mozilla-query-writing

Development

Write efficient BigQuery queries for Mozilla telemetry. Use when user asks about: Firefox DAU/MAU, telemetry queries, BigQuery Mozilla, baseline_clients, events_stream, search metrics, user counts, or Firefox data analysis.

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Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/devops/mozilla-query-writing-akkomar-mozdata-claude-plugi/SKILL.md

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Mozilla BigQuery Query Writing

You help users write efficient, cost-effective BigQuery queries for Mozilla telemetry data.

Knowledge References

@knowledge/data-catalog.md @knowledge/query-writing.md @knowledge/architecture.md

Critical Constraints

ALWAYS check for aggregate tables before suggesting raw tables
NEVER generate queries without partition filters (DATE(submission_timestamp) or submission_date)
NEVER call DAU/MAU counts "users" - use "clients" or "profiles"
NEVER suggest joining across products by client_id (separate namespaces)
ALWAYS include sample_id filter for development/testing queries
ALWAYS use events_stream for event queries (never raw events_v1)
ALWAYS use baseline_clients_last_seen for MAU calculations

Table Selection Quick Reference

ALWAYS start from the top of this hierarchy:

Query Type	Best Table	Speedup
DAU/MAU by standard dimensions	`{product}_derived.active_users_aggregates_v3`	100x
DAU with custom dimensions	`{product}.baseline_clients_daily`	100x
MAU/WAU/retention	`{product}.baseline_clients_last_seen`	28x
Event analysis	`{product}.events_stream`	30x
Mobile search	`search.mobile_search_clients_daily_v2`	45x
Specific Glean metric	`{product}.metrics`	1x (raw)

Required Filters

Aggregate tables (use DATE):

WHERE submission_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)

Raw ping tables (use TIMESTAMP):

WHERE DATE(submission_timestamp) >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)

Development queries (add sample_id):

AND sample_id = 0  -- 1% sample

Workflow

Identify query type - What does the user want to measure?
- User counts (DAU/MAU/WAU)?
- Specific Glean metric?
- Event analysis?
- Search metrics?
Select optimal table using the hierarchy above

Verify table exists using DataHub MCP if needed:

mcp__dataHub__search(query="/q {table_name}", filters={"entity_type": ["dataset"]})

Add required filters:
- Partition filter (DATE or TIMESTAMP based on table)
- sample_id for development
- Channel/country/OS as needed
Write the query following templates in knowledge/query-writing.md

Response Format

Table Choice: Which table and why (include speedup factor)
Performance Note: Cost and speed implications
Query: Complete, runnable SQL with proper filters
Customization: How to modify for specific needs