Elasticsearch Cluster Setup for Web Applications

Elasticsearch Cluster Setup for Web Applications

When moving from a single Elasticsearch instance to a cluster, you often run into non‑obvious problems: split‑brain, unbalanced shard distribution, memory leaks. We've prepared a step‑by‑step guide for setting up a production cluster on 3 nodes — from role configuration to ILM. Our experience shows that a properly configured cluster pays for itself after just a month under load.

Why a Three‑Node Cluster?

A single node is only suitable for development. Production requires a cluster for fault tolerance, horizontal scaling, and load isolation. A 3‑node cluster is 10 times more reliable than a single instance: it survives the loss of one node without data loss and handles parallel queries efficiently. For most web applications, this is the optimal balance of cost and performance.

Node Roles

In Elasticsearch 8.x each node can perform multiple roles. For small clusters (3–5 nodes) all nodes typically combine all roles. For clusters of 10+ nodes, separation is mandatory.

Master‑eligible node – participates in master election, manages cluster state. Minimum 3 master‑eligible nodes for quorum.

Data node – stores shards, executes searches. Most resource‑intensive: needs fast disks (NVMe) and plenty of RAM for JVM heap and OS file cache.

Coordinating node (client node) – receives client requests, distributes to data nodes, collects results. Relieves data nodes during the scatter‑gather phase.

Ingest node – processes documents through ingest pipelines (parsing, enrichment, transformation).

Role configuration in elasticsearch.yml:

# Master-only node
node.roles: [ master ]

# Data node
node.roles: [ data, data_content, data_hot, data_warm, data_cold ]

# Coordinating only
node.roles: []

Minimum Production Configuration: 3 Nodes

All three nodes are master‑eligible + data. This provides quorum (2 out of 3) and data storage.

elasticsearch.yml for node 1:

cluster.name: myapp-prod
node.name: es-node-01
node.roles: [master, data, ingest]

network.host: 0.0.0.0
http.port: 9200
transport.port: 9300

discovery.seed_hosts:
  - es-node-01:9300
  - es-node-02:9300
  - es-node-03:9300

cluster.initial_master_nodes:
  - es-node-01
  - es-node-02
  - es-node-03

path.data: /var/lib/elasticsearch
path.logs: /var/log/elasticsearch

xpack.security.enabled: true
xpack.security.transport.ssl.enabled: true
xpack.security.transport.ssl.keystore.path: elastic-certificates.p12
xpack.security.transport.ssl.truststore.path: elastic-certificates.p12

On nodes 2 and 3 only node.name changes.

cluster.initial_master_nodes is used only on the first cluster boot. After the cluster forms, comment out this line — otherwise a restart might cause split‑brain.

JVM and Memory

Elasticsearch defaults to 1 GB heap — critically low for production. Rule: heap = half of available RAM, but not more than 31 GB (above 32 GB JVM loses compressed oops).

In /etc/elasticsearch/jvm.options.d/heap.options:

-Xms16g
-Xmx16g

The remaining memory goes to OS file cache — Elasticsearch uses mmap heavily for reading Lucene segments. On a 64 GB RAM server: 31 GB heap + 30+ GB OS cache is optimal.

System tuning:

# /etc/sysctl.conf
vm.max_map_count=262144
vm.swappiness=1

# /etc/security/limits.conf
elasticsearch soft memlock unlimited
elasticsearch hard memlock unlimited
elasticsearch soft nofile 65536
elasticsearch hard nofile 65536

TLS Certificate Generation and Security Setup

# Generate CA and cluster certificates
/usr/share/elasticsearch/bin/elasticsearch-certutil ca --out /etc/elasticsearch/elastic-ca.p12
/usr/share/elasticsearch/bin/elasticsearch-certutil cert \
  --ca /etc/elasticsearch/elastic-ca.p12 \
  --out /etc/elasticsearch/elastic-certificates.p12

# Set elastic user password
/usr/share/elasticsearch/bin/elasticsearch-setup-passwords auto

HTTP TLS (for client connections) uses a separate certificate:

xpack.security.http.ssl.enabled: true
xpack.security.http.ssl.keystore.path: http.p12

How to Configure ILM to Save Resources?

For logs and temporary data, an ILM policy is mandatory. Without it, indices grow forever and fill the disk. Example policy with four phases:

PUT _ilm/policy/logs-policy
{
  "policy": {
    "phases": {
      "hot": {
        "min_age": "0ms",
        "actions": {
          "rollover": {
            "max_age": "7d",
            "max_size": "50gb"
          },
          "set_priority": { "priority": 100 }
        }
      },
      "warm": {
        "min_age": "7d",
        "actions": {
          "shrink": { "number_of_shards": 1 },
          "forcemerge": { "max_num_segments": 1 },
          "set_priority": { "priority": 50 }
        }
      },
      "cold": {
        "min_age": "30d",
        "actions": {
          "freeze": {},
          "set_priority": { "priority": 0 }
        }
      },
      "delete": {
        "min_age": "90d",
        "actions": {
          "delete": {}
        }
      }
    }
  }
}

Step‑by‑Step Cluster Setup

1. Analysis: determine load, data volume, required node roles.
2. Design: choose number of nodes, disk size, JVM settings.
3. Installation: deploy nodes on servers or containers, configure network.
4. Security configuration: generate TLS, set passwords.
5. ILM and templates: create index lifecycle policies.
6. Testing: verify cluster health, shard distribution, fault tolerance.
7. Deployment: connect the application, enable monitoring via Kibana.

What’s Included in Turnkey Setup

• Cluster deployment on bare metal or cloud
• TLS and basic security configuration
• ILM policy and index template setup
• Integration with Kibana for monitoring
• Operations documentation
• Team training on basic administration
• 2 weeks of post‑launch support

Checking Cluster Health

# Cluster status (green/yellow/red)
curl -u elastic:changeme http://localhost:9200/_cluster/health?pretty

# Node list
curl -u elastic:changeme http://localhost:9200/_cat/nodes?v

# Unassigned shards and reasons
curl -u elastic:changeme "http://localhost:9200/_cluster/allocation/explain?pretty"

Status yellow means all primary shards are assigned but some replicas are not. On a 1‑node cluster this is normal. On a 3‑node cluster yellow indicates a problem.

Connecting from an Application

use Elastic\Elasticsearch\ClientBuilder;

$client = ClientBuilder::create()
    ->setHosts(['https://es-node-01:9200', 'https://es-node-02:9200', 'https://es-node-03:9200'])
    ->setBasicAuthentication('elastic', 'changeme')
    ->setCABundle('/path/to/ca.crt')
    ->build();

from elasticsearch import Elasticsearch

es = Elasticsearch(
    ['https://es-node-01:9200', 'https://es-node-02:9200'],
    basic_auth=('elastic', 'changeme'),
    ca_certs='/path/to/ca.crt',
    retry_on_timeout=True,
    max_retries=3,
)

Timeline and How to Order

Deployment of a 3‑node cluster with TLS, ILM, and monitoring takes from 5 working days. Migrating existing data from a single instance adds 1–2 days. The exact cost is calculated individually after an audit. We have been working for over 5 years and have completed more than 50 Elasticsearch projects. Get a consultation from our engineers — we'll tell you which configuration is optimal for your tasks.

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.