Database and Operations for Construction and Decoration

What this category of data looks like

Construction and decoration investment research data primarily comes from construction quotas published by industry associations, product parameter documents publicly released by building material manufacturers, bidding and tendering record files from local housing and urban-rural development departments, and acceptance ledgers from construction sites. Update cadence varies by data type: building material quotes are updated regularly to reflect market fluctuations, construction quotas are adjusted every quarter, and bidding documents are added in real time as project progress advances. Document formats include structured product parameter tables, semi-structured project ledgers, unstructured bidding PDFs and on-site records. Fields include material specifications, construction unit prices, project durations, qualification requirements, and more. Units include yuan per square meter, cubic meters, kilograms, and others.

Constraints on database and operations from these characteristics

Multi-source, heterogeneous data formats require the database to support structured table storage, unstructured binary storage, and semi-structured JSON fields. This increases the complexity of index design. Real-time newly added bidding documents and periodically updated building material quotes create read/write peaks. Reasonable connection pool threshold configuration balances resource usage and concurrent processing capabilities. The diversity of field units and specifications requires a standardization check step before data import. This avoids query errors caused by unit mismatches. Data sources with different update cycles require tailored incremental synchronization strategies. This prevents excessive server resource usage from full synchronization.

Configuration Settings

Configuration Item	Recommended Setting	Rationale
`DB_STORAGE_TYPE`	`mixed`	Construction and decoration investment research data includes structured quotation tables, unstructured PDF documents, and semi-structured bidding JSON files. Mixed storage balances query efficiency and storage flexibility
`CONNECTION_POOL_MAX_SIZE`	`20-30`	Multi-source data synchronization generates concurrent read/write requests. This range balances server resource usage and concurrent processing capabilities
`DATA_CLEANUP_CYCLE`	`weekly`	Building material quote data updates weekly. Regular cleanup of expired old data maintains database query performance
`SQL_VALIDATION_RULES`	`Auto-validate unit and specification fields`	Construction and decoration data includes multiple unit fields such as yuan per square meter and cubic meters. Automatic validation prevents import errors caused by unit mismatches
`QUERY_TIMEOUT`	`300 seconds`	Full-text search and join queries for large bidding documents require extended processing time. This threshold prevents normal queries from being interrupted

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material types, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Misconfigurations

Symptom: When running the same SQL statement, some queries return results successfully while others show the Query execution timed out error. Cause: Concurrent read/write requests generated by multi-source data synchronization occupy database connection pool resources. This prevents subsequent queries from obtaining available connections.
Symptom: Tool call nodes reach the database query step but return no results. Logs show the Connection refused error. Cause: No database connection timeout release policy is configured. Idle connections are not automatically reclaimed, leading to connection pool exhaustion.
Symptom: Unable to import code-type data such as Python construction scripts and Excel macro files from the construction and decoration industry. The prompt Unsupported file format appears. Cause: The default database storage does not enable binary large object storage support. This makes it incompatible with unstructured code-type files.

How to Verify Proper Configuration

Execute an SQL query that includes multi-unit fields. Check that returned result units match the original data to confirm the SQL_VALIDATION_RULES configuration takes effect.
Simulate concurrent requests for multi-source data synchronization. Check the database connection pool occupancy rate to confirm the CONNECTION_POOL_MAX_SIZE configuration adapts to concurrent load.
Import construction and decoration investment research data in multiple formats: structured Excel, unstructured PDF, and semi-structured JSON. Confirm all formats import normally to verify the DB_STORAGE_TYPE configuration is correct.
Run a join query for a large bidding document. Check that the query is not interrupted prematurely to confirm the QUERY_TIMEOUT configuration is reasonable.

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.