To troubleshoot queries causing DeepResearch to crash or fail, start by isolating the problem. First, examine the query structure and inputs. Break the query into smaller components and test each part individually to identify which segment triggers the failure. For example, if the query includes complex joins, subqueries, or aggregations, disable them incrementally to pinpoint the culprit. Log intermediate results or use debug outputs to verify if data transformations or filters behave as expected. This helps surface issues like syntax errors, invalid parameters, or unsupported operations specific to DeepResearch's query engine.
Next, analyze system resource usage during query execution. Use monitoring tools (e.g., top, htop, or application-specific dashboards) to track CPU, memory, and disk I/O. A query causing a crash might exhaust memory due to large dataset processing or inefficient algorithms. For instance, a join operation on unindexed tables could trigger excessive memory consumption. If the query hangs, check for deadlocks or thread contention using profiling tools or database-specific diagnostics (e.g., PostgreSQL's pg_stat_activity). Adjust resource limits, optimize data structures, or add indexes to mitigate bottlenecks.
Finally, review logs and error messages. DeepResearch likely generates logs detailing errors, warnings, or stack traces. Look for patterns like timeout exceptions, out-of-memory errors, or connection failures. For example, a timeout might indicate a query that exceeds DeepResearch's execution time threshold, requiring optimization or partitioning. If logs are insufficient, enable verbose logging or instrument the code to capture intermediate states. Reproduce the issue in a controlled environment (e.g., a test instance with the same data) to safely experiment with fixes, such as adjusting query timeouts, revising logic, or upgrading dependencies to resolve compatibility issues.