Distributed Key-Value Store: Requirement Gathering

What are the questions I need to ask the interviewer if he asks me to design a distributed key value store?

Designing a distributed key-value store is a complex task that requires understanding various requirements and constraints. Here are some important questions you should ask to clarify the problem:

Data Characteristics:

• What is the nature of the data being stored? Is it structured or unstructured data?
• What is the expected data size? Are they small objects or large blobs of data?
• How is the data being written? Are we dealing with write-heavy, read-heavy, or balanced workloads?

Scale & Performance:

• What is the expected scale in terms of data volume and request rate?
• What is the read to write ratio? This will impact the caching strategy and database choice.
• What are the latency requirements for read and write operations?

Durability & Consistency:

• What are the durability guarantees required? Do we need to ensure that once the data is written, it will never be lost?
• What level of consistency is required? Do we need strong consistency or can we tolerate eventual consistency?

Distribution & Replication:

• How will the data be distributed? Will it be sharded across multiple nodes?
• How will the system handle replication and ensuring data redundancy?

Fault Tolerance & High Availability:

• What are the requirements for fault tolerance? Can the system afford to lose data due to node failures?
• How will the system ensure high availability? Will there be automatic failover mechanisms in place?

Security:

• What are the security requirements? Does the data need to be encrypted? Who will have access to the data?

Evolution:

• How might the system’s requirements evolve in the future? Is the system designed to be easily scalable or adaptable to changing needs?

Why we need to ask questions on Data Characteristics? Will it impact the system design of Service?

Yes! Understanding the characteristics of the data is crucial in designing any system, especially a distributed key-value store. The nature of the data influences many aspects of system design:

Nature of the data (structured/unstructured): Different data types may influence the choice of database. For instance, structured data can be efficiently stored in relational databases (like MySQL) or key-value stores with some structure support (like DynamoDB or Google’s BigTable), while unstructured data might be more suited for a NoSQL document store (like MongoDB) or a simple key-value store.

Data size: The size of the data has implications on the storage system, network transfer costs, and the strategy used for data partitioning or sharding. Larger data size might require more efficient compression techniques and possibly more storage nodes.

Data access patterns (read-heavy, write-heavy, balanced): If the system is read-heavy, a caching layer might be required to speed up read operations. If it’s write-heavy, we need to make sure our system can handle high write throughput, which might imply using techniques like write-behind caching or buffering, log-structured merge-trees (LSM trees), etc.

Update frequency: If data is frequently updated, we need to consider the implications on system consistency, concurrency control, and cache invalidation strategies.

Temporal characteristics (time-series data, etc.): Certain types of data, like time-series data, may require special considerations in storage and indexing to ensure efficient queries.

Value distribution: If there is a high variance in the popularity of the data (known as skew), some parts of your system might be more heavily loaded than others. Techniques such as consistent hashing can help distribute load more evenly.

Why we need to ask question on Distribution & Replication?

In a real-world system design scenario, you’re not always starting from scratch. The context and constraints of the existing system often heavily influence your design decisions. Here are some reasons why understanding the requirements for distribution and replication is crucial:

Degree of Distribution: Understanding how distributed the system needs to be is crucial for your design. If data needs to be accessible from different geographical locations, then the design should probably include geographically distributed data centers. This impacts latency, cost, and system complexity.

Replication Strategy: The choice of replication strategy can have a significant impact on system performance, cost, and complexity. For example, if strong consistency is required, you might choose synchronous replication, but this could impact write performance. If eventual consistency is acceptable, asynchronous replication could be a better choice as it can offer better performance and availability.

Failover Strategy: The approach to handle failures or downtime of part of the system can influence the choice of replication strategy. If the system needs to be highly available, you might need to design for automatic failover, which can involve more complexity and infrastructure cost.

Data Locality Requirements: If the application has data locality requirements (e.g., due to data sovereignty regulations or latency considerations), this could influence the distribution and replication strategy.

By asking these questions to the interviewer, you’re trying to understand the requirements and constraints that will shape your design. This approach also demonstrates to the interviewer that you’re considering the practical implications and trade-offs of different design choices, which is a key aspect of effective system design.

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