[SPARK-42204][CORE] Add option to disable redundant logging of TaskMe…

…trics internal accumulators in event logs

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

This PR adds an off-by-default option to JsonProtocol to have it exclude certain redundant accumulator information from Spark event logs in order to save space and processing time.

Several event logs types contain both TaskMetrics and Accumulables, but there is redundancy in how the TaskMetrics data is stored:

- TaskMetrics are stored in a map called "Task Metrics" which maps from metric names to metric values.
- An "Accumulables" field contains information on accumulator updates from the task, but this field includes updates from the TaskMetrics internal accumulators (both the value from the task, plus a running "sum-so-far" from all of the tasks completed up to that point).

The redundant task metrics accumulables are not actually used by the Spark History Server: I verified this by reading AppStatusListener and SQLAppStatusListener.

I believe that this redundancy was introduced back in [SPARK-10620](https://issues.apache.org/jira/browse/SPARK-10620) when Spark 1.x's separate TaskMetrics implementation was replaced by the current accumulator-based version.

In this PR, I add logic to exclude TaskMetrics internal accumulators when writing this field (if a new flag is enabled).

The new `spark.eventLog.includeTaskMetricsAccumulators` configuration (default `false`, meaning "keep the redundant information") can be set to `true` to exclude these redundant internal accumulator updates.

For now, I am merging this off-by-default, but in a followup PR for Spark 4.0.0 we might consider a change-of-default to `true` (in which case the flag would serve as an "escape-hatch" for users who want to restore the old behavior. Although I think it's somewhat unlikely that third-party non-Spark consumers of the event logs would be relying on this redundant information, this is changing a longstanding user-facing data format and thus needs a flag.

### Why are the changes needed?

This change reduces the size of Spark event logs, especially for logs from applications that run many tasks. It should also have slight benefits on event log read and write speed (although I haven't tried to quantify this).

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

No user-facing changes in Spark History Server.

This flag's effects could be considered a user-facing change from the perspective of third-party code which does its own direct processing of Spark event logs, hence the config. However, in this PR (by itself) the flag is off-by-default. Out-of-the-box user-facing changes will be discussed / proposed in a separate flag-flip PR.

### How was this patch tested?

New unit tests in `JsonProtocolSuite`.

Manual tests of event log size in `spark-shell` with a job that runs `spark.parallelize(1 to 1000, 1000).count()`. For this toy query, this PR's change shrunk the uncompressed event log size by ~15%. The relative size reduction will be even greater once other issues like https://issues.apache.org/jira/browse/SPARK-42206 or https://issues.apache.org/jira/browse/SPARK-42203 are fixed. The relative reduction will be smaller for tasks with many SQL metrics because those accumulables cannot be excluded.

Closes #39763 from JoshRosen/SPARK-42204-remove-redundant-logging-of-taskmetrics-internal-accumulators-in-jsonprotocol.

Authored-by: Josh Rosen <joshrosen@databricks.com>
Signed-off-by: Josh Rosen <joshrosen@databricks.com>

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

@@ -271,6 +271,18 @@ package object config {
       .toSequence
       .createWithDefault(GarbageCollectionMetrics.OLD_GENERATION_BUILTIN_GARBAGE_COLLECTORS)
 
+  private[spark] val EVENT_LOG_INCLUDE_TASK_METRICS_ACCUMULATORS =
+    ConfigBuilder("spark.eventLog.includeTaskMetricsAccumulators")
+      .doc("Whether to include TaskMetrics' underlying accumulator values in the event log (as " +
+        "part of the Task/Stage/Job metrics' 'Accumulables' fields. This configuration defaults " +
+        "to false because the TaskMetrics values are already logged in the 'Task Metrics' " +
+        "fields (so the accumulator updates are redundant). This flag exists only as a " +
+        "backwards-compatibility escape hatch for applications that might rely on the old " +
+        "behavior. See SPARK-42204 for details.")
+      .version("4.0.0")
+      .booleanConf
+      .createWithDefault(false)
+
   private[spark] val EVENT_LOG_OVERWRITE =
     ConfigBuilder("spark.eventLog.overwrite")
       .version("1.0.0")

core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala

@@ -31,7 +31,7 @@ import org.apache.spark.deploy.history.EventLogFileWriter
 import org.apache.spark.executor.ExecutorMetrics
 import org.apache.spark.internal.Logging
 import org.apache.spark.internal.config._
-import org.apache.spark.util.{JsonProtocol, Utils}
+import org.apache.spark.util.{JsonProtocol, JsonProtocolOptions, Utils}
 
 /**
  * A SparkListener that logs events to persistent storage.
@@ -74,6 +74,8 @@ private[spark] class EventLoggingListener(
   private val liveStageExecutorMetrics =
     mutable.HashMap.empty[(Int, Int), mutable.HashMap[String, ExecutorMetrics]]
 
+  private[this] val jsonProtocolOptions = new JsonProtocolOptions(sparkConf)
+
   /**
    * Creates the log file in the configured log directory.
    */
@@ -84,7 +86,7 @@ private[spark] class EventLoggingListener(
 
   private def initEventLog(): Unit = {
     val metadata = SparkListenerLogStart(SPARK_VERSION)
-    val eventJson = JsonProtocol.sparkEventToJsonString(metadata)
+    val eventJson = JsonProtocol.sparkEventToJsonString(metadata, jsonProtocolOptions)
     logWriter.writeEvent(eventJson, flushLogger = true)
     if (testing && loggedEvents != null) {
       loggedEvents += eventJson
@@ -93,7 +95,7 @@ private[spark] class EventLoggingListener(
 
   /** Log the event as JSON. */
   private def logEvent(event: SparkListenerEvent, flushLogger: Boolean = false): Unit = {
-    val eventJson = JsonProtocol.sparkEventToJsonString(event)
+    val eventJson = JsonProtocol.sparkEventToJsonString(event, jsonProtocolOptions)
     logWriter.writeEvent(eventJson, flushLogger)
     if (testing) {
       loggedEvents += eventJson

core/src/main/scala/org/apache/spark/util/JsonProtocol.scala

@@ -28,6 +28,7 @@ import org.json4s.jackson.JsonMethods.compact
 
 import org.apache.spark._
 import org.apache.spark.executor._
+import org.apache.spark.internal.config._
 import org.apache.spark.metrics.ExecutorMetricType
 import org.apache.spark.rdd.{DeterministicLevel, RDDOperationScope}
 import org.apache.spark.resource.{ExecutorResourceRequest, ResourceInformation, ResourceProfile, TaskResourceRequest}
@@ -37,6 +38,16 @@ import org.apache.spark.storage._
 import org.apache.spark.util.ArrayImplicits._
 import org.apache.spark.util.Utils.weakIntern
 
+/**
+ * Helper class for passing configuration options to JsonProtocol.
+ * We use this instead of passing SparkConf directly because it lets us avoid
+ * repeated re-parsing of configuration values on each read.
+ */
+private[spark] class JsonProtocolOptions(conf: SparkConf) {
+  val includeTaskMetricsAccumulators: Boolean =
+    conf.get(EVENT_LOG_INCLUDE_TASK_METRICS_ACCUMULATORS)
+}
+
 /**
  * Serializes SparkListener events to/from JSON.  This protocol provides strong backwards-
  * and forwards-compatibility guarantees: any version of Spark should be able to read JSON output
@@ -55,30 +66,41 @@ import org.apache.spark.util.Utils.weakIntern
 private[spark] object JsonProtocol extends JsonUtils {
   // TODO: Remove this file and put JSON serialization into each individual class.
 
+  private[util]
+  val defaultOptions: JsonProtocolOptions = new JsonProtocolOptions(new SparkConf(false))
+
   /** ------------------------------------------------- *
    * JSON serialization methods for SparkListenerEvents |
    * -------------------------------------------------- */
 
+  // Only for use in tests. Production code should use the two-argument overload defined below.
   def sparkEventToJsonString(event: SparkListenerEvent): String = {
+    sparkEventToJsonString(event, defaultOptions)
+  }
+
+  def sparkEventToJsonString(event: SparkListenerEvent, options: JsonProtocolOptions): String = {
     toJsonString { generator =>
-      writeSparkEventToJson(event, generator)
+      writeSparkEventToJson(event, generator, options)
     }
   }
 
-  def writeSparkEventToJson(event: SparkListenerEvent, g: JsonGenerator): Unit = {
+  def writeSparkEventToJson(
+      event: SparkListenerEvent,
+      g: JsonGenerator,
+      options: JsonProtocolOptions): Unit = {
     event match {
       case stageSubmitted: SparkListenerStageSubmitted =>
-        stageSubmittedToJson(stageSubmitted, g)
+        stageSubmittedToJson(stageSubmitted, g, options)
       case stageCompleted: SparkListenerStageCompleted =>
-        stageCompletedToJson(stageCompleted, g)
+        stageCompletedToJson(stageCompleted, g, options)
       case taskStart: SparkListenerTaskStart =>
-        taskStartToJson(taskStart, g)
+        taskStartToJson(taskStart, g, options)
       case taskGettingResult: SparkListenerTaskGettingResult =>
-        taskGettingResultToJson(taskGettingResult, g)
+        taskGettingResultToJson(taskGettingResult, g, options)
       case taskEnd: SparkListenerTaskEnd =>
-        taskEndToJson(taskEnd, g)
+        taskEndToJson(taskEnd, g, options)
       case jobStart: SparkListenerJobStart =>
-        jobStartToJson(jobStart, g)
+        jobStartToJson(jobStart, g, options)
       case jobEnd: SparkListenerJobEnd =>
         jobEndToJson(jobEnd, g)
       case environmentUpdate: SparkListenerEnvironmentUpdate =>
@@ -112,51 +134,64 @@ private[spark] object JsonProtocol extends JsonUtils {
     }
   }
 
-  def stageSubmittedToJson(stageSubmitted: SparkListenerStageSubmitted, g: JsonGenerator): Unit = {
+  def stageSubmittedToJson(
+      stageSubmitted: SparkListenerStageSubmitted,
+      g: JsonGenerator,
+      options: JsonProtocolOptions): Unit = {
     g.writeStartObject()
     g.writeStringField("Event", SPARK_LISTENER_EVENT_FORMATTED_CLASS_NAMES.stageSubmitted)
     g.writeFieldName("Stage Info")
     // SPARK-42205: don't log accumulables in start events:
-    stageInfoToJson(stageSubmitted.stageInfo, g, includeAccumulables = false)
+    stageInfoToJson(stageSubmitted.stageInfo, g, options, includeAccumulables = false)
     Option(stageSubmitted.properties).foreach { properties =>
       g.writeFieldName("Properties")
       propertiesToJson(properties, g)
     }
     g.writeEndObject()
   }
 
-  def stageCompletedToJson(stageCompleted: SparkListenerStageCompleted, g: JsonGenerator): Unit = {
+  def stageCompletedToJson(
+      stageCompleted: SparkListenerStageCompleted,
+      g: JsonGenerator,
+      options: JsonProtocolOptions): Unit = {
     g.writeStartObject()
     g.writeStringField("Event", SPARK_LISTENER_EVENT_FORMATTED_CLASS_NAMES.stageCompleted)
     g.writeFieldName("Stage Info")
-    stageInfoToJson(stageCompleted.stageInfo, g, includeAccumulables = true)
+    stageInfoToJson(stageCompleted.stageInfo, g, options, includeAccumulables = true)
     g.writeEndObject()
   }
 
-  def taskStartToJson(taskStart: SparkListenerTaskStart, g: JsonGenerator): Unit = {
+  def taskStartToJson(
+      taskStart: SparkListenerTaskStart,
+      g: JsonGenerator,
+      options: JsonProtocolOptions): Unit = {
     g.writeStartObject()
     g.writeStringField("Event", SPARK_LISTENER_EVENT_FORMATTED_CLASS_NAMES.taskStart)
     g.writeNumberField("Stage ID", taskStart.stageId)
     g.writeNumberField("Stage Attempt ID", taskStart.stageAttemptId)
     g.writeFieldName("Task Info")
     // SPARK-42205: don't log accumulables in start events:
-    taskInfoToJson(taskStart.taskInfo, g, includeAccumulables = false)
+    taskInfoToJson(taskStart.taskInfo, g, options, includeAccumulables = false)
     g.writeEndObject()
   }
 
   def taskGettingResultToJson(
       taskGettingResult: SparkListenerTaskGettingResult,
-      g: JsonGenerator): Unit = {
+      g: JsonGenerator,
+      options: JsonProtocolOptions): Unit = {
     val taskInfo = taskGettingResult.taskInfo
     g.writeStartObject()
     g.writeStringField("Event", SPARK_LISTENER_EVENT_FORMATTED_CLASS_NAMES.taskGettingResult)
     g.writeFieldName("Task Info")
     // SPARK-42205: don't log accumulables in "task getting result" events:
-    taskInfoToJson(taskInfo, g, includeAccumulables = false)
+    taskInfoToJson(taskInfo, g, options, includeAccumulables = false)
     g.writeEndObject()
   }
 
-  def taskEndToJson(taskEnd: SparkListenerTaskEnd, g: JsonGenerator): Unit = {
+  def taskEndToJson(
+      taskEnd: SparkListenerTaskEnd,
+      g: JsonGenerator,
+      options: JsonProtocolOptions): Unit = {
     g.writeStartObject()
     g.writeStringField("Event", SPARK_LISTENER_EVENT_FORMATTED_CLASS_NAMES.taskEnd)
     g.writeNumberField("Stage ID", taskEnd.stageId)
@@ -165,7 +200,7 @@ private[spark] object JsonProtocol extends JsonUtils {
     g.writeFieldName("Task End Reason")
     taskEndReasonToJson(taskEnd.reason, g)
     g.writeFieldName("Task Info")
-    taskInfoToJson(taskEnd.taskInfo, g, includeAccumulables = true)
+    taskInfoToJson(taskEnd.taskInfo, g, options, includeAccumulables = true)
     g.writeFieldName("Task Executor Metrics")
     executorMetricsToJson(taskEnd.taskExecutorMetrics, g)
     Option(taskEnd.taskMetrics).foreach { m =>
@@ -175,7 +210,10 @@ private[spark] object JsonProtocol extends JsonUtils {
     g.writeEndObject()
   }
 
-  def jobStartToJson(jobStart: SparkListenerJobStart, g: JsonGenerator): Unit = {
+  def jobStartToJson(
+      jobStart: SparkListenerJobStart,
+      g: JsonGenerator,
+      options: JsonProtocolOptions): Unit = {
     g.writeStartObject()
     g.writeStringField("Event", SPARK_LISTENER_EVENT_FORMATTED_CLASS_NAMES.jobStart)
     g.writeNumberField("Job ID", jobStart.jobId)
@@ -186,7 +224,7 @@ private[spark] object JsonProtocol extends JsonUtils {
     // the job was submitted: it is technically possible for a stage to belong to multiple
     // concurrent jobs, so this situation can arise even without races occurring between
     // event logging and stage completion.
-    jobStart.stageInfos.foreach(stageInfoToJson(_, g, includeAccumulables = true))
+    jobStart.stageInfos.foreach(stageInfoToJson(_, g, options, includeAccumulables = true))
     g.writeEndArray()
     g.writeArrayFieldStart("Stage IDs")
     jobStart.stageIds.foreach(g.writeNumber)
@@ -386,6 +424,7 @@ private[spark] object JsonProtocol extends JsonUtils {
   def stageInfoToJson(
       stageInfo: StageInfo,
       g: JsonGenerator,
+      options: JsonProtocolOptions,
       includeAccumulables: Boolean): Unit = {
     g.writeStartObject()
     g.writeNumberField("Stage ID", stageInfo.stageId)
@@ -404,7 +443,10 @@ private[spark] object JsonProtocol extends JsonUtils {
     stageInfo.failureReason.foreach(g.writeStringField("Failure Reason", _))
     g.writeFieldName("Accumulables")
     if (includeAccumulables) {
-      accumulablesToJson(stageInfo.accumulables.values, g)
+      accumulablesToJson(
+        stageInfo.accumulables.values,
+        g,
+        includeTaskMetricsAccumulators = options.includeTaskMetricsAccumulators)
     } else {
       g.writeStartArray()
       g.writeEndArray()
@@ -418,6 +460,7 @@ private[spark] object JsonProtocol extends JsonUtils {
   def taskInfoToJson(
       taskInfo: TaskInfo,
       g: JsonGenerator,
+      options: JsonProtocolOptions,
       includeAccumulables: Boolean): Unit = {
     g.writeStartObject()
     g.writeNumberField("Task ID", taskInfo.taskId)
@@ -435,21 +478,34 @@ private[spark] object JsonProtocol extends JsonUtils {
     g.writeBooleanField("Killed", taskInfo.killed)
     g.writeFieldName("Accumulables")
     if (includeAccumulables) {
-      accumulablesToJson(taskInfo.accumulables, g)
+      accumulablesToJson(
+        taskInfo.accumulables,
+        g,
+        includeTaskMetricsAccumulators = options.includeTaskMetricsAccumulators)
     } else {
       g.writeStartArray()
       g.writeEndArray()
     }
     g.writeEndObject()
   }
 
-  private lazy val accumulableExcludeList = Set("internal.metrics.updatedBlockStatuses")
+  private[util] val accumulableExcludeList = Set(InternalAccumulator.UPDATED_BLOCK_STATUSES)
+
+  private[this] val taskMetricAccumulableNames = TaskMetrics.empty.nameToAccums.keySet.toSet
 
-  def accumulablesToJson(accumulables: Iterable[AccumulableInfo], g: JsonGenerator): Unit = {
+  def accumulablesToJson(
+      accumulables: Iterable[AccumulableInfo],
+      g: JsonGenerator,
+    includeTaskMetricsAccumulators: Boolean = true): Unit = {
     g.writeStartArray()
     accumulables
-        .filterNot(_.name.exists(accumulableExcludeList.contains))
-        .toList.sortBy(_.id).foreach(a => accumulableInfoToJson(a, g))
+        .filterNot { acc =>
+          acc.name.exists(accumulableExcludeList.contains) ||
+          (!includeTaskMetricsAccumulators && acc.name.exists(taskMetricAccumulableNames.contains))
+        }
+        .toList
+        .sortBy(_.id)
+        .foreach(a => accumulableInfoToJson(a, g))
     g.writeEndArray()
   }