diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 93d1acdd2d15..c8b1afeebac0 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -323,10 +323,7 @@ private[spark] class Executor(
     val threadName = s"Executor task launch worker for task $taskId"
     val taskName = taskDescription.name
     val mdcProperties = taskDescription.properties.asScala
-      .filter(_._1.startsWith("mdc.")).map { item =>
-        val key = item._1.substring(4)
-        (key, item._2)
-      }.toSeq
+      .filter(_._1.startsWith("mdc.")).toSeq
 
     /** If specified, this task has been killed and this option contains the reason. */
     @volatile private var reasonIfKilled: Option[String] = None
@@ -705,7 +702,7 @@ private[spark] class Executor(
     MDC.clear()
     mdc.foreach { case (key, value) => MDC.put(key, value) }
     // avoid overriding the takName by the user
-    MDC.put("taskName", taskName)
+    MDC.put("mdc.taskName", taskName)
   }
 
   /**
diff --git a/docs/configuration.md b/docs/configuration.md
index 420942f7b7bb..706c2552b1d1 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -2955,11 +2955,11 @@ Spark uses [log4j](http://logging.apache.org/log4j/) for logging. You can config
 `log4j.properties` file in the `conf` directory. One way to start is to copy the existing
 `log4j.properties.template` located there.
 
-By default, Spark adds 1 record to the MDC (Mapped Diagnostic Context): `taskName`, which shows something
-like `task 1.0 in stage 0.0`. You can add `%X{taskName}` to your patternLayout in
+By default, Spark adds 1 record to the MDC (Mapped Diagnostic Context): `mdc.taskName`, which shows something
+like `task 1.0 in stage 0.0`. You can add `%X{mdc.taskName}` to your patternLayout in
 order to print it in the logs.
-Moreover, you can use `spark.sparkContext.setLocalProperty("mdc." + name, "value")` to add user specific data into MDC.
-The key in MDC will be the string after the `mdc.` prefix.
+Moreover, you can use `spark.sparkContext.setLocalProperty(s"mdc.$name", "value")` to add user specific data into MDC.
+The key in MDC will be the string of "mdc.$name".
 
 # Overriding configuration directory