What Data for This Category Looks Like
Data related to aquaculture profit margins is used for daily financial reporting. Data sources include real-time water quality monitoring devices at aquaculture ponds, inventory and sales systems of aquaculture operators, and quotation interfaces from buyers. Data updates once per day, generating full-chain data documents for individual ponds or culture batches within the corresponding breeding cycle. Documents are grouped by aquaculture operator identifiers, and include three field categories: core production parameters, cost accounting items, and revenue accounting items. All field units use legal measurement units. No aggregated or percentage fields are included.
Constraints on Database and Operations Workflows
The need to access multi-source heterogeneous data requires operations workflows to support compatible data synchronization across multiple formats. Input types must include sensor time-series data, structured inventory and sales data, and interface return data. Daily fixed-time bulk writes create traffic peaks. Queue buffering must be configured to avoid database overload. The large number of fields with varying units requires pre-configured data validation rules to prevent dirty data from entering the storage layer. Additionally, high-frequency query demands based on culture batches and time ranges require the database to build targeted indexes to ensure query efficiency and meet the real-time requirements of daily financial reporting.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGO_VERSION | 6.0 or 7.0 | Compatible with FastGPT aggregation query logic, supports large field storage, and meets the storage requirements for multi-field aquaculture data needed for daily financial reporting |
REDIS_MAX_MEMORY | 128 GB to 256 GB | Required to cache daily aquaculture monitoring and market data snapshots. This range covers memory requirements for standard deployment scenarios |
MILVUS_COLLECTION_DIM | 1536 | Adapts to the vector dimensions of general-purpose text embedding models, used to retrieve semantically relevant information about aquaculture data |
DATA_SYNC_BATCH_SIZE | 500 records | Adapts to the daily bulk sync volume of pond data, avoiding database overload caused by single write operations |
DB_WRITE_TIMEOUT | 30 seconds | Covers the peak latency tolerance range for multi-source data writes, ensuring the stability of synchronization tasks |
PARSE_DATA_FIELD_RULE | Partition by "aquaculture operator ID + date" | Enables fast querying of daily profit margin report data by batch and time range |
The parameter values provided on this page are common recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: FastGPT deployed in a Linux environment returns a
Connection refusederror when connecting to MongoDB, with error code 111. Cause: Firewall rules for MongoDB's default port 27017 are not opened, or ports are not correctly mapped to the host machine during Docker deployment. - Symptom: MongoDB aggregation query errors with
Unrecognized pipeline stage nameafter FastGPT starts. Cause: MongoDB 4.0 or earlier versions are used, which are incompatible with FastGPT's new aggregation query logic. - Symptom: Vector retrieval latency is too high or cached data is lost. Cause: The
REDIS_MAX_MEMORYconfiguration is not adjusted based on the caching requirements of aquaculture data. 192 GB of memory covers the caching needs for standard deployment scenarios, and unnecessary upgrades are not required.
How to Verify Successful Configuration
- Run the
mongo --host <MongoDB address> --port 27017command to verify that the database connection is normal, with no authentication or port blocking issues. - Check Docker container logs to confirm that the startup status codes for the
fastgpt,mongo,redis, andmilvusservices are 0, with no abnormal error reports. - Submit a simulated daily aquaculture data entry, then query the daily data for a specified pond to verify that the data write and retrieval processes are working correctly.
- Check the running logs of daily data synchronization tasks to confirm that there are no records of failed bulk writes or timeouts.
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.