What Data for This Category Looks Like
Tourist attraction investment research data comes from the attraction’s own operation systems, public disclosure information from cultural and tourism authorities, third-party public opinion monitoring platforms, and attraction event announcements. Passenger flow and equipment operation data updates hourly or in real time. Revenue and merchant operation data updates daily. Announcement-style data updates on a trigger basis.
Document structures include basic attraction information, real-time passenger flow snapshots, detailed time-segmented revenue records, visitor profile tags, merchant operation fields, and more. Field units include person-times, yuan, square meters, and others. Individual document lengths range from hundreds to thousands of characters.
What Constraints Do These Characteristics Impose on Database and Operations Workflows
The multi-source heterogeneous nature of attraction investment research data requires the database to support relational, time-series, and document-style storage at the same time. Data with different update frequencies must be stored in tiers to balance performance and cost.
The low-latency write requirement for real-time passenger flow data means database connection pool configurations must adapt to high-concurrency write scenarios, avoiding connection blocking. The need to standardize multi-unit fields requires database operations teams to configure field mapping and verification rules. This prevents analysis errors caused by mismatched data types and units.
Additionally, compliance retention requirements for attraction investment research data require regular archiving and persistence strategies to avoid data loss. It also requires safeguards for data integrity during abnormal scenarios such as power outages.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_CONNECTION_TIMEOUT | 30 seconds | Adapts to the stability requirements of multi-source data connections for attractions. Prevents normal connections from being interrupted by short timeouts, and avoids blocking subsequent requests from long timeouts |
PG_MAX_CONNECTIONS | 100-150 | Covers concurrent connection needs from multiple data sources including ticketing, revenue, and public opinion. Prevents service interruptions caused by exhausted connections |
MONGO_WRITE_CONCERN | w:1 | Adapts to the write efficiency requirements of real-time passenger flow data for attractions. Does not require strongly consistent writes, reducing write latency |
DATA_ARCHIVE_RETENTION_DAYS | 180 days | Meets compliance retention requirements for attraction investment research data, while controlling cold data storage costs |
DB_POOL_MIN_SIZE | 20 | Ensures a basic number of connections for high-concurrency write scenarios, avoiding queueing delays caused by an undersized connection pool |
DB_CONNECT_RETRY_TIMES | 3 times | Addresses temporary connection failures caused by network fluctuations in attractions, reducing manual troubleshooting costs |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: An error
Failed to connect to jyfkk:1433occurs during orchestration, but the MongoDB visualization tool can connect normally. The cause is incorrect port mapping for MSSQL in the container network, or the database instance’s access permissions have not opened the corresponding IP range. - Symptom: After a power outage restart following Docker deployment, PostgreSQL and MongoDB fail to start. The cause is failure to mount the database data directory to persistent storage on the host machine. The power outage causes corruption of data files inside the container.
- Symptom: Field values are empty or units are abnormal in investment research knowledge base recall results. The cause is failure to standardize mapping for fields from multi-source attraction data, and failure to verify consistency between field types and units.
How to Verify Successful Configuration
- Run the
pg_isreadycommand to check the connection status of the PostgreSQL instance, and verify that theDB_CONNECTION_TIMEOUTandPG_MAX_CONNECTIONSconfigurations take effect. - Use the
mongo --eval "db.stats()"command to view MongoDB’s write strategy and connection pool parameters, confirming that theMONGO_WRITE_CONCERNconfiguration meets requirements. - Manually stop the database container and restart it, then check if the database service starts automatically. Verify that the persistent storage and automatic restart strategy take effect.
- Import a test set of attraction passenger flow data, verify field mapping and unit conversion results, and confirm that the standardization configuration works properly.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.