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When a high-concurrency OLTP environment experiences intermittent latency spikes or sudden CPU exhaustion, enabling verbose logging or attaching full debuggers can exacerbate the problem. In production, stability is the primary constraint. Diagnostic procedures that introduce significant overhead risk triggering the very outages they aim to prevent.<\/p>\n
Effective database diagnostics<\/strong> require a phased, evidence-based approach. This protocol prioritizes non-blocking observation techniques\u2014such as per-second sampling and wait event analysis\u2014over intrusive tracing. The goal is to distinguish between transient resource contention, persistent lock bottlenecks, and query plan inefficiencies without disrupting active transactions.<\/p>\n This guide outlines a diagnostic framework. It covers prerequisites, isolation techniques for CPU and lock issues, execution plan correlation, and critical rollback procedures.<\/p>\n Before initiating any diagnostic procedure, you must establish strict safety boundaries. The objective is to gather data without becoming part of the workload.<\/p>\n Environment Constraints<\/strong><\/p>\n Prerequisite Checklist<\/strong><\/p>\n The first step in root cause analysis is determining whether the database is CPU-bound (executing complex logic) or I\/O-bound (waiting for data).<\/p>\n Vendor-Neutral Approach<\/strong><\/p>\n Standard SQL databases expose wait event data through system views. While specific view names vary, the underlying concept remains consistent: query the session state to identify what the process is waiting for.<\/p>\n KingbaseES Implementation: Using For KingbaseES V9, the Procedure:<\/strong><\/p>\n Verification:<\/strong><\/p>\n Lock contention is a common cause of latency in OLTP systems. Traditional methods of querying lock tables can sometimes block the very transactions you are trying to diagnose.<\/p>\n Low-Impact Identification<\/strong><\/p>\n Instead of querying lock tables directly, which may acquire shared locks, rely on session-level metadata.<\/p>\n Procedure:<\/strong><\/p>\n Risk Mitigation:<\/strong><\/p>\n Once you have isolated the resource type (CPU or Lock), the next step is to analyze the query plan. High CPU usage is often the result of suboptimal execution plans, such as full table scans or missing joins.<\/p>\n KingbaseES Implementation: Performance Inspection Platform<\/strong><\/p>\n KingbaseES offers a Performance Inspection Platform that supports the collection and management of slow queries. This tool is designed to capture slow queries without requiring manual intervention for every spike.<\/p>\n Procedure:<\/strong><\/p>\n Example Scenario:<\/strong><\/p>\n Verification:<\/strong><\/p>\n A critical failure mode in database diagnostics is the diagnostic tool itself becoming the bottleneck. You must validate that your diagnostic steps are not contributing to resource exhaustion.<\/p>\n Validation Checklist<\/strong><\/p>\n Evidence-Based Validation:<\/strong><\/p>\n If a diagnostic step or configuration change degrades performance, you must have a clear rollback path. This is often overlooked in generic troubleshooting guides.<\/p>\n KingbaseES Configuration Management<\/strong><\/p>\n KingbaseES uses a control file and Rollback Steps:<\/strong><\/p>\n Best Practice:<\/strong><\/p>\n Not all issues can be solved with low-impact sampling. Sometimes, deep tracing is required. However, deep tracing carries significant risk. Use the following matrix to decide when to escalate.<\/p>\nPrerequisites and Safety Boundaries for Production Diagnostics<\/h2>\n
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sys_ksh<\/code> are available for low-overhead sampling.<\/li>\n<\/ul>\n\n
SELECT<\/code> privileges on system views, but no UPDATE<\/code> or DELETE<\/code> permissions.<\/li>\nPhase 1: Isolating CPU Spikes from I\/O Boundaries via Wait Events<\/h2>\n
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CPU<\/code> or IO<\/code> related wait states.<\/li>\nQueryId<\/code> or session identifiers.<\/li>\n<\/ol>\nsys_ksh<\/code><\/strong><\/p>\nsys_ksh<\/code> tool provides a mechanism for low-overhead sampling. It performs per-second sampling of sessions and wait events, storing data in an in-memory Ringbuf queue. This approach minimizes overhead compared to continuous SQL polling.<\/p>\n\n
sys_ksh<\/code> configuration and usage. Specific commands and parameters for activating sampling or setting intervals are not provided here and must be verified against official vendor documentation.<\/li>\nQueryId<\/code> patterns. Note that specific capabilities for retrieving or analyzing this data require verification against the tool’s actual output structure.<\/li>\n<\/ol>\n\n
Phase 2: Detecting Lock Contention Without Blocking<\/h2>\n
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session_time<\/code> or start_time<\/code> exceeding the average transaction duration.<\/li>\nwaiting<\/code> state.<\/li>\nsys_ksh<\/code>, review the historical lock status data.<\/li>\n<\/ol>\n\n
sys_ksh<\/code> output for sessions marked as waiting<\/code> on lock events.<\/li>\n\n
SELECT * FROM pg_locks<\/code> (or equivalent) on a heavily locked system without caution. Use the sampled data from sys_ksh<\/code> which captures the state at the moment of sampling.<\/li>\n<\/ul>\nPhase 3: Correlating Latency Spikes with Execution Plans<\/h2>\n
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sys_ksh<\/code> shows high CPU usage correlated with specific QueryId<\/code>s.<\/li>\nQueryId<\/code>s are executing full table scans on a frequently updated table.<\/li>\n\n
Verification: Ensuring Diagnostics Do Not Cause Overhead<\/h2>\n
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sys_ksh<\/code>, verify that the in-memory Ringbuf queue operations do not cause significant CPU jitter. The sampling should be lightweight.<\/li>\nsys_ksh<\/code> uses in-memory buffers; ensure these do not compete with the buffer pool for critical data pages.<\/li>\n\n
Rollback Procedures and Configuration Restoration<\/h2>\n
kingbase.conf<\/code> for configuration. A key feature is that the control file updates automatically when kingbase.conf<\/code> parameters are modified, reducing manual maintenance errors.<\/p>\n\n
sys_ksh<\/code> or the Performance Inspection Platform was enabled for a specific session, disable it immediately.<\/li>\n\n
kingbase.conf<\/code> parameters (e.g., increased logging verbosity), revert the changes.<\/li>\nsys_ksh<\/code> data is causing memory pressure, clear the Ringbuf queue.<\/li>\n\n
Decision Matrix: Escalating from Sampling to Deep Tracing<\/h2>\n