What data in this category looks like
Game investment research data primarily comes from public financial reports of game publishers, official license announcement lists, player sentiment from game communities, information disclosed at industry expos, and in-game operation backend data. Update rhythms vary significantly: license announcements are released in periodic batches, financial reports are updated quarterly or annually, sentiment data is real-time incremental, and operation data is synced daily. Individual documents typically include fields such as license number, launch date, daily active users, revenue breakdown, and core gameplay description, with units including person-times, ten thousand RMB, batch numbers, and others.
What constraints do these characteristics impose on database and operations
Multi-source heterogeneous data sources require the database to support hybrid storage structures, compatible with structured financial data, unstructured sentiment text, and time-series operation data. Real-time incremental sentiment data creates high-frequency write pressure, while periodic batch license and financial data creates centralized bulk write requirements. Differences in units and formats across fields require the database to support flexible field mapping and type validation to prevent data import errors. Additionally, game investment research documents are often lengthy, so appropriate vector splitting and storage strategies must be configured to ensure retrieval efficiency.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
vector_store_type | milvus (paired with OceanBase for metadata storage) | Game investment research includes real-time sentiment and long-text financial reports. Milvus vector retrieval performance adapts to high-frequency query demands, and OceanBase supports high-concurrency structured data storage |
db_connection_pool_max | 60–80 | Address concurrent writes from multiple data sources, avoid database connection pool exhaustion, and adapt to mixed scenarios of license announcement batch imports and real-time sentiment writes |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Game investment research documents often contain ten-thousand-word financial or operation reports. Extending the timeout prevents parsing failures for long documents |
ob_vector_dimension | 1536 | Matches the output dimension of generic text embedding models, ensuring consistency across vector storage and retrieval |
retrieve_batch_size | 200–300 entries | Adapts to bulk data import scenarios for game investment research, balancing import efficiency and database load |
milvus_compaction_interval | 3600 seconds | Periodically compresses vector indexes to reduce disk usage, adapting to the continuous incremental write scenario of game investment research data |
The parameter values provided on this page are common recommendations to serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Abnormal increase in database write traffic, rapid disk space exhaustion. Cause: No reasonable bulk write parameters are configured, leading to excessively large single-batch write data, triggering frequent disk flushing operations in OceanBase.
- Phenomenon: Milvus deployment fails, container startup error reports inability to connect to PostgreSQL. Cause: Vector storage type and metadata storage configurations are confused in the Milvus compose file, retaining irrelevant PostgreSQL mount configurations.
- Phenomenon: Dataset creation interface call returns parameter error, prompting invalid username. Cause: The username parameter in the database configuration is not correctly modified, retaining the default
usernameplaceholder without replacing it with the actual OceanBase username.
How to confirm the configuration is correct
- Call the dataset creation interface, check that the return status code is
200 OKto verify that the database connection configuration is effective. - Upload a ten-thousand-word game financial report document, wait for parsing to complete, then check the parsing status to confirm no timeout errors are triggered.
- Perform a vector retrieval test, query keywords related to game sentiment, check that the number of returned results matches the expected configuration.
- View the database monitoring dashboard, confirm that the connection pool usage is within a reasonable range, and disk read/write traffic aligns with business expectations.
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.