# Understanding Amazon EMR node allocation strategy and scenarios
This section gives an overview of node allocation strategy and common scaling scenarios that you can use with Amazon EMR managed scaling.
## Node allocation strategy
Amazon EMR managed scaling allocates core and task nodes based on the following scale-up and scale-down strategies:
**Scale-up strategy **
+ For Amazon EMR releases 7.2 and higher, managed scaling first adds nodes based on node labels and the application process restriction YARN property.
+ For Amazon EMR releases 7.2 and higher, if you enabled node labels and restricted application processes to `CORE` nodes, Amazon EMR managed scaling scales up core nodes and task nodes if application process demand increases and executor demand increases. Similarly, if you enabled node labels and restricted application processes to `ON_DEMAND` nodes, managed scaling scales up on-demand nodes if application process demand increases and scales up spot nodes if executor demand increases.
+ If node labels aren't enabled, application process placement aren't restricted to any node or market type.
+ By using node labels, managed scaling can scale up and scale down different instance groups and instance fleets in the same resize operation. For example, in a scenario in which `instance_group1` has `ON_DEMAND` node and `instance_group2` has a `SPOT` node, and node labels are enabled and application processes are restricted to nodes with the `ON_DEMAND` label. Managed scaling will scale down `instance_group1` and scale up `instance_group2` if application process demand decreases and executor demand increases.
+ When Amazon EMR experiences a delay in scale-up with the current instance group, clusters that use managed scaling automatically switch to a different task instance group.
+ If the `MaximumCoreCapacityUnits` parameter is set, then Amazon EMR scales core nodes until the core units reach the maximum allowed limit. All the remaining capacity is added to task nodes.
+ If the `MaximumOnDemandCapacityUnits` parameter is set, then Amazon EMR scales the cluster by using the On-Demand Instances until the On-Demand units reach the maximum allowed limit. All the remaining capacity is added using Spot Instances.
+ If both the `MaximumCoreCapacityUnits` and `MaximumOnDemandCapacityUnits` parameters are set, Amazon EMR considers both limits during scaling.
For example, if the `MaximumCoreCapacityUnits` is less than `MaximumOnDemandCapacityUnits`, Amazon EMR first scales core nodes until the core capacity limit is reached. For the remaining capacity, Amazon EMR first uses On-Demand Instances to scale task nodes until the On-Demand limit is reached, and then uses Spot Instances for task nodes.
**Scale-down strategy**
+ Similar to the scale-up strategy, Amazon EMR removes nodes based on node labels. For more information about node labels, see [Understand node types: primary, core, and task nodes](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-master-core-task-nodes.html).
+ If you haven't enabled node labels, managed scaling removes task nodes and then removes core nodes until it achieves the desired scale-down target capacity. Managed scaling never scales down the cluster below the minimum constraints specified in the managed scaling policy.
+ Amazon EMR versions 5.34.0 and higher, and Amazon EMR versions 6.4.0 and higher, support Spark shuffle data awareness, which prevents an instance from scaling down while Managed Scaling is aware of existing shuffle data. For more information on shuffle operations, see the [Spark Programming Guide](https://spark.apache.org/docs/latest/rdd-programming-guide.html#shuffle-operations). Managed Scaling makes best effort to prevent scaling-down nodes with shuffle data from the current and previous stage of any active Spark application, up to a maximum of 30 minutes. This helps minimize unintended shuffle data loss, avoiding the need for job re-attempts and recomputation of intermediate data. However, prevention of shuffle data loss is not guaranteed. For improved Spark shuffle protection, we recommend shuffle awareness on clusters with release label 7.4 or higher. Add the following flags to the cluster configuration to enable improved Spark shuffle protection.
+ If either the `yarn.nodemanager.shuffledata-monitor.interval-ms` flag (default 30000 ms) or the `spark.dynamicAllocation.executorIdleTimeout` (default 60 sec) has been changed from the default values, ensure the condition `spark.dynamicAllocation.executorIdleTimeout > yarn.nodemanager.shuffledata-monitor.interval-ms` remains `true` by updating the necessary flag.
```
[
{
"Classification": "yarn-site",
"Properties": {
"yarn.resourcemanager.decommissioning-nodes-watcher.wait-for-shuffle-data": "true"
}
},
{
"Classification": "spark-defaults",
"Properties": {
"spark.dynamicAllocation.enabled": "true",
"spark.shuffle.service.removeShuffle": "true"
}
}
]
```
+ Managed scaling first removes task nodes and then removes core nodes until it achieves the desired scale-down target capacity. The cluster never scales below the minimum constraints specified in the managed scaling policy.
+ For clusters that are launched with Amazon EMR 5.x releases 5.34.0 and higher, and 6.x releases 6.4.0 and higher, Amazon EMR Managed Scaling doesn’t scale down nodes that have `ApplicationMaster` for Apache Spark, if there are active stages in the applications running on them. This minimizes job failures and retries, which helps to improve job performance and reduce costs. To confirm which nodes in your cluster are running `ApplicationMaster`, visit the Spark History Server and filter for the driver under the **Executors** tab of your Spark application ID.
+ While the intelligent scaling with EMR Managed Scaling minimizes shuffle data loss for Spark, there can be instances when transient shuffle data might be not be protected during a scale-down. To provide enhanced resiliency of shuffle data during scale-down, we recommend enabling **Graceful Decommissioning for Shuffle Data** in YARN. When **Graceful Decommissioning for Shuffle Data** is enabled in YARN, nodes selected for scale-down that have shuffle data will enter the **Decommissioning** state and continue to serve shuffle files. The YARN ResourceManager waits until nodes report no shuffle files present before removing the nodes from the cluster.
+ Amazon EMR version 6.11.0 and higher support Yarn-based graceful decommissioning for **Hive** shuffle data for both the Tez and MapReduce Shuffle Handlers.
+ Enable Graceful Decommissioning for Shuffle Data by setting `yarn.resourcemanager.decommissioning-nodes-watcher.wait-for-shuffle-data` to `true`.
+ Amazon EMR version 7.4.0 and higher support Yarn-based graceful decommissioning for Spark shuffle data when the external shuffle service is enabled (enabled by default in EMR on EC2).
+ The default behavior of the Spark external shuffle service, when running Spark on Yarn, is for the Yarn NodeManager to remove application shuffle files at time of application termination. This may have an impact on the speed of node decommissioning and compute utilization. For long running applications, consider setting `spark.shuffle.service.removeShuffle` to `true` to remove shuffle files no longer in use to enable faster decommissioning of nodes with no active shuffle data.
+ To minimize Spark shuffle data loss in Amazon EMR version 7.4.0 and higher, consider setting the following flags.
+ If either the `yarn.nodemanager.shuffledata-monitor.interval-ms` flag (default 30000 ms) or the `spark.dynamicAllocation.executorIdleTimeout` (default 60 sec) has been changed from the default values, ensure that the condition `spark.dynamicAllocation.executorIdleTimeout > yarn.nodemanager.shuffledata-monitor.interval-ms` remains `true` by updating the necessary flag.
```
[
{
"Classification": "yarn-site",
"Properties": {
"yarn.resourcemanager.decommissioning-nodes-watcher.wait-for-shuffle-data": "true"
}
},
{
"Classification": "spark-defaults",
"Properties": {
"spark.dynamicAllocation.enabled": "true",
"spark.shuffle.service.removeShuffle": "true"
}
}
]
```
If the cluster does not have any load, then Amazon EMR cancels the addition of new instances from a previous evaluation and performs scale-down operations. If the cluster has a heavy load, Amazon EMR cancels the removal of instances and performs scale-up operations.
## Node allocation considerations
We recommend that you use the On-Demand purchasing option for core nodes to avoid HDFS data loss in case of Spot reclamation. You can use the Spot purchasing option for task nodes to reduce costs and get faster job execution when more Spot Instances are added to task nodes.
## Node allocation scenarios
You can create various scaling scenarios based on your needs by setting up the Maximum, Minimum, On-Demand limit, and Maximum core node parameters in different combinations.
**Scenario 1: Scale Core Nodes Only**
To scale core nodes only, the managed scaling parameters must meet the following requirements:
+ The On-Demand limit is equal to the maximum boundary.
+ The maximum core node is equal to the maximum boundary.
When the On-Demand limit and the maximum core node parameters are not specified, both parameters default to the maximum boundary.
This scenario isn't applicable if you use managed scaling with node labels and restrict your application processes to only run on `CORE` nodes, because managed scaling scales task nodes to accommodate executor demand.
The following examples demonstrate the scenario of scaling cores nodes only.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/emr/latest/ManagementGuide/managed-scaling-allocation-strategy.html)
**Scenario 2: Scale task nodes only **
To scale task nodes only, the managed scaling parameters must meet the following requirement:
+ The maximum core node must be equal to the minimum boundary.
The following examples demonstrate the scenario of scaling task nodes only.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/emr/latest/ManagementGuide/managed-scaling-allocation-strategy.html)
**Scenario 3: Only On-Demand Instances in the cluster **
To have On-Demand Instances only, your cluster and the managed scaling parameters must meet the following requirement:
+ The On-Demand limit is equal to the maximum boundary.
When the On-Demand limit is not specified, the parameter value defaults to the maximum boundary. The default value indicates that Amazon EMR scales On-Demand Instances only.
If the maximum core node is less than the maximum boundary, the maximum core node parameter can be used to split capacity allocation between core and task nodes.
To enable this scenario in a cluster composed of instance groups, all node groups in the cluster must use the On-Demand market type during initial configuration.
This scenario is not applicable if you use managed scaling with node labels and restrict your application processes to only run on `ON_DEMAND` nodes, because managed scaling scales `Spot` nodes to accommodate executor demand.
The following examples demonstrate the scenario of having On-Demand Instances in the entire cluster.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/emr/latest/ManagementGuide/managed-scaling-allocation-strategy.html)
**Scenario 4: Only Spot Instances in the cluster**
To have Spot Instances only, the managed scaling parameters must meet the following requirement:
+ On-Demand limit is set to 0.
If the maximum core node is less than the maximum boundary, the maximum core node parameter can be used to split capacity allocation between core and task nodes.
To enable this scenario in a cluster composed of instance groups, the core instance group must use the Spot purchasing option during initial configuration. If there is no Spot Instance in the task instance group, Amazon EMR managed scaling creates a task group using Spot Instances when needed.
This scenario isn't applicable if you use managed scaling with node labels and restrict your application processes to only run on `ON_DEMAND` nodes, because managed scaling scales `ON_DEMAND` nodes to accommodate application process demand.
The following examples demonstrate the scenario of having Spot Instances in the entire cluster.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/emr/latest/ManagementGuide/managed-scaling-allocation-strategy.html)
**Scenario 5: Scale On-Demand Instances on core nodes and Spot Instances on task nodes **
To scale On-Demand Instances on core nodes and Spot Instances on task nodes, the managed scaling parameters must meet the following requirements:
+ The On-Demand limit must be equal to the maximum core node.
+ Both the On-Demand limit and the maximum core node must be less than the maximum boundary.
To enable this scenario in a cluster composed of instance groups, the core node group must use the On-Demand purchasing option.
This scenario isn't applicable if you use managed scaling with node labels and restrict your application processes to only run on `ON_DEMAND` nodes or `CORE` nodes.
The following examples demonstrate the scenario of scaling On-Demand Instances on core nodes and Spot Instances on task nodes.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/emr/latest/ManagementGuide/managed-scaling-allocation-strategy.html)
**Scenario 6: Scale `CORE` instances for application process demand and `TASK` instances for executor demand.**
This scenario is only applicable if you use managed scaling with node labels and restrict application processes to only run on `CORE` nodes.
To scale `CORE` nodes based on application process demand and `TASK` nodes based on executor demand, you must set the following configurations at cluster launch:
+ `yarn.node-labels.enabled:true`
+ `yarn.node-labels.am.default-node-label-expression: 'CORE'`
If you don't specify the `ON_DEMAND` limit and the maximum `CORE` node parameters, both parameters default to the maximum boundary.
If the maximum `ON_DEMAND` node is less than the maximum boundary, managed scaling uses the maximum `ON_DEMAND` node parameter to split capacity allocation between `ON_DEMAND` and `SPOT` nodes. If you set the the maximum `CORE` node parameter to less than or equal to the minimum capacity parameter, `CORE` nodes remain static at the maximum core capacity.
The following examples demonstrate the scenario of scaling CORE instances based on application process demand and TASK instances based on executor demand.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/emr/latest/ManagementGuide/managed-scaling-allocation-strategy.html)
**Scenario 7: Scale `ON_DEMAND` instances for application process demand and `SPOT` instances for executor demand.**
This scenario is only applicable if you use managed scaling with node labels and restrict application processes to only run on `ON_DEMAND` nodes.
To scale `ON_DEMAND` nodes based on application process demand and `SPOT` nodes based on executor demand, you must set the following configurations at cluster launch:
+ `yarn.node-labels.enabled:true`
+ `yarn.node-labels.am.default-node-label-expression: 'ON_DEMAND'`
If you don't specify the `ON_DEMAND` limit and the maximum `CORE` node parameters, both parameters default to the maximum boundary.
If the maximum `CORE` node is less than the maximum boundary, managed scaling uses the maximum `CORE` node parameter to split capacity allocation between `CORE` and `TASK` nodes. If you set the the maximum `CORE` node parameter to less than or equal to the minimum capacity parameter, `CORE` nodes remain static at the maximum core capacity.
The following examples demonstrate the scenario of scaling On-Demand Instances based on application process demand and Spot instances based on executor demand.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/emr/latest/ManagementGuide/managed-scaling-allocation-strategy.html)