What the data for this category looks like
Data related to logistics revenue yield comes from the waybill settlement system, fuel purchase ledger, road and bridge toll receipts, and site rental fee records. The update schedule pulls full daily operational data from the previous day in bulk each early morning, generating a daily revenue document for a single operational unit. Document fields include Operational Unit ID, Statistical Date, Total Revenue, Variable Costs, Fixed Cost Allocation, and Daily Revenue Yield. Their units are string, date format, Chinese yuan, Chinese yuan, Chinese yuan, and proportional value, respectively.
What constraints these characteristics impose on the "database and operations" domain
Multi-source data access requires a pre-data validation step to prevent abnormal revenue yield calculations caused by dirty data. The fixed daily bulk update schedule is suitable for configuring scheduled write tasks; sufficient bulk write buffer space must be reserved. Multi-dimensional combinations of operational unit and cost fields require the creation of composite indexes to optimize query efficiency. Data volume continues to grow as operational units expand; storage must be partitioned by statistical date, and expired data must be archived to reduce single-table query pressure. Revenue yield calculation relies on atomic consistency across multiple cost fields; transaction integrity during data modifications must be ensured.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGO_BULK_INSERT_BATCH_SIZE | 500–1000 | The volume of daily logistics report data per batch typically ranges from hundreds to thousands of entries. This parameter controls the batch size for MongoDB bulk writes, avoiding timeouts caused by too much data in a single write operation. |
DATA_CLEANUP_RETENTION_DAYS | 180 days | Logistics revenue yield daily reports require at least six months of historical data for review. Data older than this duration can be automatically archived or deleted to free disk space. |
EMBEDDING_BATCH_SIZE | 32–64 | Individual logistics daily report entries have moderate length. This parameter controls the batch size for vector model embedding, balancing memory usage and processing speed, and is optimized for 8-core 64GB memory hardware configurations. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Bulk import of historical logistics data takes longer per batch; this parameter prevents parse tasks from timing out prematurely. |
QUERY_INDEX_FIELDS | ["Statistical Date", "Operational Unit ID"] | Queries for logistics revenue yield typically filter by statistical date and operational unit. This parameter configures the composite MongoDB index to improve query efficiency. |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Bulk import files for logistics historical data are typically large. This parameter limits the maximum allowed upload file size to prevent upload failures. |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: The
getaddrinfo EAI_AGAIN mongoerror appears after restarting the service, and the database cannot be connected. Cause: Static DNS resolution for the MongoDB service is not configured. Local DNS cache expires after container network restart, leading to failure to resolve the database address. - Symptom: Knowledge base search responses are slow, and the number of recalled results is far below the configured limit. Cause: A composite index for the
Statistical DateandOperational Unit IDfields is not created. Full table scanning of large volumes of logistics daily report data causes excessive query latency. - Symptom: Disk space is exhausted during bulk import of logistics data, the service freezes, and normal write operations cannot resume after restart. Cause: The
DATA_CLEANUP_RETENTION_DAYSautomatic cleanup policy is not configured. Historical daily report data continues to occupy disk storage space, and expired data is not archived in a timely manner.
How to confirm the configuration is complete
- Run a bulk write test with simulated data of the corresponding volume, verify that database write success rate and latency meet expectations, and confirm that the bulk write parameter configuration is adapted to current business data volume.
- Query historical data using combinations of statistical date and operational unit ID, check query response time, and confirm that the composite index is active.
- View disk space usage, confirm that expired data is automatically cleaned up according to the configured retention period, and that no disk exhaustion risk exists.
- Simulate a cross-major-version upgrade process, such as upgrading from v4.6.7 to v4.8.10, export all database data, then re-import it into the upgraded instance, and confirm that database metadata is compatible.
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.