What the data for this category looks like
Data for this category primarily comes from public biologic batch release ledgers from regulatory agencies, real-time securities trading market APIs, and public industry aggregated data APIs. Market data updates daily after market close. Batch release related data updates per the fixed disclosure cycle of regulatory agencies. Aggregated sector data is generated daily. Single market document includes: trade date, ticker code, ticker name, daily trading price, daily trading volume. Single batch release document includes: product name, batch number, release date, approved quantity. Trading price is measured in yuan, trading volume in share units, and approved quantity in physical circulation units.
What Constraints Do These Data Characteristics Impose on Workflow Orchestration
This category's data characteristics impose three core constraints on workflow orchestration. First, the update rhythms of the two data sources differ significantly. Market data updates daily, while batch release data updates per regulatory cycles. The workflow must support mixed scheduling: it can pull market data at fixed times, and pull batch release data via event triggers. This avoids unnecessary pulls or data lag. Second, the field structures of the two data types differ greatly. A unified field mapping rule must be configured to convert heterogeneous data into an aggregatable standard format. Empty field handling logic must also be reserved to adapt to undisclosed batch release data. Third, different data sources have different authentication requirements. Independent access credentials and timeout parameters must be configured for each data source to ensure stable data pulling.
How to Set the Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
trigger_type | ["fixed", "event"] | Supports mixed scheduling for fixed-time market data pulls and event-triggered batch release data pulls per regulatory disclosure cycles |
field_mapping_template | {"trade_date": "statistical_date", "product_name": "subject_name", "price": "daily_transaction_price", "approval_num": "approved_quantity"} | Unifies heterogeneous market and batch release data fields into a standard aggregated format, ensuring consistency for subsequent yield rate calculations |
api_request_timeout | 30 seconds | Biologics-related data source APIs have stable response speeds; 30 seconds covers most normal requests and prevents workflow blocking |
error_retry_max_times | 3 times | Addresses network fluctuations or temporary interface failures; retries reduce the probability of data pull failures, meeting the access stability requirements of both data sources |
auth_credentials | {"market_source": "api_key_1", "batch_source": "api_key_2"} | Configures independent authentication credentials for different data sources, ensuring access permission isolation and meeting the security requirements of both data sources |
empty_field_policy | retain_null | Adapts to scenarios where batch release data has undisclosed fields; retains null values without forced population, avoiding interference with subsequent aggregation calculations |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Each situation requires individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: When configuring a database connection node, the log returns the error
Failed to connect to jyfkk:1433 - 38BBDDC3AF7F0000, causing workflow execution to fail. Cause: The access address of the database server was not configured correctly, or network policies restrict the workflow node's access permissions to the target database port. - Phenomenon: The classification node used to filter biologics category data returns results mixed with non-biologics category data. Cause: Precise matching rules were not configured for the exclusive field characteristics of biologics, leading to overly broad classification logic.
- Phenomenon: In daily generated yield aggregation results, batch release related data always shows historical old values and fails to update. Cause: The trigger mode for batch release data was set to fixed-time pulling, which does not match the actual disclosure cycle of regulatory agencies, resulting in pulled data being outdated historical data.
How to Confirm Proper Configuration
- Manually trigger a single workflow execution, review the output logs of each node, and confirm that both market and batch release data sources successfully pulled raw data in the corresponding format.
- Import a single test heterogeneous data set, and verify that the field mapping node correctly converts fields from different data sources into the standard aggregated format.
- Simulate a regulatory disclosure event to trigger the workflow, and confirm that the workflow can respond to non-fixed-time scheduling requests and pull the latest batch release data.
- Check the authentication configuration items, confirm that access credentials for different data sources have been correctly bound, with no credential confusion or omissions.
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.