What this category's data looks like
Data sources include public quotation databases from domestic auto parts industry associations, upstream metal raw material futures market data, linked data from financial market application programming interfaces, and monthly supply settlement ledgers from partner manufacturers. The update rhythm is divided into three categories: core raw material-linked parts data updated daily, parts supply data for designated supporting vehicle models updated weekly, and overall profit margin reports for the full product category updated monthly.
Documents are stored in structured JSON or CSV format, including the following fields: part SKU code, upstream raw material proportion coefficient, factory reference price, channel supply price, per-unit gross profit. Units: Factory reference price and channel supply price are measured in yuan per piece, per-unit gross profit is measured in yuan per piece, and the raw material proportion coefficient is a unitless ratio.
What constraints do these characteristics impose on workflow orchestration
This category’s data comes from multiple sources and uses multiple update frequencies, which create multiple constraints for workflow orchestration. Pull multi-source data separately before correlating and matching it. Configure field mapping nodes in workflows to unify SKU codes and field formats. Bind data sources with different update frequencies to differentiated trigger rules: Pull real-time raw material data daily, synchronize designated supply data weekly, and generate full-category reports monthly.
Single-batch data volume increases with the report cycle. Enable pagination pull configuration in workflows to avoid exceeding API limits in a single request. Additionally, field order varies across different sources. Preset field matching rules to ensure consistent data structure after merging.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
trigger_type | Scheduled trigger + manual trigger | Covers automatic pull requirements for multiple update frequencies; manual triggers are used for temporary data corrections and emergency report generation |
data_pull_batch_size | 50-100 items per request | Auto parts have a large number of SKUs; overly large batches risk triggering API rate limits, while overly small batches reduce pull efficiency |
field_mapping_rule | Associate upstream raw material data by SKU code | SKU is the unique identifier for data matching in this category, enabling accurate correlation of multi-source data |
workflow_timeout | 600 seconds | Full report generation requires processing multi-source data correlations; typical processing time is approximately 8-10 minutes, so this setting reserves reasonable buffer time |
error_retry_strategy | Retry 3 times, 60-second interval | API rate limits and temporary network fluctuations are common exception scenarios; this retry interval aligns with industry API recovery cycles |
data_merge_mode | Left join matching | All part SKUs must be retained, even if some SKUs have no matching upstream raw material data, to avoid loss of core data |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material types, data volume, and business rules. Specific issues require case-by-case analysis, and testing on independent samples is recommended before finalizing settings.
Three common mistakes
- Phenomenon: An error is reported after publishing to a channel link, with the prompt "undefined" is not valid JSON. Local workflow testing works normally. Cause: Variable mapping for the channel environment is not configured. Local test variables used in the workflow are not assigned values in the channel environment, resulting in missing fields in the generated JSON structure.
- Phenomenon: Some data fields pulled by the workflow are missing, and supply prices for some niche parts are not retrieved. Cause: Left join data merge mode is not enabled. Only SKUs with matching results are retained, resulting in filtering of data from non-popular product categories.
- Phenomenon: Workflow runs are terminated by the system due to timeout. Cause: Pull batch size is set too large, exceeding the data limit for a single API request. Pagination pull rules are not configured, causing single request processing time to exceed the preset threshold.
How to confirm proper configuration
- View workflow trigger logs to confirm that tasks with different update frequencies start normally according to preset scheduled rules.
- Randomly select some SKUs to check whether the pulled fields match the preset mapping rules.
- Run a full report generation task once to confirm that the task is not forcibly terminated by the system and that the generated result format meets expectations.
- Publish to a test channel, run a temporary task once, and confirm that the returned results have no missing fields or format errors.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.