Master-Master Replication Setup for Web Applications

Imagine your online store operates in Europe and Asia. Writing to the database from Asia via Europe takes over 100 ms — critical for catalog and cart performance. We faced this with an online retailer having 500k products and solved it by implementing Master-Master replication. Now writes are local in each region, and data syncs between nodes without loss. With over 5 years of replication tuning and 30+ high-complexity projects, we deliver robust solutions.

Why Master-Master Over Master-Slave?

Master-Slave is the classic setup: one node writes, others read. But when the application runs in a distributed environment, write latency becomes a bottleneck. Master-Master lifts this limitation: every node can accept writes. According to Galera documentation, synchronous multi-master replication ensures strong consistency and zero failover time. Galera Cluster switches in <1 sec — 10x faster than manual Master-Slave failover.

When Is Master-Master Needed?

• Applications in different regions must write locally with synchronization.
• Fault tolerance without a single point of failure is required.
• Write latency through a single master exceeds the tolerable 50 ms.
• ROI of 2–3 months at loads over 10k requests per second.

How to Set Up a Galera Cluster on Three Nodes

1. Install Galera on all nodes (e.g., Ubuntu 22.04).
2. Configure /etc/mysql/conf.d/galera.cnf as shown below.
3. Initialize the first node with galera_new_cluster.
4. Start MySQL on the other nodes — they join automatically.
5. Check status: SHOW STATUS LIKE 'wsrep_cluster_size'; — should return 3.

# /etc/mysql/conf.d/galera.cnf
[mysqld]
binlog_format = ROW
default_storage_engine = InnoDB
innodb_autoinc_lock_mode = 2
bind-address = 0.0.0.0

# Galera Provider
wsrep_on = ON
wsrep_provider = /usr/lib/galera/libgalera_smm.so
wsrep_cluster_name = "production_cluster"
wsrep_cluster_address = "gcomm://192.168.1.10,192.168.1.11,192.168.1.12"
wsrep_sst_method = rsync

# Unique for each node
wsrep_node_address = "192.168.1.10"
wsrep_node_name = "node1"

How to Avoid Data Loss During Replication?

Data loss in multi-master can occur if a node fails before synchronization. To minimize risks, use synchronous replication (Galera) with automatic recovery after failure. In BDR, set up replication slots with a delay no longer than 1 second. Regular backups with xtrabackup or pg_dump reduce losses to under 1 minute in case of complete cluster failure.

Setting Up PostgreSQL BDR

BDR (Bi-Directional Replication) is an extension for asynchronous multi-master replication in PostgreSQL. It suits tasks where eventual consistency is acceptable (sync delay up to 1–2 seconds).

-- Enable the extension
CREATE EXTENSION bdr;

-- Initialize the first node
SELECT bdr.bdr_group_create(
  local_node_name := 'node1',
  node_external_dsn := 'host=192.168.1.10 port=5432 dbname=myapp'
);

-- Join the second node
SELECT bdr.bdr_group_join(
  local_node_name := 'node2',
  node_external_dsn := 'host=192.168.1.11 port=5432 dbname=myapp',
  join_using_dsn := 'host=192.168.1.10 port=5432 dbname=myapp'
);

Comparison: Galera vs BDR

Resolving Write Conflicts

Conflicts arise when two nodes modify the same record simultaneously. In the retailer project, we applied regional partitioning — each table handles its own geographic segment. This eliminated overlaps and reduced conflicts by 95%.

Load Balancing and Monitoring

We use ProxySQL for even request distribution. Simple configuration:

INSERT INTO mysql_servers(hostgroup_id, hostname, port, weight)
VALUES (10, '192.168.1.10', 3306, 1),
       (10, '192.168.1.11', 3306, 1),
       (10, '192.168.1.12', 3306, 1);

Monitoring is done via Prometheus + Grafana. Key metrics: wsrep_local_recv_queue_avg (transaction apply queue should be <1) and wsrep_local_cert_failures (certification conflicts near zero).

Limitations and Pitfalls

• Galera does not support MyISAM or MEMORY tables.
• AUTO_INCREMENT requires innodb_autoinc_lock_mode=2.
• DDL locks the cluster — use pt-online-schema-change.
• Inter-node latency >5 ms reduces write performance by 30–40%. For low-latency networks, use dedicated links.

Comprehensive Turnkey Setup

We offer a full cycle: load analysis, solution selection, node deployment, replication and load balancer configuration, stress testing, monitoring setup, documentation, and team training. Warranty support: 1 month.

Timeline: 3–4 working days for a three-node cluster. Pricing is determined individually based on complexity and node count.

Order a replication audit — we will identify bottlenecks and propose optimizations. Contact us for a consultation on your architecture.

Further reading: Galera Cluster.

We regularly encounter a situation: "The site is not opening" at 3 a.m. — and it turns out that the VPS disk is full because nginx logs haven't been rotated for six months. Or the server went down under load on the day of an advertising campaign launch because the shared hosting had a limit of 50 concurrent connections. Setting up hosting and deployment is not about "where it's cheaper" but about what happens when something goes wrong. Our team helps avoid such incidents by designing infrastructure that accounts for real load patterns.

When to choose Vercel and Netlify?

Vercel is built for Next.js — deploy in one push, preview deployments for every PR, automatic CDN, Edge Functions, ISR without configuration. For frontend projects and JAMstack, it's the optimal choice: no operational overhead, time-to-deploy measured in minutes.

Real limitations: Vercel Serverless Functions run in us-east-1 by default (latency for Europe +80–100ms), Function timeout 300 seconds on Pro, Bandwidth 1TB/month on Pro. For heavy backend, you need workers or a separate server.

Netlify is closer to static sites and Edge Functions based on Deno Deploy. Build minutes are the main limitation on the free tier.

Why is Docker the foundation of predictable deployment?

"It works on my machine" — classic. Docker solves this through environment containerization. But a bad Dockerfile creates new problems.

A typical mistake: copying everything into the image without .dockerignore, resulting in an 800MB image instead of 80MB. node_modules inside the image weighs as much. Correct approach: multi-stage build.

FROM node:20-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
RUN npm run build

FROM node:20-alpine AS runner
WORKDIR /app
COPY --from=builder /app/.next ./.next
COPY --from=builder /app/node_modules ./node_modules
COPY --from=builder /app/package.json ./package.json
EXPOSE 3000
CMD ["npm", "start"]

Final image: 180MB instead of 1.2GB. CI build time is reduced due to layer caching — if package.json hasn't changed, the layer with npm ci is taken from cache.

Docker Compose for local development and simple production scenarios: application + PostgreSQL + Redis in one configuration. For production on a single server, it's a perfectly viable option if there's no requirement for horizontal scaling.

More about containerization — Wikipedia: Docker.

How to set up Nginx as a reverse proxy?

Nginx in front of the application is standard for VPS and dedicated servers. Main functions: SSL termination, gzip, static files, rate limiting, upstream load balancing.

A configuration often done incorrectly: worker_processes auto — number of processes equals CPU count. worker_connections 1024 — that's 1024 per worker process. With 4 CPUs and 1024 connections = 4096 concurrent connections. For a high-traffic site, you need worker_connections 4096 and set keepalive_timeout 65.

For static assets with hash in the filename:

location ~* \.(js|css|woff2|png|webp)$ {
    expires 1y;
    add_header Cache-Control "public, immutable";
}

immutable tells the browser: don't revalidate this file even on hard refresh. This only works correctly with content-hashed filenames (which Vite/webpack do by default). Documentation — Wikipedia: Nginx.

AWS: flexibility and complexity

EC2 + Auto Scaling Group — classic for horizontal scaling. AMI with pre-installed application, Launch Template, ASG with min/desired/max instances, Application Load Balancer. When CPU > 70% for 3 minutes — scale out, when CPU < 30% for 15 minutes — scale in. Health check via ALB removes unhealthy instances from rotation.

ECS Fargate — containers without managing EC2. Deploy a Docker image, specify CPU/memory (512 CPU units = 0.5 vCPU, from 512MB memory), Fargate launches it. More expensive than Lambda, but no cold start and no timeout limitations. Suitable for long-running processes, WebSocket servers, heavy workers.

RDS for PostgreSQL with Multi-AZ: automatic failover in 1–2 minutes when primary fails. Read Replicas for scaling reads. RDS Proxy for connection pooling — Lambda functions cannot hold long-term connections, the proxy buffers this.

Kubernetes: when it is justified

K8s adds significant operational complexity. Justified when: multiple teams deploy independent services, fine-grained resource allocation per service is needed, canary deployments and blue/green without downtime are required.

AWS EKS, GKE, or managed k8s from Hetzner (cheaper). Helm charts for standard services. Horizontal Pod Autoscaler based on CPU and custom metrics (RPS via Prometheus).

For most startups and medium-sized projects, Kubernetes is overkill. ECS or Fly.io provide 80% of the capabilities with 20% of the operational complexity.

Monitoring and alerting

A server without monitoring is waiting for an incident. Minimal stack: Prometheus + Grafana (or Grafana Cloud for managed), alerting on disk > 80%, memory > 85%, CPU > 90% over 5 minutes, error rate > 1%. Uptime via Better Uptime or Upptime (self-hosted).

Logs: Loki + Grafana or CloudWatch Logs Insights. Structured JSON logs (winston, pino) are mandatory — otherwise, log searching becomes a pain.

What is included in hosting setup

• Audit of current infrastructure and load profiling
• Selection of target architecture (VPS, AWS, serverless, Kubernetes)
• Setting up CI/CD pipeline (GitHub Actions, GitLab CI) with automatic deployment
• IaC via Terraform or Pulumi (infrastructure as code)
• Configuration of Nginx, SSL certificates, HTTP/2, brotli
• Monitoring and alerting (Prometheus + Grafana, PagerDuty)
• Documentation of runbooks and team training

Additionally, contact us if you need migration from current hosting or integration with external services.

Work process

1. Audit of current infrastructure (2–5 days)
2. Selection of target architecture with load and budget justification (1–3 days)
3. Setting up CI/CD pipeline (GitHub Actions, GitLab CI) (2–5 days)
4. IaC via Terraform or Pulumi (3–10 days)
5. Setting up monitoring and alerting (2–5 days)
6. Documentation of runbooks and team training (1–3 days)

Our experience — 7 years on the market, over 50 projects, guarantee of operability after deployment.

Timeline

• Basic deployment on VPS with Docker + Nginx + CI/CD: 1–2 weeks.
• Setting up AWS infrastructure with Auto Scaling, RDS, CDN: 3–6 weeks.
• Migration to EKS from scratch: 6–12 weeks.
• Setting up Vercel/Netlify for JAMstack: 3–5 days.

The cost is calculated individually depending on complexity and scope of work. Get a consultation — we'll evaluate your architecture in one day.