Anti-patterns (distributed monolith, chatty services) in Microservices - Deep Dive

Overview - Anti-patterns (distributed monolith, chatty services)

What is it?

Anti-patterns in microservices are common design mistakes that cause problems instead of solving them. Two important anti-patterns are the distributed monolith and chatty services. A distributed monolith looks like many small services but behaves like one big tightly connected system. Chatty services are microservices that talk too much to each other, causing delays and complexity.

Why it matters

These anti-patterns make microservices slow, hard to maintain, and unreliable. Without understanding them, teams waste time fixing bugs and scaling issues. Systems become fragile and expensive to run. Avoiding these anti-patterns helps build fast, scalable, and easy-to-change software that users enjoy.

Where it fits

Before learning this, you should know what microservices are and how they work. After this, you can learn about best practices for designing microservices, like API design, service boundaries, and communication patterns.

Mental Model

Core Idea

A good microservice system is loosely connected and communicates efficiently, while anti-patterns create hidden tight links and excessive chatter that slow everything down.

Think of it like...

Imagine a group project where everyone is supposed to work independently but instead keeps calling each other every minute to ask small questions. It feels like one big team doing everything together, not separate people doing their parts.

┌─────────────────────────────┐
│       Microservices          │
├─────────────┬───────────────┤
│ Service A   │ Service B     │
│ (independent)│ (independent)│
└─────┬───────┴─────┬─────────┘
      │             │
      │ Minimal     │ Minimal
      │ Calls       │ Calls
      ▼             ▼
  Fast & Scalable  Fast & Scalable


Distributed Monolith Example:

┌─────────────────────────────┐
│       Microservices          │
├─────────────┬───────────────┤
│ Service A   │ Service B     │
│ (tightly    │ (tightly      │
│ coupled)   │ coupled)      │
└─────┬───────┴─────┬─────────┘
      │             │
      │ Many Calls  │ Many Calls
      │ (chatty)    │ (chatty)
      ▼             ▼
  Slow & Fragile   Slow & Fragile

Build-Up - 7 Steps

1

Foundation

What is a Microservice?

Concept: Introduce the basic idea of microservices as small, independent services.

Microservices are small programs that do one job well. Each service runs on its own and talks to others using simple messages. This helps teams build and change parts without breaking the whole system.

Result

You understand microservices as independent pieces working together.

Understanding microservices as independent units is key to seeing why tight coupling causes problems.

2

Foundation

Service Communication Basics

Concept: Explain how microservices communicate and why it matters.

Microservices talk using messages or APIs. Good communication is simple and happens only when needed. This keeps the system fast and easy to fix.

Result

You know that communication should be minimal and clear between services.

Knowing communication basics helps spot when services talk too much or depend too tightly.

3

Intermediate

Understanding Distributed Monolith

🤔Before reading on: do you think having many services always means a well-designed system? Commit to your answer.

Concept: Introduce the distributed monolith anti-pattern where services are tightly linked despite being separate.

A distributed monolith looks like many services but they depend on each other so much that changing one breaks others. They share databases or call each other too often, making the system slow and hard to fix.

Result

You can identify when a system is a distributed monolith, not true microservices.

Understanding distributed monoliths helps avoid the trap of false microservices that cause more harm than good.

4

Intermediate

What Are Chatty Services?

🤔Before reading on: do you think more communication between services always improves system coordination? Commit to your answer.

Concept: Explain chatty services where microservices make too many calls to each other.

Chatty services send many small messages back and forth. This wastes time and network resources, causing delays and failures. It also makes debugging harder because problems spread across many calls.

Result

You recognize chatty services as a cause of slow and fragile systems.

Knowing chatty services helps design communication that is efficient and robust.

5

Intermediate

Why Distributed Monoliths and Chatty Services Harm

Concept: Show the combined effect of these anti-patterns on system performance and maintenance.

When services are tightly coupled and chat a lot, the system becomes slow and fragile. Deploying one service needs many others to be ready. Bugs spread easily. Scaling is hard because services depend on each other.

Result

You see the real-world impact of these anti-patterns on software teams and users.

Understanding the harm motivates careful design and testing to avoid these traps.

6

Advanced

Detecting and Fixing Distributed Monoliths

🤔Before reading on: do you think splitting a big database into many smaller ones always fixes distributed monoliths? Commit to your answer.

Concept: Teach how to find and reduce tight coupling and shared state in microservices.

Look for services that share databases or require many synchronous calls. Fix by defining clear service boundaries, using asynchronous communication, and owning data per service. This reduces dependencies and improves independence.

Result

You can diagnose and start fixing distributed monoliths in real systems.

Knowing how to detect and fix coupling is crucial for evolving microservices safely.

7

Expert

Balancing Communication and Independence

🤔Before reading on: do you think zero communication between microservices is ideal? Commit to your answer.

Concept: Explore the tradeoff between service independence and necessary communication.

Microservices must communicate to work together, but too much causes chatty services. Experts design APIs that bundle data and use event-driven patterns to reduce calls. They also accept some coupling when it improves performance or consistency.

Result

You understand the nuanced balance needed for scalable microservice design.

Grasping this balance prevents over-engineering and under-engineering microservice communication.

Under the Hood

Distributed monoliths happen when microservices share databases or synchronous calls, creating hidden dependencies. Chatty services cause many network calls, increasing latency and failure points. Internally, this leads to tight coupling, blocking, and cascading failures that reduce system resilience.

Why designed this way?

Microservices were designed to be independent to allow teams to work separately and scale easily. However, early attempts often kept shared databases or synchronous calls for simplicity, causing distributed monoliths. Chatty services arise from naive splitting of functionality without considering communication cost.

┌───────────────┐      ┌───────────────┐
│ Service A    │─────▶│ Service B    │
│ (owns data)  │      │ (owns data)  │
└──────┬────────┘      └──────┬────────┘
       │                     │
       │ Shared Database      │
       └─────────────────────┘

This shared database creates tight coupling.


Chatty Services:

Service A ──▶ Service B ──▶ Service C ──▶ Service D
   │           │            │            │
   ◀───────────◀────────────◀────────────◀

Many small calls cause delays and failures.

Myth Busters - 4 Common Misconceptions

Quick: Do you think having many small services always means a well-designed microservice system? Commit yes or no.

Common Belief: More services automatically mean better modularity and scalability.

Reality: Simply splitting a system into many services without clear boundaries can create a distributed monolith with tight coupling.

Why it matters: Believing this leads to complex, fragile systems that are hard to maintain and scale.

Quick: Do you think more communication between services always improves coordination? Commit yes or no.

Common Belief: Frequent communication between services is good because it keeps data fresh and consistent.

Reality: Too much communication (chatty services) causes delays, network overload, and harder debugging.

Why it matters: Ignoring this causes slow systems and cascading failures.

Quick: Do you think sharing a database between microservices is acceptable if it simplifies development? Commit yes or no.

Common Belief: Sharing a database is fine as long as services are logically separated.

Reality: Shared databases create hidden dependencies that break service independence and cause distributed monoliths.

Why it matters: This misconception leads to deployment and scaling problems.

Quick: Do you think zero communication between microservices is ideal? Commit yes or no.

Common Belief: Microservices should never communicate to stay independent.

Reality: Some communication is necessary; the goal is minimal and efficient communication, not none.

Why it matters: Thinking zero communication is ideal can lead to isolated services that cannot work together.

Expert Zone

1

Distributed monoliths often hide behind asynchronous calls that still create tight coupling through shared state or synchronous dependencies.

2

Chatty services can sometimes be optimized by batching calls or using event-driven architectures to reduce network chatter.

3

Sometimes a small amount of coupling is acceptable for performance or consistency, but it must be intentional and well-understood.

When NOT to use

Avoid microservices when the system is small or simple, as the overhead of managing many services can outweigh benefits. Instead, use a modular monolith. Also, avoid microservices if your team lacks experience with distributed systems. Alternatives include modular monoliths or service-oriented architecture (SOA).

Production Patterns

Real-world systems use API gateways to reduce chatty calls, event buses for asynchronous communication, and database per service to avoid distributed monoliths. Teams use monitoring and tracing tools to detect chatty patterns and coupling. Incremental refactoring helps evolve legacy distributed monoliths into true microservices.

Connections

Modular Monolith

Alternative approach

Understanding modular monoliths helps see when microservices are overkill and how to design clean boundaries within a single deployable unit.

Event-Driven Architecture

Solution pattern

Knowing event-driven design helps reduce chatty services by enabling asynchronous, decoupled communication.

Human Team Communication

Analogy in organizational behavior

Studying how teams communicate efficiently or inefficiently reveals parallels to service chatter and coupling, improving system design.

Common Pitfalls

#1 Splitting a big system into many services but keeping a shared database.

Wrong approach:

Multiple services connect directly to the same database tables without clear ownership.

Correct approach:

Each service owns its own database or schema and accesses data only through APIs.

Root cause:

Misunderstanding that database sharing breaks service independence.

#2 Designing services that call each other for every small piece of data.

Wrong approach:

Service A calls Service B for user info, then Service B calls Service C for address, then Service C calls Service D for zip code, all synchronously.

Correct approach:

Design APIs to return all needed data in one call or use event-driven updates to reduce calls.

Root cause:

Not considering the cost of network calls and latency.

#3 Trying to eliminate all communication between services.

Wrong approach:

Services are isolated with no data sharing or messaging, leading to duplicated data and inconsistent states.

Correct approach:

Allow minimal, well-defined communication using asynchronous events or APIs.

Root cause:

Misunderstanding that some communication is necessary for collaboration.

Key Takeaways

Microservices must be designed for independence with clear boundaries to avoid becoming distributed monoliths.

Excessive communication between services, known as chatty services, causes slow and fragile systems.

Sharing databases across services creates hidden dependencies that break microservice principles.

Balancing communication and independence is key; use asynchronous patterns and well-designed APIs.

Recognizing and fixing these anti-patterns improves system scalability, reliability, and maintainability.

Practice

1. Which of the following best describes a distributed monolith in microservices architecture?

easy

A. Services are fully independent and communicate rarely.

B. Services are tightly coupled and require coordinated deployment.

C. Services use asynchronous messaging to reduce latency.

D. Services are stateless and scale automatically.

Solution

1. Step 1: Understand distributed monolith characteristics

   A distributed monolith looks like microservices but behaves like a single app with tight coupling.

2. Step 2: Identify deployment and coupling issues

   Such services require coordinated deployment and cannot scale independently.

3. Final Answer:

   Services are tightly coupled and require coordinated deployment. -> Option B

4. Quick Check:

   Distributed monolith = tight coupling [OK]

Hint: Distributed monolith means tight coupling, not independence [OK]

Common Mistakes:

• Confusing distributed monolith with loosely coupled microservices
• Thinking distributed monolith scales independently
• Assuming distributed monolith uses asynchronous calls

2. Which syntax correctly describes a common symptom of chatty services in microservices communication?

easy

A. Service A uses event-driven messaging to notify Service B.

B. Service A calls Service B once per user request.

C. Service A calls Service B multiple times per user request.

D. Service A caches data to reduce calls to Service B.

Solution

1. Step 1: Define chatty services behavior

   Chatty services make many small calls between services per user request.

2. Step 2: Identify the correct syntax describing chatty calls

   Multiple calls per request indicate chatty communication.

3. Final Answer:

   Service A calls Service B multiple times per user request. -> Option C

4. Quick Check:

   Chatty services = many calls [OK]

Hint: Chatty means many calls, not just one [OK]

Common Mistakes:

• Choosing event-driven messaging as chatty behavior
• Assuming caching causes chatty services
• Thinking one call per request is chatty

3. Given a microservices system where Service A calls Service B 5 times and Service B calls Service C 3 times per user request, what is the total number of service calls triggered by one user request?

medium

A. 20

B. 15

C. 30

D. 8

Solution

1. Step 1: Calculate calls from Service A to B

   Service A calls Service B 5 times per request.

2. Step 2: Calculate calls from Service B to C triggered by A's calls

   Each of the 5 calls from A causes 3 calls from B to C, so 5 * 3 = 15 calls.

3. Step 3: Sum all calls

   Total calls = 5 (A->B) + 15 (B->C) = 20 calls.

4. Final Answer:

   20 -> Option A

5. Quick Check:

   5 + (5*3) = 20 [OK]

Hint: Multiply nested calls, then add all [OK]

Common Mistakes:

• Adding 5 + 3 instead of multiplying
• Ignoring nested calls from B to C
• Choosing sum as 18 instead of 20

4. You notice your microservices system has high latency due to many small synchronous calls between services. Which change would best fix this chatty service anti-pattern?

medium

A. Use asynchronous messaging or batch requests to reduce calls.

B. Combine tightly coupled services into a single service.

C. Add more synchronous calls to improve data freshness.

D. Increase the number of service instances to handle load.

Solution

1. Step 1: Identify chatty service problem

   Many small synchronous calls cause latency and network overhead.

2. Step 2: Choose solution to reduce call frequency

   Using asynchronous messaging or batching reduces calls and latency.

3. Final Answer:

   Use asynchronous messaging or batch requests to reduce calls. -> Option A

4. Quick Check:

   Reduce calls with async or batching [OK]

Hint: Reduce calls by batching or async messaging [OK]

Common Mistakes:

• Combining services creates distributed monolith
• Adding more sync calls worsens latency
• Scaling instances doesn't reduce call count

5. A company has a microservices system suffering from both distributed monolith and chatty services anti-patterns. Which combined approach best improves scalability and deployment independence?

hard

A. Merge all services into one large application to simplify deployment.

B. Increase hardware resources and add load balancers to handle traffic.

C. Use synchronous REST calls extensively to keep services tightly connected.

D. Refactor services to reduce dependencies and use asynchronous communication.

Solution

1. Step 1: Address distributed monolith by reducing dependencies

   Refactoring services to be loosely coupled allows independent deployment and scaling.

2. Step 2: Fix chatty services by adopting asynchronous communication

   Using async messaging reduces frequent synchronous calls and network overhead.

3. Step 3: Combine both improvements for better scalability and independence

   This combined approach solves both anti-patterns effectively.

4. Final Answer:

   Refactor services to reduce dependencies and use asynchronous communication. -> Option D

5. Quick Check:

   Loose coupling + async = scalable microservices [OK]

Hint: Loose coupling + async communication fixes both issues [OK]

Common Mistakes:

• Merging services worsens distributed monolith
• Adding hardware doesn't fix design flaws
• Using more sync calls increases chatty problems