What the Data for This Category Looks Like
This service delivers daily reports on revenue yield and market trends for software development output. Data is primarily sourced from market APIs, trading system operation logs, and valuation accounting modules. Two update rhythms are used: real-time push of incremental data for individual trades, and aggregated full daily report data generated after daily market close. Each data document uses structured JSON format, containing strategy ID, trading target code, holding quantity, daily revenue value, cumulative revenue value, number of trades, and data generation timestamp. Field characteristics include: trading target code is a 6-digit numeric string, holding quantity is a positive integer, revenue values are floating-point numbers with four decimal places, and data generation timestamps use ISO 8601 format.
Constraints on Database and Operations From These Characteristics
Multi-source data access and dual update rhythms require the database to support both real-time streaming writes and daily batch imports, while balancing read and write concurrent performance. Fixed-time daily batch aggregation tasks require a configured scheduled scheduling mechanism. These tasks must also handle duplicate reported daily data to avoid redundant storage. High-frequency queries mostly use combined conditions of strategy and target, so corresponding composite indexes must be created to reduce query latency. Data timeliness requirements mandate monitoring of write latency thresholds to prevent aggregation tasks from timing out after market close. Historical data proportion grows over time, so hot and cold data must be differentiated. Non-recent data must be migrated to low-cost storage media to control online storage costs.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
mongodb.writeConcern | w:1, j: true | Ensures persistence of batch-imported daily report data, preventing data loss from node outages |
mongodb.maxPoolSize | 200–300 | Balances concurrent connection needs for real-time market data writes and batch aggregation, avoiding service interruptions from exhausted connections |
mongodb.compression | snappy | Reduces transmission and storage overhead for batch-imported daily report data, balancing compression efficiency and performance |
index.policy_ticker | { strategyId: 1, ticker: 1 } | Accelerates high-frequency queries based on combined strategy and target conditions, matching the retrieval scenario for daily report broadcasts |
batchImport.timeout | 1800 seconds | Adapts to the aggregation import duration for full daily data after market close, preventing tasks from being forcibly terminated by the system |
coldData.archiveThreshold | 90 days | Migrates historical daily report data older than 90 days to object storage, reducing cost pressure on online storage |
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 targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: A
Connection refusederror is returned when connecting to MongoDB in a Linux environment, and logs show failure to bind to the specified port. Cause: The default MongoDB listening port 27017 is not open, or firewall rules restrict external access. - Symptom: When using MongoDB 7.0, batch writing daily report data triggers a
QueryExceededMemoryLimitNoDiskUseAllowederror. Cause: MongoDB 7.0 enables memory usage limits by default, and batch aggregation tasks are not configured with disk temporary storage permissions. - Symptom: Calling the API to retrieve the knowledge base returns a number of results that does not match the configuration, and system memory usage does not change significantly with configuration adjustments. Cause: The configuration for the number of retrieval results is mistakenly bound to physical memory. In reality, this parameter only controls the number of entries returned by the retrieval and has no direct association with system memory.
How to Verify Proper Configuration
- Run a batch import test script to verify whether data completes writing within the set timeout period, and check whether the write success rate meets the preset threshold.
- Connect to the database to run composite index queries, compare query latency with and without indexes to confirm that the index configuration has taken effect.
- View MongoDB connection pool monitoring metrics to confirm that the current number of active connections does not exceed the configured maximum connection threshold.
- Trigger a real-time market data write task to check whether data is synchronized to the database in a timely manner with no latency buildup.
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.