Request aggregation in Microservices - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is request aggregation in microservices?

Request aggregation is a design pattern where a single service collects data from multiple microservices and combines it into one response for the client.

beginner

Why is request aggregation useful in microservices?

It reduces the number of client requests by combining multiple service calls into one, improving performance and simplifying client logic.

intermediate

What is a common challenge when implementing request aggregation?

Handling latency and failures from multiple services while ensuring the aggregated response is timely and reliable.

intermediate

Name two common approaches to implement request aggregation.

1. API Gateway pattern, where the gateway aggregates responses before sending to client. 2. Backend for Frontend (BFF), a dedicated service per client type that aggregates data.

beginner

How does request aggregation improve user experience?

By reducing the number of network calls and combining data, it lowers wait times and simplifies the client’s data handling.

What does request aggregation primarily aim to reduce?

A Server CPU usage

B Number of microservices

C Number of client requests

D Database size

Request aggregation reduces the number of client requests by combining multiple service calls into one.

Which pattern often uses request aggregation to serve clients?

A Circuit Breaker

B API Gateway

C Event Sourcing

D CQRS

API Gateway often aggregates responses from multiple services before sending to the client.

What is a key risk when aggregating requests from multiple services?

A Overloading a single microservice

B Data duplication in database

C Client-side caching issues

D Increased latency due to waiting on all services

Waiting for multiple services can increase latency and affect response time.

Which service is responsible for request aggregation in the Backend for Frontend pattern?

A A dedicated service per client type

B Database service

C Authentication service

D Load balancer

BFF uses a dedicated service per client type to aggregate data tailored for that client.

Request aggregation helps improve user experience by:

A Reducing network calls and wait times

B Increasing the number of microservices

C Adding more client-side logic

D Duplicating data across services

Reducing network calls lowers wait times and simplifies client logic, improving user experience.

Explain request aggregation and why it is important in microservices architecture.

Think about how a single client request can get data from many services efficiently.

Describe common challenges and solutions when implementing request aggregation.

Consider what happens if one service is slow or fails during aggregation.

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