What the data for this category looks like
Game revenue yield data is sourced from real-time revenue reporting APIs of game operation backends and compliant APIs of third-party game data monitoring platforms. Data is aggregated and updated on a calendar day basis. Full data for the previous calendar day is compiled and synchronized each early morning. Each data entry is a structured item containing fields including game identifier, server group identifier, statistical date, total daily revenue, channel settlement ratio, daily net income, active user count, average revenue per user, and more. Uniform unit standards apply: revenue and net income are measured in Chinese Yuan, active user count is measured in individual users, and average revenue per user is measured in Yuan per user.
What constraints do these characteristics impose on deployment and upgrade workflows
The multi-data-source access feature of game revenue yield data requires configuring multiple sets of API keys and rate-limiting policies during deployment, to avoid triggering access restrictions from third-party interfaces. The daily full-data aggregation update rhythm requires configuring sufficiently long request timeouts during deployment, to prevent interruptions during data pulling. The fixed structured field structure requires strictly matching vector database index field mappings during deployment and upgrade, to avoid data parsing failures. Additionally, game businesses have high requirements for data timeliness. A gray release strategy must be used during upgrade, to ensure service availability is not impacted while maintaining compatibility with legacy configuration logic.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
SYNC_DATA_INTERVAL | 86400 seconds | Game revenue yield data updates on a calendar day basis. Synchronizing once daily balances resource usage and data timeliness |
PARSE_FIELD_MAPPING | {"game_id": "game_identifier", "server_group": "server_group_identifier", "stat_date": "statistical_date", "total_revenue": "daily_total_revenue", "channel_ratio": "channel_settlement_coefficient", "net_income": "daily_net_income", "dau": "dau", "arpu": "arpu"} | Matches the standard field structure of game revenue yield data, ensuring correct association between vector database indexes and data source fields |
UPGRADE_GRAY_SCALE_RATE | 0.1 | Game businesses have high requirements for service availability. Gray upgrades reduce the impact scope of failures and enable gradual verification of new version compatibility |
API_REQUEST_TIMEOUT | 300 seconds | Game data aggregation involves pulling from multiple interfaces. A longer timeout avoids interruptions during synchronization tasks and ensures full data pulling is completed |
VECTOR_DB_INDEX_BATCH_SIZE | 1000 | Game revenue yield data has large per-batch volumes. A reasonable batch size balances indexing efficiency and memory usage, avoiding service freezes |
HEALTH_CHECK_INTERVAL | 60 seconds | Detects data source and service status in a timely manner, ensuring the timeliness and accuracy of daily yield reports |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by data format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: After cross-version upgrade, scheduled synchronization tasks fail to pull game data normally, and field parsing failure prompts appear in logs. Cause: Upgrade scripts are not executed in version order, and skipped versions include field mapping change scripts, resulting in a mismatch between configurations and data sources.
- Symptom: Game data indexed in the knowledge base returns empty results or partial missing data during search. Cause:
VECTOR_DB_INDEX_BATCH_SIZEis not configured correctly, leading to partial batch data failing to complete indexing, orAPI_REQUEST_TIMEOUTis set too short, leading to partial interface request timeouts and incomplete data synchronization. - Symptom: Only a single GPU is used after service startup, and GPU usage shows only single-card utilization. Cause: The
CUDA_VISIBLE_DEVICESparameter is not specified in the startup command, causing the framework to only recognize the default GPU 0 and fail to fully utilize multi-GPU resources to accelerate vector retrieval.
How to confirm proper configuration
- Run a manual synchronization task, check for field parsing failure prompts in synchronization logs, and confirm that the
PARSE_FIELD_MAPPINGconfiguration fully matches data source fields. - Check the total data volume of the corresponding index in the vector database, confirm it matches the total data volume of the data source, and verify the rationality of the
VECTOR_DB_INDEX_BATCH_SIZEconfiguration. - View the GPU usage distribution in the service monitoring panel, confirm that multi-GPU resources are being called normally, and verify the correctness of GPU allocation configuration.
- Trigger a daily report generation task, check that the generated daily report document contains all expected fields, and confirm that scheduled tasks and data flow links are working 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.