Hadoop cluster for mapreducer, mapreduce and tall arrays
A parallel.cluster.Hadoop object provides access to a cluster for configuring mapreducer, mapreduce, and tall arrays.
A parallel.cluster.Hadoop object has the following properties.
| Property | Description |
|---|---|
AdditionalPaths | Paths to be added to MATLAB command search path on workers |
AttachedFiles | Files transferred to the workers during a mapreduce call |
AutoAttachFiles | Specifies whether automatically attach files |
ClusterMatlabRoot | Specifies path to MATLAB for workers to use |
HadoopConfigurationFile | Application configuration file to be given to Hadoop |
HadoopInstallFolder | Installation location of Hadoop on the local machine |
HadoopProperties | Map of name-value property pairs to be given to Hadoop |
LicenseNumber | License number to use with MathWorks hosted licensing |
RequiresMathWorksHostedLicensing | Specify whether cluster uses MathWorks hosted licensing |
SparkInstallFolder | Installation location of Spark on the local machine |
SparkProperties | Map of name-value property pairs to be given to Spark |
HadoopProperties allows you to override
configuration properties for Hadoop. See the list of properties in
the Hadoop® documentation.
The SparkInstallFolder is by default set
to the SPARK_HOME environment variable. This
is required for tall array evaluation on Hadoop (but not for mapreduce).
For a correctly configured cluster, you only need to set the installation
folder.
SparkProperties allows you to override
configuration properties for Spark. See the list of properties in
the Spark® documentation.
For further help, type:
help parallel.cluster.Hadoop
Spark enabled Hadoop clusters place limits on how much memory is available. You must adjust these limits to support your workflow.
The amount of data gathered to the client is limited by the Spark properties:
spark.driver.memory
spark.executor.memory
The amount of data to gather from a single Spark task must fit in these properties. A single Spark task processes one block of data from HDFS, which is 128 MB of data by default. If you gather a tall array containing most of the original data, you must ensure these properties are set to fit.
If these properties are set too small, you see an error like the following.
Error using tall/gather (line 50) Out of memory; unable to gather a partition of size 300m from Spark. Adjust the values of the Spark properties spark.driver.memory and spark.executor.memory to fit this partition.
Adjust the properties either in the default settings of the cluster or
directly in MATLAB. To adjust the properties in MATLAB, add name-value pairs to
the SparkProperties property of the cluster. For
example:
cluster = parallel.cluster.Hadoop; cluster.SparkProperties('spark.driver.memory') = '2048m'; cluster.SparkProperties('spark.executor.memory') = '2048m'; mapreducer(cluster);
The amount of working memory for a MATLAB Worker is limited by the Spark property:
spark.yarn.executor.memoryOverhead
By default, this is set to 2.5 GB. You typically need to increase this if you
use arrayfun, cellfun, or custom
datastores to generate large amounts of data in one go. It is advisable to
increase this if you come across lost or crashed Spark Executor
processes.
You can adjust these properties either in the default settings of the cluster or directly in MATLAB. To adjust the properties in MATLAB, add name-value pairs to the SparkProperties property of the cluster. For example:
cluster = parallel.cluster.Hadoop; cluster.SparkProperties('spark.yarn.executor.memoryOverhead') = '4096m'; mapreducer(cluster);