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Updating dataproc container cost to be multiplied by number of cores #648

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merged 9 commits into from
Nov 3, 2023
Merged

Updating dataproc container cost to be multiplied by number of cores #648

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5915598
Updating dataproc container cost to be multiplied by number of cores
mattahrens Nov 2, 2023
7dcd26c
Simplifying changes for only Dataproc and not Dataproc GKE
mattahrens Nov 2, 2023
a9ceef6
Fixing bug with extraneous dataproc_cost reference
mattahrens Nov 3, 2023
644a2a3
Fixing pylint with lines too long
mattahrens Nov 3, 2023
3697e16
Fixing flake issue with indentation
mattahrens Nov 3, 2023
34bb615
Fixing flake issue with indentation
mattahrens Nov 3, 2023
30d8381
Fixing flake issue with whitespace
mattahrens Nov 3, 2023
a7592b4
Fixing Dataproc GKE costs for dataproc container
mattahrens Nov 3, 2023
b4683c6
Fixing Dataproc GKE costs for dataproc container
mattahrens Nov 3, 2023
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6 changes: 3 additions & 3 deletions user_tools/src/spark_rapids_pytools/cloud_api/dataproc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -534,16 +534,16 @@ def _calculate_group_cost(self, cluster_inst: ClusterGetAccessor, node_type: Spa
mem_gb = float(mem_mb) / 1024
cores_cost = self.price_provider.get_cpu_price(node_mc_type) * int(cores_count)
memory_cost = self.price_provider.get_ram_price(node_mc_type) * mem_gb
dataproc_cost = self.price_provider.get_container_cost() * int(cores_count)
# calculate the GPU cost
gpu_per_machine, gpu_type = cluster_inst.get_gpu_per_node(node_type)
gpu_cost = 0.0
if gpu_per_machine > 0:
gpu_unit_price = self.price_provider.get_gpu_price(gpu_type)
gpu_cost = gpu_unit_price * gpu_per_machine
return nodes_cnt * (cores_cost + memory_cost + gpu_cost)
return nodes_cnt * (cores_cost + memory_cost + dataproc_cost + gpu_cost)

def _get_cost_per_cluster(self, cluster: ClusterGetAccessor):
master_cost = self._calculate_group_cost(cluster, SparkNodeType.MASTER)
workers_cost = self._calculate_group_cost(cluster, SparkNodeType.WORKER)
dataproc_cost = self.price_provider.get_container_cost()
return master_cost + workers_cost + dataproc_cost
return master_cost + workers_cost
3 changes: 1 addition & 2 deletions user_tools/src/spark_rapids_pytools/cloud_api/dataproc_gke.py
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Expand Up @@ -196,5 +196,4 @@ class DataprocGkeSavingsEstimator(DataprocSavingsEstimator):
def _get_cost_per_cluster(self, cluster: ClusterGetAccessor):
master_cost = self._calculate_group_cost(cluster, SparkNodeType.MASTER)
workers_cost = self._calculate_group_cost(cluster, SparkNodeType.WORKER)
dataproc_gke_cost = self.price_provider.get_container_cost()
return master_cost + workers_cost + dataproc_gke_cost
return master_cost + workers_cost
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6 changes: 3 additions & 3 deletions user_tools/src/spark_rapids_pytools/cloud_api/onprem.py
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Expand Up @@ -300,18 +300,18 @@ def __calculate_dataproc_group_cost(self, cluster_inst: ClusterGetAccessor, node

cores_cost = self.price_provider.get_cpu_price(node_mc_type) * int(cores_count)
memory_cost = self.price_provider.get_ram_price(node_mc_type) * mem_gb
dataproc_cost = self.price_provider.get_container_cost() * int(cores_count)
# calculate the GPU cost
gpu_per_machine, gpu_type = cluster_inst.get_gpu_per_node(node_type)
gpu_cost = 0.0
if gpu_per_machine > 0:
gpu_unit_price = self.price_provider.get_gpu_price(gpu_type)
gpu_cost = gpu_unit_price * gpu_per_machine
return nodes_cnt * (cores_cost + memory_cost + gpu_cost)
return nodes_cnt * (cores_cost + memory_cost + dataproc_cost + gpu_cost)
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Need to remove dataproc_cost reference here


def _get_cost_per_cluster(self, cluster: ClusterGetAccessor):
if self.price_provider.name.casefold() == 'dataproc':
master_cost = self.__calculate_dataproc_group_cost(cluster, SparkNodeType.MASTER)
workers_cost = self.__calculate_dataproc_group_cost(cluster, SparkNodeType.WORKER)
dataproc_cost = self.price_provider.get_container_cost()
total_cost = master_cost + workers_cost + dataproc_cost
total_cost = master_cost + workers_cost
return total_cost
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