Log Aggregation at Scale: ELK vs Alternatives

September 5, 2016

In distributed systems, logs are scattered across dozens or hundreds of instances. SSH-ing into individual servers to grep logs doesn’t scale. Centralized log aggregation—collecting logs from all sources into a searchable system—is essential for debugging, monitoring, and security.

The ELK stack (Elasticsearch, Logstash, Kibana) dominates this space, but it’s not the only option. Understanding ELK’s strengths and weaknesses helps you choose the right approach for your scale and requirements.

Why Centralized Logging

Before comparing solutions, let’s establish why centralized logging matters:

Debugging distributed systems: A user request might touch ten services. Debugging requires correlating logs across all services. Without centralization, this means SSH-ing into multiple servers and trying to align timestamps manually.

Alerting: Log-based alerts detect errors, security events, and anomalies. This requires a system that ingests logs in near-real-time and supports alert rules.

Security and compliance: Security teams need to search historical logs for investigation. Compliance requirements may mandate log retention and audit capabilities.

Operational visibility: Aggregate metrics from logs: error rates, response times, request volumes. Dashboards and visualizations help teams understand system behavior.

The ELK Stack

ELK combines three open-source components:

Elasticsearch: Distributed search and analytics engine. Stores logs and provides fast full-text search.

Logstash: Data processing pipeline. Ingests logs from various sources, transforms them, and sends to Elasticsearch.

Kibana: Visualization layer. Dashboards, search interface, and analytics on Elasticsearch data.

Elastic added Beats (lightweight data shippers) and other components, making “Elastic Stack” the official name. But “ELK” persists in common usage.

ELK Strengths

Full-text search: Elasticsearch excels at searching unstructured text. Complex queries, fuzzy matching, and relevance ranking work well.

Flexibility: Handle any log format. Parse structured and unstructured logs. Add custom fields during processing.

Ecosystem: Extensive integrations, community plugins, and documentation. Most logging scenarios have existing solutions.

Visualization: Kibana provides powerful visualization and dashboarding. Build custom dashboards for different audiences.

Open source: Run entirely on your infrastructure without licensing costs. Enterprise features available through paid offerings.

ELK Weaknesses

Operational complexity: Elasticsearch requires careful tuning for production use. Cluster management, index lifecycle, memory configuration, and shard management all need attention.

Resource intensive: Elasticsearch is memory-hungry. Indexing and storing high log volumes requires significant infrastructure.

Data model limitations: Elasticsearch isn’t a database. No transactions, limited update capability, eventual consistency by default.

Scaling challenges: While Elasticsearch scales horizontally, managing large clusters requires expertise. Index design significantly affects performance.

Running ELK at Scale

For moderate log volumes (hundreds of GB per day), ELK works well with reasonable effort:

For larger volumes (TB per day), complexity increases substantially. Consider dedicated teams or managed services.

Managed ELK Options

If running your own ELK is too much overhead:

Elastic Cloud: Elastic’s hosted offering. Full feature access, managed infrastructure, scales with usage.

Amazon Elasticsearch Service: AWS-managed Elasticsearch. Integrates with AWS ecosystem but lags Elastic’s version.

Logz.io, Logit.io: Third-party managed ELK services with additional features.

Managed services trade cost for operational simplicity. For many teams, this tradeoff is worthwhile.

Alternatives to ELK

ELK isn’t the only option. Several alternatives serve different needs.

Graylog

Open-source log management built on Elasticsearch but with its own interface and features.

Strengths:

Weaknesses:

Graylog suits teams who want Elasticsearch’s search power with simpler management.

Splunk

The enterprise log management incumbent. Powerful, mature, and expensive.

Strengths:

Weaknesses:

Splunk suits enterprises with budget and compliance requirements that justify the cost.

Loki

Grafana’s log aggregation system, designed for cost efficiency by not indexing log content.

Strengths:

Weaknesses:

Loki suits teams already using Grafana who want efficient log storage without full-text search needs.

Cloud Provider Services

CloudWatch Logs (AWS), Stackdriver (GCP), Azure Monitor: Native logging for cloud platforms.

Strengths:

Weaknesses:

Cloud logging suits cloud-native applications that don’t need advanced log analytics.

Hosted Logging Services

Datadog Logs, Sumo Logic, Papertrail: SaaS logging platforms.

Strengths:

Weaknesses:

SaaS logging suits teams prioritizing simplicity over cost and control.

Choosing the Right Solution

Decision factors:

Volume and Budget

Operational Capability

Query Requirements

Integration Needs

Architecture Patterns

Regardless of backend, certain patterns apply:

Structured Logging

Log structured data (JSON) rather than unstructured text:

{
  "timestamp": "2016-09-05T10:23:45Z",
  "level": "error",
  "service": "payment-api",
  "trace_id": "abc123",
  "message": "Payment processing failed",
  "error": "Connection timeout",
  "customer_id": "cust_456"
}

Structured logs enable filtering, aggregation, and visualization that unstructured text doesn’t support.

Correlation IDs

Include correlation IDs that trace requests across services:

{"trace_id": "abc123", "service": "api-gateway", "message": "Request received"}
{"trace_id": "abc123", "service": "auth-service", "message": "Token validated"}
{"trace_id": "abc123", "service": "payment-api", "message": "Payment processed"}

Search for a trace ID to see the complete request flow.

Buffer and Backpressure

Log ingestion systems can be overwhelmed. Use message queues (Kafka, Redis) as buffers:

Application → Shipper → Kafka → Processor → Storage

Queues absorb traffic spikes and provide backpressure if processing falls behind.

Retention Policies

Define how long to keep logs:

Implement automatic tiering and deletion policies to manage costs.

Key Takeaways