Centralized logging (ELK stack) in Microservices - System Design Guide

Problem Statement

When multiple microservices generate logs independently, it becomes impossible to trace issues across services quickly. Logs scattered in different locations cause delays in debugging and increase the risk of missing critical errors during incidents.

Solution

Centralized logging collects logs from all microservices into a single system where they are indexed and searchable. The ELK stack (Elasticsearch, Logstash, Kibana) ingests logs, stores them efficiently, and provides a dashboard for real-time analysis and troubleshooting.

Architecture

Microservice 1

Microservice 2

↓

Kibana UI

Kibana UI

This diagram shows multiple microservices sending logs to Logstash, which processes and forwards them to Elasticsearch for storage and indexing. Kibana provides a user interface to visualize and analyze the logs.

Trade-offs

✓ Pros

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Enables quick troubleshooting by aggregating logs in one place.

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Supports complex queries and real-time monitoring through Elasticsearch and Kibana.

→

Improves operational visibility across distributed microservices.

✗ Cons

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Introduces additional infrastructure components that require maintenance.

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Logstash can become a bottleneck if not scaled properly.

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Requires careful configuration to handle log volume and avoid data loss.

When running multiple microservices generating high volumes of logs needing centralized analysis, especially in production environments with frequent incidents.

For small systems with fewer than 5 services or low log volume where centralized logging overhead outweighs benefits.

Real World Examples

Netflix

Uses ELK stack to aggregate logs from thousands of microservices to quickly detect and resolve streaming issues.

Uber

Centralizes logs from diverse services to monitor ride requests and driver activities in real-time.

Shopify

Employs ELK to analyze logs for troubleshooting payment processing and order fulfillment microservices.

Alternatives

Fluentd + Elasticsearch + Kibana

Uses Fluentd instead of Logstash for log collection and processing, which can be lighter and more flexible.

Use when: When needing a more lightweight or cloud-native log collector with similar centralized logging capabilities.

Cloud-native logging services (e.g., AWS CloudWatch, Google Cloud Logging)

Uses managed cloud services for log aggregation and analysis instead of self-hosted ELK stack.

Use when: When operating primarily on cloud platforms and preferring managed services to reduce operational overhead.

Distributed tracing (e.g., Jaeger, Zipkin)

Focuses on tracing requests across services rather than collecting logs, providing a different perspective on system behavior.

Use when: When needing to understand request flows and latency across microservices rather than raw log data.

Summary

Centralized logging aggregates logs from multiple microservices into one searchable system.

The ELK stack processes, stores, and visualizes logs to speed up troubleshooting and monitoring.

It is best suited for complex systems with many services and high log volumes.

Practice

1. What is the main purpose of the ELK stack in microservices architecture?

easy

A. To manage database transactions

B. To deploy microservices automatically

C. To collect, store, and visualize logs from multiple services in one place

D. To monitor network traffic between services

Solution

1. Step 1: Understand ELK stack components

   ELK stands for Elasticsearch (storage), Logstash (processing), and Kibana (visualization), all focused on logs.

2. Step 2: Identify ELK stack role in microservices

   It centralizes logs from many services to one place for easier monitoring and troubleshooting.

3. Final Answer:

   To collect, store, and visualize logs from multiple services in one place -> Option C

4. Quick Check:

   ELK stack = centralized logging [OK]

Hint: ELK = Elasticsearch + Logstash + Kibana for logs [OK]

Common Mistakes:

• Confusing ELK with deployment tools
• Thinking ELK manages databases
• Assuming ELK monitors network traffic

2. Which of the following is the correct Docker Compose service name for running Elasticsearch in an ELK stack?

easy

A. elasticsearch

B. kibana

C. logstash

D. filebeat

Solution

1. Step 1: Recall ELK stack components

   Elasticsearch stores logs, Logstash processes, Kibana visualizes, Filebeat ships logs.

2. Step 2: Identify correct service name in Docker Compose

   The service running Elasticsearch is named "elasticsearch" in Docker Compose files.

3. Final Answer:

   elasticsearch -> Option A

4. Quick Check:

   Elasticsearch service = elasticsearch [OK]

Hint: Elasticsearch service is named 'elasticsearch' in Docker Compose [OK]

Common Mistakes:

• Confusing Logstash or Kibana as Elasticsearch service
• Using 'filebeat' as ELK core service
• Misspelling service names

3. Given this Logstash configuration snippet:

input { beats { port => 5044 } } output { elasticsearch { hosts => ["http://elasticsearch:9200"] } }

What happens when Logstash receives logs on port 5044?

medium

A. Logs are discarded because port 5044 is incorrect

B. Logs are sent to Elasticsearch at http://elasticsearch:9200

C. Logs are visualized directly by Kibana

D. Logs are stored locally on Logstash server

Solution

1. Step 1: Analyze Logstash input configuration

   Logstash listens for logs from Beats agents on port 5044.

2. Step 2: Analyze Logstash output configuration

   Logs received are forwarded to Elasticsearch at the specified host and port.

3. Final Answer:

   Logs are sent to Elasticsearch at http://elasticsearch:9200 -> Option B

4. Quick Check:

   Logstash input port 5044 forwards logs to Elasticsearch [OK]

Hint: Logstash input port 5044 sends logs to Elasticsearch host [OK]

Common Mistakes:

• Assuming logs go directly to Kibana
• Thinking port 5044 is invalid
• Believing logs are stored locally on Logstash

4. You configured Logstash to receive logs on port 5044, but no logs appear in Elasticsearch. Which is the most likely cause?

medium

A. Docker Compose file is missing Kibana service

B. Kibana is not running

C. Logstash input port is set to 9200 instead of 5044

D. Elasticsearch service is down or unreachable

Solution

1. Step 1: Check connectivity between Logstash and Elasticsearch

   If Elasticsearch is down or unreachable, Logstash cannot send logs to it.

2. Step 2: Verify other options

   Kibana not running or missing does not stop logs from reaching Elasticsearch; wrong input port would prevent Logstash from receiving logs, not sending.

3. Final Answer:

   Elasticsearch service is down or unreachable -> Option D

4. Quick Check:

   Logs missing usually means Elasticsearch unreachable [OK]

Hint: Check Elasticsearch status if logs don't appear [OK]

Common Mistakes:

• Blaming Kibana for missing logs in Elasticsearch
• Confusing input port with Elasticsearch port
• Ignoring Elasticsearch service health

5. You want to add a new microservice that sends logs to the ELK stack using Filebeat. Which steps should you take to ensure logs appear in Kibana?

hard

A. Install Filebeat on the microservice host, configure it to send logs to Logstash on port 5044, and verify Elasticsearch and Kibana are running

B. Install Kibana on the microservice host and configure it to collect logs directly

C. Configure Elasticsearch to pull logs from the microservice host automatically

D. Run Logstash on the microservice host and send logs directly to Kibana

Solution

1. Step 1: Setup Filebeat on microservice host

   Filebeat collects logs locally and forwards them to Logstash on port 5044.

2. Step 2: Ensure ELK stack components are running

   Logstash processes logs, sends them to Elasticsearch, and Kibana visualizes them.

3. Final Answer:

   Install Filebeat on the microservice host, configure it to send logs to Logstash on port 5044, and verify Elasticsearch and Kibana are running -> Option A

4. Quick Check:

   Filebeat -> Logstash -> Elasticsearch -> Kibana [OK]

Hint: Filebeat sends logs to Logstash; Kibana visualizes them [OK]

Common Mistakes:

• Trying to send logs directly to Kibana
• Expecting Elasticsearch to pull logs automatically
• Running Logstash on microservice host unnecessarily