Read Heavy vs Write Heavy System PDF

Course Discussions Read Heavy vs Write Heavy System Designing systems for read-heavy versus write-heavy workloads involves different strategies, as each type of system has unique demands and challenges. Designing for Read-Heavy Systems Read-heavy systems are characterized by a h...

Course Discussions Read Heavy vs Write Heavy System Designing systems for read-heavy versus write-heavy workloads involves different strategies, as each type of system has unique demands and challenges. Designing for Read-Heavy Systems Read-heavy systems are characterized by a high volume of read operations compared to writes. Common in scenarios like content delivery networks, reporting systems, or read-intensive APIs. Key Strategies 1. Caching: Implement extensive caching mechanisms to reduce database read operations. Technologies like Redis or Memcached can be used to cache frequent queries or results. Cache at different levels (application level, database level, or using a dedicated caching service). Example: A news website experiences high traffic with users frequently accessing the same articles. Implementing a caching layer using a technology like Redis or Memcached stores the most accessed articles in  memory. When a user requests an article, the system first checks the cache. If the article is there, it’s served directly from the cache, significantly reducing database read operations. 2. Database Replication: Use database replication to create read replicas of the primary database. Read operations are distributed across these replicas, while write operations are directed to the primary database. Ensure eventual consistency between the primary database and the replicas. Example: An e-commerce platform uses a primary database for all transactions. To optimize for read operations (like browsing products), it replicates its database across multiple read replicas. User queries for product information are handled by these replicas, distributing the load and preserving the primary database for write operations. 3. Content Delivery Network (CDN): Use CDNs to cache static content geographically closer to users, reducing latency and offloading traffic from the origin server. Example: An online content provider uses a CDN to store static assets like images, videos, and CSS files. When a user accesses this content, it is delivered from the nearest CDN node rather than the origin server, enhancing speed and efficiency. 4. Load Balancing: Employ load balancers to distribute incoming read requests evenly across multiple servers or replicas.  Example: A cloud-based application service uses a load balancer to distribute user requests across a cluster of servers, each capable of handling read operations. This setup ensures that no single server becomes a performance bottleneck. 5. Optimized Data Retrieval: Design efficient data access patterns and optimize queries for read operations. Use data indexing to speed up searches and retrievals. Example: An analytics dashboard that aggregates data for reports optimizes its SQL queries to fetch only relevant data, use proper indexes, and avoid costly join operations whenever possible. 6. Data Partitioning: Partition data to distribute the load across different servers or databases (sharding or horizontal partitioning). Example: A social media platform with millions of users implements database sharding. User data is partitioned based on user IDs or geographic location, allowing read queries to be directed to specific shards, thus reducing the read load on any single database server. 7. Asynchronous Processing Use asynchronous processing for operations that don’t need to be done in real-time. Example: A financial application performs complex data aggregation and reporting. It uses asynchronous processing to pre-compute and store these reports, which can then be quickly retrieved on demand. Designing for Write-Heavy Systems Write-heavy systems are characterized by a high volume of write operations, such as logging systems, real-time data collection systems, or transactional databases. Key Strategies 1. Database Optimization for Writes: Choose a database optimized for high write throughput (like NoSQL databases: Cassandra, MongoDB). Optimize database schema and indexes to improve write performance. Example: For a real-time analytics system, using a NoSQL database like Cassandra, which is optimized for high write throughput, can be more effective than a traditional SQL database. Cassandra's distributed architecture allows it to handle large write volumes efficiently. 2. Write Batching and Buffering: Batch multiple write operations together to reduce the number of write requests. Example: In a logging system where numerous log entries are generated every second, instead of writing each entry to the database individually, the system batches multiple log entries together and writes them in a single transaction, reducing the overhead of database writes. 3. Asynchronous Processing: Handle write operations asynchronously, allowing the application to  continue processing without waiting for the write operation to complete. Example: A video sharing platform like YouTube processes user uploaded videos asynchronously. When a video is uploaded, it's added to a queue, and the user receives an immediate confirmation. The video processing, including encoding and thumbnails generations, happens in the background. 4. CQRS (Command Query Responsibility Segregation) Separate the write (command) and read (query) operations into different models. Example: In a financial system, transaction processing (writes) is handled separately from account balance inquiries (reads). This separation allows optimizing the write model for transactional integrity and the read model for performance. 5. Data Partitioning: Use sharding or partitioning to distribute write operations across different database instances or servers. Example: A social media application uses sharding to distribute user data across multiple databases based on user IDs. When new posts are created, they are written to the shard corresponding to the user's ID, distributing the write load across the database infrastructure. 6. Use of Write-Ahead Logging (WAL) First write changes to a log before applying them to the database. This ensures data integrity and improves write performance. Example: A database management system uses WAL to handle  transactions. Changes are first written to a log file, ensuring that in case of a crash, the database can recover and apply missing writes, thus maintaining data integrity. 7. Event Sourcing: Persist changes as a sequence of immutable events rather than modifying the database state directly. Example: In an order management system, instead of updating an order record directly, each change (like order placed, order shipped) is stored as a separate event. This stream of events can be processed and persisted efficiently and replayed to reconstruct the order state. Conclusion Read-heavy systems benefit significantly from caching and data replication to reduce database read operations and latency. Write-heavy systems, on the other hand, require optimized database writes, effective data distribution, and asynchronous processing to handle high volumes of write operations efficiently. The choice of technologies and architecture patterns should align with the specific demands of the workload. Next Previous System Design Interviews - A Token Bucket Vs Leaky Bucket Step By Step Guide Mark as Completed