Top tricky Microservices Interview Questions You’re Most Likely to Encounter in 2025

Q) What are some anti-patterns in microservices, and how do you avoid them?

Microservices help in building scalable and flexible applications. But if not used properly, they can cause a lot of problems. These problems are often due to poor design choices and are called "anti-patterns." Let's look at some common anti-patterns and how to avoid them in an easy-to-understand way.

1. Distributed Monolith

What is it? This happens when your microservices are technically separated but still depend on each other too much. They are like a monolith in disguise.

Why is it bad?

• If one service fails, others may also fail.

• Hard to scale and deploy independently.

How to fix it?

• Make sure each service works on its own.

• Use async messaging (like Kafka) instead of waiting for replies.

• Let each service have its own database.

• Stick to one clear purpose per service.

2. God Service

What is it? A God Service is a microservice that tries to do too many things. It becomes too large and complex.

Why is it bad?

• Becomes hard to manage, deploy, and test.

How to fix it?

• Break it down into smaller services.

• Follow "one responsibility per service."

• Use business logic boundaries to separate services.

3. Shared Database

What is it? When multiple services use the same database directly.

Why is it bad?

• Creates tight coupling.

• One change can affect many services.

How to fix it?

• Each service should have its own database.

• Communicate using APIs.

• Use events to sync data instead of direct DB access.

4. Too Much Synchronous Communication

What is it? Services talk to each other using synchronous APIs (like REST) too often.

Why is it bad?

• If one service is slow or down, others are also affected.

How to fix it?

• Use async messaging like Kafka or RabbitMQ.

• Add retries, timeouts, and fallback mechanisms.

• Use circuit breakers to prevent failures from spreading.

5. No Autonomy Between Services

What is it? Services can't work on their own and always depend on others.

Why is it bad?

• Slows down deployment.

• Hard to scale or debug.

How to fix it?

• Design services to be independently deployable.

• Use their own logic and data.

• Accept eventual consistency when possible.

6. No Monitoring or Tracing

What is it? No tools to trace or monitor how requests flow across services.

Why is it bad?

• Hard to find issues or performance problems.

How to fix it?

• Use tools like Jaeger, Zipkin, or OpenTelemetry.

• Centralize logging using ELK Stack or Prometheus/Grafana.

• Add health checks for every service.

7. Poorly Designed APIs

What is it? APIs are too big, confusing, or do too much.

Why is it bad?

• Makes it hard for clients to use.

• Can break things when updated.

How to fix it?

• Keep APIs simple and focused.

• Use versioning.

• Use REST or GraphQL based on your needs.

8. Ignoring Security

What is it? No proper security between services or user access.

Why is it bad?

• Opens up the system to attacks.

How to fix it?

• Use secure protocols like HTTPS.

• Use JWT or OAuth2 for authentication.

• Implement access control using RBAC.

9. Over-Engineering

What is it? Making things too complex too early with unnecessary tools and setups.

Why is it bad?

• Wastes time and resources.

How to fix it?

• Start simple.

• Migrate step-by-step from monolith.

• Only add tools when there's a real need.

Final Thoughts:

Microservices give you power, but with that comes complexity. Avoiding these common anti-patterns helps you build clean, scalable, and maintainable systems. Always focus on simplicity, independence, and reliability when designing microservices.

Q: How do you handle data consistency across services in a distributed system?

In a distributed system like microservices, managing data consistency is a big challenge because the data is spread across different services and databases. Here's how we can deal with it step-by-step:

🔹 1. Understand What Level of Consistency You Need

Not all systems need the same level of data accuracy at the same time.

• Strong Consistency: Every part of the system sees the same data at the same time. For example, in banking, if ₹500 is debited, everyone should see that immediately.

• Eventual Consistency: It’s okay if services have slightly outdated data for a short time. They’ll all become consistent eventually. Useful in social media apps or shopping carts.

👉 First, decide whether you need strong or eventual consistency based on your business needs.

🔹 2. Different Ways to Achieve Consistency

Let’s look at different patterns and methods:

a. Two-Phase Commit (2PC)

• Imagine you're booking a flight and a hotel together. Both must succeed or both must fail.

• In 2PC, there's a coordinator service. It asks all services: “Are you ready to commit?”

  • If all say yes, it tells them to commit.

  • If anyone says no, it tells everyone to roll back.

✅ Good for strong consistency, but...

⚠️ Bad if one service crashes – it can freeze the whole process.

b. Three-Phase Commit (3PC)

• Like 2PC but adds a buffer phase to reduce the risk of freezing when something goes wrong.

• It helps avoid getting stuck, but it’s more complex and still not perfect.

c. SAGA Pattern (Eventual Consistency)

• Think of SAGA as a series of small transactions.

• If one fails, the system will undo the previous steps with “compensating actions”.

Example:

1. Order service creates an order

2. Payment service deducts money

3. Inventory reserves items
   If payment fails, order is canceled automatically.

✅ Best for long-running transactions in microservices.

d. Event Sourcing & CQRS

• Instead of storing the final state, store events like “User Registered” or “Order Placed”.

• Use CQRS (Command Query Responsibility Segregation) to separate writes (commands) from reads (queries).

✅ Works well for audit logs and replaying history
⚠️ Needs careful design for replay logic and event storage.

e. Idempotency & Retries

• Sometimes, the same request might be sent twice due to network issues.

• Idempotent operations ensure that repeating the same action doesn’t cause issues.

Example:
If you click "Pay Now" twice, your account shouldn't be charged twice.

✅ This helps maintain data accuracy during retries.

f. Event-Driven Architecture

• Services send and receive events using tools like Kafka, RabbitMQ, etc.

• These events are processed asynchronously.

Example:
Order Service → sends “Order Placed” event
Inventory Service → listens and reserves items
Notification Service → sends confirmation email

✅ Best for loose coupling and eventual consistency

Sample Interview Answer:

“To maintain data consistency in microservices, I first analyze whether the system needs strong or eventual consistency.

For strong consistency, I consider 2PC or 3PC, especially if transactions across services must succeed or fail together.

In most microservices, I go with eventual consistency using patterns like SAGA, event sourcing, or event-driven architecture using Kafka.

I also make sure services are idempotent, so retries don’t cause data duplication.

Additionally, I use CQRS to handle commands and queries separately and ensure clear communication between services. This way, the system remains reliable and scalable.”

What are the challenges of implementing microservices, and how do you address them?

Service Communication Complexity

What’s the Problem?

In a microservices setup, each feature is a separate service. These services need to talk to each other (e.g., “Order” needs info from “Inventory”). As your system grows, the number of services increases, and managing these communications becomes hard and risky.

• If one service is slow or crashes, others might also get stuck.

• Synchronous (real-time) communication like REST can create a chain of waiting — a bottleneck.

• Managing dependencies between services becomes complex.

How to Handle It?

1. Asynchronous Communication

   • Instead of waiting for a reply, services send messages and move on.

   • Example tools: Kafka, RabbitMQ, Amazon SQS.

   • This removes dependency on the other service being online.

2. Event-Driven Architecture

   • Services respond to events like “OrderPlaced” or “ItemOutOfStock.”

   • Helps services stay independent and scalable.

3. API Gateway

   • Acts as a single point for client communication (like a receptionist).

   • Tools: Kong, Zuul, Spring Cloud Gateway.

   • Handles routing, authentication, rate limiting, and logging.

4. Service Mesh

   • A powerful way to manage service-to-service traffic.

   • Example: Istio or Linkerd.

   • Provides retries, monitoring, timeouts, and secure connections automatically, without adding code in each service.

2. Data Management and Consistency

What’s the Problem?

Each microservice should manage its own data (own DB). But this brings new problems:

• How to keep data in sync across services?

• What if multiple services need to update data together (like in a payment flow)?

• You cannot use traditional database transactions across services easily.

How to Handle It?

1. Eventual Consistency

   • Accept that data won’t be updated immediately everywhere.

   • For example, the “Order” service confirms an order, and sends an event. Later, “Inventory” updates its stock.

2. SAGA Pattern

   • A way to manage distributed transactions.

   • Break one big operation into local transactions. If one fails, run compensation logic to undo the previous steps.

3. Event Sourcing + CQRS

   • CQRS: Separate how you write data vs how you read it.

   • Event Sourcing: Save changes as a sequence of events instead of just the current state.

4. Database-per-Service

   • Each service should own its DB. Never share databases.

   • Keeps services independent and loosely coupled.

5. Data Replication

   • For read-only purposes, you may replicate some data.

   • Use Kafka or event streams to keep replicated data up-to-date.

3. Service Discovery and Management

What’s the Problem?

In microservices, services move around — they might be on different machines, IPs may change (especially in containers or cloud).

• How do other services know where to find them?

• How do we know if a service is healthy or down?

How to Handle It?

1. Service Discovery

   • Use tools like Eureka, Consul, or Kubernetes built-in service discovery.

   • These tools keep track of where each service is running.

2. Health Checks

   • Services should expose health endpoints (like /health).

   • Kubernetes or your orchestrator checks these and restarts unhealthy services.

3. Centralized Configuration

   • Manage all your config in one place using Spring Cloud Config, Consul KV store, or Kubernetes ConfigMaps.

4. Fault Tolerance and Resilience

What’s the Problem?

If one service crashes or slows down, it might affect other services — causing a cascading failure across the system.

How to Handle It?

1. Circuit Breaker

   • Stops making calls to a service if it's failing.

   • After a while, tries again.

   • Libraries: Resilience4j, Hystrix (deprecated but known).

2. Retry with Backoff

   • Try again on failure, but with a delay.

   • Avoid retry storms (many retries at once) using exponential backoff.

3. Timeouts

   • Don’t let services hang forever. Always set timeouts.

4. Bulkheads

   • Limit resource usage by each service. Prevent one service from consuming all resources.

5. Chaos Engineering

   • Tools like Chaos Monkey can randomly stop services in testing.

   • Helps you build confidence in failure handling.

5. Monitoring, Logging, and Debugging

What’s the Problem?

Since your system is split into many services, it's harder to see the full picture when something breaks.

How to Handle It?

1. Distributed Tracing

   • Track how a request flows across services.

   • Tools: Zipkin, Jaeger, OpenTelemetry.

2. Centralized Logging

   • Send logs from all services to one place.

   • Tools: ELK Stack (Elasticsearch + Logstash + Kibana) or Fluentd + Graylog.

3. Metrics and Dashboards

   • Use Prometheus to collect metrics (like response time, error count).

   • Use Grafana to visualize them.

4. Alerting

   • Set up alerts when errors or latency cross thresholds.

   • Tools: Alertmanager, Datadog, New Relic.

6. Distributed Transactions

What’s the Problem?

Microservices can't do a simple transaction (like a single database commit) when it involves multiple services. You can’t just say BEGIN...COMMIT.

How to Handle It?

1. SAGA Pattern

   • Each service does its part and sends an event.

   • If something fails, undo the previous steps.

2. Event-Driven Updates

   • Instead of forcing sync updates, send an event (e.g., “PaymentSuccess”) and let other services react in their own time.

7. Security

What’s the Problem?

You need to secure communication between many services and users — and handle different levels of access.

How to Handle It?

1. API Gateway

   • First point of contact — handles login, token checks, etc.

2. OAuth2 + JWT

   • Use secure tokens (JWT) for user sessions.

   • Validate them in each service or at the gateway.

3. Encrypt Communication (TLS)

   • Use HTTPS between all services. No plain-text traffic.

4. Service Mesh Security

   • Istio and others support mTLS (mutual TLS), which secures traffic automatically between services.

8. CI/CD and Deployment

🧠 What’s the Problem?

Managing builds, tests, and deployment for 10s or 100s of services is hard.

✅ How to Handle It?

1. CI/CD Pipelines

   • Use tools like Jenkins, GitLab CI, or CircleCI to build, test, and deploy code automatically.

2. Docker + Kubernetes

   • Package each service in a Docker container.

   • Use Kubernetes to deploy, scale, and manage these containers.

3. Blue/Green or Canary Deployments

   • Deploy new versions to a small % of users first.

   • If it works, roll out to all. If not, roll back safely.

9. Team Organization and Communication

What’s the Problem?

Microservices aren’t just technical — they change how teams work. One big team can’t manage everything well.

How to Handle It?

1. Domain-Driven Design (DDD)

   • Break the system into bounded contexts based on business functions (like payments, orders, users).

   • Assign one team per context.

2. Cross-Functional Teams

   • Each team should include developers, testers, DevOps — so they can take full ownership.

3. DevOps Culture

   • Encourage teams to be responsible for their code from development to production.

Conclusion

Microservices give you many benefits like flexibility and scalability. But they also bring some challenges. To handle these challenges properly, you need to plan carefully and use the right design patterns — like SAGA, event-driven architecture, or CQRS. It's also important to follow best practices for:

• how services talk to each other (communication),

• handling failures (fault tolerance),

• keeping your system secure,

• and using automated deployment (CI/CD).

How to maintain a session between two microservices?

What is the Challenge?

In microservices, each service works independently and is usually stateless — meaning it doesn’t remember user data between requests. But in many real-world applications, we need to keep track of user sessions (like login info) across multiple services. This is tricky because microservices are spread out and don’t share memory.

Let’s look at some simple and effective ways to manage sessions between microservices:

1. Central Session Store (like Redis)

What It Is:

Store all session data (like login info) in a central place (e.g., Redis). Any service that needs session info will read it from there.

How It Works:

• When a user logs in, session data (like user ID, role, etc.) is stored in Redis.

• All other microservices use a session ID to fetch this data from Redis.

Advantages:

• Easy to manage sessions across services.

• Good for scaling.

Disadvantages:

• If Redis is down, session fails.

• Adds extra network calls, which may slow things slightly.

2. JWT (JSON Web Tokens)

What It Is:

Store session data inside a token called JWT. This token is passed with every request.

How It Works:

• When a user logs in, the auth service creates a JWT token with session details.

• The token is sent to the client and passed to other services with each request.

• Services decode this token to get user info.

Advantages:

• No need to store sessions on the server.

• Fast and scalable.

Disadvantages:

• Cannot easily change or remove a token after issuing it.

• Large tokens can slow things down.

3. Sticky Sessions (Session Affinity)

What It Is:

Make sure that all requests from a user go to the same server during a session.

How It Works:

• A load balancer tracks the user (based on IP or cookie).

• It always sends the user’s requests to the same microservice instance.

Advantages:

• Easy to implement.

• No need to store session externally.

Disadvantages:

• Not scalable for big systems.

• If the server crashes, the session is lost.

4. Database-Backed Sessions

What It Is:

Store session data in a relational or NoSQL database instead of cache.

How It Works:

• When a user logs in, a session ID is saved in the database with user details.

• Other services use this ID to fetch session data.

Advantages:

• Data is persistent (doesn’t disappear if server restarts).

• More durable than cache.

Disadvantages:

• Slower than cache like Redis.

• Needs good database scaling design.

5. Session via HTTP Headers

What It Is:

Pass session or user data in custom headers between services.

How It Works:

• The Gateway or main service adds headers like X-User-Id, X-User-Role, etc.

• Downstream services read these headers to know who the user is.

Advantages:

• No session storage needed.

• Works well in combination with other methods (like JWT).

Disadvantages:

• Must be careful not to leak sensitive info.

• Works only for that single request.

6. Distributed Cache for Sessions

What It Is:

Use a fast, distributed caching system like Redis or Memcached to store session data.

How It Works:

• When a user logs in, session data is saved in the cache.

• All services access the cache to get session data.

Advantages:

• Very fast.

• Good for high-traffic systems.

Disadvantages:

• Data might expire or be deleted.

• Consistency can be a problem in some cache setups.

Conclusion:

When two microservices need to share session or user-related data, choosing the right method depends on your project’s needs — like how big the system is, how fast it should be, and how complex it gets.

Here’s a quick summary of the best approaches for different situations:

1. JWT (JSON Web Tokens)

• Best for stateless microservices (services that don’t remember anything between requests).

• Ideal for systems that need to scale easily and keep services independent.

• 💡 Example: Modern APIs or mobile backends where each service validates the token on its own.

2. Centralized Session Store (e.g., Redis)

• Useful when multiple services need to share session data (like user login info).

• Good for persistent sessions — the data stays even if services restart.

• 💡 Example: An e-commerce platform where the cart and user info are stored in Redis.

3. Sticky Sessions

• Easy to implement in smaller or older systems using a load balancer.

• Not suitable for large systems that scale up/down frequently.

• 💡 Example: A legacy web app with minimal load balancing needs.

4. Distributed Cache (e.g., Redis or Memcached)

• Great for high-speed access to session data.

• Supports large systems with many concurrent users.

• 💡 Example: High-traffic applications like food delivery or travel booking apps.

Real Time Example for session and stateless in Microservices?

Scenario: E-commerce App

Imagine you're using an online shopping app that is built using microservices.

The Main Microservices:

1. Auth Service – Logs users in and issues tokens (like JWT).

2. Order Service – Lets users place orders.

3. Notification Service – Sends emails or SMS when an order is placed.

4. User Service – Manages user profiles and preferences.

What Happens When a User Places an Order?

1. Login

   • You enter your username and password.

   • Auth Service verifies and returns a JWT token.

   • This token contains your user ID, roles, etc.

2. Place Order

   • You click "Buy Now".

   • Frontend sends a request to the Order Service, with the JWT in the header.

   • Order Service checks the token, extracts the user ID (user123), and saves the order.

3. Send Notification

   • After saving the order, Order Service calls Notification Service to send an SMS/email.

   • It passes user123 in the request header or payload.

   • Notification Service then fetches user contact info and sends the message.

Where Does Session Fit?

In a monolithic app, the server may store session data like:

Map<String, Object> session = new HashMap<>();
session.put("user_id", "user123");
session.put("cart", Arrays.asList("item1", "item2"));
session.put("is_logged_in", true);

But in microservices:

Each microservice is stateless

• Order Service doesn’t remember anything about previous requests.

• It depends on the JWT or session ID passed in every request to know who the user is.

So, the session data is not stored inside the service — it’s stored:

• In the JWT token (stateless session), or

• In Redis or DB, keyed by a session ID (central session store)

You notice that a newly deployed microservice significantly increases system latency. How do you identify and resolve the issue without rolling back the deployment?

When you deploy a new microservice, sometimes it causes the overall system to respond slower. This could be due to:

• Bugs or inefficient code in the service

• Misconfigured resources

• Database or network slowness

• Integration issues with other services

The key is to analyze the issue without rolling back immediately — because rollbacks can interrupt your release process or hide deeper problems.

Step 1: Start by Observing the System — Gather Metrics and Logs

What does this mean?

Before guessing or changing anything, first observe the system. Use monitoring tools to collect real-time data — like a doctor checking your vitals(Like heart beat, BP check..etc).

How to do it:

Use tools like:

• Grafana / Prometheus for graphs and alerts

• Datadog, New Relic for application performance monitoring

What should you look at?

1. Latency

   • How long is the service taking to respond?

   • Did this increase after the deployment?

2. Traffic (Request Volume)

   • Did more users start using the service?

   • High traffic with low resources can cause delays.

3. Errors

   • Are you getting more timeouts or failures?

   • Errors may cause retries → which adds more load.

4. System Resource Usage

   • Is the service using too much CPU, Memory, or Disk I/O?

   • If a service is starved of resources, it slows down.

🧪 Example: You see that latency went from 100ms to 800ms after deployment. Logs show timeout errors and CPU is 95%. This tells you it's probably overloaded.

Step 2: Use Distributed Tracing to Find Where the Delay Happens

What is distributed tracing?

It's like tracking a courier delivery. You can see:

• Where it started

• Which steps it passed

• Where it got delayed

Tools: Jaeger, Zipkin

Why this helps:

Instead of guessing, tracing shows you:

• Is the delay inside your new service?

• Or is it in a downstream call (like a DB or another microservice)?

• Or maybe the network is slow?

A downstream call refers to any external dependency that a microservice communicates with to complete its work. This could be a database call, an API call to another microservice, or even a third-party service.

🧪 Example: You trace a request and see the actual processing time inside the microservice is fine, but a database query is taking 3 seconds.

Step 3: Check Internal and External Dependencies

1. Does your microservice call other services?

• A slow downstream service can make yours look slow.

• Are those services healthy?

2. Does it talk to a database?

• Look for slow queries, missing indexes, or locking issues.

3. Are you using an API Gateway?

• API Gateways (like Spring Cloud Gateway or Kong) may:

  • Add delays due to logging, request transformation, etc.

  • Have rate limits that slow things down

4. Network Delays

• Network between services might be slow or unstable.

Note:

→ services talk to each other over a network — usually through HTTP, gRPC, or some message queue.

API Gateways (like Spring Cloud Gateway or Kong) may Add delays due to logging:

If your microservice is behind an API Gateway like Spring Cloud Gateway, Kong, or Nginx, the gateway itself might introduce some delay. This happens because API Gateways often do extra processing before passing the request to your service — like logging, authentication, modifying headers, or transforming the request.

Also, gateways are usually configured with rate limits, which control how many requests can be sent per second or minute. If traffic is too high, the gateway might start throttling requests, meaning it intentionally slows them down or rejects them. So even if your microservice is healthy, the delay at the gateway level can make the entire request slower.

Throttling means intentionally slowing down or limiting the number of requests that are allowed to reach a service within a certain time period.

🧪 Example: Your microservice makes a call to another service that has rate-limiting. Because of this, your requests are queued and delayed.

Step 4: Check Service Configuration Settings

Why configuration matters:

Even if your code is perfect, wrong settings can slow things down.

What to check:

1. Thread Pool Size

   • If thread pools are too small, incoming requests get queued.

   • Fix: Increase pool size or use async processing.

2. Connection Pool Size

   • Are you running out of database or HTTP connections?

   • Fix: Increase pool size or optimize usage.

3. Timeouts and Retries

   • If retries are happening due to timeouts, the system gets overloaded.

   • Fix: Configure proper retry policies.

Note: Fix: Increase pool size or use async processing.

First, What is Synchronous (Normal) Processing?

In a typical microservice, the flow is like this:

1. A request comes in.

2. A thread picks it up.

3. The thread waits for everything to finish — DB call, API call, processing, etc.

4. Then it sends back a response.

This thread is occupied the whole time, even when it’s just waiting for a response from the DB.

Now, What is Asynchronous or Non-Blocking Processing?

Async or non-blocking means:

• The thread does not wait.

• Instead, it starts the work (like sending a DB request) and moves on.

• When the response is ready, a callback or handler finishes the remaining work.

So one thread can start many tasks, and finish them as responses come back.

🧪 Example: You set database connection pool to 5, but traffic needs 20. The remaining requests wait, causing delays.

Step 5: Check If Resources Are Saturated

Check your system’s health:

• CPU Usage: If it’s above 80–90%, the service may be throttled(slowing down).

• Memory Usage: If too high, it may use swap, causing major delays.

• Disk I/O: For services writing to disk, slow I/O = slow responses

swap:

Every time your microservice runs, it stores data in RAM (memory) → If your microservice uses too much memory, and there’s no free RAM left, the system tries to help by using something called swap memory.

What is Swap?

Swap is a backup memory space on your hard disk (or SSD).
When RAM is full, the system moves some data from RAM to the disk to make room.

But there’s a big problem:

Disk is much slower than RAM.
Accessing data from swap is like walking instead of flying.

So if your microservice starts using swap:

• It becomes very slow

• Requests take longer

• GC becomes slow

• Latency increases

• Eventually it may crash if memory keeps growing

JVM Garbage Collection (GC)

If you're using Java, the JVM cleans up unused memory using Garbage Collection (GC).

But GC can cause "stop-the-world" pauses, where:

• The JVM pauses your entire service for a short time to clean up memory.

• During this pause, no requests are processed.

If GC runs too often or takes too long, your service will:

• Respond slowly

• Show latency spikes

• Possibly fail under load

🧪 Example: Frequent Full GCs of 2 seconds each cause sudden spikes in latency.

Step 6: Review Logs for Hints

Go to your logs and check:

• Timeout errors: Often show up in downstream service calls

• Retry logs: Multiple retries may mean the first attempt is failing

• Unhandled exceptions: Can cause slow or partial responses

🧪 Example: You see logs filled with “timeout after 5 seconds”. That’s a clue that your downstream service is slow or the timeout value is too low. →

Step 7: Use Feature Flags or Gradual Rollout

Instead of rolling back, do this:

• Turn off features one by one using feature flags

• Split traffic: Send only 10% of traffic to the new service using A/B testing or Canary Deployments

This helps isolate which part of the new service is problematic.

Step 8: Scale the Service

If the service is just getting too much traffic, consider:

• Vertical Scaling: Give more CPU or RAM

• Horizontal Scaling: Run more instances (pods in Kubernetes)

• Enable auto-scaling: So it automatically adjusts to load

Step 9: Test in Isolation

Deploy the same service to a test environment and:

• Use tools like Apache JMeter, Gatling, Artillery to simulate real load

• Profile the app with JProfiler or VisualVM to find code bottlenecks

🧪 Example: In test, you find that one function takes 700ms due to inefficient JSON parsing.

Step 10: Fix the Issue and Keep Monitoring

Based on what you found:

• Optimize queries, code, and resource settings

• Tune timeouts, connection/thread pools

• Add resilience patterns like:

  • Circuit Breakers

  • Bulkheads (isolate services)

  • Retry with backoff

After deploying the fix, watch metrics closely to ensure latency drops.

Conclusion:

When a new microservice is deployed and you start seeing slow response times (latency), don’t panic or roll it back immediately. Instead, follow a step-by-step method to fix it:

1. Collect data — Look at logs, monitoring dashboards, error rates, and resource usage.

2. Analyze metrics — Check which part is slow: the service itself, a database call, or a downstream service.

3. Find the root cause — Is it CPU overload, memory issues, bad queries, too many retries, or poor network?

4. Fix it — Tune code, adjust configs, scale up, or fix a dependency.

Use tools like:

• Monitoring (Prometheus, Grafana)

• Distributed tracing (Jaeger, Zipkin)

• Good practices like async processing, load balancing, and auto-scaling.

This way, you can solve the latency problem without rolling back your deployment — keeping the system stable and avoiding downtime.

Is the service using too much CPU, Memory, or Disk I/O causes any issues?

When a microservice uses too much CPU, it can slow down the entire service. This happens because the CPU becomes overloaded and doesn't have enough capacity to process incoming requests quickly. As a result, each request takes longer to complete, leading to higher latency. If your service handles requests using thread pools, those threads might get delayed or stuck waiting for CPU time, causing requests to queue up or even time out. In Java-based services, high CPU usage also affects garbage collection. When the CPU is busy, garbage collection slows down, leading to long pauses that freeze the application temporarily.

If your service is running in Kubernetes or a cloud platform, high CPU usage may trigger auto-scaling or even throttling. However, scaling takes time, and throttling can reduce performance even further. All of this results in slow responses, timeouts, and sometimes errors for the users.

To fix this, you should optimize any CPU-heavy code, like tight loops or large data processing, and make sure your thread and connection pool sizes are tuned correctly. You can also scale the service by adding more CPU or replicas and use profiling tools to identify what parts of your code are consuming the most CPU. Monitoring CPU usage regularly is key to avoiding these kinds of performance issues.