def fetch_all(initial_response):
resp = initial_response
scroll_id = resp.get('_scroll_id')
if scroll_id is None:
return
iteration = 0
while True:
start = int(time.time() * 1000)
resp = client.scroll(scroll_id, scroll=scroll)
datadog_histogram('commcare.es_scroll', (time.time() * 1000) - start, tags=[
u'iteration:{}'.format(iteration),
])
for hit in resp['hits']['hits']:
yield hit
# check if we have any errrors
if resp["_shards"]["failed"]:
logging.getLogger('elasticsearch.helpers').warning(
'Scroll request has failed on %d shards out of %d.',
resp['_shards']['failed'], resp['_shards']['total']
)
scroll_id = resp.get('_scroll_id')
# end of scroll
if scroll_id is None or not resp['hits']['hits']:
break
iteration += 1
def save_document(doc_ids):
lock_keys = []
for doc_id in doc_ids:
lock_keys.append(get_async_indicator_modify_lock_key(doc_id))
indicator_config_ids = None
timer = TimingContext()
with CriticalSection(lock_keys):
indicators = AsyncIndicator.objects.filter(doc_id__in=doc_ids)
if not indicators:
return
first_indicator = indicators[0]
processed_indicators = []
failed_indicators = []
for i in indicators:
assert i.domain == first_indicator.domain
assert i.doc_type == first_indicator.doc_type
indicator_by_doc_id = {i.doc_id: i for i in indicators}
doc_store = get_document_store(first_indicator.domain,
first_indicator.doc_type)
indicator_config_ids = first_indicator.indicator_config_ids
related_docs_to_rebuild = set()
with timer:
for doc in doc_store.iter_documents(indicator_by_doc_id.keys()):
indicator = indicator_by_doc_id[doc['_id']]
successfully_processed, to_remove, rebuild_related_docs = _save_document_helper(
indicator, doc)
if rebuild_related_docs:
related_docs_to_rebuild = related_docs_to_rebuild.union(
icds_get_related_docs_ids(doc['_id']))
if successfully_processed:
processed_indicators.append(indicator.pk)
else:
failed_indicators.append((indicator, to_remove))
num_processed = len(processed_indicators)
num_failed = len(failed_indicators)
AsyncIndicator.objects.filter(pk__in=processed_indicators).delete()
with transaction.atomic():
for indicator, to_remove in failed_indicators:
indicator.update_failure(to_remove)
indicator.save()
# remove any related docs that were just rebuilt
related_docs_to_rebuild = related_docs_to_rebuild - set(doc_ids)
# queue the docs that aren't already queued
_queue_indicators(
AsyncIndicator.objects.filter(doc_id__in=related_docs_to_rebuild,
date_queued=None))
datadog_counter('commcare.async_indicator.processed_success',
num_processed)
datadog_counter('commcare.async_indicator.processed_fail', num_failed)
datadog_histogram('commcare.async_indicator.processing_time',
timer.duration,
tags=[u'config_ids:{}'.format(indicator_config_ids)])
def __record_change_metric_in_datadog(self,
metric,
change,
processor=None,
processing_time=None,
add_case_type_tag=False):
if change.metadata is not None:
common_tags = [
'datasource:{}'.format(change.metadata.data_source_name),
'is_deletion:{}'.format(change.metadata.is_deletion),
'pillow_name:{}'.format(self.get_name()),
'processor:{}'.format(processor.__class__.__name__
if processor else "all_processors"),
]
metric_tags = list(common_tags)
if add_case_type_tag and settings.ENTERPRISE_MODE and change.metadata.document_type == 'CommCareCase':
metric_tags.append('case_type:{}'.format(
change.metadata.document_subtype))
datadog_counter(metric, tags=metric_tags)
change_lag = (datetime.utcnow() -
change.metadata.publish_timestamp).total_seconds()
datadog_gauge('commcare.change_feed.change_lag',
change_lag,
tags=[
'pillow_name:{}'.format(self.get_name()),
_topic_for_ddog(change.topic),
])
if processing_time:
datadog_histogram('commcare.change_feed.processing_time',
processing_time,
tags=common_tags)
def _record_datadog_metrics(self, changes_chunk, processing_time):
tags = ["pillow_name:{}".format(self.get_name()), "mode:chunked"]
# Since success/fail count is tracked per processor, to get sense of
# actual operations count, multiply by number of processors
count = len(changes_chunk) * len(self.processors)
datadog_counter('commcare.change_feed.changes.count', count, tags=tags)
max_change_lag = (
datetime.utcnow() -
changes_chunk[0].metadata.publish_timestamp).total_seconds()
min_change_lag = (
datetime.utcnow() -
changes_chunk[-1].metadata.publish_timestamp).total_seconds()
datadog_gauge('commcare.change_feed.chunked.min_change_lag',
min_change_lag,
tags=tags)
datadog_gauge('commcare.change_feed.chunked.max_change_lag',
max_change_lag,
tags=tags)
datadog_histogram('commcare.change_feed.chunked.processing_time_total',
processing_time,
tags=tags +
["chunk_size:{}".format(str(len(changes_chunk)))])
if len(changes_chunk) == self.processor_chunk_size:
# don't report offset chunks to ease up datadog calculations
datadog_histogram('commcare.change_feed.processing_time',
processing_time / len(changes_chunk),
tags=tags +
["chunk_size:".format(str(len(changes_chunk)))])
def __record_change_metric_in_datadog(self,
metric,
change,
processor=None,
processing_time=None):
if change.metadata is not None:
tags = [
'datasource:{}'.format(change.metadata.data_source_name),
'is_deletion:{}'.format(change.metadata.is_deletion),
'pillow_name:{}'.format(self.get_name()),
'processor:{}'.format(processor.__class__.__name__
if processor else "all_processors"),
]
count = 1 if processor else len(self.processors)
datadog_counter(metric, value=count, tags=tags)
change_lag = (datetime.utcnow() -
change.metadata.publish_timestamp).total_seconds()
datadog_gauge('commcare.change_feed.change_lag',
change_lag,
tags=[
'pillow_name:{}'.format(self.get_name()),
_topic_for_ddog(change.topic),
])
if processing_time:
datadog_histogram('commcare.change_feed.processing_time',
processing_time,
tags=tags)
def _record_datadog_metrics(self, changes_chunk, processing_time):
tags = ["pillow_name:{}".format(self.get_name()), "mode:chunked"]
change_count = len(changes_chunk)
if settings.ENTERPRISE_MODE:
type_counter = Counter([
change.metadata.document_subtype
for change in changes_chunk if change.metadata.document_type == 'CommCareCase'
])
for case_type, type_count in type_counter.items():
tags_with_type = tags + ['case_type:{}'.format(case_type)]
datadog_counter('commcare.change_feed.changes.count', type_count, tags=tags_with_type)
remainder = change_count - sum(type_counter.values())
if remainder:
datadog_counter('commcare.change_feed.changes.count', remainder, tags=tags)
else:
datadog_counter('commcare.change_feed.changes.count', change_count, tags=tags)
max_change_lag = (datetime.utcnow() - changes_chunk[0].metadata.publish_timestamp).total_seconds()
min_change_lag = (datetime.utcnow() - changes_chunk[-1].metadata.publish_timestamp).total_seconds()
datadog_gauge('commcare.change_feed.chunked.min_change_lag', min_change_lag, tags=tags)
datadog_gauge('commcare.change_feed.chunked.max_change_lag', max_change_lag, tags=tags)
# processing_time per change
datadog_histogram('commcare.change_feed.processing_time', processing_time / change_count, tags=tags)
if change_count == self.processor_chunk_size:
# don't report offset chunks to ease up datadog calculations
datadog_histogram('commcare.change_feed.chunked.processing_time_total', processing_time,
tags=tags + ["chunk_size:{}".format(str(change_count))])
def custom_data(self, selected_location, domain):
timer = TimingContext()
with timer:
to_ret = self._custom_data(selected_location, domain)
if selected_location:
loc_type = selected_location.location_type.name
else:
loc_type = None
datadog_histogram("commcare.icds.block_reports.custom_data_time",
timer.duration,
tags="location_type:{}, report_slug:{}".format(
loc_type, self.slug))
return to_ret
def checkpoint_updated(self):
total_duration = sum(self.domain_timing_context.values())
duration_seen = 0
top_half_domains = {}
for domain, duration in self.domain_timing_context.most_common():
top_half_domains[domain] = duration
duration_seen += duration
if duration_seen >= total_duration // 2:
break
for domain, duration in top_half_domains.items():
datadog_histogram('commcare.change_feed.ucr_slow_log', duration, tags=[
'domain:{}'.format(domain)
])
self.domain_timing_context.clear()
def checkpoint_updated(self):
total_duration = sum(self.domain_timing_context.values())
duration_seen = 0
top_half_domains = {}
for domain, duration in self.domain_timing_context.most_common():
top_half_domains[domain] = duration
duration_seen += duration
if duration_seen >= total_duration // 2:
break
for domain, duration in top_half_domains.items():
datadog_histogram('commcare.change_feed.ucr_slow_log', duration, tags=[
'domain:{}'.format(domain)
])
self.domain_timing_context.clear()
def custom_data(self, selected_location, domain):
timer = TimingContext()
with timer:
to_ret = self._custom_data(selected_location, domain)
if selected_location:
loc_type = selected_location.location_type.name
else:
loc_type = None
datadog_histogram(
"commcare.icds.block_reports.custom_data_time",
timer.duration,
tags="location_type:{}, report_slug:{}".format(
loc_type, self.slug
)
)
return to_ret
def custom_data(self, selected_location, domain):
timer = TimingContext()
with timer:
to_ret = self._custom_data(selected_location, domain)
if selected_location:
loc_type = selected_location.location_type.name
else:
loc_type = None
tags = ["location_type:{}".format(loc_type), "report_slug:{}".format(self.slug)]
if self.allow_conditional_agg:
tags.append("allow_conditional_agg:yes")
datadog_histogram(
"commcare.icds.block_reports.custom_data_duration",
timer.duration,
tags=tags
)
return to_ret
def __record_change_metric_in_datadog(self, metric, change, timer=None):
if change.metadata is not None:
tags = [
'datasource:{}'.format(change.metadata.data_source_name),
'is_deletion:{}'.format(change.metadata.is_deletion),
'pillow_name:{}'.format(self.get_name()),
]
datadog_counter(metric, tags=tags)
change_lag = (datetime.utcnow() - change.metadata.publish_timestamp).seconds
datadog_gauge('commcare.change_feed.change_lag', change_lag, tags=[
'pillow_name:{}'.format(self.get_name()),
_topic_for_ddog(change.topic),
])
if timer:
datadog_histogram('commcare.change_feed.processing_time', timer.duration, tags=tags)
def save_document(doc_ids):
lock_keys = []
for doc_id in doc_ids:
lock_keys.append(get_async_indicator_modify_lock_key(doc_id))
indicator_config_ids = None
timer = TimingContext()
with CriticalSection(lock_keys):
indicators = AsyncIndicator.objects.filter(doc_id__in=doc_ids)
if not indicators:
return
first_indicator = indicators[0]
processed_indicators = []
failed_indicators = []
for i in indicators:
assert i.domain == first_indicator.domain
assert i.doc_type == first_indicator.doc_type
indicator_by_doc_id = {i.doc_id: i for i in indicators}
doc_store = get_document_store(first_indicator.domain, first_indicator.doc_type)
indicator_config_ids = first_indicator.indicator_config_ids
with timer:
for doc in doc_store.iter_documents(doc_ids):
indicator = indicator_by_doc_id[doc['_id']]
successfully_processed, to_remove = _save_document_helper(indicator, doc)
if successfully_processed:
processed_indicators.append(indicator.pk)
else:
failed_indicators.append((indicator, to_remove))
AsyncIndicator.objects.filter(pk__in=processed_indicators).delete()
with transaction.atomic():
for indicator, to_remove in failed_indicators:
indicator.update_failure(to_remove)
indicator.save()
datadog_histogram(
'commcare.async_indicator.processing_time', timer.duration,
tags=[
u'config_ids:{}'.format(indicator_config_ids)
]
)
def __record_change_metric_in_datadog(self, metric, change, processor=None, processing_time=None):
if change.metadata is not None:
tags = [
'datasource:{}'.format(change.metadata.data_source_name),
'is_deletion:{}'.format(change.metadata.is_deletion),
'pillow_name:{}'.format(self.get_name()),
'processor:{}'.format(processor.__class__.__name__ if processor else "all_processors"),
]
count = 1 if processor else len(self.processors)
datadog_counter(metric, value=count, tags=tags)
change_lag = (datetime.utcnow() - change.metadata.publish_timestamp).total_seconds()
datadog_gauge('commcare.change_feed.change_lag', change_lag, tags=[
'pillow_name:{}'.format(self.get_name()),
_topic_for_ddog(change.topic),
])
if processing_time:
datadog_histogram('commcare.change_feed.processing_time', processing_time, tags=tags)
def restore(request, domain, app_id=None):
"""
We override restore because we have to supply our own
user model (and have the domain in the url)
"""
response, timing_context = get_restore_response(
domain, request.couch_user, app_id, **get_restore_params(request))
tags = [
u'status_code:{}'.format(response.status_code),
]
datadog_counter('commcare.restores.count', tags=tags)
if timing_context is not None:
for timer in timing_context.to_list(exclude_root=True):
# Only record leaf nodes so we can sum to get the total
if timer.is_leaf_node:
datadog_histogram(
'commcare.restores.timings',
timer.duration,
tags=tags + [u'segment:{}'.format(timer.name)],
)
return response
def _record_datadog_metrics(self, changes_chunk, processing_time):
tags = ["pillow_name:{}".format(self.get_name()), "mode:chunked"]
# Since success/fail count is tracked per processor, to get sense of
# actual operations count, multiply by number of processors
count = len(changes_chunk) * len(self.processors)
datadog_counter('commcare.change_feed.changes.count', count, tags=tags)
max_change_lag = (datetime.utcnow() - changes_chunk[0].metadata.publish_timestamp).total_seconds()
min_change_lag = (datetime.utcnow() - changes_chunk[-1].metadata.publish_timestamp).total_seconds()
datadog_gauge('commcare.change_feed.chunked.min_change_lag', min_change_lag, tags=tags)
datadog_gauge('commcare.change_feed.chunked.max_change_lag', max_change_lag, tags=tags)
# processing_time per change
datadog_histogram(
'commcare.change_feed.processing_time',
processing_time / len(changes_chunk),
tags=tags + ["chunk_size:".format(str(len(changes_chunk)))]
)
if len(changes_chunk) == self.processor_chunk_size:
# don't report offset chunks to ease up datadog calculations
datadog_histogram('commcare.change_feed.chunked.processing_time_total', processing_time,
tags=tags + ["chunk_size:{}".format(str(len(changes_chunk)))])
def _record_metrics(base_tags,
submission_type,
response,
result=None,
timer=None):
base_tags += 'submission_type:{}'.format(submission_type),
datadog_counter('commcare.xform_submissions.count',
tags=base_tags +
['status_code:{}'.format(response.status_code)])
if response.status_code == 201 and timer and result:
datadog_histogram('commcare.xform_submissions.timings',
timer.duration,
tags=base_tags)
# normalize over number of items (form or case) saved
normalized_time = timer.duration / (1 + len(result.cases))
datadog_histogram('commcare.xform_submissions.normalized_timings',
normalized_time,
tags=base_tags)
datadog_counter('commcare.xform_submissions.case_count',
len(result.cases),
tags=base_tags)
datadog_counter('commcare.xform_submissions.ledger_count',
len(result.ledgers),
tags=base_tags)
def __record_datadog_export(duration, doc_bytes, n_rows, metric, tags):
datadog_histogram(metric, duration, tags=tags)
if doc_bytes:
datadog_histogram(
'{}_normalized_by_size'.format(metric),
duration / doc_bytes,
tags=tags,
)
if n_rows:
datadog_histogram(
'{}_normalized_by_rows'.format(metric),
duration / n_rows,
tags=tags,
)
def __record_datadog_export(duration, doc_bytes, n_rows, metric, tags):
datadog_histogram(metric, duration, tags=tags)
if doc_bytes:
datadog_histogram(
'{}_normalized_by_size'.format(metric),
duration // doc_bytes,
tags=tags,
)
if n_rows:
datadog_histogram(
'{}_normalized_by_rows'.format(metric),
duration // n_rows,
tags=tags,
)
def __record_datadog_export(duration, doc_bytes, n_rows, metric, tags):
# deprecated: datadog histograms used this way are usually meaningless
# because they are aggregated on a 10s window (so unless you're constantly
# processing at least 100 measurements per 10s window then the percentile
# will approximate the average). Also more nuanced reasons. See
# https://confluence.dimagi.com/x/4ArDAQ#TroubleshootingPasteboard/HQchatdumpingground-Datadog
datadog_histogram(metric, duration, tags=tags)
if doc_bytes:
datadog_histogram(
'{}_normalized_by_size'.format(metric),
duration // doc_bytes,
tags=tags,
)
if n_rows:
datadog_histogram(
'{}_normalized_by_rows'.format(metric),
duration // n_rows,
tags=tags,
)
def __record_datadog_export(duration, doc_bytes, n_rows, metric, tags):
# deprecated: datadog histograms used this way are usually meaningless
# because they are aggregated on a 10s window (so unless you're constantly
# processing at least 100 measurements per 10s window then the percentile
# will approximate the average). Also more nuanced reasons. See
# https://confluence.dimagi.com/x/4ArDAQ#TroubleshootingPasteboard/HQchatdumpingground-Datadog
datadog_histogram(metric, duration, tags=tags)
if doc_bytes:
datadog_histogram(
'{}_normalized_by_size'.format(metric),
duration // doc_bytes,
tags=tags,
)
if n_rows:
datadog_histogram(
'{}_normalized_by_rows'.format(metric),
duration // n_rows,
tags=tags,
)
def _custom_data(self, selected_location, domain):
data = {}
for config in self.sources['data_source']:
filters = {}
if selected_location:
key = selected_location.location_type.name.lower() + '_id'
filters = {
key: [Choice(value=selected_location.location_id, display=selected_location.name)]
}
if 'date_filter_field' in config:
filters.update({config['date_filter_field']: self.config['date_span']})
if 'filter' in config:
for fil in config['filter']:
if 'type' in fil:
now = datetime.now()
start_date = now if 'start' not in fil else now - timedelta(days=fil['start'])
end_date = now if 'end' not in fil else now - timedelta(days=fil['end'])
datespan = DateSpan(start_date, end_date)
filters.update({fil['column']: datespan})
else:
filters.update({
fil['column']: {
'operator': fil['operator'],
'operand': fil['value']
}
})
timer = TimingContext()
with timer:
report_data = ICDSData(domain, filters, config['id']).data()
if selected_location:
loc_type = selected_location.location_type.name
else:
loc_type = None
datadog_histogram(
"commcare.icds.block_reports.ucr_querytime",
timer.duration,
tags="config:{}, location_type:{}, report_slug:{}".format(
config['id'], loc_type, self.slug
)
)
for column in config['columns']:
column_agg_func = column['agg_fun']
column_name = column['column_name']
column_data = 0
if column_agg_func == 'sum':
column_data = sum([x.get(column_name, 0) or 0 for x in report_data])
elif column_agg_func == 'count':
column_data = len(report_data)
elif column_agg_func == 'count_if':
value = column['condition']['value']
op = column['condition']['operator']
def check_condition(v):
if isinstance(v, six.string_types):
soft_assert_type_text(v)
fil_v = str(value)
elif isinstance(v, int):
fil_v = int(value)
else:
fil_v = value
if op == "in":
return OPERATORS[op](fil_v, v)
else:
return OPERATORS[op](v, fil_v)
column_data = len([val for val in report_data if check_condition(val[column_name])])
elif column_agg_func == 'avg':
values = [x.get(column_name, 0) for x in report_data]
column_data = sum(values) / (len(values) or 1)
column_display = column_name if 'column_in_report' not in column else column['column_in_report']
data.update({
column_display: data.get(column_display, 0) + column_data
})
return data
def _build_async_indicators(indicator_doc_ids):
def handle_exception(exception, config_id, doc, adapter):
metric = None
if isinstance(exception, (ProtocolError, ReadTimeout)):
metric = 'commcare.async_indicator.riak_error'
elif isinstance(exception, (ESError, ConnectionTimeout)):
# a database had an issue so log it and go on to the next document
metric = 'commcare.async_indicator.es_error'
elif isinstance(exception, (DatabaseError, InternalError)):
# a database had an issue so log it and go on to the next document
metric = 'commcare.async_indicator.psql_error'
else:
# getting the config could fail before the adapter is set
if adapter:
adapter.handle_exception(doc, exception)
if metric:
datadog_counter(metric,
1,
tags={
'config_id': config_id,
'doc_id': doc['_id']
})
def doc_ids_from_rows(rows):
formatted_rows = [{
column.column.database_column_name.decode('utf-8'): column.value
for column in row
} for row in rows]
return set(row['doc_id'] for row in formatted_rows)
# tracks processed/deleted configs to be removed from each indicator
configs_to_remove_by_indicator_id = defaultdict(list)
def _mark_config_to_remove(config_id, indicator_ids):
for _id in indicator_ids:
configs_to_remove_by_indicator_id[_id].append(config_id)
timer = TimingContext()
lock_keys = [
get_async_indicator_modify_lock_key(indicator_id)
for indicator_id in indicator_doc_ids
]
with CriticalSection(lock_keys):
all_indicators = AsyncIndicator.objects.filter(
doc_id__in=indicator_doc_ids)
if not all_indicators:
return
doc_store = get_document_store_for_doc_type(all_indicators[0].domain,
all_indicators[0].doc_type)
failed_indicators = set()
rows_to_save_by_adapter = defaultdict(list)
indicator_by_doc_id = {i.doc_id: i for i in all_indicators}
config_ids = set()
with timer:
for doc in doc_store.iter_documents(
list(indicator_by_doc_id.keys())):
indicator = indicator_by_doc_id[doc['_id']]
eval_context = EvaluationContext(doc)
for config_id in indicator.indicator_config_ids:
config_ids.add(config_id)
try:
config = _get_config_by_id(config_id)
except (ResourceNotFound,
StaticDataSourceConfigurationNotFoundError):
celery_task_logger.info(
"{} no longer exists, skipping".format(config_id))
# remove because the config no longer exists
_mark_config_to_remove(config_id, [indicator.pk])
continue
except ESError:
celery_task_logger.info(
"ES errored when trying to retrieve config")
failed_indicators.add(indicator)
continue
adapter = None
try:
adapter = get_indicator_adapter(config)
rows_to_save_by_adapter[adapter].extend(
adapter.get_all_values(doc, eval_context))
eval_context.reset_iteration()
except Exception as e:
failed_indicators.add(indicator)
handle_exception(e, config_id, doc, adapter)
for adapter, rows in six.iteritems(rows_to_save_by_adapter):
doc_ids = doc_ids_from_rows(rows)
indicators = [
indicator_by_doc_id[doc_id] for doc_id in doc_ids
]
try:
adapter.save_rows(rows)
except Exception as e:
failed_indicators.union(indicators)
message = six.text_type(e)
notify_exception(
None,
"Exception bulk saving async indicators:{}".format(
message))
else:
# remove because it's sucessfully processed
_mark_config_to_remove(config_id,
[i.pk for i in indicators])
# delete fully processed indicators
processed_indicators = set(all_indicators) - failed_indicators
AsyncIndicator.objects.filter(
pk__in=[i.pk for i in processed_indicators]).delete()
# update failure for failed indicators
with transaction.atomic():
for indicator in failed_indicators:
indicator.update_failure(
configs_to_remove_by_indicator_id.get(indicator.pk, []))
indicator.save()
datadog_counter('commcare.async_indicator.processed_success',
len(processed_indicators))
datadog_counter('commcare.async_indicator.processed_fail',
len(failed_indicators))
datadog_histogram('commcare.async_indicator.processing_time',
timer.duration / len(indicator_doc_ids),
tags=[
'config_ids:{}'.format(config_ids),
])
def _custom_data(self, selected_location, domain):
data = {}
for config in self.sources['data_source']:
filters = {}
if selected_location:
key = selected_location.location_type.name.lower() + '_id'
filters = {
key: [Choice(value=selected_location.location_id, display=selected_location.name)]
}
if 'date_filter_field' in config:
filters.update({config['date_filter_field']: self.config['date_span']})
if 'filter' in config:
for fil in config['filter']:
if 'type' in fil:
now = datetime.now()
start_date = now if 'start' not in fil else now - timedelta(days=fil['start'])
end_date = now if 'end' not in fil else now - timedelta(days=fil['end'])
datespan = DateSpan(start_date, end_date)
filters.update({fil['column']: datespan})
else:
filters.update({
fil['column']: {
'operator': fil['operator'],
'operand': fil['value']
}
})
timer = TimingContext()
with timer:
report_data = ICDSData(domain, filters, config['id']).data()
if selected_location:
loc_type = selected_location.location_type.name
else:
loc_type = None
datadog_histogram(
"commcare.icds.block_reports.ucr_querytime",
timer.duration,
tags="config:{}, location_type:{}, report_slug:{}".format(
config['id'], loc_type, self.slug
)
)
for column in config['columns']:
column_agg_func = column['agg_fun']
column_name = column['column_name']
column_data = 0
if column_agg_func == 'sum':
column_data = sum([x.get(column_name, 0) or 0 for x in report_data])
elif column_agg_func == 'count':
column_data = len(report_data)
elif column_agg_func == 'count_if':
value = column['condition']['value']
op = column['condition']['operator']
def check_condition(v):
if isinstance(v, six.string_types):
soft_assert_type_text(v)
fil_v = str(value)
elif isinstance(v, int):
fil_v = int(value)
else:
fil_v = value
if op == "in":
return OPERATORS[op](fil_v, v)
else:
return OPERATORS[op](v, fil_v)
column_data = len([val for val in report_data if check_condition(val[column_name])])
elif column_agg_func == 'avg':
values = [x.get(column_name, 0) for x in report_data]
column_data = sum(values) / (len(values) or 1)
column_display = column_name if 'column_in_report' not in column else column['column_in_report']
data.update({
column_display: data.get(column_display, 0) + column_data
})
return data
def _build_async_indicators(indicator_doc_ids):
def handle_exception(exception, config_id, doc, adapter):
metric = None
if isinstance(exception, (ProtocolError, ReadTimeout)):
metric = 'commcare.async_indicator.riak_error'
elif isinstance(exception, (ESError, ConnectionTimeout)):
# a database had an issue so log it and go on to the next document
metric = 'commcare.async_indicator.es_error'
elif isinstance(exception, (DatabaseError, InternalError)):
# a database had an issue so log it and go on to the next document
metric = 'commcare.async_indicator.psql_error'
else:
# getting the config could fail before the adapter is set
if adapter:
adapter.handle_exception(doc, exception)
if metric:
datadog_counter(metric, 1,
tags={'config_id': config_id, 'doc_id': doc['_id']})
def doc_ids_from_rows(rows):
formatted_rows = [
{column.column.database_column_name.decode('utf-8'): column.value for column in row}
for row in rows
]
return set(row['doc_id'] for row in formatted_rows)
# tracks processed/deleted configs to be removed from each indicator
configs_to_remove_by_indicator_id = defaultdict(list)
def _mark_config_to_remove(config_id, indicator_ids):
for _id in indicator_ids:
configs_to_remove_by_indicator_id[_id].append(config_id)
timer = TimingContext()
lock_keys = [
get_async_indicator_modify_lock_key(indicator_id)
for indicator_id in indicator_doc_ids
]
with CriticalSection(lock_keys):
all_indicators = AsyncIndicator.objects.filter(
doc_id__in=indicator_doc_ids
)
if not all_indicators:
return
doc_store = get_document_store_for_doc_type(
all_indicators[0].domain, all_indicators[0].doc_type,
load_source="build_async_indicators",
)
failed_indicators = set()
rows_to_save_by_adapter = defaultdict(list)
indicator_by_doc_id = {i.doc_id: i for i in all_indicators}
config_ids = set()
with timer:
for doc in doc_store.iter_documents(list(indicator_by_doc_id.keys())):
indicator = indicator_by_doc_id[doc['_id']]
eval_context = EvaluationContext(doc)
for config_id in indicator.indicator_config_ids:
config_ids.add(config_id)
try:
config = _get_config_by_id(config_id)
except (ResourceNotFound, StaticDataSourceConfigurationNotFoundError):
celery_task_logger.info("{} no longer exists, skipping".format(config_id))
# remove because the config no longer exists
_mark_config_to_remove(config_id, [indicator.pk])
continue
except ESError:
celery_task_logger.info("ES errored when trying to retrieve config")
failed_indicators.add(indicator)
continue
adapter = None
try:
adapter = get_indicator_adapter(config, load_source='build_async_indicators')
rows_to_save_by_adapter[adapter].extend(adapter.get_all_values(doc, eval_context))
eval_context.reset_iteration()
except Exception as e:
failed_indicators.add(indicator)
handle_exception(e, config_id, doc, adapter)
for adapter, rows in six.iteritems(rows_to_save_by_adapter):
doc_ids = doc_ids_from_rows(rows)
indicators = [indicator_by_doc_id[doc_id] for doc_id in doc_ids]
try:
adapter.save_rows(rows)
except Exception as e:
failed_indicators.union(indicators)
message = six.text_type(e)
notify_exception(None,
"Exception bulk saving async indicators:{}".format(message))
else:
# remove because it's sucessfully processed
_mark_config_to_remove(
config_id,
[i.pk for i in indicators]
)
# delete fully processed indicators
processed_indicators = set(all_indicators) - failed_indicators
AsyncIndicator.objects.filter(pk__in=[i.pk for i in processed_indicators]).delete()
# update failure for failed indicators
with transaction.atomic():
for indicator in failed_indicators:
indicator.update_failure(
configs_to_remove_by_indicator_id.get(indicator.pk, [])
)
indicator.save()
datadog_counter('commcare.async_indicator.processed_success', len(processed_indicators))
datadog_counter('commcare.async_indicator.processed_fail', len(failed_indicators))
datadog_histogram(
'commcare.async_indicator.processing_time', timer.duration / len(indicator_doc_ids),
tags=[
'config_ids:{}'.format(config_ids),
]
)
