What data for this category looks like
Data related to commercial real estate yield rates comes primarily from commercial operation management systems and public data from third-party commercial real estate monitoring service providers. Data is updated once daily, matching the generation cycle of daily reports. Each data entry corresponds to a single commercial real estate project. The document structure includes fields such as project unique identifier, project location, business type, report cycle, average daily rent per unit area, total operating costs, rentable area, and actual rented area. The unit for average daily rent per unit area is yuan/square meter/day. Rentable area is measured in square meters. Total operating costs are measured in yuan/month.
What constraints these characteristics impose on database and operations work
The need to integrate data from multiple sources requires the database to support cross-system format adaptation. This prevents failed data ingestion due to inconsistent data formats. The daily update feature requires scheduled synchronization tasks to precisely match the data generation cycle. It also requires controlling concurrent write volumes to avoid excessive database load during peak business hours. Multi-field query scenarios require indexes for frequently used filter fields. Without these indexes, query delays will exceed business tolerance thresholds. The need to archive historical data requires the database to use a reasonable storage strategy. This avoids long-term storage resource occupation by full-volume data, while ensuring historical data remains queryable.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
api_max_concurrency | 20–30 concurrent requests | Adapts to the single-batch data volume of commercial real estate daily reports, balances query response speed and cluster load |
db_connection_timeout | 30 seconds | Covers typical network latency for pulling data across service providers, prevents unintended synchronization task interruptions |
mongo_batch_insert_size | 500 entries/batch | Adapts to the size of individual commercial real estate data entries, improves bulk write efficiency, and is compatible with bulk write optimization logic in v4.14.9 and later versions |
data_sync_cron | 0 1 * * * | Matches the overnight generation schedule of most commercial real estate daily reports, avoids daytime peak business hours |
query_index_fields | ["project_id", "report_date"] | Covers the most frequent daily query scenarios by project and cycle, accelerates retrieval efficiency |
backup_retention_days | 180 days | Meets the standard review and retention cycle for commercial real estate operation data, avoids excessive storage resource usage |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by data 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
- Symptom: Database connection nodes cannot accept port number input. Cause: The advanced configuration switch for the corresponding database node is not enabled. In v4.14.9 and later versions, non-required port configuration items are hidden by default.
- Symptom: Workflow API calls return a 429 status code. Cause: Concurrency settings exceed the healthy carrying capacity of the current cluster, and do not adapt to the bulk update requirements of commercial real estate data.
- Symptom: After importing knowledge base data via mongorestore, no matching results are returned in searches. Cause: Only raw structured data has been ingested, and the vector database incremental update process has not been triggered, causing searches to fail to match vectorized content.
How to confirm configurations are correctly set
- View database synchronization logs to confirm scheduled synchronization tasks execute according to configured trigger rules, with no connection timeout errors.
- Submit query requests targeting project identifiers and report cycles, to verify query response speed meets business expectations.
- Run a single bulk data import test to confirm the volume of written data matches the total source data volume, with no abnormal interruptions or data loss.
- Check the backup storage path to confirm backup files are generated according to the configured cycle, and retention duration meets operational requirements.
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.