Deployment and Upgrade for Auto Parts Profit Margin

What Data for This Category Looks Like

Data related to auto parts profit margin comes primarily from upstream supply chain quotation APIs, original equipment manufacturer (OEM) supporting settlement ledgers, and third-party industrial data collection platforms. Two update cadences apply: core supporting parts are updated weekly, while general spare parts are updated monthly. Documents use structured CSV or JSON format, stored grouped by part identification code. Each group includes supplier number, supporting vehicle model code, settlement cycle, benchmark profit coefficient, and cost fluctuation related fields. For field units: part identification code is a 17-character vehicle identification associated code, supplier number is a 6-digit enterprise credit code prefix, and benchmark profit coefficient uses thousandths values.

What Constraints Do These Characteristics Impose on Deployment and Upgrade

The weekly and monthly incremental update feature requires configuring incremental synchronization logic during deployment. This avoids excessive resource usage caused by full data pulls. The structured format grouped by part identification code requires configuring a shard index on this field during deployment. This improves subsequent retrieval and reporting response speeds. Data sources that rely on third-party APIs require configuring reasonable retry and timeout thresholds to handle temporary interface fluctuations. The upgrade process must retain the association relationship of historical supporting ledgers. It cannot directly overwrite old data, and must use an incremental compatible upgrade mode.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`SYNC_INTERVAL`	`3600 seconds`	Adapts to the weekly and monthly update rhythm of auto parts data. Hourly synchronization covers incremental updates while controlling resource consumption
`DATA_PARTITION_KEY`	`part identification code`	Matches the storage structure of data grouped by parts. Sharding by this field improves retrieval efficiency
`PARSE_TIMEOUT_SECONDS`	`600 seconds`	Single batch spare parts data files have large volume, requiring sufficient time for parsing and format conversion
`RETRY_TIMES`	`3 times`	Addresses temporary fluctuations in third-party supply chain APIs. Limited retries ensure data pull success rates
`UPLOAD_FILE_MAX_SIZE`	`2000 MB`	Adapts to large file upload requirements for batch spare part ledgers
`VERSION_COMPATIBLE_MODE`	`incremental compatible`	Retains the association relationship of historical supporting ledgers to avoid loss of business data during upgrades

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.

Three Common Mistakes

Phenomenon: The deployed service returns ERR_SSL_PROTOCOL_ERROR, and the page fails to load properly. Cause: The certificate and key files generated by mkcert are not mounted in the Nginx service configuration of docker-compose.yml.
Phenomenon: Calling the profit margin reporting process returns a 404 Not Found error, and the process node fails to trigger normally. Cause: The old version API call path is still used, and the data source association node has not been updated according to the new version's plugin configuration requirements.
Phenomenon: The service fails to start after deployment, and the log shows a config file not found error. Cause: The custom configuration file is not mounted to the specified path inside the container, or the configuration file format does not comply with YAML syntax specifications.

How to Confirm Proper Configuration

View the data source synchronization log to confirm that the latest spare parts data is pulled according to the set synchronization cycle, and check that the part identification code field is complete and not missing.
Initiate a test call to confirm that the process returns results containing the expected fields, and that the number of results matches the currently synchronized dataset.
Check the team permission configuration to confirm that designated members have editing and calling permissions for the process, and can access the deployed service normally.
View the service operation log to confirm that no PARSE_TIMEOUT or RETRY_EXCEEDED error messages appear.

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.