib.get('data-sku'),
            'url': base_url + item.css('a::attr(href)').get(''),
            'in_stock': bool(item.css('.in-stock')),
            'image_url': item.css('img::attr(src)').get(),
        })

    return products

For SPA with XHR — intercept API requests via Playwright. Many modern online stores, when opening a page, make a fetch request to their own API that returns JSON with product data:

from playwright.async_api import async_playwright
import json

async def intercept_catalog_api(catalog_url: str) -> list[dict]:
    products = []

    async with async_playwright() as p:
        browser = await p.chromium.launch(headless=True)
        page = await browser.new_page()

        async def handle_response(response):
            if '/api/catalog' in response.url and response.status == 200:
                try:
                    data = await response.json()
                    if 'products' in data:
                        products.extend(data['products'])
                except Exception:
                    pass

        page.on('response', handle_response)
        await page.goto(catalog_url, wait_until='networkidle')
        await browser.close()

    return products

If the API returns JSON directly — we can call it directly bypassing the browser, which is 10–20 times faster. To find the endpoint — use DevTools Network tab while manually browsing the catalog.

How does SPA with XHR parsing work?

In an SPA, the main challenge is not the HTML but the API requests that load data. We intercept these requests via Playwright and get clean JSON. This is more reliable than parsing dynamically generated DOM. If the API is open — we call it directly, saving resources.

Pagination and full crawl

For pagination via ?page=N — sequential crawl until an empty page:

async def scrape_full_catalog(base_url: str) -> list[dict]:
    all_products = []
    page_num = 1

    async with httpx.AsyncClient() as session:
        while True:
            url = f'{base_url}?page={page_num}'
            html = await fetch_page(session, url)
            products = await parse_catalog_page(html, base_url)

            if not products:
                break

            all_products.extend(products)
            page_num += 1
            await asyncio.sleep(random.uniform(1.5, 3.0))  # polite delay

    return all_products

For category tree — first recursively collect all category URLs, then crawl each category with pagination.

Storage and incremental updates

CREATE TABLE competitor_products (
  id           SERIAL PRIMARY KEY,
  source       VARCHAR(100) NOT NULL,      -- 'competitor_a', 'competitor_b'
  external_id  VARCHAR(255) NOT NULL,
  title        TEXT NOT NULL,
  price        DECIMAL(10,2),
  price_sale   DECIMAL(10,2),
  in_stock     BOOLEAN DEFAULT TRUE,
  category     VARCHAR(500),
  url          TEXT NOT NULL,
  image_url    TEXT,
  attributes   JSONB DEFAULT '{}',
  first_seen   TIMESTAMPTZ DEFAULT NOW(),
  last_seen    TIMESTAMPTZ DEFAULT NOW(),
  UNIQUE(source, external_id)
);

CREATE TABLE competitor_price_history (
  id         BIGSERIAL PRIMARY KEY,
  product_id INT REFERENCES competitor_products(id),
  price      DECIMAL(10,2),
  price_sale DECIMAL(10,2),
  in_stock   BOOLEAN,
  scraped_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE INDEX ON competitor_price_history(product_id, scraped_at DESC);

On subsequent crawls — INSERT ... ON CONFLICT (source, external_id) DO UPDATE SET last_seen = NOW(), price = EXCLUDED.price, .... History entry is made only if price or availability changed (compare with last entry via LAG() or store price in the main table).

Scheduling and alerts

Celery Beat or Node.js cron. Recommended frequency for a competitor's catalog — every 4–12 hours, depending on price dynamics in the niche. For marketplaces with fast-changing prices — every hour for top positions.

Alert when a competitor's price drops below yours — SQL query or PostgreSQL trigger with notification to Slack/Telegram via webhook. Example query:

SELECT cp.title, cp.price AS competitor_price, mp.price AS my_price
FROM competitor_products cp
JOIN my_products mp ON mp.sku = cp.external_id
WHERE cp.source = 'competitor_a'
  AND cp.price < mp.price
  AND cp.in_stock = TRUE
ORDER BY (mp.price - cp.price) DESC;

How to set up alerts for competitor price drops?

1. Set a threshold: SELECT ... WHERE cp.price < mp.price * 0.95 — alert on a 5% drop.
2. Configure a webhook in Telegram/Slack.
3. Run the SQL query after each crawl and send the result.

We implement this logic as part of the parser: you receive a notification in messenger with a table of products where the competitor became cheaper.

How to ensure uninterrupted parser operation?

Competitors' sites change — the parser periodically breaks. We set up monitoring: alert if in the last run less than 50% of the average product count is collected. When the structure changes, updating usually takes 2–4 hours. We guarantee support and adaptation to new site versions.

What's included in the work?

• Exhaustive analysis of the target site (structure, protection, API)
• Development of the parser with pagination, categories, incremental updates
• Database setup for storing price and assortment history
• Scheduling (cron) and alert configuration (Telegram/Slack)
• Documentation for operation and access
• Training of your staff to use the system
• Warranty support for 1 month and response to failures within 2–4 hours

We will evaluate your project — contact us, we will offer the optimal end-to-end solution. Order parser development and get a tool that will bring real benefits in competitive struggle. Wikipedia: Web scraping

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.