Custom Varnish VCL Rules: Boost Hit Rate & Reduce Load

Default Varnish configuration often works inefficiently: it caches everything or fails to cache critical pages, ignores authorization headers, and breaks on edge cases with cookies. The result is a low hit rate (30–40%) and excessive backend load. If you're facing this issue, request an audit of your current Varnish configuration. With over 7 years of experience and 50+ projects focused on optimization, we guarantee an increase in hit rate to 90%+ while preserving full business logic. One of our clients—an e-commerce store with 500,000 requests per hour—saw their hit rate jump from 35% to 93% in just 2 days after implementing custom rules. This reduced backend load by 8x and saved up to 70% on server costs.

Varnish Cache uses VCL to describe caching policies.

How Custom VCL Rules Accelerate Your Site?

Custom VCL rules address specific needs: conditional cache control, request normalization, bypassing CDN for certain routes, routing to different backends, and grace mode when the origin is down. Let's dive into key aspects.

Request normalization

Without normalization, the same resource is stored under dozens of keys: /?utm_source=google, /?utm_source=facebook—these are separate cache entries even though the content is identical. Statistics show that URLs with utm tags waste up to 30% of cache space. Normalization solves this.

vcl 4.1;

import std;
import directors;

backend default {
    .host = "127.0.0.1";
    .port = "8080";
    .connect_timeout = 2s;
    .first_byte_timeout = 60s;
    .between_bytes_timeout = 10s;
    .probe = {
        .url = "/healthz";
        .timeout = 1s;
        .interval = 5s;
        .window = 5;
        .threshold = 3;
    }
}

backend api {
    .host = "127.0.0.1";
    .port = "8081";
    .connect_timeout = 1s;
    .first_byte_timeout = 30s;
}

sub vcl_recv {
    # Remove marketing parameters
    if (req.url ~ "(\\?|&)(utm_source|utm_medium|utm_campaign|utm_term|utm_content|fbclid|gclid|yclid|_ga|mc_eid)=") {
        set req.url = regsuball(req.url, "&(utm_source|utm_medium|utm_campaign|utm_term|utm_content|fbclid|gclid|yclid|_ga|mc_eid)=[^&]*", "");
        set req.url = regsuball(req.url, "\\?(utm_source|utm_medium|utm_campaign|utm_term|utm_content|fbclid|gclid|yclid|_ga|mc_eid)=[^&]*&", "?");
        set req.url = regsub(req.url, "\\?$", "");
    }

    # Normalize Accept-Encoding
    if (req.http.Accept-Encoding) {
        if (req.url ~ "\\.(jpg|jpeg|png|gif|webp|gz|tgz|bz2|tbz|mp3|ogg|swf|flv|mp4|woff2?)$") {
            unset req.http.Accept-Encoding;
        } else if (req.http.Accept-Encoding ~ "br") {
            set req.http.Accept-Encoding = "br";
        } else if (req.http.Accept-Encoding ~ "gzip") {
            set req.http.Accept-Encoding = "gzip";
        } else {
            unset req.http.Accept-Encoding;
        }
    }

    # Normalize cookies
    if (req.http.Cookie) {
        set req.http.Cookie = ";" + req.http.Cookie;
        set req.http.Cookie = regsuball(req.http.Cookie, "; +", ";");
        set req.http.Cookie = regsuball(req.http.Cookie, ";(session|auth_token|XSRF-TOKEN)=", "; \\1=");
        set req.http.Cookie = regsuball(req.http.Cookie, ";[^ ][^;]*", "");
        set req.http.Cookie = regsuball(req.http.Cookie, "^[; ]+|[; ]+$", "");
        if (req.http.Cookie == "") {
            unset req.http.Cookie;
        }
    }

    # Device detection for adaptive caching
    if (req.http.User-Agent ~ "(?i)mobile|android|iphone|ipod|blackberry|opera mini|iemobile") {
        set req.http.X-Device-Type = "mobile";
    } else {
        set req.http.X-Device-Type = "desktop";
    }

    # Route by content type
    if (req.url ~ "^/api/") {
        return(pass);
    }
    if (req.http.Authorization || req.http.Cookie ~ "auth_token=") {
        return(pass);
    }
    if (req.method != "GET" && req.method != "HEAD") {
        return(pass);
    }
    if (req.url ~ "\\.(css|js|jpg|jpeg|png|gif|ico|svg|woff|woff2|ttf|eot|webp|avif)(\\?.*)?$") {
        unset req.http.Cookie;
        return(hash);
    }
    return(hash);
}

sub vcl_hash {
    hash_data(req.url);
    if (req.http.host) {
        hash_data(req.http.host);
    }
    hash_data(req.http.X-Device-Type);
    return(lookup);
}

What is grace mode and how does it work?

Grace mode allows serving stale cache while the backend is overloaded or temporarily unavailable. This is critical for high-traffic sites.

sub vcl_backend_response {
    set beresp.grace = 24h;

    if (beresp.status >= 500) {
        set beresp.ttl = 0s;
        set beresp.grace = 60s;
        return(deliver);
    }

    # Custom TTL by content type
    if (bereq.url ~ "^/news/") {
        set beresp.ttl = 10m;
    } else if (bereq.url ~ "^/static/") {
        set beresp.ttl = 30d;
        unset beresp.http.Set-Cookie;
    } else if (bereq.url ~ "^/product/") {
        set beresp.ttl = 1h;
    } else {
        set beresp.ttl = 5m;
    }

    if (beresp.http.Cache-Control ~ "no-store|private") {
        set beresp.uncacheable = true;
        return(deliver);
    }
}

sub vcl_hit {
    if (obj.ttl >= 0s) {
        return(deliver);
    }
    if (obj.ttl + obj.grace > 0s) {
        return(deliver);
    }
    return(restart);
}

How to implement cache invalidation via VCL?

Tag-based purge (via xkey) is the right approach for CMS with object dependencies.

import xkey;

acl purge_acl {
    "127.0.0.1";
}

sub vcl_recv {
    if (req.method == "PURGE") {
        if (!client.ip ~ purge_acl) {
            return(synth(405, "Not allowed"));
        }
        return(purge);
    }

    if (req.method == "XKEY-PURGE") {
        if (!client.ip ~ purge_acl) {
            return(synth(405, "Not allowed"));
        }
        set req.http.n-gone = xkey.softpurge(req.http.xkey-purge);
        return(synth(200, "Purged " + req.http.n-gone + " objects"));
    }
}

sub vcl_backend_response {
    if (beresp.http.Surrogate-Key) {
        set beresp.http.xkey = beresp.http.Surrogate-Key;
    }
}

Comparison of invalidation methods:

Debugging and monitoring

sub vcl_deliver {
    if (obj.hits > 0) {
        set resp.http.X-Cache = "HIT";
        set resp.http.X-Cache-Hits = obj.hits;
    } else {
        set resp.http.X-Cache = "MISS";
    }
    set resp.http.X-Served-By = server.hostname;
    unset resp.http.X-Powered-By;
    unset resp.http.Server;
    unset resp.http.X-Varnish;
    unset resp.http.Via;
}

# Current hit rate
varnishstat -f MAIN.cache_hit,MAIN.cache_miss
# Live log filtered by URL
varnishlog -q 'ReqURL ~ "^/news/"' -g request
# Top cache miss by URL
varnishtop -i ReqURL -q 'VCL_call eq "MISS"'

Why request normalization is critical for hit rate?

Request normalization is the foundation of efficient caching. Without it, hit rate can be below 50% even with properly configured TTL. For example, URLs with utm tags waste up to 30% of cache space. Normalization also prevents issues with cookie-dependent content and incorrect caching of dynamic pages. Custom VCL rules boost hit rate by 2–3x compared to default configuration, as confirmed by our projects.

How to set up request normalization?

1. Identify parameters to remove (utm, fbclid, gclid, etc.).
2. Write vcl_recv that strips these parameters from req.url.
3. Normalize Accept-Encoding—prioritize br, then gzip.
4. Process cookies: remove all but session-essential ones.
5. Add device-aware hashing for mobile/desktop separation.
6. Test with varnishtest and monitoring.

Implementation process

A typical custom VCL rules project includes the following stages:

• Analysis (1–2 days): audit current traffic, analyze backend response headers, identify non-cacheable patterns (cookies, Cache-Control: private).
• Design (1 day): develop VCL rule architecture, define cache keys, grace periods, and routing.
• Implementation (2–3 days): write VCL scripts, configure health checks, integrate with CDN.
• Testing (1 day): validate on staging with varnishtest, measure hit rate, compare with baseline.
• Deployment (1 day): roll out to production, set up monitoring, document.

Complex cases (A/B testing via Varnish, ESI includes, multi-level caching with Nginx) add 3–5 days.

Scope of work

• Custom VCL rules tailored to project architecture.
• URL, cookie, and Accept-Encoding normalization.
• Grace mode and stale-while-revalidate.
• Cache invalidation via PURGE/xkey.
• Integration with CDN and deployment systems.
• Load testing and monitoring setup (varnishstat, metrics).
• Documentation of implemented rules and invalidation procedures.

Comparison: default config vs custom VCL

Request an audit of your current Varnish configuration—we'll identify bottlenecks and propose custom solutions. Contact us for a tailored offer.

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.