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

To run the hdp-configuration-utils.py script, execute the following command from the folder containing the script hdp-configuration-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:

python hdp-configuration-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 a 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, 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 = Reserved for stack memory + Reserved for HBase Memory (If HBase is on the same node).

Use the following table to determine the Reserved Memory per node.

The next calculation is to determine the maximum number of containers allowed per node. The following formula can be used:

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

Where DISKS is the value for dfs.data.dirs (number of data disks) per machine.

And MIN_CONTAINER_SIZE is the minimum container size (in RAM). This value is dependent on the amount of RAM available -- in smaller memory nodes, the minimum container size should also be smaller. The following table outlines the recommended values:

The final calculation is to determine the amount of RAM per container:

RAM-per-container = max(MIN_CONTAINER_SIZE, (Total Available RAM) / containers))

With these calculations, the YARN and MapReduce configurations can be set.

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

Examples

Cluster nodes have 12 CPU cores, 48 GB RAM, and 12 disks.

Reserved Memory = 6 GB reserved for system memory + (if HBase) 8 GB for HBase Min container size = 2 GB

If there is no HBase:

# 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: