diff --git a/python/docs/source/reference/pyspark.rst b/python/docs/source/reference/pyspark.rst
index 6d4d0b55477c..bf4e66ee3353 100644
--- a/python/docs/source/reference/pyspark.rst
+++ b/python/docs/source/reference/pyspark.rst
@@ -53,6 +53,7 @@ Spark Context APIs
 
     SparkContext.PACKAGE_EXTENSIONS
     SparkContext.accumulator
+    SparkContext.addArchive
     SparkContext.addFile
     SparkContext.addPyFile
     SparkContext.applicationId
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 68f748e68faa..2f1746b0a434 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -1278,6 +1278,50 @@ def addPyFile(self, path: str) -> None:
 
         importlib.invalidate_caches()
 
+    def addArchive(self, path: str) -> None:
+        """
+        Add an archive to be downloaded with this Spark job on every node.
+        The `path` passed can be either a local file, a file in HDFS
+        (or other Hadoop-supported filesystems), or an HTTP, HTTPS or
+        FTP URI.
+
+        To access the file in Spark jobs, use :meth:`SparkFiles.get` with the
+        filename to find its download/unpacked location. The given path should
+        be one of .zip, .tar, .tar.gz, .tgz and .jar.
+
+        .. versionadded:: 3.3.0
+
+        Notes
+        -----
+        A path can be added only once. Subsequent additions of the same path are ignored.
+        This API is experimental.
+
+        Examples
+        --------
+        Creates a zipped file that contains a text file written '100'.
+
+        >>> import zipfile
+        >>> from pyspark import SparkFiles
+        >>> path = os.path.join(tempdir, "test.txt")
+        >>> zip_path = os.path.join(tempdir, "test.zip")
+        >>> with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zipped:
+        ...     with open(path, "w") as f:
+        ...         _ = f.write("100")
+        ...     zipped.write(path, os.path.basename(path))
+        >>> sc.addArchive(zip_path)
+
+        Reads the '100' as an integer in the zipped file, and processes
+        it with the data in the RDD.
+
+        >>> def func(iterator):
+        ...    with open("%s/test.txt" % SparkFiles.get("test.zip")) as f:
+        ...        v = int(f.readline())
+        ...        return [x * int(v) for x in iterator]
+        >>> sc.parallelize([1, 2, 3, 4]).mapPartitions(func).collect()
+        [100, 200, 300, 400]
+        """
+        self._jsc.sc().addArchive(path)
+
     def setCheckpointDir(self, dirName: str) -> None:
         """
         Set the directory under which RDDs are going to be checkpointed. The