🧩 ORA-00600 During Parallel Execution in RAC

 🧩 Introduction

ORA-00600 errors can occur during parallel execution in RAC environments when internal consistency checks fail across multiple nodes. These errors may impact a single session or multiple parallel processes, causing query failures, job interruptions, or node instability. Understanding the cause and resolution is critical for DBAs managing RAC clusters.

Error Message Example:

ORA-00600: internal error code, arguments: [kghpxx1], [56789], [NULL], ...

Alert logs and trace files indicate the node and process affected.

Common Causes in Parallel Execution on RAC

Inconsistent data block handling across RAC nodes.

Bugs in parallel query coordination and cache fusion.

Memory corruption in PGA or SGA for parallel slaves.

SQL statements consuming large resources and triggering internal errors.

Differences in patch levels or OS libraries between RAC nodes.

Step 1: Identify Node and Session

Check alert logs for each RAC node to identify the affected session and parallel process.

Step 2: Examine Trace Files

Trace files for parallel slaves show stack information and SQL context:

$ORACLE_BASE/diag/rdbms/<db_name>/<SID>/trace

Look for modules related to parallel execution and cache fusion.

Step 3: Analyze Error Arguments

Use the first ORA-00600 argument to search Oracle Support for known bugs or patches specific to parallel execution scenarios.

Step 4: Apply Fixes or Workarounds

Apply recommended patches for the ORA-00600 argument.

Optimize or rewrite parallel SQL queries causing the error.

Reboot affected nodes if memory corruption is suspected.

Ensure consistency in patch levels and OS libraries across all RAC nodes.

Step 5: Prevent Recurrence

Monitor parallel execution workloads across RAC nodes.

Use Oracle Health Checks for memory, parallel slaves, and RAC interconnect.

Schedule large parallel operations during off-peak hours.

Regularly review alert logs for early warning signs.

Conclusion

ORA-00600 during parallel execution in RAC can disrupt cluster operations, but with careful analysis, patch management, and workload optimization, DBAs can maintain high availability and stable performance.