This rule provides comprehensive guidance for handling transactions in CockroachDB, focusing on serializable isolation, retry logic, and optimistic concurrency control. CockroachDB uses serializable isolation by default, which requires specific patterns to handle transaction conflicts and retries effectively.

This rule provides comprehensive guidance for setting up CockroachDB in development and production environments. It covers initial cluster configuration, node setup, and essential security configurations to ensure optimal performance and reliability.

This rule provides comprehensive security testing methodologies for CockroachDB deployments, covering authentication vulnerabilities, authorization flaws, network security, data exposure risks, and configuration weaknesses. It includes practical testing scripts, common attack vectors, and defensive measures to ensure robust database security.

This rule provides a comprehensive approach to performance testing CockroachDB by populating tables with large datasets to simulate real-world scenarios with millions of users. It includes data generation strategies, performance monitoring techniques, and bottleneck identification methods.

This rule provides a comprehensive guide for designing and implementing a scalable e-commerce database architecture using CockroachDB. It covers schema design, table relationships, indexing strategies, and performance optimizations specific to e-commerce workloads including high-volume transactions, inventory management, and global distribution.

This rule provides comprehensive guidance for monitoring CockroachDB performance, covering cluster health metrics, query performance analysis, resource utilization tracking, and alerting strategies. It focuses on CockroachDB-specific monitoring approaches using built-in observability features and external tools.

This rule provides comprehensive guidance for designing efficient schemas in CockroachDB's distributed environment. It covers primary key selection, partitioning strategies, index optimization, and data distribution patterns that maximize performance while avoiding common pitfalls like hotspots and range splits.