What's New
Function Overview
Product Bulletin
- Product Notice
- Version Support Notes
Service Overview
- GaussDB(DWS) Infographics
- What Is GaussDB(DWS)?
- Data Warehouse Types
- Data Warehouse Flavors
- Advantages
- Application Scenarios
- Functions
- Concepts
- Related Services
- Security
- GaussDB(DWS) Permissions Management
- GaussDB(DWS) Access
- Restrictions
- Technical Support
- Service Quotas
- GaussDB(DWS) Technical Specifications
Billing
- GaussDB(DWS) Billing Overview
- Billing Modes
- Item
- Billing Examples
- Billing Mode Change
- Renewal
  - Overview
  - Manual Renewal
- Bills
- Arrears
- Stopping Billing
- Cost Management
- Billing FAQs
Getting Started
- Checkpoint Vehicle Analysis
- Supply Chain Requirement Analysis of a Company
- Operations Status Analysis of a Retail Department Store
- Creating a Time Series Table
- Best Practices of Hot and Cold Data Management
- Best Practices for Automatic Partition Management
- Creating a Cluster and Connecting to It
- Using CDM to Migrate MySQL Data to the GaussDB(DWS) Cluster
- Using DLI Flink Jobs to Write Kafka Data to GaussDB(DWS) in Real Time
- Basic SQL Operations
- Database Quick Start
- Getting Started with Common Practices
User Guide
- Using GaussDB(DWS)
- Preparations
- Creating a GaussDB(DWS) Cluster
- Connecting to a GaussDB(DWS) Cluster
- Creating a GaussDB(DWS) Database and User
- Migrating Service Data to a GaussDB(DWS) Cluster
  - Migrating Data to a GaussDB(DWS) Cluster Using GDS-Kafka
  - Data Source Management
- GaussDB(DWS) Cluster Data Security and Encryption
- GaussDB(DWS) Cluster Management
- GaussDB(DWS) Cluster O&M
Best Practices
- Import and Export
- Data Migration
- Data Analytics
- Decoupled Storage and Compute
  - DWS 3.0 Decoupled Storage and Compute Usage Suggestions and Performance Optimization
- Data Development
- Database Management
- Performance Tuning
  - Optimizing Table Structure Design to Enhance GaussDB(DWS) Query Performance
  - Analyzing SQL Statements That Are Being Executed to Handle GaussDB(DWS) Performance Issues
- Cluster Management
  - Binding Different Resource Pools to Two Types of Jobs to Balance Load for GaussDB(DWS)
  - Scaling Options for GaussDB(DWS) with a Coupled Storage-Compute Architecture
Data Migration and Synchronization
- Data Migration to GaussDB(DWS)
- Importing Data
- Full Database Migration
  - Using CDM to Migrate Data to GaussDB(DWS)
  - Using DSC to Migrate SQL Scripts
- Real-time Import
  - Using DRS to Import Data to GaussDB(DWS)
  - Real-time Data Import From Kafka GaussDB(DWS)
- Metadata Migration
  - Using gs_dump and gs_dumpall to Export Metadata
  - Using gs_restore to Import Data
- Exporting Data
- Other Operations
Developer Guide
- Standard data warehouse (9.1.0.x)
- Standard data warehouse (8.1.3.x)
- Hybrid Data Warehouse (9.1.0.x）
- Hybrid Data Warehouse (8.1.3.x）
- Historical Versions
SQL Syntax Reference
- SQL Syntax Reference (9.1.0.x)
- SQL Syntax Reference (8.1.3.x)
- Historical Versions
Performance Tuning
- Overview of Query Performance Optimization
- Query Execution Process
- SQL Execution Plan
- SQL Optimization Guide
- Optimization Cases
- SQL Execution Troubleshooting
- query_band Load Identification
- Common Performance Parameter Optimization Design
Tool Guide
- Overview
- Downloading Related Tools
- gsql
- Data Studio
- GDS
- DSC
- DataCheck
- DWS-Connector
- Server Tool
API Reference
- Before You Start
- API Overview
- Calling APIs
- Getting Started
- API Description
- Application Cases
  - Using Postman to Call the API for Creating a Cluster
  - Using Postman to Call the API for Creating a Snapshot
- Introduction
- Appendix
SDK Reference
- SDK Overview
FAQs
- Top FAQs
- Product Consulting
- Database Connections
- Data Migration
- Database Usage
- Cluster Management
- Account Permissions
- Database Performance
- Backup and Restoration
  1. Why Does It Take a Long Time to Create an Automated Snapshot in GaussDB(DWS)?
  2. Does a GaussDB(DWS) Snapshot Have the Same Function as an EVS Snapshot?
Troubleshooting
- Database Connections
- JDBC/ODBC
- Data Import and Export
- Database Parameter Modification
- Account/Permission/Password
- Cluster Performance
- Cluster Exceptions
  - The Disk Usage Alarm Is Frequently Generated
- Database Use
Videos
Performance White Paper
- Overview
- Test Result
  - TPC-H Single Query Test
  - TPC-DS Single Query Test
- Test Methods
- Appendixes
  - TPC-H Test Sets
  - TPC-DS Test Sets
Technical White Paper
- GaussDB(DWS)
- Platforms and Technical Specifications Supported by GaussDB(DWS)
  - Technical Specifications
- GaussDB(DWS) Core Technologies
- GaussDB(DWS) Tools
  - Client Tools
  - Database Monitoring Tool
- External APIs
Error Code Reference
- 8.2.0 and earlier versions
  - Management Console Error Code
  - Data Warehouse Service Error Codes
- 8.2.1 or later versions
Glossary
More Documents
- User Guide
- API Reference (ME-Abu Dhabi Region)
- Developer Guide (ME-Abu Dhabi Region)
- SQL Syntax Reference (ME-Abu Dhabi Region)
- Tool Guide (ME-Abu Dhabi Region)
- Error Code Reference (ME-Abu Dhabi Region)
  - Management Console Error Code
- User Guide (Paris Region)
- API Reference (Paris Region)
- Developer Guide (Paris Region)
- SQL Syntax Reference (Paris Region)
- Tool Guide (Paris Region)
- Error Code Reference (Paris Region)
  - Management Console Error Code
- User Guide (Kuala Lumpur Region)
- API Reference (Kuala Lumpur Region)
- Developer Guide (Kuala Lumpur Region)
- SQL Syntax Reference (Kuala Lumpur Region)
- Tool Guide (Kuala Lumpur Region)
- Error Code Reference (Kuala Lumpur Region)
  - Management Console Error Code
General Reference
- Glossary
- Service Level Agreement
- White Papers
- Endpoints
- Permissions

On this page

Show all

Help Center/ GaussDB(DWS)/ Developer Guide/ Standard data warehouse (9.1.0.x)/ GaussDB(DWS) Performance Tuning/ SQL Tuning/ Hint-based Tuning/ Join Order Hints

Join Order Hints

Updated on 2024-12-18 GMT+08:00

View PDF

Function

Theses hints specify the join order and outer/inner tables.

Syntax

Single-layer parentheses (), specifying only the join order. The order of internal and foreign tables is not specified.

    
       leading(join_table_list) 
leading(@block_name join_table_list)

Double parentheses (()), specifying the join order and outer/inner tables. The outer/inner tables are specified by the outermost parentheses.

    
       leading((join_table_list)) 
leading(@block_name (join_table_list))

Single-layer square brackets [], specifying the join order of [] and the sequence of internal and foreign tables.

   
      leading[join_table_list]
leading[@block_name join_table_list]

Combination of single-layer parentheses () and single-layer square brackets [], specifying the join order and the sequence of internal and foreign tables at any layer. The parentheses () specify only the join order, but not the sequence of internal and foreign tables. The square brackets [] specify both the join order and the sequence of internal and foreign tables.

   
      leading(join_table_list1 [join_table_list2])
leading[join_table_list1 [join_table_list2]]
leading[join_table_list1 (join_table_list2)]
leading(@block_name join_table_list1 [join_table_list2])
leading[@block_name join_table_list1 [join_table_list2]]
leading[@block_name join_table_list1 (join_table_list2)]

NOTICE:

Single-layer square brackets [] can be used together with single-layer parentheses () to specify the sequence of internal and foreign tables of any layer. Single-layer [] and double-layer () cannot be used together.

Parameter Description

join_table_list
Specifies the tables to be joined. The values can be table names or table aliases. If a subquery is pulled up, the value can also be the subquery alias. Separate the values with spaces. You can add parentheses to specify the join priorities of tables.
To prevent semantic errors, tables in the list must meet the following requirements:
- The tables must exist in the query or its subquery to be pulled up.
- The table names must be unique in the query or subquery to be pulled up. If they are not, their aliases must be unique.
- A table appears only once in the list.
- An alias (if any) is used to represent a table.
NOTE:
- The syntax format of the table is as follows:
  [schema.]table[@block_name]
  
  The table name can contain the schema name or block name before the subquery statement block is promoted. If the subquery statement block is optimized and rewritten by the optimizer, the value of block_name is different from that of block_name in leading.
- If a table has an alias, the alias is preferentially used to represent the table.

block_name

Specifies the block name of the statement block. It indicates that the hint takes effect in the subquery statement block corresponding to the block name.

NOTICE:

By default, a block name is generated for a statement.

CN lightweight statements do not generate block names.

Block names can be generated for the CREATE TABLE AS SELECT, SELECT INTO, SELECT, INSERT, UPDATE, DELETE, and MERGE statements.

The naming rule of a block name is as follows:

A block name is automatically generated for the SELECT, INSERT, UPDATE, DELETE, and MERGE statements. The naming format of a block name for these statements is sel$n, ins$n, upd$n, del$n, and mer$n, respectively, where n starts from 1. The number of statements of different types is not accumulated, but the number of statements of the same type is accumulated.

Example:

           
              INSERT INTO t SELECT * FROM t1 WHERE a1 IN (select * from t2);

--------sel$2-------

-----------------------sel$1----------------------

--------------------------------ins$1---------------------------

Recursively assigns a block name to each statement block before the optimizer is used.
First, assign block names to the existing statements block based on the statement type, then traverse the statement blocks in the following sequence, and assign block name to the statement blocks in the statement blocks:
1. Traverse the target column.
2. Traverse the target column in the source table of the MERGE statement.
3. Traverse actions (update or insert) in the MERGE statement.
4. Traverse the returning clause.
5. Traverse the Join and Where conditions in From. (The join condition takes precedence over the Where condition.)
6. For a set operation, traverse each branch of the set (UNION, INTERSECT, and EXCEPT).
7. Traverse the HAVING clause.
8. Traverse the LIMIT OFFSET clause.
9. Traverse the LIMIT COUNT clause.
10. Traverse CTE
11. Traverse the table after From.
12. Traverse the UPSERT clause.
In the rewriting phase of the optimizer, rewriting optimization is performed due to FUL LJOIN, cte inline, materialized view rewriting, INLIST2JOIN, OR conversion, multi count(distinct), Magic Set, lazyagg, and subquery/sublink promotion, a new subquery is constructed. In this case, the recursive processing during block name assignment is also applied to the newly constructed subquery. The number of block names is accumulated.
In the optimizer rewriting phase, when a subquery is promoted, the table in the inner subquery is promoted to the outer query, and the inner subquery is eliminated. In this case, the promoted table may have the same name as the table in the outer queries. Therefore, the block name to which the promoted table belongs is recorded in the table to distinguish two tables with the same name but are from different query blocks.

For example:

leading(t1 t2 t3 t4 t5): t1, t2, t3, t4, and t5 are joined. The join order and outer/inner tables are not specified.

leading(t1 t2 t3 t4 t5): t1, t2, t3, t4, and t5 are joined in sequence. The table on the right is used as the inner table in each join.

leading(t1 (t2 t3 t4) t5): First, t2, t3, and t4 are joined and the outer/inner tables are not specified. Then, the result is joined with t1 and t5, and the outer/inner tables are not specified.

leading(t1 (t2 t3 t4) t5): First, t2, t3, and t4 are joined and the outer/inner tables are not specified. Then, the result is joined with t1, and (t2 t3 t4) is used as the inner table. Finally, the result is joined with t5, and t5 is used as the inner table.

leading((t1 (t2 t3) t4 t5)) leading((t3 t2)): First, t2 and t3 are joined and t2 is used as the inner table. Then, the result is joined with t1, and (t2 t3) is used as the inner table. Finally, the result is joined with t4 and then t5, and the table on the right in each join is used as the inner table.

leading[t1 [t2 t3]] is equivalent to leading((t1 (t2 t3))) leading((t2 t3)).

leading(t1 [t2 t3]) is equivalent to leading(t1 t2 t3) leading((t2 t3)).

leading[@sel$1 t1@sel$1 [t2@sel$2 t3@sel$2]] indicates that t2 and t3 are located in the subquery. After the subquery is promoted, t2 and t3 are joined, and then the join table is joined to t1. Where t2 is a foreign table, t3 is an internal table, t1 is a foreign table. The join table of t2 and t3 is an internal table.

Examples

Hint the query plan in Examples as follows:

   
      explain
select /*+ leading((((((store_sales store) promotion) item) customer) ad2) store_returns) leading((store store_sales))*/ i_product_name product_name ...

First, store_sales and store are joined and store_sales is the inner table. Then, The result is joined with promotion, item, customer, ad2, and store_returns in sequence. The optimized plan is as follows:

Click to enlarge

For details about the warning at the top of the plan, see Hint Errors, Conflicts, and Other Warnings.

Parent topic: Hint-based Tuning

Previous topic: Plan Hint Optimization

Next topic: Join Operation Hints

Feedback

Was this page helpful?

Helpful Not helpful

Provide feedback

Thank you very much for your feedback. We will continue working to improve the documentation.See the reply and handling status in My Cloud VOC.

The system is busy. Please try again later.

Which of the following issues have you encountered?

Content is inconsistent with the product UI

Unclear descriptions

Lack of examples or code

Incorrect steps

Can't find what I need

Lack of best practices

Feedback (optional)

0/500

Select at least one type of issue, and enter your comments or suggestions.

Enter a maximum of 500 characters.

Submit Cancel

For any further questions, feel free to contact us through the chatbot.

Chatbot