Apache Kafka Cluster Setup for Web Applications

Setting up an Apache Kafka Cluster for a Web Application

Imagine your web application processes thousands of orders per minute. Every event — sending an email, updating a search index, syncing data — happens synchronously. Servers choke, latency grows, and failures cause data loss. Apache Kafka solves these problems, but incorrect cluster configuration (e.g., single broker with replication factor = 1) leads to disaster. One of our clients — an e-commerce platform handling 500,000 events per second — migrated to Kafka after RabbitMQ failed under peak load.

Kafka is a distributed log with ordering and replication guarantees. It decouples services, provides an audit log, and enables real-time analytics. A production cluster requires careful setup: mode selection, broker configuration, security, and monitoring. Our team has deployed over 30 clusters for highload projects — from fintech to e-commerce. Proper configuration can reduce infrastructure costs by up to 40% compared to alternatives, with payback within six months.

How to Set Up an Apache Kafka Cluster: KRaft or ZooKeeper?

For new projects, use only KRaft (no ZooKeeper). Since KRaft became production-ready in version 3.3, it simplifies architecture, reduces failure points, and speeds up recovery. ZooKeeper remains for legacy setups, but we do not recommend starting with it.

Source: Kafka official documentation

Installation and Configuration on Ubuntu

Install the latest stable version, set up the systemd service, and initialize storage:

apt install -y openjdk-21-jdk-headless
KAFKA_VERSION=3.7.0
SCALA_VERSION=2.13
wget https://downloads.apache.org/kafka/${KAFKA_VERSION}/kafka_${SCALA_VERSION}-${KAFKA_VERSION}.tgz
tar -xzf kafka_${SCALA_VERSION}-${KAFKA_VERSION}.tgz -C /opt/
ln -s /opt/kafka_${SCALA_VERSION}-${KAFKA_VERSION} /opt/kafka
useradd -r -s /bin/false kafka
chown -R kafka:kafka /opt/kafka
mkdir -p /var/log/kafka /data/kafka
chown kafka:kafka /var/log/kafka /data/kafka
cat > /etc/systemd/system/kafka.service << 'EOF'
[Unit]
Description=Apache Kafka
After=network.target
[Service]
Type=simple
User=kafka
Environment="KAFKA_HEAP_OPTS=-Xmx4g -Xms4g"
Environment="KAFKA_JVM_PERFORMANCE_OPTS=-server -XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:+ExplicitGCInvokesConcurrent -Djava.awt.headless=true"
ExecStart=/opt/kafka/bin/kafka-server-start.sh /opt/kafka/config/kraft/server.properties
ExecStop=/opt/kafka/bin/kafka-server-stop.sh
Restart=on-failure
RestartSec=5
LimitNOFILE=65536
[Install]
WantedBy=multi-user.target
EOF
CLUSTER_UUID=$(kafka-storage.sh random-uuid)
kafka-storage.sh format -t $CLUSTER_UUID -c /opt/kafka/config/kraft/server.properties

To tailor the configuration to your project, contact us.

KRaft configuration on each node (example for node 1; for nodes 2 and 3 change node.id and advertised.listeners):

node.id=1
process.roles=broker,controller
controller.quorum.voters=1@kafka-1:9093,2@kafka-2:9093,3@kafka-3:9093
listeners=PLAINTEXT://0.0.0.0:9092,CONTROLLER://0.0.0.0:9093
advertised.listeners=PLAINTEXT://kafka-1.internal:9092
inter.broker.listener.name=PLAINTEXT
controller.listener.names=CONTROLLER
listener.security.protocol.map=PLAINTEXT:PLAINTEXT,CONTROLLER:PLAINTEXT
log.dirs=/data/kafka
num.recovery.threads.per.data.dir=4
num.io.threads=16
num.network.threads=8
socket.send.buffer.bytes=1048576
socket.receive.buffer.bytes=1048576
socket.request.max.bytes=104857600
default.replication.factor=3
min.insync.replicas=2
num.partitions=6
offsets.topic.replication.factor=3
transaction.state.log.replication.factor=3
transaction.state.log.min.isr=2
log.retention.hours=168
log.segment.bytes=1073741824
log.retention.check.interval.ms=300000
compression.type=lz4

Configuring TLS Between Brokers

Without TLS, traffic is transmitted in plain text. For protection, use your own CA and certificates for each broker. Detailed instructions for generating certificates will be provided as part of the project.

Add to server.properties on each broker:

listeners=PLAINTEXT://0.0.0.0:9092,SSL://0.0.0.0:9094,CONTROLLER://0.0.0.0:9093
ssl.keystore.location=/etc/kafka/ssl/kafka-1.keystore.jks
ssl.keystore.password=changeit
ssl.key.password=changeit
ssl.truststore.location=/etc/kafka/ssl/kafka.truststore.jks
ssl.truststore.password=changeit
ssl.client.auth=required
ssl.enabled.protocols=TLSv1.3,TLSv1.2

Monitoring: Key Metrics and Alerts

Track under-replicated partitions, controller activity, p99 producer/consumer latency, consumer lag. Use JMX Exporter + Prometheus for collection, Grafana for visualization. Example JMX Exporter configuration:

startDelaySeconds: 0
hostPort: 127.0.0.1:9999
lowercaseOutputName: true
rules:
  - pattern: kafka.server<type=BrokerTopicMetrics, name=MessagesInPerSec><>OneMinuteRate
    name: kafka_server_broker_topic_messages_in_per_sec
  - pattern: kafka.server<type=ReplicaManager, name=UnderReplicatedPartitions><>Value
    name: kafka_server_under_replicated_partitions
  - pattern: kafka.controller<type=KafkaController, name=ActiveControllerCount><>Value
    name: kafka_controller_active_count
  - pattern: kafka.network<type=RequestMetrics, name=TotalTimeMs, request=Produce><>99thPercentile
    name: kafka_network_produce_total_time_ms_p99

Key alerts: kafka_server_under_replicated_partitions > 0, kafka_controller_active_count != 1, consumer lag above threshold. Contact us to implement monitoring and get ready-made dashboards for your load.

How to Ensure Fault Tolerance?

1. Create a test topic with replication factor 3.
2. Shut down one broker and verify that producers do not lose data (with acks=all and min.insync.replicas=2).
3. Bring the broker back and ensure replicas synchronize.
4. Check consumer lag — it should return to zero.
5. Repeat for each broker.

kafka-topics.sh --bootstrap-server kafka-1:9092 --create --topic test-topic --partitions 6 --replication-factor 3
kafka-producer-perf-test.sh --topic test-topic --num-records 1000000 --record-size 1024 --throughput -1 --producer-props bootstrap.servers=kafka-1:9092,kafka-2:9092,kafka-3:9092 acks=all compression.type=lz4
kafka-consumer-perf-test.sh --bootstrap-server kafka-1:9092 --topic test-topic --messages 1000000 --group perf-test-group

We guarantee SLA 99.99% for your cluster. Order deployment — contact us for a consultation. Get a preliminary cost and timeline estimate.

Process and Timelines

Total: 5-7 working days for a basic cluster. Cost is calculated individually.

What's Included

• Cluster architecture design (roles, partitions, replication)
• Installation and configuration in KRaft mode
• TLS configuration between brokers and clients
• Monitoring implementation (JMX Exporter + Prometheus + Grafana)
• Creation of production topics with optimal parameters
• Load testing and fault tolerance verification
• Documentation (cluster diagram, instructions, runbook)
• Team training (basic administration, CLI usage)
• Technical support for 2 weeks after launch

Results and Guarantees

Over 30 deployed clusters with loads up to 1 million messages per second. Average infrastructure cost reduction — 35%. Payback time — up to 6 months. Contact our engineers — we will help configure a cluster for your load.

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.