Why Anti-patterns (distributed monolith, chatty services) in Microservices? - Purpose & Use Cases

The Big Idea

What if your 'microservices' were actually just a slow, tangled mess in disguise?

The Scenario

Imagine a team building a big app by splitting it into many small parts, but these parts keep calling each other too much, like a group of friends who can't stop texting back and forth every minute.

The Problem

This constant back-and-forth slows everything down, causes confusion, and makes fixing problems a nightmare because changing one part breaks many others unexpectedly.

The Solution

Recognizing these anti-patterns helps teams design services that work independently and communicate efficiently, avoiding tangled dependencies and slowdowns.

Before vs After

✗ Before

serviceA calls serviceB for data, then serviceB calls serviceC, then serviceC calls serviceA again

✓ After

serviceA handles its own data or calls serviceB once with all needed info, minimizing calls

What It Enables

It enables building fast, reliable systems where each part can evolve without breaking the whole.

Real Life Example

A shopping app where the payment service doesn't need to ask the inventory service every second, but gets all info upfront, making checkout smooth and quick.

Key Takeaways

Too many small services talking too much cause delays and bugs.

Distributed monoliths hide tight coupling behind service boundaries.

Good design means clear, minimal communication between independent parts.

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