Fixing Slow Query Performance in GBase Database: A Practical DBA Checklist

Slow queries are one of the most common database performance problems.

When a query becomes slow in GBase Database, the solution is usually not to immediately increase memory or add indexes. A better approach is to identify the bottleneck, understand the execution plan, make one targeted change, and then measure the result.

The same methodology works across most GBase Database workloads, although the optimization strategy can differ between transactional and analytical systems.

Step 1: Find the Slow Queries

Start by identifying which SQL statements are actually causing the problem.

Look at:

• Execution time
• Execution frequency
• Rows processed
• CPU consumption
• I/O activity
• Concurrent executions

A query that takes 10 seconds but runs once a day may be less important than a query that takes 500 ms and runs thousands of times per minute.

For GBase Database production systems, prioritize queries based on their total workload impact rather than execution time alone.

A useful starting point is:

Total impact = execution time × execution frequency

This helps DBAs focus on the queries that matter most.

Step 2: Read the Execution Plan

Once a slow query has been identified, check its execution plan.

For example:

SET EXPLAIN ON;

SELECT customer_id, SUM(amount)
FROM orders
WHERE order_date >= '2026-01-01'
GROUP BY customer_id;

SET EXPLAIN OFF;

The exact plan output and diagnostic tools can vary by GBase Database version and product, but the questions are the same:

• Is the query scanning more data than necessary?
• Are indexes being used appropriately?
• Are joins processing large intermediate result sets?
• Is sorting or grouping expensive?
• Are estimated rows significantly different from actual workload behavior?

Common warning signs

Large sequential scans

A full scan is not automatically bad. It can be efficient when a query needs a large percentage of a table.

The problem is a large scan for a highly selective query.

Expensive joins

Check join conditions, join order, and the amount of data flowing between operations.

Large sorts or aggregations

Sorting and grouping can become expensive when the query processes millions of rows unnecessarily.

The goal is to understand why the database is processing so much data before changing configuration.

Step 3: Check Indexes

For transactional workloads, indexes are often one of the most important performance tools.

Before adding an index, check:

• Which columns are used in WHERE conditions?
• Which columns are used for joins?
• Are frequently queried columns selective?
• Does an existing index already cover the query?
• Will the new index increase write overhead?

For example:

SELECT *
FROM orders
WHERE customer_id = 10001
  AND order_date >= '2026-01-01';

An appropriate index may reduce the amount of data that needs to be examined.

But more indexes do not automatically mean better performance.

Every additional index can increase storage requirements and the cost of INSERT, UPDATE, and DELETE operations.

Step 4: Check Data Volume and Statistics

Sometimes the SQL itself is reasonable, but the optimizer does not have enough accurate information to choose a good execution strategy.

During GBase Database performance troubleshooting, check whether:

• Table statistics are current.
• Data distribution has changed significantly.
• Large amounts of data were recently loaded.
• Table growth has changed the original query behavior.

This is especially important after a major migration.

A query that performed well on a small test dataset may behave very differently after the GBase Database system reaches production-scale data volumes.

Step 5: Optimize for the Workload

Not every GBase Database workload should be optimized in the same way.

Transactional workloads

For GBase Database(GBase 8s) and similar OLTP workloads, focus on:

• Index efficiency
• Short transactions
• Lock contention
• Connection concurrency
• Efficient point lookups
• Avoiding unnecessary full-table operations

For example, fetching a few rows should not require processing millions of rows.

Analytical workloads

For analytical workloads such as GBase Database(GBase 8a MPP Cluster), the optimization priorities are different.

Avoid unnecessary columns:

-- Avoid
SELECT *
FROM fact_sales;

Prefer:

SELECT date_id, SUM(amount)
FROM fact_sales
WHERE date_id >= 20260101
GROUP BY date_id;

The principle is simple:

Process less data whenever possible.

Column-oriented analytical systems can be extremely efficient for large scans, but selecting unnecessary columns or processing far more rows than required still creates avoidable work.

Step 6: Change One Thing at a Time

A common DBA mistake is changing several variables simultaneously:

• Add an index
• Increase memory
• Change configuration
• Rewrite SQL
• Restart the database

If performance improves, you may not know which change actually helped.

A better process is:

Identify
   ↓
Measure
   ↓
Explain
   ↓
Change one thing
   ↓
Measure again
   ↓
Keep or roll back

This makes performance tuning much easier to reproduce.

GBase Database Performance Checklist

When troubleshooting a slow query, ask:

• [ ] Which SQL statements are actually slow?
• [ ] How frequently do they execute?
• [ ] What does the execution plan show?
• [ ] Is the query processing unnecessary rows or columns?
• [ ] Are indexes appropriate for the workload?
• [ ] Are statistics current?
• [ ] Is the problem CPU, I/O, memory, locking, or concurrency?
• [ ] Has the data volume changed?
• [ ] Was the performance improvement measured after the change?

Final Takeaway

The most reliable GBase Database performance tuning strategy is not to start with configuration parameters.

Start with the workload.

Find the expensive SQL → understand the execution plan → identify the real bottleneck → make one targeted change → measure again.

For transactional GBase Database workloads, this often means better indexing and shorter transactions. For analytical workloads, it may mean reducing data scanned, improving query structure, and taking advantage of the underlying storage architecture.

The tools may differ across GBase Database products and versions, but the troubleshooting methodology remains the same.