Your distributed system is facing a surge in data volume. How will you manage the strain on storage capacity?

Data volume spike typically happens in write heavy systems, for such systems always consider having buffer capacity, and consider having a pipeline in front of your data store which acts as buffer, with this data loss won't happen and you'll have enough time to scale the data store, also you can consider having autoscaling at workers based on data store performance.

We could also focus on sharding the database across the distributed network to reduce the strain on each node. In higher contention systems, this could free up memory usage by 50-60%. Including write buffers and streams to write data to cloud backup allows for on-time upscaling as needed. Here, predicting the future network load and adjusting upscaling as necessary would make the system fault-tolerant. Sharding + On-demand scaling + effective prediction of rise in network usage can help us to decide on provisioning more nodes to service the requests and increase system availability.

When designing a system that can handle spikes in storage, it's essential to use a reliable platform from trusted service providers like AWS or Azure. It's important to balance the load between read-heavy and write-heavy parts of the system. For instance, in the design of a data lake with multiple ETL processes ingesting, cleaning, and aggregating data for analysis, it's crucial to minimize data duplication and consider denormalization to manage data volume effectively.

One critical aspect of achieving storage scalability is implementing a smart caching strategy. Speaking of caching, Redis being the no-brainer option, implementing an in-memory cache to hold data per backend service thus decreasing call frequency to Redis and DB servers can prove to be a smart choice. With smart synchronization of cache invalidation by adopting RabbiqMQ to inform other services to invalidate cache instances we can achieve a form of multi-instance data cache per service that can render you not only resource-effective but cost-effective as well.

Compression is CPU intensive, varying compression levels impacts CPU but it optimizes storage, hence it's worthwhile to consider this trade-off while designing the system. Moving storage to cheaper storage is also a great option consider that data is less frequently accessed, most of the times read-only kind of data with very less read requests can be moved to this, so for Tiered storage consider frequency of accessing this data before moving.

The Data has to be stored in a different type of database depending on the type and usage in real-time. Cloud providers offer Cache, Data lakes, data warehouses, cold storage, document type storage, Graph DB, Timestream DB, Ledger database, and many more. These can be a great way to separate highly used and less likely used data. Understanding the business point of view and how this data could help us fetch the insights would also give a different perspective in storing the data in the specific type of database and Agreeing upon data retrieval rates and latencies helps. This could lead to a highly available and cost-effective data storage method.

This redirects to Operating System basics where Cache maintenance needs to be at the top of the pivot. Similarly, in System design, we need to identify what data is needed in the cache based on the data model. Eviction strategies are something that maintains the overall performance of the system by maintaining the cache size without exponentially increasing.

Database sharding improves scalability, performance, data storage utilization, availability, and thereby increasing copies throughout the network without overloading the system with backups. While implementing sharding in a system, we need to consider how our data model is and how the usage is throughout the network. Sharding strategies, balancing principles, and Data integrity policies are a must to ensure consistency in the system without faults and complexity in cross-shard requests.