[SPARK-38194][YARN][MESOS][K8S] Make memory overhead factor configurable

### What changes were proposed in this pull request?

Add a new config to set the memory overhead factor for drivers and executors. Currently the memory overhead is hard coded to 10% (except in Kubernetes), and the only way to set it higher is to set it to a specific memory amount.

### Why are the changes needed?

In dynamic environments where different people or use cases need different memory requirements, it would be helpful to set a higher memory overhead factor instead of having to set a higher specific memory overhead value. The kubernetes resource manager already makes this configurable. This makes it configurable across the board.

### Does this PR introduce _any_ user-facing change?

No change to default behavior, just adds a new config users can change.

### How was this patch tested?

New UT to check the memory calculation.

Closes apache#35504 from Kimahriman/yarn-configurable-memory-overhead-factor.

Authored-by: Adam Binford <[email protected]>
Signed-off-by: Thomas Graves <[email protected]>

core/src/main/scala/org/apache/spark/SparkConf.scala

@@ -636,7 +636,9 @@ private[spark] object SparkConf extends Logging {
       DeprecatedConfig("spark.blacklist.killBlacklistedExecutors", "3.1.0",
         "Please use spark.excludeOnFailure.killExcludedExecutors"),
       DeprecatedConfig("spark.yarn.blacklist.executor.launch.blacklisting.enabled", "3.1.0",
-        "Please use spark.yarn.executor.launch.excludeOnFailure.enabled")
+        "Please use spark.yarn.executor.launch.excludeOnFailure.enabled"),
+      DeprecatedConfig("spark.kubernetes.memoryOverheadFactor", "3.3.0",
+        "Please use spark.driver.memoryOverheadFactor and spark.executor.memoryOverheadFactor")
     )
 
     Map(configs.map { cfg => (cfg.key -> cfg) } : _*)

core/src/main/scala/org/apache/spark/internal/config/package.scala

@@ -105,6 +105,22 @@ package object config {
     .bytesConf(ByteUnit.MiB)
     .createOptional
 
+  private[spark] val DRIVER_MEMORY_OVERHEAD_FACTOR =
+    ConfigBuilder("spark.driver.memoryOverheadFactor")
+      .doc("Fraction of driver memory to be allocated as additional non-heap memory per driver " +
+        "process in cluster mode. This is memory that accounts for things like VM overheads, " +
+        "interned strings, other native overheads, etc. This tends to grow with the container " +
+        "size. This value defaults to 0.10 except for Kubernetes non-JVM jobs, which defaults to " +
+        "0.40. This is done as non-JVM tasks need more non-JVM heap space and such tasks " +
+        "commonly fail with \"Memory Overhead Exceeded\" errors. This preempts this error " +
+        "with a higher default. This value is ignored if spark.driver.memoryOverhead is set " +
+        "directly.")
+      .version("3.3.0")
+      .doubleConf
+      .checkValue(factor => factor > 0,
+        "Ensure that memory overhead is a double greater than 0")
+      .createWithDefault(0.1)
+
   private[spark] val DRIVER_LOG_DFS_DIR =
     ConfigBuilder("spark.driver.log.dfsDir").version("3.0.0").stringConf.createOptional
 
@@ -315,6 +331,18 @@ package object config {
     .bytesConf(ByteUnit.MiB)
     .createOptional
 
+  private[spark] val EXECUTOR_MEMORY_OVERHEAD_FACTOR =
+    ConfigBuilder("spark.executor.memoryOverheadFactor")
+      .doc("Fraction of executor memory to be allocated as additional non-heap memory per " +
+        "executor process. This is memory that accounts for things like VM overheads, " +
+        "interned strings, other native overheads, etc. This tends to grow with the container " +
+        "size. This value is ignored if spark.executor.memoryOverhead is set directly.")
+      .version("3.3.0")
+      .doubleConf
+      .checkValue(factor => factor > 0,
+        "Ensure that memory overhead is a double greater than 0")
+      .createWithDefault(0.1)
+
   private[spark] val CORES_MAX = ConfigBuilder("spark.cores.max")
     .doc("When running on a standalone deploy cluster or a Mesos cluster in coarse-grained " +
       "sharing mode, the maximum amount of CPU cores to request for the application from across " +

docs/configuration.md

@@ -183,7 +183,7 @@ of the most common options to set are:
 </tr>
 <tr>
   <td><code>spark.driver.memoryOverhead</code></td>
-  <td>driverMemory * 0.10, with minimum of 384 </td>
+  <td>driverMemory * <code>spark.driver.memoryOverheadFactor</code>, with minimum of 384 </td>
   <td>
     Amount of non-heap memory to be allocated per driver process in cluster mode, in MiB unless
     otherwise specified. This is memory that accounts for things like VM overheads, interned strings,
@@ -198,6 +198,21 @@ of the most common options to set are:
   </td>
   <td>2.3.0</td>
 </tr>
+<tr>
+  <td><code>spark.driver.memoryOverheadFactor</code></td>
+  <td>0.10</td>
+  <td>
+    Fraction of driver memory to be allocated as additional non-heap memory per driver process in cluster mode.
+    This is memory that accounts for things like VM overheads, interned strings,
+    other native overheads, etc. This tends to grow with the container size.
+    This value defaults to 0.10 except for Kubernetes non-JVM jobs, which defaults to
+    0.40. This is done as non-JVM tasks need more non-JVM heap space and such tasks
+    commonly fail with "Memory Overhead Exceeded" errors. This preempts this error
+    with a higher default.
+    This value is ignored if <code>spark.driver.memoryOverhead</code> is set directly.
+  </td>
+  <td>3.3.0</td>
+</tr>
 <tr>
  <td><code>spark.driver.resource.{resourceName}.amount</code></td>
   <td>0</td>
@@ -272,7 +287,7 @@ of the most common options to set are:
 </tr>
 <tr>
  <td><code>spark.executor.memoryOverhead</code></td>
-  <td>executorMemory * 0.10, with minimum of 384 </td>
+  <td>executorMemory * <code>spark.executor.memoryOverheadFactor</code>, with minimum of 384 </td>
   <td>
     Amount of additional memory to be allocated per executor process, in MiB unless otherwise specified.
     This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc.
@@ -287,6 +302,17 @@ of the most common options to set are:
   </td>
   <td>2.3.0</td>
 </tr>
+<tr>
+  <td><code>spark.executor.memoryOverheadFactor</code></td>
+  <td>0.10</td>
+  <td>
+    Fraction of executor memory to be allocated as additional non-heap memory per executor process.
+    This is memory that accounts for things like VM overheads, interned strings,
+    other native overheads, etc. This tends to grow with the container size.
+    This value is ignored if <code>spark.executor.memoryOverhead</code> is set directly.
+  </td>
+  <td>3.3.0</td>
+</tr>
 <tr>
  <td><code>spark.executor.resource.{resourceName}.amount</code></td>
   <td>0</td>

docs/running-on-kubernetes.md

@@ -1137,15 +1137,6 @@ See the [configuration page](configuration.html) for information on Spark config
   </td>
   <td>3.0.0</td>
 </tr>
-<tr>
-  <td><code>spark.kubernetes.memoryOverheadFactor</code></td>
-  <td><code>0.1</code></td>
-  <td>
-    This sets the Memory Overhead Factor that will allocate memory to non-JVM memory, which includes off-heap memory allocations, non-JVM tasks, various systems processes, and <code>tmpfs</code>-based local directories when <code>spark.kubernetes.local.dirs.tmpfs</code> is <code>true</code>. For JVM-based jobs this value will default to 0.10 and 0.40 for non-JVM jobs.
-    This is done as non-JVM tasks need more non-JVM heap space and such tasks commonly fail with "Memory Overhead Exceeded" errors. This preempts this error with a higher default.
-  </td>
-  <td>2.4.0</td>
-</tr>
 <tr>
   <td><code>spark.kubernetes.pyspark.pythonVersion</code></td>
   <td><code>"3"</code></td>

resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/features/BasicDriverFeatureStep.scala

@@ -53,18 +53,23 @@ private[spark] class BasicDriverFeatureStep(conf: KubernetesDriverConf)
 
   // Memory settings
   private val driverMemoryMiB = conf.get(DRIVER_MEMORY)
+  private val memoryOverheadFactor = if (conf.contains(DRIVER_MEMORY_OVERHEAD_FACTOR)) {
+    conf.get(DRIVER_MEMORY_OVERHEAD_FACTOR)
+  } else {
+    conf.get(MEMORY_OVERHEAD_FACTOR)
+  }
 
   // The memory overhead factor to use. If the user has not set it, then use a different
   // value for non-JVM apps. This value is propagated to executors.
   private val overheadFactor =
     if (conf.mainAppResource.isInstanceOf[NonJVMResource]) {
-      if (conf.contains(MEMORY_OVERHEAD_FACTOR)) {
-        conf.get(MEMORY_OVERHEAD_FACTOR)
+      if (conf.contains(MEMORY_OVERHEAD_FACTOR) || conf.contains(DRIVER_MEMORY_OVERHEAD_FACTOR)) {
+        memoryOverheadFactor
       } else {
         NON_JVM_MEMORY_OVERHEAD_FACTOR
       }
     } else {
-      conf.get(MEMORY_OVERHEAD_FACTOR)
+      memoryOverheadFactor
     }
 
   private val memoryOverheadMiB = conf
@@ -164,7 +169,7 @@ private[spark] class BasicDriverFeatureStep(conf: KubernetesDriverConf)
       KUBERNETES_DRIVER_POD_NAME.key -> driverPodName,
       "spark.app.id" -> conf.appId,
       KUBERNETES_DRIVER_SUBMIT_CHECK.key -> "true",
-      MEMORY_OVERHEAD_FACTOR.key -> overheadFactor.toString)
+      DRIVER_MEMORY_OVERHEAD_FACTOR.key -> overheadFactor.toString)
     // try upload local, resolvable files to a hadoop compatible file system
     Seq(JARS, FILES, ARCHIVES, SUBMIT_PYTHON_FILES).foreach { key =>
       val uris = conf.get(key).filter(uri => KubernetesUtils.isLocalAndResolvable(uri))

resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/features/BasicExecutorFeatureStep.scala

@@ -59,11 +59,16 @@ private[spark] class BasicExecutorFeatureStep(
   private val isDefaultProfile = resourceProfile.id == ResourceProfile.DEFAULT_RESOURCE_PROFILE_ID
   private val isPythonApp = kubernetesConf.get(APP_RESOURCE_TYPE) == Some(APP_RESOURCE_TYPE_PYTHON)
   private val disableConfigMap = kubernetesConf.get(KUBERNETES_EXECUTOR_DISABLE_CONFIGMAP)
+  private val memoryOverheadFactor = if (kubernetesConf.contains(EXECUTOR_MEMORY_OVERHEAD_FACTOR)) {
+    kubernetesConf.get(EXECUTOR_MEMORY_OVERHEAD_FACTOR)
+  } else {
+    kubernetesConf.get(MEMORY_OVERHEAD_FACTOR)
+  }
 
   val execResources = ResourceProfile.getResourcesForClusterManager(
     resourceProfile.id,
     resourceProfile.executorResources,
-    kubernetesConf.get(MEMORY_OVERHEAD_FACTOR),
+    memoryOverheadFactor,
     kubernetesConf.sparkConf,
     isPythonApp,
     Map.empty)

resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/features/BasicDriverFeatureStepSuite.scala

@@ -134,7 +134,7 @@ class BasicDriverFeatureStepSuite extends SparkFunSuite {
       KUBERNETES_DRIVER_POD_NAME.key -> "spark-driver-pod",
       "spark.app.id" -> KubernetesTestConf.APP_ID,
       "spark.kubernetes.submitInDriver" -> "true",
-      MEMORY_OVERHEAD_FACTOR.key -> MEMORY_OVERHEAD_FACTOR.defaultValue.get.toString)
+      DRIVER_MEMORY_OVERHEAD_FACTOR.key -> DRIVER_MEMORY_OVERHEAD_FACTOR.defaultValue.get.toString)
     assert(featureStep.getAdditionalPodSystemProperties() === expectedSparkConf)
   }
 
@@ -193,21 +193,21 @@ class BasicDriverFeatureStepSuite extends SparkFunSuite {
   // Memory overhead tests. Tuples are:
   //   test name, main resource, overhead factor, expected factor
   Seq(
-    ("java", JavaMainAppResource(None), None, MEMORY_OVERHEAD_FACTOR.defaultValue.get),
+    ("java", JavaMainAppResource(None), None, DRIVER_MEMORY_OVERHEAD_FACTOR.defaultValue.get),
     ("python default", PythonMainAppResource(null), None, NON_JVM_MEMORY_OVERHEAD_FACTOR),
     ("python w/ override", PythonMainAppResource(null), Some(0.9d), 0.9d),
     ("r default", RMainAppResource(null), None, NON_JVM_MEMORY_OVERHEAD_FACTOR)
   ).foreach { case (name, resource, factor, expectedFactor) =>
     test(s"memory overhead factor: $name") {
       // Choose a driver memory where the default memory overhead is > MEMORY_OVERHEAD_MIN_MIB
       val driverMem =
-        ResourceProfile.MEMORY_OVERHEAD_MIN_MIB / MEMORY_OVERHEAD_FACTOR.defaultValue.get * 2
+        ResourceProfile.MEMORY_OVERHEAD_MIN_MIB / DRIVER_MEMORY_OVERHEAD_FACTOR.defaultValue.get * 2
 
       // main app resource, overhead factor
       val sparkConf = new SparkConf(false)
         .set(CONTAINER_IMAGE, "spark-driver:latest")
         .set(DRIVER_MEMORY.key, s"${driverMem.toInt}m")
-      factor.foreach { value => sparkConf.set(MEMORY_OVERHEAD_FACTOR, value) }
+      factor.foreach { value => sparkConf.set(DRIVER_MEMORY_OVERHEAD_FACTOR, value) }
       val conf = KubernetesTestConf.createDriverConf(
         sparkConf = sparkConf,
         mainAppResource = resource)
@@ -218,10 +218,63 @@ class BasicDriverFeatureStepSuite extends SparkFunSuite {
       assert(mem === s"${expected}Mi")
 
       val systemProperties = step.getAdditionalPodSystemProperties()
-      assert(systemProperties(MEMORY_OVERHEAD_FACTOR.key) === expectedFactor.toString)
+      assert(systemProperties(DRIVER_MEMORY_OVERHEAD_FACTOR.key) === expectedFactor.toString)
     }
   }
 
+  test(s"SPARK-38194: memory overhead factor precendence") {
+    // Choose a driver memory where the default memory overhead is > MEMORY_OVERHEAD_MIN_MIB
+    val driverMem =
+      ResourceProfile.MEMORY_OVERHEAD_MIN_MIB / DRIVER_MEMORY_OVERHEAD_FACTOR.defaultValue.get * 2
+
+    // main app resource, overhead factor
+    val sparkConf = new SparkConf(false)
+      .set(CONTAINER_IMAGE, "spark-driver:latest")
+      .set(DRIVER_MEMORY.key, s"${driverMem.toInt}m")
+
+    // New config should take precedence
+    val expectedFactor = 0.2
+    sparkConf.set(DRIVER_MEMORY_OVERHEAD_FACTOR, expectedFactor)
+    sparkConf.set(MEMORY_OVERHEAD_FACTOR, 0.3)
+
+    val conf = KubernetesTestConf.createDriverConf(
+      sparkConf = sparkConf)
+    val step = new BasicDriverFeatureStep(conf)
+    val pod = step.configurePod(SparkPod.initialPod())
+    val mem = amountAndFormat(pod.container.getResources.getRequests.get("memory"))
+    val expected = (driverMem + driverMem * expectedFactor).toInt
+    assert(mem === s"${expected}Mi")
+
+    val systemProperties = step.getAdditionalPodSystemProperties()
+    assert(systemProperties(DRIVER_MEMORY_OVERHEAD_FACTOR.key) === expectedFactor.toString)
+  }
+
+  test(s"SPARK-38194: old memory factor settings is applied if new one isn't given") {
+    // Choose a driver memory where the default memory overhead is > MEMORY_OVERHEAD_MIN_MIB
+    val driverMem =
+      ResourceProfile.MEMORY_OVERHEAD_MIN_MIB / DRIVER_MEMORY_OVERHEAD_FACTOR.defaultValue.get * 2
+
+    // main app resource, overhead factor
+    val sparkConf = new SparkConf(false)
+      .set(CONTAINER_IMAGE, "spark-driver:latest")
+      .set(DRIVER_MEMORY.key, s"${driverMem.toInt}m")
+
+    // Old config still works if new config isn't given
+    val expectedFactor = 0.3
+    sparkConf.set(MEMORY_OVERHEAD_FACTOR, expectedFactor)
+
+    val conf = KubernetesTestConf.createDriverConf(
+      sparkConf = sparkConf)
+    val step = new BasicDriverFeatureStep(conf)
+    val pod = step.configurePod(SparkPod.initialPod())
+    val mem = amountAndFormat(pod.container.getResources.getRequests.get("memory"))
+    val expected = (driverMem + driverMem * expectedFactor).toInt
+    assert(mem === s"${expected}Mi")
+
+    val systemProperties = step.getAdditionalPodSystemProperties()
+    assert(systemProperties(DRIVER_MEMORY_OVERHEAD_FACTOR.key) === expectedFactor.toString)
+  }
+
   test("SPARK-35493: make spark.blockManager.port be able to be fallen back to in driver pod") {
     val initPod = SparkPod.initialPod()
     val sparkConf = new SparkConf()

resource-managers/kubernetes/core/src/test/scala/org/apache/spark/deploy/k8s/features/BasicExecutorFeatureStepSuite.scala

@@ -441,6 +441,60 @@ class BasicExecutorFeatureStepSuite extends SparkFunSuite with BeforeAndAfter {
     ))
   }
 
+  test(s"SPARK-38194: memory overhead factor precendence") {
+    // Choose an executor memory where the default memory overhead is > MEMORY_OVERHEAD_MIN_MIB
+    val defaultFactor = EXECUTOR_MEMORY_OVERHEAD_FACTOR.defaultValue.get
+    val executorMem = ResourceProfile.MEMORY_OVERHEAD_MIN_MIB / defaultFactor * 2
+
+    // main app resource, overhead factor
+    val sparkConf = new SparkConf(false)
+      .set(CONTAINER_IMAGE, "spark-driver:latest")
+      .set(EXECUTOR_MEMORY.key, s"${executorMem.toInt}m")
+
+    // New config should take precedence
+    val expectedFactor = 0.2
+    sparkConf.set(EXECUTOR_MEMORY_OVERHEAD_FACTOR, expectedFactor)
+    sparkConf.set(MEMORY_OVERHEAD_FACTOR, 0.3)
+
+    val conf = KubernetesTestConf.createExecutorConf(
+      sparkConf = sparkConf)
+    ResourceProfile.clearDefaultProfile()
+    val resourceProfile = ResourceProfile.getOrCreateDefaultProfile(sparkConf)
+    val step = new BasicExecutorFeatureStep(conf, new SecurityManager(baseConf),
+      resourceProfile)
+    val pod = step.configurePod(SparkPod.initialPod())
+    val mem = amountAndFormat(pod.container.getResources.getRequests.get("memory"))
+    val expected = (executorMem + executorMem * expectedFactor).toInt
+    assert(mem === s"${expected}Mi")
+  }
+
+  test(s"SPARK-38194: old memory factor settings is applied if new one isn't given") {
+    // Choose an executor memory where the default memory overhead is > MEMORY_OVERHEAD_MIN_MIB
+    val defaultFactor = EXECUTOR_MEMORY_OVERHEAD_FACTOR.defaultValue.get
+    val executorMem = ResourceProfile.MEMORY_OVERHEAD_MIN_MIB / defaultFactor * 2
+
+    // main app resource, overhead factor
+    val sparkConf = new SparkConf(false)
+      .set(CONTAINER_IMAGE, "spark-driver:latest")
+      .set(EXECUTOR_MEMORY.key, s"${executorMem.toInt}m")
+
+    // New config should take precedence
+    val expectedFactor = 0.3
+    sparkConf.set(MEMORY_OVERHEAD_FACTOR, expectedFactor)
+
+    val conf = KubernetesTestConf.createExecutorConf(
+      sparkConf = sparkConf)
+    ResourceProfile.clearDefaultProfile()
+    val resourceProfile = ResourceProfile.getOrCreateDefaultProfile(sparkConf)
+    val step = new BasicExecutorFeatureStep(conf, new SecurityManager(baseConf),
+      resourceProfile)
+    val pod = step.configurePod(SparkPod.initialPod())
+    val mem = amountAndFormat(pod.container.getResources.getRequests.get("memory"))
+    val expected = (executorMem + executorMem * expectedFactor).toInt
+    assert(mem === s"${expected}Mi")
+  }
+
+
   // There is always exactly one controller reference, and it points to the driver pod.
   private def checkOwnerReferences(executor: Pod, driverPodUid: String): Unit = {
     assert(executor.getMetadata.getOwnerReferences.size() === 1)

resource-managers/mesos/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala

@@ -105,6 +105,7 @@ private[mesos] class MesosSubmitRequestServlet(
     val superviseDriver = sparkProperties.get(config.DRIVER_SUPERVISE.key)
     val driverMemory = sparkProperties.get(config.DRIVER_MEMORY.key)
     val driverMemoryOverhead = sparkProperties.get(config.DRIVER_MEMORY_OVERHEAD.key)
+    val driverMemoryOverheadFactor = sparkProperties.get(config.DRIVER_MEMORY_OVERHEAD_FACTOR.key)
     val driverCores = sparkProperties.get(config.DRIVER_CORES.key)
     val name = request.sparkProperties.getOrElse("spark.app.name", mainClass)
 
@@ -121,8 +122,10 @@ private[mesos] class MesosSubmitRequestServlet(
       mainClass, appArgs, environmentVariables, extraClassPath, extraLibraryPath, javaOpts)
     val actualSuperviseDriver = superviseDriver.map(_.toBoolean).getOrElse(DEFAULT_SUPERVISE)
     val actualDriverMemory = driverMemory.map(Utils.memoryStringToMb).getOrElse(DEFAULT_MEMORY)
+    val actualDriverMemoryFactor = driverMemoryOverheadFactor.map(_.toDouble).getOrElse(
+      MEMORY_OVERHEAD_FACTOR)
     val actualDriverMemoryOverhead = driverMemoryOverhead.map(_.toInt).getOrElse(
-      math.max((MEMORY_OVERHEAD_FACTOR * actualDriverMemory).toInt, MEMORY_OVERHEAD_MIN))
+      math.max((actualDriverMemoryFactor * actualDriverMemory).toInt, MEMORY_OVERHEAD_MIN))
     val actualDriverCores = driverCores.map(_.toDouble).getOrElse(DEFAULT_CORES)
     val submitDate = new Date()
     val submissionId = newDriverId(submitDate)

resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala

@@ -387,8 +387,7 @@ trait MesosSchedulerUtils extends Logging {
     }
   }
 
-  // These defaults copied from YARN
-  private val MEMORY_OVERHEAD_FRACTION = 0.10
+  // This default copied from YARN
   private val MEMORY_OVERHEAD_MINIMUM = 384
 
   /**
@@ -400,8 +399,9 @@ trait MesosSchedulerUtils extends Logging {
    *         (whichever is larger)
    */
   def executorMemory(sc: SparkContext): Int = {
+    val memoryOverheadFactor = sc.conf.get(EXECUTOR_MEMORY_OVERHEAD_FACTOR)
     sc.conf.get(mesosConfig.EXECUTOR_MEMORY_OVERHEAD).getOrElse(
-      math.max(MEMORY_OVERHEAD_FRACTION * sc.executorMemory, MEMORY_OVERHEAD_MINIMUM).toInt) +
+      math.max(memoryOverheadFactor * sc.executorMemory, MEMORY_OVERHEAD_MINIMUM).toInt) +
       sc.executorMemory
   }
 
@@ -415,7 +415,8 @@ trait MesosSchedulerUtils extends Logging {
    *         `MEMORY_OVERHEAD_FRACTION (=0.1) * driverMemory`
    */
   def driverContainerMemory(driverDesc: MesosDriverDescription): Int = {
-    val defaultMem = math.max(MEMORY_OVERHEAD_FRACTION * driverDesc.mem, MEMORY_OVERHEAD_MINIMUM)
+    val memoryOverheadFactor = driverDesc.conf.get(DRIVER_MEMORY_OVERHEAD_FACTOR)
+    val defaultMem = math.max(memoryOverheadFactor * driverDesc.mem, MEMORY_OVERHEAD_MINIMUM)
     driverDesc.conf.get(mesosConfig.DRIVER_MEMORY_OVERHEAD).getOrElse(defaultMem.toInt) +
       driverDesc.mem
   }