Elasticsearch Monitoring and Alert Configuration (Kibana)

We often encounter this situation: an Elasticsearch cluster silently goes red, disk fills to 95%, and the team learns about it from users. Without alerts, an incident turns into an outage. Our experience shows that monitoring configuration is a key step that prevents downtime and data loss. In this article, we'll cover how to set up full monitoring and alerts via Kibana using Metricbeat, Watcher, and Prometheus. Below are battle-tested configs and production tips.

Why Elasticsearch monitoring is critical?

Elasticsearch powers search and analytics in many projects. If the cluster goes down, business processes stop. Monitoring lets you spot degradation early: rising JVM heap, disk filling, increasing search latency. Without it, you hear about problems from users. We offer a ready monitoring scheme with Telegram/Slack alerts so you always know your cluster state.

Which monitoring tool to choose?

For production, we recommend Metricbeat + Stack Monitoring for basic metrics and Watcher for alerts. If you already use Prometheus, integrate elasticsearch_exporter.

How does Stack Monitoring work in Kibana?

Kibana Stack Monitoring is a built-in tool for collecting metrics from Elasticsearch, Logstash, and Kibana. Data can be collected via Metricbeat (recommended) or the built-in agent (deprecated). We strongly advise sending metrics to a separate monitoring cluster — otherwise a primary cluster failure means losing monitoring. We use Metricbeat with X-Pack enabled.

Metricbeat configuration for Elasticsearch metrics:

# metricbeat.yml
metricbeat.modules:
  - module: elasticsearch
    xpack.enabled: true
    period: 10s
    hosts: ["https://localhost:9200"]
    username: "remote_monitoring_user"
    password: "${ES_MONITOR_PASSWORD}"
    ssl.certificate_authorities: ["/etc/elasticsearch/certs/ca.crt"]
    scope: cluster
    metricsets:
      - ccr
      - cluster_stats
      - enrich
      - index
      - index_recovery
      - index_summary
      - ml_job
      - node
      - node_stats
      - pending_tasks
      - shard

output.elasticsearch:
  hosts: ["https://monitoring-es:9200"]
  username: "metricbeat_writer"
  password: "${MONITOR_WRITER_PASSWORD}"

Key metrics: what to watch

Cluster health — first thing to check: green means all shards assigned, yellow means replicas not assigned (normal for single node, problem for production), red means data unavailable.

JVM heap usage — critical: below 75% normal, 75–85% monitor, >85% degradation, >95% JVM freezes on GC and cluster stops responding.

Disk usage per node — Elasticsearch blocks indexing when disk fills. Flood_stage threshold (95%) makes indices read-only; high_watermark (90%) triggers shard rebalancing; low_watermark (85%) normal.

Setting up alerts via Watcher

Watcher is X-Pack's built-in alerting system. Configured via API or Kibana UI. Here's an example alert for red cluster status:

PUT _watcher/watch/cluster_status_red
{
  "trigger": {
    "schedule": {
      "interval": "1m"
    }
  },
  "input": {
    "http": {
      "request": {
        "host": "localhost",
        "port": 9200,
        "path": "/_cluster/health",
        "auth": {
          "basic": {
            "username": "elastic",
            "password": "{{ctx.metadata.es_password}}"
          }
        }
      }
    }
  },
  "condition": {
    "compare": {
      "ctx.payload.status": {
        "eq": "red"
      }
    }
  },
  "actions": {
    "send_telegram": {
      "webhook": {
        "scheme": "https",
        "host": "api.telegram.org",
        "port": 443,
        "method": "post",
        "path": "/bot{{ctx.metadata.telegram_token}}/sendMessage",
        "params": {
          "chat_id": "{{ctx.metadata.telegram_chat_id}}",
          "text": "ALERT: Elasticsearch cluster status is RED! Time: {{ctx.execution_time}}"
        }
      }
    }
  }
}

Alert for disk usage >85%:

PUT _watcher/watch/disk_usage_high
{
  "trigger": {
    "schedule": { "interval": "5m" }
  },
  "input": {
    "http": {
      "request": {
        "path": "/_nodes/stats/fs",
        "auth": { "basic": { "username": "elastic", "password": "changeme" } }
      }
    }
  },
  "condition": {
    "script": {
      "source": """
        for (node in ctx.payload.nodes.values()) {
          def total = node.fs.total.total_in_bytes;
          def free = node.fs.total.free_in_bytes;
          def used_pct = (total - free) / total * 100;
          if (used_pct > 85) return true;
        }
        return false;
      """
    }
  },
  "actions": {
    "log": {
      "logging": {
        "level": "warn",
        "text": "High disk usage detected on Elasticsearch node"
      }
    }
  }
}

How to set up alerts via Kibana UI?

In Kibana 8.x, use Alerts & Actions (Stack Management > Rules). Visual rule builder without writing JSON manually. Ready templates: Elasticsearch cluster health, nodes changed, version mismatch, CPU usage, JVM memory. Notification channels: Email, Slack, PagerDuty, Webhook (Telegram, Teams).

Monitoring via Prometheus and Grafana

If your infrastructure already uses Prometheus, connect elasticsearch_exporter:

docker run -d \
  --name elasticsearch_exporter \
  -p 9114:9114 \
  prometheuscommunity/elasticsearch-exporter:latest \
  --es.uri=https://elastic:changeme@localhost:9200 \
  --es.ssl-skip-verify \
  --es.all \
  --es.indices \
  --es.shards

Prometheus scrape_config:

- job_name: 'elasticsearch'
  static_configs:
    - targets: ['localhost:9114']
  scrape_interval: 30s

Import Grafana dashboard ID 6483 (Elasticsearch Overview) — a ready dashboard with key metrics. As noted in the Elasticsearch documentation, this simplifies visualization.

What's included in our work

We provide: deployment of Metricbeat and Stack Monitoring dashboards; configuration of alerts via Watcher or Kibana Rules with Telegram/Slack/PagerDuty channels; integration with Prometheus and Grafana (if infrastructure exists); documentation and guidance for your team; 3 months of support and threshold adjustments. Our experience — 5 years in Elasticsearch administration, over 20 projects. We guarantee SLA on response time. Our engineers hold Elastic Certified Engineer certifications.

Timelines

Basic monitoring via Metricbeat and Stack Monitoring — 1 day. Alerts — 1 day. Advanced monitoring with Prometheus+Grafana — 1–2 days. Total from 2 to 4 days. Cost is calculated individually.

Contact us for a consultation — we'll assess your cluster and propose the optimal solution. Get in touch to discuss details.

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.