What Data for This Category Looks Like
This category of data typically comes from landing page access logs, user heatmaps, form submission data, and advertising source parameters. Daily associated documents include landing page plans, buried point requirement lists, AB test reports, and form field configuration tables. Data updates happen weekly or within a single campaign cycle. Core analysis steps include page path breakdown, form bottleneck troubleshooting, and experimental effect evaluation. Data fields usually include access time, source channel, form field values, and heatmap click coordinates. Units adjust dynamically based on analysis dimensions.
Constraints Imposed on Database and Operations Workflows
The volume of operational data for landing page conversion optimization fluctuates with campaign scale. Concentrated writes during weekly or campaign cycles create database connection pressure. Multi-dimensional analysis requires databases that support flexible field expansion and complex queries, avoiding fixed table structures that limit analysis dimensions. High-frequency real-time heatmap and access log collection requires proper caching and read-write separation configurations to reduce primary database load. Correlation analysis between form data and advertising source parameters needs databases that support cross-table join queries, while ensuring data attribution accuracy. Missing fields or inconsistent formats can cause analysis failures. Additionally, post-campaign historical data archiving requires reasonable data retention and cold backup configurations, balancing storage costs and query efficiency.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:xxxx@192.168.xx.xx:27017/fastgpt_ads?authSource=admin | Core operational logs and form data for landing page conversion optimization are stored using MongoDB. Specify the authentication source and target business database to ensure correct data read/write permissions and paths, and comply with FastGPT v4.17.0 storage specifications |
DB_MAX_LINK | 60–100 units | Data write and query concurrency for this category fluctuates with campaign scale. This range balances connection pool usage and server resource utilization, preventing service interruptions from exhausted connections |
REDIS_URL | redis://:xxxx@192.168.xx.xx:6379/0 | Used to cache frequently accessed heatmap data and advertising source parameter mapping tables, reducing direct database query pressure and improving analysis response speed |
PG_URL | postgresql://fastgpt:xxxx@192.168.xx.xx:5432/fastgpt_report?sslmode=prefer | Some aggregated analysis reports require structured storage via PostgreSQL. Enable SSL encryption to ensure data security during cross-table join queries |
| Automatic database backup cycle | Every 7 days | Data for this category is updated weekly or within a single campaign cycle. Weekly backups meet historical data retention requirements while balancing backup frequency and storage costs |
Three Common Configuration Errors
- Symptom: Errors like
Failed to connect to 192.168.xx.xx.:1433appear during page loading, or MongoDB connections fail continuously during local development. Cause: Connection addresses, ports, and authentication information forMONGODB_URIorPG_URLare not configured correctly, preventing database connections from being established. - Symptom: No response occurs after exceeding the set threshold during high-concurrency testing, and model server resources do not reach their upper limits. Cause:
DB_MAX_LINKis not configured reasonably, exhausting the database connection pool and blocking new data analysis requests. - Symptom: Form data has empty fields or attribution errors, and cannot be associated with advertising sources. Cause: Redis caching is not enabled or configured incorrectly, causing the advertising source parameter mapping table to not update in time, leading to failed data association.
How to Verify Configurations Are Correct
- Run a database connection test script to confirm that configurations for
MONGODB_URIandPG_URLcan establish normal connections, with no authentication or address errors. - Adjust concurrent request volume and observe changes in database connection counts, confirming that the value of
DB_MAX_LINKcovers current business peak requirements. - Import test landing page data and advertising source parameters to verify that cross-table join queries return normal results, with no missing fields or format errors.
- View automatic backup logs to confirm that database automatic backup tasks execute normally according to the set cycle, and backup files are complete and restorable.
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.