Database and Operations for Shipping Port Investment

What data looks like for this category

Data sources for shipping port investment research include port operation management systems, maritime dispatching platforms, customs clearance databases, shipping freight index data sources, and first-hand documents such as berth operation logs and vessel schedule announcements. Data update cycles vary significantly. Real-time operation data such as berth loading and unloading progress updates at minute-level intervals. Vessel schedules update hourly. Annual operation reports update quarterly. Document structures include structured tabular data such as berth throughput and vessel draft. They also include semi-structured JSON files for operation logs, and unstructured documents such as port planning PDFs and maritime notices. Standard business fields include berth ID, vessel IMO number, cargo volume (unit: tons), operation duration (unit: hours), and additional relevant fields.

What constraints these characteristics impose on database and operations workflows

Multi-source heterogeneous data requires mixed storage. Support must be provided for both relational databases for structured business data and vector databases for vector indexes of unstructured documents. High-frequency real-time data creates synchronization pressure. Minute-level updated berth data requires low-latency database synchronization mechanisms. This prevents data lag that affects investment research accuracy. Business fields require strict validation. Fields with specific units such as tons and hours must undergo format validation during data ingestion. This stops dirty data from entering the knowledge base. Cross-source data association has performance requirements. Investment research often requires associating vessel schedule, freight rate, and berth operation data. Databases must support efficient JOIN queries to avoid association timeouts. Large documents create parsing and storage pressure. Single port operation reports often exceed 10,000 words. Databases must use chunked storage strategies adapted for long text.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000–12000 characters`	Single port monthly operation reports often exceed 5000 characters, so complete context must be retained to support cross-document investment research association
`RECALL_TOP_K`	`Top 10–15 results`	Investment research requires balancing multi-dimensional recall results for berths, vessel schedules, and freight rates to avoid missing key information from single-dimensional recall
`DB_SYNC_INTERVAL`	`30 seconds`	Real-time vessel schedule and berth operation data require high-frequency synchronization to ensure the timeliness of investment research data
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Large port annual operation reports take longer to parse, so extending the timeout period avoids forced task termination
`MYSQL_CONN_POOL_SIZE`	`20–30`	Multiple concurrent investment research requests require sufficient connection pool capacity to avoid request failures caused by exhausted database connections
`MILVUS_VECTOR_DIM`	`1536`	The output dimension of general text embedding models adapts to port business text, ensuring vector retrieval accuracy

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material 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 Configuration Mistakes

Symptom: After configuring MYSQL_CONN_STRING, running SQL queries returns empty results or the 1045 Access denied error. Cause: Public network access permissions for the port business database are not enabled, or the configured database account lacks SELECT permissions for the corresponding business tables.
Symptom: The investment research application returns 429 Too Many Requests after sending multiple parallel requests. Cause: The CONCURRENT_REQUEST_LIMIT parameter has not been adjusted. The default concurrency limit cannot support parallel query requirements for multiple berths and vessel schedules.
Symptom: Using the attu tool to connect to the MILVUS database fails, and vector indexes cannot be viewed. Cause: The IP address of the FastGPT deployment node is not added to the access whitelist of the MILVUS instance, or the default port 19530 is not open.

How to Confirm Successful Configuration

Run the database connection test script to verify that the database configured with MYSQL_CONN_STRING can normally query port business data. Confirm that the units and business definitions of returned fields match expectations.
Send multiple sets of parallel investment research requests. Observe that no 429 Too Many Requests errors appear in the logs. Confirm that the concurrency parameter configuration meets peak business requirements.
Open the attu console, enter the MILVUS instance address and port. Verify that the connection is successful and the created port business vector indexes can be viewed.
Upload a port monthly operation report. Verify that the parsing task completes within the preset timeout period, and no parsing failure logs are generated.

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.