What the data for this category looks like
The data for this category covers the entire workflow of enterprises engaged in color printing, labels, cartons, flexible packaging, and pharmaceutical packaging materials. Daily documents include pre-press files, imposition sheets, printing process sheets, ink color matching records, first-piece proofing records, barcode inspection records, die-cutting and lamination ledgers. Data is sourced from customer submissions, color management systems, printing press operation data, ink and substrate batch ledgers, spectrophotometer test results, and barcode detector readings. It is updated per order, per batch, or each time a plate is changed. Fields include plate version number, color deviation value, batch number, inspection qualification status, and other related items.
What constraints do these characteristics impose on the database and operations link?
The high-frequency updates and multi-document association of data in this category require the database to support high-frequency writes and multi-table join queries. Multi-source heterogeneous data sources include device readings, manual records, and unstructured files, so the system needs compatible structured and unstructured storage capabilities. New data generated each time a plate is changed must be quickly associated with historical batches, so targeted index configuration is required to improve query efficiency. Defect analysis requires tracing full-process data, so complete audit logs must be retained. Additionally, the data backup and rollback mechanism must support rapid recovery of bulk data.
The following configurations apply to FastGPT v4.17.0.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://user:pass@cluster-host:27017/printpack?replicaSet=rs0 | Stores unstructured documents and metadata such as pre-press files and first-piece proofing records, supports multi-replica high availability |
PG_URL | postgresql://user:pass@db-host:5432/printpack_trace?sslmode=require | Stores structured data such as batch ledgers, process parameters, and inspection results, supports complex join queries |
DB_MAX_LINK | 200–300 | Adapts to high-frequency data writes per order and per batch, prevents database connection exhaustion |
REDIS_URL | redis://:redis-pass@cache-host:6379/1 | Caches frequently accessed batch association information, reduces query pressure on the primary database |
| Database Backup Strategy | Daily full backup + hourly incremental backup | Adapts to high-frequency updated data, meets requirements for rapid traceability during defect analysis and fault recovery |
Three common errors
- A login failure occurs after a version upgrade, with the interface returning a 502 status code. The cause is that the service was not restarted correctly after modifying the configuration file corresponding to
MONGODB_URI, resulting in invalid database connection parameters. - A field parsing error occurs when attempting to replace PostgreSQL with another database. The cause is failure to adapt the structured storage format of the target database, causing category-specific fields such as batch number and color deviation value to fail to write correctly.
- A dependency download timeout occurs during upgrades in an environment without external network access. The cause is failure to pre-download offline images and dependency packages required for the upgrade, preventing the upgrade process from proceeding normally.
How to confirm the configuration is properly set up
- Perform a simulated plate change data write operation, check that there are no errors in the database connection logs, and confirm that the
MONGODB_URIandPG_URLconfigurations are valid. - Trigger a batch association data query, observe the cache hit rate, and confirm that the cache service configured via
REDIS_URLis running normally. - Simulate a database connection exhaustion scenario, verify that the
DB_MAX_LINKconfiguration value can limit the number of connections and prevent service crashes. - Check the execution logs of the backup script, confirm that the daily full backup and hourly incremental backup tasks are completed normally.
The values provided are common starting points and should be measured against the reader's own samples.