What Data Looks Like for This Category
This work typically serves client engineers, engine engineers, and performance testing teams. Daily deliverables include performance test reports, device compatibility lists, optimization records, and crash analysis reports. Data is collected from engine Profiler tools, crash reporting platforms, real-device test equipment, and frame rate collection systems. Update frequency is either daily or per version iteration. Data fields include frame rate, memory usage, resource load duration, and crash stacks. There is no unified fixed format, so parsing rules must be adjusted based on the collection tool used.
Constraints Imposed on Workflow Orchestration by These Characteristics
Multi-source heterogeneous data sources mean workflows must support connecting different types of data source nodes. These nodes separately process Profiler logs, crash data, and real-device test results. Daily or per-version update cycles mean workflows must support scheduled triggers or triggers tied to version submissions. This matches the team’s iteration rhythm. Diverse daily document types mean workflows need configured field mapping rules. These rules unify core fields from different documents into a processable format. Time-consuming steps such as low-end device reproduction and resource analysis mean workflows must set reasonable timeout thresholds. This prevents workflow interruptions. Inaccurate handling of abnormal performance data may cause stutters, crashes, and app store negative reviews. So workflows must focus on filtering and routing abnormal entries.
How to Configure
The following configurations are based on the workflow module of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Mode | Trigger at 2:00 AM daily, trigger after version submission | Matches the daily/per-version data update frequency, adapts to the iteration rhythm of performance testing |
variables | Bind frame rate, memory usage, crash stack fields | Corresponds to core data items in performance test reports and crash analysis reports, enables automated data pulling |
nodeId | Assign independent IDs to Profiler data nodes and real-device test nodes | Distinguishes processing nodes for different data sources, avoids data confusion and overwriting |
| Timeout Setting | 1800 seconds | Covers the maximum time required for low-end device reproduction and resource usage analysis, prevents abnormal termination of the workflow mid-run |
| Result Filtering Rules | Retain entries with frame rate below 60 fps | Matches the core judgment standard for game client performance optimization, focuses on abnormal performance data |
| Output Format | Structured JSON | Adapts to the input requirements of subsequent HTTP requests and AI chat modules, simplifies cross-node data transfer |
Three Common Misconfigurations
- The workflow throws a "Cannot convert undefined or null to object" error during runtime. The cause is failure to bind fields of the corresponding data source to
variables, resulting in empty node input and incomplete data parsing. - The output of the AI chat module is displayed directly on the page, without being processed by a text splicing component before output. The cause is failure to configure forwarding rules for the output node, and failure to direct the processed results to the specified reply channel.
- Knowledge base retrieval results cannot be correctly referenced when passed to the AI chat module. The cause is failure to convert retrieval results into a structured format matching
variables, resulting in the AI being unable to recognize associated performance data.
How to Verify Proper Configuration
- Trigger a test workflow, check logs of each node, confirm no uncaught null values or format errors.
- Check the output of the output node, confirm only abnormal data entries matching the performance judgment standard are retained.
- Test the input of the AI chat module, confirm the passed structured data can be correctly identified and associated with performance analysis content.
- Verify the trigger rules, confirm the workflow starts automatically at the specified time or after version submission.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.