In a famous I Love Lucy episode, Lucy and Ethel take jobs at a chocolate factory, tasked with wrapping each piece of candy as it passes by on a conveyor belt. Over time, the conveyor belt speeds up, making it difficult for them to keep pace with the work. As the chocolates start piling up, Lucy begins eating chocolates, shoving them into her hat, and hiding them in her shirt. Quickly, the chaos becomes unmanageable.
Similarly, in a microservices architecture, a problem with one service can lead to chaos across the entire application. For most companies, an application starts out small with a single developer working on the project. Over time, the organization hires more developers and the codebase grows into a tangled web of a deployment. As the team and app scale, the organization starts to break the application into different services so that everyone can work on their part without getting in anyone else’s way. However, when a single service has an issue, identifying the root cause becomes overwhelming across multiple services and environments.
While a distributed system enables organizations to manage application availability and usage needs, microservices troubleshooting becomes unwieldy as the application expands and adds more services.
What Are Microservices?
A microservices architecture breaks a large application into small, loosely coupled services that each handle a specific task. Each microservice:
- Owns its data.
- Runs its processes.
- Communicates with other services through lightweight APIs, like HTTP/REST or message queues.
The key characteristics of microservices are:
- Single responsibility: Each service does one thing and does it well.
- Independent deployment: Services ship on their own CI/CD pipelines.
- Decentralized data: Each service manages its own database.
- Failure isolation: One service going down fails to cause a large-scale system outage.
- Technology flexibility: Different services can use different languages or databases.
What are the benefits of microservices?
Using a distributed system is often a strategic business decision that offers the following benefits:
- Scalability: Handle massive traffic spikes in specific areas without over-provisioning the entire infrastructure, like scaling a checkout during a flash sale.
- Independent deployment: Build, test, and ship a service without waiting on other teams.
- Fault isolation: Contain crashes and bugs to a single service without taking down the whole application.
- Simpler codebases: Understand, change, and test smaller focused services faster than one giant monolith.
- Technology flexibility: Choose different languages, frameworks, or databases per service without forcing the entire system onto one stack.
- Lower-risk releases: Roll out new features incrementally without exposing the entire application to potential failure.
- Easier modernization: Retire or replace individual services without rewriting the whole app.
- Clear ownership: Assign one team per service without creating ambiguity over accountability or cross-team bottlenecks.
- Organizational alignment: Structure engineering teams around services without the coordination overhead of a shared codebase.
- Targeted resilience: Implement redundancy and failover exactly where the system needs it without applying it uniformly everywhere.
How are microservices different from monolithic applications?
Understanding the differences between microservices and monolithic applications helps explain why organizations adopt distributed systems and accept the challenges that come with them:
- Architecture: While a monolith bundles all functionality into a single deployable unit, microservices split that functionality into independent services that communicate over APIs.
- Deployment: While a monolith requires redeploying the entire application for any change, microservices allow individual services to deploy on their own schedule.
- Scaling: While a monolith scales as a whole even when only one component is under load, microservices scale only the services that need it.
- Fault tolerance: While a monolith can let a single bug or crash bring down the entire application, microservices contain failures to the affected service.
- Codebase: While a monolith lives in one shared codebase that grows increasingly complex over time, microservices maintain smaller, focused codebases with clear boundaries.
- Technology stack: While a monolith typically commits the entire application to one language and framework, microservices allow different technology choices per service.
- Team structure: While a monolith requires tight coordination across the entire engineering team, microservices allow teams to work autonomously on their own service.
- Testing: While a monolith is tested as one unit in a single place, microservices require testing both individual services and their interactions.
- Observability: While a monolith is relatively straightforward to monitor and debug in one place, microservices require distributed tracing and centralized logging across many services.
- Operational complexity: While a monolith is simpler to run and maintain early on, microservices introduce significant infrastructure and tooling overhead.
Troubleshooting 10 Common Microservices Issues
Debugging a microservices environment creates additional challenges since the distributed system means abstraction layers can hide an issue’s root cause.
1. Distributed Tracing
Distributed tracing tracks a single request as it travels through multiple services to pinpoint where a failure occurred. When teams fail to build it into every service from the start, they struggle to locate a failure that originates in one place and silently manifests as an error somewhere completely different.
When investigating this problem, look for:
- Requests that enter the system successfully but never return a response to determine whether the failure is buried in a middle service.
- Services that are individually healthy but slow, which can indicate a downstream dependency is timing out and the latency is cascading upstream.
- Log entries that share no common identifier across services, making it impossible to follow a single request through the system.
2. Distributed Transactions and Data Consistency
Distributed transactions manage saves across multiple services that each own their own database, ensuring data stays consistent across the system. Without a deliberate strategy, a partial failure mid-transaction leaves data in a broken state with no automatic way to fix the issue.
When investigating this problem, look for:
- Records that exist in one service’s database with no corresponding record in another service that should have been updated at the same time.
- Events in a message queue that were published but never consumed, indicating a service went down before it could complete its part of the transaction.
- Duplicate records or double charges that might suggest a retry failed even though the original operation was partially completed.
3. Cascade Failures
A cascade failure happens when one slow or failing service causes the services depending on it to back up, timeout, and fail in turn. Without deliberate protection, a single struggling service can take down an entire system that was otherwise healthy.
When investigating this problem, look for:
- A wave of errors that started in one service and spread outward to its callers to identify the first service showing errors which may indicate the origin.
- Connection pool exhaustion in upstream services that can help identify a downstream service no longer responding.
- A sudden spike in response times in one service immediately before errors start appearing in others that can help identify the slowdown’s starting point.
4. API Contract Drift and Breaking Changes
API contract drift happens when one service changes its interface in a way that silently breaks the other services consuming it. REST and JSON often fail to enforce contracts at compile time so these mismatches typically surface at runtime and in production.
When investigating this problem, look for:
- Errors referencing missing or unexpected fields in a response that may indicate one updated service failed to notify others.
- A deployment timestamp that lines up exactly with when errors started appearing in a consuming service to help identify the change that introduced the mismatch.
- Services that are returning 200 status codes but passing malformed or incomplete data.
5. Local Development Environment
In microservices, no services work in isolation. Each service depends on others so it can function, meaning that testing a small change on a laptop requires running multiple other services simultaneously.
When investigating this problem, look for:
- Bugs that only reproduce in staging or production but not locally that could indicate a missing dependency or different behavior in the local environment.
- Errors that reference services that were replaced with simplified stand-ins locally, suggesting the stand-in failed to reflect how the real service behaves.
- Configuration differences between local and production environments, like mismatched environment variables, connection strings, or feature flags are a common culprit
6. Lack of Centralized Observability
Centralized observability means aggregating logs, metrics, and traces from every service into one place for correlation and faster search. Without centralized observability, incident debugging means hunting manually through individual pod logs across various services without the ability to create a complete timeline.
When investigating this problem, look for:
- Errors that appear in one service’s logs without corresponding upstream or downstream service entries.
- Time gaps in log entries where a service goes quiet that may indicate a service crash or restart leading to lost logs.
- Inconsistent log formats across services that make it rapid search or filtering difficult during an active incident.
7. Coordinating Services During Deployment
Organizations adopt microservices because they want the benefit of independent deployment, but services that share APIs or data contracts are never truly independent. Coordinating a breaking change across multiple services, multiple teams, and multiple deployment pipelines without causing an outage is more difficult than rolling back a single monolith.
When investigating this problem, look for:
- Errors that started immediately after a deployment that may suggest an updated version is incompatible with an non-updated service.
- Services running mismatched versions, like one using the update while dependency runs and older one.
- Rollback attempts fail to resolve the issue, indicating the problem may involve a data or schema change.
8. Authentication and Secrets Management
Authentication and secrets management ensure that services can securely authenticate to the other services upon which they depend without credentials being hardcoded, committed to repositories, or left to expire unnoticed. Managing credentials across various services and environments can lead to a security incident or a production outage.
When investigating this problem, look for:
- Authentication errors or 401/403 responses in service logs that appear suddenly without any code change that may indicate an expired credential or certificate.
- Services that previously worked correctly stopped working without any deployment that may indicate a rotated or expired secret.
- Errors referencing specific credentials or tokens that point to a particular service or integration can help identify the secret that requires refreshing or reissuing.
9. Service Discovery and Network Disruptions
Service discovery is how services dynamically find and connect to each other as they spin up, shut down, and move around in the background. Sometimes service communication failures arise from network connectivity issues, like stale DNS records, failed health checks, or network partitions.
When investigating this problem, look for:
- Connection refused errors pointing to an IP address or hostname that no longer exists, indicating a potentially moved or restarted service.
- Intermittent failures that resolve themselves after a short time that may indicate a refreshed DNS record.
- A healthy service that does not receive any traffic that may indicate incorrect registration or failed health check.
10. End-to-End Testing
End-to-end testing in a microservices system means launching enough of the distributed system to verify that services work correctly together, not just in isolation. Often, the most difficult bugs to trace exist in the interactions between services, but testing at this level can be slow, expensive, and unstable.
When investigating this problem, look for:
- Bugs that passed all unit and integration tests but only appeared in production that may indicate the test environment failed to accurately reflect real world service interactions.
- Failures at the boundaries between services, like in the request or response payloads being passed between them.
- Test failures that are inconsistent and hard to reproduce that may indicate timing issues or race conditions between services.
Graylog: Centralized Observability for Troubleshooting Microservices Faster
Graylog enables DevOps teams to aggregate, search, and correlate log data from every service in one place. When something breaks, teams can start the investigation immediately instead of after twenty minutes of hunting through individual service logs.
In a microservices environment, most failures leave a trail across multiple services. Graylog keeps that trail intact and searchable, giving teams the ability to move from a symptom to a root cause without switching between tools or reconstructing events manually from disconnected sources.
