v2.1

About MogDB
- MogDB Introduction
- Comparison Between MogDB and openGauss
- MogDB Release Notes
  - MogDB 2.1.1
  - MogDB 2.1.0
- Open Source Components
- Usage Limitations
- Terms of Use
Quick Start
- MogDB Playground
- Container-based MogDB Installation
- Installation on a Single Node
- MogDB Access
  - Use CLI to Access MogDB
    - gsql
    - pgcli
  - Use GUI to Access MogDB
    - Data Studio
    - DBeaver
  - Use Middleware to Access MogDB
    - Use WebLogic to Configure MogDB Data Sources
    - Use WebSphere to Configure MogDB Data Sources
  - Use Programming Language to Access MogDB
    - Java
    - C/C++
    - Python
    - Go
    - Rust
    - NodeJS
- Using Sample Dataset Mogila
Characteristic Description
Installation Guide
- Container Installation
- Simplified Installation Process
- Standard Installation
  - Installation Overview
  - Preparing for Installation
    - Environment Requirement
    - Modifying OS Configuration
  - Installing MogDB
  - Verifying the Installation
  - Recommended Parameter Settings
  - Uninstalling MogDB
- Manual Installation
Administrator Guide
AI Features Guide
- Overview
- Predictor: AI Query Time Forecasting
- X-Tuner: Parameter Optimization and Diagnosis
- SQLdiag: Slow SQL Discovery
- A-Detection: Status Monitoring
- Index-advisor: Index Recommendation
- DeepSQL
- AI-Native Database (DB4AI)
Security Guide
- Database Security Management
Developer Guide
- Application Development Guide
  - Development Specifications
  - Development Based on JDBC
  - Development Based on ODBC
  - Development Based on libpq
    - Development Based on libpq
    - libpq API Reference
      - Database Connection Control Functions
        Description
        PQconnectdbParams
        PQconnectdb
        PQconninfoParse
        PQconnectStart
        PQerrorMessage
        PQsetdbLogin
        PQfinish
        PQreset
        PQstatus
      - Database Statement Execution Functions
        PQclear
        PQexec
        PQexecParams
        PQexecParamsBatch
        PQexecPrepared
        PQexecPreparedBatch
        PQfname
        PQgetvalue
        PQnfields
        PQntuples
        PQprepare
        PQresultStatus
      - Functions for Asynchronous Command Processing
        Description
        PQsendQuery
        PQsendQueryParams
        PQsendPrepare
        PQsendQueryPrepared
        PQflush
      - Functions for Canceling Queries in Progress
        PQgetCancel
        PQfreeCancel
        PQcancel
      - Example
      - Connection Characters
  - Psycopg-Based Development
  - Commissioning
  - Appendices
- Stored Procedure
- User Defined Functions
- PL/pgSQL-SQL Procedural Language
- Scheduled Jobs
  - PKG_SERVICE API
- Autonomous Transaction
- Logical Replication
  - Logical Decoding
    - Overview
    - Logical Decoding by SQL Function Interfaces
- Foreign Data Wrapper
- Materialized View
  - Materialized View Overview
  - Full Materialized View
  - Incremental Materialized View
- Resource Load Management
  - Overview
  - Resource Management Preparation
Performance Tuning Guide
Reference Guide
Common Faults and Identification Guide
- Common Fault Locating Methods
- Common Fault Locating Cases
  - Core Fault Locating
  - Permission/Session/Data Type Fault Location
  - Service/High Availability/Concurrency Fault Location
  - Table/Partition Table Fault Location
    - Table Size Does not Change After VACUUM FULL Is Executed on the Table
    - An Error Is Reported When the Table Partition Is Modified
  - File System/Disk/Memory Fault Location
  - SQL Fault Location
  - Index Fault Location
Source Code Parsing
FAQs
Glossary

Best Practices

For details about the parameters, see GS_OPT_MODEL.

Table 1

Model Parameter	Recommended Value
template_name	rlstm
model_name	The value can be customized, for example, open_ai. The value must meet the unique constraint.
datname	Name of the database to be served, for example, postgres.
ip	IP address of the AI Engine, for example, 127.0.0.1.
port	AI Engine listening port number, for example, 5000.
max_epoch	Iteration times. A large value is recommended to ensure the convergence effect, for example, 2000.
learning_rate	(0, 1] is a floating-point number. A large learning rate is recommended to accelerate convergence.
dim_red	Number of feature values to be reduced. -1: Do not use PCA for dimension reduction. All features are supported. Floating point number in the range of (0, 1]: A smaller value indicates a smaller training dimension and a faster convergence speed, but affects the training accuracy.
hidden_units	If the feature value dimension is high, you are advised to increase the value of this parameter to increase the model complexity. For example, set this parameter to 64, 128, and so on.
batch_size	You are advised to increase the value of this parameter based on the amount of encoded data to accelerate model convergence. For example, set this parameter to 256, 512, and so on.
Other parameters	See GS_OPT_MODEL.

Recommended parameter settings:

INSERT INTO gs_opt_model values('rlstm', 'open_ai', 'postgres', '127.0.0.1', 5000, 2000, 1, -1, 64, 512, 0, false, false, '{S, T}', '{0,0}', '{0,0}', 'Text');