ELK Stack Deep Dive: Elasticsearch, Logstash, Kibana, and Beats

The ELK Stack is a popular open-source solution for centralized logging. It lets you collect logs from multiple sources, transform them into structured format, store them efficiently, and query them interactively.

This guide covers each component in depth. If you are new to logging concepts, start with our Logging Best Practices guide first.

Introduction

graph LR
    A[Log Sources] -->|Shippers| B[Beats]
    B --> C[Logstash]
    C --> D[Elasticsearch]
    D --> E[Kibana]
    A -->|Direct| D

The ELK Stack has four main components:

• Beats: Lightweight shippers that collect data from various sources
• Logstash: Transforms and enriches data during transit
• Elasticsearch: Stores and indexes data for fast search
• Kibana: Visualizes and explores data

Core Concepts

Elasticsearch is a distributed search and analytics engine built on Apache Lucene. It stores documents in JSON format and provides powerful query capabilities.

Elasticsearch Key Concepts

Concept	Description
Index	Collection of documents, similar to a database
Document	A single JSON record, similar to a row
Shard	A partition of an index for horizontal scaling
Replica	A copy of a shard for high availability

Index Lifecycle Management

Define policies to manage index data from creation to deletion:

PUT _ilm/policy/logs-policy
{
  "policy": {
    "phases": {
      "hot": {
        "actions": {
          "rollover": {
            "max_age": "7d",
            "max_primary_shard_size": "50gb"
          },
          "set_priority": 100
        }
      },
      "warm": {
        "min_age": "7d",
        "actions": {
          "shrink": {
            "number_of_shards": 1
          },
          "forcemerge": {
            "max_num_segments": 1
          },
          "set_priority": 50
        }
      },
      "cold": {
        "min_age": "30d",
        "actions": {
          "freeze": {},
          "set_priority": 0
        }
      },
      "delete": {
        "min_age": "365d",
        "actions": {
          "delete": {}
        }
      }
    }
  }
}

Mapping and Index Templates

Index templates define mappings and settings for new indices:

PUT _index_template/logs-template
{
  "index_patterns": ["logs-*"],
  "template": {
    "settings": {
      "number_of_shards": 3,
      "number_of_replicas": 1,
      "index.lifecycle.name": "logs-policy"
    },
    "mappings": {
      "properties": {
        "@timestamp": {
          "type": "date"
        },
        "level": {
          "type": "keyword"
        },
        "message": {
          "type": "text",
          "fields": {
            "keyword": {
              "type": "keyword",
              "ignore_above": 256
            }
          }
        },
        "service": {
          "type": "keyword"
        },
        "trace_id": {
          "type": "keyword"
        },
        "user_id": {
          "type": "keyword"
        },
        "duration_ms": {
          "type": "long"
        },
        "host": {
          "properties": {
            "name": { "type": "keyword" },
            "ip": { "type": "ip" }
          }
        }
      }
    }
  }
}

Querying Elasticsearch

GET logs-2026.03.22/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "service": "api-gateway" } },
        { "range": { "@timestamp": { "gte": "now-1h" } } }
      ],
      "filter": [
        { "term": { "level": "ERROR" } }
      ]
    }
  },
  "sort": [
    { "@timestamp": "desc" }
  ],
  "aggs": {
    "error_by_service": {
      "terms": { "field": "service" },
      "aggs": {
        "error_rate": {
          "avg": { "field": "error_count" }
        }
      }
    }
  }
}

Logstash

Logstash processes and transforms data before it reaches Elasticsearch. It handles complex parsing, enrichment, and filtering.

Logstash Pipeline

graph TB
    A[Input] --> B[Filter]
    B --> C[Output]

A pipeline has three sections: input, filter, and output.

Input Plugins

# Receive logs from Beats
input {
  beats {
    port => 5044
    ssl => true
    ssl_certificate => "/etc/ssl/certs/logstash.crt"
    ssl_key => "/etc/ssl/private/logstash.key"
  }

  # Alternative: direct HTTP
  http {
    port => 8080
    content_type => "application/json"
  }
}

Filter Plugins

Filters transform and enrich data:

filter {
  # Parse JSON logs
  json {
    source => "message"
    target => "parsed"
  }

  # Parse timestamp
  date {
    match => ["parsed.timestamp", "ISO8601"]
    target => "@timestamp"
  }

  # Extract fields from message
  grok {
    match => {
      "parsed.message" => "%{DATA:level}\s*%{DATA:logger}\s*%{GREEDYDATA:log_message}"
    }
    overwrite => ["message"]
  }

  # Add computed fields
  mutate {
    add_field => {
      "environment" => "%{[parsed][env]}"
      "[@metadata][index_prefix]" => "logs-%{[parsed][service]}"
    }
  }

  # Enrich with GeoIP
  geoip {
    source => "[parsed][client_ip]"
    target => "[parsed][geoip]"
    database => "/etc/logstash/GeoLite2-City.mmdb"
  }

  # Parse query string
  kv {
    source => "[parsed][request_params]"
    field_split => "&"
    prefix => "param_"
  }
}

Output Plugins

output {
  elasticsearch {
    hosts => ["https://elasticsearch:9200"]
    manage_template => false
    index => "%{[@metadata][index_prefix]}-%{+YYYY.MM.dd}"
    ssl => true
    cacert => "/etc/ssl/certs/ca.crt"
    user => "${ELASTICSEARCH_USER}"
    password => "${ELASTICSEARCH_PASSWORD}"
  }

  # Also send to stdout for debugging
  stdout {
    codec => rubydebug
  }
}

Complete Pipeline Example

input {
  beats {
    port => 5044
  }
}

filter {
  if [fields][log_type] == "application" {
    json {
      source => "message"
      target => "parsed"
    }

    date {
      match => ["parsed.timestamp", "ISO8601"]
      target => "@timestamp"
    }

    if [parsed][level] {
      mutate {
        add_field => { "level" => "%{parsed[level]}" }
      }
    }

    if [parsed][exception] {
      mutate {
        add_tag => ["error"]
      }
    }
  }

  if [fields][log_type] == "access" {
    grok {
      match => {
        "message" => '%{IPORHOST:client_ip} %{DATA:ident} %{DATA:auth} \[%{HTTPDATE:timestamp}\] "%{WORD:method} %{URIPATHPARAM:request} HTTP/%{NUMBER:httpversion}" %{NUMBER:status:int} %{NUMBER:bytes:int} "%{DATA:referrer}" "%{DATA:user_agent}"'
      }
    }

    date {
      match => ["timestamp", "dd/MMM/yyyy:HH:mm:ss Z"]
      target => "@timestamp"
    }

    geoip {
      source => "client_ip"
      target => "geoip"
    }

    useragent {
      source => "user_agent"
      target => "ua"
    }
  }
}

output {
  if "error" in [tags] {
    elasticsearch {
      hosts => ["https://elasticsearch:9200"]
      index => "logs-error-%{+YYYY.MM.dd}"
    }
  } else {
    elasticsearch {
      hosts => ["https://elasticsearch:9200"]
      index => "logs-%{[fields][log_type]}-%{+YYYY.MM.dd}"
    }
  }
}

Beats

Beats are lightweight data shippers that send data from servers to Logstash or Elasticsearch.

Filebeat

Filebeat tails log files and ships them:

# filebeat.yml
filebeat.inputs:
  - type: log
    enabled: true
    paths:
      - /var/log/containers/*.log
    json:
      keys_under_root: true
      add_error_key: true
      message_key: log
    fields:
      log_type: container
    processors:
      - add_kubernetes_metadata:
          host: ${NODE_NAME}
          matchers:
            - logs_path:
                logs_path: "/var/log/containers/"

  - type: log
    enabled: true
    paths:
      - /var/log/nginx/*.log
    fields:
      log_type: nginx
    processors:
      - add_locale: ~

processors:
  - add_host_metadata:
      when.not.contains.tags: forwarded
  - add_cloud_metadata: ~
  - add_docker_metadata: ~

output.logstash:
  hosts: ["logstash:5044"]
  ssl.enabled: true
  ssl.certificate_authorities: ["/etc/filebeat/ca.crt"]
  ssl.certificate: "/etc/filebeat/filebeat.crt"
  ssl.key: "/etc/filebeat/filebeat.key"

logging.level: info
logging.to_files: true
logging.files:
  path: /var/log/filebeat
  name: filebeat
  keepfiles: 7
  permissions: 0644

Metricbeat

Metricbeat collects system and service metrics:

metricbeat.modules:
  - module: system
    metricsets:
      - cpu
      - memory
      - network
      - process
      - diskio
    period: 10s
    processes: [".*"]

  - module: docker
    metricsets:
      - container
      - cpu
      - diskio
      - healthcheck
      - info
      - memory
      - network
    hosts: ["unix:///var/run/docker.sock"]
    period: 10s

  - module: nginx
    metricsets:
      - stubstatus
    hosts: ["http://nginx:8080/nginx_status"]
    period: 10s

output.elasticsearch:
  hosts: ["https://elasticsearch:9200"]
  ssl.enabled: true
  ssl.certificate_authorities: ["/etc/metricbeat/ca.crt"]

Heartbeat

Heartbeat monitors service availability with synthetic checks:

heartbeat.monitors:
  - type: http
    name: api-health-check
    schedule: "@every 30s"
    urls:
      - https://api.example.com/health
    check.response:
      status: 200
    fields:
      service: api-gateway

  - type: tcp
    name: redis-connectivity
    schedule: "@every 60s"
    hosts: ["redis:6379"]
    timeout: 5s

  - type: icmp
    name: host-ping
    schedule: "@every 5m"
    hosts: ["elasticsearch"]

output.elasticsearch:
  hosts: ["https://elasticsearch:9200"]

Kibana

Kibana provides the visualization and exploration interface for your Elasticsearch data.

Index Pattern Setup

Before exploring data, create an index pattern in Kibana:

1. Navigate to Management > Stack Management > Index Patterns
2. Click “Create index pattern”
3. Enter logs-* as the pattern
4. Select @timestamp as the time field

Building Visualizations

Error Rate Over Time

{
  "title": "Error Rate",
  "type": "line",
  "params": {
    "type": "line",
    "grid": { "categoryLines": false },
    "categoryAxes": [
      {
        "id": "CategoryAxis-1",
        "type": "category",
        "position": "bottom"
      }
    ],
    "valueAxes": [
      {
        "id": "ValueAxis-1",
        "name": "LeftAxis-1",
        "type": "value",
        "position": "left",
        "scale": {
          "type": "linear",
          "mode": "normal"
        }
      }
    ]
  },
  "aggs": [
    {
      "id": "1",
      "type": "avg",
      "schema": "metric",
      "params": {
        "field": "error_rate"
      }
    },
    {
      "id": "2",
      "type": "date_histogram",
      "schema": "segment",
      "params": {
        "field": "@timestamp",
        "interval": "auto"
      }
    }
  ]
}

Service Error Distribution

{
  "title": "Errors by Service",
  "type": "pie",
  "aggs": [
    {
      "id": "1",
      "type": "count",
      "schema": "metric"
    },
    {
      "id": "2",
      "type": "terms",
      "schema": "segment",
      "params": {
        "field": "service.keyword",
        "size": 10
      }
    }
  ]
}

Kibana Discover

Discover provides ad-hoc search and exploration:

// Sample Discover query
{
  "query": {
    "bool": {
      "must": [
        { "match": { "level": "ERROR" } },
        { "range": { "@timestamp": { "gte": "now-24h" } } }
      ]
    }
  },
  "sort": [{ "@timestamp": "desc" }],
  "fields": ["@timestamp", "level", "message", "service", "trace_id"],
  "filter": [
    {
      "meta": {
        "index": "logs-*",
        "negate": false,
        "params": {},
        "type": "phrase"
      },
      "query": {
        "match_phrase": {
          "service": "api-gateway"
        }
      }
    }
  ]
}

Kibana Dashboard Example

A complete dashboard might include:

• Time series of log volume by level
• Pie chart of error distribution by service
• Table of recent errors with context
• Heat map of errors over time by host
• Metric visualization of error rate and latency percentiles

Deployment Considerations

Hardware Requirements

Component	CPU	RAM	Disk
Elasticsearch (per node)	4+ cores	8GB+	SSD, 500GB+
Logstash	2+ cores	4GB+	Minimal
Kibana	2 cores	2GB+	Minimal
Beats	1 core	512MB+	Minimal

Elasticsearch is I/O intensive. Use SSDs and ensure adequate disk throughput.

Security

# Enable security in elasticsearch.yml
xpack.security.enabled: true
xpack.security.transport.ssl.enabled: true
xpack.security.http.ssl.enabled: true

# API key authentication
xpack.security.api.key.enabled: true

# Role-based access control
xpack.security.authorization:
  roles_path: /etc/elasticsearch/roles.yml

Scaling

Scale Elasticsearch horizontally by adding nodes. The cluster automatically rebalances shards.

# Minimum master nodes for cluster stability
discovery.zen.minimum_master_nodes: 2  # for 3-node cluster

# Adjust shard allocation
PUT _cluster/settings
{
  "transient": {
    "cluster.routing.allocation.enable": "all",
    "cluster.routing.allocation.cluster_concurrent_rebalance": 2
  }
}

When to Use the ELK Stack

Use the ELK Stack when:

• You need centralized logging from multiple services and environments
• You need full-text search across log entries and application data
• You need log analysis and pattern detection with Kibana
• You need security analytics and threat detection
• You need compliance audit logging and archival
• You need infrastructure log aggregation (syslog, nginx, apache)

Don’t use the ELK Stack when:

• You have simple applications with minimal logging needs
• You only need metrics and dashboards (use Prometheus + Grafana instead)
• You have high-volume streaming use cases (Kafka is better suited)
• You need real-time alerting on log data (use dedicated alerting tools)
• You need large-scale time-series metrics (Elasticsearch is not optimized for pure metrics)

ELK Stack vs Alternatives

Aspect	ELK Stack	Loki	Splunk
Cost	Open source (self-hosted)	Open source (self-hosted)	Commercial (expensive)
Storage efficiency	Medium (indexed)	High (log-structured)	Medium
Query language	KQL (Kibana)	LogQL (Prometheus-style)	SPL
Scalability	Excellent (horizontal)	Excellent	Excellent
Ease of setup	Moderate	Easy	Easy
Full-text search	Excellent	Limited	Excellent
Metrics integration	Via Metricbeat	Native Prometheus	Native
Best for	Complex log analysis, security analytics	High-volume Kubernetes logs	Enterprise compliance, security

Capacity Planning

Choosing the right hardware for Elasticsearch prevents performance issues down the line. These are rough guidelines for typical workloads.

Elasticsearch Node Sizing

Tier	RAM	CPU	Disk (SSD)	Use Case
Hot	64GB+	8+ cores	1TB+	Active indexing, recent data
Warm	32GB	4+ cores	2TB+	Read-only, older indices
Cold	16GB	2 cores	4TB+	Archival, rare queries

The heap size should be at most 50% of available RAM. Keep heap under 32GB if possible to benefit from compressed object pointers. Set -Xms and -Xmx to the same value to avoid heap resizing during runtime.

# Check JVM settings
GET _nodes/jvm?filter_path=nodes.*.jvm.memory

Estimating Storage Requirements

Calculate expected index size using this formula:

index_size = source_log_volume × compression_ratio × replica_factor

For JSON logs with ILM enabled, expect 3-5x compression from raw log size. Without ILM, indices can grow 10-20x beyond raw log volume due to normalization and extra fields.

Log Volume Estimation

# Estimate daily log volume per service
# Assume: 1000 requests/min × 10KB avg log size × 60 min × 24h = ~14GB/day

# With 3 replicas and 30% overhead:
# 14GB × 4 (3 replicas + overhead) × 30 days = ~1.7TB/month per service

Scaling Triggers

Watch these metrics to decide when to add nodes:

• Cluster health: Yellow or red status means you need capacity
• Indexing latency: P95 above 500ms indicates saturation
• Search latency: P95 above 1s for interactive queries
• Disk usage: Nodes approaching 80% capacity
• JVM heap pressure: Old generation spending more than 30% time in GC

Observability Stack Integration

ELK works best as part of a broader observability setup. Here is how it fits with other tools.

Prometheus + Grafana Integration

Beats can export metrics to Prometheus:

# metricbeat.yml - Enable prometheus output
metricbeat.modules:
  - module: elasticsearch
    metricsets:
      - node
      - node_stats
    period: 10s
    hosts: ["https://elasticsearch:9200"]

output.prometheus:
  enabled: true
  host: "0.0.0.0"
  port: 9424

Then scrape those metrics in Prometheus and build Grafana dashboards for cluster health, indexing throughput, and search latency.

Distributed Tracing with Jaeger

Add trace context to your logs so you can jump from a Kibana error directly into Jaeger:

# Logstash filter to extract Jaeger trace context
filter {
  if [message] =~ /trace_id:/ {
    grok {
      match => { "message" => "trace_id:%{DATA:trace_id}\s+span_id:%{DATA:span_id}" }
    }
    mutate {
      add_field => {
        "trace_url" => "https://jaeger.example.com/trace/%{trace_id}"
      }
    }
  }
}

This lets you correlate log entries with the full request trace in Jaeger.

Trade-off Analysis

Factor	ELK Stack (Self-Hosted)	Loki (Grafana Cloud)	Splunk Enterprise	CloudWatch Logs
Deployment Model	Fully self-managed	SaaS / managed	Self-managed	Fully managed SaaS
Storage Cost	Infrastructure + ops	Pay-per-GB stored	License + infra	Pay-per-GB ingested
Query Performance	Excellent on large data	Good	Excellent	Moderate (throttling)
Operational Burden	High (cluster ops)	Low	High (infra + license)	Very low
Scalability	Manual shard management	Auto-scaling	Manual	Auto-scaling
Learning Curve	Steep (ES DSL)	Low (LogQL)	Steep (SPL)	Low (CloudWatch)
Ecosystem	Beats, Fluentd, Logstash	Promtail, Grafana	Heavy Agents	AWS-native

Production Failure Scenarios

Failure	Impact	Mitigation
Elasticsearch cluster red/yellow	Logs not indexing; search degraded	Monitor cluster health; provision more shards; adjust replica settings
Logstash pipeline errors	Logs stuck in queue; processing backlog	Monitor pipeline errors; implement dead-letter queues; alert on queue depth
Hot tier disk saturation	New indices cannot be created; ingestion fails	Monitor disk usage; implement ILM rollover; add nodes
Kibana performance degradation	Slow searches; dashboards timeout	Optimize queries; use filter context; limit time ranges
Beats shipper failure	Logs not forwarded; blind spots in coverage	Monitor Beats health; implement local buffering; alert on forward failures
Index template mismatch	Fields not indexed correctly; search failures	Version index templates; validate mappings; test before deployment

Common Pitfalls / Anti-Patterns

1. Too Many Indices with Few Documents

Each index has overhead. Too many small indices overwhelms the cluster:

// Bad: Index per day per service creates thousands of indices
PUT logs-service-a-2026.03.22
PUT logs-service-b-2026.03.22
// ... thousands more

// Good: Use rollover with larger time intervals
PUT logs-service-a
{
  "aliases": {
    "logs-service-a": { "is_write_index": true }
  }
}

2. Dynamic Field Mapping Without Controls

Dynamic mapping can create unexpected field types and blow up cardinality:

// Bad: Unrestricted dynamic mapping
{
  "mappings": {
    "dynamic": "true" // Creates any field
  }
}

// Good: Strict dynamic mapping or disabled
{
  "mappings": {
    "dynamic": "strict",
    "properties": {
      "@timestamp": { "type": "date" },
      "level": { "type": "keyword" },
      "message": { "type": "text" }
    }
  }
}

3. Not Using Filter Context for Simple Queries

Filter context is faster because it does not score:

// Bad: Query context for term filter
{
  "query": {
    "match": { "level": "ERROR" } // Scores, slower
  }
}

// Good: Filter context for exact match
{
  "query": {
    "bool": {
      "filter": [
        { "term": { "level": "ERROR" } } // No scoring, faster
      ]
    }
  }
}

4. Ignoring Index Lifecycle Management

Without ILM, indices grow unbounded and performance degrades:

// Good: ILM with hot/warm/cold/delete
{
  "policy": {
    "phases": {
      "hot": {
        "min_age": "0ms",
        "actions": { "rollover": { "max_age": "7d" } }
      },
      "warm": { "min_age": "7d", "actions": { "shrink": 1, "forcemerge": 1 } },
      "cold": { "min_age": "30d", "actions": { "freeze": {} } },
      "delete": { "min_age": "365d", "actions": { "delete": {} } }
    }
  }
}

5. Loading Too Much Data into Memory

Kibana visualizations on large time ranges cause OOM:

// Bad: Visualize 90 days of minute-level data
{
  "query": { "range": { "@timestamp": { "gte": "now-90d" } } }
}

// Good: Use date histogram with appropriate interval
{
  "aggs": {
    "over_time": {
      "date_histogram": {
        "field": "@timestamp",
        "fixed_interval": "1h" // Or auto with proper configuration
      }
    }
  }
}

Real-world Failure Scenarios

Scenario 1: Elasticsearch Cluster Red/Yellow

What happened: After an unexpected traffic spike, the Elasticsearch cluster’s hot tier disk became saturated. New indices could not be created, causing log ingestion to fail across multiple services simultaneously.

Root cause: Index Lifecycle Management (ILM) was not configured, and disk alerts were set too high to catch gradual saturation before it became critical.

Impact: Approximately 4 hours of log data were lost. Engineers lost visibility into production systems during a post-incident analysis window.

Lesson learned: Configure ILM policies before going to production. Set disk usage alerts at 70% as a warning threshold and 80% as a critical threshold.

Scenario 2: Logstash Pipeline Backlog

What happened: A misconfigured Logstash filter caused an infinite loop in a conditional expression, causing the pipeline to process zero events while the input queue grew to millions of pending messages.

Root cause: A Grok filter pattern with an overly broad regex caused Logstash to CPU-thrash while failing to match any events.

Impact: Logs were delayed by 12 hours before the queue depth alert fired. Correlating logs with real-time events during the incident window was impossible.

Lesson learned: Implement dead-letter queues for failed events. Monitor pipeline-to-queue depth. Test filter patterns on sample data before deploying to production.

Scenario 3: Kibana Dashboard Timeout

What happened: A dashboard aggregating logs across 30-day windows with complex Vega visualizations began timing out. Users reported that the Kibana UI became unresponsive for all users on the cluster.

Root cause: The cluster’s coordination node was memory-constrained. Large aggregations caused heap pressure and triggered long GC pauses, affecting all coordination operations.

Impact: All Kibana users lost access to dashboards for approximately 45 minutes during peak business hours.

Lesson learned: Set query timeout limits in Kibana. Limit the default time range for users. Implement query caching and use rollup indices for historical data.

Interview Questions

Further Reading

• Metrics, Monitoring & Alerting - Complete observability stack integration
• Distributed Tracing - End-to-end request tracing with correlation IDs
• OpenTelemetry Official Docs - Vendor-neutral instrumentation standard
• Elasticsearch ILM Documentation - Index lifecycle management
• Fluent Bit Documentation - High-performance log agent
• SLO Embedded on-call Handbook - SLO-based incident management

Quick Recap

Key Takeaways:

• Beats collect, Logstash transforms, Elasticsearch stores, Kibana visualizes
• Index lifecycle management prevents unbounded growth
• Use filter context for exact matches; query context only when scoring needed
• Monitor cluster health and pipeline metrics proactively
• Implement security early: authentication, TLS, RBAC
• Design index templates carefully to control field mapping

Copy/Paste Checklist:

# Check cluster health
GET _cluster/health?pretty

# Monitor index size and document count
GET _cat/indices?v&s=store.size:desc

# Check Logstash pipeline status
GET _nodes/stats/ingest?filter_path=nodes.*.ingest

# ILM policy check
GET _ilm/policy/logs-policy?pretty

# Dead letter queue inspection
GET _all/_doc/_search?q=tags:_dead_letter_queue

# Index template validation
GET _index_template/logs-template?pretty

# Secure your cluster (Elasticsearch)
PUT _security/user/kibana_admin
{
  "password": "${KIBA...ORD}",
  "roles": ["kibana_admin"]
}

Observability Checklist

Infrastructure Monitoring

• Elasticsearch cluster health (green/yellow/red)
• Primary shard and replica distribution
• Index count and size per index
• Node resource utilization (CPU, heap, disk)
• Search and indexing latency percentiles
• JVM heap usage and GC frequency
• Segment count and merge queue depth

Log Pipeline Monitoring

• Beats shipper metrics (bytes sent, errors, lag)
• Logstash pipeline throughput and latency
• Logstash queue depth and worker utilization
• Dead-letter queue size and age
• Log parsing error rate

Kibana Monitoring

• Search response time (p95, p99)
• Dashboard load time
• Visualization render time
• Active users and session count

Data Management

• Index count within expected bounds
• Document count growth rate
• Disk usage trend and forecasting
• ILM policy execution success/failure
• Archive tier accessibility

Security Checklist

• Elasticsearch security enabled (XPack Security)
• User authentication configured (LDAP, SAML, or built-in)
• Role-based access control for indices and spaces
• TLS encryption for all network traffic
• API keys rotated regularly
• Kibana spaces isolation (dev/staging/prod separation)
• Audit logging enabled for security events
• No sensitive data in index names or field names
• Snapshot repositories secured and access logged
• Cross-cluster search secured if used

Conclusion

The ELK Stack provides a powerful platform for centralized logging and analysis. Beats collect data efficiently, Logstash transforms it into structured format, Elasticsearch stores and indexes it, and Kibana makes it explorable.

Start with Filebeat shipping container logs to Elasticsearch, and build from there. Add Logstash for complex parsing, Kibana for visualizations, and ILM policies for efficient data retention.

For monitoring beyond logs, see our Prometheus & Grafana guide for metrics visualization. For distributed tracing, see the Jaeger and Distributed Tracing guides for correlating logs with request traces.