What data looks like for this category
Data sources for refractory materials primarily come from industry association monitoring platforms, upstream mine quotation systems, and ex-factory price databases of downstream steel and building material enterprises. Update rhythms vary: raw material prices are updated daily, finished product ex-factory prices are updated weekly, and monthly inventory data is updated monthly. Most data is provided as structured CSV files or returned via standardized JSON APIs. Fields include product grade, origin, bulk density, compressive strength, tax-included ex-factory price, and inventory surplus. Corresponding units are g/cm³, MPa, yuan/ton, and ton respectively.
What constraints these characteristics impose on workflow orchestration
Differing update frequencies across multiple data sources require aligning pull nodes for each source using timestamps. This prevents yield calculation errors caused by mismatched data cycles. Fields include specialized physical quantities and price units, so a unit validation node must be embedded in the workflow to stop format exceptions from disrupting subsequent calculations. Single batches of data can include thousands of product grades, so the workflow must support batch pagination pull and processing to avoid single request timeouts. Downstream yield calculation requires linking raw material and finished product price data, so a custom calculation node must be added to complete cross-data source field association. Additionally, the authority of data sources requires adding a data validity check step to filter invalid quotations or abnormal inventory data.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_BATCH_SIZE | 500–1000 records/batch | Adapts to thousands of refractory product data per batch, avoids single pull timeouts |
HTTP_REQUEST_TIMEOUT | 300 seconds | Covers network latency across industry associations and enterprise quotation systems, ensures complete data pulling |
WORKFLOW_LOOP_MAX_DEPTH | 2 levels | Meets nested loop requirements for multi-source data alignment, avoids excessive resource usage from unlimited loops |
ENV_VAR_OVERRIDE | Configure dedicated keys per data source | Adapts authentication permissions for different data sources, avoids security risks from universal keys |
DATA_FIELD_MAPPING | {"tax-included ex-factory price":"price","bulk density":"density"} | Unifies field names across data sources, reduces adaptation costs for custom calculation nodes |
ERROR_RETRY_MAX_TIMES | 3 times | Handles pull failures caused by network fluctuations or temporary interface exceptions, improves workflow stability |
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. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: A
connect ETIMEDOUTdatabase connection error pops up during workflow execution, while the target database can be accessed normally locally. Cause: No intranet access whitelist is added to the network configuration of the workflow container, or the container network mode is not set to host mode, preventing the container from accessing the local database. - Symptom: The workflow returns a
COMPOSE_FILE_MISSING_ENVerror after startup, indicating that a specified environment variable is missing. Cause: No authentication environment variable for the corresponding data source is added to the docker-compose.yml file, or the variable name does not match the reference in the workflow configuration. - Symptom: Only partial data is returned after multi-level nested loop execution, or the workflow enters an unresponsive state. Cause: The
WORKFLOW_LOOP_MAX_DEPTHparameter is not set, or the set value is less than the actual required number of nested levels, causing the loop to fail to complete normally or exceed system limits.
How to confirm the configuration is correct
- Run a workflow test, check whether the fields pulled from each data source match the configured
DATA_FIELD_MAPPING, with no missing or format errors. - Check the docker-compose.yml file, confirm that all environment variables referenced by the workflow are included, and the variable names fully match the configuration items.
- View the workflow's loop node settings, confirm that the
WORKFLOW_LOOP_MAX_DEPTHparameter matches the actual required number of nested levels. - Run the workflow with simulated large-batch data, confirm that the
PARSE_BATCH_SIZEsetting adapts to the current data volume, with no timeouts or data truncation.
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.