Distributed SQL Optimization

PawSQL Distributed SQL Optimization targets performance problems that are specific to distributed databases by analyzing data distribution, cross-node movement, join paths, distribution keys, and index strategies. Unlike traditional single-node databases, distributed SQL performance depends not only on access paths, but also on where data is located and whether computation can be pushed down to the nodes that own the data.

Overview

In a distributed database, a SQL statement may be logically correct and still perform poorly because of inefficient data distribution. Common problems include:

Join columns that do not align with distribution keys
Large Shuffle / Redistribute operations between nodes
Small tables that are not replicated
Predicates that are not pushed down
Data skew caused by poor distribution-key selection
Inefficient use of global or local indexes

PawSQL analyzes both SQL structure and distribution metadata.

Why Distributed SQL Needs Specialized Optimization

Traditional SQL optimization focuses on:

Indexes
Join algorithms
Scan types
Predicate pushdown
Aggregation and sorting

Distributed databases add another set of questions:

Which node owns the data?
Can the join be completed locally?
Must data move between nodes?
Which side is suitable for broadcast?
Does the distribution key match the dominant access pattern?
Is data heavily skewed?

As a result, SQL that is efficient on a single-node database may be inefficient in a distributed environment.

Key Capabilities

Distribution key analysis

Evaluate whether table distribution keys align with common filter conditions, join predicates, and access patterns. Typical issues include:

Frequently joined tables using unrelated distribution keys
Filters that do not benefit from the distribution strategy
Low-cardinality distribution keys
Hot partitions or node-level skew

Cross-shard join detection

Identify joins that require data to cross shards or nodes. For large business tables, cross-shard joins can introduce:

Network transfer
Shuffle
Intermediate-result explosion
Coordinator pressure

PawSQL can further analyze potential optimization paths.

Data movement analysis

Identify execution-plan operators such as:

Redistribute
Broadcast
Exchange
Motion
Shuffle

and determine whether some of that movement can be avoided.

Replicated table recommendation

For relatively small tables that are frequently joined, such as configuration, dimension, or parameter tables, PawSQL can evaluate whether replicated or broadcast-table designs are more appropriate. Potential benefits include:

Fewer cross-node joins
Lower network overhead
Better join pushdown

Predicate and join pushdown

Analyze whether filters and joins can be pushed closer to the data nodes to reduce intermediate row volume.

Global and local index analysis

For databases that support global and local indexes, PawSQL can evaluate:

Index coverage
Partition / shard access paths
Global-index maintenance cost
Whether local indexes satisfy the query pattern

Common Optimization Strategies

Example

Suppose two large distributed tables use different distribution keys:

orders       DISTRIBUTED BY customer_id
payments     DISTRIBUTED BY payment_id

SQL:

SELECT *
FROM orders o
JOIN payments p
  ON o.customer_id = p.customer_id;

Because the two tables are distributed differently, the database may need to redistribute one side before completing the join. Possible optimization strategies include:

Changing the distribution key
Reducing the joined data set using filters
Broadcasting the smaller side
Rewriting or decomposing the SQL to reduce data movement
Introducing an additional replica or replicated table

The correct strategy depends on data volume, database capabilities, and the business data model.

Supported Scenarios

Distributed SQL Optimization applies to distributed database environments such as:

TDSQL PostgreSQL
TDSQLx-MySQL
OceanBase
GaussDB
openGauss
PolarDB-X
GoldenDB
Greenplum

Distributed SQL Optimization

Overview

Why Distributed SQL Needs Specialized Optimization

Key Capabilities

Distribution key analysis

Cross-shard join detection

Data movement analysis

Replicated table recommendation

Predicate and join pushdown

Global and local index analysis

Common Optimization Strategies

Example

Supported Scenarios

Query Rewrite

Index Recommendation

Performance Validation

Execution Plan Analysis

Big Data SQL Optimization

DBA Batch Slow SQL Governance

Database Migration SQL Governance

Enterprise SQL Governance Platform

​Overview

​Why Distributed SQL Needs Specialized Optimization

​Key Capabilities

​Distribution key analysis

​Cross-shard join detection

​Data movement analysis

​Replicated table recommendation

​Predicate and join pushdown

​Global and local index analysis

​Common Optimization Strategies

​Example

​Supported Scenarios

​Related Capabilities

Query Rewrite

Index Recommendation

Performance Validation

Execution Plan Analysis

Big Data SQL Optimization

​Related Use Cases

DBA Batch Slow SQL Governance

Database Migration SQL Governance

Enterprise SQL Governance Platform

Overview

Why Distributed SQL Needs Specialized Optimization

Key Capabilities

Distribution key analysis

Cross-shard join detection

Data movement analysis

Replicated table recommendation

Predicate and join pushdown

Global and local index analysis

Common Optimization Strategies

Example

Supported Scenarios

Related Capabilities

Related Use Cases