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. 限制條件
      • 5.4. 系統欄位
      • 5.5. 表格變更
      • 5.6. 權限
      • 5.7. 資料列安全原則
      • 5.8. Schemas
      • 5.9. 繼承
      • 5.10. 分割資料表
      • 5.11. 外部資料
      • 5.12. 其他資料庫物件
      • 5.13. 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. 遞迴查詢(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. 指標型別
      • 8.19. pg_lsn型別
      • 8.20. 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
    • 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. 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 SSH Tunnels
      • 18.11. 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. Run-time Statistics
      • 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
    • 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. File System Level Backup
      • 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. Recovery Configuration
      • 27.1. Archive Recovery Settings
      • 27.2. Recovery Target Settings
      • 27.3. Standby Server Settings
    • 28. 監控資料庫活動
      • 28.1. Standard Unix Tools
      • 28.2. 統計資訊收集器
      • 28.3. Viewing Locks
      • 28.4. Progress Reporting
      • 28.5. Dynamic Tracing
    • 29. Monitoring Disk Usage
      • 29.1. Determining Disk Usage
      • 29.2. Disk Full Failure
    • 30. 高可靠度及預寫日誌
      • 30.1. Reliability
      • 30.2. Write-Ahead Logging (WAL)
      • 30.3. Asynchronous Commit
      • 30.4. WAL Configuration
      • 30.5. WAL Internals
    • 31. 邏輯複寫(Logical Replication)
      • 31.1. 發佈(Publication)
      • 31.2. 訂閱(Subscription)
      • 31.3. 衝突處理
      • 31.4. 限制
      • 31.5. 架構
      • 31.6. 監控
      • 31.7. 安全性
      • 31.8. 系統設定
      • 31.9. 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
      • 34.1. 資料庫連線控制函數
      • 34.2. 連線狀態函數
      • 34.3. Command Execution Functions
      • 34.4. Asynchronous Command Processing
      • 34.5. Retrieving Query Results Row-By-Row
      • 34.6. Canceling Queries in Progress
      • 34.7. The Fast-Path Interface
      • 34.8. Asynchronous Notification
      • 34.9. Functions Associated with the COPY Command
      • 34.10. Control Functions
      • 34.11. Miscellaneous Functions
      • 34.12. Notice Processing
      • 34.13. Event System
      • 34.14. 環境變數
      • 34.15. 密碼檔
      • 34.16. The Connection Service File
      • 34.17. LDAP Lookup of Connection Parameters
      • 34.18. SSL Support
      • 34.19. Behavior in Threaded Programs
      • 34.20. Building libpq Programs
      • 34.21. 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
      • 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
    • 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
      • 37.6. 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_domain_usage
      • 37.13. column_options
      • 37.14. column_privileges
      • 37.15. column_udt_usage
      • 37.16. columns
      • 37.17. 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
      • 37.31. key_column_usage
      • 37.32. parameters
      • 37.33. 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
      • 37.41. 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
      • 37.50. 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
    • 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. User-defined Aggregates
      • 38.12. User-defined Types
      • 38.13. User-defined Operators
      • 38.14. Operator Optimization Information
      • 38.15. Interfacing Extensions To Indexes
      • 38.16. Packaging Related Objects into an Extension
      • 38.17. Extension Building Infrastructure
    • 39. Triggers
    • 40. Event Triggers
    • 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
      • 43.5. 基本語法
    • 44. PL/Tcl - Tcl Procedural Language
    • 45. PL/Perl - Perl Procedural Language
    • 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 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
      • 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
      • pgbench
      • pg_dump
      • psql
      • vacuumdb
    • III. PostgreSQL 伺服器應用程式
      • pg_test_timing
      • postgres
  • VII. 資料庫進階
    • 52. 系統目錄
      • 52.3. pg_am
      • 52.7. pg_attribute
      • 52.8. pg_authid
      • 52.9. pg_auth_members
      • 52.11 pg_class
      • 52.12. pg_collation
      • 52.13. pg_constraint
      • 52.15 pg_database
      • 52.26 pg_index
      • 52.29. pg_language
      • 52.32. pg_namespace
      • 52.33. pg_opclass
      • 52.38. pg_policy
      • 52.39. pg_proc
      • 52.44. pg_rewrite
      • 52.50. pg_statistic
      • 52.51. pg_statistic_ext
      • 52.54. pg_tablespace
      • 52.56. pg_trigger
      • 52.62. pg_type
      • 52.79. pg_replication_origin_status
      • 52.81 pg_roles
      • 52.85. pg_settings
      • 52.87. pg_stats
    • 53. Frontend/Backend Protocol
      • 53.1. Overview
      • 53.2. Message Flow
      • 53.3. SASL Authentication
      • 53.4. Streaming Replication Protocol
      • 53.5. Logical Streaming Replication Protocol
      • 53.6. Message Data Types
      • 53.7. Message Formats
      • 53.8. Error and Notice Message Fields
      • 53.9. Logical Replication Message Formats
      • 53.10. Summary of Changes since Protocol 2.0
    • 54. PostgreSQL 程式撰寫慣例
      • 54.1. Formatting
      • 54.2. Reporting Errors Within the Server
      • 54.3. Error Message Style Guide
      • 54.4. Miscellaneous Coding Conventions
    • 56. Writing A Procedural Language Handler
    • 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. Extensibility
      • 66.4. Implementation
      • 66.5. GIN Tips and Tricks
      • 66.6. Limitations
      • 66.7. Examples
    • 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
      • 68.2. TOAST
      • 68.4 可視性映射表(Visibility Map)
    • 70. How the Planner Uses Statistics
      • 70.2. Multivariate Statistics Examples
  • 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.11. dblink
        • dblink
      • F.33. pg_visibility
    • G. Additional Supplied Programs
      • G.1. Client Applications
        • oid2name
        • vacuumlo
      • G.2. Server Applications
        • pg_standby
    • H. 外部專案
      • H.1. 用戶端介面
      • H.2. Administration Tools
      • H.3. Procedural Languages
      • H.4. Extensions
    • I. The Source Code Repository
      • I.1. Getting The Source via Git
    • J. 文件取得
    • K. 縮寫字
  • 參考書目
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  • 38.5.1. Arguments for SQL Functions
  • 38.5.2. SQL Functions on Base Types
  • 38.5.3. SQL Functions on Composite Types
  • 38.5.4. SQL Functions with Output Parameters
  • 38.5.5. SQL Functions with Variable Numbers of Arguments
  • 38.5.6. SQL Functions with Default Values for Arguments
  • 38.5.7. SQL Functions as Table Sources
  • 38.5.8. SQL Functions Returning Sets
  • 38.5.9. SQL Functions Returning TABLE
  • 38.5.10. Polymorphic SQL Functions
  • 38.5.11. SQL Functions with Collations

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  1. V. 資料庫程式設計
  2. 38. SQL 延伸功能

38.5. Query Language (SQL) Functions

版本:11

Previous38.4. User-defined ProceduresNext38.6. Function Overloading

Last updated 6 years ago

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SQL functions execute an arbitrary list of SQL statements, returning the result of the last query in the list. In the simple (non-set) case, the first row of the last query's result will be returned. (Bear in mind that “the first row” of a multirow result is not well-defined unless you use ORDER BY.) If the last query happens to return no rows at all, the null value will be returned.

Alternatively, an SQL function can be declared to return a set (that is, multiple rows) by specifying the function's return type as SETOF sometype, or equivalently by declaring it as RETURNS TABLE(columns). In this case all rows of the last query's result are returned. Further details appear below.

The body of an SQL function must be a list of SQL statements separated by semicolons. A semicolon after the last statement is optional. Unless the function is declared to return void, the last statement must be a SELECT, or an INSERT, UPDATE, or DELETE that has a RETURNING clause.

Any collection of commands in the SQL language can be packaged together and defined as a function. Besides SELECT queries, the commands can include data modification queries (INSERT, UPDATE, and DELETE), as well as other SQL commands. (You cannot use transaction control commands, e.g. COMMIT, SAVEPOINT, and some utility commands, e.g. VACUUM, in SQL functions.) However, the final command must be a SELECT or have a RETURNING clause that returns whatever is specified as the function's return type. Alternatively, if you want to define a SQL function that performs actions but has no useful value to return, you can define it as returning void. For example, this function removes rows with negative salaries from the emp table:

CREATE FUNCTION clean_emp() RETURNS void AS '
    DELETE FROM emp
        WHERE salary < 0;
' LANGUAGE SQL;

SELECT clean_emp();

 clean_emp
-----------

(1 row)

Note

The entire body of a SQL function is parsed before any of it is executed. While a SQL function can contain commands that alter the system catalogs (e.g., CREATE TABLE), the effects of such commands will not be visible during parse analysis of later commands in the function. Thus, for example, CREATE TABLE foo (...); INSERT INTO foo VALUES(...); will not work as desired if packaged up into a single SQL function, since foo won't exist yet when the INSERT command is parsed. It's recommended to use PL/pgSQL instead of a SQL function in this type of situation.

The syntax of the CREATE FUNCTION command requires the function body to be written as a string constant. It is usually most convenient to use dollar quoting (see ) for the string constant. If you choose to use regular single-quoted string constant syntax, you must double single quote marks (') and backslashes (\) (assuming escape string syntax) in the body of the function (see ).

38.5.1. Arguments for SQL Functions

Arguments of a SQL function can be referenced in the function body using either names or numbers. Examples of both methods appear below.

To use a name, declare the function argument as having a name, and then just write that name in the function body. If the argument name is the same as any column name in the current SQL command within the function, the column name will take precedence. To override this, qualify the argument name with the name of the function itself, that is function_name.argument_name. (If this would conflict with a qualified column name, again the column name wins. You can avoid the ambiguity by choosing a different alias for the table within the SQL command.)

In the older numeric approach, arguments are referenced using the syntax $n: $1 refers to the first input argument, $2 to the second, and so on. This will work whether or not the particular argument was declared with a name.

If an argument is of a composite type, then the dot notation, e.g., argname.fieldname or $1.fieldname, can be used to access attributes of the argument. Again, you might need to qualify the argument's name with the function name to make the form with an argument name unambiguous.

SQL function arguments can only be used as data values, not as identifiers. Thus for example this is reasonable:

INSERT INTO mytable VALUES ($1);

but this will not work:

INSERT INTO $1 VALUES (42);

Note

The ability to use names to reference SQL function arguments was added in PostgreSQL9.2. Functions to be used in older servers must use the $n notation.

38.5.2. SQL Functions on Base Types

The simplest possible SQL function has no arguments and simply returns a base type, such as integer:

CREATE FUNCTION one() RETURNS integer AS $$
    SELECT 1 AS result;
$$ LANGUAGE SQL;

-- Alternative syntax for string literal:
CREATE FUNCTION one() RETURNS integer AS '
    SELECT 1 AS result;
' LANGUAGE SQL;

SELECT one();

 one
-----
   1

Notice that we defined a column alias within the function body for the result of the function (with the name result), but this column alias is not visible outside the function. Hence, the result is labeled one instead of result.

It is almost as easy to define SQL functions that take base types as arguments:

CREATE FUNCTION add_em(x integer, y integer) RETURNS integer AS $$
    SELECT x + y;
$$ LANGUAGE SQL;

SELECT add_em(1, 2) AS answer;

 answer
--------
      3

Alternatively, we could dispense with names for the arguments and use numbers:

CREATE FUNCTION add_em(integer, integer) RETURNS integer AS $$
    SELECT $1 + $2;
$$ LANGUAGE SQL;

SELECT add_em(1, 2) AS answer;

 answer
--------
      3

Here is a more useful function, which might be used to debit a bank account:

CREATE FUNCTION tf1 (accountno integer, debit numeric) RETURNS numeric AS $$
    UPDATE bank
        SET balance = balance - debit
        WHERE accountno = tf1.accountno;
    SELECT 1;
$$ LANGUAGE SQL;

A user could execute this function to debit account 17 by $100.00 as follows:

SELECT tf1(17, 100.0);

In this example, we chose the name accountno for the first argument, but this is the same as the name of a column in the bank table. Within the UPDATE command, accountno refers to the column bank.accountno, so tf1.accountno must be used to refer to the argument. We could of course avoid this by using a different name for the argument.

In practice one would probably like a more useful result from the function than a constant 1, so a more likely definition is:

CREATE FUNCTION tf1 (accountno integer, debit numeric) RETURNS numeric AS $$
    UPDATE bank
        SET balance = balance - debit
        WHERE accountno = tf1.accountno;
    SELECT balance FROM bank WHERE accountno = tf1.accountno;
$$ LANGUAGE SQL;

which adjusts the balance and returns the new balance. The same thing could be done in one command using RETURNING:

CREATE FUNCTION tf1 (accountno integer, debit numeric) RETURNS numeric AS $$
    UPDATE bank
        SET balance = balance - debit
        WHERE accountno = tf1.accountno
    RETURNING balance;
$$ LANGUAGE SQL;

A SQL function must return exactly its declared result type. This may require inserting an explicit cast. For example, suppose we wanted the previous add_em function to return type float8 instead. This won't work:

CREATE FUNCTION add_em(integer, integer) RETURNS float8 AS $$
    SELECT $1 + $2;
$$ LANGUAGE SQL;

even though in other contexts PostgreSQL would be willing to insert an implicit cast to convert integer to float8. We need to write it as

CREATE FUNCTION add_em(integer, integer) RETURNS float8 AS $$
    SELECT ($1 + $2)::float8;
$$ LANGUAGE SQL;

38.5.3. SQL Functions on Composite Types

When writing functions with arguments of composite types, we must not only specify which argument we want but also the desired attribute (field) of that argument. For example, suppose that emp is a table containing employee data, and therefore also the name of the composite type of each row of the table. Here is a function double_salary that computes what someone's salary would be if it were doubled:

CREATE TABLE emp (
    name        text,
    salary      numeric,
    age         integer,
    cubicle     point
);

INSERT INTO emp VALUES ('Bill', 4200, 45, '(2,1)');

CREATE FUNCTION double_salary(emp) RETURNS numeric AS $$
    SELECT $1.salary * 2 AS salary;
$$ LANGUAGE SQL;

SELECT name, double_salary(emp.*) AS dream
    FROM emp
    WHERE emp.cubicle ~= point '(2,1)';

 name | dream
------+-------
 Bill |  8400

Notice the use of the syntax $1.salary to select one field of the argument row value. Also notice how the calling SELECT command uses table_name.* to select the entire current row of a table as a composite value. The table row can alternatively be referenced using just the table name, like this:

SELECT name, double_salary(emp) AS dream
    FROM emp
    WHERE emp.cubicle ~= point '(2,1)';

Sometimes it is handy to construct a composite argument value on-the-fly. This can be done with the ROW construct. For example, we could adjust the data being passed to the function:

SELECT name, double_salary(ROW(name, salary*1.1, age, cubicle)) AS dream
    FROM emp;

It is also possible to build a function that returns a composite type. This is an example of a function that returns a single emp row:

CREATE FUNCTION new_emp() RETURNS emp AS $$
    SELECT text 'None' AS name,
        1000.0 AS salary,
        25 AS age,
        point '(2,2)' AS cubicle;
$$ LANGUAGE SQL;

In this example we have specified each of the attributes with a constant value, but any computation could have been substituted for these constants.

Note two important things about defining the function:

  • The select list order in the query must be exactly the same as that in which the columns appear in the table associated with the composite type. (Naming the columns, as we did above, is irrelevant to the system.)

  • We must ensure each expression's type matches the corresponding column of the composite type, inserting a cast if necessary. Otherwise we'll get errors like this:

    
    ERROR:  function declared to return emp returns varchar instead of text at column 1
    

    As with the base-type case, the function will not insert any casts automatically.

A different way to define the same function is:

CREATE FUNCTION new_emp() RETURNS emp AS $$
    SELECT ROW('None', 1000.0, 25, '(2,2)')::emp;
$$ LANGUAGE SQL;

Here we wrote a SELECT that returns just a single column of the correct composite type. This isn't really better in this situation, but it is a handy alternative in some cases — for example, if we need to compute the result by calling another function that returns the desired composite value. Another example is that if we are trying to write a function that returns a domain over composite, rather than a plain composite type, it is always necessary to write it as returning a single column, since there is no other way to produce a value that is exactly of the domain type.

We could call this function directly either by using it in a value expression:

SELECT new_emp();

         new_emp
--------------------------
 (None,1000.0,25,"(2,2)")

or by calling it as a table function:

SELECT * FROM new_emp();

 name | salary | age | cubicle
------+--------+-----+---------
 None | 1000.0 |  25 | (2,2)

When you use a function that returns a composite type, you might want only one field (attribute) from its result. You can do that with syntax like this:

SELECT (new_emp()).name;

 name
------
 None

The extra parentheses are needed to keep the parser from getting confused. If you try to do it without them, you get something like this:

SELECT new_emp().name;
ERROR:  syntax error at or near "."
LINE 1: SELECT new_emp().name;
                        ^

Another option is to use functional notation for extracting an attribute:

SELECT name(new_emp());

 name
------
 None

Another way to use a function returning a composite type is to pass the result to another function that accepts the correct row type as input:

CREATE FUNCTION getname(emp) RETURNS text AS $$
    SELECT $1.name;
$$ LANGUAGE SQL;

SELECT getname(new_emp());
 getname
---------
 None
(1 row)

38.5.4. SQL Functions with Output Parameters

An alternative way of describing a function's results is to define it with output parameters, as in this example:

CREATE FUNCTION add_em (IN x int, IN y int, OUT sum int)
AS 'SELECT x + y'
LANGUAGE SQL;

SELECT add_em(3,7);
 add_em
--------
     10
(1 row)
CREATE FUNCTION sum_n_product (x int, y int, OUT sum int, OUT product int)
AS 'SELECT x + y, x * y'
LANGUAGE SQL;

 SELECT * FROM sum_n_product(11,42);
 sum | product
-----+---------
  53 |     462
(1 row)

What has essentially happened here is that we have created an anonymous composite type for the result of the function. The above example has the same end result as

CREATE TYPE sum_prod AS (sum int, product int);

CREATE FUNCTION sum_n_product (int, int) RETURNS sum_prod
AS 'SELECT $1 + $2, $1 * $2'
LANGUAGE SQL;

but not having to bother with the separate composite type definition is often handy. Notice that the names attached to the output parameters are not just decoration, but determine the column names of the anonymous composite type. (If you omit a name for an output parameter, the system will choose a name on its own.)

Notice that output parameters are not included in the calling argument list when invoking such a function from SQL. This is because PostgreSQL considers only the input parameters to define the function's calling signature. That means also that only the input parameters matter when referencing the function for purposes such as dropping it. We could drop the above function with either of

DROP FUNCTION sum_n_product (x int, y int, OUT sum int, OUT product int);
DROP FUNCTION sum_n_product (int, int);

Parameters can be marked as IN (the default), OUT, INOUT, or VARIADIC. An INOUT parameter serves as both an input parameter (part of the calling argument list) and an output parameter (part of the result record type). VARIADIC parameters are input parameters, but are treated specially as described next.

38.5.5. SQL Functions with Variable Numbers of Arguments

SQL functions can be declared to accept variable numbers of arguments, so long as all the “optional” arguments are of the same data type. The optional arguments will be passed to the function as an array. The function is declared by marking the last parameter as VARIADIC; this parameter must be declared as being of an array type. For example:

CREATE FUNCTION mleast(VARIADIC arr numeric[]) RETURNS numeric AS $$
    SELECT min($1[i]) FROM generate_subscripts($1, 1) g(i);
$$ LANGUAGE SQL;

SELECT mleast(10, -1, 5, 4.4);
 mleast 
--------
     -1
(1 row)

Effectively, all the actual arguments at or beyond the VARIADIC position are gathered up into a one-dimensional array, as if you had written

SELECT mleast(ARRAY[10, -1, 5, 4.4]);    -- doesn't work

You can't actually write that, though — or at least, it will not match this function definition. A parameter marked VARIADIC matches one or more occurrences of its element type, not of its own type.

SELECT mleast(VARIADIC ARRAY[10, -1, 5, 4.4]);

This prevents expansion of the function's variadic parameter into its element type, thereby allowing the array argument value to match normally. VARIADIC can only be attached to the last actual argument of a function call.

Specifying VARIADIC in the call is also the only way to pass an empty array to a variadic function, for example:

SELECT mleast(VARIADIC ARRAY[]::numeric[]);

Simply writing SELECT mleast() does not work because a variadic parameter must match at least one actual argument. (You could define a second function also named mleast, with no parameters, if you wanted to allow such calls.)

SELECT mleast(VARIADIC arr => ARRAY[10, -1, 5, 4.4]);

but not these:

SELECT mleast(arr => 10);
SELECT mleast(arr => ARRAY[10, -1, 5, 4.4]);

38.5.6. SQL Functions with Default Values for Arguments

For example:

CREATE FUNCTION foo(a int, b int DEFAULT 2, c int DEFAULT 3)
RETURNS int
LANGUAGE SQL
AS $$
    SELECT $1 + $2 + $3;
$$;

SELECT foo(10, 20, 30);
 foo 
-----
  60
(1 row)

SELECT foo(10, 20);
 foo 
-----
  33
(1 row)

SELECT foo(10);
 foo 
-----
  15
(1 row)

SELECT foo();  -- fails since there is no default for the first argument
ERROR:  function foo() does not exist

The = sign can also be used in place of the key word DEFAULT.

38.5.7. SQL Functions as Table Sources

All SQL functions can be used in the FROM clause of a query, but it is particularly useful for functions returning composite types. If the function is defined to return a base type, the table function produces a one-column table. If the function is defined to return a composite type, the table function produces a column for each attribute of the composite type.

Here is an example:

CREATE TABLE foo (fooid int, foosubid int, fooname text);
INSERT INTO foo VALUES (1, 1, 'Joe');
INSERT INTO foo VALUES (1, 2, 'Ed');
INSERT INTO foo VALUES (2, 1, 'Mary');

CREATE FUNCTION getfoo(int) RETURNS foo AS $$
    SELECT * FROM foo WHERE fooid = $1;
$$ LANGUAGE SQL;

SELECT *, upper(fooname) FROM getfoo(1) AS t1;

 fooid | foosubid | fooname | upper
-------+----------+---------+-------
     1 |        1 | Joe     | JOE
(1 row)

As the example shows, we can work with the columns of the function's result just the same as if they were columns of a regular table.

Note that we only got one row out of the function. This is because we did not use SETOF. That is described in the next section.

38.5.8. SQL Functions Returning Sets

When an SQL function is declared as returning SETOF sometype, the function's final query is executed to completion, and each row it outputs is returned as an element of the result set.

This feature is normally used when calling the function in the FROM clause. In this case each row returned by the function becomes a row of the table seen by the query. For example, assume that table foo has the same contents as above, and we say:

CREATE FUNCTION getfoo(int) RETURNS SETOF foo AS $$
    SELECT * FROM foo WHERE fooid = $1;
$$ LANGUAGE SQL;

SELECT * FROM getfoo(1) AS t1;

Then we would get:

 fooid | foosubid | fooname
-------+----------+---------
     1 |        1 | Joe
     1 |        2 | Ed
(2 rows)

It is also possible to return multiple rows with the columns defined by output parameters, like this:

CREATE TABLE tab (y int, z int);
INSERT INTO tab VALUES (1, 2), (3, 4), (5, 6), (7, 8);

CREATE FUNCTION sum_n_product_with_tab (x int, OUT sum int, OUT product int)
RETURNS SETOF record
AS $$
    SELECT $1 + tab.y, $1 * tab.y FROM tab;
$$ LANGUAGE SQL;

SELECT * FROM sum_n_product_with_tab(10);
 sum | product
-----+---------
  11 |      10
  13 |      30
  15 |      50
  17 |      70
(4 rows)

The key point here is that you must write RETURNS SETOF record to indicate that the function returns multiple rows instead of just one. If there is only one output parameter, write that parameter's type instead of record.

SELECT * FROM nodes;
   name    | parent
-----------+--------
 Top       |
 Child1    | Top
 Child2    | Top
 Child3    | Top
 SubChild1 | Child1
 SubChild2 | Child1
(6 rows)

CREATE FUNCTION listchildren(text) RETURNS SETOF text AS $$
    SELECT name FROM nodes WHERE parent = $1
$$ LANGUAGE SQL STABLE;

SELECT * FROM listchildren('Top');
 listchildren
--------------
 Child1
 Child2
 Child3
(3 rows)

SELECT name, child FROM nodes, LATERAL listchildren(name) AS child;
  name  |   child
--------+-----------
 Top    | Child1
 Top    | Child2
 Top    | Child3
 Child1 | SubChild1
 Child1 | SubChild2
(5 rows)

This example does not do anything that we couldn't have done with a simple join, but in more complex calculations the option to put some of the work into a function can be quite convenient.

Functions returning sets can also be called in the select list of a query. For each row that the query generates by itself, the set-returning function is invoked, and an output row is generated for each element of the function's result set. The previous example could also be done with queries like these:

SELECT listchildren('Top');
 listchildren
--------------
 Child1
 Child2
 Child3
(3 rows)

SELECT name, listchildren(name) FROM nodes;
  name  | listchildren
--------+--------------
 Top    | Child1
 Top    | Child2
 Top    | Child3
 Child1 | SubChild1
 Child1 | SubChild2
(5 rows)

In the last SELECT, notice that no output row appears for Child2, Child3, etc. This happens because listchildren returns an empty set for those arguments, so no result rows are generated. This is the same behavior as we got from an inner join to the function result when using the LATERAL syntax.

PostgreSQL's behavior for a set-returning function in a query's select list is almost exactly the same as if the set-returning function had been written in a LATERAL FROM-clause item instead. For example,

SELECT x, generate_series(1,5) AS g FROM tab;

is almost equivalent to

SELECT x, g FROM tab, LATERAL generate_series(1,5) AS g;

It would be exactly the same, except that in this specific example, the planner could choose to put g on the outside of the nestloop join, since g has no actual lateral dependency on tab. That would result in a different output row order. Set-returning functions in the select list are always evaluated as though they are on the inside of a nestloop join with the rest of the FROM clause, so that the function(s) are run to completion before the next row from the FROM clause is considered.

If there is more than one set-returning function in the query's select list, the behavior is similar to what you get from putting the functions into a single LATERAL ROWS FROM( ... ) FROM-clause item. For each row from the underlying query, there is an output row using the first result from each function, then an output row using the second result, and so on. If some of the set-returning functions produce fewer outputs than others, null values are substituted for the missing data, so that the total number of rows emitted for one underlying row is the same as for the set-returning function that produced the most outputs. Thus the set-returning functions run “in lockstep” until they are all exhausted, and then execution continues with the next underlying row.

Set-returning functions can be nested in a select list, although that is not allowed in FROM-clause items. In such cases, each level of nesting is treated separately, as though it were a separate LATERAL ROWS FROM( ... ) item. For example, in

SELECT srf1(srf2(x), srf3(y)), srf4(srf5(z)) FROM tab;

the set-returning functions srf2, srf3, and srf5 would be run in lockstep for each row of tab, and then srf1 and srf4 would be applied in lockstep to each row produced by the lower functions.

Set-returning functions cannot be used within conditional-evaluation constructs, such as CASE or COALESCE. For example, consider

SELECT x, CASE WHEN x > 0 THEN generate_series(1, 5) ELSE 0 END FROM tab;

It might seem that this should produce five repetitions of input rows that have x > 0, and a single repetition of those that do not; but actually, because generate_series(1, 5) would be run in an implicit LATERAL FROM item before the CASE expression is ever evaluated, it would produce five repetitions of every input row. To reduce confusion, such cases produce a parse-time error instead.

Note

If a function's last command is INSERT, UPDATE, or DELETE with RETURNING, that command will always be executed to completion, even if the function is not declared with SETOF or the calling query does not fetch all the result rows. Any extra rows produced by the RETURNING clause are silently dropped, but the commanded table modifications still happen (and are all completed before returning from the function).

Note

Before PostgreSQL 10, putting more than one set-returning function in the same select list did not behave very sensibly unless they always produced equal numbers of rows. Otherwise, what you got was a number of output rows equal to the least common multiple of the numbers of rows produced by the set-returning functions. Also, nested set-returning functions did not work as described above; instead, a set-returning function could have at most one set-returning argument, and each nest of set-returning functions was run independently. Also, conditional execution (set-returning functions inside CASEetc) was previously allowed, complicating things even more. Use of the LATERAL syntax is recommended when writing queries that need to work in older PostgreSQL versions, because that will give consistent results across different versions. If you have a query that is relying on conditional execution of a set-returning function, you may be able to fix it by moving the conditional test into a custom set-returning function. For example,

SELECT x, CASE WHEN y > 0 THEN generate_series(1, z) ELSE 5 END FROM tab;

could become

CREATE FUNCTION case_generate_series(cond bool, start int, fin int, els int)
  RETURNS SETOF int AS $$
BEGIN
  IF cond THEN
    RETURN QUERY SELECT generate_series(start, fin);
  ELSE
    RETURN QUERY SELECT els;
  END IF;
END$$ LANGUAGE plpgsql;

SELECT x, case_generate_series(y > 0, 1, z, 5) FROM tab;

This formulation will work the same in all versions of PostgreSQL.

38.5.9. SQL Functions Returning TABLE

There is another way to declare a function as returning a set, which is to use the syntax RETURNS TABLE(columns). This is equivalent to using one or more OUT parameters plus marking the function as returning SETOF record (or SETOF a single output parameter's type, as appropriate). This notation is specified in recent versions of the SQL standard, and thus may be more portable than using SETOF.

For example, the preceding sum-and-product example could also be done this way:

CREATE FUNCTION sum_n_product_with_tab (x int)
RETURNS TABLE(sum int, product int) AS $$
    SELECT $1 + tab.y, $1 * tab.y FROM tab;
$$ LANGUAGE SQL;

It is not allowed to use explicit OUT or INOUT parameters with the RETURNS TABLE notation — you must put all the output columns in the TABLE list.

38.5.10. Polymorphic SQL Functions

CREATE FUNCTION make_array(anyelement, anyelement) RETURNS anyarray AS $$
    SELECT ARRAY[$1, $2];
$$ LANGUAGE SQL;

SELECT make_array(1, 2) AS intarray, make_array('a'::text, 'b') AS textarray;
 intarray | textarray
----------+-----------
 {1,2}    | {a,b}
(1 row)

Notice the use of the typecast 'a'::text to specify that the argument is of type text. This is required if the argument is just a string literal, since otherwise it would be treated as type unknown, and array of unknown is not a valid type. Without the typecast, you will get errors like this:


ERROR:  could not determine polymorphic type because input has type "unknown"

It is permitted to have polymorphic arguments with a fixed return type, but the converse is not. For example:

CREATE FUNCTION is_greater(anyelement, anyelement) RETURNS boolean AS $$
    SELECT $1 > $2;
$$ LANGUAGE SQL;

SELECT is_greater(1, 2);
 is_greater
------------
 f
(1 row)

CREATE FUNCTION invalid_func() RETURNS anyelement AS $$
    SELECT 1;
$$ LANGUAGE SQL;
ERROR:  cannot determine result data type
DETAIL:  A function returning a polymorphic type must have at least one polymorphic argument.

Polymorphism can be used with functions that have output arguments. For example:

CREATE FUNCTION dup (f1 anyelement, OUT f2 anyelement, OUT f3 anyarray)
AS 'select $1, array[$1,$1]' LANGUAGE SQL;

SELECT * FROM dup(22);
 f2 |   f3
----+---------
 22 | {22,22}
(1 row)

Polymorphism can also be used with variadic functions. For example:

CREATE FUNCTION anyleast (VARIADIC anyarray) RETURNS anyelement AS $$
    SELECT min($1[i]) FROM generate_subscripts($1, 1) g(i);
$$ LANGUAGE SQL;

SELECT anyleast(10, -1, 5, 4);
 anyleast 
----------
       -1
(1 row)

SELECT anyleast('abc'::text, 'def');
 anyleast 
----------
 abc
(1 row)

CREATE FUNCTION concat_values(text, VARIADIC anyarray) RETURNS text AS $$
    SELECT array_to_string($2, $1);
$$ LANGUAGE SQL;

SELECT concat_values('|', 1, 4, 2);
 concat_values 
---------------
 1|4|2
(1 row)

38.5.11. SQL Functions with Collations

SELECT anyleast('abc'::text, 'ABC');

will depend on the database's default collation. In C locale the result will be ABC, but in many other locales it will be abc. The collation to use can be forced by adding a COLLATE clause to any of the arguments, for example

SELECT anyleast('abc'::text, 'ABC' COLLATE "C");

Alternatively, if you wish a function to operate with a particular collation regardless of what it is called with, insert COLLATE clauses as needed in the function definition. This version of anyleast would always use en_US locale to compare strings:

CREATE FUNCTION anyleast (VARIADIC anyarray) RETURNS anyelement AS $$
    SELECT min($1[i] COLLATE "en_US") FROM generate_subscripts($1, 1) g(i);
$$ LANGUAGE SQL;

But note that this will throw an error if applied to a non-collatable data type.

If no common collation can be identified among the actual arguments, then a SQL function treats its parameters as having their data types' default collation (which is usually the database's default collation, but could be different for parameters of domain types).

The behavior of collatable parameters can be thought of as a limited form of polymorphism, applicable only to textual data types.

but this usage is deprecated since it's easy to get confused. (See for details about these two notations for the composite value of a table row.)

The second way is described more fully in .

As explained in , the field notation and functional notation are equivalent.

This is not essentially different from the version of add_em shown in . The real value of output parameters is that they provide a convenient way of defining functions that return several columns. For example,

Sometimes it is useful to be able to pass an already-constructed array to a variadic function; this is particularly handy when one variadic function wants to pass on its array parameter to another one. Also, this is the only secure way to call a variadic function found in a schema that permits untrusted users to create objects; see . You can do this by specifying VARIADIC in the call:

The array element parameters generated from a variadic parameter are treated as not having any names of their own. This means it is not possible to call a variadic function using named arguments (), except when you specify VARIADIC. For example, this will work:

Functions can be declared with default values for some or all input arguments. The default values are inserted whenever the function is called with insufficiently many actual arguments. Since arguments can only be omitted from the end of the actual argument list, all parameters after a parameter with a default value have to have default values as well. (Although the use of named argument notation could allow this restriction to be relaxed, it's still enforced so that positional argument notation works sensibly.) Whether or not you use it, this capability creates a need for precautions when calling functions in databases where some users mistrust other users; see .

It is frequently useful to construct a query's result by invoking a set-returning function multiple times, with the parameters for each invocation coming from successive rows of a table or subquery. The preferred way to do this is to use the LATERAL key word, which is described in . Here is an example using a set-returning function to enumerate elements of a tree structure:

SQL functions can be declared to accept and return the polymorphic types anyelement, anyarray, anynonarray, anyenum, and anyrange. See for a more detailed explanation of polymorphic functions. Here is a polymorphic function make_array that builds up an array from two arbitrary data type elements:

When a SQL function has one or more parameters of collatable data types, a collation is identified for each function call depending on the collations assigned to the actual arguments, as described in . If a collation is successfully identified (i.e., there are no conflicts of implicit collations among the arguments) then all the collatable parameters are treated as having that collation implicitly. This will affect the behavior of collation-sensitive operations within the function. For example, using the anyleast function described above, the result of

Section 4.1.2.4
Section 4.1.2.1
Section 8.16.5
Section 38.5.7
Section 8.16.5
Section 38.5.2
Section 10.3
Section 4.3
Section 10.3
Section 7.2.1.5
Section 38.2.5
Section 23.2