15 System Architecture Interview Questions and Answers

System architecture is a critical aspect of software development, encompassing the design and organization of a system’s components and their interactions. It involves making high-level decisions about the structure and behavior of a system, ensuring scalability, reliability, and performance. Understanding system architecture is essential for creating robust and efficient systems that can meet the demands of modern applications.

This article provides a curated selection of interview questions focused on system architecture. By exploring these questions and their detailed answers, you will gain a deeper understanding of key architectural principles and be better prepared to discuss and design complex systems in a professional setting.

System Architecture Interview Questions and Answers

1. What are the key differences between monolithic and microservices architectures?

Monolithic and microservices architectures are two approaches to designing software systems.

Monolithic architecture builds the entire application as a single unit, with tightly coupled components running as one process. This can simplify development but poses challenges in scalability and maintenance as the application grows.

Microservices architecture breaks down the application into smaller, independent services that communicate through APIs. Each service handles specific functionality and can be developed, deployed, and scaled independently. This offers flexibility and scalability but adds complexity in service coordination.

Key differences include:

• Scalability: Monolithic applications are challenging to scale horizontally, as the entire application must be replicated. Microservices allow individual services to be scaled independently.
• Deployment: Monolithic architecture requires redeploying the entire application for any change. Microservices enable continuous deployment, as individual services can be updated independently.
• Maintenance: Monolithic systems can become difficult to maintain due to tightly coupled components. Microservices promote better maintainability by isolating functionalities.
• Technology Stack: Monolithic applications typically use a single technology stack. Microservices allow for different technologies and languages for different services.
• Fault Isolation: In a monolithic system, a failure in one component can affect the entire application. Microservices provide better fault isolation.

2. Write pseudocode for a basic round-robin load balancer.

A round-robin load balancer distributes client requests across a group of servers by cycling through the list of servers and assigning each incoming request to the next server. This ensures an even distribution of requests.

Pseudocode for a basic round-robin load balancer:

initialize server_list as a list of servers
initialize current_index to 0

function get_next_server():
    server = server_list[current_index]
    current_index = (current_index + 1) % length of server_list
    return server

function handle_request(request):
    server = get_next_server()
    forward request to server

3. Explain the CAP theorem and its implications for distributed systems.

The CAP theorem, or Brewer’s theorem, states that a distributed data store can only achieve two out of three guarantees: Consistency, Availability, and Partition Tolerance. Network partitions are inevitable, so designers must choose between consistency and availability during a partition. This leads to three types of systems:

• CP (Consistency and Partition Tolerance): Prioritizes consistency and partition tolerance over availability.
• AP (Availability and Partition Tolerance): Prioritizes availability and partition tolerance over consistency.
• CA (Consistency and Availability): Theoretically impossible to achieve in the presence of network partitions.

4. How would you ensure data consistency in a distributed database?

Ensuring data consistency in a distributed database involves balancing the trade-offs described by the CAP theorem. Strategies include:

• Consistency Models: Choose an appropriate model based on application requirements. Strong consistency ensures all nodes see the same data, while eventual consistency allows temporary discrepancies.
• Distributed Transactions: Use protocols like Two-Phase Commit to ensure all nodes commit or abort a transaction together.
• Consensus Algorithms: Use algorithms like Paxos or Raft to ensure all nodes agree on the data state.
• Data Replication: Maintain multiple data copies across nodes. Synchronous replication ensures strong consistency, while asynchronous may lead to eventual consistency.
• Conflict Resolution: Implement mechanisms to handle inconsistencies from concurrent updates, like version vectors or CRDTs.

5. Write pseudocode for an LRU (Least Recently Used) cache.

An LRU (Least Recently Used) cache stores a limited number of items and removes the least recently used item when capacity is reached. This keeps frequently accessed items readily available.

Pseudocode for an LRU cache:

class LRUCache:
    Initialize(capacity)
        self.capacity = capacity
        self.cache = {}
        self.order = []

    Get(key)
        if key in self.cache:
            Move key to the end of self.order
            return self.cache[key]
        else:
            return -1

    Put(key, value)
        if key in self.cache:
            Update the value of self.cache[key]
            Move key to the end of self.order
        else:
            if len(self.cache) >= self.capacity:
                Remove the first item from self.order
                Delete the corresponding key from self.cache
            Add key to the end of self.order
            Set self.cache[key] = value

6. How would you design a system to handle real-time data processing?

Designing a system for real-time data processing involves:

• Data Ingestion: Use technologies like Apache Kafka or Amazon Kinesis for high-throughput data streams.
• Data Processing: Use stream processing frameworks like Apache Flink or Apache Spark Streaming for low-latency processing.
• Data Storage: Use NoSQL databases like Apache Cassandra for high write throughput and low-latency reads.
• Scalability: Design for horizontal scaling using distributed systems and technologies like Kubernetes.
• Fault Tolerance: Ensure continuous operation by replicating data across nodes and using fault-tolerant technologies.
• Latency: Optimize components to reduce processing time and ensure minimal delay.

7. Describe the process of sharding a database and its benefits.

Sharding a database involves dividing data into independent pieces stored on different servers to improve performance and scalability. Steps include:

1. Determine the Sharding Key: Choose an attribute to distribute data evenly.
2. Partition the Data: Use the sharding key to partition data into shards.
3. Distribute the Shards: Distribute shards across multiple servers.
4. Routing Queries: Use a routing mechanism to direct queries to the appropriate shard.

Benefits include improved performance, scalability, and fault isolation.

8. How would you design a monitoring and alerting system for a large-scale application?

Designing a monitoring and alerting system for a large-scale application involves:

1. Data Collection: Use tools like Prometheus and ELK Stack to collect metrics and logs.
2. Data Storage: Store data in scalable systems like InfluxDB or Elasticsearch.
3. Data Analysis: Analyze data to identify patterns and performance issues.
4. Visualization: Use dashboards like Grafana for real-time insights.
5. Alerting: Set up alerting rules with tools like Prometheus Alertmanager.
6. Scalability and Reliability: Ensure the monitoring system is scalable and highly available.
7. Security and Compliance: Protect monitoring data and ensure compliance with regulations.

9. Explain the concept of service discovery in microservices and how it can be implemented.

Service discovery in microservices involves automatically detecting and tracking service instances’ network locations. This enables services to communicate without hardcoding addresses, which can change dynamically.

There are two main types:

1. Client-Side Discovery: The client determines service locations by querying a service registry. Tools like Netflix Eureka are used for this.
2. Server-Side Discovery: A load balancer queries the service registry and forwards requests to service instances. AWS Elastic Load Balancing is an example.

Service registries maintain a dynamic list of available service instances and their locations.

10. Write pseudocode for implementing a circuit breaker pattern.

class CircuitBreaker:
    def __init__(self, failure_threshold, recovery_timeout):
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.failure_count = 0
        self.state = 'CLOSED'
        self.last_failure_time = None

    def call(self, func):
        if self.state == 'OPEN':
            if self._timeout_expired():
                self.state = 'HALF-OPEN'
            else:
                raise Exception("Circuit is open")

        try:
            result = func()
            self._reset()
            return result
        except Exception as e:
            self._record_failure()
            if self.failure_count >= self.failure_threshold:
                self.state = 'OPEN'
                self.last_failure_time = current_time()
            raise e

    def _record_failure(self):
        self.failure_count += 1

    def _reset(self):
        self.failure_count = 0
        self.state = 'CLOSED'

    def _timeout_expired(self):
        return current_time() - self.last_failure_time > self.recovery_timeout

def current_time():
    # Returns the current time in seconds
    pass

11. How would you design a caching strategy for a high-traffic web application?

To design a caching strategy for a high-traffic web application, consider:

1. Types of Caches:

• Client-Side Cache: Cache static assets on the client side.
• Server-Side Cache: Use server-side caching mechanisms like Redis.
• Content Delivery Network (CDN): Distribute content across multiple locations.

2. Cache Invalidation:

• Time-Based Invalidation: Set a time-to-live (TTL) for cached data.
• Event-Based Invalidation: Invalidate cache entries based on specific events.

3. Data Access Patterns:

• Read-Heavy Workloads: Cache read operations to improve performance.
• Write-Heavy Workloads: Use write-through or write-back caching strategies.

4. Cache Granularity:

• Fine-Grained Caching: Cache individual database queries or API responses.
• Coarse-Grained Caching: Cache entire web pages or large data sets.

5. Cache Consistency:

• Strong Consistency: Ensure cached data is up-to-date with the source of truth.
• Eventual Consistency: Allow some delay in cache updates.

6. Cache Storage:

• In-Memory Cache: Store cache data in memory for fast access.
• Distributed Cache: Use a distributed caching system to scale horizontally.

12. What are some common security measures you would implement in a system architecture?

Common security measures in system architecture include:

• Encryption: Encrypt sensitive data both at rest and in transit.
• Access Control: Implement role-based access control (RBAC) and multi-factor authentication (MFA).
• Firewalls: Deploy firewalls to monitor and control network traffic.
• Intrusion Detection and Prevention Systems (IDPS): Use IDPS to detect and respond to potential security breaches.
• Regular Updates and Patch Management: Keep software and systems up to date with security patches.
• Data Backup and Recovery: Regularly back up data and have a disaster recovery plan.
• Security Audits and Penetration Testing: Conduct regular audits and testing to identify vulnerabilities.
• Logging and Monitoring: Implement logging and monitoring to track system activities.
• Secure Software Development Lifecycle (SDLC): Integrate security practices into the development lifecycle.

13. How would you implement API rate limiting to protect your services?

API rate limiting controls the number of requests a client can make to an API within a specified time frame. This helps protect services from being overwhelmed and ensures fair usage. One approach is the token bucket algorithm, where tokens are added to a bucket at a fixed rate. Each request consumes a token, and if the bucket is empty, the request is denied.

Example:

import time
from collections import defaultdict

class RateLimiter:
    def __init__(self, rate, per):
        self.rate = rate
        self.per = per
        self.allowance = rate
        self.last_check = time.time()
        self.clients = defaultdict(lambda: {'allowance': rate, 'last_check': time.time()})

    def is_allowed(self, client_id):
        current = time.time()
        time_passed = current - self.clients[client_id]['last_check']
        self.clients[client_id]['last_check'] = current
        self.clients[client_id]['allowance'] += time_passed * (self.rate / self.per)

        if self.clients[client_id]['allowance'] > self.rate:
            self.clients[client_id]['allowance'] = self.rate

        if self.clients[client_id]['allowance'] < 1.0:
            return False
        else:
            self.clients[client_id]['allowance'] -= 1.0
            return True

rate_limiter = RateLimiter(5, 60)  # 5 requests per minute

client_id = 'client_123'
if rate_limiter.is_allowed(client_id):
    print("Request allowed")
else:
    print("Rate limit exceeded")

14. Describe the principles of event-driven architecture and its advantages.

Event-driven architecture (EDA) is based on producing, detecting, consuming, and reacting to events. Core components include:

• Event Producers: Sources that generate events.
• Event Consumers: Entities that consume and react to events.
• Event Channels: Pathways through which events are transmitted.
• Event Processors: Components that process events and may trigger further actions.

Advantages of EDA include:

• Scalability: Allows for horizontal scaling by adding new event consumers.
• Flexibility: The decoupled nature makes it easier to modify or replace components.
• Responsiveness: Enables real-time processing and immediate reaction to events.
• Resilience: The asynchronous nature helps in building fault-tolerant systems.

15. How do DevOps practices influence system architecture and deployment?

DevOps practices influence system architecture and deployment by:

• Automation: Emphasizing automation of tasks like testing and deployment to reduce errors and speed up releases.
• Continuous Integration and Continuous Deployment (CI/CD): Ensuring code changes are automatically tested and deployed, leading to faster releases.
• Microservices Architecture: Aligning with microservices architecture for independent development and deployment of services.
• Infrastructure as Code (IaC): Managing infrastructure through code for consistency and easy replication.
• Monitoring and Logging: Emphasizing monitoring and logging for insights into system performance.
• Collaboration and Communication: Fostering collaboration between development and operations teams for better alignment and problem resolution.