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. LIMIT 和 OFFSET
      • 7.7. VALUES 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. UUID Functions
      • 9.15. XML 函式
      • 9.16. JSON 函式及運算子
      • 9.17. 序列函式
      • 9.18. 條件表示式
      • 9.19. 陣列函式及運算子
      • 9.20. 範圍函式及運算子
      • 9.21. 彙總函數
      • 9.22. Window 函式
      • 9.23. 子查詢
      • 9.24. 資料列與陣列的比較運算
      • 9.25. 集合回傳函數
      • 9.26. 系統資訊函數
      • 9.27. 系統管理函式
      • 9.28. 觸發函式
      • 9.29. 事件觸發函式
      • 9.30. 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. Index-Only Scans and Covering Indexes
      • 11.10. 運算子物件及家族
      • 11.11. 索引與排序規則
      • 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(Concurrency Control)
      • 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. Serialization Failure Handling
      • 13.6. 特別提醒
      • 13.7. 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. 以預編譯套件安裝
    • 17. 以原始碼安裝
      • 17.1. 簡要步驟
      • 17.2. 環境需求
      • 17.3. Getting The Source
      • 17.4. 安裝流程
      • 17.5. Post-Installation Setup
      • 17.6. Supported Platforms
      • 17.7. 平台相關的注意事項
    • 18. 以原始碼在 Windows 上安裝
      • 18.1. Building with Visual C++ or the Microsoft Windows SDK
    • 19. 服務配置與維運
      • 19.1. PostgreSQL 使用者帳號
      • 19.2. Creating a Database Cluster
      • 19.3. Starting the Database Server
      • 19.4. 核心資源管理
      • 19.5. Shutting Down the Server
      • 19.6. Upgrading a PostgreSQL Cluster
      • 19.7. Preventing Server Spoofing
      • 19.8. Encryption Options
      • 19.9. Secure TCP/IP Connections with SSL
      • 19.10. Secure TCP/IP Connections with GSSAPI Encryption
      • 19.11. Secure TCP/IP Connections with SSH Tunnels
      • 19.12. 在 Windows 註冊事件日誌
    • 20. 服務組態設定
      • 20.1. Setting Parameters
      • 20.2. File Locations
      • 20.3. 連線與認證
      • 20.4. 資源配置
      • 20.5. Write Ahead Log
      • 20.6. 複寫(Replication)
      • 20.7. 查詢規畫
      • 20.8. 錯誤回報與日誌記錄
      • 20.9. 執行階段統計資訊
      • 20.10. 自動資料庫清理
      • 20.11. 用戶端連線預設參數
      • 20.12. 交易鎖定管理
      • 20.13. 版本與平台的相容性
      • 20.14. Error Handling
      • 20.15. 預先配置的參數
      • 20.16. Customized Options
      • 20.17. Developer Options
      • 20.18. Short Options
    • 21. 使用者認證
      • 21.1. 設定檔:pg_hba.conf
      • 21.2. User Name Maps
      • 21.3. Authentication Methods
      • 21.4. Trust Authentication
      • 21.5. Password Authentication
      • 21.6. GSSAPI Authentication
      • 21.7. SSPI Authentication
      • 21.8. Ident Authentication
      • 21.9. Peer Authentication
      • 21.10. LDAP Authentication
      • 21.11. RADIUS Authentication
      • 21.12. Certificate Authentication
      • 21.13. PAM Authentication
      • 21.14. BSD Authentication
      • 21.15. Authentication Problems
    • 22. 資料庫角色
      • 22.1. Database Roles
      • 22.2. Role Attributes
      • 22.3. Role Membership
      • 22.4. 移除角色
      • 22.5. Default Roles
      • 22.6. Function Security
    • 23. 管理資料庫
      • 23.1. Overview
      • 23.2. Creating a Database
      • 23.3. 樣版資料庫
      • 23.4. Database Configuration
      • 23.5. Destroying a Database
      • 23.6. Tablespaces
    • 24. 語系
      • 24.1. 語系支援
      • 24.2. Collation Support
      • 24.3. 字元集支援
    • 25. 例行性資料庫維護工作
      • 25.1. 例行性資料清理
      • 25.2. 定期重建索引
      • 25.3. Log 檔案維護
    • 26. 備份及還原
      • 26.1. SQL Dump
      • 26.2. 檔案系統層級備份
      • 26.3. 持續封存及 Point-in-Time Recovery (PITR)
    • 27. High Availability, Load Balancing, and Replication
      • 27.1. 比較不同的解決方案
      • 27.2. 日誌轉送備用伺服器 Log-Shipping Standby Servers
      • 27.3. Failover
      • 27.4. Hot Standby
    • 28. 監控資料庫活動
      • 28.1. 標準的 Unix 工具
      • 28.2. 統計資訊收集器
      • 28.3. Viewing Locks
      • 28.4. Progress Reporting
      • 28.5. Dynamic Tracing
    • 29. 監控磁碟使用情況
      • 29.1. 瞭解磁碟使用情形
      • 29.2. 磁碟空間不足錯誤
    • 30. 高可靠度及預寫日誌
      • 30.1. 可靠度
      • 30.2. Data Checksums
      • 30.3. Write-Ahead Logging(WAL)
      • 30.4. Asynchronous Commit
      • 30.5. WAL Configuration
      • 30.6. WAL Internals
    • 31. 邏輯複寫(Logical Replication)
      • 31.1. 發佈(Publication)
      • 31.2. 訂閱(Subscription)
      • 31.3. Row Filters
      • 31.4. Column Lists
      • 31.5. 衝突處理
      • 31.6. 限制
      • 31.7. 架構
      • 31.8. 監控
      • 31.9. 安全性
      • 31.10. 系統設定
      • 31.11. 快速設定
    • 32. Just-in-Time Compilation(JIT)
      • 32.1. What is JIT compilation?
      • 32.2. When to JIT?
      • 32.3. Configuration
      • 32.4. Extensibility
    • 33. 迴歸測試
      • 33.1. Running the Tests
      • 33.2. Test Evaluation
      • 33.3. Variant Comparison Files
      • 33.4. TAP Tests
      • 33.5. Test Coverage Examination
  • IV. 用戶端介面
    • 34. 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
      • 34.16. 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
    • 35. Large Objects
      • 35.1. Introduction
      • 35.2. Implementation Features
      • 35.3. Client Interfaces
      • 35.4. Server-side Functions
      • 35.5. Example Program
    • 36. ECPG - Embedded SQL in C
      • 35.1. The Concept
      • 35.2. Managing Database Connections
      • 35.3. Running SQL Commands
      • 35.4. Using Host Variables
      • 35.5. Dynamic SQL
      • 35.6. pgtypes Library
      • 35.7. Using Descriptor Areas
      • 35.8. Error Handling
      • 35.9. Preprocessor Directives
      • 35.10. Processing Embedded SQL Programs
      • 35.11. Library Functions
      • 35.12. Large Objects
      • 35.13. C++ Applications
      • 35.14. Embedded SQL Commands
      • 35.15. Informix Compatibility Mode
      • 35.16. Internals
    • 37. The Information Schema
      • 37.1. The Schema
      • 37.2. Data Types
      • 37.3. information_schema_catalog_name
      • 37.4. administrable_role_authorizations
      • 37.5. applicable_roles
      • 37.7. attributes
      • 37.7. character_sets
      • 37.8. check_constraint_routine_usage
      • 37.9. check_constraints
      • 37.10. collations
      • 37.11. collation_character_set_applicability
      • 37.12. column_column_usage
      • 37.13. column_domain_usage
      • 37.14. column_options
      • 37.15. column_privileges
      • 37.16. column_udt_usage
      • 37.17. columns
      • 37.18. constraint_column_usage
      • 37.19. constraint_table_usage
      • 37.20. data_type_privileges
      • 37.21. 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
      • 37.33. parameters
      • 36.34. referential_constraints
      • 37.34. role_column_grants
      • 37.35. role_routine_grants
      • 37.37. role_table_grants
      • 37.38. role_udt_grants
      • 37.39. role_usage_grants
      • 37.40. routine_column_usage
      • 37.41. routine_privileges
      • 37.45. routines
      • 37.46. schemata
      • 37.47. sequences
      • 37.48. sql_features
      • 37.49. sql_implementation_info
      • 37.50. sql_parts
      • 37.51. sql_sizing
      • 36.51. table_constraints
      • 36.49. 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
      • 37.63. view_column_usage
      • 37.64. view_routine_usage
      • 37.65. view_table_usage
      • 37.66. views
  • V. 資料庫程式設計
    • 38. SQL 延伸功能
      • 38.1. How Extensibility Works
      • 38.2. The PostgreSQL Type System
      • 38.3. 使用者自訂函數
      • 38.4. User-defined Procedures
      • 38.5. Query Language (SQL) Functions
      • 38.6. Function Overloading
      • 38.7. 函數易變性類別
      • 38.8. Procedural Language Functions
      • 38.9. Internal Functions
      • 38.10. C-Language Functions
      • 38.11. Function Optimization Information
      • 38.12. User-defined Aggregates
      • 38.13. User-defined Types
      • 38.14. User-defined Operators
      • 38.15. Operator Optimization Information
      • 38.16. Interfacing Extensions To Indexes
      • 38.17. 封裝相關物件到延伸功能中
      • 38.18. Extension Building Infrastructure
    • 39. Triggers
      • 39.1. Overview of Trigger Behavior
      • 39.2. Visibility of Data Changes
      • 39.3. Writing Trigger Functions in C
      • 39.4. A Complete Trigger Example
    • 40. Event Triggers (事件觸發)
      • 40.1. Overview of Event Trigger Behavior
      • 40.2. Event Trigger Firing Matrix
      • 40.3. Writing Event Trigger Functions in C
      • 40.4. A Complete Event Trigger Example
    • 41. 規則系統
      • 41.1. The Query Tree
      • 41.2. Views and the Rule System
      • 41.3. Materialized Views
      • 41.4. Rules on INSERT, UPDATE, and DELETE
      • 41.5. 規則及權限
      • 41.6. Rules and Command Status
      • 41.7. Rules Versus Triggers
    • 42. Procedural Languages(程序語言)
      • 42.1. Installing Procedural Languages
    • 43. PL/pgSQL - SQL Procedural Language
      • 43.1. Overview
      • 43.2. Structure of PL/pgSQL
      • 43.3. Declarations
      • 43.4. Expressions
      • 43.5. 基本語法
      • 43.6. Control Structures
      • 43.7. Cursors
      • 43.8. Transaction Management
      • 43.9. Errors and Messages
      • 43.10. Trigger Functions
      • 43.11. PL/pgSQL under the Hood
      • 43.12. Tips for Developing in PL/pgSQL
      • 43.13. Porting from Oracle PL/SQL
    • 44. PL/Tcl - Tcl Procedural Language
    • 45. PL/Perl — Perl Procedural Language
    • 46. PL/Python - Python Procedural Language
      • 46.1. PL/Python Functions
      • 46.2. Data Values
      • 46.3. Sharing Data
      • 46.4. Anonymous Code Blocks
      • 46.5. Trigger Functions
      • 46.6. Database Access
      • 46.7. Explicit Subtransactions
      • 46.8. Transaction Management
      • 46.9. Utility Functions
      • 46.10. Python 2 vs. Python 3
      • 46.11. Environment Variables
    • 47. Server Programming Interface
    • 48. Background Worker Processes
    • 49. Logical Decoding
      • 48.1. Logical Decoding Examples
      • 48.2. Logical Decoding Concepts
      • 48.3. Streaming Replication Protocol Interface
      • 48.4. Logical Decoding SQL Interface
      • 48.5. System Catalogs Related to Logical Decoding
      • 48.6. Logical Decoding Output Plugins
      • 48.7. Logical Decoding Output Writers
      • 48.8. Synchronous Replication Support for Logical Decoding
    • 50. Replication Progress Tracking
    • 51. Archive Modules
      • 51.1. Initialization Functions
      • 51.2. Archive Module Callbacks
  • 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 USER
      • ALTER VIEW
      • ANALYZE
      • CLUSTER
      • COMMENT
      • COMMIT PREPARED
      • COPY
      • CREATE ACCESS METHOD
      • CREATE CAST
      • CREATE DATABASE
      • CREATE EVENT TRIGGER
      • 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
      • DEALLOCATE
      • DELETE
      • DO
      • DROP ACCESS METHOD
      • DROP DATABASE
      • DROP EXTENSION
      • DROP FUNCTION
      • DROP INDEX
      • DROP LANGUAGE
      • DROP MATERIALIZED VIEW
      • DROP OWNED
      • DROP POLICY
      • DROP PUBLICATION
      • 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
      • MERGE
      • NOTIFY
      • PREPARE
      • PREPARE TRANSACTION
      • REASSIGN OWNED
      • REFRESH MATERIALIZED VIEW
      • REINDEX
      • RESET
      • REVOKE
      • ROLLBACK PREPARED
      • 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_isready
      • pg_receivewal
      • pg_recvlogical
      • pg_restore
      • pg_verifybackup
      • psql
      • vacuumdb
    • III. PostgreSQL 伺服器應用程式
      • initdb
      • pg_archivecleanup
      • pg_ctl
      • pg_standby
      • pg_test_fsync
      • pg_test_timing
      • pg_upgrade
      • postgres
  • VII. 資料庫進階
    • 52. PostgreSQL 的內部架構
      • 52.1. 處理查詢語句的流程
      • 52.2. 連線是如何被建立的
      • 52.3. 解析器階段
      • 52.4. The PostgreSQL Rule System
      • 52.5. Planner/Optimizer
      • 52.6. Executor
    • 53. 系統資訊目錄
      • 51.3. pg_am
      • 51.7. pg_attribute
      • 51.8. pg_authid
      • 51.9. pg_auth_members
      • 51.10. pg_cast
      • 51.11 pg_class
      • 51.12. pg_collation
      • 51.13. pg_constraint
      • 51.15 pg_database
      • 51.21. pg_event_trigger
      • 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
      • 51.49. pg_statistic
      • 51.50. pg_statistic_ext
      • 51.52. pg_subscription
      • 51.53. pg_subscription_rel
      • 51.54. pg_tablespace
      • 51.56. pg_trigger
      • 51.62. pg_type
      • 51.66. pg_available_extensions
      • 51.67. pg_available_extension_versions
      • 51.71. pg_hba_file_rules
      • 51.72. pg_indexes
      • 51.73. pg_locks
      • 51.77. pg_prepared_xacts
      • 51.79. pg_replication_origin_status
    • 54. System Views
      • 54.1. Overview
      • 54.19. pg_replication_slots
      • 54.20 pg_roles
      • 54.24. pg_settings
      • 54.25. pg_shadow
      • 54.26. pg_shmem_allocations
      • 54.27. pg_stats
      • 54.30. pg_tables
      • 54.31. pg_timezone_abbrevs
      • 54.32. pg_timezone_names
      • 54.33. pg_user
      • 54.35. pg_views
    • 55. 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
    • 56. 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
    • 57. Native Language Support
      • 54.1. For the Translator
      • 54.2. For the Programmer
    • 58. 撰寫程序語言的處理程序
    • 59. 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
    • 60. Writing a Table Sampling Method
    • 61. Writing a Custom Scan Provider
    • 62. 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
    • 63. Table Access Method Interface Definition
    • 64. Index Access Method Interface Definition
    • 65. Generic WAL Records
    • 66. Custom WAL Resource Managers
    • 67. B-Tree Indexes
      • 67.1. Introduction
      • 67.2. Behavior of B-Tree Operator Classes
      • 67.3. B-Tree Support Functions
      • 67.4. Implementation
    • 68. GiST Indexes
      • 64.1. Introduction
      • 64.2. Built-in Operator Classes
      • 64.3. Extensibility
      • 64.4. Implementation
      • 64.5. Examples
    • 69. SP-GiST Indexes
      • 65.1. Introduction
      • 65.2. Built-in Operator Classes
      • 65.3. Extensibility
      • 65.4. Implementation
      • 65.5. Examples
    • 70. GIN 索引
      • 70.1. 簡介
      • 70.2. 內建運算子類
      • 70.3. 延伸介面
      • 70.4. 實作說明
      • 70.5. GIN 小技巧
      • 70.6. 限制
      • 70.7. 範例
    • 71. BRIN Indexes
      • 67.1. Introduction
      • 67.2. Built-in Operator Classes
      • 67.3. Extensibility
    • 72. Hash Indexes
    • 73. 資料庫實體儲存格式
      • 73.1. Database File Layout
      • 73.3. 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
    • 74. System Catalog Declarations and Initial Contents
    • 75. 查詢計畫如何使用統計資訊
      • 70.1. Row Estimation Examples
      • 70.2. 多元統計資訊範例
      • 70.3. Planner Statistics and Security
    • 76. Backup Manifest Format
  • 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. Handling of Invalid or Ambiguous Timestamps
      • B.3. 日期時間慣用字
      • B.4. 日期時間設定檔
      • B.5. POSIX Time Zone Specifications
      • B.6. 日期時間的沿革
      • B.7. Julian Dates
    • C. SQL 關鍵字
    • D. SQL 相容性
      • D.1. Supported Features
      • D.2. Unsupported Features
      • D.3. XML Limits and Conformance to SQL/XML
    • E. 版本資訊
      • E.1. Release 15.2
      • E.2. Release 15.1
      • E.3. Release 15
      • E.4. Prior Releases
    • F. 延伸支援模組
      • F.1. adminpack
      • F.2. amcheck
      • F.3. auth_delay
      • F.4. auto_explain
      • F.5. bloom
      • 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.16. hstore
      • F.24. pg_buffercache
      • F.26. passwordcheck
      • F.29. pg_stat_statements
      • F.30. pgstattuple
      • F.31. pg_trgm
      • F.32. pg_visibility
      • F.38. postgres_fdw
      • F.35. sepgsql
      • F.43. tablefunc
      • F.45. test_decoding
      • F.46. tsm_system_rows
      • F.47. tsm_system_time
      • F.49. uuid-ossp
    • 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
      • J.1. DocBook
      • J.2. Tool Sets
      • J.3. Building the Documentation
      • J.4. Documentation Authoring
      • J.5. Style Guide
    • K. PostgreSQL Limits
    • L. 縮寫字
    • M. Glossary
    • N. 色彩支援
      • N.1. When Color is Used
      • N.2. Configuring the Colors
    • O. Obsolete or Renamed Features
  • 參考書目
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  • 46.2.1. Data Type Mapping
  • 46.2.2. Null, None
  • 46.2.3. Arrays, Lists
  • 46.2.4. Composite Types
  • 46.2.5. Set-Returning Functions

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  1. V. 資料庫程式設計
  2. 46. PL/Python - Python Procedural Language

46.2. Data Values

Generally speaking, the aim of PL/Python is to provide a “natural” mapping between the PostgreSQL and the Python worlds. This informs the data mapping rules described below.

46.2.1. Data Type Mapping

When a PL/Python function is called, its arguments are converted from their PostgreSQL data type to a corresponding Python type:

  • PostgreSQL boolean is converted to Python bool.

  • PostgreSQL smallint, int, bigint and oid are converted to Python int.

  • PostgreSQL real and double are converted to Python float.

  • PostgreSQL numeric is converted to Python Decimal. This type is imported from the cdecimal package if that is available. Otherwise, decimal.Decimal from the standard library will be used. cdecimal is significantly faster than decimal. In Python 3.3 and up, however, cdecimal has been integrated into the standard library under the name decimal, so there is no longer any difference.

  • PostgreSQL bytea is converted to Python bytes.

  • All other data types, including the PostgreSQL character string types, are converted to a Python str (in Unicode like all Python strings).

  • For nonscalar data types, see below.

When a PL/Python function returns, its return value is converted to the function's declared PostgreSQL return data type as follows:

  • When the PostgreSQL return type is boolean, the return value will be evaluated for truth according to the Python rules. That is, 0 and empty string are false, but notably 'f' is true.

  • When the PostgreSQL return type is bytea, the return value will be converted to Python bytes using the respective Python built-ins, with the result being converted to bytea.

  • For all other PostgreSQL return types, the return value is converted to a string using the Python built-in str, and the result is passed to the input function of the PostgreSQL data type. (If the Python value is a float, it is converted using the repr built-in instead of str, to avoid loss of precision.)

    Strings are automatically converted to the PostgreSQL server encoding when they are passed to PostgreSQL.

  • For nonscalar data types, see below.

Note that logical mismatches between the declared PostgreSQL return type and the Python data type of the actual return object are not flagged; the value will be converted in any case.

46.2.2. Null, None

CREATE FUNCTION pymax (a integer, b integer)
  RETURNS integer
AS $$
  if (a is None) or (b is None):
    return None
  if a > b:
    return a
  return b
$$ LANGUAGE plpython3u;

As shown above, to return an SQL null value from a PL/Python function, return the value None. This can be done whether the function is strict or not.

46.2.3. Arrays, Lists

SQL array values are passed into PL/Python as a Python list. To return an SQL array value out of a PL/Python function, return a Python list:

CREATE FUNCTION return_arr()
  RETURNS int[]
AS $$
return [1, 2, 3, 4, 5]
$$ LANGUAGE plpython3u;

SELECT return_arr();
 return_arr
-------------
 {1,2,3,4,5}
(1 row)

Multidimensional arrays are passed into PL/Python as nested Python lists. A 2-dimensional array is a list of lists, for example. When returning a multi-dimensional SQL array out of a PL/Python function, the inner lists at each level must all be of the same size. For example:

CREATE FUNCTION test_type_conversion_array_int4(x int4[]) RETURNS int4[] AS $$
plpy.info(x, type(x))
return x
$$ LANGUAGE plpython3u;

SELECT * FROM test_type_conversion_array_int4(ARRAY[[1,2,3],[4,5,6]]);
INFO:  ([[1, 2, 3], [4, 5, 6]], <type 'list'>)
 test_type_conversion_array_int4
---------------------------------
 {{1,2,3},{4,5,6}}
(1 row)

Other Python sequences, like tuples, are also accepted for backwards-compatibility with PostgreSQL versions 9.6 and below, when multi-dimensional arrays were not supported. However, they are always treated as one-dimensional arrays, because they are ambiguous with composite types. For the same reason, when a composite type is used in a multi-dimensional array, it must be represented by a tuple, rather than a list.

Note that in Python, strings are sequences, which can have undesirable effects that might be familiar to Python programmers:

CREATE FUNCTION return_str_arr()
  RETURNS varchar[]
AS $$
return "hello"
$$ LANGUAGE plpython3u;

SELECT return_str_arr();
 return_str_arr
----------------
 {h,e,l,l,o}
(1 row)

46.2.4. Composite Types

Composite-type arguments are passed to the function as Python mappings. The element names of the mapping are the attribute names of the composite type. If an attribute in the passed row has the null value, it has the value None in the mapping. Here is an example:

CREATE TABLE employee (
  name text,
  salary integer,
  age integer
);

CREATE FUNCTION overpaid (e employee)
  RETURNS boolean
AS $$
  if e["salary"] > 200000:
    return True
  if (e["age"] < 30) and (e["salary"] > 100000):
    return True
  return False
$$ LANGUAGE plpython3u;

There are multiple ways to return row or composite types from a Python function. The following examples assume we have:

CREATE TYPE named_value AS (
  name   text,
  value  integer
);

A composite result can be returned as a:

Sequence type (a tuple or list, but not a set because it is not indexable)

Returned sequence objects must have the same number of items as the composite result type has fields. The item with index 0 is assigned to the first field of the composite type, 1 to the second and so on. For example:

CREATE FUNCTION make_pair (name text, value integer)
  RETURNS named_value
AS $$
  return ( name, value )
  # or alternatively, as list: return [ name, value ]
$$ LANGUAGE plpython3u;

To return an SQL null for any column, insert None at the corresponding position.

When an array of composite types is returned, it cannot be returned as a list, because it is ambiguous whether the Python list represents a composite type, or another array dimension.

Mapping (dictionary)

The value for each result type column is retrieved from the mapping with the column name as key. Example:

CREATE FUNCTION make_pair (name text, value integer)
  RETURNS named_value
AS $$
  return { "name": name, "value": value }
$$ LANGUAGE plpython3u;

Any extra dictionary key/value pairs are ignored. Missing keys are treated as errors. To return an SQL null value for any column, insert None with the corresponding column name as the key.

Object (any object providing method __getattr__)

This works the same as a mapping. Example:

CREATE FUNCTION make_pair (name text, value integer)
  RETURNS named_value
AS $$
  class named_value:
    def __init__ (self, n, v):
      self.name = n
      self.value = v
  return named_value(name, value)

  # or simply
  class nv: pass
  nv.name = name
  nv.value = value
  return nv
$$ LANGUAGE plpython3u;

Functions with OUT parameters are also supported. For example:

CREATE FUNCTION multiout_simple(OUT i integer, OUT j integer) AS $$
return (1, 2)
$$ LANGUAGE plpython3u;

SELECT * FROM multiout_simple();

Output parameters of procedures are passed back the same way. For example:

CREATE PROCEDURE python_triple(INOUT a integer, INOUT b integer) AS $$
return (a * 3, b * 3)
$$ LANGUAGE plpython3u;

CALL python_triple(5, 10);

46.2.5. Set-Returning Functions

A PL/Python function can also return sets of scalar or composite types. There are several ways to achieve this because the returned object is internally turned into an iterator. The following examples assume we have composite type:

CREATE TYPE greeting AS (
  how text,
  who text
);

A set result can be returned from a:

Sequence type (tuple, list, set)

CREATE FUNCTION greet (how text)
  RETURNS SETOF greeting
AS $$
  # return tuple containing lists as composite types
  # all other combinations work also
  return ( [ how, "World" ], [ how, "PostgreSQL" ], [ how, "PL/Python" ] )
$$ LANGUAGE plpython3u;

Iterator (any object providing __iter__ and next methods)

CREATE FUNCTION greet (how text)
  RETURNS SETOF greeting
AS $$
  class producer:
    def __init__ (self, how, who):
      self.how = how
      self.who = who
      self.ndx = -1

    def __iter__ (self):
      return self

    def next (self):
      self.ndx += 1
      if self.ndx == len(self.who):
        raise StopIteration
      return ( self.how, self.who[self.ndx] )

  return producer(how, [ "World", "PostgreSQL", "PL/Python" ])
$$ LANGUAGE plpython3u;

Generator (yield)

CREATE FUNCTION greet (how text)
  RETURNS SETOF greeting
AS $$
  for who in [ "World", "PostgreSQL", "PL/Python" ]:
    yield ( how, who )
$$ LANGUAGE plpython3u;

Set-returning functions with OUT parameters (using RETURNS SETOF record) are also supported. For example:

CREATE FUNCTION multiout_simple_setof(n integer, OUT integer, OUT integer) RETURNS SETOF record AS $$
return [(1, 2)] * n
$$ LANGUAGE plpython3u;

SELECT * FROM multiout_simple_setof(3);
Previous46.1. PL/Python FunctionsNext46.3. Sharing Data

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If an SQL null value is passed to a function, the argument value will appear as None in Python. For example, the function definition of pymax shown in will return the wrong answer for null inputs. We could add STRICT to the function definition to make PostgreSQL do something more reasonable: if a null value is passed, the function will not be called at all, but will just return a null result automatically. Alternatively, we could check for null inputs in the function body:

Section 46.1