What this category’s data looks like
Data related to airport aviation revenue yield comes from public APIs of civil aviation authorities, internal airport ERP systems, and revenue ledgers. Two update cycles apply: Real-time flight takeoff and landing data syncs every 15 minutes. Daily revenue and yield statistics update full previous day data each early morning. Individual data records use structured format, including airport_iata_code (3-letter IATA airport code), stat_date (date in YYYY-MM-DD format), departure_arrival_count (takeoff and landing count, integer), passenger_volume (passenger trips, integer), cargo_volume (cargo tonnage, float), total_revenue (total revenue, yuan), profit_per_operation (profit per operating unit, yuan), and additional fields.
Constraints on Database and Operations
High-frequency real-time takeoff and landing data is written every 15 minutes. This requires the database to support high-concurrency, low-latency write operations to avoid queue backlogs. Daily full revenue data imports run each early morning. These imports require batch write capabilities and data deduplication mechanisms to prevent duplicate statistics. Mixed numeric field requirements demand the database support precise integer and float storage to avoid precision deviations. Significant differences in data volume across airports require table structures partitioned by date or airport code to optimize query and storage efficiency. Multi-data source integration requires compatibility with varying input formats. Unified ETL validation rules must be configured to ensure consistent data formats.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
database_write_batch_size | 50–80 records/batch | Balances write performance and memory usage for high-frequency real-time takeoff and landing data, avoids timeouts caused by overly large single writes |
etl_cron_expression | 0 2 * * * | Matches the scheduling rhythm for updating previous day's revenue data at 2 AM daily, avoids conflicting with business peak hours |
partition_strategy | Monthly partition by stat_date | Date-based partitioning enables fast retrieval of daily report data by time range, optimizes query efficiency and simplifies historical data archiving |
connection_pool_max_size | 150–250 | Meets concurrent write demands from multiple data sources, prevents service interruptions caused by exhausted connections |
replica_set_auto_reconnect | Enabled | Adapts to high availability requirements for multi-node database clusters, avoids write interruptions during primary node failover |
disk_io_throttle_threshold | 450 MB/s | Limits peak disk IO from real-time data writes, prevents disk resource exhaustion from impacting other business operations |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by data format, volume, and business rules. Specific issues require targeted analysis. Test on own samples before finalizing configuration.
Three Common Misconfigurations
- Symptom: Sustained high disk IO usage, daily read/write volume far exceeds expectations, eventually leading to disk resource exhaustion. Cause: No reasonable value configured for
database_write_batch_size, single-record write mode is used. High-frequency real-time data triggers a large number of small IO requests, resulting in excessive disk reads and writes over time. - Symptom: Database connection failure during deployment, logs indicate username or password mismatch. Cause: Incorrect naming convention for database connection parameters.
MONGO_USERNAMEis incorrectly set tousername, and a business-specific database account is not used, leading to authentication failure. - Symptom: Data write interrupts after primary node switchover, with no automatic recovery. Cause:
replica_set_auto_reconnectconfiguration is not enabled. When the database cluster primary node fails over, no reconnection logic is triggered, resulting in broken write links.
How to Verify Proper Configuration
- View the database monitoring dashboard, verify real-time write batch count matches the
database_write_batch_sizeconfiguration value, confirm write frequency aligns with expectations. - Trigger an ETL scheduling task, check execution at the time specified by
etl_cron_expression, confirm imported data format matches expected structure. - Manually trigger a database primary node switchover, verify automatic recovery of the write link, confirm
replica_set_auto_reconnectconfiguration takes effect. - Review disk IO monitoring metrics, confirm peak values do not exceed the
disk_io_throttle_thresholdsetting, confirm no abnormal resource consumption.
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.