Performance Considerations

What does BigQuery use to help manage resource consumption and costs?

• Virtual Machines

• Slots

• Containers

• Nodes

Answer explanation

In BigQuery, slots are the fundamental unit of computational capacity. Each slot represents a virtual CPU that can process a portion of a query. BigQuery automatically allocates slots to queries based on factors like query size and complexity. You can think of slots as a way to measure the processing power needed to run a BigQuery job.

By using slots as a metric, BigQuery can:

• Control costs: The cost of running queries is often determined by the number of slots used and the duration of the query.

• Manage resource consumption: By limiting the number of slots available to a project or user, BigQuery can prevent any single job from consuming too many resources and impacting the performance of other jobs.

• Optimize performance: BigQuery dynamically allocates and releases slots to ensure that queries are executed efficiently and in a timely manner.

Which of the following factors does BigQuery consider when calculating the required slots for a query?

• Query language and user

• Query size and complexity

• Query execution time

• Number of datasets

Answer explanation

BigQuery automatically calculates the number of slots required for a query based on:

• Query size: This refers to the amount of data that the query needs to process. Larger datasets typically require more slots for efficient processing.
  

• Query complexity: Complex queries with multiple joins, aggregations, or subqueries require more processing power than simple queries. Therefore, BigQuery allocates more slots to complex queries to ensure timely completion.

In what tool can you monitor BigQuery slot usage?

• Cloud Console

• Cloud Monitoring

• Stackdriver

• Cloud Trace

Answer explanation

Cloud Monitoring: This is a more advanced monitoring tool that allows you to create custom dashboards, alerts, and metrics to track your BigQuery slot usage in detail.

Under what circumstance might additional slot capacity improve BigQuery performance?

• Small datasets with simple queries

• Large datasets with simple queries

• Very large datasets with highly concurrent workloads

• Small datasets with highly concurrent workloads

Answer explanation

• Very Large Datasets: BigQuery is designed for large-scale data processing. When dealing with massive datasets, increasing slot capacity provides more computational power to process the data in parallel, leading to faster query execution times.
  
  

• Highly Concurrent Workloads: When multiple queries are running simultaneously, increasing slot capacity ensures that each query has enough resources to execute efficiently, preventing bottlenecks and reducing overall query latency.

What feature is common across all BigQuery Edition tiers and the pay-as-you-go model?

• Manual scaling

• Autoscaling capability

• Dedicated hardware

• Static pricing

Answer explanation

BigQuery's autoscaling capability is a fundamental feature available across all editions, including the pay-as-you-go model. It automatically adjusts resources based on workload demands, ensuring optimal performance and cost efficiency.

What is a benefit of using BigQuery’s compressed storage option?

• Lower storage costs with the same storage space

• Higher per unit pricing for uncompressed storage

• Increased storage capacity without additional costs

• Shorter time travel window

What is the time travel window for data stored in compressed storage in BigQuery?

• 7 days

• 4 days

• 2 days

• 1 day

Answer explanation

The time travel window for data stored in compressed storage in BigQuery is 7 days by default. This means you can query or restore data from any point within the past seven days.

https://cloud.google.com/bigquery/docs/time-travel

What happens if a second query is executed while a first query is still running in BigQuery?

• The second query is queued until the first query completes

• Both queries share the total available slots equally

• The first query is prioritized over the second

• The second query is cancelled

What does BigQuery use to ensure fair resource allocation across projects, users, and queries?

• Hierarchical reservations

• Dedicated resources

• Dynamic partitioning

• Concurrency rules

Answer explanation

BigQuery does not directly use hierarchical reservations, dedicated resources, or dynamic partitioning to ensure fair resource allocation.

Explanation:

BigQuery uses a fair scheduling algorithm to allocate resources (slots) among different projects, users, and queries. This algorithm aims to distribute resources equitably so that no single job monopolizes all the resources. It ensures that every query gets a fair share of the available slots and that capacity is dynamically re-allocated as the demand for resources changes.

• Fair scheduling works by periodically re-evaluating the resource allocation and adjusting it based on the current needs of all running queries. This prevents long-running or resource-intensive queries from starving other queries and ensures that all jobs make progress.

How can you prioritize one BigQuery project over another?

• Increasing the slot limit for the higher priority project

• Assigning more VMs to the higher priority project

• Setting up a hierarchical reservation

• Manually managing the query execution order

Answer explanation

BigQuery allows you to create reservations, which are allocations of slots (a unit of computational power) for specific projects or users. By setting up a hierarchical reservation, you can create a parent reservation with a certain number of slots, and then allocate sub-reservations to different projects with their own slot limits. This allows you to ensure that the higher-priority project always has access to the resources it needs, while the lower-priority project can utilize any remaining slots.