Overview
Query timeout errors in Canopy Insights reports and dashboards occur when the system is unable to process a query within the allocated resource or time limits. These errors are often caused by large, complex datasets or unoptimized query practices. Below, we outline common causes, practical troubleshooting steps, and an acknowledgment of known limitations.
Common Causes
Several factors can lead to query timeout errors:
Large or Complex Datasets: - Queries involving high volumes of data, such as all-time data across multiple clients or extensive use of joins, filters, or subqueries, take longer to process.
Unoptimized Filter Usage: - Applying filters too late in the query, using insufficient filters, or not leveraging indexed fields (e.g., filter by client or date fields first) can drastically increase execution times.
Overly Broad Date Ranges: - Querying extensive time frames, such as months or years, increases the data being scanned, leading to slowdowns.
Lack of Indexing or Partitioning: - Missing data partitioning or incorrect field usage (e.g., not querying partitioned fields like dates) reduces system efficiency.
Platform or Infrastructure Issues: - Temporary service interruptions, resource competition in a multi-tenant system, or infrastructure slowdowns within the Canopy platform.
Troubleshooting Steps
To resolve or reduce query timeout errors, follow these steps:
Simplify Queries: - Limit the date range—e.g., query shorter time periods instead of larger historical datasets. - Focus filters on specific clients, team members, or other narrow criteria before running the query.
Optimize Filtering Practices: - Ensure filters are applied early within queries and utilize indexed fields where available. - Remove irrelevant data or duplicates to improve processing speed.
Resolve Syntax Issues: - Double-check for formula conflicts, such as mixing aggregated and unaggregated attributes, and uncheck problematic formulas when needed.
Refresh the Dashboard: - Save and re-run the dashboard query or edit the board and save it without changes to trigger data reloads.
Re-login to Canopy: - If issues persist following recent updates or changes, logging out and back into the system can resolve residual platform inconsistencies.
Known Limitations
Query timeout errors in Canopy Insights are a recognized constraint, primarily when handling significantly large data queries. Recent updates have enhanced performance, and logging back in after these updates may yield improvements.However, limitations remain on large-scale data queries. Canopy is actively working on expanding system capacity and introducing optimizations to reduce query resource demands.
By understanding the causes and applying these solutions, you can minimize query timeout errors and improve performance using Canopy Insights.