What the data for this category looks like
PCB manufacturing quality traceability data sources include Gerber files, drill files, and BOM lists provided by customers, as well as engineering documents generated by CAM, drilling and lamination parameters from production equipment, bath solution test records, AOI inspection reports, electrical testing data, and laboratory slice reports. Data is updated by production batch and shift. Some equipment parameters require continuous collection. A single batch of data includes fields such as engineering production instructions, analysis logs, process parameters, and test results. Fields must correspond to board numbers, processes, parameter values, and test results. There are no unified fixed units, and units vary based on parameter type.
Constraints on database and operations maintenance
The characteristics of PCB manufacturing quality traceability data impose multiple constraints on database and operations maintenance. Multi-source heterogeneous data sources need to support access to different formats. Batch updates by production batch and shift, plus high-frequency writes from continuous equipment data collection, require adaptation to fluctuating concurrent write demands. Core fields such as board numbers, processes, and parameter values must maintain global consistency to support cross-process defect association and traceability. Data must be retained long-term for post-event analysis. This requires balancing read/write performance and data immutability, while avoiding production disruptions caused by delayed storage expansion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | Set to an authenticated cluster connection string, such as mongodb://user:pass@host1:27017,host2:27017/pcb_trace?replicaSet=rs1 | Adapts to batch writes of multi-batch data and cross-process associated queries, ensuring availability under high concurrency |
DB_MAX_LINK | 200–300 connections | Addresses high-frequency writes from continuous equipment collection and parallel queries across multiple processes, preventing database connection exhaustion |
REDIS_URL | Set to a persistent single-node or cluster connection string, such as redis://:pass@host:6379/0 | Caches frequently accessed data such as board number association indexes and AOI inspection results, reducing load on the primary database |
PG_URL | Set to an SSL-encrypted structured data connection string, such as postgresql://user:pass@host:5432/pcb_engineering?sslmode=require | Stores structured business data requiring strong consistency, such as BOM lists and engineering production instructions |
Three Common Misconfigurations
- Symptom: Unable to log in to the system after a version upgrade, with an error indicating database connection failure. Cause: The binding address or authentication parameters were accidentally modified when editing the MongoDB configuration file, and the FastGPT
MONGODB_URIconfiguration item was not updated synchronously. This results in a connection failure. - Symptom: Unable to complete a version upgrade in an offline environment, with the upgrade package download failing. Cause: By default, FastGPT pulls images and dependency packages from official sources. Without a local image repository or offline upgrade package configured, downloads cannot be completed in an environment without external network access.
- Symptom: Timeout errors occur during concurrent system queries, with a 504 status code returned. Cause: The
DB_MAX_LINKconfiguration was not adjusted. The connection pool is exhausted, preventing new database requests from being established and causing timeouts.
How to Confirm Correct Configuration
For FastGPT v4.17.0, perform the following verification steps:
- Run a database connection test command to verify that the connection strings configured for
MONGODB_URIandPG_URLcan establish normal connections, with no authentication or address errors. - Import a single batch of test data, check that data write speed and storage usage meet expectations, and confirm that the
DB_MAX_LINKconfiguration can support current concurrent write requirements. - Access the Redis management interface, check cache hit rates, and confirm that the cache service configured via
REDIS_URLis running normally and can store specified data. - Simulate parallel query operations across multiple processes, confirm that the database connection pool is not exhausted, and that no timeout errors occur.
The values provided are common starting points and should be measured against the reader's own samples.