Automatic Scaling of Task Nodes in an MRS Cluster

In big data application scenarios, especially real-time data analysis and processing, the number of cluster nodes needs to be dynamically adjusted according to data volume changes to ensure that the required resources are provided.

MRS supports auto scaling of Task nodes based on cluster loads. If the data volume changes periodically, you can configure an auto scaling rule so that the number of task nodes can be automatically adjusted in a fixed period of time before the data volume changes.

Load-based auto scaling of Task nodes: The number of Task nodes is adjusted based on the real-time cluster load metrics or cluster resource pool metrics. When the data volume changes, scaling is triggered with a delay. For details about the metrics that can be configured, see Node Auto Scaling Metrics.
Time-based Task node auto scaling: The number of Task nodes is adjusted based on the time ranges. If the data volume changes periodically, you can specify a resource plan to scale in or out the cluster before the data volume changes so as to avoid scaling delay.
Automation script: In some scaling scenarios, you may need to manually reallocate resources or modify service logic based on the number of nodes after cluster scaling. MRS allows you to customize automation scripts for auto scaling to solve this problem. Automation scripts can be executed to adjust clusters to service load changes, freeing you from manual operations. In addition, the automation scripts can be fully customized according to various requirements, making the auto scaling more flexible. For details about how to configure automation scripts, see Configuring Bootstrap Actions for an MRS Cluster Node.

You can configure load-based or time-based auto scaling of Task nodes. You can also configure both of them, which is recommended in case of unexpected data peaks.

Node Auto Scaling Metrics

Node group dimension policy

When adding a rule, you can refer to Table 1 to configure the corresponding metrics.

When the value type is percentage or ratio in Table 1, the valid value can be accurate to percentile. The percentage-type metric values omit the percent sign (%). For example, 16.80 indicates 16.80%.
Hybrid clusters support all metrics of analytics clusters and streaming clusters.

**Table 1** Auto scaling metrics
Cluster Type	Metric	Value Type	Description
Streaming cluster	StormSlotAvailable	Integer	Number of available Storm slots Value range: 0 to 2147483646
	StormSlotAvailablePercentage	Percentage	Percentage of available Storm slots, that is, the proportion of the available slots Value range: 0 to 100
	StormSlotUsed	Integer	Number of the used Storm slots Value range: 0 to 2147483646
	StormSlotUsedPercentage	Percentage	Percentage of the used Storm slots, that is, the proportion of the used slots Value range: 0 to 100
	StormSupervisorMemAverageUsage	Integer	Average memory usage of the Supervisor process of Storm Value range: 0 to 2147483646
	StormSupervisorMemAverageUsagePercentage	Percentage	Average percentage of the memory used by the Supervisor process of Storm to the total memory of the system Value range: 0 to 100
	StormSupervisorCPUAverageUsagePercentage	Percentage	Average percentage of the CPUs used by the Supervisor process of Storm to the total CPUs Value range: 0 to 6000
Analysis cluster	YARNAppPending	Integer	Number of pending tasks on YARN Value range: 0 to 2147483646
	YARNAppPendingRatio	Ratio	Ratio of pending tasks on YARN, that is, the ratio of pending tasks to running tasks on YARN Value range: 0 to 2147483646
	YARNAppRunning	Integer	Number of running tasks on YARN Value range: 0 to 2147483646
	YARNContainerAllocated	Integer	Number of containers allocated to YARN Value range: 0 to 2147483646
	YARNContainerPending	Integer	Number of pending containers on YARN Value range: 0 to 2147483646
	YARNContainerPendingRatio	Ratio	Ratio of pending containers on YARN, that is, the ratio of pending containers to running containers on YARN. Value range: 0 to 2147483646
	YARNCPUAllocated	Integer	Number of virtual CPUs (vCPUs) allocated to YARN Value range: 0 to 2147483646
	YARNCPUAvailable	Integer	Number of available vCPUs on YARN Value range: 0 to 2147483646
	YARNCPUAvailablePercentage	Percentage	Percentage of available vCPUs on YARN, that is, the proportion of available vCPUs to total vCPUs Value range: 0 to 100
	YARNCPUPending	Integer	Number of pending vCPUs on YARN Value range: 0 to 2147483646
	YARNMemoryAllocated	Integer	Memory allocated to YARN. The unit is MB. Value range: 0 to 2147483646
	YARNMemoryAvailable	Integer	Available memory on YARN. The unit is MB. Value range: 0 to 2147483646
	YARNMemoryAvailablePercentage	Percentage	Percentage of available memory on YARN, that is, the proportion of available memory to total memory on YARN Value range: 0 to 100
	YARNMemoryPending	Integer	Pending memory on YARN Value range: 0 to 2147483646

Resource pool policy

Only the following versions support auto scaling by resource pool:

Common MRS: MRS 3.1.5 or later
LTS MRS: MRS 3.3.0-LTS or later

When adding a rule, you can refer to Table 2 to configure the corresponding metrics.

**Table 2** Rule configuration description
Cluster Type	Metric	Value Type	Description
Analysis/Custom cluster	ResourcePoolMemoryAvailable	Integer	Available memory on YARN in the resource pool. The unit is MB. Value range: 0 to 2147483646
	ResourcePoolMemoryAvailablePercentage	Percentage	Percentage of available memory on YARN in the resource pool, that is, the proportion of available memory to total memory on YARN Value range: 0 to 100
	ResourcePoolCPUAvailable	Integer	Number of available vCPUs on YARN in the resource pool Value range: 0 to 2147483646
	ResourcePoolCPUAvailablePercentage	Percentage	Percentage of available vCPUs on YARN in the resource pool. that is, the proportion of available vCPUs to total vCPUs Value range: 0 to 100

When adding a resource plan, you can set parameters by referring to Table 3.

**Table 3** Configuration items of a resource plan
Configuration Item	Example Value	Description
Effective On	Monday	The effective date of a resource plan. Daily is selected by default. You can also select one or multiple days from Monday to Sunday.
Time Range	08:00-10:00	Start time and End time of a resource plan are accurate to minutes, with the value ranging from 00:00 to 23:59. For example, if a resource plan starts at 8:00 and ends at 10:00, set this parameter to 8:00-10:00. The end time must be at least 30 minutes later than the start time.
Node Range	4-5	The number of nodes in a resource plan ranges from 0 to 500. In the time range specified in the resource plan, if the number of Task nodes is less than the specified minimum number of nodes, it will be increased to the specified minimum value of the node range at a time. If the number of Task nodes is greater than the maximum number of nodes specified in the resource plan, the auto scaling function reduces the number of Task nodes to the maximum value of the node range at a time. The minimum number of nodes must be less than or equal to the maximum number of nodes.

When a resource plan is enabled, the Default Range value on the auto scaling page forcibly takes effect beyond the time range specified in the resource plan. For example, if Default Range is set to 1-2, Time Range is between 08:00-10:00, and Node Range is 4-5 in a resource plan, the number of Task nodes in other periods (0:00-8:00 and 10:00-23:59) of a day is forcibly limited to the default node range (1 to 2). If the number of nodes is greater than 2, auto scale-in is triggered; if the number of nodes is less than 1, auto scale-out is triggered.
When a resource plan is not enabled, the Default Range takes effect in all time ranges. If the number of nodes is not within the default node range, the number of Task nodes is automatically increased or decreased to the default node range.
Time ranges of resource plans cannot be overlapped. The overlapped time range indicates that two effective resource plans exist at a time point. For example, if resource plan 1 takes effect from 08:00 to 10:00 and resource plan 2 takes effect from 09:00 to 11:00, the time range between 09:00 to 10:00 is overlapped.
The time range of a resource plan must be on the same day. For example, you cannot configure a resource plan from 23:00 to 01:00 (the next day). Instead, you need to configure two resource plans, one from 23:00 to 00:00 and the other from 00:00 to 01:00.

Automation scripts

When adding an automation script, you can set related parameters by referring to Table 4.

**Table 4** Configuration items of an automation script
Configuration Item	Example Value	Description
Name	test	Automation script name. The value can contain only digits, letters, spaces, hyphens (-), and underscores (_) and cannot start with a space. The value can contain 1 to 64 characters. A name must be unique in the same cluster. You can set the same name for different clusters.
Script Path	obs://mrs-samples/test.sh	Script path. The value can be an OBS file system path or a local VM path. An OBS file system path must start with obs:// and end with .sh, for example, obs://mrs-samples/xxx.sh. A local VM path must start with a slash (/) and end with .sh. For example, the path of the example script for installing the Zepelin is /opt/bootstrap/zepelin/zepelin_install.sh.
Execution Node	Master	Select a type of the node where an automation script is executed. If you select Master nodes, you can choose whether to run the script only on the active Master nodes by enabling or disabling Active Master. If you enable it, the script runs only on the active Master nodes. If you disable it, the script runs on all Master nodes. It is disabled by default.
Parameter	-	Automation script parameter. The following predefined variables can be imported to obtain auto scaling information: ${mrs_scale_node_num}: Number of auto scaling nodes. The value is always positive. ${mrs_scale_type}: Scale-out/in type. The value can be scale_out or scale_in. ${mrs_scale_node_hostnames}: Host names of the auto scaling nodes. Use commas (,) to separate multiple host names. ${mrs_scale_node_ips}: IP address of the auto scaling nodes. Use commas (,) to separate multiple IP addresses. ${mrs_scale_rule_name}: Name of the triggered auto scaling rule. For a resource plan, this parameter is set to resource_plan.
Executed	Before scale-out	Time for executing an automation script. The following four options are supported: Before scale-out, After scale-out, Before scale-in, and After scale-in. Assume that the execution nodes include Task nodes. The automation script executed before scale-out cannot run on the Task nodes to be added. The automation script executed after scale-out can run on the added Task nodes. The automation script executed before scale-in can run on the Task nodes to be deleted. The automation script executed after scale-in cannot run on the deleted Task nodes.
Action upon Failure	Continue	Whether to continue to execute subsequent scripts and scale-out/in after the script fails to be executed. You are advised to set this parameter to Continue in the commissioning phase so that the cluster can continue the scale-out/in operation no matter whether the script is executed successfully. If the script fails to be executed, view the failure logs in the /var/log/Bigdata/Bootstrap directory on the cluster VM. The scale-in operation cannot be rolled back. Therefore, the Action upon Failure can only be set to Continue after scale-in.