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. How Connections Are Established
      • 52.3. The Parser Stage
      • 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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  1. V. 資料庫程式設計
  2. 41. 規則系統

41.4. Rules on INSERT, UPDATE, and DELETE

Rules that are defined on INSERT, UPDATE, and DELETE are significantly different from the view rules described in the previous section. First, their CREATE RULE command allows more:

  • They are allowed to have no action.

  • They can have multiple actions.

  • They can be INSTEAD or ALSO (the default).

  • The pseudorelations NEW and OLD become useful.

  • They can have rule qualifications.

Second, they don't modify the query tree in place. Instead they create zero or more new query trees and can throw away the original one.

Caution

In many cases, tasks that could be performed by rules on INSERT/UPDATE/DELETE are better done with triggers. Triggers are notationally a bit more complicated, but their semantics are much simpler to understand. Rules tend to have surprising results when the original query contains volatile functions: volatile functions may get executed more times than expected in the process of carrying out the rules.

Also, there are some cases that are not supported by these types of rules at all, notably including WITH clauses in the original query and multiple-assignment sub-SELECTs in the SET list of UPDATEqueries. This is because copying these constructs into a rule query would result in multiple evaluations of the sub-query, contrary to the express intent of the query's author.

40.4.1. How Update Rules Work

Keep the syntax:

CREATE [ OR REPLACE ] RULE name AS ON event
    TO table [ WHERE condition ]
    DO [ ALSO | INSTEAD ] { NOTHING | command | ( command ; command ... ) }

in mind. In the following, update rules means rules that are defined on INSERT, UPDATE, or DELETE.

Update rules get applied by the rule system when the result relation and the command type of a query tree are equal to the object and event given in the CREATE RULE command. For update rules, the rule system creates a list of query trees. Initially the query-tree list is empty. There can be zero (NOTHING key word), one, or multiple actions. To simplify, we will look at a rule with one action. This rule can have a qualification or not and it can be INSTEAD or ALSO (the default).

What is a rule qualification? It is a restriction that tells when the actions of the rule should be done and when not. This qualification can only reference the pseudorelations NEW and/or OLD, which basically represent the relation that was given as object (but with a special meaning).

So we have three cases that produce the following query trees for a one-action rule.No qualification, with either ALSO or INSTEAD

the query tree from the rule action with the original query tree's qualification addedQualification given and ALSO

the query tree from the rule action with the rule qualification and the original query tree's qualification addedQualification given and INSTEAD

the query tree from the rule action with the rule qualification and the original query tree's qualification; and the original query tree with the negated rule qualification added

Finally, if the rule is ALSO, the unchanged original query tree is added to the list. Since only qualified INSTEAD rules already add the original query tree, we end up with either one or two output query trees for a rule with one action.

For ON INSERT rules, the original query (if not suppressed by INSTEAD) is done before any actions added by rules. This allows the actions to see the inserted row(s). But for ON UPDATE and ON DELETE rules, the original query is done after the actions added by rules. This ensures that the actions can see the to-be-updated or to-be-deleted rows; otherwise, the actions might do nothing because they find no rows matching their qualifications.

The query trees generated from rule actions are thrown into the rewrite system again, and maybe more rules get applied resulting in more or less query trees. So a rule's actions must have either a different command type or a different result relation than the rule itself is on, otherwise this recursive process will end up in an infinite loop. (Recursive expansion of a rule will be detected and reported as an error.)

The query trees found in the actions of the pg_rewrite system catalog are only templates. Since they can reference the range-table entries for NEW and OLD, some substitutions have to be made before they can be used. For any reference to NEW, the target list of the original query is searched for a corresponding entry. If found, that entry's expression replaces the reference. Otherwise, NEW means the same as OLD (for an UPDATE) or is replaced by a null value (for an INSERT). Any reference to OLD is replaced by a reference to the range-table entry that is the result relation.

After the system is done applying update rules, it applies view rules to the produced query tree(s). Views cannot insert new update actions so there is no need to apply update rules to the output of view rewriting.

40.4.1.1. A First Rule Step by Step

Say we want to trace changes to the sl_avail column in the shoelace_data relation. So we set up a log table and a rule that conditionally writes a log entry when an UPDATE is performed on shoelace_data.

CREATE TABLE shoelace_log (
    sl_name    text,          -- shoelace changed
    sl_avail   integer,       -- new available value
    log_who    text,          -- who did it
    log_when   timestamp      -- when
);

CREATE RULE log_shoelace AS ON UPDATE TO shoelace_data
    WHERE NEW.sl_avail <> OLD.sl_avail
    DO INSERT INTO shoelace_log VALUES (
                                    NEW.sl_name,
                                    NEW.sl_avail,
                                    current_user,
                                    current_timestamp
                                );

Now someone does:

UPDATE shoelace_data SET sl_avail = 6 WHERE sl_name = 'sl7';

and we look at the log table:

SELECT * FROM shoelace_log;

 sl_name | sl_avail | log_who | log_when                        
---------+----------+---------+----------------------------------
 sl7     |        6 | Al      | Tue Oct 20 16:14:45 1998 MET DST
(1 row)

That's what we expected. What happened in the background is the following. The parser created the query tree:

UPDATE shoelace_data SET sl_avail = 6
  FROM shoelace_data shoelace_data
 WHERE shoelace_data.sl_name = 'sl7';

There is a rule log_shoelace that is ON UPDATE with the rule qualification expression:

NEW.sl_avail <> OLD.sl_avail

and the action:

INSERT INTO shoelace_log VALUES (
       new.sl_name, new.sl_avail,
       current_user, current_timestamp )
  FROM shoelace_data new, shoelace_data old;

(This looks a little strange since you cannot normally write INSERT ... VALUES ... FROM. The FROM clause here is just to indicate that there are range-table entries in the query tree for new and old. These are needed so that they can be referenced by variables in the INSERT command's query tree.)

The rule is a qualified ALSO rule, so the rule system has to return two query trees: the modified rule action and the original query tree. In step 1, the range table of the original query is incorporated into the rule's action query tree. This results in:

INSERT INTO shoelace_log VALUES (
       new.sl_name, new.sl_avail,
       current_user, current_timestamp )
  FROM shoelace_data new, shoelace_data old,
       shoelace_data shoelace_data;

In step 2, the rule qualification is added to it, so the result set is restricted to rows where sl_avail changes:

INSERT INTO shoelace_log VALUES (
       new.sl_name, new.sl_avail,
       current_user, current_timestamp )
  FROM shoelace_data new, shoelace_data old,
       shoelace_data shoelace_data
 WHERE new.sl_avail <> old.sl_avail;

(This looks even stranger, since INSERT ... VALUES doesn't have a WHERE clause either, but the planner and executor will have no difficulty with it. They need to support this same functionality anyway for INSERT ... SELECT.)

In step 3, the original query tree's qualification is added, restricting the result set further to only the rows that would have been touched by the original query:

INSERT INTO shoelace_log VALUES (
       new.sl_name, new.sl_avail,
       current_user, current_timestamp )
  FROM shoelace_data new, shoelace_data old,
       shoelace_data shoelace_data
 WHERE new.sl_avail <> old.sl_avail
   AND shoelace_data.sl_name = 'sl7';

Step 4 replaces references to NEW by the target list entries from the original query tree or by the matching variable references from the result relation:

INSERT INTO shoelace_log VALUES (
       shoelace_data.sl_name, 6,
       current_user, current_timestamp )
  FROM shoelace_data new, shoelace_data old,
       shoelace_data shoelace_data
 WHERE 6 <> old.sl_avail
   AND shoelace_data.sl_name = 'sl7';

Step 5 changes OLD references into result relation references:

INSERT INTO shoelace_log VALUES (
       shoelace_data.sl_name, 6,
       current_user, current_timestamp )
  FROM shoelace_data new, shoelace_data old,
       shoelace_data shoelace_data
 WHERE 6 <> shoelace_data.sl_avail
   AND shoelace_data.sl_name = 'sl7';

That's it. Since the rule is ALSO, we also output the original query tree. In short, the output from the rule system is a list of two query trees that correspond to these statements:

INSERT INTO shoelace_log VALUES (
       shoelace_data.sl_name, 6,
       current_user, current_timestamp )
  FROM shoelace_data
 WHERE 6 <> shoelace_data.sl_avail
   AND shoelace_data.sl_name = 'sl7';

UPDATE shoelace_data SET sl_avail = 6
 WHERE sl_name = 'sl7';

These are executed in this order, and that is exactly what the rule was meant to do.

The substitutions and the added qualifications ensure that, if the original query would be, say:

UPDATE shoelace_data SET sl_color = 'green'
 WHERE sl_name = 'sl7';

no log entry would get written. In that case, the original query tree does not contain a target list entry for sl_avail, so NEW.sl_avail will get replaced by shoelace_data.sl_avail. Thus, the extra command generated by the rule is:

INSERT INTO shoelace_log VALUES (
       shoelace_data.sl_name, shoelace_data.sl_avail,
       current_user, current_timestamp )
  FROM shoelace_data
 WHERE shoelace_data.sl_avail <> shoelace_data.sl_avail
   AND shoelace_data.sl_name = 'sl7';

and that qualification will never be true.

It will also work if the original query modifies multiple rows. So if someone issued the command:

UPDATE shoelace_data SET sl_avail = 0
 WHERE sl_color = 'black';

four rows in fact get updated (sl1, sl2, sl3, and sl4). But sl3 already has sl_avail = 0. In this case, the original query trees qualification is different and that results in the extra query tree:

INSERT INTO shoelace_log
SELECT shoelace_data.sl_name, 0,
       current_user, current_timestamp
  FROM shoelace_data
 WHERE 0 <> shoelace_data.sl_avail
   AND shoelace_data.sl_color = 'black';

being generated by the rule. This query tree will surely insert three new log entries. And that's absolutely correct.

Here we can see why it is important that the original query tree is executed last. If the UPDATE had been executed first, all the rows would have already been set to zero, so the logging INSERT would not find any row where 0 <> shoelace_data.sl_avail.

40.4.2. Cooperation with Views

A simple way to protect view relations from the mentioned possibility that someone can try to run INSERT, UPDATE, or DELETE on them is to let those query trees get thrown away. So we could create the rules:

CREATE RULE shoe_ins_protect AS ON INSERT TO shoe
    DO INSTEAD NOTHING;
CREATE RULE shoe_upd_protect AS ON UPDATE TO shoe
    DO INSTEAD NOTHING;
CREATE RULE shoe_del_protect AS ON DELETE TO shoe
    DO INSTEAD NOTHING;

If someone now tries to do any of these operations on the view relation shoe, the rule system will apply these rules. Since the rules have no actions and are INSTEAD, the resulting list of query trees will be empty and the whole query will become nothing because there is nothing left to be optimized or executed after the rule system is done with it.

A more sophisticated way to use the rule system is to create rules that rewrite the query tree into one that does the right operation on the real tables. To do that on the shoelace view, we create the following rules:

CREATE RULE shoelace_ins AS ON INSERT TO shoelace
    DO INSTEAD
    INSERT INTO shoelace_data VALUES (
           NEW.sl_name,
           NEW.sl_avail,
           NEW.sl_color,
           NEW.sl_len,
           NEW.sl_unit
    );

CREATE RULE shoelace_upd AS ON UPDATE TO shoelace
    DO INSTEAD
    UPDATE shoelace_data
       SET sl_name = NEW.sl_name,
           sl_avail = NEW.sl_avail,
           sl_color = NEW.sl_color,
           sl_len = NEW.sl_len,
           sl_unit = NEW.sl_unit
     WHERE sl_name = OLD.sl_name;

CREATE RULE shoelace_del AS ON DELETE TO shoelace
    DO INSTEAD
    DELETE FROM shoelace_data
     WHERE sl_name = OLD.sl_name;

If you want to support RETURNING queries on the view, you need to make the rules include RETURNING clauses that compute the view rows. This is usually pretty trivial for views on a single table, but it's a bit tedious for join views such as shoelace. An example for the insert case is:

CREATE RULE shoelace_ins AS ON INSERT TO shoelace
    DO INSTEAD
    INSERT INTO shoelace_data VALUES (
           NEW.sl_name,
           NEW.sl_avail,
           NEW.sl_color,
           NEW.sl_len,
           NEW.sl_unit
    )
    RETURNING
           shoelace_data.*,
           (SELECT shoelace_data.sl_len * u.un_fact
            FROM unit u WHERE shoelace_data.sl_unit = u.un_name);

Note that this one rule supports both INSERT and INSERT RETURNING queries on the view — the RETURNING clause is simply ignored for INSERT.

Now assume that once in a while, a pack of shoelaces arrives at the shop and a big parts list along with it. But you don't want to manually update the shoelace view every time. Instead we set up two little tables: one where you can insert the items from the part list, and one with a special trick. The creation commands for these are:

CREATE TABLE shoelace_arrive (
    arr_name    text,
    arr_quant   integer
);

CREATE TABLE shoelace_ok (
    ok_name     text,
    ok_quant    integer
);

CREATE RULE shoelace_ok_ins AS ON INSERT TO shoelace_ok
    DO INSTEAD
    UPDATE shoelace
       SET sl_avail = sl_avail + NEW.ok_quant
     WHERE sl_name = NEW.ok_name;

Now you can fill the table shoelace_arrive with the data from the parts list:

SELECT * FROM shoelace_arrive;

 arr_name | arr_quant
----------+-----------
 sl3      |        10
 sl6      |        20
 sl8      |        20
(3 rows)

Take a quick look at the current data:

SELECT * FROM shoelace;

 sl_name  | sl_avail | sl_color | sl_len | sl_unit | sl_len_cm
----------+----------+----------+--------+---------+-----------
 sl1      |        5 | black    |     80 | cm      |        80
 sl2      |        6 | black    |    100 | cm      |       100
 sl7      |        6 | brown    |     60 | cm      |        60
 sl3      |        0 | black    |     35 | inch    |      88.9
 sl4      |        8 | black    |     40 | inch    |     101.6
 sl8      |        1 | brown    |     40 | inch    |     101.6
 sl5      |        4 | brown    |      1 | m       |       100
 sl6      |        0 | brown    |    0.9 | m       |        90
(8 rows)

Now move the arrived shoelaces in:

INSERT INTO shoelace_ok SELECT * FROM shoelace_arrive;

and check the results:

SELECT * FROM shoelace ORDER BY sl_name;

 sl_name  | sl_avail | sl_color | sl_len | sl_unit | sl_len_cm
----------+----------+----------+--------+---------+-----------
 sl1      |        5 | black    |     80 | cm      |        80
 sl2      |        6 | black    |    100 | cm      |       100
 sl7      |        6 | brown    |     60 | cm      |        60
 sl4      |        8 | black    |     40 | inch    |     101.6
 sl3      |       10 | black    |     35 | inch    |      88.9
 sl8      |       21 | brown    |     40 | inch    |     101.6
 sl5      |        4 | brown    |      1 | m       |       100
 sl6      |       20 | brown    |    0.9 | m       |        90
(8 rows)

SELECT * FROM shoelace_log;

 sl_name | sl_avail | log_who| log_when                        
---------+----------+--------+----------------------------------
 sl7     |        6 | Al     | Tue Oct 20 19:14:45 1998 MET DST
 sl3     |       10 | Al     | Tue Oct 20 19:25:16 1998 MET DST
 sl6     |       20 | Al     | Tue Oct 20 19:25:16 1998 MET DST
 sl8     |       21 | Al     | Tue Oct 20 19:25:16 1998 MET DST
(4 rows)

It's a long way from the one INSERT ... SELECT to these results. And the description of the query-tree transformation will be the last in this chapter. First, there is the parser's output:

INSERT INTO shoelace_ok
SELECT shoelace_arrive.arr_name, shoelace_arrive.arr_quant
  FROM shoelace_arrive shoelace_arrive, shoelace_ok shoelace_ok;

Now the first rule shoelace_ok_ins is applied and turns this into:

UPDATE shoelace
   SET sl_avail = shoelace.sl_avail + shoelace_arrive.arr_quant
  FROM shoelace_arrive shoelace_arrive, shoelace_ok shoelace_ok,
       shoelace_ok old, shoelace_ok new,
       shoelace shoelace
 WHERE shoelace.sl_name = shoelace_arrive.arr_name;

and throws away the original INSERT on shoelace_ok. This rewritten query is passed to the rule system again, and the second applied rule shoelace_upd produces:

UPDATE shoelace_data
   SET sl_name = shoelace.sl_name,
       sl_avail = shoelace.sl_avail + shoelace_arrive.arr_quant,
       sl_color = shoelace.sl_color,
       sl_len = shoelace.sl_len,
       sl_unit = shoelace.sl_unit
  FROM shoelace_arrive shoelace_arrive, shoelace_ok shoelace_ok,
       shoelace_ok old, shoelace_ok new,
       shoelace shoelace, shoelace old,
       shoelace new, shoelace_data shoelace_data
 WHERE shoelace.sl_name = shoelace_arrive.arr_name
   AND shoelace_data.sl_name = shoelace.sl_name;

Again it's an INSTEAD rule and the previous query tree is trashed. Note that this query still uses the view shoelace. But the rule system isn't finished with this step, so it continues and applies the _RETURN rule on it, and we get:

UPDATE shoelace_data
   SET sl_name = s.sl_name,
       sl_avail = s.sl_avail + shoelace_arrive.arr_quant,
       sl_color = s.sl_color,
       sl_len = s.sl_len,
       sl_unit = s.sl_unit
  FROM shoelace_arrive shoelace_arrive, shoelace_ok shoelace_ok,
       shoelace_ok old, shoelace_ok new,
       shoelace shoelace, shoelace old,
       shoelace new, shoelace_data shoelace_data,
       shoelace old, shoelace new,
       shoelace_data s, unit u
 WHERE s.sl_name = shoelace_arrive.arr_name
   AND shoelace_data.sl_name = s.sl_name;

Finally, the rule log_shoelace gets applied, producing the extra query tree:

INSERT INTO shoelace_log
SELECT s.sl_name,
       s.sl_avail + shoelace_arrive.arr_quant,
       current_user,
       current_timestamp
  FROM shoelace_arrive shoelace_arrive, shoelace_ok shoelace_ok,
       shoelace_ok old, shoelace_ok new,
       shoelace shoelace, shoelace old,
       shoelace new, shoelace_data shoelace_data,
       shoelace old, shoelace new,
       shoelace_data s, unit u,
       shoelace_data old, shoelace_data new
       shoelace_log shoelace_log
 WHERE s.sl_name = shoelace_arrive.arr_name
   AND shoelace_data.sl_name = s.sl_name
   AND (s.sl_avail + shoelace_arrive.arr_quant) <> s.sl_avail;

After that the rule system runs out of rules and returns the generated query trees.

So we end up with two final query trees that are equivalent to the SQL statements:

INSERT INTO shoelace_log
SELECT s.sl_name,
       s.sl_avail + shoelace_arrive.arr_quant,
       current_user,
       current_timestamp
  FROM shoelace_arrive shoelace_arrive, shoelace_data shoelace_data,
       shoelace_data s
 WHERE s.sl_name = shoelace_arrive.arr_name
   AND shoelace_data.sl_name = s.sl_name
   AND s.sl_avail + shoelace_arrive.arr_quant <> s.sl_avail;

UPDATE shoelace_data
   SET sl_avail = shoelace_data.sl_avail + shoelace_arrive.arr_quant
  FROM shoelace_arrive shoelace_arrive,
       shoelace_data shoelace_data,
       shoelace_data s
 WHERE s.sl_name = shoelace_arrive.sl_name
   AND shoelace_data.sl_name = s.sl_name;

The result is that data coming from one relation inserted into another, changed into updates on a third, changed into updating a fourth plus logging that final update in a fifth gets reduced into two queries.

There is a little detail that's a bit ugly. Looking at the two queries, it turns out that the shoelace_data relation appears twice in the range table where it could definitely be reduced to one. The planner does not handle it and so the execution plan for the rule systems output of the INSERT will be

Nested Loop
  ->  Merge Join
        ->  Seq Scan
              ->  Sort
                    ->  Seq Scan on s
        ->  Seq Scan
              ->  Sort
                    ->  Seq Scan on shoelace_arrive
  ->  Seq Scan on shoelace_data

while omitting the extra range table entry would result in a

Merge Join
  ->  Seq Scan
        ->  Sort
              ->  Seq Scan on s
  ->  Seq Scan
        ->  Sort
              ->  Seq Scan on shoelace_arrive

which produces exactly the same entries in the log table. Thus, the rule system caused one extra scan on the table shoelace_data that is absolutely not necessary. And the same redundant scan is done once more in the UPDATE. But it was a really hard job to make that all possible at all.

Now we make a final demonstration of the PostgreSQL rule system and its power. Say you add some shoelaces with extraordinary colors to your database:

INSERT INTO shoelace VALUES ('sl9', 0, 'pink', 35.0, 'inch', 0.0);
INSERT INTO shoelace VALUES ('sl10', 1000, 'magenta', 40.0, 'inch', 0.0);

We would like to make a view to check which shoelace entries do not fit any shoe in color. The view for this is:

CREATE VIEW shoelace_mismatch AS
    SELECT * FROM shoelace WHERE NOT EXISTS
        (SELECT shoename FROM shoe WHERE slcolor = sl_color);

Its output is:

SELECT * FROM shoelace_mismatch;

 sl_name | sl_avail | sl_color | sl_len | sl_unit | sl_len_cm
---------+----------+----------+--------+---------+-----------
 sl9     |        0 | pink     |     35 | inch    |      88.9
 sl10    |     1000 | magenta  |     40 | inch    |     101.6

Now we want to set it up so that mismatching shoelaces that are not in stock are deleted from the database. To make it a little harder for PostgreSQL, we don't delete it directly. Instead we create one more view:

CREATE VIEW shoelace_can_delete AS
    SELECT * FROM shoelace_mismatch WHERE sl_avail = 0;

and do it this way:

DELETE FROM shoelace WHERE EXISTS
    (SELECT * FROM shoelace_can_delete
             WHERE sl_name = shoelace.sl_name);

Voilà:

SELECT * FROM shoelace;

 sl_name | sl_avail | sl_color | sl_len | sl_unit | sl_len_cm
---------+----------+----------+--------+---------+-----------
 sl1     |        5 | black    |     80 | cm      |        80
 sl2     |        6 | black    |    100 | cm      |       100
 sl7     |        6 | brown    |     60 | cm      |        60
 sl4     |        8 | black    |     40 | inch    |     101.6
 sl3     |       10 | black    |     35 | inch    |      88.9
 sl8     |       21 | brown    |     40 | inch    |     101.6
 sl10    |     1000 | magenta  |     40 | inch    |     101.6
 sl5     |        4 | brown    |      1 | m       |       100
 sl6     |       20 | brown    |    0.9 | m       |        90
(9 rows)

A DELETE on a view, with a subquery qualification that in total uses 4 nesting/joined views, where one of them itself has a subquery qualification containing a view and where calculated view columns are used, gets rewritten into one single query tree that deletes the requested data from a real table.

There are probably only a few situations out in the real world where such a construct is necessary. But it makes you feel comfortable that it works.

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