#Hbase archive cleaner driver

The maximum allocation for every container request at the resource manager, in terms of virtual CPU cores. Number of CPU cores that can be allocated for containers. The maximum allocation for every container request at the resource manager. memory-mbĪmount of physical memory, in MB, that can be allocated for containers.

The amount of off-heap memory to be allocated per executor. Īmount of memory to use per executor process.

#Hbase archive cleaner driver

The number of cores to use on each executor.Īmount of memory to use for the driver process. The number of executors for static allocation. The amount of off-heap memory to be allocated per driver in cluster mode. Number of cores to use for the driver process, only in cluster mode. Queue capacity in percentage (%) as absolute resource queue minimum capacity for root queue. The capacity scheduler with predefined queue called root.Ĭ. The ResourceCalculator implementation to be used to compare Resources in the scheduler. Maximum number of applications in the system which can be concurrently active both running and pending.Ĭ.resource-calculator System-managed settings are not included. The Spark settings below are those that have BDC-specific defaults but are user configurable. Big Data Clusters-specific default Spark settings See Configure Apache Spark and Apache Hadoop in Big Data Clusters for instructions. To include Spark in the Storage pool, set the boolean value includeSpark in the bdc.json configuration file at .settings.spark. The settings we do not support configuring are also listed below.

You can find all possible configurations and the defaults for each at the associated Apache documentation site: Other than the gateway resource, there is no difference between settings that are configurable at the service scope and the resource scope. The settings that we do change are listed below along with a description and their default value. Big Data Clusters uses the same default configuration values as the respective open source project for most settings. For more information, see Big data options on the Microsoft SQL Server platform.īig Data Clusters supports deployment time and post-deployment time configuration of Apache Spark and Hadoop components at the service and resource scopes. Support for SQL Server 2019 Big Data Clusters will end on February 28, 2025. The Microsoft SQL Server 2019 Big Data Clusters add-on will be retired.