PostgreSQL 正體中文使用手冊
PostgreSQL.TW官方使用手冊小島故事加入社團
12
12
  • 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
Powered by GitBook
On this page
  • 7.8.1. SELECT in WITH
  • Note
  • Tip
  • Tip
  • 7.8.2. Data-Modifying Statements in WITH

Was this helpful?

Export as PDF
  1. II. SQL 查詢語言
  2. 7. 資料查詢

7.8. WITH Querys(Common Table Expressions)

WITH provides a way to write auxiliary statements for use in a larger query. These statements, which are often referred to as Common Table Expressions or CTEs, can be thought of as defining temporary tables that exist just for one query. Each auxiliary statement in a WITH clause can be a SELECT, INSERT, UPDATE, or DELETE; and the WITH clause itself is attached to a primary statement that can also be a SELECT, INSERT, UPDATE, or DELETE.

7.8.1. SELECT in WITH

The basic value of SELECT in WITH is to break down complicated queries into simpler parts. An example is:

WITH regional_sales AS (
    SELECT region, SUM(amount) AS total_sales
    FROM orders
    GROUP BY region
), top_regions AS (
    SELECT region
    FROM regional_sales
    WHERE total_sales > (SELECT SUM(total_sales)/10 FROM regional_sales)
)
SELECT region,
       product,
       SUM(quantity) AS product_units,
       SUM(amount) AS product_sales
FROM orders
WHERE region IN (SELECT region FROM top_regions)
GROUP BY region, product;

which displays per-product sales totals in only the top sales regions. The WITH clause defines two auxiliary statements named regional_sales and top_regions, where the output of regional_sales is used in top_regions and the output of top_regions is used in the primary SELECT query. This example could have been written without WITH, but we'd have needed two levels of nested sub-SELECTs. It's a bit easier to follow this way.

The optional RECURSIVE modifier changes WITH from a mere syntactic convenience into a feature that accomplishes things not otherwise possible in standard SQL. Using RECURSIVE, a WITH query can refer to its own output. A very simple example is this query to sum the integers from 1 through 100:

WITH RECURSIVE t(n) AS (
    VALUES (1)
  UNION ALL
    SELECT n+1 FROM t WHERE n < 100
)
SELECT sum(n) FROM t;

The general form of a recursive WITH query is always a non-recursive term, then UNION (or UNION ALL), then a recursive term, where only the recursive term can contain a reference to the query's own output. Such a query is executed as follows:

Recursive Query Evaluation

  1. Evaluate the non-recursive term. For UNION (but not UNION ALL), discard duplicate rows. Include all remaining rows in the result of the recursive query, and also place them in a temporary working table.

  2. So long as the working table is not empty, repeat these steps:

    1. Evaluate the recursive term, substituting the current contents of the working table for the recursive self-reference. For UNION (but not UNION ALL), discard duplicate rows and rows that duplicate any previous result row. Include all remaining rows in the result of the recursive query, and also place them in a temporary intermediate table.

    2. Replace the contents of the working table with the contents of the intermediate table, then empty the intermediate table.

Note

Strictly speaking, this process is iteration not recursion, but RECURSIVE is the terminology chosen by the SQL standards committee.

In the example above, the working table has just a single row in each step, and it takes on the values from 1 through 100 in successive steps. In the 100th step, there is no output because of the WHERE clause, and so the query terminates.

Recursive queries are typically used to deal with hierarchical or tree-structured data. A useful example is this query to find all the direct and indirect sub-parts of a product, given only a table that shows immediate inclusions:

WITH RECURSIVE included_parts(sub_part, part, quantity) AS (
    SELECT sub_part, part, quantity FROM parts WHERE part = 'our_product'
  UNION ALL
    SELECT p.sub_part, p.part, p.quantity
    FROM included_parts pr, parts p
    WHERE p.part = pr.sub_part
)
SELECT sub_part, SUM(quantity) as total_quantity
FROM included_parts
GROUP BY sub_part

When working with recursive queries it is important to be sure that the recursive part of the query will eventually return no tuples, or else the query will loop indefinitely. Sometimes, using UNION instead of UNION ALL can accomplish this by discarding rows that duplicate previous output rows. However, often a cycle does not involve output rows that are completely duplicate: it may be necessary to check just one or a few fields to see if the same point has been reached before. The standard method for handling such situations is to compute an array of the already-visited values. For example, consider the following query that searches a table graph using a link field:

WITH RECURSIVE search_graph(id, link, data, depth) AS (
    SELECT g.id, g.link, g.data, 1
    FROM graph g
  UNION ALL
    SELECT g.id, g.link, g.data, sg.depth + 1
    FROM graph g, search_graph sg
    WHERE g.id = sg.link
)
SELECT * FROM search_graph;

This query will loop if the link relationships contain cycles. Because we require a “depth” output, just changing UNION ALL to UNION would not eliminate the looping. Instead we need to recognize whether we have reached the same row again while following a particular path of links. We add two columns path and cycle to the loop-prone query:

WITH RECURSIVE search_graph(id, link, data, depth, path, cycle) AS (
    SELECT g.id, g.link, g.data, 1,
      ARRAY[g.id],
      false
    FROM graph g
  UNION ALL
    SELECT g.id, g.link, g.data, sg.depth + 1,
      path || g.id,
      g.id = ANY(path)
    FROM graph g, search_graph sg
    WHERE g.id = sg.link AND NOT cycle
)
SELECT * FROM search_graph;

Aside from preventing cycles, the array value is often useful in its own right as representing the “path” taken to reach any particular row.

In the general case where more than one field needs to be checked to recognize a cycle, use an array of rows. For example, if we needed to compare fields f1 and f2:

WITH RECURSIVE search_graph(id, link, data, depth, path, cycle) AS (
    SELECT g.id, g.link, g.data, 1,
      ARRAY[ROW(g.f1, g.f2)],
      false
    FROM graph g
  UNION ALL
    SELECT g.id, g.link, g.data, sg.depth + 1,
      path || ROW(g.f1, g.f2),
      ROW(g.f1, g.f2) = ANY(path)
    FROM graph g, search_graph sg
    WHERE g.id = sg.link AND NOT cycle
)
SELECT * FROM search_graph;

Tip

Omit the ROW() syntax in the common case where only one field needs to be checked to recognize a cycle. This allows a simple array rather than a composite-type array to be used, gaining efficiency.

Tip

The recursive query evaluation algorithm produces its output in breadth-first search order. You can display the results in depth-first search order by making the outer query ORDER BY a “path” column constructed in this way.

A helpful trick for testing queries when you are not certain if they might loop is to place a LIMIT in the parent query. For example, this query would loop forever without the LIMIT:

WITH RECURSIVE t(n) AS (
    SELECT 1
  UNION ALL
    SELECT n+1 FROM t
)
SELECT n FROM t LIMIT 100;

This works because PostgreSQL's implementation evaluates only as many rows of a WITH query as are actually fetched by the parent query. Using this trick in production is not recommended, because other systems might work differently. Also, it usually won't work if you make the outer query sort the recursive query's results or join them to some other table, because in such cases the outer query will usually try to fetch all of the WITH query's output anyway.

A useful property of WITH queries is that they are normally evaluated only once per execution of the parent query, even if they are referred to more than once by the parent query or sibling WITH queries. Thus, expensive calculations that are needed in multiple places can be placed within a WITH query to avoid redundant work. Another possible application is to prevent unwanted multiple evaluations of functions with side-effects. However, the other side of this coin is that the optimizer is not able to push restrictions from the parent query down into a multiply-referenced WITH query, since that might affect all uses of the WITH query's output when it should affect only one. The multiply-referenced WITH query will be evaluated as written, without suppression of rows that the parent query might discard afterwards. (But, as mentioned above, evaluation might stop early if the reference(s) to the query demand only a limited number of rows.)

但是,如果 WITH 查詢是非遞迴且不會在執行中變動的(即它是一個不包含 volatile 函數的 SELECT),則可以將其合併到父查詢之中,從而可以對兩個查詢等級進行聯合語法最佳化。預設情況下,如果父查詢僅引用一次 WITH 語句,而不是多次引用 WITH 一次查詢,則會觸發這個機制。您可以透過指定 MATERIALIZED 強制執行 WITH 查詢的單獨計算,或者透過指定 NOT MATERIALIZED 強制執行將其合併到父查詢中來覆蓋該查詢計畫。後面一種選擇可能會冒著重複計算 WITH 查詢的風險,但如果 WITH 查詢的每次使用只需要 WITH 查詢全部輸出的一小部分,那麼它仍然可以節省成本。

A simple example of these rules is

WITH w AS (
    SELECT * FROM big_table
)
SELECT * FROM w WHERE key = 123;

This WITH query will be folded, producing the same execution plan as

SELECT * FROM big_table WHERE key = 123;

In particular, if there's an index on key, it will probably be used to fetch just the rows having key = 123. On the other hand, in

WITH w AS (
    SELECT * FROM big_table
)
SELECT * FROM w AS w1 JOIN w AS w2 ON w1.key = w2.ref
WHERE w2.key = 123;

the WITH query will be materialized, producing a temporary copy of big_table that is then joined with itself — without benefit of any index. This query will be executed much more efficiently if written as

WITH w AS NOT MATERIALIZED (
    SELECT * FROM big_table
)
SELECT * FROM w AS w1 JOIN w AS w2 ON w1.key = w2.ref
WHERE w2.key = 123;

so that the parent query's restrictions can be applied directly to scans of big_table.

An example where NOT MATERIALIZED could be undesirable is

WITH w AS (
    SELECT key, very_expensive_function(val) as f FROM some_table
)
SELECT * FROM w AS w1 JOIN w AS w2 ON w1.f = w2.f;

Here, materialization of the WITH query ensures that very_expensive_function is evaluated only once per table row, not twice.

The examples above only show WITH being used with SELECT, but it can be attached in the same way to INSERT, UPDATE, or DELETE. In each case it effectively provides temporary table(s) that can be referred to in the main command.

7.8.2. Data-Modifying Statements in WITH

You can use data-modifying statements (INSERT, UPDATE, or DELETE) in WITH. This allows you to perform several different operations in the same query. An example is:

WITH moved_rows AS (
    DELETE FROM products
    WHERE
        "date" >= '2010-10-01' AND
        "date" < '2010-11-01'
    RETURNING *
)
INSERT INTO products_log
SELECT * FROM moved_rows;

This query effectively moves rows from products to products_log. The DELETE in WITH deletes the specified rows from products, returning their contents by means of its RETURNING clause; and then the primary query reads that output and inserts it into products_log.

A fine point of the above example is that the WITH clause is attached to the INSERT, not the sub-SELECT within the INSERT. This is necessary because data-modifying statements are only allowed in WITH clauses that are attached to the top-level statement. However, normal WITH visibility rules apply, so it is possible to refer to the WITH statement's output from the sub-SELECT.

WITH t AS (
    DELETE FROM foo
)
DELETE FROM bar;

This example would remove all rows from tables foo and bar. The number of affected rows reported to the client would only include rows removed from bar.

Recursive self-references in data-modifying statements are not allowed. In some cases it is possible to work around this limitation by referring to the output of a recursive WITH, for example:

WITH RECURSIVE included_parts(sub_part, part) AS (
    SELECT sub_part, part FROM parts WHERE part = 'our_product'
  UNION ALL
    SELECT p.sub_part, p.part
    FROM included_parts pr, parts p
    WHERE p.part = pr.sub_part
)
DELETE FROM parts
  WHERE part IN (SELECT part FROM included_parts);

This query would remove all direct and indirect subparts of a product.

Data-modifying statements in WITH are executed exactly once, and always to completion, independently of whether the primary query reads all (or indeed any) of their output. Notice that this is different from the rule for SELECT in WITH: as stated in the previous section, execution of a SELECT is carried only as far as the primary query demands its output.

WITH t AS (
    UPDATE products SET price = price * 1.05
    RETURNING *
)
SELECT * FROM products;

the outer SELECT would return the original prices before the action of the UPDATE, while in

WITH t AS (
    UPDATE products SET price = price * 1.05
    RETURNING *
)
SELECT * FROM t;

the outer SELECT would return the updated data.

Trying to update the same row twice in a single statement is not supported. Only one of the modifications takes place, but it is not easy (and sometimes not possible) to reliably predict which one. This also applies to deleting a row that was already updated in the same statement: only the update is performed. Therefore you should generally avoid trying to modify a single row twice in a single statement. In particular avoid writing WITH sub-statements that could affect the same rows changed by the main statement or a sibling sub-statement. The effects of such a statement will not be predictable.

At present, any table used as the target of a data-modifying statement in WITH must not have a conditional rule, nor an ALSO rule, nor an INSTEAD rule that expands to multiple statements.

Previous7.7. 列舉資料Next8. 資料型別

Last updated 3 years ago

Was this helpful?

Data-modifying statements in WITH usually have RETURNING clauses (see ), as shown in the example above. It is the output of the RETURNING clause, not the target table of the data-modifying statement, that forms the temporary table that can be referred to by the rest of the query. If a data-modifying statement in WITH lacks a RETURNING clause, then it forms no temporary table and cannot be referred to in the rest of the query. Such a statement will be executed nonetheless. A not-particularly-useful example is:

The sub-statements in WITH are executed concurrently with each other and with the main query. Therefore, when using data-modifying statements in WITH, the order in which the specified updates actually happen is unpredictable. All the statements are executed with the same snapshot (see ), so they cannot “see” one another's effects on the target tables. This alleviates the effects of the unpredictability of the actual order of row updates, and means that RETURNING data is the only way to communicate changes between different WITH sub-statements and the main query. An example of this is that in

Section 6.4
Chapter 13