Compute resources in a distributed environment need to be managed so that resource utilization is efficient and every job gets a fair chance to run. Spark comes with its own cluster manager, which is conveniently called standalone mode. Spark also supports working with YARN and Mesos cluster managers.

The cluster manager you choose should be mostly driven by both legacy concerns and whether other frameworks, such as MapReduce, share the same compute resource pool. If your cluster has legacy MapReduce jobs running and all of them cannot be converted into Spark jobs, it is a good idea to use YARN as the cluster manager. Mesos is emerging as a data center operating system to conveniently manage jobs across frameworks, and it is very compatible with Spark.

If the Spark framework is the only framework in your cluster, then the standalone mode is good enough. As Spark is evolving as a technology, you will see more and more use cases of Spark being used as the standalone framework, serving all your big data compute needs. For example, some jobs may use Apache Mahout at present because MLlib does not have a specific machine-learning library, which the job needs. As soon as MLlib gets its library, this particular job can be moved to Spark.