Monitor Usage with INFORMATION_SCHEMA in BigQuery

When you work with BigQuery at any meaningful scale, understanding your usage patterns becomes critical for both cost management and performance optimization. The ability to monitor usage with INFORMATION_SCHEMA in BigQuery gives you unprecedented visibility into how your data warehouse operates, who's using it, and where your resources are going. This metadata layer built directly into Google Cloud's data warehouse provides real-time insights without requiring external monitoring tools or additional infrastructure.

Many organizations discover too late that their BigQuery costs have spiraled because they lacked visibility into query patterns, storage growth, or inefficient data access. The challenge isn't just about tracking numbers. It's about understanding the trade-offs between granular monitoring (which requires more complex queries and potentially impacts performance) versus simpler aggregate monitoring (which might miss important details). This article walks through the five critical areas you need to monitor and shows you how to balance comprehensive oversight with practical implementation.

Understanding INFORMATION_SCHEMA in BigQuery

INFORMATION_SCHEMA is a collection of system-defined views that contain metadata about your BigQuery resources. Unlike traditional databases where metadata queries can be expensive operations, BigQuery's implementation is designed specifically for cost-effective monitoring. These views are automatically updated and queryable just like regular tables, but they don't consume slot capacity in the same way that querying your actual data does.

The key architectural decision Google Cloud made here was to separate metadata access from data access. In traditional data warehouses, querying system catalogs often competes for resources with your production workloads. BigQuery treats metadata as a first-class concern with its own infrastructure. This means you can run monitoring queries frequently without worrying about impacting your analytical workloads.

1. Query Execution Patterns and Slot Consumption

The single greatest factor in BigQuery costs for compute-heavy workloads is how much slot time your queries consume. Slots are BigQuery's unit of computational capacity, and understanding which queries consume significant slot resources helps you identify optimization opportunities.

The JOBS_BY_PROJECT and JOBS_BY_USER views provide detailed information about every query executed in your project. Here's a practical query that identifies your highest slot-consuming queries over the past week:

SELECT
  user_email,
  job_id,
  query,
  total_slot_ms,
  ROUND(total_bytes_processed / POW(10, 12), 2) AS tb_processed,
  total_slot_ms / (1000 * 60 * 60) AS slot_hours,
  creation_time
FROM
  `region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHERE
  creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)
  AND job_type = 'QUERY'
  AND state = 'DONE'
ORDER BY
  total_slot_ms DESC
LIMIT 20;

This query reveals patterns that aggregate dashboards might miss. For example, you might discover that a particular data analyst runs a weekly report that consumes 50 slot hours each time because it performs a full table scan on a poorly partitioned table. The cost difference between optimizing that single query and letting it continue could be hundreds of dollars monthly.

The trade-off here is between detailed job-level tracking and higher-level aggregate monitoring. Querying JOBS_BY_PROJECT for detailed analysis means processing potentially millions of rows if you have a busy environment. For a trading platform processing thousands of queries daily, storing and analyzing this metadata externally might make more sense than repeatedly querying INFORMATION_SCHEMA. However, for organizations with moderate query volumes, the direct approach works efficiently and keeps your monitoring stack simpler.

2. Storage Growth and Table Size Tracking

Storage costs in BigQuery accumulate silently. Unlike compute costs that spike when someone runs an expensive query, storage costs grow steadily as tables expand. Many organizations only notice when their monthly bill shows a significant increase that's already several weeks old.

The TABLE_STORAGE view provides current information about every table's size, including logical bytes (the actual data) and physical bytes (what you're billed for after compression). Here's how a hospital network managing electronic health records might track storage growth:

SELECT
  table_schema AS dataset_name,
  table_name,
  ROUND(size_bytes / POW(10, 9), 2) AS size_gb,
  ROUND(active_logical_bytes / POW(10, 9), 2) AS active_gb,
  ROUND(long_term_logical_bytes / POW(10, 9), 2) AS longterm_gb,
  TIMESTAMP_MILLIS(creation_time) AS created,
  TIMESTAMP_MILLIS(CAST(last_modified_time AS INT64)) AS last_modified
FROM
  `region-us`.INFORMATION_SCHEMA.TABLE_STORAGE
WHERE
  table_schema = 'patient_records'
ORDER BY
  size_bytes DESC;

This query distinguishes between active storage (data modified in the past 90 days) and long-term storage (data older than 90 days), which BigQuery bills at different rates. For a hospital network, this distinction matters significantly because patient records might be legally required for years but accessed infrequently after initial treatment episodes.

The design decision here involves tracking frequency versus storage efficiency. You could run this query daily and store results in a monitoring table to track trends over time. However, that approach creates additional storage costs and query overhead. Alternatively, you could query INFORMATION_SCHEMA on demand when investigating cost increases. The right choice depends on whether you need proactive alerting (requiring regular monitoring) or reactive investigation (ad-hoc queries are sufficient).

3. User Access Patterns and Cost Attribution

Understanding who uses your BigQuery environment and how they use it is essential for cost allocation and identifying training opportunities. Some users might not realize that selecting all columns from a multi-terabyte table costs significantly more than selecting only needed columns.

The JOBS_BY_USER view aggregates query activity by user, allowing you to track both usage patterns and costs per person or team. For a mobile game studio analyzing player behavior, this becomes critical when multiple teams share the same data warehouse:

SELECT
  user_email,
  COUNT(*) AS query_count,
  SUM(total_bytes_processed) / POW(10, 12) AS total_tb_processed,
  AVG(total_slot_ms) / 1000 AS avg_slot_seconds,
  SUM(total_bytes_billed) / POW(10, 12) AS total_tb_billed,
  COUNTIF(error_result IS NOT NULL) AS error_count,
  ROUND(COUNTIF(error_result IS NOT NULL) / COUNT(*) * 100, 2) AS error_rate_pct
FROM
  `region-us`.INFORMATION_SCHEMA.JOBS_BY_USER
WHERE
  creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
  AND job_type = 'QUERY'
GROUP BY
  user_email
ORDER BY
  total_tb_processed DESC;

This analysis helps the game studio's data team identify which departments need additional training on query optimization. If the monetization team processes 10 TB weekly while the retention team processes only 2 TB, that information helps justify resource allocation and training investments differently for each group.

The trade-off involves privacy and granularity. Some organizations prefer not to track individual user activity this closely, instead focusing on service accounts or team-level aggregations. However, without user-level visibility, you lose the ability to provide targeted feedback and training. GCP allows you to configure access controls so that only data platform administrators can query these views, balancing oversight with privacy concerns.

How BigQuery's Architecture Changes Traditional Monitoring

Traditional data warehouse monitoring often requires separate enterprise monitoring tools, agent installations, and complex integration work. BigQuery's INFORMATION_SCHEMA fundamentally changes this equation by making metadata queryable using the same SQL interface you use for analytics.

This architectural decision has significant implications. First, you can use BigQuery itself to analyze BigQuery usage. You can create scheduled queries that run daily, aggregating usage patterns into summary tables that cost pennies to maintain. A solar farm monitoring service tracking sensor data could create a materialized view that updates nightly with usage statistics, avoiding repeated scans of the full JOBS history.

Second, BigQuery's serverless architecture means INFORMATION_SCHEMA queries don't require dedicated monitoring infrastructure. You don't need to provision servers, configure databases, or maintain separate monitoring stacks. The metadata simply exists alongside your data, queryable on demand.

However, this convenience comes with considerations. INFORMATION_SCHEMA views reflect current and recent state but aren't designed for long-term historical analysis spanning years. For compliance or detailed trend analysis, you need to periodically export this metadata to dedicated monitoring tables. Google Cloud customers often use scheduled queries to copy daily snapshots into permanent tables, creating a historical record without manual intervention.

4. Partition and Clustering Effectiveness

Partitioning and clustering are BigQuery's primary mechanisms for reducing query costs by limiting data scans. Monitoring how effectively your tables use these features directly impacts your bottom line.

The PARTITIONS view shows detailed information about each partition in your partitioned tables. For a freight logistics company tracking shipment events, this visibility is crucial:

SELECT
  table_name,
  partition_id,
  total_rows,
  ROUND(total_logical_bytes / POW(10, 9), 2) AS logical_gb,
  TIMESTAMP_MILLIS(last_modified_time) AS last_modified
FROM
  `region-us`.INFORMATION_SCHEMA.PARTITIONS
WHERE
  table_schema = 'shipment_tracking'
  AND table_name = 'delivery_events'
ORDER BY
  partition_id DESC
LIMIT 10;

This query helps the logistics company verify that their partitioning strategy works as intended. If they see partitions with dramatically different sizes or discover that queries still scan many partitions unnecessarily, they can adjust their partition column or query patterns.

The design challenge involves balancing partition granularity with query patterns. Daily partitions work well for time-series queries but create overhead if you have thousands of tiny partitions. Monthly partitions reduce overhead but might force broader scans. INFORMATION_SCHEMA gives you the data to make informed decisions based on actual usage rather than assumptions.

5. Reservation and Commitment Utilization

For organizations using BigQuery slots reservations instead of on-demand pricing, monitoring reservation utilization becomes critical for maximizing your investment. Reservations commit you to paying for a fixed number of slots whether you use them or not, so understanding utilization patterns helps you right-size your commitment.

The JOBS_TIMELINE and CAPACITY_COMMITMENT views provide insights into how fully you're using your reserved capacity. A video streaming service that purchased a baseline reservation needs to know if their queries are hitting capacity limits or if they're paying for unused slots:

SELECT
  TIMESTAMP_TRUNC(period_start, HOUR) AS hour,
  reservation_name,
  AVG(period_slot_ms) / (1000 * 60 * 60) AS avg_slot_hours_used
FROM
  `region-us`.INFORMATION_SCHEMA.JOBS_TIMELINE_BY_PROJECT
WHERE
  period_start >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)
GROUP BY
  hour,
  reservation_name
ORDER BY
  hour DESC;

This analysis reveals usage patterns over time. If the streaming service sees consistent peak usage during evening hours when users watch content but low utilization during early morning hours, they might consider autoscaling reservations or restructuring their commitment.

The fundamental trade-off for reservation monitoring is between commitment flexibility and cost predictability. Detailed utilization monitoring lets you optimize commitments, but requires investment in building monitoring dashboards and analysis workflows. Organizations with variable workloads benefit greatly from this visibility, while those with steady, predictable usage might find simpler monitoring sufficient.

Practical Implementation Scenario

Consider a telehealth platform that processes patient appointment data, video consultation logs, and prescription records across BigQuery. They started with on-demand pricing but noticed monthly costs reaching $15,000 as usage grew. The data engineering team decided to implement comprehensive INFORMATION_SCHEMA monitoring to understand their usage before committing to reservations.

They began by querying JOBS_BY_PROJECT to identify their costliest queries. They discovered that their patient matching algorithm ran hourly and scanned 3 TB each time because it lacked proper filtering on their date partition column. Adding a WHERE clause reduced scans to 50 GB, cutting that workload's cost by 98%.

Next, they used TABLE_STORAGE monitoring to find that consultation video metadata had grown to 8 TB, with 6 TB in long-term storage. They implemented a lifecycle policy to archive consultation logs older than two years to Cloud Storage, reducing active BigQuery storage costs by 40%.

Finally, JOBS_BY_USER analysis revealed that their data science team was experimenting with different patient outcome models, generating hundreds of queries daily during business hours. The platform team created a separate project for experimentation with its own budget, preventing exploratory work from impacting production analytics costs.

After three months of optimization guided by INFORMATION_SCHEMA monitoring, they reduced their monthly costs to $6,000 while supporting 30% more queries. They then purchased a 500-slot baseline reservation at $4,000 monthly, knowing from their monitoring data that they'd fully utilize it.

Comparing Monitoring Approaches

When deciding how to implement BigQuery usage monitoring, you face several approaches with distinct trade-offs:

Approach	Benefits	Drawbacks	Best For
Ad-hoc INFORMATION_SCHEMA queries	No infrastructure needed, zero additional cost, flexible analysis	No historical trends, requires manual investigation, reactive not proactive	Small teams, low query volumes, occasional cost reviews
Scheduled queries with summary tables	Historical tracking, automated updates, low maintenance	Some storage costs, limited to predefined metrics, requires initial setup	Medium-sized organizations, regular reporting needs, cost attribution requirements
Export to external monitoring platforms	Integration with enterprise tools, advanced alerting, cross-platform visibility	Additional infrastructure costs, complexity, data transfer overhead	Large enterprises, multiple cloud platforms, sophisticated governance requirements
Built-in BigQuery admin panel	Zero setup, visual dashboards, accessible to non-technical users	Limited customization, basic metrics only, no programmatic access	Quick overview, executive reporting, initial assessment

Your choice depends on organizational size, technical sophistication, and monitoring requirements. A startup with five data analysts needs far less sophisticated monitoring than a healthcare network with hundreds of users across multiple departments.

Relevance to Google Cloud Certification Exams

Understanding how to monitor usage with INFORMATION_SCHEMA in BigQuery is directly relevant to several Google Cloud certification exams. The Professional Data Engineer certification may test your knowledge of cost optimization strategies and your ability to use INFORMATION_SCHEMA views to identify performance problems. You might encounter scenarios where you need to recommend monitoring approaches for organizations with specific requirements.

A sample exam question might present a scenario like this: "A retail analytics team notices their BigQuery costs increased 40% last quarter but doesn't know why. They need to identify which users or queries caused the increase. What's the quickest way to investigate?" The correct answer would involve querying JOBS_BY_PROJECT or JOBS_BY_USER views filtered to the relevant time period, not implementing complex external monitoring or waiting for monthly billing reports.

The Cloud Architect certification can include questions about designing cost-effective data architectures. You might need to evaluate monitoring strategies as part of a broader data platform design. Understanding that INFORMATION_SCHEMA provides built-in monitoring without additional infrastructure helps you recommend simpler, more maintainable solutions.

For exam preparation, focus on understanding which INFORMATION_SCHEMA views contain specific types of information (JOBS views for query history, TABLE_STORAGE for size data, PARTITIONS for partition details) and when to use each. Practice writing queries against these views, as you may need to identify correct SQL syntax or recommend appropriate queries for given scenarios.

Conclusion

Effective BigQuery usage monitoring through INFORMATION_SCHEMA views gives you the visibility needed to control costs, optimize performance, and ensure your data warehouse operates efficiently. The five critical areas covered in this article (query execution patterns, storage growth, user access patterns, partition effectiveness, and reservation utilization) provide a comprehensive framework for understanding your BigQuery environment.

The architectural decision to make metadata queryable through standard SQL fundamentally simplifies monitoring compared to traditional data warehouses. You don't need separate tools or infrastructure. You simply query metadata the same way you query data. However, this convenience shouldn't lead to complacency. Thoughtful engineering means deciding which metrics matter for your organization, how frequently to monitor them, and whether to build historical tracking or rely on point-in-time analysis.

Whether you're managing a small project or a large enterprise data platform on Google Cloud, these monitoring capabilities help you make informed decisions about optimization priorities and resource allocation. Start with the basics: understand your query patterns and storage growth. Then expand to more sophisticated monitoring as your needs evolve. The key is using these insights not just to understand what happened, but to proactively shape how your team uses BigQuery going forward.