Column Description Finder

Column Description Finder

Composable: Invoked by schema-enricher (Step 0c) and base-schema-audit (Step 3) for base schema coverage audits When to use: Finding column descriptions, auditing base schema coverage, listing available columns in global/app/dataset schemas

Overview

Mozilla bigquery-etl maintains base schema YAML files that define standard column descriptions for fields used across many tables:

• bigquery_etl/schema/global.yaml — common telemetry fields, read live from: https://raw.githubusercontent.com/mozilla/bigquery-etl/main/bigquery_etl/schema/global.yaml
• bigquery_etl/schema/app_<name>.yaml — application-specific fields (e.g., app_newtab.yaml), read live from: https://raw.githubusercontent.com/mozilla/bigquery-etl/main/bigquery_etl/schema/app_<name>.yaml
• bigquery_etl/schema/<dataset>.yaml — dataset-specific fields, read live from: https://raw.githubusercontent.com/mozilla/bigquery-etl/main/bigquery_etl/schema/<dataset_name>.yaml

This skill helps:

1. Find a column's description, type, and aliases in base schemas
2. List all columns defined in a base schema file
3. Audit a table's schema.yaml to see which columns are covered by base schemas
4. Identify columns missing descriptions in a table

🚨 REQUIRED - Read These Files on Every Invocation

ALWAYS fetch and read the live YAML files before answering — never rely on cached or assumed field data.

1. App-specific schema (read first — highest priority):

   • https://raw.githubusercontent.com/mozilla/bigquery-etl/main/bigquery_etl/schema/app_<name>.yaml
   • Check bigquery_etl/schema/ for available app schema files (app_*.yaml pattern).
   • Use WebFetch to retrieve the file; if it returns 404, no app schema exists for that application.

2. Dataset-specific schema (read second):

   • https://raw.githubusercontent.com/mozilla/bigquery-etl/main/bigquery_etl/schema/<dataset_name>.yaml
   • Check bigquery_etl/schema/ for available dataset schema files.
   • Use WebFetch to retrieve the file; if it returns 404, no dataset schema exists for that dataset.

3. Global schema (read third — fallback):

   • https://raw.githubusercontent.com/mozilla/bigquery-etl/main/bigquery_etl/schema/global.yaml

4. Format and conventions: READ references/column_definition_yaml_guide.md

Quick Start

Find a Column's Description

# Search global.yaml for a column
python scripts/find_column_description.py submission_date

# Search global.yaml + app_newtab.yaml (named file + global)
python scripts/find_column_description.py pocket_clicks --dataset app_newtab

# Search global.yaml + ads_derived.yaml (named file + global)
python scripts/find_column_description.py clicks --dataset ads_derived

# Search all available base schemas (app-specific first, then dataset-specific, then global)
python scripts/find_column_description.py my_column --all-datasets

Output shows: name, source file, type, mode, aliases, description

List All Columns in a Base Schema

# List global.yaml columns
python scripts/find_column_description.py --list-all

# List ads_derived.yaml columns
python scripts/find_column_description.py --list-all --dataset ads_derived

# List all available base schema files
python scripts/audit_base_schema_coverage.py --list-schemas

Audit a Table's Base Schema Coverage

# Check which columns in a table have base schema descriptions available.
# If metadata.yaml contains app_schema: <name>, that app schema is auto-applied.
python scripts/audit_base_schema_coverage.py telemetry_derived.clients_daily_v1

# Override or explicitly specify an app-specific schema (takes priority over metadata.yaml)
python scripts/audit_base_schema_coverage.py telemetry_derived.newtab_daily_interactions_aggregates_v1 --app-schema app_newtab

# Check coverage including dataset-specific schema
python scripts/audit_base_schema_coverage.py ads_derived.impressions_v1 --dataset-schema

# Check coverage including both app-specific and dataset-specific schemas
python scripts/audit_base_schema_coverage.py telemetry_derived.newtab_daily_interactions_aggregates_v1 --app-schema app_newtab --dataset-schema

# Show only columns missing descriptions
python scripts/audit_base_schema_coverage.py ads_derived.impressions_v1 --missing-only --dataset-schema

Output shows:

• Columns covered by base schemas (and which file)
• Columns with custom descriptions (defined in schema.yaml but not in any base schema)
• Columns with no description at all

Note: Only top-level columns are matched against base schemas. Nested RECORD fields are not included in coverage analysis.

Common Workflows

Workflow 1: Looking Up a Column Description

When a user asks "what does the country column mean?" or "what is dau?":

1. Run find_column_description.py <column_name>
2. If not found, try --all-datasets to search all schemas
3. Report the description and source
4. If still not found, note the column is not in any base schema

Workflow 2: Before Creating a Schema

When creating schema.yaml for a new derived table:

1. Run audit_base_schema_coverage.py <dataset>.<table> after initial schema generation
   • Add --app-schema <app_name> if the table belongs to an app (or set app_schema in metadata.yaml)
   • Add --dataset-schema if the dataset has a matching <dataset_name>.yaml
2. Review which columns are covered by base schemas
3. Apply base schema descriptions directly from the audit output
4. For uncovered columns, use the schema-enricher skill to fill descriptions from upstream schemas, query context, or application context

Workflow 3: Identify Missing Descriptions

When checking metadata completeness for a table:

1. Run audit_base_schema_coverage.py <dataset>.<table> --missing-only
2. Show the user which columns have no description
3. For columns in base schemas → recommend applying base schema
4. For custom columns → generate descriptions based on field name and context

Workflow 4: Adding New Column Definitions to Base Schemas

When a column is used in multiple derived tables and needs a standard description:

1. Determine if it belongs in global.yaml (used everywhere), an app-specific yaml (app_.yaml, cross-dataset for a specific app), or a dataset-specific yaml (.yaml)
   • For auditing an entire dataset or table prefix to classify many columns at once, use the base-schema-audit skill instead of doing this manually
2. READ assets/example_global_entries.yaml to see the correct format
3. Add the entry with name, type, mode, description, and aliases
4. Verify description quality using the checklist in references/column_definition_yaml_guide.md

Script Reference

find_column_description.py

Searches base schemas for a column by name or alias.

Usage: python scripts/find_column_description.py <column_name> [options]

Options:
  --dataset DATASET      Named base schema file to search (in addition to global.yaml), e.g., ads_derived or app_newtab
  --all-datasets         Search all available schemas (app-specific first, then dataset-specific, then global)
  --list-all             List all columns in the selected schema(s)
  --base-schemas-dir     Path to bigquery_etl/schema/ (default: bigquery_etl/schema)

audit_base_schema_coverage.py

Audits a table's schema.yaml against base schemas.

Usage: python scripts/audit_base_schema_coverage.py <dataset>.<table> [options]

Options:
  --app-schema APP_SCHEMA  App-specific schema to check first (e.g., app_newtab)
  --dataset-schema         Include dataset-specific schema (inferred from dataset name)
  --missing-only           Show only columns with no description
  --list-schemas           List all available base schema files
  --sql-dir                Path to sql/ directory (default: sql)
  --base-schemas-dir       Path to bigquery_etl/schema/ (default: bigquery_etl/schema)

Key Files