def fact_delete_query_task(self, worker_id: int):
worker_object = DeleteFactsByQueryTask.objects.get(pk=worker_id)
try:
show_progress = ShowProgress(worker_object.task, multiplier=1)
show_progress.update_step(
'Scrolling through the indices to delete the facts.')
# Get the necessary fields.
indices: List[str] = worker_object.get_indices()
target_facts = json.loads(worker_object.facts)
scroll_size = worker_object.scroll_size
searcher = ElasticSearcher(
query=json.loads(worker_object.query),
indices=indices,
field_data=[TEXTA_TAGS_KEY],
output=ElasticSearcher.OUT_RAW,
callback_progress=show_progress,
scroll_size=scroll_size,
scroll_timeout=f"{worker_object.es_timeout}m")
ed = ElasticDocument(index=None)
actions = query_delete_actions_generator(searcher, target_facts)
ed.bulk_update(actions)
worker_object.task.complete()
worker_object.save()
return worker_id
except Exception as e:
worker_object.task.handle_failed_task(e)
raise e
def update_documents_in_es(documents: List[dict]):
"""
Updates the documents inside Elasticsearch, either with the MLP results or the
error messages.
:param documents: Full Elasticsearch documents..
"""
ed = ElasticDocument(index=None)
ed.bulk_update(actions=documents)
def apply_analyzers_on_indices(self, worker_id: int):
worker_object = ApplyESAnalyzerWorker.objects.get(pk=worker_id)
task_object = worker_object.task
try:
show_progress = ShowProgress(task_object, multiplier=1)
show_progress.update_step(
'scrolling through the indices to apply lang')
# Get the necessary fields.
indices: List[str] = worker_object.get_indices()
fields = json.loads(worker_object.fields)
detect_lang = worker_object.detect_lang
snowball_language = worker_object.stemmer_lang
scroll_timeout = f"{worker_object.es_timeout}m"
scroll_size = worker_object.bulk_size
analyzers = json.loads(worker_object.analyzers)
tokenizer = worker_object.tokenizer
strip_html = worker_object.strip_html
searcher = ElasticSearcher(query=json.loads(worker_object.query),
indices=indices,
field_data=fields,
output=ElasticSearcher.OUT_RAW,
callback_progress=show_progress,
scroll_size=scroll_size,
scroll_timeout=scroll_timeout)
task_object.set_total(searcher.count())
actions = process_analyzer_actions(generator=searcher,
worker=worker_object,
detect_lang=detect_lang,
snowball_language=snowball_language,
fields_to_parse=fields,
analyzers=analyzers,
tokenizer=tokenizer,
strip_html=strip_html)
# Send the data towards Elasticsearch
ed = ElasticDocument("_all")
ed.bulk_update(actions=actions, chunk_size=scroll_size)
worker_object.task.complete()
return worker_id
except Exception as e:
task_object.handle_failed_task(e)
raise e
def apply_search_fields_tagger_on_index(object_id: int):
search_fields_tagger = SearchFieldsTagger.objects.get(pk=object_id)
task_object = search_fields_tagger.task
"""Apply Search Fields Tagger to index."""
try:
progress = ShowProgress(task_object)
progress.update_step('scrolling search fields')
# Get the necessary fields.
indices: List[str] = search_fields_tagger.get_indices()
fields: List[str] = json.loads(search_fields_tagger.fields)
fact_name: str = search_fields_tagger.fact_name
scroll_timeout = search_fields_tagger.es_timeout
scroll_size = search_fields_tagger.bulk_size
use_breakup = search_fields_tagger.use_breakup
breakup_character = search_fields_tagger.breakup_character
ec = ElasticCore()
[ec.add_texta_facts_mapping(index) for index in indices]
searcher = ElasticSearcher(
indices=indices,
field_data=fields +
["texta_facts"], # Get facts to add upon existing ones.
query=json.loads(search_fields_tagger.query),
output=ElasticSearcher.OUT_RAW,
scroll_timeout=f"{scroll_timeout}m",
callback_progress=progress,
scroll_size=scroll_size)
actions = update_search_fields_generator(
generator=searcher,
ec=ec,
fields=fields,
fact_name=fact_name,
search_field_tagger_object=search_fields_tagger,
use_breakup=use_breakup,
breakup_character=breakup_character)
# Send the data towards Elasticsearch
ed = ElasticDocument("_all")
elastic_response = ed.bulk_update(actions=actions)
return object_id
except Exception as e:
logging.getLogger(ERROR_LOGGER).exception(e)
task_object.add_error(str(e))
task_object.update_status(Task.STATUS_FAILED)
raise e
def apply_rakun_extractor_to_index(self, object_id: int, indices: List[str],
fields: List[str], query: dict,
es_timeout: int, bulk_size: int,
fact_name: str, add_spans: bool):
"""Apply Rakun Keyword Extractor to index."""
logging.getLogger(INFO_LOGGER).info(
f"Starting task 'apply_rakun_extractor_to_index' with ID: {object_id}!"
)
rakun_extractor_object = RakunExtractor.objects.get(id=object_id)
try:
progress = ShowProgress(rakun_extractor_object.task)
# retrieve fields
field_data = fields
ec = ElasticCore()
[ec.add_texta_facts_mapping(index) for index in indices]
searcher = ElasticSearcher(
indices=indices,
field_data=field_data +
["texta_facts"], # Get facts to add upon existing ones.
query=query,
timeout=f"{es_timeout}m",
output=ElasticSearcher.OUT_RAW,
callback_progress=progress,
scroll_size=bulk_size)
keyword_detector = rakun_extractor_object.load_rakun_keyword_detector()
actions = update_generator(
keyword_detector=keyword_detector,
generator=searcher,
ec=ec,
fields=field_data,
rakun_extractor_object=rakun_extractor_object,
fact_name=fact_name,
fact_value="",
add_spans=add_spans)
# Send the data towards Elasticsearch
ed = ElasticDocument("_all")
elastic_response = ed.bulk_update(actions=actions)
rakun_extractor_object.task.complete()
return True
except Exception as e:
logging.getLogger(ERROR_LOGGER).exception(e)
error_message = f"{str(e)[:100]}..." # Take first 100 characters in case the error message is massive.
rakun_extractor_object.task.add_error(error_message)
rakun_extractor_object.task.update_status(Task.STATUS_FAILED)
def apply_lang_on_indices(self, apply_worker_id: int):
worker_object = ApplyLangWorker.objects.get(pk=apply_worker_id)
task_object = worker_object.task
try:
load_mlp()
show_progress = ShowProgress(task_object, multiplier=1)
show_progress.update_step(
'scrolling through the indices to apply lang')
# Get the necessary fields.
indices: List[str] = worker_object.get_indices()
field = worker_object.field
scroll_size = 100
searcher = ElasticSearcher(query=json.loads(worker_object.query),
indices=indices,
field_data=[field],
output=ElasticSearcher.OUT_RAW,
callback_progress=show_progress,
scroll_size=scroll_size,
scroll_timeout="15m")
for index in indices:
searcher.core.add_texta_facts_mapping(index=index)
actions = process_lang_actions(generator=searcher,
field=field,
worker_id=apply_worker_id,
mlp_class=mlp)
# Send the data towards Elasticsearch
ed = ElasticDocument("_all")
elastic_response = ed.bulk_update(actions=actions)
worker_object.task.complete()
return apply_worker_id
except Exception as e:
task_object.handle_failed_task(e)
raise e
def apply_summarizer_on_index(self, summarizer_id: int):
summarizer_object = Summarizer.objects.get(pk=summarizer_id)
task_object = summarizer_object.task
try:
load_sumy()
show_progress = ShowProgress(task_object, multiplier=1)
show_progress.update_step('scrolling summarizer')
# Get the necessary fields.
indices: List[str] = summarizer_object.get_indices()
field_data: List[str] = json.loads(summarizer_object.fields)
ratio_data: float[str] = summarizer_object.ratio
algorithm_data: List[str] = summarizer_object.algorithm
scroll_size = 100
searcher = ElasticSearcher(query=json.loads(summarizer_object.query),
indices=indices,
field_data=field_data,
output=ElasticSearcher.OUT_RAW,
callback_progress=show_progress,
scroll_size=scroll_size,
scroll_timeout="30m")
actions = process_actions(searcher,
field_data,
ratio_data,
algorithm=algorithm_data,
summarizer_class=sumy,
summarizer_id=summarizer_id)
# Send the data towards Elasticsearch
ed = ElasticDocument("_all")
elastic_response = ed.bulk_update(actions=actions)
return summarizer_id
except Exception as e:
logging.getLogger(ERROR_LOGGER).exception(e)
task_object.add_error(str(e))
task_object.update_status(Task.STATUS_FAILED)
raise e
