What data looks like for this category
Data sources for this category include raw business datasets, ETL process logs, annotation platform output results, and sampling quality inspection results. Data updates follow a daily batch schedule or are triggered by individual batch tasks. Daily deliverables include cleaning rule configuration files, quality inspection sampling statistics tables, abnormal data lists, and final delivery reports. Data fields cover raw data identifiers, cleaning tags, exception types, quality inspection scores, and sampling batch numbers. There are no unified fixed units; values adjust based on specific business scenarios.
Constraints imposed on the database and operations link
Bulk updates of raw datasets and ETL logs require database connection pools to adapt to high-concurrency short connection scenarios. Frequent writes of abnormal data lists and delivery reports require configuring appropriate write buffers to avoid blocking. The daily or per-batch update feature requires operations workflows to support scheduled task-triggered data synchronization and verification. Field differences across business scenarios require database table structures to reserve extended fields, avoiding frequent table alterations. Additionally, associated storage of cleaning rules and quality inspection results requires guaranteeing data read-write consistency to prevent dirty data.
Configuration guidelines
This guide applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://user:pass@host:27017/fastgpt_data?replicaSet=rs0 | Adapts to bulk data storage and query needs; replica set configuration ensures read-write consistency, matching storage scenarios for raw datasets and abnormal lists |
PG_URL | postgresql://user:pass@host:5432/metadata_db?sslmode=require | Used to store structured metadata such as cleaning rules and quality inspection reports; SSL encryption ensures data security, adapting to structured metadata storage needs |
DB_MAX_LINK | 200–300 connections | Matches concurrent connection requirements for daily or per-batch bulk data processing, avoiding task interruptions caused by exhausted connections |
REDIS_URL | redis://:pass@host:6379/0 | Used to cache temporary cleaning task status and sampling results, reducing database read-write pressure, adapting to high-speed read-write scenarios for temporary data |
| Bulk write threshold | 500 items per batch | Adapts to bulk write scenarios for abnormal data lists, reducing database IO overhead and improving write efficiency |
Three common configuration mistakes
- Symptom: Fields appear empty or import fails when uploading Excel to the knowledge base, with status code 400 returned in the interface. Cause: The replica set parameter for
MONGODB_URIis not configured, and only a single-node connection configuration is used, leading to data consistency check failure during bulk writes. - Symptom: MongoDB reports missing replica set configuration during local deployment. Cause: The replicaSet parameter for
MONGODB_URIis not set as required, and only single-node connection information is configured, which cannot support consistency requirements for bulk data processing. - Symptom: Duplicate data entries appear after importing ETL logs. Cause: A reasonable value for
DB_MAX_LINKis not configured, and excessive concurrent connections lead to duplicate insert operations, breaking data uniqueness.
How to verify correct configuration
- Execute a database connection test script to verify connectivity for
MONGODB_URIandPG_URL, and confirm that the actual connection count does not exceed the configuredDB_MAX_LINKvalue. - Upload a single batch of test datasets, trigger the cleaning and quality inspection process, and check whether temporary cached data can be written normally to the cache service corresponding to
REDIS_URL. - View the generation logs for the abnormal data list, and confirm that the execution status of bulk write operations matches the configured threshold requirements.
- Check the associated storage of cleaning rules and quality inspection results, and confirm that data read-write consistency meets business requirements.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.