Request aggregation in Microservices - Architecture Diagram

System Overview - Request aggregation

This system collects data from multiple microservices and combines the results into a single response for the user. It is designed to improve user experience by reducing the number of separate requests the user must make and to handle data aggregation efficiently.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  v
Aggregator Service
 /      |       \
v       v        v
Service A  Service B  Service C
  |         |         |
  v         v         v
Database A Database B Database C
  \
   v
  Cache

Components

User

client

Initiates requests to the system

Load Balancer

load_balancer

Distributes incoming requests evenly across API Gateway instances

API Gateway

api_gateway

Receives user requests and routes them to the Aggregator Service

Aggregator Service

service

Sends parallel requests to multiple microservices and combines their responses

Service A

service

Handles specific domain logic and data retrieval from Database A

Service B

service

Handles specific domain logic and data retrieval from Database B

Service C

service

Handles specific domain logic and data retrieval from Database C

Database A

database

Stores data for Service A

Database B

database

Stores data for Service B

Database C

database

Stores data for Service C

Cache

cache

Stores frequently accessed aggregated data to reduce latency

Request Flow - 21 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Aggregator Service

Aggregator Service → Cache

Cache → Aggregator Service

Aggregator Service → Service A

Aggregator Service → Service B

Aggregator Service → Service C

Service A → Database A

Service B → Database B

Service C → Database C

Database A → Service A

Database B → Service B

Database C → Service C

Service A → Aggregator Service

Service B → Aggregator Service

Service C → Aggregator Service

Aggregator Service → Cache

Aggregator Service → API Gateway

API Gateway → Load Balancer

Load Balancer → User

Failure Scenario

Component Fails:Cache

Impact:Aggregator Service cannot retrieve cached aggregated data, causing all requests to fetch data from microservices and databases, increasing latency.

Mitigation:System continues working by fetching fresh data from services and databases; cache can be restored asynchronously. Optionally, implement cache replication or fallback caches.

Architecture Quiz - 3 Questions

Which component is responsible for combining data from multiple microservices?

A Load Balancer

B API Gateway

C Aggregator Service

D Cache

Design Principle

This architecture demonstrates the principle of request aggregation by centralizing multiple service calls into one. It improves performance by using caching and parallel service calls, reducing user wait time and system load.

Practice

1. What is the main purpose of request aggregation in microservices?

easy

A. To cache responses from a single microservice

B. To split a large service into smaller microservices

C. To handle database transactions across services

D. To combine data from multiple microservices into a single response

Solution

1. Step 1: Understand request aggregation concept

   Request aggregation means collecting data from multiple microservices to form one combined response.

2. Step 2: Identify the main goal

   The goal is to reduce multiple client calls into one, improving efficiency and user experience.

3. Final Answer:

   To combine data from multiple microservices into a single response -> Option D

4. Quick Check:

   Request aggregation = combine multiple responses [OK]

Hint: Aggregation means combining multiple service responses [OK]

Common Mistakes:

• Confusing aggregation with service splitting
• Thinking it only caches data
• Mixing aggregation with transaction management

2. Which of the following is the correct way to implement a request aggregator in a microservices architecture?

easy

A. Make parallel calls to all required microservices and aggregate responses asynchronously

B. Make sequential calls to each microservice and combine results synchronously

C. Call only one microservice and ignore others

D. Use a database trigger to combine data from microservices

Solution

1. Step 1: Review aggregator call patterns

   Efficient aggregators call multiple services in parallel to reduce total wait time.

2. Step 2: Identify correct implementation

   Parallel asynchronous calls improve performance and user experience compared to sequential calls.

3. Final Answer:

   Make parallel calls to all required microservices and aggregate responses asynchronously -> Option A

4. Quick Check:

   Parallel async calls = best aggregator practice [OK]

Hint: Use parallel async calls for faster aggregation [OK]

Common Mistakes:

• Using sequential calls causing slow responses
• Ignoring some microservices in aggregation
• Trying to use database triggers for aggregation

3. Consider this pseudocode for a request aggregator:

async function aggregate() {
  const user = await getUser();
  const orders = await getOrders(user.id);
  const payments = await getPayments(user.id);
  return { user, orders, payments };
}

What is the main problem with this code?

medium

A. It does not handle errors from getUser

B. It calls getOrders and getPayments sequentially, increasing total response time

C. It returns data in the wrong format

D. It calls getUser multiple times unnecessarily

Solution

1. Step 1: Analyze call sequence

   The code waits for getUser, then calls getOrders and waits, then calls getPayments and waits, all sequentially.

2. Step 2: Identify inefficiency

   Calling getOrders and getPayments one after another increases total wait time unnecessarily.

3. Final Answer:

   It calls getOrders and getPayments sequentially, increasing total response time -> Option B

4. Quick Check:

   Sequential calls = slower aggregation [OK]

Hint: Parallelize independent calls to reduce wait time [OK]

Common Mistakes:

• Assuming error handling is missing
• Thinking return format is incorrect
• Believing getUser is called multiple times

4. You have a request aggregator that calls three microservices in parallel. Sometimes, one service fails and causes the whole aggregation to fail. How can you fix this?

medium

A. Cache the failed service response permanently

B. Retry the failed service indefinitely until it succeeds

C. Ignore errors and return partial data with error info for failed services

D. Stop calling other services if one fails

Solution

1. Step 1: Understand error impact in aggregation

   If one service fails, the aggregator should still return available data to avoid full failure.

2. Step 2: Choose error handling strategy

   Returning partial data with error info improves user experience and system resilience.

3. Final Answer:

   Ignore errors and return partial data with error info for failed services -> Option C

4. Quick Check:

   Partial data + error info = robust aggregation [OK]

Hint: Return partial results with errors, don't fail whole aggregation [OK]

Common Mistakes:

• Retrying endlessly causing delays
• Stopping all calls on one failure
• Caching errors permanently causing stale data

5. You design a request aggregator for a shopping app that calls user, orders, and payment microservices. To improve scalability, which design choice is best?

hard

A. Use asynchronous parallel calls with timeout and fallback data for each microservice

B. Call microservices sequentially and cache all responses for 24 hours

C. Aggregate data in a single monolithic service instead of microservices

D. Make synchronous calls and block until all microservices respond

Solution

1. Step 1: Consider scalability needs

   Parallel async calls reduce latency and improve throughput under load.

2. Step 2: Add timeout and fallback

   Timeouts prevent long waits; fallback data keeps user experience smooth if a service is slow or down.

3. Step 3: Evaluate other options

   Sequential calls and long caching reduce freshness and responsiveness; monolith loses microservices benefits; synchronous blocking hurts scalability.

4. Final Answer:

   Use asynchronous parallel calls with timeout and fallback data for each microservice -> Option A

5. Quick Check:

   Async parallel + timeout + fallback = scalable aggregator [OK]

Hint: Combine async calls with timeout and fallback for best scalability [OK]

Common Mistakes:

• Using sequential calls causing slow response
• Relying on stale cached data too long
• Ignoring microservices benefits by monolith design