System Design Reading Notes 5: Design a Key-Value Store

This is my reading notes for Chapter 6 in book “System Design Interview – An insider’s guide (Vol. 1)”.

Overview

Chapter 6 focuses on the design of a key-value store, a type of non-relational database where data is stored as key-value pairs. The chapter walks through the key considerations in designing such a system, including understanding the trade-offs between consistency, availability, and partition tolerance, as well as implementing the necessary components for a scalable and reliable key-value store.

Key Concepts

1. Key-Value Pair:
   • A key-value pair consists of a unique key and its associated value. The key is often a short string or hashed value, while the value can be a string, list, or object.
   • Example: “last_logged_in_at” could be a key with a value of “2024-08-11 10:00:00.”
2. Operations Supported:
   • put(key, value): Inserts the value associated with the key.
   • get(key): Retrieves the value associated with the key.
3. Design Scope:
   • The chapter emphasizes that there is no perfect design; instead, designs must balance trade-offs like read/write efficiency, memory usage, consistency, and availability.
   • The key characteristics of the key-value store designed in this chapter include small size for key-value pairs, ability to store large data, high availability, scalability, automatic scaling, tunable consistency, and low latency.

Single Server Key-Value Store

• A single server key-value store is simple to implement using a hash table, storing key-value pairs in memory for fast access. However, this approach has limitations due to memory constraints.
• Optimizations:
  • Data Compression: Compress data to save space.
  • Memory Management: Store frequently used data in memory while keeping the rest on disk.

Distributed Key-Value Store

• To support large-scale applications, a distributed key-value store, also known as a distributed hash table (DHT), is necessary.
• CAP Theorem:
  • The CAP theorem states that in a distributed system, it is impossible to achieve all three: consistency, availability, and partition tolerance simultaneously.
  • Consistency: All clients see the same data.
  • Availability: The system remains operational even during some failures.
  • Partition Tolerance: The system continues to function despite network partitions.
  • Key-value stores are typically designed to prioritize two of these properties, leading to classifications like CP (Consistency and Partition Tolerance) or AP (Availability and Partition Tolerance) systems.

System Components

1. Data Partitioning:
   • Consistent Hashing: A technique used to evenly distribute data across multiple servers and minimize data movement when servers are added or removed.
   • Servers are placed on a hash ring, and keys are assigned to servers based on their position on this ring.
2. Data Replication:
   • Data is replicated across multiple servers to ensure high availability and reliability.
   • The number of replicas (N) and the quorum sizes for reads (R) and writes (W) are configured based on the desired trade-off between consistency and latency.
3. Consistency Models:
   • Strong Consistency: Guarantees that every read returns the most recent write.
   • Weak Consistency: Allows for reads that might return stale data.
   • Eventual Consistency: Ensures that, given enough time, all replicas will converge to the same value.
   • Dynamo and Cassandra, popular key-value stores, use eventual consistency.
4. Inconsistency Resolution:
   • Versioning: Each modification is treated as a new version of data, helping to detect and resolve conflicts.
   • Vector Clocks: A technique used to track the version history of each key-value pair across different replicas.
5. Failure Handling:
   • Merkle Trees: Used for efficient synchronization of data across replicas by comparing hash trees of data.
   • Data Center Outage: Data is replicated across multiple data centers to ensure availability even during outages.

System Architecture

• Coordinator Node: Acts as a proxy between clients and the key-value store, managing read and write requests.
• Decentralized Architecture: Ensures there is no single point of failure, with every node having similar responsibilities.

Write Path

1. The write request is first persisted in a commit log file.
2. Data is then saved in a memory cache.
3. When the memory cache reaches a threshold, data is flushed to a sorted-string table (SSTable) on disk.

Read Path

1. Data is first checked in memory; if not found, the system checks the Bloom filter.
2. If the Bloom filter suggests a possible match, the SSTables are queried to retrieve the data.

Summary of Techniques

• Data Partitioning: Consistent Hashing
• Data Replication: Replication factor (N), Quorum consensus (R and W)
• Consistency: Eventual consistency
• Failure Handling: Merkle Trees, Vector Clocks

This chapter provides a comprehensive guide to designing a scalable and reliable key-value store, covering essential concepts like the CAP theorem, data partitioning and replication, consistency models, and failure handling mechanisms. The chapter also references real-world systems like Dynamo, Cassandra, and BigTable, providing practical insights into how these concepts are applied in large-scale distributed systems .

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Detailed Reading Notes and Summary for “Chapter 6: Design a Key-Value Store” from System Design Interview: An Insider’s Guide by Alex Xu

Introduction

Chapter 6 delves into designing a key-value store, a fundamental building block in distributed systems. Key-value stores are pivotal for applications requiring low-latency data access, high availability, and scalability. The chapter explores various aspects of key-value store design, from basic concepts to advanced techniques used in large-scale distributed systems.

Key Concepts and Basic Operations

1. Key-Value Pair:
   • A key-value store uses a simple yet powerful structure where data is stored as pairs of keys and values. The key is unique and can be used to retrieve the associated value efficiently.
   • Example:
     • Key: "user_12345"
     • Value: {"name": "John Doe", "email": "john.doe@example.com", "last_login": "2024-08-11T10:00:00Z"}
2. Supported Operations:
   • Put (key, value): This operation inserts or updates the value associated with the key.
     • Example: put("user_12345", {"name": "John Doe", "email": "john.doe@example.com"}) inserts the user information.
   • Get (key): This operation retrieves the value associated with the key.
     • Example: get("user_12345") returns {"name": "John Doe", "email": "john.doe@example.com"}.

Design Scope and Considerations

1. Design Requirements:
   • The key-value store should be capable of handling millions of read and write requests per second while ensuring high availability, low latency, and scalability.
   • Scalability: The system should be able to scale horizontally by adding more nodes.
   • Consistency: The design should balance between consistency, availability, and partition tolerance (CAP theorem).
2. Trade-offs in Design:
   • Consistency vs. Availability: In the face of network partitions, a trade-off must be made between consistency (all nodes see the same data) and availability (the system is always available).
   • Example: Dynamo prioritizes availability and partition tolerance over strict consistency, leading to eventual consistency.

Single Server Key-Value Store

1. In-Memory Hash Table:
   • A simple implementation uses a hash table where keys are hashed to determine the location of their corresponding values in memory. This allows for constant time complexity (O(1)) for both put and get operations.
   • Limitations: The in-memory approach is constrained by the amount of available memory and lacks durability (data is lost on server failure).
2. Optimizations:
   • Data Compression: Compressing values can save memory space, but this introduces additional computational overhead during compression and decompression.
   • Memory Management: Implementing a Least Recently Used (LRU) cache can keep frequently accessed data in memory, while less frequently accessed data is persisted to disk.

Distributed Key-Value Store

1. Motivation for Distribution:
   • To handle large datasets and provide high availability, the key-value store must be distributed across multiple servers. This distribution introduces challenges such as data partitioning, replication, and ensuring consistency across nodes.
2. CAP Theorem:
   • The CAP theorem states that a distributed system can only provide two out of three guarantees: Consistency, Availability, and Partition Tolerance. This leads to different design strategies:
     • CP Systems: Prioritize Consistency and Partition Tolerance (e.g., HBase).
     • AP Systems: Prioritize Availability and Partition Tolerance, accepting eventual consistency (e.g., Cassandra, Dynamo).

System Components and Techniques

1. Data Partitioning:
   • Consistent Hashing: Used to evenly distribute data across nodes. Consistent hashing minimizes data movement when nodes are added or removed, making the system more scalable and fault-tolerant.
   • Example: In a consistent hashing ring, servers (nodes) and keys are both hashed to a position on the ring. A key is assigned to the first server encountered as you move clockwise around the ring.
2. Data Replication:
   • To ensure availability and durability, data is replicated across multiple nodes.
   • Replication Factor (N): The number of copies of each piece of data.
   • Quorum Mechanism: Used to achieve a balance between consistency and availability.
     • R (Read Quorum): The minimum number of nodes that must respond to a read request.
     • W (Write Quorum): The minimum number of nodes that must acknowledge a write.
   • Example: If N = 3, R = 2, and W = 2, the system can tolerate one node failure while still ensuring data consistency.
3. Consistency Models:
   • Strong Consistency: Guarantees that every read reflects the latest write.
     • Example: In a banking system, account balance reads must always reflect the latest transaction.
   • Eventual Consistency: Guarantees that, given enough time, all replicas will converge to the same value.
     • Example: In a social media platform, a user’s post might not immediately appear to all followers but will eventually be consistent across all replicas.
4. Inconsistency Resolution:
   • Versioning: Each write operation creates a new version of the data, allowing the system to detect and resolve conflicts.
     • Example: If two versions of a user profile exist, the system might merge them or select the latest based on a timestamp.
   • Vector Clocks: Used to track the causal relationships between different versions of a key’s value, helping to resolve conflicts by understanding the order of operations.
5. Failure Handling:
   • Merkle Trees: Data structures that allow efficient comparison and synchronization of data across replicas by comparing hash trees of data segments.
     • Example: Used in Cassandra to detect and repair inconsistencies between replicas.
   • Data Center Outage: Data is replicated across multiple geographically distributed data centers to ensure availability even if one data center fails.

System Architecture

1. Coordinator Node:
   • In systems like Dynamo, a coordinator node is responsible for managing read and write requests, ensuring that the appropriate replicas are accessed or updated.
2. Decentralized Architecture:
   • Nodes in the system are designed to have equal responsibilities, eliminating single points of failure and enabling the system to scale horizontally.

Write Path

1. Commit Log:
   • When a write request is received, it is first persisted in a commit log (write-ahead log) to ensure durability.
   • Example: A new user registration is logged before being written to the in-memory database.
2. Memory Cache:
   • After being logged, the data is stored in an in-memory cache for fast access.
   • Example: User session data is cached in memory for quick retrieval.
3. SSTables:
   • Once the in-memory cache reaches a certain threshold, the data is flushed to disk in the form of Sorted String Tables (SSTables), which are immutable and can be merged efficiently.

Read Path

1. Memory Lookup:
   • The system first checks if the data is in memory. If found, it is returned directly.
   • Example: If a user profile is cached, it’s returned immediately.
2. Bloom Filter:
   • If the data is not in memory, a Bloom filter (a probabilistic data structure) is checked to quickly determine if the data might be in the SSTables on disk.
   • Example: Before searching SSTables, the Bloom filter can suggest whether the data is likely present, reducing unnecessary disk I/O.
3. SSTable Query:
   • If the Bloom filter indicates a possible match, the relevant SSTables are queried to retrieve the data.
   • Example: The system searches the disk-based SSTables if the data is not found in memory.

Real-World Examples

1. Dynamo (Amazon):
   • Dynamo uses consistent hashing for data distribution, a quorum mechanism for consistency, and vector clocks for conflict resolution. It is designed to prioritize availability and partition tolerance.
2. Cassandra (Apache):
   • Cassandra, inspired by Dynamo, is an AP system that supports eventual consistency, tunable consistency levels, and uses SSTables for efficient storage and retrieval of data.
3. BigTable (Google):
   • BigTable uses a distributed architecture, supports strong consistency, and employs SSTables and a hierarchical structure for managing large-scale data across multiple nodes.

Conclusion

Chapter 6 provides a thorough understanding of the design principles behind key-value stores, balancing the need for high availability, scalability, and consistency. By exploring real-world systems like Dynamo, Cassandra, and BigTable, the chapter illustrates how these concepts are applied in practice, offering valuable insights for designing robust and scalable key-value stores in distributed environments.