What the data for this category looks like
Core data for this category comes from search ad backends, search term reports, bidding ranking data, and landing page data. Update frequency is daily or weekly. Daily business documents include structured keyword lists, negative keyword tables, bid records, and unstructured search term reports. Data fields include keyword text, match type, single bid amount, search trigger volume, conversion data, and additional fields. Units include yuan per click, times, entries, and others. Field combinations vary across documents. For example, the negative keyword table only includes two fields: keyword and filter type.
What constraints these characteristics impose on database and operations workflows
Data from multiple sources requires compatibility with different import and storage formats. The database must support flexible field mapping and multi-type data storage. Daily and weekly scheduled updates require automated synchronization tasks to avoid delays and errors caused by manual operations. Differences in fields across business documents require splitting data storage structures by business scenario to prevent data confusion. Data synchronization or storage failures directly affect the effectiveness of keyword management. Real-time operations monitoring and error alert mechanisms must be configured to ensure the integrity and timeliness of business data.
How to configure settings
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://ad_admin:secure_pass@db-host:27017/ad_keyword_db?authSource=admin | Business data such as keywords and bid records for this category require persistent storage. The standard MongoDB connection format supports batch import requirements for multiple data sources |
REDIS_URL | redis://:redis_pass@redis-host:6379/1 | High-frequency access search term report snapshots and negative keyword filter rules must be cached to reduce repeated query pressure on the database |
DB_MAX_LINK | 20–30 | Batch import of daily update data generates concurrent connections. This range balances server resource usage and synchronization efficiency, as calibrated through actual testing |
PG_URL | postgresql://ad_report:db_pass@pg-host:5432/ad_report_db?schema=sem_management | Data storage for keywords, negative keywords, bids, and other items must be split by business type. PostgreSQL’s structured query supports the generation of multi-dimensional operation reports |
Three common configuration mistakes
- Symptom: The
init root user mongo start connect init root user errorerror appears when starting the service. Cause: Authentication parameters forMONGODB_URIare not configured correctly, leading to failed database connection verification. - Symptom: Some fields are empty after batch importing a keyword list. Cause: Import data is not mapped according to the field structure of the business document, leading to some business fields not being correctly written to the database.
- Symptom: Tasks time out when synchronizing search term reports. Cause:
DB_MAX_LINKis not configured reasonably, insufficient concurrent connections cause synchronization tasks to block, and full report data cannot be pulled in a timely manner.
How to confirm configurations are properly set
- Check service startup logs to confirm that the connection status of
MONGODB_URIandREDIS_URLis normal, with no timeout or authentication failure error messages. - Import a test keyword list to verify that data can be correctly written to the database and corresponding fields can be read, such as match type, bid amount, and others.
- Manually trigger a scheduled synchronization task to confirm that search term report update records can be correctly generated and stored in the specified database.
- Check the server’s database connection monitoring panel to confirm that the current number of connections falls within the configuration range of
DB_MAX_LINK, with no connection overflow.
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.