Perbandingan komprehensif PostgreSQL dan MySQL

pengenalan

Mari kita lihat dengan pantas pada PostgreSQL dan MySQL. Kedua-dua ini ialah sistem pengurusan pangkalan data hubungan sumber terbuka yang penting yang digunakan secara meluas merentas aplikasi yang berbeza.

Seni Bina Teknikal dan Falsafah Reka Bentuk

Asal usul dan Perkembangan

PostgreSQL berasal daripada projek POSTGRES di Berkeley pada tahun 1986, yang bertujuan untuk memajukan sistem pengurusan pangkalan data melalui penyelidikan akademik yang ketat dan piawaian SQL yang ketat. Latar belakang ini menyediakan PostgreSQL dengan asas teori yang kukuh yang memfokuskan pada ketekalan data, menjadikannya sesuai untuk pertanyaan kompleks dan jenis data lanjutan. Reka bentuknya menekankan kestabilan jangka panjang, skalabiliti dan inovasi dipacu komuniti.

Sebaliknya, MySQL telah dicipta pada tahun 1995 oleh Michael Widenius dan David Axmark, mengutamakan kepraktisan dan kemudahan penggunaan untuk memenuhi keperluan aplikasi internet yang pesat membangun. Ia memudahkan pengurusan pangkalan data dan meningkatkan prestasi, dengan cepat menjadi pilihan pilihan untuk pembangun web semasa ledakan internet. Fokus MySQL secara konsisten adalah pada prestasi dan kemudahan penggunaan.

Enjin Pangkalan Data

Ciri ketara MySQL ialah sokongannya untuk berbilang enjin pangkalan data, membolehkan pengguna memilih kaedah storan terbaik untuk keperluan mereka. Sejak versi 5.5, InnoDB telah menjadi enjin lalai, menyokong transaksi dan penguncian peringkat baris untuk keselarasan tinggi dan ketekalan data. MyISAM, sambil menawarkan prestasi bacaan yang lebih baik, tidak mempunyai sokongan urus niaga dan sesuai untuk senario membaca berat. MySQL juga menyediakan enjin seperti Memori dan Arkib untuk kes penggunaan tertentu.

PostgreSQL, sebaliknya, menggunakan enjin teras bersatu, memastikan konsistensi dan saling kendali untuk semua ciri. Reka bentuk ini menyokong pertanyaan kompleks, pengurusan transaksi dan jenis data lanjutan sambil memudahkan penyelenggaraan. Walaupun kurang fleksibel daripada MySQL dalam beberapa kes, fleksibiliti dan kebolehskalaan dalaman PostgreSQL dipertingkatkan dengan ciri seperti pembahagian dan pengoptimuman pertanyaan.

Membandingkan Sintaks dan Ciri SQL

Analisis perbandingan mendedahkan perbezaan dan persamaan dalam bidang seperti sokongan jenis tatasusunan, pengendalian JSON, pengurusan transaksi, jadual sementara, fungsi tetingkap, pertanyaan rekursif, kekayaan jenis data, kekangan nilai lalai dan sensitiviti huruf besar:

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.

참고: 시간이 지남에 따라 두 시스템 모두 지속적으로 업데이트되며 특정 기능의 지원 및 성능이 변경될 수 있습니다. 데이터베이스를 선택할 때 최신 공식 문서나 릴리스 노트를 참조하는 것이 가장 좋습니다.

고급 기능 비교: 데이터 유형 및 트랜잭션 처리

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.

데이터 유형 및 기능적 특징

• 고급 데이터 유형: PostgreSQL은 배열, JSONB, hstore와 같은 더욱 풍부한 옵션을 제공하므로 복잡한 데이터 처리에 적합합니다.
• 창 함수 및 분석 쿼리: PostgreSQL은 이전에 이를 지원했고 MySQL은 최신 릴리스에 추가했습니다.
• 트랜잭션 처리 및 동시성 제어: 격리 수준, MVCC 구현 및 잠금 메커니즘을 비교하면 주요 차이점을 알 수 있습니다.

성능 및 확장성 비교

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.

성능과 확장성의 심층 비교

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.

성능 및 확장성

• 벤치마킹 및 작업 부하: 읽기 작업이 많은 상황에서 MySQL의 장점과 복잡한 쿼리에서 PostgreSQL의 효율성을 언급하면서 서로 다른 작업 부하에서 두 시스템의 성능을 분석합니다.
• 확장성: MySQL의 샤딩 전략과 PostgreSQL의 연결 풀링, 파티셔닝 및 병렬 쿼리 기능 등 수평적 확장성 기능에 대해 논의하세요.

보안 및 규정 준수 비교

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.

보안 및 규정 준수

• 사용자 권한 관리: PostgreSQL은 세분화된 제어 및 행 수준 보안에 탁월합니다. MySQL은 애플리케이션 계층 보안 적응에 중점을 둡니다.
• 암호화 기능: 둘 다 SSL/TLS를 지원하지만 PostgreSQL에는 확장을 통한 TDE 옵션과 필드 수준 암호화에 대한 고급 제어가 포함되어 있습니다.
• 규정 준수 인증: 두 데이터베이스 모두 다양한 보안 표준을 준수하지만 PostgreSQL은 인정받는 인증 범위가 더 넓은 반면 MySQL의 엔터프라이즈 버전은 규정 준수 기능을 향상시킵니다.

응용 시나리오 및 선택 권장 사항

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.

의사결정 프레임워크

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

고려사항

데이터베이스를 선택할 때 모든 용도에 맞는 것은 없습니다. 대신 귀하의 요구 사항에 가장 적합한 것이 무엇인지에 집중하십시오. 이러한 요소를 고려하면서 최종 결정을 내리기 전에 소규모 POC(개념 증명)를 실행하여 특정 워크로드에서 데이터베이스 성능을 테스트하는 것을 고려해 보십시오. 또한 두 데이터베이스 시스템 모두 지속적으로 개선되고 새로운 기능을 도입하므로 정보에 입각한 선택을 하려면 최신 개발 정보를 계속 업데이트하는 것이 필수적입니다.

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