Slots and BigQuery

A slot is a dynamic placeholder on a Web page that waits for content (a passive slot) or calls out to the content repository to deliver it (an active slot). A slot is defined by a slot> element and can have global attributes. Slots work in conjunction with scenarios to deliver content and the renderers to specify how it will be presented on a Web page.

The Pay Table

A pay table is a key piece of information that gives players detailed knowledge about the symbols in a slot game and how much they can win for landing (typically) three, four or five matching symbols on a winning combination. The pay table also contains information on any bonus features that a slot game may have and how to trigger them. Typically, the pay table will match the theme of the slot and include pictures of each symbol, alongside their payout values.

Initially, slot machines were programmed to only display one of 22 possible symbols on the reels, which limited the number of combinations and jackpot sizes. When microprocessors were introduced, manufacturers could program the slots to weight certain symbols more than others. This resulted in a situation where it might appear that a symbol was “so close” to appearing on a paying line, but actually had a much lower probability.

This is why it’s important to always read the pay tables before playing a slot machine. You might be tempted to play machine A because it offers a large jackpot, but it’s really best to pick a machine with a smaller jackpot and a higher average payback.

In order to avoid this, BigQuery utilizes a sophisticated scheduling algorithm that automatically re-evaluates the capacity demands of each query as data is added and removed from the query’s dynamic DAG. This allows for the optimal use of available resources and to guarantee that all queries receive an equal amount of time in the queue. In addition, BigQuery maintains a pool of reserved capacity for all queries and reserves the right to reclaim any or all slots for other workloads if necessary. This way, BigQuery can continue to provide high performance for all applications despite fluctuations in the overall workload. This is what makes the service so scalable and reliable. It’s also what enables it to scale out to support a wide range of use cases, from simple to complex. To learn more about how BigQuery schedules queries across a cluster, see the Sizing and Scheduling section of this documentation.