Two methods can be used to 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.
This section describes how to use the hdp-configuration-utils.py Python 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.
To run the hdp-configuration-utils.py script, execute the following command from the folder containing the script
python hdp-configuration-utils.py options where options are as follows:
Table 1.5. hdp-configuration-utils.py Options
Option | Description |
|---|---|
-c CORES | The number of cores on each host. |
-m MEMORY | The amount of memory on each host in GB. |
-d DISKS | The number of disks on each host. |
-k HBASE | "True" if HBase is installed, "False" if not. |
![[Note]](../common/images/admon/note.png) | 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 hive.auto.convert.join.noconditionaltask.size=1342177000
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:
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.
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 |
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:
Table 1.7. 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:
Table 1.8. YARN and MapReduce Configuration Setting Value Calculations
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 | yarn.app.mapreduce.am.resource.mb | = 2 * RAM-per-container |
yarn-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
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
Table 1.9. Example Value Calculations
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
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. causes changes the number of containers per node.
If your installation has a large amount of RAM but not many disks/cores, you can free up 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 pre-configured 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 our example cluster, with the above configurations, YARN will be 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).
