YARN+Spark an der ETHZ - ETH Zurich | Karteikarten & Zusammenfassungen

# Lernmaterialien für YARN+Spark an der ETHZ - ETH Zurich

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TESTE DEIN WISSEN

With a FAIR scheduler. What happens when everyone tries to access more resources than there are available at the same time?

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The one who needs it the most gets it. So the one whos usage is the most different from its instantaneous fair share.

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How many instances of a ApplicationMaster are in a cluster in YARN?

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Many per cluster, but usually not per every node

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What is instantaneous fair share?

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The share the remaining users should get if a user (or multiple) are not using the cluster. This should be proportional to their steady fair shares and renormalized to 100%.

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List 5 main shortcomings of MapReduce v1, which are addressed by YARN design.

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1. Scalability issues: MapReduce has limited scalability, while YARN can scale to 10,000 nodes.
2. Rigidity issues: MapReduce v1 only supports MapReduce specific jobs. There is a need, however, for scheduling non-MapReduce workloads. For instance, we would like the ability to share cluster with MPI, graph processing, and any user code.
3. Resource utilization isues: in MapReduce v1, the reducers wait on the mappers to finish (and vice-versa), leaving large fractions of time when either the reducers or the mappers are idle. Ideally all resources should be used at any given time.
4. Flexibility issues: mapper and reducer roles are decided at configuration time, and cannot be reconfigured.
5. Ability to maintain MapReduce frameworks of different versions.
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TESTE DEIN WISSEN

What are Capacity schedulers?

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The CapacityScheduler gives each user certain minimum capacity guarantees. With this strategy cluster resources are allocated over a set of predetermined queues. Each queue gets only a fraction of the cluster resources. As a result, each queue has a minimum guaranteed resource allocation.

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Communications between the ResourceManager and NodeManagers are heartbeat-based.

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True

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Whose responsibility is providing leases to use containers?

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ResourceManager

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Does YARN plan to allow applications to only request resources in terms of memory usage and number of CPUs?

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Yes

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Whose responsibility is asking for resources needed for an application?

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ResourceManager

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Whose responsibility is fault Tolerance of running applications?

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ResourceManager

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Can the container allocation/deallocation take place in a dynamic fashion as the application progresses?

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Yes

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ResourceManager has the ability to request resources back from a running application.

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True

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Q:

With a FAIR scheduler. What happens when everyone tries to access more resources than there are available at the same time?

A:

The one who needs it the most gets it. So the one whos usage is the most different from its instantaneous fair share.

Q:

How many instances of a ApplicationMaster are in a cluster in YARN?

A:

Many per cluster, but usually not per every node

Q:

What is instantaneous fair share?

A:

The share the remaining users should get if a user (or multiple) are not using the cluster. This should be proportional to their steady fair shares and renormalized to 100%.

Q:

List 5 main shortcomings of MapReduce v1, which are addressed by YARN design.

A:
1. Scalability issues: MapReduce has limited scalability, while YARN can scale to 10,000 nodes.
2. Rigidity issues: MapReduce v1 only supports MapReduce specific jobs. There is a need, however, for scheduling non-MapReduce workloads. For instance, we would like the ability to share cluster with MPI, graph processing, and any user code.
3. Resource utilization isues: in MapReduce v1, the reducers wait on the mappers to finish (and vice-versa), leaving large fractions of time when either the reducers or the mappers are idle. Ideally all resources should be used at any given time.
4. Flexibility issues: mapper and reducer roles are decided at configuration time, and cannot be reconfigured.
5. Ability to maintain MapReduce frameworks of different versions.
Q:

What are Capacity schedulers?

A:

The CapacityScheduler gives each user certain minimum capacity guarantees. With this strategy cluster resources are allocated over a set of predetermined queues. Each queue gets only a fraction of the cluster resources. As a result, each queue has a minimum guaranteed resource allocation.

Q:

Communications between the ResourceManager and NodeManagers are heartbeat-based.

A:

True

Q:

Whose responsibility is providing leases to use containers?

A:

ResourceManager

Q:

Does YARN plan to allow applications to only request resources in terms of memory usage and number of CPUs?

A:

Yes

Q:

Whose responsibility is asking for resources needed for an application?

A:

ResourceManager

Q:

Whose responsibility is fault Tolerance of running applications?

A:

ResourceManager

Q:

Can the container allocation/deallocation take place in a dynamic fashion as the application progresses?

A:

Yes

Q:

ResourceManager has the ability to request resources back from a running application.

A:

True

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