What the data for this category looks like
Marketing and business data for residential development comes primarily from project sales ledgers, housing and urban-rural development department pre-sale filing systems, unit parameter manuals, and offline visit registration systems.
Data update schedules vary:
- Sales ledgers are updated daily
- Pre-sale filing data is synchronized each calendar month
- Unit parameters are only updated when unit designs or project plans are adjusted
A single sales data table includes fields such as building number, internal floor area, filing unit price, number of visiting customers, and subscription date. Supported units include square meters, yuan per square meter, person-times, and standard date format timestamps.
What constraints these characteristics impose on workflow orchestration
Sales data volumes are large, with a single table containing up to 100,000 records. Workflows must support bulk data reading and sharded processing to avoid exceeding node memory limits with single loads.
Multiple data sources have distinct format differences. Structured ledgers and semi-structured visit registration records require separate parsing rules. This increases configuration costs for format conversion between nodes.
Update rhythms vary widely across data types. Trigger periods must be configured per data type. This avoids repeatedly pulling static unit parameter data, while ensuring real-time access to sales data.
Some pre-sale filing data is sensitive. Permission verification nodes must be added to workflows. This ensures only authorized roles can access relevant content.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
workflow_batch_size | 2000–5000 records | Adapts to sharded processing of single 100,000-record sales data tables, avoiding exceeding node memory limits with single loads |
data_source_sync_interval | 86400 seconds (daily) | Matches the daily update rhythm of sales ledgers, ensuring data timeliness for marketing content generation |
code_run_timeout | 600 seconds | Reserves sufficient time to process bulk data calculations and format conversions, preventing workflow interruptions from timeouts |
node_error_retry_count | 2–3 retries | Addresses temporary network fluctuations during single data pulls, reducing workflow failure rates |
trigger_type | Scheduled trigger | Adapts to fixed generation requirements for monthly marketing reports and daily sales data summaries |
cache_ttl | 2592000 seconds (30 days) | Matches the static update cycle of unit parameters, reducing resource consumption from repeated pulls |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material formats, data volumes, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- No log output after code run node execution: The workflow’s log persistence switch is not enabled, and
console.logoutput can only be viewed in real-time debug sessions. - Code run node errors in local deployment environments: The local runtime environment lacks required code dependency libraries, and no dependency installation steps were added in the node configuration.
- Missing result records after bulk sales data processing:
workflow_batch_sizeis set too large, exceeding the node memory capacity and causing sharded data loss.
How to Confirm Proper Configuration
- Trigger a test workflow, review the node log panel, and confirm
console.logoutput displays normally. - Upload 100 simulated sales data records, and verify that the number of workflow output records matches the number of input records.
- Check the data synchronization records, and confirm that the latest sales ledger data is automatically pulled at the preset cycle.
- Review the dependency configuration of the code run node, and confirm that the corresponding dependency packages are installed in the local deployment environment.
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.