What This Category's Data Looks Like
Film theater investment research data primarily comes from theater ticket system APIs, public data from the National Film Special Fund Office, industry information platforms, and public announcements from film production companies. Update frequencies vary across data types: real-time box office data updates hourly, daily screening data is updated in batches each early morning, and quarterly industry research reports are updated quarterly. Most data takes the form of structured tabular data, including fields such as film_id, cinema_id, show_time, ticket_price, daily_box_office, and avg_audience. The corresponding units are string, string, datetime, yuan, ten thousand yuan, and people per session. Unstructured content includes research report documents and promotional material text.
Constraints on Database and Operations Work
High-frequency writes of hourly real-time box office data require database connection pool configurations optimized for high-concurrency write scenarios, to avoid connection exhaustion. Mixed structured and unstructured data from multiple heterogeneous sources requires a vector database that supports hybrid queries combining structured indexing and vector retrieval. Daily batch updates of screening data need scheduled task windows that avoid peak business hours, to prevent excessive system resource usage. Multi-dimensional associated fields for films, cinemas, and screenings require properly designed joint indexes to optimize query efficiency. Segmented storage of long-text research reports requires adjusting document splitting parameters to fit content of varying lengths, preventing retrieval accuracy degradation caused by improper segmentation.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
CONCURRENT_WRITE_LIMIT | 200–300 operations/second | Fits the high-frequency write requirements of hourly real-time box office data for film theaters, avoiding database connection overload |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Matches the parsing time required for long-text industry research reports, preventing interruptions to large file parsing tasks |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports batch upload requirements for complete quarterly industry research reports and high-definition promotional materials |
VECTOR_RECALL_TOP_K | Top 10 entries | Balances recall coverage for investment research retrieval and resource consumption per single query |
DB_POOL_MIN_IDLE | 50 connections | Ensures basic connection resources during scheduled batch screening data updates, preventing connection pool exhaustion |
SIMILARITY_THRESHOLD | Calibrated via actual testing | Adapts to the text similarity distribution of film theater investment research scenarios, avoiding false recalls or insufficient recall |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test against your own samples before finalizing settings.
Three Frequently Encountered Configuration Mistakes
- Issue: Login error after local Docker deployment, with logs showing
database connection refused. Cause: TheDB_CONNECTION_STRINGparameter is not configured correctly, or the local database service is not started and its port is not mapped to the container. - Issue:
503 Service Unavailablestatus code appears during peak weekend box office hours, with some write tasks timing out. Cause: TheCONCURRENT_WRITE_LIMITparameter was not adjusted, exceeding the database's preset write concurrency limit. - Issue: FastGPT retrieval results do not update synchronously after modifying vector database documents via an external MongoDB client. Cause: Real-time index synchronization configuration for the vector database is not enabled, so external modifications do not trigger a system vector index rebuild.
How to Verify Configurations Are Correct
- Run a batch screening data import test, check that the database write log has no abnormal errors, and confirm that the number of written data entries matches the source data.
- Send multiple concurrent real-time box office query requests, verify that the database connection count on the system monitoring panel does not exceed the preset limit.
- Upload a single long-text industry research report, confirm that the parsing task completes within the preset timeout period, with no parsing failure prompts.
- Insert a test vector data entry via an external database client, confirm that FastGPT can successfully recall this data during retrieval.
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.