Planner changes for CPU usage

This release brings some changes to the query planner to improve parallel sizing and resource estimation. This is done for workload-aware autoscaling and will be available as query options. These additional query options are added for tuning purposes. This new functionality will allow additional customers to enable multi-threaded queries globally for improved performance.

Before you begin

  • You must obtain the entitlement to use this feature.


Until this release, estimating the peak memory was the scaling factor in selecting an executor group to run large queries. This estimation was calculated during the query compilation time. When large queries were identified, they were enabled to run on larger executor groups mapped to different resource groups.

This release adds a new CPU costing algorithm as the scaling factor by establishing an infrastructure to allow the weighted total amount of data to process to be used as a new factor in the selection of an executor group. The new costing algorithm returns a number representing an ideal CPU core count required for a query to run efficiently. This number will be compared against the total CPU count of an Impala executor group to determine if it fits to run in that executor group or not. If the CPU requirement is higher than the max-query-cpu-core-per-node-limit, even in the largest exec group, the query may be rejected.

The following query options are added to control this CPU costing algorithm.

    Option to enable this CPU costing algorithm. Set MT_DOP > 0 to enable this query option.

    Option to control the minimum number of fragment instances (threads) that the costing algorithm is allowed to adjust. The costing algorithm is in charge of increasing the fragment's instance count beyond this minimum number through producer-consumer rate comparison. The maximum number of fragments is max between PROCESSING_COST_MIN_THREADS, MT_DOP, and number of cores per executor.

You can also use the following backend flags to tune the algorithm.

  • query_cpu_count_divisor

    Use this flag to divide the CPU requirement of a query to fit the total available CPU in the executor group. For example, setting the value to 2 will fit the query with CPU requirement 2X to an executor group with total available CPU X. Note that setting with a fractional value less than 1 effectively multiplies the query CPU requirement. A valid value is > 0.0. The default value is 1.

  • processing_cost_use_equal_expr_weight
    The default value of this flag is True. When the default value is retained, all expression evaluations are weighted equally to 1 during the plan node's processing cost calculation.
  • min_processing_per_thread

    This flag defines the minimum processing load (in processing cost unit) that a fragment instance needs to work on before the planner considers increasing the instance count based on the processing cost rather than the MT_DOP setting. The decision is per fragment. If you set this flag to a higher number, it will reduce parallelism of a fragment (more workload per fragment), and when set to a lower number, it will increase parallelism (less workload per fragment). Actual parallelism might still be constrained by the total number of cores in the selected executor groups, MT_DOP, or the PROCESSING_COST_MIN_THREADS query option. The value of this flag must be a positive integer and it defaults to 10M.

  • skip_resource_checking_on_last_executor_group_set

    This flag defines if the resource checking will be skipped on the last executor group set. If this backend flag is set to true, memory and cpu resource checking will be skipped when a query is being planned against the last (largest) executor group set. Setting true will ensure that query will always get admitted into the last executor group set if it does not fit in any other group set.