cold_reports.yaml Deep Dive

cold_reports.yaml is the configuration file for the cold_reports service (compiled from cold_reports.ts). It defines the catalogue of reports that COLD can generate: what shell command to run, how to name the output file, what MIME type to declare, and what user-supplied parameters (if any) the command requires.

This file is read in two contexts:

1. At startup by the cold_reports service, which builds an in-memory map of Runnable objects — one per report entry.
2. At request time by the middleware’s handleReportRequest function, which reads cold_reports.yaml directly to retrieve input definitions for the named report before creating the queue entry in reports.ds.

Top-level fields

report_directory: ./htdocs/rpt
reports:
  <report_name>:
    ...

Note: The report_directory value in cold_reports.yaml is not yet consumed by the Runnable.run() method — the output path ./htdocs/rpt and the URL prefix rpt are currently hardcoded in cold_reports.ts at lines 520–522. The YAML field exists as a declaration of intent for a future fix.

Report definition block

<report_name>:
  cmd: ./run_something.bash
  basename: something
  append_datestamp: true|false
  content_type: text/csv
  inputs:            # optional
    - id: clpid
      type: text
      required: true

content_type to file extension mapping

The Runnable.run() method in cold_reports.ts maps MIME types to extensions:

Input definition fields

Reports catalogue

run_people_csv

cmd: ./run_people_csv.bash
basename: people
append_datestamp: false
content_type: text/csv

Runs run_people_csv.bash, which calls dsquery with a SQL statement against people.ds and outputs a full CSV export of all people records. Output: htdocs/rpt/people.csv.

The SQL query used by dsquery in the bash script is a superset of the people_csv named query in cold_api.yaml — it includes additional fields like snac. When debugging column differences between the live API and the CSV export, compare the SQL in run_people_csv.bash against the people_csv query in cold_api.yaml.

run_groups_csv

cmd: ./run_groups_csv.bash
basename: groups
append_datestamp: false
content_type: text/csv

Exports all group records from groups.ds as CSV. Output: htdocs/rpt/groups.csv.

run_division_people_csv

cmd: ./run_division_people.bash
basename: division_people
append_datestamp: false
content_type: text/csv

Produces a CSV with columns division, clpid, orcid, family_name, given_name, plus one column per additional group membership. This uses the division_people named query in cold_api.yaml. Output: htdocs/rpt/division_people.csv.

run_people_membership_csv

cmd: ./run_people_membership.bash
basename: people_membership
append_datestamp: false
content_type: text/csv

Produces a flattened CSV with one row per person-per-group membership, using the people_membership named query. Output: htdocs/rpt/people_membership.csv.

run_group_people_crosswalk_csv

cmd: ./run_group_people_crosswalk.bash
basename: group_people_crosswalk
append_datestamp: false
content_type: text/csv

Produces a crosswalk CSV mapping groups to people. Output: htdocs/rpt/group_people_crosswalk.csv.

run_division_people_crosswalk_csv

cmd: ./run_division_people_crosswalk.bash
basename: division_people_crosswalk
append_datestamp: false
content_type: text/csv

Produces a crosswalk CSV mapping divisions to people. Output: htdocs/rpt/division_people_crosswalk.csv.

run_people_identifier_csv

cmd: ./run_people_identifier_csv.bash
basename: people_identifier
append_datestamp: true
content_type: text/csv

Exports people records focused on external identifiers (ORCID, ISNI, VIAF, etc.). append_datestamp: true means each run produces a dated file such as htdocs/rpt/people_identifier_2026-04-09.csv, preserving historical snapshots rather than overwriting.

journal_vocabulary

cmd: ./run_journal_vocabulary.bash
basename: journal_vocabulary
append_datestamp: false
content_type: application/yaml

Generates a YAML vocabulary file for journals by running bin/journal_vocabulary, which calls the journal_names query on journals.ds. Output: htdocs/rpt/journal_vocabulary.yaml.

group_vocabulary

cmd: ./run_group_vocabulary.bash
basename: group_vocabulary
append_datestamp: false
content_type: application/yaml

Generates a YAML vocabulary file for groups by running bin/group_vocabulary. The bash script can also be called with the argument push_to_cold to enqueue itself via POST /reports instead of running directly — a useful pattern for automated vocabulary refreshes. Output: htdocs/rpt/group_vocabulary.yaml.

people_vocabulary

cmd: ./run_people_vocabulary.bash
basename: people_vocabulary
append_datestamp: false
content_type: application/yaml

Generates a YAML vocabulary file for people, using the people_vocabulary named query in cold_api.yaml. That query shapes each record for use in the authors vocabulary (identifiers, affiliations). Output: htdocs/rpt/people_vocabulary.yaml.

thesis_option_vocabulary

cmd: ./run_thesis_option_vocabulary.bash
basename: thesis_option_vocabulary
append_datestamp: false
content_type: application/yaml

Generates a YAML vocabulary file for thesis options. Output: htdocs/rpt/thesis_option_vocabulary.yaml.

run_authors_records_csv

cmd: ./run_authors_records_csv.bash
basename: authors_records
append_datestamp: false
content_type: text/csv

Exports author records for integration with other Caltech Library systems. Output: htdocs/rpt/authors_records.csv.

run_authors_review_queue_csv

cmd: ./run_authors_review_queue_csv.bash
basename: authors_review_queue
append_datestamp: false
content_type: text/csv

Exports a CSV view of the RDM review queue for author reconciliation work. Output: htdocs/rpt/authors_review_queue.csv.

run_collaborator_report

cmd: ./run_collaborator_report.bash
inputs:
  - id: clpid
    type: text
    required: true
basename: "{{clpid}}_nsf_collaborator_report"
append_datestamp: false
content_type: text/csv

This is the only parameterized report. It generates an NSF collaborator table for a specific person, identified by their clpid.

inputs: A single required text field with id: clpid. The user supplies a clpid value in the browser form. That value is:

1. Stored in the Report object’s inputs list in reports.ds
2. Passed as a positional command-line argument ($1) to run_collaborator_report.bash
3. Substituted into the basename template via {{clpid}}, producing filenames like Briney-Kristin-A_nsf_collaborator_report.csv

How run_collaborator_report.bash works: The script validates that the clpid exists in people.ds via dataset read people.ds "$1", then calls bin/generate_collaborator_rpt "$1" --record_ids. That binary (compiled from generate_collaborator_rpt.ts) fetches the person’s publication records from the CaltechAUTHORS API and formats them as an NSF collaborator table.

Debugging this report: If the output file is not generated: 1. Check that $1 (the clpid) exists in people.ds: dataset read people.ds <clpid> 2. Check that bin/generate_collaborator_rpt exists and is executable 3. Run run_collaborator_report.bash <clpid> directly from the COLD working directory to see stderr

The runner: how cold_reports.ts processes the queue

Startup

cold_reports reads cold_reports.yaml at startup and builds a Runner object with a report_map dictionary. Each key is a report name; each value is a Runnable instance holding the command, basename, inputs schema, datestamp flag, and content type. The YAML file is not re-read while the service is running — restart cold_reports after any changes to cold_reports.yaml.

Poll loop

cold_reports calls servicing_requests() on a 10-second interval:

setInterval(async () => { await report_runner(config_yaml); }, 10000);

servicing_requests() calls the next_request query on reports.ds (defined in cold_api.yaml). That query returns the oldest request with status = "requested", ordered by updated ascending — a strict FIFO. Only one request is dequeued and processed per poll cycle.

Request processing (process_request)

1. Set status = "processing" and write to reports.ds.
2. Resolve the command’s input schema against the values stored in the request’s inputs field using resolveCommandInputs(). This aligns inputs by position and type, substituting an empty value for any mismatch.
3. Call runnable.run([]). This executes the shell command (with validated inputs as positional args if inputs exist, or with $-shell execution otherwise).
4. The command’s stdout is captured and written to htdocs/rpt/<basename><ext>. The URL path rpt/<filename> is returned as the link.
5. If stdout contains error://, the status is set to "error" and the link holds the error string.
6. If emails is non-empty, send_email() is called with the report name, status, and link.
7. Write the final status and link to reports.ds.

Command execution details

For parameterized reports (those with inputs), Runnable.run() uses Deno.Command with args set to the ordered list of input values. This avoids shell injection — inputs are passed as discrete arguments, not interpolated into a shell string.

For non-parameterized reports, run() uses the dax $ shell helper, which allows the command string to include shell features.

Filename templating

When basename contains {{id}} placeholders, Runnable.filenameTemplate() replaces each {{id}} with the corresponding input value. If no input with that id is found, the placeholder is replaced with _id_. This is used only by run_collaborator_report, which produces <clpid>_nsf_collaborator_report.csv.

Adding a new report

1. Write the report command

Create a shell script (e.g., run_my_report.bash) in the COLD working directory. The script should:

• Write its output to stdout
• Exit 0 on success, non-zero on failure
• Print a short error description to stderr on failure (the runner captures stderr and sets status to "error" if it is non-empty)

For parameterized reports, accept inputs as positional arguments ($1, $2, …).

2. Add an entry to cold_reports.yaml

my_new_report:
  cmd: ./run_my_report.bash
  basename: my_report_output
  append_datestamp: false
  content_type: text/csv

For a parameterized report:

my_parameterized_report:
  cmd: ./run_my_parameterized_report.bash
  inputs:
    - id: some_id
      type: text
      required: true
  basename: "{{some_id}}_my_report"
  append_datestamp: false
  content_type: text/csv

3. Expose the report in the browser form

The browser report request form reads the list of available reports from the UI, not from cold_reports.yaml directly. Add the new report name and any input fields to the report request page in htdocs/ so users can request it. The report_name submitted in the form POST must exactly match the key in cold_reports.yaml.

4. Restart cold_reports

bin/cold_reports cold_reports.yaml

The runner builds its report_map at startup. Changes to cold_reports.yaml are not picked up at runtime.

Debugging the report runner

Report stays in “requested” status: - Confirm cold_reports is running and its poll interval is firing. Check the service log for INFO: entered servicing_requests (if enabled) or INFO: Processing requests for <name>. - Confirm the report_name in the queue object exactly matches a key in cold_reports.yaml.

Report status is “aborting, unknown report”: - The report_name in reports.ds does not match any key in cold_reports.yaml. Either the report was mis-named in the request, or the entry is missing from cold_reports.yaml. Check with: dataset read reports.ds <uuid> and compare report_name against the YAML.

Report status is “error”: - The link field on the report object contains the error string (prefixed with error://). Read it: dataset read reports.ds <uuid> and inspect link. - Run the command script manually: ./run_my_report.bash [args] and check exit code and stderr.

Output file is empty or malformed: - The command ran but wrote nothing meaningful to stdout. Run the command directly and inspect its output. - Check that the script is executable: ls -l run_my_report.bash.

Input values not passed correctly: - For parameterized reports, add console.log debug output in cold_reports.ts or check the existing DEBUG resolved command inputs log line. Confirm resolveCommandInputs is matching by id and type.