讓我們快速了解 PostgreSQL 和 MySQL。這兩者都是重要的開源關係型資料庫管理系統，廣泛應用於不同的應用程式。
PostgreSQL 起源於 1986 年伯克利的 POSTGRES 項目，旨在透過嚴謹的學術研究和嚴格的 SQL 標準來推進資料庫管理系統。這項背景為 PostgreSQL 提供了專注於資料一致性的堅實理論基礎，使其成為複雜查詢和進階資料類型的理想選擇。其設計強調長期穩定性、可擴展性和社區驅動的創新。
相較之下，MySQL 由 Michael Widenius 和 David Axmark 創建於 1995 年，優先考慮實用性和易用性，以滿足快速發展的互聯網應用的需求。它簡化了資料庫管理並提高了效能，在網路繁榮時期迅速成為 Web 開發人員的首選。 MySQL 的重點始終是效能和易於部署。
MySQL的顯著特點是支援多種資料庫引擎，讓使用者可以根據自己的需求選擇最佳的儲存方式。從5.5版本開始，InnoDB一直是預設引擎，支援交易和行級鎖定，以實現高並發和資料一致性。 MyISAM雖然提供了更好的讀取效能，但缺乏事務支持，適合讀密集型場景。 MySQL 也為特定用例提供了 Memory 和 Archive 等引擎。
相較之下，PostgreSQL 使用統一的核心引擎，確保所有功能的一致性和互通性。此設計支援複雜查詢、事務管理和進階資料類型，同時簡化維護。儘管在某些情況下不如 MySQL 靈活，但 PostgreSQL 的內部靈活性和可擴展性透過分區和查詢最佳化等功能得到了增強。
比較分析揭示了數組類型支援、JSON 處理、事務管理、臨時表、視窗函數、遞歸查詢、資料類型豐富性、預設值約束和區分大小寫等方面的差異和相似之處：
| SQL Syntax/Feature | PostgreSQL | MySQL | Description |
|---|---|---|---|
| Array Types | Supported | Not directly supported | PostgreSQL allows direct definition of array type fields. MySQL simulates arrays using strings or other indirect methods. |
| JSON Support | Powerful | More basic | PostgreSQL has advanced JSON support with indexing and optimized queries. MySQL’s JSON support has improved in recent versions but remains simpler. |
| Transaction Handling | Fully ACID | Default auto-commit | PostgreSQL pulls off full ACID compliance by default, ideal for high-consistency scenarios. MySQL defaults to auto-commit for each statement but can be configured for transaction handling. |
| Temporary Tables | Session/Global Scope | Session Only | PostgreSQL allows both session-level and global temporary tables, while MySQL supports only session-level ones. |
| Window Functions | Supported | Supported since later versions | PostgreSQL has long supported window functions; MySQL added full support in more recent versions. |
| CTE (Common Table Expressions) | Supported | Supported | Both support CTE, but advanced usages or performance may vary. |
| Recursive Queries | Supported | Supported since version 8.0 | PostgreSQL has supported recursive queries for a while, while MySQL started in version 8.0. |
| Data Types | More varied (like ARRAY, HSTORE, GIS types) | Basic types are comprehensive | PostgreSQL supports more specialized data types, while MySQL has a good set of basic types but not as diverse as PostgreSQL. |
| Default Value Constraints | Supports any expression | Has many limitations | PostgreSQL allows defaults to be any expression, whereas MySQL’s defaults are usually constants. |
| Case Sensitivity | Configurable | Defaults to case-insensitive | PostgreSQL can configure case sensitivity at the database or column level, while MySQL defaults to case-insensitive unless using binary collation. |
注意：隨著時間的推移，兩個系統都會不斷更新，特定功能的支援和效能可能會改變。選擇資料庫時最好查閱最新的官方文件或發行說明。
| Feature/Database | PostgreSQL | MySQL |
|---|---|---|
| Advanced Data Types | Supports arrays, JSONB, hstore, etc., for complex data structures. | Supports JSON (enhanced in newer versions), but doesn't natively support arrays or hstore, needing indirect methods. |
| Window Functions | Early support for window functions, suitable for a variety of complex data analytics scenarios. | Added window functions in newer versions, progressively improving functionality but might lag in maturity and community resources. |
| Transaction Isolation Levels | Supports READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ, SERIALIZABLE, fully compliant with SQL standards. | Also supports these four isolation levels, but defaults to REPEATABLE READ and implements them via different storage engines (like InnoDB). |
| MVCC Implementation | Strong MVCC mechanism maintains multiple versions for each row, allowing for lock-free reads to enhance concurrency. | InnoDB uses MVCC via Undo Logs to maintain transaction views, optimizing read and write concurrency with its own locking strategies. |
| Locking Mechanism | Supports row-level locking combined with multi-version concurrency control, reducing lock contention and improving concurrency efficiency. | InnoDB supports row-level locking; MyISAM and other engines use table locks. Row-level locking improves concurrency but can be influenced by locking strategies and transaction designs. |
| Feature/Database | PostgreSQL | MySQL |
|---|---|---|
| Benchmarking and Workload | - Excels in complex queries and joins, thanks to rich indexing types and an optimizer. - Good balance for write-heavy and mixed workloads. |
- Performs excellently in read-heavy scenarios, especially simple SELECT queries. - InnoDB engine optimizes read speed and handles concurrency well. |
| Scalability Strategy | - Supports partitioning for large tables to optimize query performance. - Parallel querying enhances large data processing capabilities. - Connection pooling management boosts concurrent processing. |
- Achieves scalability via third-party tools (like PgPool-II, Patroni) for high availability and extensibility. - Sharding is common for horizontally scaling, ideal for large data distribution. - Offers replication (master-slave), group replication for redundancy and separating reads and writes. |
| Horizontal Scalability | - Native support is limited but can implement complex distributed deployments with third-party tools. - Citus extension enables real distributed SQL processing. |
- Has more mature sharding solutions and clustering technologies, making horizontal scalability more flexible, especially for large internet applications. |
| Feature/Database | PostgreSQL | MySQL |
|---|---|---|
| Benchmarking and Workload | - With a powerful query optimizer and various indexing types, excels in complex query handling and analysis. - Balanced reading and writing, suitable for applications needing high-performance writing and complex analysis. - Excels in read-heavy contexts, particularly in simple SELECT queries, suited for web browsing and content distribution scenarios. - Optimizes read performance through read-write separation and caching strategies. |
- Specializes in read-heavy operations for simple SELECT queries, perfect for content management systems and e-commerce platforms, ensuring optimized reading performance. - MySQL supports InnoDB optimizations for read speed and concurrency handling. |
| Scalability Solutions | - Partitions support range, list, hash, and more, boosting large table query efficiency. - Automatically leverages multi-core CPUs for parallel querying, enhancing data retrieval speed. - 内置和第三方连接池管理优化资源使用和响应时间。 - Using extensions like Citus for distributed processing. - Sharding, either manual or automated, disperses storage and processes large datasets to improve read and write performance. - Replication mechanisms (master-slave, group) enhance data availability and reading scalability. |
- InnoDB Cluster provides integrated high availability and scalability solutions that simplify cluster management. |
| Feature/Database | PostgreSQL | MySQL |
|---|---|---|
| User Permission Management | - Fine-grained permission control with role and privilege inheritance, making it easier to manage complex permission structures. - Supports row-level security (RLS) for custom access control rules. - Provides a detailed user and permissions management system, with controls down to the database and table level. |
- Doesn't natively support row-level security but can implement it through application logic. |
| Encryption Features | - Supports SSL/TLS encrypted connections to secure data transmission. - Has field-level encryption plugins to enhance security when data is at rest. - Transparent Data Encryption (TDE) options can be implemented through third-party extensions. |
- Built-in SSL/TLS support protects network communications. - InnoDB storage engine supports table space encryption to secure data files. - MySQL Enterprise Edition offers more advanced encryption options. |
| Compliance Certification | - Complies with multiple security standards, including FIPS 140-2 and Common Criteria. - Supports data protection regulations like GDPR, but specific compliance measures need to be tailored to the environment. |
- Holds several international security certifications like PCI DSS and ISO 27001. - Supports SSL/TLS and TDE, aiding in compliance with regulations like HIPAA and GDPR. - MySQL Enterprise Edition provides enhanced auditing and security functions to strengthen compliance. |
| Database | Suitable Scenarios |
|---|---|
| PostgreSQL | - Data analytics and business intelligence: Strong capabilities for complex queries, window functions, and geospatial data processing. - High compliance industries like finance and healthcare: Robust security and compliance features. - Complex application development: Supports advanced data types and multi-version concurrency, ideal for transaction-heavy applications. |
| MySQL | - Web applications and startups: Lightweight, easy to deploy, rich community resources, quick development cycles. - Read-heavy services: Such as content management systems and e-commerce platforms with optimized read performance. - Cloud-native environments: Deep integration with various cloud providers, suited for quickly scalable internet services. |
| Decision Factor | Considerations | PostgreSQL Tendency | MySQL Tendency |
|---|---|---|---|
| Data Scale and Complexity | Volume of data, query complexity | Large datasets, complex queries, multi-dimensional analysis | Small to medium datasets, simple queries |
| Transaction Processing Needs | Complexity and consistency of transactions | High-concurrency transactions, strict ACID requirements | Simple transaction handling, read/write separation scenarios |
| Budget and Costs | Software licensing, operational costs | Open-source and free, but may require more professional support | Open-source and low cloud service costs |
| Team Familiarity and Skills | Technical stack match, learning curve | Requires strong SQL skills, suited for experienced teams | Friendlier for beginners, lower learning curve |
選擇資料庫時，沒有一刀切的選擇。相反，專注於最適合您需求的內容。在權衡這些因素時，請考慮執行小規模概念驗證 (POC)，以測試特定工作負載下的資料庫效能，然後再做出最終決定。此外，這兩個資料庫系統都在不斷改進並引入新功能，因此了解最新發展對於做出明智的選擇至關重要。
以上是PostgreSQL和MySQL的全面比較的詳細內容。更多資訊請關注PHP中文網其他相關文章！
