Read Heavy vs Write Heavy Systems PDF

! 222 Read Heavy vs Write Heavy System ** Designing systems for read-heavy versus write-heavy workloads involves different ** strategies, as each type...

! 222 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. ***