What the data for this category looks like
This category’s data includes prepress files, imposition sheets, printing process sheets, ink color matching records, first article proofing, barcode inspection, die-cutting and lamination records, and other documents. Data sources include customer submissions, color management systems, printing press operational data, ink and substrate batch records, spectrometer and barcode detector output results. Data updates are triggered per order, per production batch, and per plate change. Fields include plate number, color parameters, substrate batch number, barcode code, die-cutting accuracy, and others. There are no unified fixed units; values match dynamically based on document type.
What constraints these characteristics impose during deployment and upgrade
This category’s high-frequency, small-batch update rhythm requires storage components configured during deployment to support high-concurrency, low-latency writes. The multi-type dynamic field document structure needs support for flexible metadata management configuration. Cross-data-source correlation (such as printing press data and barcode detector data) requires establishing multi-source data synchronization links during deployment. The risk that plate errors spread rapidly to cause batch scrap requires retaining full historical ledger snapshots during upgrades, and adding a new data validation node to run compliance checks on key fields before data is stored. This page is compatible with FastGPT v4.17.0.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 200–300 connections | Adapts to the concurrency requirements of high-frequency small-batch data writes, avoids connection pool exhaustion |
MONGODB_URI | Connection string pointing to an independent sharded cluster | Stores multi-type dynamic ledger documents; sharded clusters support high-frequency updates and complex queries |
REDIS_URL | Configured as a local cache instance, memory quota 2048 MB | Caches frequently read imposition sheets and first article proofing data to reduce storage layer query pressure |
STORAGE_S3_ENDPOINT | Configured per local storage or cloud storage service provider | Stores large-volume documents such as prepress files and first article proofing, adapts to the category’s large file storage needs |
FILE_TOKEN_KEY | 32-bit random string calibrated via actual testing | Generates document access tokens to secure access to ledger files |
Three common mistakes
- After deployment, accessing core interfaces returns the
getPluginGroups 500error, with an interface prompt indicating the commercial edition link is not configured. The cause is that commercial edition-specific environment variables were not removed during open-source edition deployment, and the open-source edition permission key was not correctly configured. - After importing multiple batches of ledger data, field null exceptions occur. The cause is that the category’s dynamic field schema configuration was not adapted, and the switch to automatically identify document fields was not enabled.
- Timeout interruptions occur during batch report generation. The cause is that a reasonable value for
DB_MAX_LINKwas not configured, leading to connection pool exhaustion that prevents subsequent requests from being processed.
How to confirm configuration is complete
- Log in to the FastGPT admin backend, check that environment variable configuration items match preset values, confirm parameters such as
DB_MAX_LINKandREDIS_URLhave been correctly filled. - Upload a typical prepress file and imposition sheet for this category, trigger the data import process, confirm data can be written to the storage layer normally without field loss.
- Simulate high-frequency data write operations, check that interface response status codes are normal, confirm connection pool exhaustion has not occurred.
- Generate a test report containing barcode and plate number fields, confirm field content in the report matches source data.
The values provided are common starting points and should be measured against the reader's own samples.