ChatDBA Case Study: Resolving OceanBase 4013 Memory Error through Interactive AI‑Driven Troubleshooting

The community flagship column "One Question One Experiment: AI Edition" has published several issues (51‑59) showcasing ChatDBA’s ability to solve problems in various scenarios. The first batch of invited users is now open for experience; interested readers can fill in their contact information at the end of the article.

Problem

OceanBase performance test unexpectedly shows error 4013?

Symptom: While running a simple sysbench test on OceanBase, the client observed the following error after a period of execution:

FATAL: mysql_drv_query() returned error 4013 (No memory or reach tenant memory limit) for query 'INSERT INTO sbtest9(k, c, pad) VALUES ...'

The following sections walk through four rounds of interaction with ChatDBA to diagnose and resolve the issue.

Experiment

Full operation video

ChatDBA demonstration video is synchronized to the community video channel and Bilibili; please follow.

ChatDBA Expert Mode

In Expert Mode, after the first round of dialogue, ChatDBA generates a root‑cause analysis tree, displaying its troubleshooting logic to help DBAs quickly locate the problem.

First Round Interaction

The specific fault details are provided to ChatDBA.

ChatDBA initially analyzes the issue and suggests a preliminary solution, while prompting the user to check log files for memory‑related information.

Second Round Interaction

The user executes the command suggested by ChatDBA, retrieves the logs, and provides them back.

ChatDBA determines that the tenant’s memory usage has reached its limit, causing allocation failures, and recommends checking memory usage across different contexts.

Third Round Interaction

The user queries the tenant’s memory limit information and shares the results.

ChatDBA confirms that the tenant’s memory usage is near or at the configured limit, identifies major memory consumers, and proposes concrete solutions and further optimization steps.

Fourth Round Interaction

Following ChatDBA’s second recommendation, the user executes the command:

ALTER RESOURCE UNIT unit_name MEMORY_SIZE = '4G';

After the change, the test runs normally. The final root cause was identified in the third round of dialogue.

ChatDBA also provides additional remediation suggestions for memory‑shortage issues (see experiment summary).

Experiment Summary

Database memory shortage can be caused by the following reasons:

Memory Limits: OceanBase may set per‑tenant memory limits; exceeding them triggers memory‑shortage errors.

Improper Database Configuration: Parameters such as innodb_buffer_pool_size or ob_server_memory_limit set too low.

Queries or Operations Consuming Excessive Memory: Complex queries or massive inserts/updates.

Too Many Connections: High concurrency or connection leaks.

Memory Leaks: In the database engine or application.

System Resource Limits: OS‑level process memory caps or insufficient physical RAM.

Excessive Data Volume: Unoptimized data growth increasing memory demand.

Disk I/O Bottlenecks: Poor disk performance affecting memory release.

Memory Fragmentation: Long‑running workloads causing fragmented memory.

The issue in this case was due to the tenant’s memory usage approaching or reaching its configured limit.

ChatGPT‑4o Comparison

Award Information

Shanghai Aikexing Information Technology Co., Ltd. actively explores and applies AI technology. Its self‑developed intelligent database operation assistant ChatDBA won the Excellence Award in the Enterprise Track of the “2024 Wuxi International AI Innovation Application Competition”.

The competition, launched in August, attracted many teams; Aikexing’s solution stood out for its solid technical implementation and business integration, earning the judges’ recognition.

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