rms-filecache 1.0.0

File cache for files retrieved from the cloud

Introduction

filecache is a Python module that abstracts away the location where files used or generated by a program are stored. Files can be on the local file system, in Google Cloud Storage, on Amazon Web Services S3, or on a webserver. When files to be read are on the local file system, they are simply accessed in-place. Otherwise, they are downloaded from the remote source to a local temporary directory. When files to be written are on the local file system, they are simply written in-place. Otherwise, they are written to a local temporary directory and then uploaded to the remote location (it is not possible to upload to a webserver). When a cache is no longer needed, it is deleted from the local disk.

filecache is a product of the PDS Ring-Moon Systems Node.

Installation

The filecache module is available via the rms-filecache package on PyPI and can be installed with:

pip install rms-filecache

Getting Started

The top-level file organization is provided by the FileCache class. A FileCache instance is used to specify a particular sharing policy and lifetime. For example, a cache could be private to the current process and group a set of files that all have the same basic purpose. Once these files have been (downloaded and) read, they are deleted as a group. Another cache could be shared among all processes on the current machine and group a set of files that are needed by multiple processes, thus allowing them to be downloaded from a remote source only one time, saving time and bandwidth.

A FileCache contains one or more FileCachePrefix instances that each define access to a local or remote source/destination for files. For example, one instance could be used to access the local filesystem, while another could be used to access a particular AWS S3 bucket.

Usage examples:

from filecache import FileCache
with FileCache() as fc:  # Context manager
    # Use GS by specifying the bucket name and one directory level
    pfx1 = fc.new_prefix('gs://rms-filecache-tests/subdir1')
    # Use S3 by specifying the bucket namd and two directory levels
    pfx2 = fc.new_prefix('s3://rms-filecache-tests/subdir1/subdir2a')
    # Access GS using a directory + filename (since only one directory level
    # was specified by the prefix)
    with pfx1.open('subdir2a/binary1.bin', 'rb') as fp:
        bin1 = fp.read()
    # Access S3 using a filename only (since two directory levels were already
    # specified by the prefix))
    with pfx2.open('binary1.bin', 'rb') as fp:
        bin2 = fp.read()
    assert bin1 == bin2
# Cache automatically deleted here

# Same as above example but not using context managers for FileCache
fc = FileCache()
pfx1 = fc.new_prefix('gs://rms-filecache-tests/subdir1')
pfx2 = fc.new_prefix('s3://rms-filecache-tests/subdir1/subdir2a')
path1 = pfx1.retrieve('subdir2a/binary1.bin')
with open(path1, 'rb') as fp:
    bin1 = fp.read()
path2 = pfx2.retrieve('binary1.bin')
with open(path2, 'rb') as fp:
    bin2 = fp.read()
fc.clean_up()  # Cache manually deleted here
assert bin1 == bin2

# Write a file to a bucket and read it back
with FileCache() as fc:
    pfx = fc.new_prefix('gs://my-writable-bucket')
    with pfx.open('output.txt', 'w') as fp:
        fp.write('A')
# The cache will be deleted here so the file will have to be downloaded
with FileCache() as fc:
    pfx = fc.new_prefix('gs://my-writable-bucket')
    with pfx.open('output.txt', 'r') as fp:
        print(fp.read())

A benefit of the abstraction is that different environments can access the same files in different ways without needing to change the program code. For example, consider a program that needs to access the file COISS_2xxx/COISS_2001/voldesc.cat from the NASA PDS archives. This file might be stored on the local disk in the user's home directory in a subdirectory called pds3-holdings. Or if the user does not have a local copy, it is accessible from a webserver at https://pds-rings.seti.org/holdings/volumes/COISS_2xxx/COISS_2001/voldesc.cat. Finally, it could be accessible from Google Cloud Storage from the rms-node-holdings bucket at gs://rms-node-holdings/pds3-holdings/volumes/COISS_2xxx/COISS_2001/voldesc.cat. Before running the program, an environment variable could be set to one of these values:

$ export PDS3_HOLDINGS_DIR="~/pds3-holdings"
$ export PDS3_HOLDINGS_DIR="https://pds-rings.seti.org/holdings"
$ export PDS3_HOLDINGS_DIR="gs://rms-node-holdings/pds3-holdings"

Then the program could be written as::

from filecache import FileCache
import os
with FileCache() as fc:
    pfx = fc.new_prefix(os.getenv('PDS3_HOLDINGS_DIR'))
    with pfx.open('volumes/COISS_2xxx/COISS_2001/voldesc.cat', 'r') as fp:
        contents = fp.read()
# Cache automatically deleted here

If the program was going to be run multiple times in a row, or multiple copies were going to be run simultaneously, marking the cache as shared would allow all of the processes to share the same copy, thus requiring only a single download no matter how many times the program was run:

from filecache import FileCache
import os
with FileCache(shared=True) as fc:
    pfx = fc.new_prefix(os.getenv('PDS3_HOLDINGS_DIR'))
    with pfx.open('volumes/COISS_2xxx/COISS_2001/voldesc.cat', 'r') as fp:
        contents = fp.read()
# Cache not deleted here; must be deleted manually using fc.clean_up(final=True)
# If not deleted manually, the shared cache will persist until the temporary
# directory is purged by the operating system (which may be never)

Details of each class are available in the module documentation.

Contributing

Information on contributing to this package can be found in the Contributing Guide.

Links

• Documentation
• Repository
• Issue tracker
• PyPi

Licensing

This code is licensed under the Apache License v2.0.