How can you troubleshoot queries that consistently cause DeepResearch to crash, hang, or otherwise fail?

To troubleshoot queries that cause DeepResearch to fail, start by analyzing logs and error messages. Most crashes or hangs leave traces in system logs, which can pinpoint where the failure occurs. For example, if a query triggers an out-of-memory error, the log might show a spike in memory usage just before the crash. Look for stack traces or exceptions that highlight specific code paths, such as infinite loops, unhandled edge cases, or resource leaks. Tools like profilers or monitoring dashboards can help track CPU, memory, or I/O usage during query execution. For instance, a query with a recursive function that doesn’t terminate could max out CPU resources, while a poorly optimized JOIN operation might overload database connections. Reproducing the issue in a controlled environment with debuggers or breakpoints can isolate the root cause.

Next, simplify the query and test components incrementally. If a complex query fails, break it into smaller subqueries and validate each step. For example, if a search combines multiple filters and aggregations, disable filters one by one to identify which part causes the failure. Use mock data or a reduced dataset to test if the issue persists under lighter loads. Tools like unit tests or API testing frameworks (e.g., Postman) can automate this process. For instance, a query that crashes when sorting large result sets might reveal a bug in the sorting algorithm when tested with synthetic data. Additionally, check for dependencies—like outdated libraries or incompatible data formats—that might conflict with the query logic. If the issue is intermittent, implement retries or timeouts to handle transient failures gracefully.

Finally, review system configurations and resource limits. DeepResearch might crash if queries exceed predefined thresholds for execution time, memory, or parallel processes. For example, a timeout set too low could terminate long-running analytical queries prematurely. Adjust these limits based on typical workloads, and ensure infrastructure (e.g., databases, caching layers) can scale under load. Optimize queries by adding indexes, reducing data transfers, or caching frequent results. For instance, adding an index on a timestamp field in a database can speed up time-range queries and prevent full-table scans. Regularly update dependencies to patch known bugs, and monitor performance trends to catch regressions early. If all else fails, consult documentation or community forums for known issues with specific query patterns, and consider reaching out to the tool’s support team with a reproducible test case.