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.
|Folders to add to MATLAB search path of workers, specified as a character vector, string or string array, or cell array of character vectors
|Files and folders that are sent to workers during a
mapreduce call, specified as a character vector, string or string array, or cell array of character vectors
|Specifies whether automatically attach files
|Specifies path to MATLAB for workers to use
|Application configuration file to be given to Hadoop
|Installation location of Hadoop on the local machine
|Map of name-value property pairs to be given to Hadoop
|License number to use with online licensing
|Specify whether cluster uses online licensing
|Installation location of Spark on the local machine
|Map of name-value property pairs to be given to Spark
When you offload computations to workers, any files that the client needs for computations
must also be available on workers. By default, the client attempts to detect and attach
these files. To turn off automatic detection, set the
false. If the software cannot find all the files, or if
sending files from client to worker is slow, use one of these options.
If the files are in a folder that is not accessible on the workers, set the
AttachedFilesproperty. The cluster copies each file you specify from the client to the workers.
If the files are in a folder that is accessible on the workers, you can set the
AdditionalPathsproperty instead. Use the
AdditionalPathsproperty to add paths to the MATLAB® search path for each worker and avoid copying files unnecessarily from the client to the workers.
HadoopProperties allows you to override configuration properties for Hadoop. See the list of properties in the Hadoop® documentation.
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:
Specify Memory Properties
Spark enabled Hadoop clusters place limits on how much memory is available. You must adjust these limits to support your workflow.
Size of Data to Gather
The amount of data gathered to the client is limited by the Spark properties:
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);
Specify Working Memory Size for a MATLAB Worker
The amount of working memory for a MATLAB Worker is limited by the Spark property:
By default, this is set to 2.5 GB. You typically need to increase this if you use
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);
Introduced in R2014b