This section describes how to use the yarn-utils-py script to calculate YARN, MapReduce, Hive, and Tez memory allocation settings based on the node hardware specifications. The yarn-utils-py script is included in the HDP companion files. See Download Companion Files.

To run the yarn-utils-py script, execute the following command from the folder containing the script yarn-utils-py options, where options are as follows:

	Note
	Requires python26 to run. You can also use the -h or --help option to display a Help message that describes the options.

Example

Running the following command from the hdp_manual_install_rpm_helper_files-2.5.5.0.1245 directory:

python yarn-utils.py -c 16 -m 64 -d 4 -k True

Returns:

Using cores=16 memory=64GB disks=4 hbase=True
Profile: cores=16 memory=49152MB reserved=16GB usableMem=48GB disks=4 
Num Container=8
Container Ram=6144MB 
Used Ram=48GB
Unused Ram=16GB
yarn.scheduler.minimum-allocation-mb=6144 
yarn.scheduler.maximum-allocation-mb=49152 
yarn.nodemanager.resource.memory-mb=49152 
mapreduce.map.memory.mb=6144 
mapreduce.map.java.opts=-Xmx4096m 
mapreduce.reduce.memory.mb=6144 
mapreduce.reduce.java.opts=-Xmx4096m 
yarn.app.mapreduce.am.resource.mb=6144 
yarn.app.mapreduce.am.command-opts=-Xmx4096m 
mapreduce.task.io.sort.mb=1792 
tez.am.resource.memory.mb=6144 
tez.am.launch.cmd-opts =-Xmx4096m 
hive.tez.container.size=6144 
hive.tez.java.opts=-Xmx4096m

This section describes how to manually configure YARN and MapReduce memory allocation settings based on the node hardware specifications.

YARN takes into account all of the available compute resources on each machine in the cluster. Based on the available resources, YARN negotiates resource requests from applications running in the cluster, such as MapReduce. YARN then provides processing capacity to each application by allocating containers. A container is the basic unit of processing capacity in YARN, and is an encapsulation of resource elements such as memory and CPU.

In an Apache Hadoop cluster, it is vital to balance the use of memory (RAM), processors (CPU cores), and disks so that processing is not constrained by any one of these cluster resources. As a general recommendation, allowing for two containers per disk and per core gives the best balance for cluster utilization.

When determining the appropriate YARN and MapReduce memory configurations for a cluster node, you should start with the available hardware resources. Specifically, note the following values on each node:

The total available RAM for YARN and MapReduce should take into account the Reserved Memory. Reserved memory is the RAM needed by system processes and other Hadoop processes (such as HBase):

reserved memory = stack memory reserve + HBase memory reserve (if HBase is on the same node)

You can use the values in the following table to determine what you need for reserved memory per node:

After you determine the amount of memory you need per node, you must determine the maximum number of containers allowed per node:

# of containers = min (2*CORES, 1.8*DISKS, (total available RAM) / MIN_CONTAINER_SIZE)

DISKS is the value for dfs.datanode.data.dir (number of data disks) per machine.

MIN_CONTAINER_SIZE is the minimum container size (in RAM). This value depends on the amount of RAM available; in smaller memory nodes, the minimum container size should also be smaller.

The following table provides the recommended values:

Finally, you must determine the amount of RAM per container:

RAM per container = max(MIN_CONTAINER_SIZE, (total available RAM, per containers)

Using the results of all the previous calculations, you can configure YARN and MapReduce.

Note: After installation, both yarn-site.xml and mapred-site.xml are located in the /etc/hadoop/conf folder.

Examples

Assume that your cluster nodes have 12 CPU cores, 48 GB RAM, and 12 disks:

Reserved memory = 6 GB system memory reserve + 8 GB for HBase min container size = 2 GB

If there is no HBase, then you can use the following calculation:

# of containers = min (2*12, 1.8* 12, (48-6)/2) = min (24, 21.6, 21) = 21

RAM-per-container = max (2, (48-6)/21) = max (2, 2) = 2

If HBase is included:

# of containers = min (2*12, 1.8* 12, (48-6-8)/2) = min (24, 21.6, 17) = 17

RAM-per-container = max (2, (48-6-8)/17) = max (2, 2) = 2

Notes: