Top Questions & Answers for System Design Interview | AI Enhanced Prep

Ensuring data consistency in a distributed system is challenging due to the nature of distributed architecture, where data may be replicated across multiple nodes. Common strategies to maintain consistency include:

• Two-Phase Commit (2PC):
  • A protocol that ensures all nodes in a distributed system either commit a transaction or roll it back, ensuring atomicity and consistency.
  • It involves a prepare phase where all nodes agree to commit, followed by a commit phase where the transaction is finalized.
• Quorum-based Voting:
  • Involves reading or writing to a subset of nodes (a quorum) to ensure consistency.
  • For example, in a majority quorum, a write is considered successful if it is written to the majority of nodes. A read is considered consistent if it is read from a majority of nodes.
• Eventual Consistency:
  • A consistency model where updates to a distributed system are not immediately visible to all nodes, but eventually, all nodes converge to the same state.
  • Common in systems where high availability and partition tolerance are prioritized, such as NoSQL databases like Cassandra and DynamoDB.
• Versioning and Conflict Resolution:
  • Using version numbers or timestamps to manage different versions of data and resolve conflicts that may arise from concurrent updates.
  • Strategies such as last-write-wins or custom conflict resolution logic can be applied.
• Leader-Follower Replication:
  • A leader node is responsible for accepting writes, and followers replicate the leader's state.
  • Consistency is maintained by ensuring that followers are kept up-to-date with the leader through log replication or other synchronization mechanisms.

These strategies are often used in combination, depending on the specific requirements of the system, such as the need for strong consistency versus availability.

Caching is a technique used to store copies of frequently accessed data in a location that can be accessed more quickly than the original source. By reducing the need to repeatedly fetch the same data from the primary storage, caching can significantly improve system performance.

Benefits of Caching:

• Reduced Latency: Caching decreases the time it takes to retrieve data by storing it closer to the client, leading to faster response times.
• Reduced Load on Backend: By serving requests from the cache, the number of requests hitting the backend database or service is reduced, leading to improved scalability.
• Increased Throughput: Caching can increase the overall throughput of the system by handling more requests in less time.

Common Caching Strategies:

• Write-through Cache:
  • Data is written to both the cache and the backend storage simultaneously.
  • Ensures that the cache is always in sync with the backend, providing strong consistency.
• Write-back (Lazy-write) Cache:
  • Data is written to the cache first and then written to the backend storage asynchronously.
  • Improves write performance but may introduce inconsistency if the cache is lost before the write is committed to the backend.
• Cache-aside (Lazy-loading):
  • The application checks the cache first; if the data is not present, it is loaded from the backend and placed in the cache.
  • Common in systems where the cache is used to speed up read operations.
• Time-to-Live (TTL):
  • Each cache entry is given a TTL, after which it expires and is removed from the cache.
  • Helps to ensure that the cache does not serve stale data.
• Eviction Policies:
  • Least Recently Used (LRU): Evicts the least recently accessed items first.
  • Least Frequently Used (LFU): Evicts the least frequently accessed items.
  • First-In-First-Out (FIFO): Evicts items in the order they were added.

Caching is a powerful tool, but it must be carefully managed to avoid issues like stale data or cache thrashing.

A message queue is a communication mechanism used in distributed systems to enable asynchronous communication between different components. It allows messages (data) to be sent from one component to another, with the queue holding the messages until they are processed.

How Message Queues Support Asynchronous Communication:

• Decoupling: Message queues decouple the sender and receiver, allowing them to operate independently. The sender can send a message and continue processing without waiting for the receiver to process the message.
• Load Balancing: By queuing messages, the system can distribute the processing load across multiple consumers, ensuring that no single component is overwhelmed.
• Fault Tolerance: If a receiver is temporarily unavailable, the message queue can hold the messages until the receiver is ready to process them, ensuring that no messages are lost.
• Scalability: Message queues support horizontal scaling by allowing multiple consumers to process messages in parallel, enabling the system to handle higher loads.

Common Message Queue Implementations:

• RabbitMQ: An open-source message broker that supports various messaging protocols.
• Apache Kafka: A distributed streaming platform that provides high-throughput, low-latency messaging.
• Amazon SQS: A fully managed message queue service provided by AWS.

Message queues are essential for building resilient, scalable, and decoupled systems, especially in microservices architectures.

A Content Delivery Network (CDN) is a distributed network of servers strategically placed across various geographic locations to deliver content to users more efficiently. The primary goal of a CDN is to reduce latency and improve the performance of content delivery, particularly for web applications, streaming media, and large files.

How a CDN Works:

• Edge Servers: CDNs consist of edge servers located in different regions. These servers cache copies of content such as HTML pages, JavaScript files, images, and videos.
• Geographical Proximity: When a user requests content, the CDN routes the request to the edge server closest to the user’s location. This reduces the physical distance that data must travel, leading to faster load times.
• Load Balancing: CDNs can distribute the load across multiple edge servers, preventing any single server from becoming a bottleneck.
• Content Caching: Static content is cached on the edge servers, reducing the need to fetch the same content repeatedly from the origin server.
• Dynamic Content Acceleration: While CDNs primarily cache static content, some CDNs also support the acceleration of dynamic content by optimizing routing and reducing latency.

Benefits of Using a CDN:

• Improved Performance: By serving content from a server close to the user, CDNs reduce latency and improve page load times.
• Scalability: CDNs can handle large volumes of traffic, making it easier to scale web applications to meet demand.
• DDoS Protection: CDNs can absorb and mitigate Distributed Denial of Service (DDoS) attacks, improving the security and availability of the content.
• Reduced Bandwidth Costs: By caching content on edge servers, CDNs reduce the load on the origin server, leading to lower bandwidth costs.

CDNs are widely used by websites, streaming services, and online platforms to ensure fast and reliable content delivery to users around the globe.

Vertical scaling and horizontal scaling are two strategies used to increase the capacity of a system to handle more load.

Vertical Scaling (Scaling Up):

• Definition: Vertical scaling involves adding more resources (such as CPU, memory, or storage) to an existing server or machine to handle increased load.
• Example: Upgrading a server’s RAM from 16GB to 32GB or increasing the number of CPU cores.
• Benefits:
  • Simplicity: Vertical scaling is often easier to implement as it does not require changes to the application architecture.
  • No Need for Distributed Systems: Vertical scaling can be sufficient for smaller applications that do not require the complexity of distributed systems.
• Limitations:
  • Hardware Limits: There is a physical limit to how much a single machine can be scaled vertically.
  • Single Point of Failure: If the machine fails, the entire application may go down.

Horizontal Scaling (Scaling Out):

• Definition: Horizontal scaling involves adding more machines or nodes to a system to handle increased load. It is commonly used in distributed systems.
• Example: Adding more servers to a web farm or adding more nodes to a database cluster.
• Benefits:
  • Scalability: Horizontal scaling can handle much larger loads by distributing the work across multiple machines.
  • Fault Tolerance: By spreading the load across multiple machines, the system can continue to operate even if one or more machines fail.
• Limitations:
  • Complexity: Horizontal scaling requires more complex infrastructure, including load balancing, data replication, and network management.
  • Distributed System Challenges: Maintaining consistency, availability, and partition tolerance becomes more challenging in horizontally scaled systems.

Conclusion:

• Vertical Scaling: Suitable for smaller systems or when simplicity is desired, but limited by hardware constraints.
• Horizontal Scaling: Preferred for large-scale systems that require high availability and fault tolerance but involves more complexity in implementation.

Relational databases and NoSQL databases serve different purposes and have distinct characteristics that make them suitable for different types of applications.

Relational Databases:

• Data Model: Uses a structured schema with tables, rows, and columns. Relationships between tables are established using foreign keys.
• Schema: Schema is predefined and rigid. Any changes to the schema require modifying the database structure.
• ACID Compliance: Relational databases are typically ACID-compliant (Atomicity, Consistency, Isolation, Durability), ensuring data integrity.
• Query Language: Uses SQL (Structured Query Language) for querying and managing data.
• Examples: MySQL, PostgreSQL, Oracle, Microsoft SQL Server.
• Use Cases: Suitable for applications where data integrity and complex querying are critical, such as financial systems, enterprise applications, and CRM systems.

NoSQL Databases:

• Data Model: Uses various data models, including key-value stores, document stores, column-family stores, and graph databases.
• Schema: Schema is flexible and can evolve over time, making it easy to accommodate changes in the data structure.
• Eventual Consistency: NoSQL databases often prioritize availability and partition tolerance over strong consistency, using eventual consistency models.
• Query Language: May use different query languages or APIs depending on the database type, such as MongoDB's query language or Cassandra's CQL.
• Examples: MongoDB (document store), Cassandra (column-family store), Redis (key-value store), Neo4j (graph database).
• Use Cases: Suitable for applications requiring high scalability, handling large volumes of unstructured or semi-structured data, such as social networks, IoT applications, and big data analytics.

Conclusion:

• Relational Databases: Best for structured data with complex relationships and strong consistency requirements.
• NoSQL Databases: Best for unstructured or semi-structured data, high scalability, and flexibility.

A rate-limiting system is designed to control the number of requests a user or client can make to an API within a specific time frame. This helps prevent abuse, such as DDoS attacks or excessive usage that could degrade the service for other users.

Key Components of a Rate-Limiting System:

• Rate-Limit Policy: Defines the maximum number of requests allowed within a specific time window. For example, 100 requests per minute per user.
• Client Identification: Each client (user or IP address) is uniquely identified, often using API keys, tokens, or IP addresses.
• Token Bucket Algorithm: A common rate-limiting algorithm that uses tokens to represent the number of allowed requests. Tokens are added to the bucket at a fixed rate, and each request consumes a token. If the bucket is empty, the request is denied or delayed.
• Sliding Window Algorithm: An alternative algorithm that tracks requests within a sliding time window. If the number of requests within the window exceeds the limit, the request is denied.
• Enforcement: The system tracks the number of requests made by each client and enforces the rate limit by rejecting requests that exceed the limit with an appropriate HTTP status code (e.g., 429 Too Many Requests).
• Logging and Monitoring: Logs and monitors rate-limiting events to detect patterns of abuse or to adjust rate limits based on usage trends.

Considerations:

• Burst Handling: Allow short bursts of traffic without exceeding the rate limit by temporarily exceeding the limit within a smaller time frame (e.g., allowing 200 requests in 30 seconds within a 100 requests per minute limit).
• Distributed Systems: In a distributed system, the rate-limiting logic must be synchronized across all nodes, often using a centralized datastore like Redis to track usage.
• User Experience: Provide users with information about their remaining quota and retry-after headers to indicate when they can make new requests.

Eventual consistency is a consistency model used in distributed systems where updates to a system may not be immediately visible to all nodes, but eventually, all nodes will converge to the same state given enough time.

How Eventual Consistency Works:

• When a write operation is performed, the change is propagated to all replicas, but not necessarily immediately.
• During the propagation period, different nodes may have different views of the data, leading to temporary inconsistencies.
• Eventually, all replicas receive the update, and the system converges to a consistent state.

Trade-offs of Eventual Consistency:

• Advantages:
  • High Availability: Systems using eventual consistency can continue to operate even if some nodes are temporarily unavailable or partitioned.
  • Scalability: Eventual consistency allows for horizontal scaling by distributing data across multiple nodes without requiring immediate synchronization.
  • Performance: Reduces the latency of write operations by not requiring immediate consistency across all nodes, leading to faster response times.
• Disadvantages:
  • Temporary Inconsistency: Users may see stale or outdated data during the period when updates are propagating, which may not be acceptable for certain applications.
  • Complexity in Conflict Resolution: If two nodes accept conflicting updates simultaneously, additional logic is needed to resolve these conflicts, such as versioning or custom resolution rules.
  • Limited Use Cases: Eventual consistency is not suitable for applications that require strong consistency, such as financial transactions or critical systems.

Use Cases: Eventual consistency is often used in distributed databases like DynamoDB, Cassandra, and Riak, where high availability and partition tolerance are prioritized over immediate consistency.

In system design, different types of databases are used based on the application's requirements, such as data structure, scalability, performance, and consistency needs. The main types of databases include:

• Relational Databases (RDBMS):
  • Description: Structured databases that store data in tables with predefined schemas and support complex queries using SQL.
  • Examples: MySQL, PostgreSQL, Oracle, Microsoft SQL Server.
  • Use Cases: Applications that require strong consistency, complex transactions, and structured data relationships, such as financial systems, ERP systems, and content management systems.
• Document Databases:
  • Description: NoSQL databases that store data in semi-structured documents (e.g., JSON, BSON) with flexible schemas.
  • Examples: MongoDB, CouchDB.
  • Use Cases: Applications with unstructured or semi-structured data, such as content management, e-commerce catalogs, and real-time analytics.
• Key-Value Stores:
  • Description: Simple NoSQL databases that store data as key-value pairs, providing fast access to data using unique keys.
  • Examples: Redis, DynamoDB, Riak.
  • Use Cases: Caching, session management, real-time data processing, and applications requiring fast read/write operations.
• Column-Family Stores:
  • Description: NoSQL databases that store data in columns rather than rows, optimized for read and write operations on large datasets.
  • Examples: Apache Cassandra, HBase.
  • Use Cases: Applications requiring high write throughput and scalability, such as time-series data, logging, and big data analytics.
• Graph Databases:
  • Description: Databases designed to store and query graph structures, where data is represented as nodes, edges, and properties.
  • Examples: Neo4j, Amazon Neptune.
  • Use Cases: Applications with complex relationships between entities, such as social networks, recommendation engines, and fraud detection.
• Time-Series Databases:
  • Description: Databases optimized for storing and querying time-series data, where data points are indexed by time.
  • Examples: InfluxDB, TimescaleDB.
  • Use Cases: Applications that require the storage and analysis of time-stamped data, such as IoT monitoring, financial market data, and performance metrics.

Designing a highly available system involves ensuring that the system is resilient to failures and can continue to operate with minimal downtime. The key components and strategies involved in achieving high availability include:

• Redundancy:
  • Description: Deploying multiple instances of critical components (e.g., servers, databases) to avoid single points of failure.
  • Example: Using multiple web servers behind a load balancer or replicating databases across different data centers.
• Load Balancing:
  • Description: Distributing incoming traffic across multiple servers to ensure that no single server becomes a bottleneck.
  • Example: Using a load balancer to route requests to the healthiest and least loaded servers.
• Failover:
  • Description: Automatically switching to a backup system or component when the primary one fails.
  • Example: Implementing database failover using replication and automatic promotion of standby nodes.
• Geographic Distribution:
  • Description: Deploying the system across multiple geographic regions or availability zones to protect against regional failures.
  • Example: Using cloud providers like AWS or Azure to deploy applications across multiple regions.
• Data Replication:
  • Description: Copying data across multiple locations to ensure that it is available even if one location fails.
  • Example: Replicating databases across different data centers using synchronous or asynchronous replication.
• Monitoring and Alerting:
  • Description: Continuously monitoring the system’s health and performance, with alerts configured to notify administrators of potential issues.
  • Example: Using tools like Prometheus, Grafana, or CloudWatch to monitor system metrics and trigger alerts based on predefined thresholds.
• Automated Recovery:
  • Description: Implementing automated recovery mechanisms that can detect failures and trigger actions to restore services.
  • Example: Using auto-scaling groups in AWS to automatically replace failed instances.
• Backup and Restore:
  • Description: Regularly backing up critical data and having a reliable restore process in place to recover from data loss.
  • Example: Implementing automated backups of databases and storing them in a separate, secure location.

Synchronous and asynchronous communication are two fundamental paradigms for how components in a distributed system interact with each other.

Synchronous Communication:

• Definition: In synchronous communication, the sender sends a request and waits for a response before proceeding. The sender and receiver are tightly coupled, and the communication requires both parties to be active and available at the same time.
• Example: A typical HTTP request-response model where a client sends a request to a server and waits for a response before continuing with other tasks.
• Use Cases:
  • Real-time Systems: Applications that require immediate processing of requests, such as online banking transactions or live video streaming.
  • Simple Interactions: Situations where the interaction is simple and quick, and blocking the sender while waiting for a response does not impact performance.
  • Strong Consistency Requirements: Systems that require immediate consistency and cannot tolerate delays in communication.

Asynchronous Communication:

• Definition: In asynchronous communication, the sender sends a request and continues processing without waiting for a response. The sender and receiver are loosely coupled, and the communication does not require both parties to be active simultaneously.
• Example: Message queues like RabbitMQ or Kafka, where a message is sent to a queue and processed by a consumer at a later time.
• Use Cases:
  • Decoupling Components: Asynchronous communication is ideal for decoupling components in a distributed system, allowing each component to operate independently.
  • High Latency Tolerance: Applications that can tolerate delays in processing, such as email systems or background job processing.
  • Scalability: Systems that require high scalability, where components can process requests at their own pace, without being blocked by other components.

Conclusion:

• Synchronous Communication: Suitable for scenarios requiring immediate responses, strong consistency, and real-time processing.
• Asynchronous Communication: Suitable for decoupling components, improving scalability, and handling high-latency or delayed processing scenarios.

Database partitioning is the process of dividing a large database into smaller, more manageable pieces called partitions. Each partition is stored separately, which can improve performance, manageability, and scalability.

Types of Database Partitioning:

• Horizontal Partitioning (Sharding):
  • Description: Divides a table into rows and distributes these rows across multiple partitions or shards. Each shard contains a subset of the table’s rows.
  • Example: A user table could be partitioned by geographical region, where users from Asia are stored in one shard and users from Europe in another.
  • Benefits: Improves query performance by reducing the amount of data each query needs to scan. It also enables horizontal scaling by distributing data across multiple servers.
• Vertical Partitioning:
  • Description: Divides a table into columns and stores different columns in different partitions. This is often used when different columns are accessed with different frequencies.
  • Example: A table with user profiles might store frequently accessed columns (e.g., username, email) in one partition and less frequently accessed columns (e.g., address, bio) in another.
  • Benefits: Improves performance by reducing the amount of data read during queries, especially when only a subset of columns is needed.
• Range Partitioning:
  • Description: Partitions data based on a range of values in a specific column, such as date or numeric ranges.
  • Example: An order table could be partitioned by order date, with one partition for each month.
  • Benefits: Efficient for queries that access a specific range of data, as it reduces the amount of data scanned.
• List Partitioning:
  • Description: Partitions data based on predefined lists of values.
  • Example: A table could be partitioned by product category, with each partition containing products from a specific category.
  • Benefits: Useful when data naturally falls into distinct categories, improving query performance for those specific categories.
• Hash Partitioning:
  • Description: Uses a hash function to determine the partition in which each row is stored. The hash function distributes rows evenly across partitions.
  • Example: A table might use the hash of a user ID to distribute rows evenly across several partitions.
  • Benefits: Ensures an even distribution of data across partitions, preventing hotspots and improving overall system performance.

How Partitioning Improves Performance:

• Parallel Processing: Partitioning allows queries to be processed in parallel, as each partition can be scanned independently, leading to faster query performance.
• Reduced I/O: By narrowing down queries to specific partitions, the amount of data read and processed is reduced, leading to lower I/O operations and faster response times.
• Scalability: Partitioning enables horizontal scaling by distributing data across multiple servers or disks, making it easier to handle large volumes of data.
• Improved Manageability: Partitioning simplifies database management by allowing maintenance operations, such as backups or indexing, to be performed on individual partitions rather than the entire database.

The circuit breaker pattern is a design pattern used in software architecture to improve the resilience and stability of a system. It prevents cascading failures and reduces the load on failing components by short-circuiting the flow of requests when a service or operation is consistently failing.

How the Circuit Breaker Pattern Works:

• Closed State: In the closed state, the circuit breaker allows requests to flow through as usual. It monitors the success and failure of requests.
• Open State: If the failure rate exceeds a certain threshold (e.g., 50% of requests fail), the circuit breaker trips to the open state, and all subsequent requests are immediately failed without attempting to execute the operation.
• Half-Open State: After a certain time, the circuit breaker transitions to the half-open state, allowing a limited number of test requests to pass through. If these requests succeed, the circuit breaker closes again. If they fail, the circuit breaker reopens.

Benefits of the Circuit Breaker Pattern:

• Improved Resilience: By preventing continuous retries on a failing service, the circuit breaker reduces the risk of overwhelming the service and causing further instability.
• Faster Failure Detection: The circuit breaker quickly identifies when a service is down or degraded, allowing the system to take appropriate action, such as falling back to a secondary service or returning an error message.
• Graceful Degradation: The circuit breaker enables graceful degradation by failing fast and providing alternative responses, rather than allowing the system to become completely unresponsive.
• Reduced Latency: When a service is known to be down, the circuit breaker prevents long timeouts by failing requests immediately, reducing overall system latency.

Use Cases: The circuit breaker pattern is commonly used in microservices architectures, distributed systems, and environments where services may be unreliable or subject to temporary outages.

A reverse proxy is a server that sits between client devices and a backend server, forwarding client requests to the backend and returning the server's response to the client. Unlike a forward proxy, which routes outgoing traffic from clients, a reverse proxy manages incoming traffic from clients to servers.

How a Reverse Proxy Works:

• When a client sends a request, the reverse proxy receives it and forwards it to the appropriate backend server.
• The backend server processes the request and sends the response back to the reverse proxy.
• The reverse proxy then returns the response to the client, acting as an intermediary between the client and the backend server.

Common Use Cases for Reverse Proxies:

• Load Balancing: Distributing incoming requests across multiple backend servers to ensure that no single server becomes overloaded.
• Security: Hiding the identity and structure of backend servers from clients, making it harder for attackers to target specific servers.
• SSL Termination: Offloading the SSL/TLS encryption and decryption process from backend servers, improving their performance.
• Web Acceleration: Caching static content, compressing responses, and reducing load times by serving cached content to clients.
• Application Firewall: Protecting backend servers from malicious traffic by filtering out harmful requests before they reach the servers.

Reverse proxies are widely used in web applications, content delivery networks (CDNs), and cloud computing environments to improve performance, security, and scalability.

The Domain Name System (DNS) is a critical component of the internet infrastructure that translates human-readable domain names (e.g., www.example.com) into IP addresses that computers use to identify each other on the network. DNS is essentially the phonebook of the internet, allowing users to access websites and services using easy-to-remember domain names.

Role of DNS in Internet Infrastructure:

• Name Resolution: DNS resolves domain names to their corresponding IP addresses, enabling users to access websites and services without needing to remember numeric IP addresses.
• Redundancy and Reliability: DNS uses a hierarchical structure with multiple layers of servers, providing redundancy and ensuring that domain name resolution remains reliable even if some servers fail.
• Distributed Architecture: DNS is distributed globally, with millions of DNS servers working together to provide fast and reliable name resolution.
• Security: DNSSEC (DNS Security Extensions) adds security to DNS by ensuring that responses to DNS queries are authentic and have not been tampered with.

DNS Load Balancing:

• How It Works: DNS load balancing distributes traffic across multiple servers by returning different IP addresses in response to DNS queries. This allows traffic to be spread evenly across servers, improving performance and availability.
• Round-Robin DNS: A simple form of DNS load balancing where DNS responses rotate through a list of IP addresses, distributing traffic evenly across multiple servers.
• GeoDNS: A more advanced form of DNS load balancing that directs users to the closest or most appropriate server based on their geographic location.
• Weighted DNS: Allows administrators to assign different weights to servers, directing more traffic to higher-capacity servers and less to lower-capacity ones.
• Failover DNS: Redirects traffic to a backup server if the primary server is unavailable, ensuring continuity of service.

DNS load balancing is a key technique used to improve the scalability, performance, and reliability of web applications, cloud services, and content delivery networks.