SIRP Data Center
****************

The SIRP data center cares for managing CSV files and importing then.

.. :doctest:
.. :layer: waeup.sirp.testing.SIRPUnitTestLayer

Creating a data center
======================

A data center can be created easily:

    >>> from waeup.sirp.datacenter import DataCenter
    >>> mydatacenter = DataCenter()
    >>> mydatacenter
    <waeup.sirp.datacenter.DataCenter object at 0x...>

Each data center has a location in file system where files are stored:

    >>> storagepath = mydatacenter.storage
    >>> storagepath
    '/tmp/tmp...'


Managing the storage path
-------------------------

We can set another storage path:

    >>> import os
    >>> os.mkdir('newlocation')
    >>> newpath = os.path.abspath('newlocation')
    >>> mydatacenter.setStoragePath(newpath)
    []

The result here is a list of filenames, that could not be
copied. Luckily, this list is empty.

When we set a new storage path, we can tell to move all files in the
old location to the new one. To see this feature in action, we first
have to put a file into the old location:

    >>> open(os.path.join(newpath, 'myfile.txt'), 'wb').write('hello')

Now we can set a new location and the file will be copied:

    >>> verynewpath = os.path.abspath('verynewlocation')
    >>> os.mkdir(verynewpath)

    >>> mydatacenter.setStoragePath(verynewpath, move=True)
    []

    >>> storagepath = mydatacenter.storage
    >>> 'myfile.txt' in os.listdir(verynewpath)
    True

We remove the created file to have a clean testing environment for
upcoming examples:

    >>> os.unlink(os.path.join(storagepath, 'myfile.txt'))

Uploading files
===============

We can get a list of files stored in that location:

    >>> mydatacenter.getFiles()
    []

Let's put some file in the storage:

    >>> import os
    >>> filepath = os.path.join(storagepath, 'data.csv')
    >>> open(filepath, 'wb').write('Some Content\n')

Now we can find a file:

    >>> mydatacenter.getFiles()
    [<waeup.sirp.datacenter.DataCenterFile object at 0x...>]

As we can see, the actual file is wrapped by a convenience wrapper,
that enables us to fetch some data about the file. The data returned
is formatted in strings, so that it can easily be put into output
pages:

    >>> datafile = mydatacenter.getFiles()[0]
    >>> datafile.getSize()
    '13 bytes'

    >>> datafile.getDate() # Nearly current datetime...
    '...'

Clean up:

    >>> import shutil
    >>> shutil.rmtree(newpath)
    >>> shutil.rmtree(verynewpath)


Distributing processed files
============================

When files were processed by a batch processor, we can put the
resulting files into desired destinations.

We recreate the datacenter root in case it is missing:

    >>> import os
    >>> dc_root = mydatacenter.storage
    >>> fin_dir = os.path.join(dc_root, 'finished')
    >>> unfin_dir = os.path.join(dc_root, 'unfinished')

    >>> def recreate_dc_storage():
    ...   if os.path.exists(dc_root):
    ...     shutil.rmtree(dc_root)
    ...   os.mkdir(dc_root)
    ...   mydatacenter.setStoragePath(mydatacenter.storage)
    >>> recreate_dc_storage()

We define a function that creates a set of faked result files:

    >>> import os
    >>> import tempfile
    >>> def create_fake_results(source_basename, create_pending=True):
    ...   tmp_dir = tempfile.mkdtemp()
    ...   src = os.path.join(dc_root, source_basename)
    ...   pending_src = None
    ...   if create_pending:
    ...     pending_src = os.path.join(tmp_dir, 'mypendingsource.csv')
    ...   finished_src = os.path.join(tmp_dir, 'myfinishedsource.csv')
    ...   for path in (src, pending_src, finished_src):
    ...     if path is not None:
    ...       open(path, 'wb').write('blah')
    ...   return tmp_dir, src, finished_src, pending_src

Now we can create the set of result files, that typically come after a
successful processing of a regular source:

Now we can try to distribute those files. Let's start with a source
file, that was processed successfully:

    >>> tmp_dir, src, finished_src, pending_src = create_fake_results(
    ...  'mysource.csv', create_pending=False)
    >>> mydatacenter.distProcessedFiles(True, src, finished_src,
    ...                            pending_src, mode='create')
    >>> sorted(os.listdir(dc_root))
    ['finished', 'logs', 'unfinished']

    >>> sorted(os.listdir(fin_dir))
    ['mysource.create.finished.csv', 'mysource.csv']

    >>> sorted(os.listdir(unfin_dir))
    []

The created dir will be removed for us by the datacenter. This way we
can assured, that less temporary dirs are left hanging around:

    >>> os.path.exists(tmp_dir)
    False

The root dir is empty, while the original file and the file containing
all processed data were moved to'finished/'.

Now we restart, but this time we fake an erranous action:

    >>> recreate_dc_storage()
    >>> tmp_dir, src, finished_src, pending_src = create_fake_results(
    ...  'mysource.csv')
    >>> mydatacenter.distProcessedFiles(False, src, finished_src,
    ...                                 pending_src, mode='create')
    >>> sorted(os.listdir(dc_root))
    ['finished', 'logs', 'mysource.create.pending.csv', 'unfinished']

    >>> sorted(os.listdir(fin_dir))
    ['mysource.create.finished.csv']

    >>> sorted(os.listdir(unfin_dir))
    ['mysource.csv']

While the original source was moved to the 'unfinished' dir, the
pending file went to the root and the set of already processed items
are stored in finished/.

We fake processing the pending file and assume that everything went
well this time:

    >>> tmp_dir, src, finished_src, pending_src = create_fake_results(
    ...  'mysource.create.pending.csv', create_pending=False)
    >>> mydatacenter.distProcessedFiles(True, src, finished_src,
    ...                                 pending_src, mode='create')

    >>> sorted(os.listdir(dc_root))
    ['finished', 'logs', 'unfinished']

    >>> sorted(os.listdir(fin_dir))
    ['mysource.create.finished.csv', 'mysource.csv']

    >>> sorted(os.listdir(unfin_dir))
    []

The result is the same as in the first case shown above.

We restart again, but this time we fake several non-working imports in
a row.

We start with a faulty start-import:

    >>> recreate_dc_storage()
    >>> tmp_dir, src, finished_src, pending_src = create_fake_results(
    ...  'mysource.csv')
    >>> mydatacenter.distProcessedFiles(False, src, finished_src,
    ...                                 pending_src, mode='create')

We try to process the pending file, which fails again:

    >>> tmp_dir, src, finished_src, pending_src = create_fake_results(
    ...  'mysource.create.pending.csv')
    >>> mydatacenter.distProcessedFiles(False, src, finished_src,
    ...                                 pending_src, mode='create')

We try to process the new pending file:

    >>> tmp_dir, src, finished_src, pending_src = create_fake_results(
    ...  'mysource.create.pending.csv')
    >>> mydatacenter.distProcessedFiles(False, src, finished_src,
    ...                                 pending_src, mode='create')

    >>> sorted(os.listdir(dc_root))
    ['finished', 'logs', 'mysource.create.pending.csv', 'unfinished']

    >>> sorted(os.listdir(fin_dir))
    ['mysource.create.finished.csv']

    >>> sorted(os.listdir(unfin_dir))
    ['mysource.csv']

Finally, we process the pending file and everything works:

    >>> tmp_dir, src, finished_src, pending_src = create_fake_results(
    ...  'mysource.create.pending.csv', create_pending=False)
    >>> mydatacenter.distProcessedFiles(True, src, finished_src,
    ...                                 pending_src, mode='create')

    >>> sorted(os.listdir(dc_root))
    ['finished', 'logs', 'unfinished']

    >>> sorted(os.listdir(fin_dir))
    ['mysource.create.finished.csv', 'mysource.csv']

    >>> sorted(os.listdir(unfin_dir))
    []

The root dir is empty (contains no input files) and only the files in
finished-subdirectory remain.

Clean up:

    >>> shutil.rmtree(verynewpath)