SparkContext.addArchive(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 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.


A path can be added only once. Subsequent additions of the same path are ignored. This API is experimental.


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, "")
>>> 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("")) 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]