diff --git a/docs/sql-migration-guide-upgrade.md b/docs/sql-migration-guide-upgrade.md
index c4d2157de8b6..7e6a0c097d24 100644
--- a/docs/sql-migration-guide-upgrade.md
+++ b/docs/sql-migration-guide-upgrade.md
@@ -40,6 +40,9 @@ displayTitle: Spark SQL Upgrading Guide
   - In Spark version 2.4 and earlier, JSON datasource and JSON functions like `from_json` convert a bad JSON record to a row with all `null`s in the PERMISSIVE mode when specified schema is `StructType`. Since Spark 3.0, the returned row can contain non-`null` fields if some of JSON column values were parsed and converted to desired types successfully.
 
   - Since Spark 3.0, the `unix_timestamp`, `date_format`, `to_unix_timestamp`, `from_unixtime`, `to_date`, `to_timestamp` functions use java.time API for parsing and formatting dates/timestamps from/to strings by using ISO chronology (https://docs.oracle.com/javase/8/docs/api/java/time/chrono/IsoChronology.html) based on Proleptic Gregorian calendar. In Spark version 2.4 and earlier, java.text.SimpleDateFormat and java.util.GregorianCalendar (hybrid calendar that supports both the Julian and Gregorian calendar systems, see https://docs.oracle.com/javase/7/docs/api/java/util/GregorianCalendar.html) is used for the same purpuse. New implementation supports pattern formats as described here https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html and performs strict checking of its input. For example, the `2015-07-22 10:00:00` timestamp cannot be parse if pattern is `yyyy-MM-dd` because the parser does not consume whole input. Another example is the `31/01/2015 00:00` input cannot be parsed by the `dd/MM/yyyy hh:mm` pattern because `hh` supposes hours in the range `1-12`. To switch back to the implementation used in Spark 2.4 and earlier, set `spark.sql.legacy.timeParser.enabled` to `true`.
+
+  - Since Spark 3.0, JSON datasource and JSON function `schema_of_json` infer TimestampType from string values if they matches to the pattern defined by the JSON option `timestampFormat`. Set JSON option `inferTimestamp` to `false` to disable such type inferring.
+
 ## Upgrading From Spark SQL 2.3 to 2.4
 
   - In Spark version 2.3 and earlier, the second parameter to array_contains function is implicitly promoted to the element type of first array type parameter. This type promotion can be lossy and may cause `array_contains` function to return wrong result. This problem has been addressed in 2.4 by employing a safer type promotion mechanism. This can cause some change in behavior and are illustrated in the table below.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JSONOptions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JSONOptions.scala
index eaff3fa7bec2..1ec9d5093a78 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JSONOptions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JSONOptions.scala
@@ -117,6 +117,12 @@ private[sql] class JSONOptions(
    */
   val pretty: Boolean = parameters.get("pretty").map(_.toBoolean).getOrElse(false)
 
+  /**
+   * Enables inferring of TimestampType from strings matched to the timestamp pattern
+   * defined by the timestampFormat option.
+   */
+  val inferTimestamp: Boolean = parameters.get("inferTimestamp").map(_.toBoolean).getOrElse(true)
+
   /** Sets config options on a Jackson [[JsonFactory]]. */
   def setJacksonOptions(factory: JsonFactory): Unit = {
     factory.configure(JsonParser.Feature.ALLOW_COMMENTS, allowComments)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JsonInferSchema.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JsonInferSchema.scala
index 3203e626ea40..0bf3f03cdb72 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JsonInferSchema.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JsonInferSchema.scala
@@ -128,7 +128,8 @@ private[sql] class JsonInferSchema(options: JSONOptions) extends Serializable {
         }
         if (options.prefersDecimal && decimalTry.isDefined) {
           decimalTry.get
-        } else if ((allCatch opt timestampFormatter.parse(field)).isDefined) {
+        } else if (options.inferTimestamp &&
+            (allCatch opt timestampFormatter.parse(field)).isDefined) {
           TimestampType
         } else {
           StringType
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/json/JsonInferSchemaSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/json/JsonInferSchemaSuite.scala
index 9307f9b47b80..9a6f4f5f9b0c 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/json/JsonInferSchemaSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/json/JsonInferSchemaSuite.scala
@@ -99,4 +99,10 @@ class JsonInferSchemaSuite extends SparkFunSuite with SQLHelper {
       }
     }
   }
+
+  test("disable timestamp inferring") {
+    val json = """{"a": "2019-01-04T21:11:10.123Z"}"""
+    checkType(Map("inferTimestamp" -> "true"), json, TimestampType)
+    checkType(Map("inferTimestamp" -> "false"), json, StringType)
+  }
 }