Advanced Examples

Advanced Topics for System Design

1. Distributed Caching

Definition:

Distributed caching involves storing frequently accessed data in memory across multiple nodes to improve performance and reduce database load.

Key Concepts:

1. Cache Invalidation: Ensuring cached data is updated when the underlying data changes.
2. Consistency: Balancing between strong consistency (e.g., write-through cache) and eventual consistency (e.g., write-back cache).
3. Eviction Policies: Deciding which data to remove when the cache is full (e.g., LRU, LFU).

Use Cases:

• Reducing latency for frequently accessed data (e.g., user sessions, product details).
• Offloading database queries in high-traffic systems.

Tools:

• Redis, Memcached.

Interview Response Script:

Interviewer: “How would you implement distributed caching in a high-traffic system?”

You:
“To implement distributed caching, I’d start by identifying the data that is frequently accessed and can tolerate slight staleness, such as user sessions or product details.
I’d use a distributed cache like Redis or Memcached to store this data in memory across multiple nodes.

Here’s how I’d approach it:

1. Cache Invalidation:

   • For strong consistency, I’d use a write-through cache, where data is written to both the cache and the database simultaneously.
   • For eventual consistency, I’d use a write-back cache, where data is written to the cache first and later synced to the database.

2. Eviction Policies:

   • I’d use an LRU (Least Recently Used) policy to evict the least accessed data when the cache is full.

3. Scalability:

   • The cache would be distributed across multiple nodes to handle high traffic.
   • A consistent hashing algorithm would ensure even distribution of data across nodes.

Trade-offs:

• Write-through caches ensure strong consistency but increase write latency.
• Write-back caches improve write performance but risk data loss if the cache fails.

Tools:

• Redis for distributed caching.

This design ensures the system is performant, scalable, and maintains data consistency.”

2. Data Replication and Consistency

Definition:

Data replication involves creating multiple copies of data across different nodes to improve availability and fault tolerance. Consistency ensures all copies of the data are synchronized.

Key Concepts:

1. Replication Models:
   • Master-Slave: One master handles writes, and slaves handle reads.
   • Multi-Master: Multiple nodes can handle writes.
2. Consistency Models:
   • Strong Consistency: All reads reflect the most recent write.
   • Eventual Consistency: Reads may return stale data initially but will eventually converge.

Use Cases:

• High availability and fault tolerance in distributed systems.
• Reducing read latency by serving data from replicas.

Tools:

• Cassandra, MySQL Replication.

Interview Response Script:

Interviewer: “How would you handle data replication and consistency in a distributed database?”

You:
“To handle data replication and consistency, I’d start by choosing a replication model based on the system’s requirements.

Here’s my approach:

1. Replication Model:

   • For systems with high read traffic, I’d use a master-slave model, where the master handles writes and slaves handle reads.
   • For systems requiring high availability, I’d use a multi-master model, where multiple nodes can handle writes.

2. Consistency Model:

   • For systems requiring strong consistency (e.g., financial systems), I’d ensure all replicas are updated synchronously before acknowledging a write.
   • For systems where eventual consistency is acceptable (e.g., social media), I’d allow replicas to be updated asynchronously.

3. Conflict Resolution:

   • In a multi-master setup, I’d use techniques like vector clocks or last-write-wins to resolve conflicts.

Trade-offs:

• Strong consistency ensures data accuracy but increases latency.
• Eventual consistency improves performance but may return stale data.

Tools:

• Cassandra for multi-master replication.
• MySQL for master-slave replication.

This design ensures the system is highly available, fault-tolerant, and maintains the desired level of consistency.”

3. Rate Limiting and Throttling

Definition:

Rate limiting controls the number of requests a user or service can make within a specific time period. Throttling slows down requests when a limit is reached.

Key Concepts:

1. Algorithms:
   • Token Bucket: Allows a fixed number of requests per time interval.
   • Leaky Bucket: Limits the rate of requests to a fixed rate.
2. Implementation:
   • In-memory counters (e.g., Redis) for distributed systems.
   • Middleware or API gateways for centralized control.

Use Cases:

• Preventing abuse of APIs or services.
• Ensuring fair usage of resources.

Tools:

• NGINX, Redis, AWS API Gateway.

Interview Response Script:

Interviewer: “How would you implement rate limiting in a high-traffic API?”

You:
“To implement rate limiting, I’d start by defining the limits (e.g., 100 requests per minute per user).

Here’s my approach:

1. Algorithm:

   • I’d use the token bucket algorithm, where each user gets a fixed number of tokens per time interval.
   • Each request consumes a token, and requests are rejected when tokens are exhausted.

2. Implementation:

   • For a distributed system, I’d use Redis to store and manage token counts across multiple nodes.
   • For a centralized system, I’d use an API gateway like NGINX or AWS API Gateway to enforce rate limits.

3. Throttling:

   • If a user exceeds the limit, I’d throttle their requests by delaying responses or returning a 429 (Too Many Requests) status code.

Trade-offs:

• Strict rate limiting prevents abuse but may block legitimate users.
• Throttling ensures fair usage but increases latency.

Tools:

• Redis for distributed rate limiting.
• NGINX for centralized rate limiting.

This design ensures the API is protected from abuse while maintaining fair usage.”

4. Distributed Locking

Definition:

Distributed locking ensures that only one process can access a shared resource at a time in a distributed system.

Key Concepts:

1. Implementation:
   • Using a distributed coordination service like ZooKeeper or etcd.
   • Using a distributed database with support for atomic operations (e.g., Redis).
2. Challenges:
   • Deadlocks: Ensuring locks are released properly.
   • Performance: Minimizing the overhead of acquiring and releasing locks.

Use Cases:

• Preventing race conditions in distributed systems.
• Ensuring exclusive access to shared resources (e.g., updating a configuration file).

Tools:

• ZooKeeper, Redis, etcd.

Interview Response Script:

Interviewer: “How would you implement distributed locking in a distributed system?”

You:
“To implement distributed locking, I’d use a distributed coordination service like ZooKeeper or Redis.

Here’s my approach:

1. Lock Acquisition:

   • A process requests a lock by creating an ephemeral node in ZooKeeper or setting a key in Redis.
   • If the lock is available, the process acquires it; otherwise, it waits.

2. Lock Release:

   • The process releases the lock by deleting the node or key.
   • To handle process failures, I’d use timeouts to automatically release locks.

3. Deadlock Prevention:

   • I’d ensure locks are always released, even in case of failures, by using timeouts and monitoring.

Trade-offs:

• Using ZooKeeper ensures strong consistency but adds complexity.
• Using Redis is simpler but may have weaker consistency guarantees.

Tools:

• ZooKeeper for strong consistency.
• Redis for simplicity.

This design ensures exclusive access to shared resources in a distributed system.”

5. Handling Large-Scale Data (MapReduce, BigTable)

Definition:

Handling large-scale data involves processing and storing massive datasets efficiently using distributed systems.

Key Concepts:

1. MapReduce:
   • A programming model for processing large datasets in parallel across a distributed cluster.
   • Consists of two phases: Map (process data) and Reduce (aggregate results).
2. BigTable:
   • A distributed storage system for managing structured data at scale.
   • Uses a sparse, distributed, and persistent multi-dimensional sorted map.

Use Cases:

• Batch processing of large datasets (e.g., log analysis, ETL).
• Storing and querying massive structured data (e.g., web indexing).

Tools:

• Hadoop (MapReduce), Google BigTable, Apache HBase.

Interview Response Script:

Interviewer: “How would you process and store large-scale data in a distributed system?”

You:
“To handle large-scale data, I’d use a combination of MapReduce for processing and BigTable for storage.

Here’s my approach:

1. MapReduce:

   • For batch processing (e.g., log analysis), I’d use the MapReduce model.
   • The Map phase processes data in parallel, and the Reduce phase aggregates the results.
   • I’d use Hadoop to implement MapReduce on a distributed cluster.

2. BigTable:

   • For storing massive structured data (e.g., web indexing), I’d use BigTable or its open-source equivalent, HBase.
   • BigTable organizes data into rows, columns, and timestamps, allowing efficient querying.

3. Scalability:

   • Both MapReduce and BigTable are designed to scale horizontally across thousands of nodes.

Trade-offs:

• MapReduce is efficient for batch processing but not suitable for real-time processing.
• BigTable provides low-latency access but requires careful schema design.

Tools:

• Hadoop for MapReduce.
• HBase for BigTable-like storage.

This design ensures efficient processing and storage of large-scale data in a distributed system.”