Data characteristics for this category
Data related to hotel and catering yield rates primarily comes from store POS terminals, back-of-house inventory management systems, and room management modules. Business transaction records are synced hourly. Full daily summary documents are generated after store closing each day. Each document includes fields such as unique store identifier, statistical time period, dish SKU code, actual received amount, in-store customer traffic, table turnover count, and more. Amounts use RMB yuan as the unit. Customer traffic uses person-times as the unit. Table turnover counts use times as the unit.
Constraints on database and operations
High-frequency hourly syncs of business transaction records create continuous write concurrency pressure. Adopt short-cycle batch write logic to reduce overhead per single write operation. Full daily summary generation after store closing triggers short-term peak write requests. Reserve dedicated database resources for peak periods to avoid blocking. Multi-dimensional combined queries (such as searches filtered by store, time period, and SKU code) require composite indexes. This prevents full table scans that slow query speed. Data volume varies significantly across different stores. Implement dynamic storage partitioning strategies to prevent single-table data overload from harming overall performance.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGO_BATCH_INSERT_SIZE | 200–300 records | Matches the single-batch write volume for hourly business transaction records in hotel and catering scenarios, balancing write efficiency and single-request overhead |
DB_WRITE_TIMEOUT_MS | 15000 milliseconds | Addresses peak write latency during daily summary generation, preventing timeout interruptions of full data synchronization |
CONCURRENT_REQUEST_LIMIT | 80–120 | Matches the reasonable upper limit for concurrent store queries and external interface calls, avoiding exceeding resource limits |
INDEX_CREATION_STRATEGY | Create composite indexes using store ID + statistical time period + SKU code | Optimizes retrieval speed for multi-dimensional combined queries, avoiding full table scans |
DATA_VALIDATION_ENABLE | Enabled | Performs pre-validation of format and unit consistency for fields such as actual received amount and customer traffic |
STORAGE_PARTITION_POLICY | Partition by store ID + month | Adapts to differences in data volume across stores, simplifying historical data archiving operations |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by data format, 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
- A
429 Too Many Requestserror code is returned when calling interfaces associated with external models. This occurs when no reasonable concurrency limit parameter is configured, causing requests to exceed the concurrency upper limit of the database or external interfaces. - Database query latency increases significantly when full daily summary data is generated. This happens when no composite index is created, leading multi-dimensional combined queries to trigger full table scans and consume excessive system resources.
- Missing fields or format errors appear in data extracted from the database after uploading a PPT-format revenue report file. This stems from missing parsing adaptation rules for PPT-format documents, which prevents correct extraction of structured data fields.
How to verify proper configuration
- Initiate simulated concurrent query requests, observe interface return status codes, and adjust the
CONCURRENT_REQUEST_LIMITparameter until no429errors are returned. - Manually trigger a write operation for full daily summary data, check database query logs, and confirm that composite indexes are active and no full table scan records exist.
- Upload a PPT-format report file containing structured fields, and verify that the database can correctly extract content such as store identifiers and revenue amounts.
- Check the database storage partition monitoring dashboard, confirm that data is stored in partitions according to preset rules, and no single-table data overload occurs.
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.