Command Line Installation
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Determining HDP Memory Configuration Settings

You can use either of two methods determine YARN and MapReduce memory configuration settings:

The HDP utility script is the recommended method for calculating HDP memory configuration settings, but information about manually calculating YARN and MapReduce memory configuration settings is also provided for reference.

Running the YARN Utility Script

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:

Table 1.5. yarn-utils.py Options

Option

Description

-c CORES

The number of cores on each host

-m MEMORY

The amount of memory on each host, in gigabytes

-d DISKS

The number of disks on each host

-k HBASE

"True" if HBase is installed; "False" if not


[Note]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-2.6.1.0.$BUILD 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

Calculating YARN and MapReduce Memory Requirements

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:

  • 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 = 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:

Table 1.6. 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

24 GB

2 GB

4 GB

2 GB

4 GB

48 GB

6 GB

8 GB

64 GB

8 GB

8 GB

72 GB

8 GB

8 GB

96 GB

128 GB

12 GB

24 GB

16 GB

24 GB

256 GB

32 GB

32 GB

512 GB

64 GB

64 GB


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:

Table 1.7. Recommended Container Size 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


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.

Table 1.8. YARN and MapReduce Configuration Values

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

mapred-site.xml

yarn.app.mapreduce.am.resource.mb

= 2 * RAM-per-container

mapred-site.xml

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

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

Table 1.9. Example Value Calculations Without HBase

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

Table 1.10. Example Value Calculations with HBase

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


Notes:

  • Updating values for yarn.scheduler.minimum-allocation-mb without also changing yarn.nodemanager.resource.memory-mb, or changing yarn.nodemanager.resource.memory-mb without also changing yarn.scheduler.minimum-allocation-mb changes the number of containers per node.

  • If your installation has a large amount of RAM but not many disks or cores, you can free RAM for other tasks by lowering both yarn.scheduler.minimum-allocation-mb and yarn.nodemanager.resource.memory-mb.

  • With MapReduce on YARN, there are no longer preconfigured static slots for Map and Reduce tasks.

    The entire cluster is available for dynamic resource allocation of Map and Reduce tasks as needed by each job. In the previous example cluster, with the previous configurations, YARN is able to allocate up to 10 Mappers (40/4) or 5 Reducers (40/8) on each node (or some other combination of Mappers and Reducers within the 40 GB per node limit).