What This Category of Data Looks Like
Coking coal investment research data comes from domestic spot trading platforms, public futures exchange databases, monthly industry association reports, and port loading and unloading ledgers. Data types include structured and unstructured formats. Structured data covers spot quotes, futures delivery details, and fields such as origin identifiers, coal grade labels, moisture content, ash content, caking index, daily trading prices, and port inventory. Prices use yuan per ton as the unit. Inventory uses tons as the unit. Update frequencies vary across data types: Spot trading data updates daily. Futures delivery data updates with each trading day. Research notes update monthly. Regulatory capacity data updates quarterly.
Constraints Imposed on Database and Operations Work
Use unified field mapping rules for heterogeneous data from multiple sources. This prevents format conflicts across coking coal data from different channels. High-frequency daily spot data updates create sustained write pressure. Adjust write concurrency and batch parameters to avoid exhausting database connections. Apply distinct storage strategies for different document structures. Store structured data in relational tables. Store unstructured research notes in vector databases. Set appropriate database field types to ensure accurate data storage. Coking coal data has high field precision requirements. Synchronize data sources with different update frequencies in batches. This avoids peak write task conflicts that impact business availability.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
vector_recall_top_k | Top 10-15 entries | Coking coal investment research data has long individual entries. Excessive recall increases context processing pressure. Insufficient recall loses critical industrial chain information |
db_write_batch_size | 50-100 entries per batch | Coking coal spot data has large daily update volume. Batch writing avoids database connection timeouts and write blocking |
parse_file_timeout_seconds | 600 seconds | Coking coal industrial chain research notes are mostly long documents with extensive industry terminology and tables. Parsing takes longer |
pg_vector_extension_version | 0.5.0 and above | Older versions have vector field index failure issues. Vector recall for coking coal data relies on stable index performance |
db_connection_pool_size | 20-30 connections | Concurrent investment research queries and data synchronization tasks run simultaneously. The connection pool must support concurrent requests |
upload_file_max_size | 1000 MB | Large coking coal delivery warehouse receipt scans and quarterly research note files have significant size. Relax upload limits to accommodate these files |
The parameter values provided on this page are common starting points for configuration work. Actual values are affected by material form, data volume, and business rules. Analyze specific issues on a case-by-case basis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Vector queries return
column vector does not exist. Cause: After reinstalling the PostgreSQL vector extension, the extension initialization command was not re-run in the target business database. System tables and functions were not created correctly. - Symptom: Docker image deployment fails to connect to the database, returning
connection refusedor connection timeout. Cause: The database port was not mapped to the host machine during deployment, or the connection string used the host's local address instead of the service address within the container network. - Symptom: Coking coal long document parsing takes longer than expected. Cause: The
parse_file_chunk_sizeparameter was not adjusted. The default segment length is too short, leading to an excessive number of segments. Or parallel parsing configuration was not enabled, leading to insufficient single-threaded parsing efficiency.
How to Verify Proper Configuration
- Run a vector query statement to confirm the
vectorfield can be created and retrieved normally. Verify the extension is installed and initialized correctly. - After starting the Docker container, use internal container network tools to test database connections. Confirm the connection string configuration is correct.
- Upload a single coking coal document larger than 10 MB. Observe parsing progress and duration. Confirm parsing parameters are adapted to long document scenarios.
- Simulate concurrent query requests. Observe database connection pool usage. Confirm the connection count configuration meets peak business 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.