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python/pyspark/resource/requests.py
556 строк
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Cheng Pan
[SPARK-58192][CORE] Support fractional spark.task.cpus
26 июл 2026, 13:59
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26 июл 2026, 13:59
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# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from typing import overload, Optional, Dict, TYPE_CHECKING from pyspark.util import _parse_memory if TYPE_CHECKING: from py4j.java_gateway import JavaObject, JVMView class ExecutorResourceRequest: """ An Executor resource request. This is used in conjunction with the ResourceProfile to programmatically specify the resources needed for an RDD that will be applied at the stage level. This is used to specify what the resource requirements are for an Executor and how Spark can find out specific details about those resources. Not all the parameters are required for every resource type. Resources like GPUs are supported and have same limitations as using the global spark configs spark.executor.resource.gpu.*. The amount, discoveryScript, and vendor parameters for resources are all the same parameters a user would specify through the configs: spark.executor.resource.{resourceName}.{amount, discoveryScript, vendor}. For instance, a user wants to allocate an Executor with GPU resources on YARN. The user has to specify the resource name (gpu), the amount or number of GPUs per Executor, the discovery script would be specified so that when the Executor starts up it can discovery what GPU addresses are available for it to use because YARN doesn't tell Spark that, then vendor would not be used because its specific for Kubernetes. See the configuration and cluster specific docs for more details. Use :class:`pyspark.ExecutorResourceRequests` class as a convenience API. .. versionadded:: 3.1.0 Parameters ---------- resourceName : str Name of the resource amount : str Amount requesting discoveryScript : str, optional Optional script used to discover the resources. This is required on some cluster managers that don't tell Spark the addresses of the resources allocated. The script runs on Executors startup to discover the addresses of the resources available. vendor : str, optional Vendor, required for some cluster managers See Also -------- :class:`pyspark.resource.ResourceProfile` Notes ----- This API is evolving. """ def __init__( self, resourceName: str, amount: int, discoveryScript: str = "", vendor: str = "", ): self._name = resourceName self._amount = amount self._discovery_script = discoveryScript self._vendor = vendor @property def resourceName(self) -> str: """ Returns ------- str Name of the resource """ return self._name @property def amount(self) -> int: """ Returns ------- str Amount requesting """ return self._amount @property def discoveryScript(self) -> str: """ Returns ------- str Amount requesting """ return self._discovery_script @property def vendor(self) -> str: """ Returns ------- str Vendor, required for some cluster managers """ return self._vendor class ExecutorResourceRequests: """ A set of Executor resource requests. This is used in conjunction with the :class:`pyspark.resource.ResourceProfileBuilder` to programmatically specify the resources needed for an RDD that will be applied at the stage level. .. versionadded:: 3.1.0 See Also -------- :class:`pyspark.resource.ResourceProfile` Notes ----- This API is evolving. """ _CORES = "cores" _MEMORY = "memory" _OVERHEAD_MEM = "memoryOverhead" _PYSPARK_MEM = "pyspark.memory" _OFFHEAP_MEM = "offHeap" @overload def __init__(self, _jvm: "JVMView"): ... @overload def __init__( self, _jvm: None = ..., _requests: Optional[Dict[str, ExecutorResourceRequest]] = ..., ): ... def __init__( self, _jvm: Optional["JVMView"] = None, _requests: Optional[Dict[str, ExecutorResourceRequest]] = None, ): from pyspark.sql import is_remote jvm = None if not is_remote(): from pyspark.core.context import SparkContext jvm = _jvm or SparkContext._jvm if jvm is not None: self._java_executor_resource_requests = getattr( jvm, "org.apache.spark.resource.ExecutorResourceRequests" )() if _requests is not None: for k, v in _requests.items(): if k == self._MEMORY: self._java_executor_resource_requests.memory(str(v.amount)) elif k == self._OVERHEAD_MEM: self._java_executor_resource_requests.memoryOverhead(str(v.amount)) elif k == self._PYSPARK_MEM: self._java_executor_resource_requests.pysparkMemory(str(v.amount)) elif k == self._CORES: self._java_executor_resource_requests.cores(v.amount) else: self._java_executor_resource_requests.resource( v.resourceName, v.amount, v.discoveryScript, v.vendor ) else: self._java_executor_resource_requests = None self._executor_resources: Dict[str, ExecutorResourceRequest] = {} def memory(self, amount: str) -> "ExecutorResourceRequests": """ Specify heap memory. The value specified will be converted to MiB. This is a convenient API to add :class:`ExecutorResourceRequest` for "memory" resource. Parameters ---------- amount : str Amount of memory. In the same format as JVM memory strings (e.g. 512m, 2g). Default unit is MiB if not specified. Returns ------- :class:`ExecutorResourceRequests` """ if self._java_executor_resource_requests is not None: self._java_executor_resource_requests.memory(amount) else: self._executor_resources[self._MEMORY] = ExecutorResourceRequest( self._MEMORY, _parse_memory(amount) ) return self def memoryOverhead(self, amount: str) -> "ExecutorResourceRequests": """ Specify overhead memory. The value specified will be converted to MiB. This is a convenient API to add :class:`ExecutorResourceRequest` for "memoryOverhead" resource. Parameters ---------- amount : str Amount of memory. In the same format as JVM memory strings (e.g. 512m, 2g). Default unit is MiB if not specified. Returns ------- :class:`ExecutorResourceRequests` """ if self._java_executor_resource_requests is not None: self._java_executor_resource_requests.memoryOverhead(amount) else: self._executor_resources[self._OVERHEAD_MEM] = ExecutorResourceRequest( self._OVERHEAD_MEM, _parse_memory(amount) ) return self def pysparkMemory(self, amount: str) -> "ExecutorResourceRequests": """ Specify pyspark memory. The value specified will be converted to MiB. This is a convenient API to add :class:`ExecutorResourceRequest` for "pyspark.memory" resource. Parameters ---------- amount : str Amount of memory. In the same format as JVM memory strings (e.g. 512m, 2g). Default unit is MiB if not specified. Returns ------- :class:`ExecutorResourceRequests` """ if self._java_executor_resource_requests is not None: self._java_executor_resource_requests.pysparkMemory(amount) else: self._executor_resources[self._PYSPARK_MEM] = ExecutorResourceRequest( self._PYSPARK_MEM, _parse_memory(amount) ) return self def offheapMemory(self, amount: str) -> "ExecutorResourceRequests": """ Specify off heap memory. The value specified will be converted to MiB. This value only take effect when MEMORY_OFFHEAP_ENABLED is true. This is a convenient API to add :class:`ExecutorResourceRequest` for "offHeap" resource. Parameters ---------- amount : str Amount of memory. In the same format as JVM memory strings (e.g. 512m, 2g). Default unit is MiB if not specified. Returns ------- :class:`ExecutorResourceRequests` """ if self._java_executor_resource_requests is not None: self._java_executor_resource_requests.offHeapMemory(amount) else: self._executor_resources[self._OFFHEAP_MEM] = ExecutorResourceRequest( self._OFFHEAP_MEM, _parse_memory(amount) ) return self def cores(self, amount: int) -> "ExecutorResourceRequests": """ Specify number of cores per Executor. This is a convenient API to add :class:`ExecutorResourceRequest` for "cores" resource. Parameters ---------- amount : int Number of cores to allocate per Executor. Returns ------- :class:`ExecutorResourceRequests` """ if self._java_executor_resource_requests is not None: self._java_executor_resource_requests.cores(amount) else: self._executor_resources[self._CORES] = ExecutorResourceRequest(self._CORES, amount) return self def resource( self, resourceName: str, amount: int, discoveryScript: str = "", vendor: str = "", ) -> "ExecutorResourceRequests": """ Amount of a particular custom resource(GPU, FPGA, etc) to use. The resource names supported correspond to the regular Spark configs with the prefix removed. For instance, resources like GPUs are gpu (spark configs `spark.executor.resource.gpu.*`). If you pass in a resource that the cluster manager doesn't support the result is undefined, it may error or may just be ignored. This is a convenient API to add :class:`ExecutorResourceRequest` for custom resources. Parameters ---------- resourceName : str Name of the resource. amount : str amount of that resource per executor to use. discoveryScript : str, optional Optional script used to discover the resources. This is required on some cluster managers that don't tell Spark the addresses of the resources allocated. The script runs on Executors startup to of the resources available. vendor : str Optional vendor, required for some cluster managers Returns ------- :class:`ExecutorResourceRequests` """ if self._java_executor_resource_requests is not None: self._java_executor_resource_requests.resource( resourceName, amount, discoveryScript, vendor ) else: self._executor_resources[resourceName] = ExecutorResourceRequest( resourceName, amount, discoveryScript, vendor ) return self @property def requests(self) -> Dict[str, ExecutorResourceRequest]: """ Returns ------- dict Returns all the resource requests for the executor. """ if self._java_executor_resource_requests is not None: result = {} execRes = self._java_executor_resource_requests.requestsJMap() for k, v in execRes.items(): result[k] = ExecutorResourceRequest( v.resourceName(), v.amount(), v.discoveryScript(), v.vendor() ) return result else: return self._executor_resources class TaskResourceRequest: """ A task resource request. This is used in conjunction with the :class:`pyspark.resource.ResourceProfile` to programmatically specify the resources needed for an RDD that will be applied at the stage level. The amount is specified as a float to allow for saying you want more than 1 task per resource. For custom resources, valid values are less than or equal to 0.5 or whole numbers, since a task's amount must map onto discrete resource addresses. CPUs (resource name "cpus") are a plain quantity drawn from the executor's core pool rather than an addressable resource, so any positive amount is valid, e.g. 1.5. Use :class:`pyspark.resource.TaskResourceRequests` class as a convenience API. Parameters ---------- resourceName : str Name of the resource amount : float Amount requesting as a float to support fractional resource requests. For custom resources, valid values are less than or equal to 0.5 or whole numbers; this essentially lets you configure X number of tasks to run on a single resource, ie amount equals 0.5 translates into 2 tasks per resource address. For cpus, any positive amount is valid, including fractional ones below 1 (e.g. 0.5) or above 1 (e.g. 1.5). .. versionadded:: 3.1.0 See Also -------- :class:`pyspark.resource.ResourceProfile` Notes ----- This API is evolving. """ def __init__(self, resourceName: str, amount: float): self._name = resourceName self._amount = float(amount) @property def resourceName(self) -> str: """ Returns ------- str Name of the resource. """ return self._name @property def amount(self) -> float: """ Returns ------- str Amount requesting as a float to support fractional resource requests. """ return self._amount class TaskResourceRequests: """ A set of task resource requests. This is used in conjunction with the :class:`pyspark.resource.ResourceProfileBuilder` to programmatically specify the resources needed for an RDD that will be applied at the stage level. .. versionadded:: 3.1.0 See Also -------- :class:`pyspark.resource.ResourceProfile` Notes ----- This API is evolving. """ _CPUS = "cpus" @overload def __init__(self, _jvm: "JVMView"): ... @overload def __init__( self, _jvm: None = ..., _requests: Optional[Dict[str, TaskResourceRequest]] = ..., ): ... def __init__( self, _jvm: Optional["JVMView"] = None, _requests: Optional[Dict[str, TaskResourceRequest]] = None, ): from pyspark.sql import is_remote jvm = None if not is_remote(): from pyspark.core.context import SparkContext jvm = _jvm or SparkContext._jvm if jvm is not None: self._java_task_resource_requests: Optional["JavaObject"] = getattr( jvm, "org.apache.spark.resource.TaskResourceRequests" )() if _requests is not None: for k, v in _requests.items(): if k == self._CPUS: # Pass the amount as a float so the fractional part survives; py4j # then selects the Double overload of TaskResourceRequests.cpus. self._java_task_resource_requests.cpus(float(v.amount)) else: self._java_task_resource_requests.resource(v.resourceName, v.amount) else: self._java_task_resource_requests = None self._task_resources: Dict[str, TaskResourceRequest] = {} def cpus(self, amount: float) -> "TaskResourceRequests": """ Specify number of cpus per Task. This is a convenient API to add :class:`TaskResourceRequest` for cpus. .. versionchanged:: 4.3.0 Accepts fractional amounts. Parameters ---------- amount : float Number of cpus to allocate per Task, valid from 1e-9 to 2147483647 after rounding. Fractional values are supported: below 1 (e.g. 0.5) to let multiple tasks share a CPU core, or above 1 (e.g. 1.5). Returns ------- :class:`TaskResourceRequests` """ if self._java_task_resource_requests is not None: self._java_task_resource_requests.cpus(float(amount)) else: self._task_resources[self._CPUS] = TaskResourceRequest(self._CPUS, amount) return self def resource(self, resourceName: str, amount: float) -> "TaskResourceRequests": """ Amount of a particular custom resource(GPU, FPGA, etc) to use. This is a convenient API to add :class:`TaskResourceRequest` for custom resources. Parameters ---------- resourceName : str Name of the resource. amount : float Amount requesting as a float to support fractional resource requests. Valid values are less than or equal to 0.5 or whole numbers. This essentially lets you configure X number of tasks to run on a single resource, ie amount equals 0.5 translates into 2 tasks per resource address. Returns ------- :class:`TaskResourceRequests` """ if self._java_task_resource_requests is not None: self._java_task_resource_requests.resource(resourceName, float(amount)) else: self._task_resources[resourceName] = TaskResourceRequest(resourceName, amount) return self @property def requests(self) -> Dict[str, TaskResourceRequest]: """ Returns ------- dict Returns all the resource requests for the task. """ if self._java_task_resource_requests is not None: result = {} taskRes = self._java_task_resource_requests.requestsJMap() for k, v in taskRes.items(): result[k] = TaskResourceRequest(v.resourceName(), v.amount()) return result else: return self._task_resources