You've built an amazing app and it's ready to scale for millions of people all over the world. You're ready to collect lots of data from the people and utilize it to your fullest potential. But how will you store it all? Maybe you should consider a key value store. What's that?
According to "System Design Interview, an Insiders Guide" by Alex Xu.
A key-value store is a data storage system that stores data as a collection of key-value pairs, where each value is associated with a unique key.
Ok great! But how do you make one? Let's look at a (brief and watered down) overview of designing a key value store.
The following contains some system design concepts you should consider when designing your first key-value store:
Firstly, this type of data store is commonly used in many real world applications, such as caching systems, session stores, and distributed systems. You've already got an app that needs to store a lot of data. But what is the best way to distribute that data after the fact. The answer is, it depends. But before you make a final decision it's time to stop and consider CAP Theorem. Have you ever heard of CAP? 🧢 Of course it's not the cap you put on your head!
CAP stands for: consistency, availability, and partition-tolerance. In a nutshell the theory suggests that it is impossible for a database to maintain more than two of these concepts at a time. Click Here for an article with an in depth overview on the subject.
When designing a key-value store, you need to consider the trade-offs between consistency and availability. Consistency ensures that all nodes in the system have the same view of the data, while availability ensures that the system is always up and running. You should choose a consistency model that meets the needs of your application, such as eventual consistency or strong consistency. You will need to make a decision about the most important characteristics of your key value store. This means you'll need to select the right CAP to maintain your system accordingly.
Larger jobs require bigger storage. If your application requires more than one server you'll need to partition the data and store it on multiple servers. Depending on the size of your data set you may need to partition your data across multiple nodes or servers. Data partitioning is the process of dividing your data set into smaller subsets and distributing them across multiple machines. You should choose a partitioning scheme that allows for efficient data access and minimizes data movement. Consistent hashing with virtual nodes may be a good solution for your application.
You'll also need to decide the data model when designing your key-value store. You should determine what data you need to store and how you will store it. This is VERY important. For example, you might store strings, integers, or JSON objects as values. You may also need to consider how to handle large objects or values that are too large to fit in memory.
The storage engine is responsible for storing and retrieving data from the key-value store. You should choose a storage engine that can efficiently handle your data model and scale to meet your needs. Popular storage engines for key-value stores include LevelDB, RocksDB, and Redis.
To ensure data availability and durability, you may need to replicate your data across multiple nodes or servers. Replication ensures that if one node fails, the data can still be accessed from another node. You should choose a replication scheme that balances data consistency, availability, and performance. This system should also take accountability of handling failures into account. Failures can be minimized, but not avoided entirely (better luck next time).
Caching can help improve the performance of your key-value store by reducing the number of disk reads or network requests. You should consider implementing caching at different levels of the system, such as at the client or server level, depending on your performance requirements.
Finally, you should consider how you will monitor and manage your key-value store. You should implement tools to monitor system health, track performance metrics, and manage backups and failover procedures.
Building The System Architecture:
A key-value store is a type of database that stores data as a collection of key-value pairs. The keys are used to uniquely identify each value in the store, and the values are the actual data that is being stored. This concept may seem familiar if you know what "key value" pairs are.
Consider this, In a key-value store system architecture, there are typically three main components:
Client: The client is the user or application that wants to store or retrieve data from the key-value store. It communicates with the key-value store through a set of APIs provided by the store.
Key-Value Store: This is the main component of the system architecture that stores the key-value pairs. It receives requests from the client and processes them accordingly. The key-value store can be implemented using various storage technologies, such as in-memory, disk-based storage, or a distributed file system.
Data Access Layer: This layer is responsible for managing the storage and retrieval of data in the key-value store. It provides an interface for the key-value store to interact with the underlying storage technology.
In addition to these three components, remember to consider the other components such as load balancers, caching layers, and replication mechanisms that can help improve the performance and availability of your key-value store.
By considering all of these concepts, you can create a key-value store that meets the performance, scalability, and availability requirements of your application. That's it. If you have any questions or if you want to have a conversation about this concept. Leave me a comment below.
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Author: 👩🏽💻 Kodebae
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Top comments (4)
Hey thanks for sharing, if you are interested also 'Building Microservices', by Sam Newman got some great insights on how and where to use key-value stores as a data store in microservices. For example a microservice can use a key-value store for cache, a relational database for structured data, and event sourcing for auditing.
Can you share a link here to what you're referring to? I'd be interested in checking it out. :)
Sure , it from O'Reilly publications, samnewman.io/books/building_micros...
Thank you Krlz 😁