Memory Configuration In Hadoop

In this post, there are some recommendations on how to 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 (such as MapReduce) running in the cluster. 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 (memory, cpu etc.).

In a Hadoop cluster, it is vital to balance the usage 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:

• RAM (Amount of memory)
• CORES (Number of CPU cores)
• DISKS (Number of disks)

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.

Reserved Memory Recommendations

Total Memory per Node	Recommended Reserved System Memory	Recommended Reserved HBase Memory
4 GB	1 GB	1 GB
8 GB	2 GB	1 GB
16 GB	2 GB	2 GB
24 GB	4 GB	4 GB
48 GB	6 GB	8 GB
64 GB	8 GB	8 GB
72 GB	8 GB	8 GB
96 GB	12 GB	16 GB
128 GB	24 GB	24 GB
256 GB	32 GB	32 GB
512 GB	64 GB	64 GB

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 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:

Total RAM per Node	Recommended Minimum Container Size
Less than 4 GB	256 MB
Between 4 GB and 8 GB	512 MB
Between 8 GB and 24 GB	1024 MB
Above 24 GB	2048 MB

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:

Configuration File
Configuration Setting
Value Calculation
yarn-site.xml
yarn.nodemanager.resource.memory-mb
= containers * RAM-per-container
yarn-site.xml
yarn.scheduler.minimum-allocation-mb
= RAM-per-container
yarn-site.xml
yarn.scheduler.maximum-allocation-mb
= containers * RAM-per-container
mapred-site.xml
mapreduce.map.memory.mb
= RAM-per-container
mapred-site.xml        
mapreduce.reduce.memory.mb
= 2 * RAM-per-container
mapred-site.xml
mapreduce.map.java.opts
= 0.8 * RAM-per-container
mapred-site.xml
mapreduce.reduce.java.opts
= 0.8 * 2 * RAM-per-container
yarn-site.xml (check)
yarn.app.mapreduce.am.resource.mb
= 2 * RAM-per-container
yarn-site.xml (check)
yarn.app.mapreduce.am.command-opts
= 0.8 * 2 * RAM-per-container

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

Configuration	Value Calculation
yarn.nodemanager.resource.memory-mb	= 21 * 2 = 42*1024 MB
yarn.scheduler.minimum-allocation-mb	= 2*1024 MB
yarn.scheduler.maximum-allocation-mb	= 21 * 2 = 42*1024 MB
mapreduce.map.memory.mb	= 2*1024 MB
mapreduce.reduce.memory.mb	= 2 * 2 = 4*1024 MB
mapreduce.map.java.opts	= 0.8 * 2 = 1.6*1024 MB
mapreduce.reduce.java.opts	= 0.8 * 2 * 2 = 3.2*1024 MB
yarn.app.mapreduce.am.resource.mb	= 2 * 2 = 4*1024 MB
yarn.app.mapreduce.am.command-opts	= 0.8 * 2 * 2 = 3.2*1024 MB

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

Configuration	Value Calculation
yarn.nodemanager.resource.memory-mb	= 17 * 2 = 34*1024 MB
yarn.scheduler.minimum-allocation-mb	= 2*1024 MB
yarn.scheduler.maximum-allocation-mb	= 17 * 2 = 34*1024 MB
mapreduce.map.memory.mb	= 2*1024 MB
mapreduce.reduce.memory.mb	= 2 * 2 = 4*1024 MB
mapreduce.map.java.opts	= 0.8 * 2 = 1.6*1024 MB
mapreduce.reduce.java.opts	= 0.8 * 2 * 2 = 3.2*1024 MB
yarn.app.mapreduce.am.resource.mb	= 2 * 2 = 4*1024 MB
yarn.app.mapreduce.am.command-opts	= 0.8 * 2 * 2 = 3.2*1024 MB

Linked from http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.0.6.0/bk_installing_manually_book/content/rpm-chap1-11.html

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