def search_advising_notes(
search_phrase,
author_csid=None,
student_csid=None,
topic=None,
datetime_from=None,
datetime_to=None,
offset=0,
limit=20,
):
benchmark = get_benchmarker('search_advising_notes')
benchmark('begin')
if search_phrase:
search_terms = [
t.group(0)
for t in list(re.finditer(NOTE_SEARCH_PATTERN, search_phrase)) if t
]
search_phrase = ' & '.join(search_terms)
else:
search_terms = []
# TODO We're currently retrieving all results for the sake of subsequent offset calculations. As the number of notes in
# BOA grows (and possibly requires us to use some kind of staging table for search indexing), we'll need to revisit.
benchmark('begin local notes query')
author_uid = get_uid_for_csid(app, author_csid) if author_csid else None
local_results = Note.search(
search_phrase=search_phrase,
author_uid=author_uid,
student_csid=student_csid,
topic=topic,
datetime_from=datetime_from,
datetime_to=datetime_to,
)
benchmark('end local notes query')
local_notes_count = len(local_results)
cutoff = min(local_notes_count, offset + limit)
benchmark('begin local notes parsing')
notes_feed = _get_local_notes_search_results(local_results[offset:cutoff],
search_terms)
benchmark('end local notes parsing')
if len(notes_feed) == limit:
return notes_feed
benchmark('begin loch notes query')
loch_results = data_loch.search_advising_notes(
search_phrase=search_phrase,
author_uid=author_uid,
author_csid=author_csid,
student_csid=student_csid,
topic=topic,
datetime_from=datetime_from,
datetime_to=datetime_to,
offset=max(0, offset - local_notes_count),
limit=(limit - len(notes_feed)),
)
benchmark('end loch notes query')
benchmark('begin loch notes parsing')
notes_feed += _get_loch_notes_search_results(loch_results, search_terms)
benchmark('end loch notes parsing')
return notes_feed
def search_advising_notes(
search_phrase,
author_csid=None,
author_uid=None,
student_csid=None,
topic=None,
datetime_from=None,
datetime_to=None,
offset=0,
limit=20,
):
benchmark = get_benchmarker('search_advising_notes')
benchmark('begin')
if search_phrase:
search_terms = list({
t.group(0)
for t in list(re.finditer(TEXT_SEARCH_PATTERN, search_phrase)) if t
})
search_phrase = ' & '.join(search_terms)
else:
search_terms = []
author_uid = get_uid_for_csid(
app, author_csid) if (not author_uid and author_csid) else author_uid
# TODO We're currently retrieving all results for the sake of subsequent offset calculations. As the number of notes in
# BOA grows (and possibly requires us to use some kind of staging table for search indexing), we'll need to revisit.
benchmark('begin local notes query')
local_results = Note.search(
search_phrase=search_phrase,
author_uid=author_uid,
student_csid=student_csid,
topic=topic,
datetime_from=datetime_from,
datetime_to=datetime_to,
)
benchmark('end local notes query')
benchmark('begin local notes parsing')
# Our offset calculations are unforuntately fussy because note parsing might reveal notes associated with students no
# longer in BOA, which we won't include in the feed; so we don't actually know the length of our result set until parsing
# is complete.
cutoff = min(len(local_results), offset + limit)
notes_feed = _get_local_notes_search_results(local_results, cutoff,
search_terms)
local_notes_count = len(notes_feed)
notes_feed = notes_feed[offset:]
benchmark('end local notes parsing')
if len(notes_feed) == limit:
return notes_feed
benchmark('begin loch notes query')
loch_results = data_loch.search_advising_notes(
search_phrase=search_phrase,
author_uid=author_uid,
author_csid=author_csid,
student_csid=student_csid,
topic=topic,
datetime_from=datetime_from,
datetime_to=datetime_to,
offset=max(0, offset - local_notes_count),
limit=(limit - len(notes_feed)),
)
benchmark('end loch notes query')
benchmark('begin loch notes parsing')
notes_feed += _get_loch_notes_search_results(loch_results, search_terms)
benchmark('end loch notes parsing')
return notes_feed
