Magpie contains a number of scripts for running Big Data software in HPC environments, including Hadoop and Spark. There is support for Lustre, Slurm, Moab, Torque. LSF, and more.
Magpie contains a number of scripts for running Big Data software in HPC environments. Thus far, Hadoop, Spark, Hbase, Storm, Pig, Mahout, Phoenix, Kafka, Zeppelin, Zookeeper, and Alluxio are supported. It currently supports running over the parallel file system Lustre and running over any generic network filesytem. There is scheduler/resource manager support for Slurm, Moab, Torque, and LSF.
Some of the features presently supported:
Experimental support for several distributed machine learning frameworks has also been added. Presently tensorflow, tensorflow w/ horovod, and ray is supported.
The basic idea behind these scripts are to:
1) Submit a Magpie batch script to allocate nodes on a cluster using your HPC scheduler/resource manager. Slurm, Slurm+mpirun, Moab+Slurm, Moab+Torque and LSF+mpirun are currently supported.
2) The batch script will create configuration files for all appropriate projects (Hadoop, Spark, etc.) The configuration files will be setup so the rank 0 node is the "master". All compute nodes will have configuration files created that point to the node designated as the master server.
The configuration files will be populated with values for your filesystem choice and the hardware that exists in your cluster. Reasonable attempts are made to determine optimal values for your system and hardware (they are almost certainly better than the default values). A number of options exist in the batch scripts to adjust these values for individual jobs.
3) Launch daemons on all nodes. The rank 0 node will run master daemons, such as the Hadoop Namenode. All remaining nodes will run appropriate worker daemons, such as the Hadoop Datanodes.
4) Now you have a mini big data cluster to do whatever you want. You can log into the master node and interact with your mini big data cluster however you want. Or you could have Magpie run a script to execute your big data calculation instead.
5) When your job completes or your allocation time has run out, Magpie will cleanup your job by tearing down daemons. When appropriate, Magpie may also do some additional cleanup work to hopefully make re-execution on later runs cleaner and faster.
For a complete list of supported package versions and dependencies, please see
doc/README. The following can be considered a summary of support.
Hadoop - 2.2.0, 2.3.0, 2.4.X, 2.5.X, 2.6.X, 2.7.X, 2.8.X, 2.9.X, 3.0.X, 3.1.X, 3.2.X, 3.3.X
Spark - 0.9.X, 1.1.X, 1.2.X, 1.3.X, 1.4.X, 1.5.X, 1.6.X, 2.0.X, 2.1.X, 2.2.X, 2.3.X, 2.4.X, 3.0.X
Hbase - 0.98.X, 0.99.X, 1.0.X, 1.1.X, 1.2.X, 1.3.X, 1.4.X, 1.5.X, 1.6.X
Hive - 2.3.0
Pig - 0.13.0, 0.14.0, 0.15.0, 0.16.0, 0.17.0
Zookeeper - 3.4.X
Storm - 0.9.X, 0.10.X, 1.0.X, 1.1.X, 1.2.X
Mahout - 0.11.X, 0.12.X, 0.13.0
Phoenix - 4.5.X, 4.6.0, 4.7.0, 4.8.X, 4.9.0, 4.10.1, 4.11.0, 4.12.0, 4.13.X, 4.14.0
Kafka - 2.11-0.9.0.0
Zeppelin - 0.6.X, 0.7.X, 0.8.X
Alluxio - 2.3.0
TensorFlow - 1.9, 1.12
Ray - 0.7.0
Some packages and features were dropped in Magpie 2.0 due to lack of interest, the software becoming old/deprecated, and/or their initial experimental addition into Magpie. If you are interested in them, please look at Magpie 1.X versions for supported versions and documentation. If you are very interested in support in Magpie 2.0 beyond an experimental nature, please submit a support request and we can reconsider adding it back in.
All documentation is in the 'doc' subdirectory. Please see the doc/README file as a starting point. It provides general instructions as well as pointers to documentation for each project, setup requirements, ability to do local configurations, tips & tricks, and more information.
Magpie is release under a GPL license. For more information, see the COPYING file.