- Locality Awareness
- High Cluster Utilization
- Secure and Auditable Operation
- Support for Programming Model Diversity
- Flexible Resource Model
- Backward compatibility
To address the requirements, YARN lifts some functions into a platform layer responsible for resource management, leaving coordination of logical execution plans to a host of framework implementations. Specifically, a per-cluster ResourceManager (RM) tracks resource usage and node liveness, enforces allocation invariants, and arbitrates contention among tenants. By separating these duties in the JobTracker’s charter, the central allocator can use an abstract description of tenants’ requirements, but remain ignorant of the semantics of each allocation. That responsibility is delegated to an ApplicationMaster (AM), which coordinates the logical plan of a single job by requesting resources from the RM, generating a physical plan from the resources it receives, and coordinating the execution of that plan around faults.
The RM runs as a daemon on a dedicated machine, and acts as the central authority arbitrating resources among various competing applications in the cluster. Given this central and global view of the cluster resources, it can enforce rich, familiar properties such as fairness [R10], capacity [R10], and locality [R4] across tenants. Depending on the application demand, scheduling priorities, and resource availability, the RM dynamically allocates leases– called containers– to applications to run on particular nodes. The container is a logical bundle of resources (e.g., ⟨2GB RAM, 1 CPU⟩) bound to a particular node [R4,R9]. In order to enforce and track such assignments, the RM interacts with a special system daemon running on each node called the NodeManager (NM). Communications between RM and NMs are heartbeat-based for scalability. NMs are responsible for monitoring resource availability, reporting faults, and container lifecycle management (e.g., starting, killing). The RM assembles its global view from these snapshots of NM state.
Jobs are submitted to the RM via a public submission protocol and go through an admission control phase during which security credentials are validated and various operational and administrative checks are performed [R7]. Accepted jobs are passed to the scheduler to be run. Once the scheduler has enough resources, the application is moved from accepted to running state. Aside from internal bookkeeping, this involves allocating a container for the AM and spawning it on a node in the cluster. A record of accepted applications is written to persistent storage and recovered in case of RM restart or failure.
The ApplicationMaster is the “head” of a job, managing all lifecycle aspects including dynamically increasing and decreasing resources consumption, managing the flow of execution (e.g., running reducers against the output of maps), handling faults and computation skew, and performing other local optimizations. In fact, the AM can run arbitrary user code, and can be written in any programming language since all communication with the RM and NM is encoded using extensible communication protocols —as an example consider the Dryad port we discuss in Section 4.2. YARN makes few assumptions about the AM, although in practice we expect most jobs will use a higher level programming framework (e.g., MapReduce, Dryad, Tez, REEF, etc.). By delegating all these functions to AMs, YARN’s architecture gains a great deal of scalability [R1], programming model flexibility [R8], and improved upgrading/testing [R3] (since multiple versions of the same framework can coexist).
Typically, an AM will need to harness the resources (cpus, RAM, disks etc.) available on multiple nodes to complete a job. To obtain containers, AM issues resource requests to the RM. The form of these requests includes specification of locality preferences and properties of the containers. The RM will attempt to satisfy the resource requests coming from each application according to availability and scheduling policies. When a resource is allocated on behalf of an AM, the RM generates a lease for the resource, which is pulled by a subsequent AM heartbeat. A token-based security mechanism guarantees its authenticity when the AM presents the container lease to the NM [R4]. Once the ApplicationMaster discovers that a container is available for its use, it encodes an application-specific launch request with the lease. In MapReduce, the code running in the container is either a map task or a reduce task. If needed, running containers may communicate directly with the AM through an application-specific protocol to report status and liveness and receive framework-specific commands– YARN neither facilitates nor enforces this communication. Overall, a YARN deployment provides a basic, yet robust infrastructure for lifecycle management and monitoring of containers, while application-specific semantics are managed by each framework [R3,R8].
This concludes the architectural overview of YARN.
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