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Problems

Designing a High-Availability Database System

You are tasked with designing a database system that can withstand failures and continue to operate with minimal downtime. This is crucial for applications where data availability is paramount, such as e-commerce platforms, financial services, or critical infrastructure monitoring. The goal is to ensure that if one database instance fails, another can seamlessly take over, preventing data loss and service interruption.

Problem Description

Your objective is to design a SQL database schema and outline the architectural considerations for a high-availability setup. This involves planning for redundancy, failover mechanisms, and data consistency across multiple database instances. You need to think about how to ensure that data is replicated effectively and that the system can automatically detect and respond to failures.

Key Requirements:

  1. Redundancy: The system must have at least two identical copies of the database.
  2. Automatic Failover: If the primary database instance becomes unavailable, a secondary instance must automatically assume the role of the primary.
  3. Data Consistency: Data written to the primary database must be reflected in the secondary database(s) with minimal lag.
  4. Minimal Downtime: The transition during a failover should be as seamless as possible, with minimal or no interruption to read and write operations.
  5. Read Scalability (Optional but Recommended): The design should ideally support the ability to scale read operations across multiple secondary instances.

Expected Behavior:

  • In a normal operational state, all write operations are directed to the primary database. Read operations can be directed to the primary or one or more replicas.
  • Upon failure of the primary database (e.g., server crash, network partition), the system automatically promotes one of the secondary databases to become the new primary.
  • Applications should be able to continue performing read and write operations against the new primary database with minimal disruption.
  • The system should be resilient to temporary network glitches between database instances.

Edge Cases to Consider:

  • Split-Brain Scenario: What happens if the network partitions and the primary and secondary instances cannot communicate? How do you prevent both from thinking they are the primary?
  • Data Loss during Failover: How can data loss be minimized during a failover, especially if writes are in transit?
  • Rejoining a Failed Node: What is the process for bringing a failed database instance back online and reintegrating it into the cluster?
  • Concurrent Writes during Failover: How are concurrent write operations handled when a failover is in progress?

Examples

Example 1: Basic Replication Setup

  • Scenario: A single primary database server (DB-Primary) and a single replica database server (DB-Replica).
  • Input:
    • A users table: CREATE TABLE users (user_id INT PRIMARY KEY, username VARCHAR(50), email VARCHAR(100));
    • A products table: CREATE TABLE products (product_id INT PRIMARY KEY, product_name VARCHAR(100), price DECIMAL(10, 2));
    • A transaction: INSERT INTO users (user_id, username, email) VALUES (1, 'alice', 'alice@example.com');
  • Expected Output (after transaction and successful replication):
    • DB-Primary: Contains the new user.
    • DB-Replica: Also contains the new user.
  • Explanation: The write operation on DB-Primary is asynchronously or synchronously replicated to DB-Replica.

Example 2: Failover Scenario

  • Scenario: DB-Primary (active) and DB-Replica (standby). DB-Primary suddenly becomes unresponsive.
  • Input:
    • Application attempts to write: INSERT INTO products (product_id, product_name, price) VALUES (101, 'Laptop', 1200.00);
    • Monitoring system detects DB-Primary failure.
  • Expected Output:
    • DB-Replica is promoted to become the new DB-Primary.
    • The application can now successfully execute write operations against the new DB-Primary (which was DB-Replica).
    • If the products write was in transit or committed just before failure, the failover mechanism should ensure it is present on the new primary or handled gracefully.
  • Explanation: A failover process is initiated. A mechanism (e.g., quorum-based consensus, heartbeat monitoring) selects DB-Replica as the new primary. The application's connection string might be updated, or a load balancer/proxy redirects traffic to the new primary.

Example 3: Read Scaling with Multiple Replicas

  • Scenario: One primary DB-Primary and two replicas, DB-Replica-1 and DB-Replica-2.
  • Input:
    • Application needs to read a list of all users.
    • A read-only query: SELECT user_id, username FROM users;
  • Expected Output:
    • The read query can be distributed across DB-Replica-1 and DB-Replica-2, or directed to a load balancer that routes it to one of them.
    • DB-Primary remains available for writes.
  • Explanation: This demonstrates how read traffic can be offloaded to replicas, improving overall system performance and availability for read-heavy workloads.

Constraints

  • The system must support at least two database instances.
  • The database schema must be defined using standard SQL data types and constructs.
  • The solution should focus on the logical design and mechanisms for high availability, not on specific vendor implementations unless explicitly stated as a learning tool.
  • The latency for data replication should be within an acceptable threshold, typically measured in milliseconds or seconds, depending on the criticality.
  • The failover process should ideally complete within 60 seconds.

Notes

  • Consider different replication strategies: synchronous vs. asynchronous replication, and their trade-offs regarding consistency, performance, and complexity.
  • Think about how to manage database schemas and schema changes in a multi-instance environment.
  • Explore the role of quorum and consensus algorithms in preventing split-brain scenarios.
  • Familiarize yourself with common high-availability patterns like Primary/Replica, Multi-Primary (with caveats), and clustering.
  • The solution should involve describing the SQL schema and the logic of the high-availability mechanisms, not writing executable code for a specific database system. Pseudocode is acceptable for describing processes.
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