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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  1. VII. 資料庫進階
  2. 64. GiST Indexes

64.3. Extensibility

Traditionally, implementing a new index access method meant a lot of difficult work. It was necessary to understand the inner workings of the database, such as the lock manager and Write-Ahead Log. The GiST interface has a high level of abstraction, requiring the access method implementer only to implement the semantics of the data type being accessed. The GiST layer itself takes care of concurrency, logging and searching the tree structure.

This extensibility should not be confused with the extensibility of the other standard search trees in terms of the data they can handle. For example, PostgreSQL supports extensible B-trees and hash indexes. That means that you can use PostgreSQL to build a B-tree or hash over any data type you want. But B-trees only support range predicates (<, =, >), and hash indexes only support equality queries.

So if you index, say, an image collection with a PostgreSQL B-tree, you can only issue queries such as “is imagex equal to imagey”, “is imagex less than imagey” and “is imagex greater than imagey”. Depending on how you define “equals”, “less than” and “greater than” in this context, this could be useful. However, by using a GiST based index, you could create ways to ask domain-specific questions, perhaps “find all images of horses” or “find all over-exposed images”.

All it takes to get a GiST access method up and running is to implement several user-defined methods, which define the behavior of keys in the tree. Of course these methods have to be pretty fancy to support fancy queries, but for all the standard queries (B-trees, R-trees, etc.) they're relatively straightforward. In short, GiST combines extensibility along with generality, code reuse, and a clean interface.

There are seven methods that an index operator class for GiST must provide, and two that are optional. Correctness of the index is ensured by proper implementation of the same,consistent and union methods, while efficiency (size and speed) of the index will depend on the penalty and picksplit methods. The remaining two basic methods are compress and decompress, which allow an index to have internal tree data of a different type than the data it indexes. The leaves are to be of the indexed data type, while the other tree nodes can be of any C struct (but you still have to follow PostgreSQL data type rules here, see about varlena for variable sized data). If the tree's internal data type exists at the SQL level, theSTORAGE option of the CREATE OPERATOR CLASS command can be used. The optional eighth method is distance, which is needed if the operator class wishes to support ordered scans (nearest-neighbor searches). The optional ninth method fetch is needed if the operator class wishes to support index-only scans.consistent

Given an index entry p and a query value q, this function determines whether the index entry is “consistent” with the query; that is, could the predicate “indexed_columnindexable_operator q” be true for any row represented by the index entry? For a leaf index entry this is equivalent to testing the indexable condition, while for an internal tree node this determines whether it is necessary to scan the subtree of the index represented by the tree node. When the result is true, a recheck flag must also be returned. This indicates whether the predicate is certainly true or only possibly true. If recheck = false then the index has tested the predicate condition exactly, whereas if recheck = true the row is only a candidate match. In that case the system will automatically evaluate the indexable_operator against the actual row value to see if it is really a match. This convention allows GiST to support both lossless and lossy index structures.

The SQL declaration of the function must look like this:

CREATE OR REPLACE FUNCTION my_consistent(internal, data_type, smallint, oid, internal)
RETURNS bool
AS 'MODULE_PATHNAME'
LANGUAGE C STRICT;

And the matching code in the C module could then follow this skeleton:

PG_FUNCTION_INFO_V1(my_consistent);

Datum
my_consistent(PG_FUNCTION_ARGS)
{
    GISTENTRY  *entry = (GISTENTRY *) PG_GETARG_POINTER(0);
    data_type  *query = PG_GETARG_DATA_TYPE_P(1);
    StrategyNumber strategy = (StrategyNumber) PG_GETARG_UINT16(2);
    /* Oid subtype = PG_GETARG_OID(3); */
    bool       *recheck = (bool *) PG_GETARG_POINTER(4);
    data_type  *key = DatumGetDataType(entry->key);
    bool        retval;

    /*
     * determine return value as a function of strategy, key and query.
     *
     * Use GIST_LEAF(entry) to know where you're called in the index tree,
     * which comes handy when supporting the = operator for example (you could
     * check for non empty union() in non-leaf nodes and equality in leaf
     * nodes).
     */

    *recheck = true;        /* or false if check is exact */

    PG_RETURN_BOOL(retval);
}

Here, key is an element in the index and query the value being looked up in the index. The StrategyNumber parameter indicates which operator of your operator class is being applied — it matches one of the operator numbers in the CREATE OPERATOR CLASS command.

Depending on which operators you have included in the class, the data type of query could vary with the operator, since it will be whatever type is on the righthand side of the operator, which might be different from the indexed data type appearing on the lefthand side. (The above code skeleton assumes that only one type is possible; if not, fetching the query argument value would have to depend on the operator.) It is recommended that the SQL declaration of the consistent function use the opclass's indexed data type for the query argument, even though the actual type might be something else depending on the operator.union

This method consolidates information in the tree. Given a set of entries, this function generates a new index entry that represents all the given entries.

The SQL declaration of the function must look like this:

CREATE OR REPLACE FUNCTION my_union(internal, internal)
RETURNS storage_type
AS 'MODULE_PATHNAME'
LANGUAGE C STRICT;

And the matching code in the C module could then follow this skeleton:

PG_FUNCTION_INFO_V1(my_union);

Datum
my_union(PG_FUNCTION_ARGS)
{
    GistEntryVector *entryvec = (GistEntryVector *) PG_GETARG_POINTER(0);
    GISTENTRY  *ent = entryvec->vector;
    data_type  *out,
               *tmp,
               *old;
    int         numranges,
                i = 0;

    numranges = entryvec->n;
    tmp = DatumGetDataType(ent[0].key);
    out = tmp;

    if (numranges == 1)
    {
        out = data_type_deep_copy(tmp);

        PG_RETURN_DATA_TYPE_P(out);
    }

    for (i = 1; i < numranges; i++)
    {
        old = out;
        tmp = DatumGetDataType(ent[i].key);
        out = my_union_implementation(out, tmp);
    }

    PG_RETURN_DATA_TYPE_P(out);
}

As you can see, in this skeleton we're dealing with a data type where union(X, Y, Z) = union(union(X, Y), Z). It's easy enough to support data types where this is not the case, by implementing the proper union algorithm in this GiST support method.

The result of the union function must be a value of the index's storage type, whatever that is (it might or might not be different from the indexed column's type). The unionfunction should return a pointer to newly palloc()ed memory. You can't just return the input value as-is, even if there is no type change.

As shown above, the union function's first internal argument is actually a GistEntryVector pointer. The second argument is a pointer to an integer variable, which can be ignored. (It used to be required that the union function store the size of its result value into that variable, but this is no longer necessary.)compress

Converts the data item into a format suitable for physical storage in an index page.

The SQL declaration of the function must look like this:

CREATE OR REPLACE FUNCTION my_compress(internal)
RETURNS internal
AS 'MODULE_PATHNAME'
LANGUAGE C STRICT;

And the matching code in the C module could then follow this skeleton:

PG_FUNCTION_INFO_V1(my_compress);

Datum
my_compress(PG_FUNCTION_ARGS)
{
    GISTENTRY  *entry = (GISTENTRY *) PG_GETARG_POINTER(0);
    GISTENTRY  *retval;

    if (entry->leafkey)
    {
        /* replace entry->key with a compressed version */
        compressed_data_type *compressed_data = palloc(sizeof(compressed_data_type));

        /* fill *compressed_data from entry->key ... */

        retval = palloc(sizeof(GISTENTRY));
        gistentryinit(*retval, PointerGetDatum(compressed_data),
                      entry->rel, entry->page, entry->offset, FALSE);
    }
    else
    {
        /* typically we needn't do anything with non-leaf entries */
        retval = entry;
    }

    PG_RETURN_POINTER(retval);
}

You have to adapt compressed_data_type to the specific type you're converting to in order to compress your leaf nodes, of course.decompress

The reverse of the compress method. Converts the index representation of the data item into a format that can be manipulated by the other GiST methods in the operator class.

The SQL declaration of the function must look like this:

CREATE OR REPLACE FUNCTION my_decompress(internal)
RETURNS internal
AS 'MODULE_PATHNAME'
LANGUAGE C STRICT;

And the matching code in the C module could then follow this skeleton:

PG_FUNCTION_INFO_V1(my_decompress);

Datum
my_decompress(PG_FUNCTION_ARGS)
{
    PG_RETURN_POINTER(PG_GETARG_POINTER(0));
}

The above skeleton is suitable for the case where no decompression is needed.penalty

Returns a value indicating the “cost” of inserting the new entry into a particular branch of the tree. Items will be inserted down the path of least penalty in the tree. Values returned by penalty should be non-negative. If a negative value is returned, it will be treated as zero.

The SQL declaration of the function must look like this:

CREATE OR REPLACE FUNCTION my_penalty(internal, internal, internal)
RETURNS internal
AS 'MODULE_PATHNAME'
LANGUAGE C STRICT;  -- in some cases penalty functions need not be strict

And the matching code in the C module could then follow this skeleton:

PG_FUNCTION_INFO_V1(my_penalty);

Datum
my_penalty(PG_FUNCTION_ARGS)
{
    GISTENTRY  *origentry = (GISTENTRY *) PG_GETARG_POINTER(0);
    GISTENTRY  *newentry = (GISTENTRY *) PG_GETARG_POINTER(1);
    float      *penalty = (float *) PG_GETARG_POINTER(2);
    data_type  *orig = DatumGetDataType(origentry->key);
    data_type  *new = DatumGetDataType(newentry->key);

    *penalty = my_penalty_implementation(orig, new);
    PG_RETURN_POINTER(penalty);
}

For historical reasons, the penalty function doesn't just return a float result; instead it has to store the value at the location indicated by the third argument. The return value per se is ignored, though it's conventional to pass back the address of that argument.

The penalty function is crucial to good performance of the index. It'll get used at insertion time to determine which branch to follow when choosing where to add the new entry in the tree. At query time, the more balanced the index, the quicker the lookup.picksplit

When an index page split is necessary, this function decides which entries on the page are to stay on the old page, and which are to move to the new page.

The SQL declaration of the function must look like this:

CREATE OR REPLACE FUNCTION my_picksplit(internal, internal)
RETURNS internal
AS 'MODULE_PATHNAME'
LANGUAGE C STRICT;

And the matching code in the C module could then follow this skeleton:

PG_FUNCTION_INFO_V1(my_picksplit);

Datum
my_picksplit(PG_FUNCTION_ARGS)
{
    GistEntryVector *entryvec = (GistEntryVector *) PG_GETARG_POINTER(0);
    GIST_SPLITVEC *v = (GIST_SPLITVEC *) PG_GETARG_POINTER(1);
    OffsetNumber maxoff = entryvec->n - 1;
    GISTENTRY  *ent = entryvec->vector;
    int         i,
                nbytes;
    OffsetNumber *left,
               *right;
    data_type  *tmp_union;
    data_type  *unionL;
    data_type  *unionR;
    GISTENTRY **raw_entryvec;

    maxoff = entryvec->n - 1;
    nbytes = (maxoff + 1) * sizeof(OffsetNumber);

    v->spl_left = (OffsetNumber *) palloc(nbytes);
    left = v->spl_left;
    v->spl_nleft = 0;

    v->spl_right = (OffsetNumber *) palloc(nbytes);
    right = v->spl_right;
    v->spl_nright = 0;

    unionL = NULL;
    unionR = NULL;

    /* Initialize the raw entry vector. */
    raw_entryvec = (GISTENTRY **) malloc(entryvec->n * sizeof(void *));
    for (i = FirstOffsetNumber; i <= maxoff; i = OffsetNumberNext(i))
        raw_entryvec[i] = &(entryvec->vector[i]);

    for (i = FirstOffsetNumber; i <= maxoff; i = OffsetNumberNext(i))
    {
        int         real_index = raw_entryvec[i] - entryvec->vector;

        tmp_union = DatumGetDataType(entryvec->vector[real_index].key);
        Assert(tmp_union != NULL);

        /*
         * Choose where to put the index entries and update unionL and unionR
         * accordingly. Append the entries to either v_spl_left or
         * v_spl_right, and care about the counters.
         */

        if (my_choice_is_left(unionL, curl, unionR, curr))
        {
            if (unionL == NULL)
                unionL = tmp_union;
            else
                unionL = my_union_implementation(unionL, tmp_union);

            *left = real_index;
            ++left;
            ++(v->spl_nleft);
        }
        else
        {
            /*
             * Same on the right
             */
        }
    }

    v->spl_ldatum = DataTypeGetDatum(unionL);
    v->spl_rdatum = DataTypeGetDatum(unionR);
    PG_RETURN_POINTER(v);
}

Notice that the picksplit function's result is delivered by modifying the passed-in v structure. The return value per se is ignored, though it's conventional to pass back the address of v.

Like penalty, the picksplit function is crucial to good performance of the index. Designing suitable penalty and picksplit implementations is where the challenge of implementing well-performing GiST indexes lies.same

Returns true if two index entries are identical, false otherwise. (An “index entry” is a value of the index's storage type, not necessarily the original indexed column's type.)

The SQL declaration of the function must look like this:

CREATE OR REPLACE FUNCTION my_same(storage_type, storage_type, internal)
RETURNS internal
AS 'MODULE_PATHNAME'
LANGUAGE C STRICT;

And the matching code in the C module could then follow this skeleton:

PG_FUNCTION_INFO_V1(my_same);

Datum
my_same(PG_FUNCTION_ARGS)
{
    prefix_range *v1 = PG_GETARG_PREFIX_RANGE_P(0);
    prefix_range *v2 = PG_GETARG_PREFIX_RANGE_P(1);
    bool       *result = (bool *) PG_GETARG_POINTER(2);

    *result = my_eq(v1, v2);
    PG_RETURN_POINTER(result);
}

For historical reasons, the same function doesn't just return a Boolean result; instead it has to store the flag at the location indicated by the third argument. The return value per se is ignored, though it's conventional to pass back the address of that argument.distance

Given an index entry p and a query value q, this function determines the index entry's “distance” from the query value. This function must be supplied if the operator class contains any ordering operators. A query using the ordering operator will be implemented by returning index entries with the smallest “distance” values first, so the results must be consistent with the operator's semantics. For a leaf index entry the result just represents the distance to the index entry; for an internal tree node, the result must be the smallest distance that any child entry could have.

The SQL declaration of the function must look like this:

CREATE OR REPLACE FUNCTION my_distance(internal, data_type, smallint, oid, internal)
RETURNS float8
AS 'MODULE_PATHNAME'
LANGUAGE C STRICT;

And the matching code in the C module could then follow this skeleton:

PG_FUNCTION_INFO_V1(my_distance);

Datum
my_distance(PG_FUNCTION_ARGS)
{
    GISTENTRY  *entry = (GISTENTRY *) PG_GETARG_POINTER(0);
    data_type  *query = PG_GETARG_DATA_TYPE_P(1);
    StrategyNumber strategy = (StrategyNumber) PG_GETARG_UINT16(2);
    /* Oid subtype = PG_GETARG_OID(3); */
    /* bool *recheck = (bool *) PG_GETARG_POINTER(4); */
    data_type  *key = DatumGetDataType(entry->key);
    double      retval;

    /*
     * determine return value as a function of strategy, key and query.
     */

    PG_RETURN_FLOAT8(retval);
}

The arguments to the distance function are identical to the arguments of the consistent function.

Some approximation is allowed when determining the distance, so long as the result is never greater than the entry's actual distance. Thus, for example, distance to a bounding box is usually sufficient in geometric applications. For an internal tree node, the distance returned must not be greater than the distance to any of the child nodes. If the returned distance is not exact, the function must set *recheck to true. (This is not necessary for internal tree nodes; for them, the calculation is always assumed to be inexact.) In this case the executor will calculate the accurate distance after fetching the tuple from the heap, and reorder the tuples if necessary.

If the distance function returns *recheck = true for any leaf node, the original ordering operator's return type must be float8 or float4, and the distance function's result values must be comparable to those of the original ordering operator, since the executor will sort using both distance function results and recalculated ordering-operator results. Otherwise, the distance function's result values can be any finite float8 values, so long as the relative order of the result values matches the order returned by the ordering operator. (Infinity and minus infinity are used internally to handle cases such as nulls, so it is not recommended that distance functions return these values.)fetch

Converts the compressed index representation of a data item into the original data type, for index-only scans. The returned data must be an exact, non-lossy copy of the originally indexed value.

The SQL declaration of the function must look like this:

CREATE OR REPLACE FUNCTION my_fetch(internal)
RETURNS internal
AS 'MODULE_PATHNAME'
LANGUAGE C STRICT;

The argument is a pointer to a GISTENTRY struct. On entry, its key field contains a non-NULL leaf datum in compressed form. The return value is another GISTENTRY struct, whose key field contains the same datum in its original, uncompressed form. If the opclass's compress function does nothing for leaf entries, the fetch method can return the argument as-is.

The matching code in the C module could then follow this skeleton:

PG_FUNCTION_INFO_V1(my_fetch);

Datum
my_fetch(PG_FUNCTION_ARGS)
{
    GISTENTRY  *entry = (GISTENTRY *) PG_GETARG_POINTER(0);
    input_data_type *in = DatumGetP(entry->key);
    fetched_data_type *fetched_data;
    GISTENTRY  *retval;

    retval = palloc(sizeof(GISTENTRY));
    fetched_data = palloc(sizeof(fetched_data_type));

    /*
     * Convert 'fetched_data' into the a Datum of the original datatype.
     */

    /* fill *retval from fetch_data. */
    gistentryinit(*retval, PointerGetDatum(converted_datum),
                  entry->rel, entry->page, entry->offset, FALSE);

    PG_RETURN_POINTER(retval);
}

If the compress method is lossy for leaf entries, the operator class cannot support index-only scans, and must not define a fetch function.

All the GiST support methods are normally called in short-lived memory contexts; that is, CurrentMemoryContext will get reset after each tuple is processed. It is therefore not very important to worry about pfree'ing everything you palloc. However, in some cases it's useful for a support method to cache data across repeated calls. To do that, allocate the longer-lived data in fcinfo->flinfo->fn_mcxt, and keep a pointer to it in fcinfo->flinfo->fn_extra. Such data will survive for the life of the index operation (e.g., a single GiST index scan, index build, or index tuple insertion). Be careful to pfree the previous value when replacing a fn_extra value, or the leak will accumulate for the duration of the operation.

Previous64.2. Built-in Operator ClassesNext64.4. Implementation

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