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1:mod:`waeup.kofa.utils.batching` -- Batch processing
2****************************************************
3
4Batch processing is much more than pure data import.
5
6Overview
7========
8
9Basically, it means processing CSV files in order to mass-create,
10mass-remove, or mass-update data.
11
12So you can feed CSV files to processors, that are part of
13the batch-processing mechanism.
14
15Processors
16----------
17
18Each CSV file processor
19
20* accepts a single data type identified by an interface.
21
22* knows about the places inside a site (University) where to store,
23  remove or update the data.
24
25* can check headers before processing data.
26
27* supports the mode 'create', 'update', 'remove'.
28
29* creates log entries (optional)
30
31* creates csv files containing successful and not-successful processed
32  data respectively.
33
34Output
35------
36
37The results of processing are written to loggers, if a logger was
38given. Beside this new CSV files are created during processing:
39
40* a pending CSV file, containing datasets that could not be processed
41
42* a finished CSV file, containing datasets successfully processed.
43
44The pending file is not created if everything works fine. The
45respective path returned in that case is ``None``.
46
47The pending file (if created) is a CSV file that contains the failed
48rows appended by a column ``--ERRROR--`` in which the reasons for
49processing failures are listed.
50
51The complete paths of these files are returned. They will be in a
52temporary directory created only for this purpose. It is the caller's
53responsibility to remove the temporay directories afterwards (the
54datacenters distProcessedFiles() method takes care for that).
55
56It looks like this::
57 
58     -----+      +---------+
59    /     |      |         |              +------+
60   | .csv +----->|Batch-   |              |      |
61   |      |      |processor+----changes-->| ZODB |
62   |  +------+   |         |              |      |
63   +--|      |   |         +              +------+
64      | Mode +-->|         |                 -------+
65      |      |   |         +----outputs-+-> /       |
66      |  +----+->+---------+            |  |.pending|
67      +--|Log |  ^                      |  |        |
68         +----+  |                      |  +--------+
69           +-----++                     v
70           |Inter-|                  ----------+
71           |face  |                 /          |
72           +------+                | .finished |
73                                   |           |
74                                   +-----------+
75
76
77Creating a batch processor
78==========================
79
80We create an own batch processor for an own datatype. This datatype
81must be based on an interface that the batcher can use for converting
82data.
83
84Founding Stoneville
85-------------------
86
87We start with the interface:
88
89    >>> from zope.interface import Interface
90    >>> from zope import schema
91    >>> class ICave(Interface):
92    ...   """A cave."""
93    ...   name = schema.TextLine(
94    ...     title = u'Cave name',
95    ...     default = u'Unnamed',
96    ...     required = True)
97    ...   dinoports = schema.Int(
98    ...     title = u'Number of DinoPorts (tm)',
99    ...     required = False,
100    ...     default = 1)
101    ...   owner = schema.TextLine(
102    ...     title = u'Owner name',
103    ...     required = True,
104    ...     missing_value = 'Fred Estates Inc.')
105    ...   taxpayer = schema.Bool(
106    ...     title = u'Payes taxes',
107    ...     required = True,
108    ...     default = False)
109
110Now a class that implements this interface:
111
112    >>> import grok
113    >>> class Cave(object):
114    ...   grok.implements(ICave)
115    ...   def __init__(self, name=u'Unnamed', dinoports=2,
116    ...                owner='Fred Estates Inc.', taxpayer=False):
117    ...     self.name = name
118    ...     self.dinoports = 2
119    ...     self.owner = owner
120    ...     self.taxpayer = taxpayer
121
122We also provide a factory for caves. Strictly speaking, this not
123necessary but makes the batch processor we create afterwards, better
124understandable.
125
126    >>> from zope.component import getGlobalSiteManager
127    >>> from zope.component.factory import Factory
128    >>> from zope.component.interfaces import IFactory
129    >>> gsm = getGlobalSiteManager()
130    >>> cave_maker = Factory(Cave, 'A cave', 'Buy caves here!')
131    >>> gsm.registerUtility(cave_maker, IFactory, 'Lovely Cave')
132
133Now we can create caves using a factory:
134
135    >>> from zope.component import createObject
136    >>> createObject('Lovely Cave')
137    <Cave object at 0x...>
138
139This is nice, but we still lack a place, where we can place all the
140lovely caves we want to sell.
141
142Furthermore, as a replacement for a real site, we define a place where
143all caves can be stored: Stoneville! This is a lovely place for
144upperclass cavemen (which are the only ones that can afford more than
145one dinoport).
146
147We found Stoneville:
148
149    >>> stoneville = dict()
150
151Everything in place.
152
153Now, to improve local health conditions, imagine we want to populate
154Stoneville with lots of new happy dino-hunting natives that slept on
155the bare ground in former times and had no idea of
156bathrooms. Disgusting, isn't it?
157
158Lots of cavemen need lots of caves.
159
160Of course we can do something like:
161
162    >>> cave1 = createObject('Lovely Cave')
163    >>> cave1.name = "Fred's home"
164    >>> cave1.owner = "Fred"
165    >>> stoneville[cave1.name] = cave1
166
167and Stoneville has exactly
168
169    >>> len(stoneville)
170    1
171
172inhabitant. But we don't want to do this for hundreds or thousands of
173citizens-to-be, do we?
174
175It is much easier to create a simple CSV list, where we put in all the
176data and let a batch processor do the job.
177
178The list is already here:
179
180    >>> open('newcomers.csv', 'wb').write(
181    ... """name,dinoports,owner,taxpayer
182    ... Barneys Home,2,Barney,1
183    ... Wilmas Asylum,1,Wilma,1
184    ... Freds Dinoburgers,10,Fred,0
185    ... Joeys Drive-in,110,Joey,0
186    ... """)
187
188All we need, is a batch processor now.
189
190    >>> from waeup.kofa.utils.batching import BatchProcessor
191    >>> from waeup.kofa.interfaces import IGNORE_MARKER
192    >>> class CaveProcessor(BatchProcessor):
193    ...   util_name = 'caveprocessor'
194    ...   grok.name(util_name)
195    ...   name = 'Cave Processor'
196    ...   iface = ICave
197    ...   location_fields = ['name']
198    ...   factory_name = 'Lovely Cave'
199    ...
200    ...   def parentsExist(self, row, site):
201    ...     return True
202    ...
203    ...   def getParent(self, row, site):
204    ...     return stoneville
205    ...
206    ...   def entryExists(self, row, site):
207    ...     return row['name'] in stoneville.keys()
208    ...
209    ...   def getEntry(self, row, site):
210    ...     if not self.entryExists(row, site):
211    ...       return None
212    ...     return stoneville[row['name']]
213    ...
214    ...   def delEntry(self, row, site):
215    ...     del stoneville[row['name']]
216    ...
217    ...   def addEntry(self, obj, row, site):
218    ...     stoneville[row['name']] = obj
219    ...
220    ...   def updateEntry(self, obj, row, site, filename):
221    ...     # This is not strictly necessary, as the default
222    ...     # updateEntry method does exactly the same
223    ...     for key, value in row.items():
224    ...       if value != IGNORE_MARKER:
225    ...         setattr(obj, key, value)
226
227If we also want the results being logged, we must provide a logger
228(this is optional):
229
230    >>> import logging
231    >>> logger = logging.getLogger('stoneville')
232    >>> logger.setLevel(logging.DEBUG)
233    >>> logger.propagate = False
234    >>> handler = logging.FileHandler('stoneville.log', 'w')
235    >>> logger.addHandler(handler)
236
237Create the fellows:
238
239    >>> processor = CaveProcessor()
240    >>> result = processor.doImport('newcomers.csv',
241    ...                   ['name', 'dinoports', 'owner', 'taxpayer'],
242    ...                    mode='create', user='Bob', logger=logger)
243    >>> result
244    (4, 0, '/.../newcomers.finished.csv', None)
245
246The result means: four entries were processed and no warnings
247occured. Furthermore we get filepath to a CSV file with successfully
248processed entries and a filepath to a CSV file with erraneous entries.
249As everything went well, the latter is ``None``. Let's check:
250
251    >>> sorted(stoneville.keys())
252    [u'Barneys Home', ..., u'Wilmas Asylum']
253
254The values of the Cave instances have correct type:
255
256    >>> barney = stoneville['Barneys Home']
257    >>> barney.dinoports
258    2
259
260which is a number, not a string.
261
262Apparently, when calling the processor, we gave some more info than
263only the CSV filepath. What does it all mean?
264
265While the first argument is the path to the CSV file, we also have to
266give an ordered list of headernames. These replace the header field
267names that are actually in the file. This way we can override faulty
268headers.
269
270The ``mode`` paramter tells what kind of operation we want to perform:
271``create``, ``update``, or ``remove`` data.
272
273The ``user`` parameter finally is optional and only used for logging.
274
275We can, by the way, see the results of our run in a logfile if we
276provided a logger during the call:
277
278    >>> print open('stoneville.log').read()
279    processed: newcomers.csv, create mode, 4 lines (4 successful/ 0 failed), ... s (... s/item)
280
281
282We cleanup the temporay dir created by doImport():
283
284    >>> import shutil
285    >>> import os
286    >>> shutil.rmtree(os.path.dirname(result[2]))
287
288As we can see, the processing was successful. Otherwise, all problems
289could be read here as we can see, if we do the same operation again:
290
291    >>> result = processor.doImport('newcomers.csv',
292    ...                   ['name', 'dinoports', 'owner', 'taxpayer'],
293    ...                    mode='create', user='Bob', logger=logger)
294    >>> result
295    (4, 4, '/.../newcomers.finished.csv', '/.../newcomers.pending.csv')
296
297This time we also get a path to a .pending file.
298
299The log file will tell us this in more detail:
300
301    >>> print open('stoneville.log').read()
302    processed: newcomers.csv, create mode, 4 lines (4 successful/ 0 failed), ... s (... s/item)
303    processed: newcomers.csv, create mode, 4 lines (0 successful/ 4 failed), ... s (... s/item)
304
305
306This time a new file was created, which keeps all the rows we could not
307process and an additional column with error messages:
308
309    >>> print open(result[3]).read()
310    owner,name,taxpayer,dinoports,--ERRORS--
311    Barney,Barneys Home,1,2,This object already exists. Skipping.
312    Wilma,Wilmas Asylum,1,1,This object already exists. Skipping.
313    Fred,Freds Dinoburgers,0,10,This object already exists. Skipping.
314    Joey,Joeys Drive-in,0,110,This object already exists. Skipping.
315
316This way we can correct the faulty entries and afterwards retry without
317having the already processed rows in the way.
318
319We also notice, that the values of the taxpayer column are returned as
320in the input file. There we wrote '1' for ``True`` and '0' for
321``False`` (which is accepted by the converters).
322
323Clean up:
324
325    >>> shutil.rmtree(os.path.dirname(result[2]))
326
327
328We can also tell to ignore some cols from input by passing
329``--IGNORE--`` as col name:
330
331    >>> result = processor.doImport('newcomers.csv', ['name',
332    ...                             '--IGNORE--', '--IGNORE--'],
333    ...                    mode='update', user='Bob')
334    >>> result
335    (4, 0, '...', None)
336
337Clean up:
338
339    >>> shutil.rmtree(os.path.dirname(result[2]))
340
341If something goes wrong during processing, the respective --IGNORE--
342cols won't be populated  in the resulting pending file:
343
344    >>> result = processor.doImport('newcomers.csv', ['name', 'dinoports',
345    ...                             '--IGNORE--', '--IGNORE--'],
346    ...                    mode='create', user='Bob')
347    >>> result
348    (4, 4, '...', '...')
349
350    >>> print open(result[3], 'rb').read()
351    name,dinoports,--ERRORS--
352    Barneys Home,2,This object already exists. Skipping.
353    Wilmas Asylum,1,This object already exists. Skipping.
354    Freds Dinoburgers,10,This object already exists. Skipping.
355    Joeys Drive-in,110,This object already exists. Skipping.
356
357
358Clean up:
359
360    >>> shutil.rmtree(os.path.dirname(result[2]))
361
362
363
364
365Updating entries
366----------------
367
368To update entries, we just call the batchprocessor in a different
369mode:
370
371    >>> result = processor.doImport('newcomers.csv', ['name',
372    ...                             'dinoports', 'owner'],
373    ...                    mode='update', user='Bob')
374    >>> result
375    (4, 0, '...', None)
376
377Now we want to tell, that Wilma got an extra port for her second dino:
378
379    >>> open('newcomers.csv', 'wb').write(
380    ... """name,dinoports,owner
381    ... Wilmas Asylum,2,Wilma
382    ... """)
383
384    >>> wilma = stoneville['Wilmas Asylum']
385    >>> wilma.dinoports
386    1
387
388Clean up:
389
390    >>> shutil.rmtree(os.path.dirname(result[2]))
391
392
393We start the processor:
394
395    >>> result = processor.doImport('newcomers.csv', ['name',
396    ...                    'dinoports', 'owner'], mode='update', user='Bob')
397    >>> result
398    (1, 0, '...', None)
399
400    >>> wilma = stoneville['Wilmas Asylum']
401    >>> wilma.dinoports
402    2
403
404Wilma's number of dinoports raised.
405
406Clean up:
407
408    >>> shutil.rmtree(os.path.dirname(result[2]))
409
410
411If we try to update an unexisting entry, an error occurs:
412
413    >>> open('newcomers.csv', 'wb').write(
414    ... """name,dinoports,owner
415    ... NOT-WILMAS-ASYLUM,2,Wilma
416    ... """)
417
418    >>> result = processor.doImport('newcomers.csv', ['name',
419    ...                             'dinoports', 'owner'],
420    ...                    mode='update', user='Bob')
421    >>> result
422    (1, 1, '/.../newcomers.finished.csv', '/.../newcomers.pending.csv')
423
424Clean up:
425
426    >>> shutil.rmtree(os.path.dirname(result[2]))
427
428   
429Also invalid values will be spotted:
430
431    >>> open('newcomers.csv', 'wb').write(
432    ... """name,dinoports,owner
433    ... Wilmas Asylum,NOT-A-NUMBER,Wilma
434    ... """)
435
436    >>> result = processor.doImport('newcomers.csv', ['name',
437    ...                             'dinoports', 'owner'],
438    ...                    mode='update', user='Bob')
439    >>> result
440    (1, 1, '...', '...')
441
442Clean up:
443
444    >>> shutil.rmtree(os.path.dirname(result[2]))
445
446
447We can also update only some cols, leaving some out. We skip the
448'dinoports' column in the next run:
449
450    >>> open('newcomers.csv', 'wb').write(
451    ... """name,owner
452    ... Wilmas Asylum,Barney
453    ... """)
454
455    >>> result = processor.doImport('newcomers.csv', ['name', 'owner'],
456    ...                             mode='update', user='Bob')
457    >>> result
458    (1, 0, '...', None)
459
460    >>> wilma.owner
461    u'Barney'
462
463Clean up:
464
465    >>> shutil.rmtree(os.path.dirname(result[2]))
466
467
468We can however, not leave out the 'location field' ('name' in our
469case), as this one tells us which entry to update:
470
471    >>> open('newcomers.csv', 'wb').write(
472    ... """name,dinoports,owner
473    ... 2,Wilma
474    ... """)
475
476    >>> processor.doImport('newcomers.csv', ['dinoports', 'owner'],
477    ...                    mode='update', user='Bob')
478    Traceback (most recent call last):
479    ...
480    FatalCSVError: Need at least columns 'name' for import!
481
482This time we get even an exception!
483
484Generally, empty strings are considered as ``None``:
485
486    >>> open('newcomers.csv', 'wb').write(
487    ... """name,dinoports,owner
488    ... "Wilmas Asylum","","Wilma"
489    ... """)
490
491    >>> result = processor.doImport('newcomers.csv', ['name',
492    ...                             'dinoports', 'owner'],
493    ...                    mode='update', user='Bob')
494    >>> result
495    (1, 0, '...', None)
496
497    >>> wilma.dinoports
498    2
499
500Clean up:
501
502    >>> shutil.rmtree(os.path.dirname(result[2]))
503
504We can tell to set dinoports to ``None`` although this is not a
505number, as we declared the field not required in the interface:
506
507    >>> open('newcomers.csv', 'wb').write(
508    ... """name,dinoports,owner
509    ... "Wilmas Asylum","XXX","Wilma"
510    ... """)
511
512    >>> result = processor.doImport('newcomers.csv', ['name',
513    ...                             'dinoports', 'owner'],
514    ...                    mode='update', user='Bob', ignore_empty=False)
515    >>> result
516    (1, 0, '...', None)
517
518    >>> wilma.dinoports is None
519    True
520
521Clean up:
522
523    >>> shutil.rmtree(os.path.dirname(result[2]))
524
525Removing entries
526----------------
527
528In 'remove' mode we can delete entries. Here validity of values in
529non-location fields doesn't matter because those fields are ignored.
530
531    >>> open('newcomers.csv', 'wb').write(
532    ... """name,dinoports,owner
533    ... "Wilmas Asylum","ILLEGAL-NUMBER",""
534    ... """)
535
536    >>> result = processor.doImport('newcomers.csv', ['name',
537    ...                             'dinoports', 'owner'],
538    ...                    mode='remove', user='Bob')
539    >>> result
540    (1, 0, '...', None)
541
542    >>> sorted(stoneville.keys())
543    [u'Barneys Home', "Fred's home", u'Freds Dinoburgers', u'Joeys Drive-in']
544
545Oops! Wilma is gone.
546
547Clean up:
548
549    >>> shutil.rmtree(os.path.dirname(result[2]))
550
551
552Clean up:
553
554    >>> import os
555    >>> os.unlink('newcomers.csv')
556    >>> os.unlink('stoneville.log')
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