What the data for this category looks like
Professional chain investment research data primarily comes from store POS systems, supply chain management platforms, public industry research reports, and public competitor information. Structured data includes fields such as store code, SKU code, daily sales, per-square-meter sales efficiency, and inventory turnover days. Units are store ID, product code, yuan, yuan per square meter, and days, respectively. Unstructured data includes store operation logs, regional investment promotion plans, and competitor expansion announcements. Data update rhythms vary significantly: store sales data updates daily, supply chain inventory data updates weekly, and industry research reports and competitor information follow public channel release schedules for updates.
What constraints these characteristics impose on database and operations processes
High-frequency writes of structured store sales data require databases to support high-concurrency transaction processing to avoid write blocking. Field differences across multiple data sources require the database layer to have flexible field expansion capabilities and data mapping rules. Data synchronization from sources with different update rhythms requires configuring differentiated scheduled task scheduling to avoid resource preemption. The diversity of unstructured documents requires databases to support binary large object storage and full-text indexing, to meet semantic recall needs in investment research scenarios. The need to retrieve historical investment research data requires databases to enable full data archiving mechanisms to prevent data loss.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MCP_CONCURRENCY_LIMIT | 2–4 times/second | Single calls in professional chain investment research scenarios involve pulling data from multiple stores. Excessively high concurrency will cause downstream service timeouts, matching conventional call limits |
MONGO_CONNECTION_POOL_SIZE | 20–30 | In multi-source data synchronization and concurrent recall scenarios, the connection pool must cover simultaneous read and write requests to avoid connection exhaustion |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Unstructured documents for professional chains, such as operation manuals and regional reports, are usually lengthy, requiring sufficient parsing time |
RECALL_TOP_K | 8–12 entries | Investment research scenarios require balancing recall coverage and result relevance, avoiding excessive redundant data that reduces investment research efficiency |
DB_ARCHIVE_RETENTION_DAYS | 365 days | Investment research scenarios require retaining at least one year of historical data for trend analysis, meeting retrieval needs |
UPLOAD_FILE_MAX_SIZE | 500 MB | Professional chain supply chain reports and large regional operation documents are usually large in size, requiring adaptation to large file upload requirements |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on one’s own samples before finalizing settings.
Three common misconfigurations
- Symptom: Some requests return empty values during concurrent MCP workflow calls, with logs showing downstream service timeouts. Cause: The MCP concurrent call limit is not restricted, causing multi-store data pull requests to exceed the downstream service's carrying capacity.
- Symptom: MongoDB database connection errors occur, prompting connection exhaustion. Cause: A reasonable MongoDB connection pool size is not configured, and concurrent data synchronization and recall requests occupy too many connection resources.
- Symptom: Uploaded store operation document parsing fails, returning a format error prompt. Cause: The diversity of unstructured documents is not adapted, and the multi-format parsing switch is not enabled, causing some proprietary format documents to fail normal parsing.
How to verify successful configuration
- Run a concurrent MCP call test at 2–3 times per second, observe whether return results are normal, and adjust the
MCP_CONCURRENCY_LIMITparameter until no empty values are returned. - View the MongoDB database connection monitoring panel, confirm that active connections do not reach the configured connection pool upper limit, and adjust
MONGO_CONNECTION_POOL_SIZEbased on actual concurrent volume. - Upload professional chain operation documents in different formats, verify whether parsing results are complete, and adjust
PARSE_FILE_TIMEOUT_SECONDSand parsing configuration items until no parsing failures occur. - View the database archiving logs, confirm that historical data is automatically archived according to the configured cycle, and verify that the data retrieval function works normally.
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.