The following isOracle、MySQL、SQL Server、PostgreSQL、RedisComparative analysis of the five major databases, fromUsage, data processing method, high concurrency capability, advantages and disadvantagesExpand in equal dimensions:
1. Database classification
| database | type | Core scenario |
|---|---|---|
| Oracle | Relational database | Enterprise-level complex transaction processing |
| MySQL | Relational database | Web applications, small and medium-sized business systems |
| SQL Server | Relational database | Enterprise-level applications (especially Microsoft ecosystem) |
| PostgreSQL | Relational database | Complex query, GIS/timing and other extended scenarios |
| Redis | Key-value pair memory database | Cache, real-time data processing, queueing |
2. Comparison of core differences
1. use
| database | Typical uses |
|---|---|
| Oracle | Enterprise-level core systems such as finance and telecommunications, with high reliability and strong transaction consistency scenarios. |
| MySQL | Web applications (such as e-commerce, CMS), small and medium-sized business systems, with a wide open source ecosystem. |
| SQL Server | Enterprise-level ERP, CRM (Microsoft Ecosystem), and deep integration of Windows platforms. |
| PostgreSQL | Complex analysis, GIS geographic data, time series data (TimescaleDB extension), JSON document storage, etc. |
| Redis | Cache (such as Session storage), real-time rankings, message queues (Pub/Sub), high-frequency read/write scenarios. |
2. Data processing method
| database | Data Model | Transaction support | Extensibility |
|---|---|---|---|
| Oracle | Relational type (ACID) | Strong consistency, supporting distributed transactions | Scaling horizontally through RAC |
| MySQL | Relational type (ACID) | Support transactions (InnoDB engine) | Master-slave copy, the library and table should be manually divided |
| SQL Server | Relational type (ACID) | Strong consistency, distributed transactions | Scaling with AlwaysOn cluster |
| PostgreSQL | Relational type (ACID) | Support transactions, MVCC mechanism | Supports logical replication and rich extension plug-ins |
| Redis | Key-value pairs (supports multiple data structures) | Weak transactions (Lua script or Pipeline) | Redis Cluster |
3. High concurrency processing capability
| database | Concurrency Model | Performance Features | Applicable scenarios |
|---|---|---|---|
| Oracle | Multi-threading, lock mechanism optimization | High concurrent write capability, but high resource consumption | High load enterprise-level OLTP |
| MySQL | Multithreading (InnoDB engine) | Read more and write less scene optimization, and the writing bottleneck is more obvious | Small and medium-sized web applications |
| SQL Server | Multi-threaded, lock granularity control | Medium and high concurrency, Windows platform performance optimization | Enterprise-level OLTP (Microsoft Ecosystem) |
| PostgreSQL | Multi-process, MVCC lock-free design | Complex query performance is strong, writing concurrency is slightly weak | OLAP or hybrid load |
| Redis | Single threading (avoid lock competition) | Ultra-high performance (100,000+QPS), pure memory operation | High frequency read/write cache, real-time data processing |
4. Comparison of advantages and disadvantages
| database | Advantages | Disadvantages |
|---|---|---|
| Oracle | High reliability, comprehensive functions, enterprise-level support | Expensive, complex, and high hardware requirements |
| MySQL | Lightweight, open source, easy to deploy, and ecologically perfect | Functional limitations (such as window function support is late), weak scalability |
| SQL Server | High ease of use, Microsoft ecosystem integration, and powerful BI tools | Close source, weak cross-platform capabilities, high licensing fees |
| PostgreSQL | Rich features (JSON, GIS, full text search, etc.), strong extensibility | High resource consumption and complex writing performance optimization |
| Redis | Extremely high performance, flexible data structure (List/Hash/Set, etc.) | Data size is limited by memory, persistence may cause data loss |
3. Selection suggestions
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Enterprise-level core system: Oracle (adequate budget) or PostgreSQL (open source alternative).
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Web applications/small and medium-sized systems: MySQL (simple scenario) or PostgreSQL (complex query).
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Microsoft Eco-Integration：SQL Server。
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Real-time cache/high frequency reading and writing: Redis (used with relational database).
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GIS/Timing/JSON Extensions: PostgreSQL + extension plug-ins (such as PostGIS, TimescaleDB).
4. Additional instructions
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Redis and other databases: Usually asCache layerUse with relational databases (such as MySQL) to alleviate high concurrency pressure.
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OLTP vs OLAP: Oracle/MySQL/SQL Server focuses on OLTP; PostgreSQL can take into account OLAP; Redis is not suitable for analysis scenarios.
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Open Source vs Business: MySQL/PostgreSQL/Redis is open source; Oracle/SQL Server requires commercial authorization.
Through the above comparison, you can select the appropriate database combination based on business needs (transaction complexity, scalability, performance, cost).