PostgreSQL 正體中文使用手冊
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  • 簡介
  • 前言
    • 1. 什麼是 PostgreSQL?
    • 2. PostgreSQL 沿革
    • 3. 慣例
    • 4. 其他參考資訊
    • 5. 問題回報指南
  • I. 新手教學
    • 1. 入門指南
      • 1.1. 安裝
      • 1.2. 基礎架構
      • 1.3. 建立一個資料庫
      • 1.4. 存取一個資料庫
    • 2. SQL 查詢語言
      • 2.1. 簡介
      • 2.2. 概念
      • 2.3. 創建一個新的資料表
      • 2.4. 資料列是資料表的組成單位
      • 2.5. 資料表的查詢
      • 2.6. 交叉查詢
      • 2.7. 彙總查詢
      • 2.8. 更新資料
      • 2.9. 刪除資料
    • 3. 先進功能
      • 3.1. 簡介
      • 3.2. 檢視表(View)
      • 3.3. 外部索引鍵
      • 3.4. 交易安全
      • 3.5. 窗函數
      • 3.6. 繼承
      • 3.7. 結論
  • II. SQL 查詢語言
    • 4. SQL 語法
      • 4.1. 語法結構
      • 4.2. 參數表示式
      • 4.3. 函數呼叫
    • 5. 定義資料結構
      • 5.1. 認識資料表
      • 5.2. 預設值
      • 5.3. Generated Columns
      • 5.4. 限制條件
      • 5.5. 系統欄位
      • 5.6. 表格變更
      • 5.7. 權限
      • 5.8. 資料列安全原則
      • 5.9. Schemas
      • 5.10. 繼承
      • 5.11. 分割資料表
      • 5.12. 外部資料
      • 5.13. 其他資料庫物件
      • 5.14. 相依性追蹤
    • 6. 資料處理
      • 6.1. 新增資料
      • 6.2. 更新資料
      • 6.3. 刪除資料
      • 6.4. 修改並回傳資料
    • 7. 資料查詢
      • 7.1. 概觀
      • 7.2. 資料表表示式
      • 7.3. 取得資料列表
      • 7.4. 合併查詢結果
      • 7.5. 資料排序
      • 7.6. 指定資料範圍
      • 7.7. 列舉資料
      • 7.8. WITH Querys(Common Table Expressions)
    • 8. 資料型別
      • 8.1. 數字型別
      • 8.2. 貨幣型別
      • 8.3. 字串型別
      • 8.4. 位元組型別(bytea)
      • 8.5. 日期時間型別
      • 8.6. 布林型別
      • 8.7. 列舉型別
      • 8.8. 地理資訊型別
      • 8.9. 網路資訊型別
      • 8.10. 位元字串型別
      • 8.11. 全文檢索型別
      • 8.12. UUID 型別
      • 8.13. XML 型別
      • 8.14. JSON 型別
      • 8.15. 陣列
      • 8.16. 複合型別
      • 8.17. 範圍型別
      • 8.18. Domain Types
      • 8.19. 物件指標型別
      • 8.20. pg_lsn 型別
      • 8.21. 概念型別
    • 9. 函式及運算子
      • 9.1. 邏輯運算子
      • 9.2. 比較函式及運算子
      • 9.3. 數學函式及運算子
      • 9.4. 字串函式及運算子
      • 9.5. 位元字串函式及運算子
      • 9.6. 二元字串函式及運算子
      • 9.7. 特徵比對
      • 9.8. 型別轉換函式
      • 9.9 日期時間函式及運算子
      • 9.10. 列舉型別函式
      • 9.11. 地理資訊函式及運算子
      • 9.12. 網路位址函式及運算子
      • 9.13. 文字檢索函式及運算子
      • 9.14. XML 函式
      • 9.15. JSON 函式及運算子
      • 9.16. 序列函式
      • 9.17. 條件表示式
      • 9.18. 陣列函式及運算子
      • 9.19. 範圍函式及運算子
      • 9.20. 彙總函數
      • 9.21. Window 函式
      • 9.22. 子查詢
      • 9.23. 資料列與陣列的比較運算
      • 9.24. 集合回傳函式
      • 9.25. 系統資訊函數
      • 9.26. 系統管理函式
      • 9.27. 觸發函式
      • 9.28. 事件觸發函式
      • 9.29. Statistics Information Functions
    • 10. 型別轉換
      • 10.1. 概觀
      • 10.2. 運算子
      • 10.3. 函式
      • 10.4. 資料儲存轉換規則
      • 10.5. UNION、CASE 等相關結構
      • 10.6. SELECT 輸出規則
    • 11. 索引(Index)
      • 11.1. 簡介
      • 11.2. 索引型別
      • 11.3. 多欄位索引
      • 11.4. 索引與 ORDER BY
      • 11.5. 善用多個索引
      • 11.6. 唯一值索引
      • 11.7. 表示式索引
      • 11.8. 部份索引(partial index)
      • 11.9. 運算子物件及家族
      • 11.10. 索引與排序規則
      • 11.11. 索引限定查詢(Index-only scan)
      • 11.12. 檢查索引運用
    • 12. 全文檢索
      • 12.1. 簡介
      • 12.2. 查詢與索引
      • 12.3. 細部控制
      • 12.4. 延伸功能
      • 12.5. 斷詞
      • 12.6. 字典
      • 12.7. 組態範例
      • 12.8. 測試與除錯
      • 12.9. GIN 及 GiST 索引型別
      • 12.10. psql支援
      • 12.11. 功能限制
    • 13. 一致性管理(MVCC)
      • 13.1. 簡介
      • 13.2. 交易隔離
      • 13.3. 鎖定模式
      • 13.4. 在應用端檢視資料一致性
      • 13.5. 特別注意
      • 13.6. 鎖定與索引
    • 14. 效能技巧
      • 14.1. 善用 EXPLAIN
      • 14.2. 統計資訊
      • 14.3. 使用確切的 JOIN 方式
      • 14.4. 快速建立資料庫內容
      • 14.5. 風險性彈性設定
    • 15. 平行查詢
      • 15.1. 如何運作?
      • 15.2. 啓用時機?
      • 15.3. 平行查詢計畫
      • 15.4. 平行查詢的安全性
  • III. 系統管理
    • 16. 用原始碼安裝
      • 16.1. Short Version
      • 16.2. Requirements
      • 16.3. Getting The Source
      • 16.4. 安裝流程
      • 16.5. Post-Installation Setup
      • 16.6. Supported Platforms
      • 16.7. 平台相關的注意事項
    • 17. 用原始碼在 Windows 上安裝
      • 17.1. Building with Visual C++ or the Microsoft Windows SDK
    • 18. 服務配置與維運
      • 18.1. PostgreSQL 使用者帳號
      • 18.2. Creating a Database Cluster
      • 18.3. Starting the Database Server
      • 18.4. 核心資源管理
      • 18.5. Shutting Down the Server
      • 18.6. Upgrading a PostgreSQL Cluster
      • 18.7. Preventing Server Spoofing
      • 18.8. Encryption Options
      • 18.9. Secure TCP/IP Connections with SSL
      • 18.10. Secure TCP/IP Connections with GSSAPI Encryption
      • 18.11. Secure TCP/IP Connections with SSH Tunnels
      • 18.12. 在 Windows 註冊事件日誌
    • 19. 服務組態設定
      • 19.1. Setting Parameters
      • 19.2. File Locations
      • 19.3. 連線與認證
      • 19.4. 資源配置
      • 19.5. Write Ahead Log
      • 19.6. 複寫(Replication)
      • 19.7. 查詢規畫
      • 19.8. 錯誤回報與日誌記錄
      • 19.9. 執行階段統計資訊
      • 19.10. 自動資料庫清理
      • 19.11. 用戶端連線預設參數
      • 19.12. 交易鎖定管理
      • 19.13. 版本與平台的相容性
      • 19.14. Error Handling
      • 19.15. 預先配置的參數
      • 19.16. Customized Options
      • 19.17. Developer Options
      • 19.18. Short Options
    • 20. 使用者認證
      • 20.1. 設定檔:pg_hba.conf
      • 20.2. User Name Maps
      • 20.3. Authentication Methods
      • 20.4. Authentication Problems
      • 20.5. Password Authentication
      • 20.12. Certificate Authentication
      • 20.13. PAM Authentication
    • 21. 資料庫角色
      • 21.1. Database Roles
      • 21.2. Role Attributes
      • 21.3. Role Membership
      • 21.4. 移除角色
      • 21.5. Default Roles
      • 21.6. Function Security
    • 22. Managing Databases
      • 22.1. Overview
      • 22.2. Creating a Database
      • 22.3. 樣版資料庫
      • 22.4. Database Configuration
      • 22.5. Destroying a Database
      • 22.6. Tablespaces
    • 23. 語系
      • 23.1. 語系支援
      • 23.2. Collation Support
      • 23.3. 字元集支援
    • 24. 例行性資料庫維護工作
      • 24.1. 例行性資料清理
      • 24.2. 定期重建索引
      • 24.3. Log File Maintenance
    • 25. 備份及還原
      • 25.1. SQL Dump
      • 25.2. 檔案系統層級備份
      • 25.3. Continuous Archiving and Point-in-Time Recovery (PITR)
    • 26. High Availability, Load Balancing, and Replication
      • 26.1. Comparison of Different Solutions
      • 26.2. 日誌轉送備用伺服器 Log-Shipping Standby Servers
      • 26.3. Failover
      • 26.4. Alternative Method for Log Shipping
      • 26.5. Hot Standby
    • 27. 監控資料庫活動
      • 27.1. Standard Unix Tools
      • 27.2. 統計資訊收集器
      • 27.3. Viewing Locks
      • 27.4. Progress Reporting
      • 27.5. Dynamic Tracing
    • 28. 監控磁碟使用情況
      • 28.1. 瞭解磁碟使用情形
      • 28.2. 磁碟空間不足錯誤
    • 29. 高可靠度及預寫日誌
      • 29.1. 可靠度
      • 29.2. Write-Ahead Logging(WAL)
      • 29.3. Asynchronous Commit
      • 29.4. WAL Configuration
      • 29.5. WAL Internals
    • 30. 邏輯複寫(Logical Replication)
      • 30.1. 發佈(Publication)
      • 30.2. 訂閱(Subscription)
      • 30.3. 衝突處理
      • 30.4. 限制
      • 30.5. 架構
      • 30.6. 監控
      • 30.7. 安全性
      • 30.8. 系統設定
      • 30.9. 快速設定
    • 31. Just-in-Time Compilation(JIT)
      • 31.1. What is JIT compilation?
      • 31.2. When to JIT?
      • 31.3. Configuration
      • 31.4. Extensibility
    • 32. 迴歸測試
      • 32.1. Running the Tests
      • 32.2. Test Evaluation
      • 32.3. Variant Comparison Files
      • 32.4. TAP Tests
      • 32.5. Test Coverage Examination
  • IV. 用戶端介面
    • 33. libpq - C Library
      • 33.1. 資料庫連線控制函數
      • 33.2. 連線狀態函數
      • 33.3. Command Execution Functions
      • 33.4. Asynchronous Command Processing
      • 33.5. Retrieving Query Results Row-By-Row
      • 33.6. Canceling Queries in Progress
      • 33.7. The Fast-Path Interface
      • 33.8. Asynchronous Notification
      • 33.9. Functions Associated with the COPY Command
      • 33.10. Control Functions
      • 33.11. Miscellaneous Functions
      • 33.12. Notice Processing
      • 33.13. Event System
      • 33.14. 環境變數
      • 33.15. 密碼檔
      • 33.16. The Connection Service File
      • 33.17. LDAP Lookup of Connection Parameters
      • 33.18. SSL Support
      • 33.19. Behavior in Threaded Programs
      • 33.20. Building libpq Programs
      • 33.21. Example Programs
    • 34. Large Objects
      • 35.1. Introduction
      • 35.2. Implementation Features
      • 35.3. Client Interfaces
      • 35.4. Server-side Functions
      • 35.5. Example Program
    • 35. ECPG - Embedded SQL in C
      • 36.1. The Concept
      • 36.2. Managing Database Connections
      • 36.3. Running SQL Commands
      • 36.4. Using Host Variables
      • 36.5. Dynamic SQL
      • 36.6. pgtypes Library
      • 36.7. Using Descriptor Areas
      • 36.8. Error Handling
      • 36.9. Preprocessor Directives
      • 36.10. Processing Embedded SQL Programs
      • 36.11. Library Functions
      • 36.12. Large Objects
      • 36.13. C++ Applications
      • 36.14. Embedded SQL Commands
      • 36.15. Informix Compatibility Mode
      • 36.16. Internals
    • 36. The Information Schema
      • 36.1. The Schema
      • 36.2. Data Types
      • 36.3. information_schema_catalog_name
      • 36.4. administrable_role_authorizations
      • 36.5. applicable_roles
      • 36.6. attributes
      • 36.7. character_sets
      • 36.8. check_constraint_routine_usage
      • 36.9. check_constraints
      • 36.10. collations
      • 36.11. collation_character_set_applicability
      • 36.12. column_domain_usage
      • 36.13. column_options
      • 36.14. column_privileges
      • 36.15. column_udt_usage
      • 36.17. columns
      • 36.18. constraint_column_usage
      • 37.18. constraint_table_usage
      • 37.19. data_type_privileges
      • 37.20. domain_constraints
      • 37.21. domain_udt_usage
      • 37.22. domains
      • 37.23. element_types
      • 37.24. enabled_roles
      • 37.25. foreign_data_wrapper_options
      • 37.26. foreign_data_wrappers
      • 37.27. foreign_server_options
      • 37.28. foreign_servers
      • 37.29. foreign_table_options
      • 37.30. foreign_tables
      • 36.32. key_column_usage
      • 37.32. parameters
      • 36.34. referential_constraints
      • 37.34. role_column_grants
      • 37.35. role_routine_grants
      • 37.36. role_table_grants
      • 37.37. role_udt_grants
      • 37.38. role_usage_grants
      • 37.39. routine_privileges
      • 37.40. routines
      • 36.42. schemata
      • 37.42. sequences
      • 37.43. sql_features
      • 37.44. sql_implementation_info
      • 37.45. sql_languages
      • 37.46. sql_packages
      • 37.47. sql_parts
      • 37.48. sql_sizing
      • 37.49. sql_sizing_profiles
      • 36.51. table_constraints
      • 37.51. table_privileges
      • 37.52. tables
      • 37.53. transforms
      • 37.54. triggered_update_columns
      • 37.55. triggers
      • 37.56. udt_privileges
      • 37.57. usage_privileges
      • 37.58. user_defined_types
      • 37.59. user_mapping_options
      • 37.60. user_mappings
      • 37.61. view_column_usage
      • 37.62. view_routine_usage
      • 37.63. view_table_usage
      • 37.64. views
  • V. 資料庫程式設計
    • 37. SQL 延伸功能
      • 37.1. How Extensibility Works
      • 37.2. The PostgreSQL Type System
      • 37.3. 使用者自訂函數
      • 37.4. User-defined Procedures
      • 37.5. Query Language (SQL) Functions
      • 37.6. Function Overloading
      • 37.7. 函數易變性類別
      • 37.8. Procedural Language Functions
      • 37.9. Internal Functions
      • 37.10. C-Language Functions
      • 37.11. Function Optimization Information
      • 37.12. User-defined Aggregates
      • 37.13. User-defined Types
      • 37.14. User-defined Operators
      • 37.15. Operator Optimization Information
      • 37.16. Interfacing Extensions To Indexes
      • 37.17. 封裝相關物件到延伸功能中
      • 37.18. Extension Building Infrastructure
    • 38. Triggers
    • 39. Event Triggers
    • 40. 規則系統
      • 40.1. The Query Tree
      • 40.2. Views and the Rule System
      • 40.3. Materialized Views
      • 40.4. Rules on INSERT, UPDATE, and DELETE
      • 40.5. 規則及權限
      • 40.6. Rules and Command Status
      • 40.7. Rules Versus Triggers
    • 41. Procedural Languages(程序語言)
      • 41.1. Installing Procedural Languages
      • 41.2. Structure of PL/pgSQL
      • 41.5. Basic Statements
      • 41.11. 深入了解 PL/pgSQL
    • 43. PL/pgSQL - SQL Procedural Language
      • 43.5. 基本語法
    • 44. PL/Tcl - Tcl Procedural Language
    • 45. PL/Python - Python Procedural Language
      • 45.1. Python 2 vs. Python 3
      • 45.2. PL/Python Functions
      • 45.3. Data Values
      • 45.4. Sharing Data
      • 45.5. Anonymous Code Blocks
      • 45.6. Trigger Functions
      • 45.7. Database Access
      • 45.8. Explicit Subtransactions
      • 45.9. Transaction Management
      • 45.10. Utility Functions
      • 45.11. Environment Variables
    • 46. PL/Python - Python Procedural Language
    • 47. Server Programming Interface
    • 48. Background Worker Processes
    • 49. Logical Decoding
    • 50. Replication Progress Tracking
  • VI. 參考資訊
    • I. SQL 指令
      • ALTER DATABASE
      • ALTER DEFAULT PRIVILEGES
      • ALTER EXTENSION
      • ALTER FUNCTION
      • ALTER INDEX
      • ALTER LANGUAGE
      • ALTER MATERIALIZED VIEW
      • ALTER POLICY
      • ALTER PUBLICATION
      • ALTER ROLE
      • ALTER RULE
      • ALTER SCHEMA
      • ALTER SEQUENCE
      • ALTER STATISTICS
      • ALTER SUBSCRIPTION
      • ALTER SYSTEM
      • ALTER TABLE
      • ALTER TABLESPACE
      • ALTER TRIGGER
      • ALTER TYPE
      • ALTER VIEW
      • ANALYZE
      • CLUSTER
      • COMMENT
      • COPY
      • CREATE CAST
      • CREATE DATABASE
      • CREATE EXTENSION
      • CREATE FOREIGN TABLE
      • CREATE FOREIGN DATA WRAPPER
      • CREATE FUNCTION
      • CREATE INDEX
      • CREATE LANGUAGE
      • CREATE MATERIALIZED VIEW
      • CREATE DOMAIN
      • CREATE POLICY
      • CREATE PROCEDURE
      • CREATE PUBLICATION
      • CREATE ROLE
      • CREATE RULE
      • CREATE SCHEMA
      • CREATE SEQUENCE
      • CREATE SERVER
      • CREATE STATISTICS
      • CREATE SUBSCRIPTION
      • CREATE TABLE
      • CREATE TABLE AS
      • CREATE TABLESPACE
      • CREATE TRANSFORM
      • CREATE TRIGGER
      • CREATE TYPE
      • CREATE USER
      • CREATE USER MAPPING
      • CREATE VIEW
      • DELETE
      • DO
      • DROP DATABASE
      • DROP EXTENSION
      • DROP FUNCTION
      • DROP INDEX
      • DROP LANGUAGE
      • DROP MATERIALIZED VIEW
      • DROP OWNED
      • DROP POLICY
      • DROP ROLE
      • DROP RULE
      • DROP SCHEMA
      • DROP SEQUENCE
      • DROP STATISTICS
      • DROP SUBSCRIPTION
      • DROP TABLE
      • DROP TABLESPACE
      • DROP TRANSFORM
      • DROP TRIGGER
      • DROP TYPE
      • DROP USER
      • DROP VIEW
      • EXECUTE
      • EXPLAIN
      • GRANT
      • IMPORT FOREIGN SCHEMA
      • INSERT
      • LISTEN
      • LOAD
      • NOTIFY
      • PREPARE TRANSACTION
      • REASSIGN OWNED
      • REFRESH MATERIALIZED VIEW
      • REINDEX
      • RESET
      • REVOKE
      • SECURITY LABEL
      • SELECT
      • SELECT INTO
      • SET
      • SET CONSTRAINTS
      • SET ROLE
      • SET SESSION AUTHORIZATION
      • SET TRANSACTION
      • SHOW
      • TRUNCATE
      • UNLISTEN
      • UPDATE
      • VACUUM
      • VALUES
    • II. PostgreSQL 用戶端工具
      • createdb
      • createuser
      • dropdb
      • dropuser
      • oid2name
      • pgbench
      • pg_basebackup
      • pg_dump
      • pg_dumpall
      • pg_restore
      • psql
      • vacuumdb
    • III. PostgreSQL 伺服器應用程式
      • initdb
      • pg_archivecleanup
      • pg_ctl
      • pg_standby
      • pg_test_timing
      • postgres
  • VII. 資料庫進階
    • 50. PostgreSQL 的內部架構
      • 50.1. The Path of a Query
      • 50.2. How Connections Are Established
      • 50.3. The Parser Stage
      • 50.4. The PostgreSQL Rule System
      • 50.5. Planner/Optimizer
      • 50.6. Executor
    • 51. 系統目錄
      • 51.3. pg_am
      • 51.7. pg_attribute
      • 51.8. pg_authid
      • 51.9. pg_auth_members
      • 51.11 pg_class
      • 51.12. pg_collation
      • 51.13. pg_constraint
      • 51.15 pg_database
      • 51.22. pg_extension
      • 51.26 pg_index
      • 51.29. pg_language
      • 51.32. pg_namespace
      • 51.33. pg_opclass
      • 51.38. pg_policy
      • 51.39. pg_proc
      • 51.44. pg_rewrite
      • 51.50. pg_statistic
      • 51.51. pg_statistic_ext
      • 51.54. pg_tablespace
      • 51.56. pg_trigger
      • 51.62. pg_type
      • 51.66. pg_available_extensions
      • 51.67. pg_available_extension_versions
      • 51.72. pg_hba_file_rules
      • 51.73. pg_indexes
      • 51.74. pg_locks
      • 51.79. pg_replication_origin_status
      • 51.82 pg_roles
      • 51.85. pg_settings
      • 51.87. pg_stats
      • 51.90. pg_tables
      • 51.95. pg_views
    • 52. Frontend/Backend Protocol
      • 52.1. Overview
      • 52.2. Message Flow
      • 52.3. SASL Authentication
      • 52.4. Streaming Replication Protocol
      • 52.5. Logical Streaming Replication Protocol
      • 52.6. Message Data Types
      • 52.7. Message Formats
      • 52.8. Error and Notice Message Fields
      • 52.9. Logical Replication Message Formats
      • 52.10. Summary of Changes since Protocol 2.0
    • 53. PostgreSQL 程式撰寫慣例
      • 53.1. Formatting
      • 53.2. Reporting Errors Within the Server
      • 53.3. Error Message Style Guide
      • 53.4. Miscellaneous Coding Conventions
    • 54. Native Language Support
      • 54.1. For the Translator
      • 54.2. For the Programmer
    • 55. 撰寫程序語言的處理程序
    • 56. Writing a Foreign Data Wrapper
      • 56.1. Foreign Data Wrapper Functions
      • 56.2. Foreign Data Wrapper Callback Routines
      • 56.3. Foreign Data Wrapper Helper Functions
      • 56.4. Foreign Data Wrapper Query Planning
      • 56.5. Row Locking in Foreign Data Wrappers
    • 59. Genetic Query Optimizer
      • 59.1. Query Handling as a Complex Optimization Problem
      • 59.2. Genetic Algorithms
      • 59.3. Genetic Query Optimization (GEQO) in PostgreSQL
      • 59.4. Further Reading
    • 64. GiST Indexes
      • 64.1. Introduction
      • 64.2. Built-in Operator Classes
      • 64.3. Extensibility
      • 64.4. Implementation
      • 64.5. Examples
    • 65. SP-GiST Indexes
      • 65.1. Introduction
      • 65.2. Built-in Operator Classes
      • 65.3. Extensibility
      • 65.4. Implementation
      • 65.5. Examples
    • 66. GIN 索引
      • 66.1. 簡介
      • 66.2. 內建運算子類
      • 66.3. 延伸介面
      • 66.4. 實作說明
      • 66.5. GIN 小巧技
      • 66.6. 限制
      • 66.7. 範例
    • 67. BRIN Indexes
      • 67.1. Introduction
      • 67.2. Built-in Operator Classes
      • 67.3. Extensibility
    • 68. 資料庫實體儲存格式
      • 68.1. Database File Layout
      • 68.2. TOAST
      • 68.3. Free Space Map
      • 68.4 可視性映射表(Visibility Map)
      • 68.5. The Initialization Fork
      • 68.6. Database Page Layout
    • 70. 查詢計畫如何使用統計資訊
      • 70.1. Row Estimation Examples
      • 70.2. Multivariate Statistics Examples
      • 70.3. Planner Statistics and Security
  • VIII. 附錄
    • A. PostgreSQL 錯誤代碼
    • B. 日期時間格式支援
      • B.1. 日期時間解譯流程
      • B.2. 日期時間慣用字
      • B.3. 日期時間設定檔
      • B.4. 日期時間的沿革
    • C. SQL 關鍵字
    • D. SQL 相容性
    • E. 版本資訊
    • F. 延伸支援模組
      • F.4. auto_explain
      • F.6. btree_gin
      • F.10. dblink
        • dblink_connect
        • dblink_connect_u
        • dblink_disconnect
        • dblink
        • dblink_exec
        • dblink_open
        • dblink_fetch
        • dblink_close
        • dblink_get_connections
        • dblink_error_message
        • dblink_send_query
        • dblink_is_busy
        • dblink_get_notify
        • dblink_get_result
        • dblink_cancel_query
        • dblink_get_pkey
        • dblink_build_sql_insert
        • dblink_build_sql_delete
        • dblink_build_sql_update
      • F.13. earthdistance
      • F.14. file_fdw
      • F.29. pg_stat_statements
      • F.31. pg_trgm
      • F.32. pg_visibility
      • F.33. postgres_fdw
      • F.35. sepgsql
      • F.38. tablefunc
      • F.41. tsm_system_rows
      • F.42. tsm_system_time
    • G. Additional Supplied Programs
      • G.1. Client Applications
        • oid2name
        • vacuumlo
      • G.2. Server Applications
        • pg_standby
    • H. 外部專案
      • H.1. 用戶端介面
      • H.2. Administration Tools
      • H.3. Procedural Languages
      • H.4. Extensions
    • I. The Source Code Repository
      • I.1. Getting The Source via Git
    • J. 文件取得
    • K. 縮寫字
  • 參考書目
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  • 8.16.1. Declaration of Composite Types
  • 8.16.2. Constructing Composite Values
  • 8.16.3. Accessing Composite Types
  • 8.16.4. Modifying Composite Types
  • 8.16.5. Using Composite Types in Queries
  • Tip
  • Tip
  • 8.16.6. Composite Type Input and Output Syntax
  • Note
  • Tip

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  1. II. SQL 查詢語言
  2. 8. 資料型別

8.16. 複合型別

A composite type represents the structure of a row or record; it is essentially just a list of field names and their data types. PostgreSQL allows composite types to be used in many of the same ways that simple types can be used. For example, a column of a table can be declared to be of a composite type.

8.16.1. Declaration of Composite Types

Here are two simple examples of defining composite types:

CREATE TYPE complex AS (
    r       double precision,
    i       double precision
);

CREATE TYPE inventory_item AS (
    name            text,
    supplier_id     integer,
    price           numeric
);

The syntax is comparable to CREATE TABLE, except that only field names and types can be specified; no constraints (such as NOT NULL) can presently be included. Note that the AS keyword is essential; without it, the system will think a different kind of CREATE TYPE command is meant, and you will get odd syntax errors.

Having defined the types, we can use them to create tables:

CREATE TABLE on_hand (
    item      inventory_item,
    count     integer
);

INSERT INTO on_hand VALUES (ROW('fuzzy dice', 42, 1.99), 1000);

or functions:

CREATE FUNCTION price_extension(inventory_item, integer) RETURNS numeric
AS 'SELECT $1.price * $2' LANGUAGE SQL;

SELECT price_extension(item, 10) FROM on_hand;

Whenever you create a table, a composite type is also automatically created, with the same name as the table, to represent the table's row type. For example, had we said:

CREATE TABLE inventory_item (
    name            text,
    supplier_id     integer REFERENCES suppliers,
    price           numeric CHECK (price > 0)
);

then the same inventory_item composite type shown above would come into being as a byproduct, and could be used just as above. Note however an important restriction of the current implementation: since no constraints are associated with a composite type, the constraints shown in the table definition do not apply to values of the composite type outside the table. (To work around this, create a domain over the composite type, and apply the desired constraints as CHECK constraints of the domain.)

8.16.2. Constructing Composite Values

'( val1 , val2 , ... )'

An example is:

'("fuzzy dice",42,1.99)'

which would be a valid value of the inventory_item type defined above. To make a field be NULL, write no characters at all in its position in the list. For example, this constant specifies a NULL third field:

'("fuzzy dice",42,)'

If you want an empty string rather than NULL, write double quotes:

'("",42,)'

Here the first field is a non-NULL empty string, the third is NULL.

The ROW expression syntax can also be used to construct composite values. In most cases this is considerably simpler to use than the string-literal syntax since you don't have to worry about multiple layers of quoting. We already used this method above:

ROW('fuzzy dice', 42, 1.99)
ROW('', 42, NULL)

The ROW keyword is actually optional as long as you have more than one field in the expression, so these can be simplified to:

('fuzzy dice', 42, 1.99)
('', 42, NULL)

8.16.3. Accessing Composite Types

To access a field of a composite column, one writes a dot and the field name, much like selecting a field from a table name. In fact, it's so much like selecting from a table name that you often have to use parentheses to keep from confusing the parser. For example, you might try to select some subfields from our on_hand example table with something like:

SELECT item.name FROM on_hand WHERE item.price > 9.99;

This will not work since the name item is taken to be a table name, not a column name of on_hand, per SQL syntax rules. You must write it like this:

SELECT (item).name FROM on_hand WHERE (item).price > 9.99;

or if you need to use the table name as well (for instance in a multitable query), like this:

SELECT (on_hand.item).name FROM on_hand WHERE (on_hand.item).price > 9.99;

Now the parenthesized object is correctly interpreted as a reference to the item column, and then the subfield can be selected from it.

Similar syntactic issues apply whenever you select a field from a composite value. For instance, to select just one field from the result of a function that returns a composite value, you'd need to write something like:

SELECT (my_func(...)).field FROM ...

Without the extra parentheses, this will generate a syntax error.

8.16.4. Modifying Composite Types

Here are some examples of the proper syntax for inserting and updating composite columns. First, inserting or updating a whole column:

INSERT INTO mytab (complex_col) VALUES((1.1,2.2));

UPDATE mytab SET complex_col = ROW(1.1,2.2) WHERE ...;

The first example omits ROW, the second uses it; we could have done it either way.

We can update an individual subfield of a composite column:

UPDATE mytab SET complex_col.r = (complex_col).r + 1 WHERE ...;

Notice here that we don't need to (and indeed cannot) put parentheses around the column name appearing just after SET, but we do need parentheses when referencing the same column in the expression to the right of the equal sign.

And we can specify subfields as targets for INSERT, too:

INSERT INTO mytab (complex_col.r, complex_col.i) VALUES(1.1, 2.2);

Had we not supplied values for all the subfields of the column, the remaining subfields would have been filled with null values.

8.16.5. Using Composite Types in Queries

There are various special syntax rules and behaviors associated with composite types in queries. These rules provide useful shortcuts, but can be confusing if you don't know the logic behind them.

SELECT c FROM inventory_item c;

This query produces a single composite-valued column, so we might get output like:

           c
------------------------
 ("fuzzy dice",42,1.99)
(1 row)

Note however that simple names are matched to column names before table names, so this example works only because there is no column named c in the query's tables.

When we write

SELECT c.* FROM inventory_item c;

then, according to the SQL standard, we should get the contents of the table expanded into separate columns:

    name    | supplier_id | price
------------+-------------+-------
 fuzzy dice |          42 |  1.99
(1 row)

as if the query were

SELECT c.name, c.supplier_id, c.price FROM inventory_item c;
SELECT (myfunc(x)).* FROM some_table;
SELECT (myfunc(x)).a, (myfunc(x)).b, (myfunc(x)).c FROM some_table;

Tip

PostgreSQL handles column expansion by actually transforming the first form into the second. So, in this example, myfunc() would get invoked three times per row with either syntax. If it's an expensive function you may wish to avoid that, which you can do with a query like:

SELECT m.* FROM some_table, LATERAL myfunc(x) AS m;

Placing the function in a LATERAL FROM item keeps it from being invoked more than once per row. m.* is still expanded into m.a, m.b, m.c, but now those variables are just references to the output of the FROM item. (The LATERAL keyword is optional here, but we show it to clarify that the function is getting x from some_table.)

SELECT somefunc(c.*) FROM inventory_item c;
SELECT somefunc(c) FROM inventory_item c;

In both cases, the current row of inventory_item is passed to the function as a single composite-valued argument. Even though .* does nothing in such cases, using it is good style, since it makes clear that a composite value is intended. In particular, the parser will consider c in c.* to refer to a table name or alias, not to a column name, so that there is no ambiguity; whereas without .*, it is not clear whether c means a table name or a column name, and in fact the column-name interpretation will be preferred if there is a column named c.

Another example demonstrating these concepts is that all these queries mean the same thing:

SELECT * FROM inventory_item c ORDER BY c;
SELECT * FROM inventory_item c ORDER BY c.*;
SELECT * FROM inventory_item c ORDER BY ROW(c.*);
SELECT * FROM inventory_item c ORDER BY ROW(c.name, c.supplier_id, c.price);
SELECT * FROM inventory_item c ORDER BY (c.name, c.supplier_id, c.price);

(The last case uses a row constructor with the key word ROW omitted.)

Another special syntactical behavior associated with composite values is that we can use functional notation for extracting a field of a composite value. The simple way to explain this is that the notations field(table) and table.field are interchangeable. For example, these queries are equivalent:

SELECT c.name FROM inventory_item c WHERE c.price > 1000;
SELECT name(c) FROM inventory_item c WHERE price(c) > 1000;

Moreover, if we have a function that accepts a single argument of a composite type, we can call it with either notation. These queries are all equivalent:

SELECT somefunc(c) FROM inventory_item c;
SELECT somefunc(c.*) FROM inventory_item c;
SELECT c.somefunc FROM inventory_item c;

This equivalence between functional notation and field notation makes it possible to use functions on composite types to implement “computed fields”. An application using the last query above wouldn't need to be directly aware that somefunc isn't a real column of the table.

Tip

Because of this behavior, it's unwise to give a function that takes a single composite-type argument the same name as any of the fields of that composite type. If there is ambiguity, the field-name interpretation will be chosen if field-name syntax is used, while the function will be chosen if function-call syntax is used. However, PostgreSQL versions before 11 always chose the field-name interpretation, unless the syntax of the call required it to be a function call. One way to force the function interpretation in older versions is to schema-qualify the function name, that is, write schema.func(compositevalue).

8.16.6. Composite Type Input and Output Syntax

The external text representation of a composite value consists of items that are interpreted according to the I/O conversion rules for the individual field types, plus decoration that indicates the composite structure. The decoration consists of parentheses (( and )) around the whole value, plus commas (,) between adjacent items. Whitespace outside the parentheses is ignored, but within the parentheses it is considered part of the field value, and might or might not be significant depending on the input conversion rules for the field data type. For example, in:

'(  42)'

the whitespace will be ignored if the field type is integer, but not if it is text.

As shown previously, when writing a composite value you can write double quotes around any individual field value. You must do so if the field value would otherwise confuse the composite-value parser. In particular, fields containing parentheses, commas, double quotes, or backslashes must be double-quoted. To put a double quote or backslash in a quoted composite field value, precede it with a backslash. (Also, a pair of double quotes within a double-quoted field value is taken to represent a double quote character, analogously to the rules for single quotes in SQL literal strings.) Alternatively, you can avoid quoting and use backslash-escaping to protect all data characters that would otherwise be taken as composite syntax.

A completely empty field value (no characters at all between the commas or parentheses) represents a NULL. To write a value that is an empty string rather than NULL, write "".

The composite output routine will put double quotes around field values if they are empty strings or contain parentheses, commas, double quotes, backslashes, or white space. (Doing so for white space is not essential, but aids legibility.) Double quotes and backslashes embedded in field values will be doubled.

Note

Remember that what you write in an SQL command will first be interpreted as a string literal, and then as a composite. This doubles the number of backslashes you need (assuming escape string syntax is used). For example, to insert a text field containing a double quote and a backslash in a composite value, you'd need to write:

INSERT ... VALUES ('("\"\\")');

Tip

The ROW constructor syntax is usually easier to work with than the composite-literal syntax when writing composite values in SQL commands. In ROW, individual field values are written the same way they would be written when not members of a composite.

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To write a composite value as a literal constant, enclose the field values within parentheses and separate them by commas. You can put double quotes around any field value, and must do so if it contains commas or parentheses. (More details appear .) Thus, the general format of a composite constant is the following:

(These constants are actually only a special case of the generic type constants discussed in . The constant is initially treated as a string and passed to the composite-type input conversion routine. An explicit type specification might be necessary to tell which type to convert the constant to.)

The ROW expression syntax is discussed in more detail in .

The special field name * means “all fields”, as further explained in .

In PostgreSQL, a reference to a table name (or alias) in a query is effectively a reference to the composite value of the table's current row. For example, if we had a table inventory_item as shown , we could write:

The ordinary qualified-column-name syntax table_name.column_name can be understood as applying to the composite value of the table's current row. (For efficiency reasons, it's not actually implemented that way.)

PostgreSQL will apply this expansion behavior to any composite-valued expression, although as shown , you need to write parentheses around the value that .* is applied to whenever it's not a simple table name. For example, if myfunc() is a function returning a composite type with columns a, b, and c, then these two queries have the same result:

The composite_value.* syntax results in column expansion of this kind when it appears at the top level of a , a in INSERT/UPDATE/DELETE, a , or a . In all other contexts (including when nested inside one of those constructs), attaching .* to a composite value does not change the value, since it means “all columns” and so the same composite value is produced again. For example, if somefunc() accepts a composite-valued argument, these queries are the same:

All of these ORDER BY clauses specify the row's composite value, resulting in sorting the rows according to the rules described in . However, if inventory_item contained a column named c, the first case would be different from the others, as it would mean to sort by that column only. Given the column names previously shown, these queries are also equivalent to those above:

The string-literal processor removes one level of backslashes, so that what arrives at the composite-value parser looks like ("\"\\"). In turn, the string fed to the text data type's input routine becomes "\. (If we were working with a data type whose input routine also treated backslashes specially, bytea for example, we might need as many as eight backslashes in the command to get one backslash into the stored composite field.) Dollar quoting (see ) can be used to avoid the need to double backslashes.

below
Section 4.1.2.7
Section 4.2.13
Section 8.16.5
above
field selection
above
SELECT output list
RETURNING list
VALUES clause
row constructor
Section 9.23.6
Section 4.1.2.4