Stay organized with collections Save and categorize content based on your preferences.

Understand slots

A BigQuery slot is a virtual CPU used by BigQuery to execute SQL queries. BigQuery automatically calculates how many slots each query requires, depending on query size and complexity.

You have a choice of using an on-demand pricing model or a flat-rate pricing model. Both use slots for data processing. The flat-rate model gives you explicit control over slots and analytics capacity, whereas the on-demand model does not.

Customers on the flat-rate pricing model explicitly choose how many slots to reserve. Your queries run within that capacity, and you pay for that capacity continuously every second it's deployed. For example, if you purchase 2,000 BigQuery slots, your queries in aggregate are limited to using 2,000 virtual CPUs at any given time. You have this capacity until you delete it, and you pay for 2,000 slots until you delete them.

Projects on the BigQuery on-demand pricing model are subject to per-project slot quota with transient burst capability. Most users on the on-demand model find the default slot capacity more than sufficient. Depending on the workload, access to more slots improves query performance. To check how many slots your account uses, see BigQuery monitoring.

Query execution using slots

When BigQuery executes a query job, it converts the declarative SQL statement into a graph of execution, broken up into a series of query stages, which themselves are composed of more granular sets of execution steps. BigQuery leverages a heavily distributed parallel architecture to run these queries, and the stages model the units of work that many potential workers may execute in parallel. Stages communicate with one another by using a fast distributed shuffle architecture, which is discussed in more detail on the Google Cloud blog.

BigQuery query execution is dynamic, which means that the query plan can be modified while a query is in flight. Stages that are introduced while a query is running are often used to improve data distribution throughout query workers.

BigQuery can run multiple stages concurrently. BigQuery can leverage speculative execution to accelerate a query, and BigQuery can dynamically repartition a stage to achieve optimal parallelization.

BigQuery slots execute individual units of work at each stage of the query. For example, if BigQuery determines that a stage's optimal parallelization factor is 10, it requests 10 slots to process that stage.

Query slots.

GoogleSQL query is a dynamic DAG

Query execution under slot resource economy

If a query requests more slots than currently available, BigQuery queues up individual units of work and waits for slots to become available. As progress on query execution is made, and as slots free up, these queued up units of work get dynamically picked up for execution.

BigQuery can request any number of slots for a particular stage of a query. The number of slots requested is not related to the amount of capacity you purchase, but rather an indication of the most optimal parallelization factor chosen by BigQuery for that stage. Units of work queue up and get executed as slots become available.

When query demands exceed slots you committed to, you are not charged for additional slots, and you are not charged for additional on-demand rates. Your individual units of work simply queue up.

For example,

  1. A query stage requests 2,000 slots, but only 1,000 are available.
  2. BigQuery consumes all 1,000 slots and queues up the other 1,000 slots.
  3. Thereafter, if 100 slots finish their work, they dynamically pick up 100 units of work from the 1,000 queued up units of work. 900 units of queued up work remain.
  4. Thereafter, if 500 slots finish their work, they dynamically pick up 500 units of work from the 900 queued up units of work. 400 units of queued up work remain.
  5. And so on.

Slot scheduling.

BigQuery slots queued up if demand exceeds availability

Fair scheduling in BigQuery

Slots are distributed fairly among projects and then within the jobs in the project. This means that every query has access to all available slots at any time, and capacity is dynamically and automatically re-allocated among active queries as each query's capacity demands change. Queries complete and new queries get submitted for execution under the following conditions:

  • Whenever a new query is submitted, capacity is automatically re-allocated across executing queries. Individual units of work can be gracefully paused, resumed, and queued up as more capacity becomes available to each query.
  • Whenever a query completes, capacity consumed by that query automatically becomes immediately available for all other queries to use.
  • Whenever a query's capacity demands change due to changes in query's dynamic DAG, BigQuery automatically re-evaluates capacity availability for this and all other queries, re-allocating and pausing slots as necessary.

Multiple query scheduling.

Fair scheduling in BigQuery

Depending on complexity and size, a query might not require all the slots it has the right to, or it may require more. BigQuery dynamically ensures that, given fair scheduling, all slots can be fully used at any point in time.