def process_exception(self, request, exception):
import traceback
tb = traceback.format_exc()
logging.info("Processing exception: %s: %s" % (i18n.smart_unicode(exception),
i18n.smart_unicode(tb)))
if hasattr(exception, "response"):
return exception.response(request)
if hasattr(exception, "response_data"):
if request.ajax:
response = render_json(exception.response_data)
response[MIDDLEWARE_HEADER] = 'EXCEPTION'
return response
else:
return render("error.mako", request,
dict(error=exception.response_data.get("message")))
# We didn't handle it as a special exception, but if we're ajax we still
# need to do some kind of nicer handling than the built-in page
# Note that exception may actually be an Http404 or similar.
if request.ajax:
err = _("An error occurred: %(error)s") % {'error': exception}
logging.exception("Middleware caught an exception")
return PopupException(err, detail=None).response(request)
return None
def _invoke(self, method, relpath=None, params=None, data=None, headers=None, files=None, allow_redirects=False, clear_cookies=False, log_response=True):
"""
Invoke an API method.
@return: Raw body or JSON dictionary (if response content type is JSON).
"""
path = self._join_uri(relpath)
start_time = time.time()
resp = self._client.execute(method,
path,
params=params,
data=data,
headers=headers,
files=files,
allow_redirects=allow_redirects,
urlencode=self._urlencode,
clear_cookies=clear_cookies)
if log_response:
log_length = conf.REST_RESPONSE_SIZE.get() != -1 and conf.REST_RESPONSE_SIZE.get()
duration = time.time() - start_time
message = "%s %s Got response%s: %s%s" % (
method,
smart_unicode(path, errors='ignore'),
' in %dms' % (duration * 1000),
smart_unicode(resp.content[:log_length or None], errors='replace'),
log_length and len(resp.content) > log_length and "..." or ""
)
self._client.logger.disabled = 0
log_if_slow_call(duration=duration, message=message, logger=self._client.logger)
return resp
def _init_attributes(self):
self.queueName = i18n.smart_unicode(self.job.profile.queueName)
self.jobName = i18n.smart_unicode(self.job.profile.name)
self.user = i18n.smart_unicode(self.job.profile.user)
self.mapProgress = self.job.status.mapProgress
self.reduceProgress = self.job.status.reduceProgress
self.setupProgress = self.job.status.setupProgress
self.cleanupProgress = self.job.status.cleanupProgress
if self.job.desiredMaps == 0:
maps_percent_complete = 0
else:
maps_percent_complete = int(
round(
float(self.job.finishedMaps) / self.job.desiredMaps * 100))
self.desiredMaps = self.job.desiredMaps
if self.job.desiredReduces == 0:
reduces_percent_complete = 0
else:
reduces_percent_complete = int(
round(
float(self.job.finishedReduces) / self.job.desiredReduces *
100))
self.desiredReduces = self.job.desiredReduces
self.maps_percent_complete = maps_percent_complete
self.finishedMaps = self.job.finishedMaps
self.finishedReduces = self.job.finishedReduces
self.reduces_percent_complete = reduces_percent_complete
self.startTimeMs = self.job.startTime
self.startTimeFormatted = format_unixtime_ms(self.job.startTime)
self.launchTimeMs = self.job.launchTime
self.launchTimeFormatted = format_unixtime_ms(self.job.launchTime)
self.finishTimeMs = self.job.finishTime
self.finishTimeFormatted = format_unixtime_ms(self.job.finishTime)
self.status = self.job.status.runStateAsString
self.priority = self.job.priorityAsString
self.jobFile = self.job.profile.jobFile
finishTime = self.job.finishTime
if finishTime == 0:
finishTime = datetime.datetime.now()
else:
finishTime = datetime.datetime.fromtimestamp(finishTime / 1000)
self.duration = finishTime - datetime.datetime.fromtimestamp(
self.job.startTime / 1000)
diff = int(finishTime.strftime("%s")) * 1000 - self.startTimeMs
self.durationFormatted = format_duration_in_millis(diff)
self.durationInMillis = diff
def invoke(self, method, relpath=None, params=None, data=None, headers=None, files=None, allow_redirects=False,
clear_cookies=False):
"""
Invoke an API method.
@return: Raw body or JSON dictionary (if response content type is JSON).
"""
path = self._join_uri(relpath)
resp = self._client.execute(method,
path,
params=params,
data=data,
headers=headers,
files=files,
allow_redirects=allow_redirects,
urlencode=self._urlencode,
clear_cookies=clear_cookies)
if self._client.logger.isEnabledFor(logging.DEBUG):
self._client.logger.debug(
"%s Got response: %s%s" %
(method,
smart_unicode(resp.content[:1000], errors='replace'),
len(resp.content) > 1000 and "..." or ""))
return self._format_response(resp)
def decorator(*args, **kwargs):
response = {}
try:
return view_fn(*args, **kwargs)
except Exception, e:
LOG.exception('Error running %s' % view_fn)
response['status'] = -1
response['message'] = smart_unicode(e)
def render_to_string_normal(template_name, django_context):
data_dict = dict()
if isinstance(django_context, django.template.Context):
for d in reversed(django_context.dicts):
data_dict.update(d)
else:
data_dict = django_context
template = lookup.get_template(template_name)
result = template.render(**data_dict)
return i18n.smart_unicode(result)
def process_exception(self, request, exception):
import traceback
tb = traceback.format_exc()
logging.info("Processing exception: %s: %s" % (i18n.smart_unicode(exception),
i18n.smart_unicode(tb)))
if isinstance(exception, PopupException):
return exception.response(request)
if isinstance(exception, StructuredException):
if request.ajax:
response = render_json(exception.response_data)
response[MIDDLEWARE_HEADER] = 'EXCEPTION'
response.status_code = getattr(exception, 'error_code', 500)
return response
else:
response = render("error.mako", request,
dict(error=exception.response_data.get("message")))
response.status_code = getattr(exception, 'error_code', 500)
return response
return None
def render_to_string_normal(template_name, django_context):
data_dict = dict()
if isinstance(django_context, django.template.context.Context):
for d in reversed(django_context.dicts):
if d:
data_dict.update(d)
data_dict.update({'request': django_context.request})
else:
data_dict = django_context
template = lookup.get_template(template_name)
data_dict = dict(map(lambda k: (str(k), data_dict.get(k)), data_dict.keys()))
result = template.render(**data_dict)
return i18n.smart_unicode(result)
def rerun_oozie_coordinator(request, job_id, app_path=None):
oozie_coordinator = check_job_access_permission(request, job_id)
check_job_edition_permission(oozie_coordinator, request.user)
ParametersFormSet = formset_factory(ParameterForm, extra=0)
if app_path is None:
app_path = oozie_coordinator.coordJobPath
return_json = request.GET.get('format') == 'json'
if request.method == 'POST':
params_form = ParametersFormSet(request.POST)
rerun_form = RerunCoordForm(request.POST, oozie_coordinator=oozie_coordinator)
if sum([rerun_form.is_valid(), params_form.is_valid()]) == 2:
args = {}
args['deployment_dir'] = app_path
params = {
'type': 'action',
'scope': ','.join(oozie_coordinator.aggreate(rerun_form.cleaned_data['actions'])),
'refresh': rerun_form.cleaned_data['refresh'],
'nocleanup': rerun_form.cleaned_data['nocleanup'],
}
properties = dict([(param['name'], param['value']) for param in params_form.cleaned_data])
_rerun_coordinator(request, job_id, args, params, properties)
if rerun_form.cleaned_data['return_json']:
return JsonResponse({'status': 0, 'job_id': job_id}, safe=False)
else:
request.info(_('Coordinator re-running.'))
return redirect(reverse('oozie:list_oozie_coordinator', kwargs={'job_id': job_id}))
else:
request.error(_('Invalid submission form: %s') % smart_unicode(rerun_form.errors))
return list_oozie_coordinator(request, job_id)
else:
rerun_form = RerunCoordForm(oozie_coordinator=oozie_coordinator, return_json=return_json)
initial_params = ParameterForm.get_initial_params(oozie_coordinator.conf_dict)
params_form = ParametersFormSet(initial=initial_params)
popup = render('dashboard/rerun_coord_popup.mako', request, {
'rerun_form': rerun_form,
'params_form': params_form,
'action': reverse('oozie:rerun_oozie_coord', kwargs={'job_id': job_id, 'app_path': app_path}),
'return_json': return_json,
'is_mini': request.GET.get('is_mini', False),
}, force_template=True).content
return JsonResponse(popup, safe=False)
def rerun_oozie_coordinator(request, job_id, app_path):
oozie_coordinator = check_job_access_permission(request, job_id)
check_job_edition_permission(oozie_coordinator, request.user)
ParametersFormSet = formset_factory(ParameterForm, extra=0)
if request.method == "POST":
params_form = ParametersFormSet(request.POST)
rerun_form = RerunCoordForm(request.POST, oozie_coordinator=oozie_coordinator)
if sum([rerun_form.is_valid(), params_form.is_valid()]) == 2:
args = {}
args["deployment_dir"] = app_path
params = {
"type": "action",
"scope": ",".join(oozie_coordinator.aggreate(rerun_form.cleaned_data["actions"])),
"refresh": rerun_form.cleaned_data["refresh"],
"nocleanup": rerun_form.cleaned_data["nocleanup"],
}
properties = dict([(param["name"], param["value"]) for param in params_form.cleaned_data])
_rerun_coordinator(request, job_id, args, params, properties)
request.info(_("Coordinator re-running."))
return redirect(reverse("oozie:list_oozie_coordinator", kwargs={"job_id": job_id}))
else:
request.error(_("Invalid submission form: %s") % smart_unicode(rerun_form.errors))
return list_oozie_coordinator(request, job_id)
else:
rerun_form = RerunCoordForm(oozie_coordinator=oozie_coordinator)
initial_params = ParameterForm.get_initial_params(oozie_coordinator.conf_dict)
params_form = ParametersFormSet(initial=initial_params)
popup = render(
"dashboard/rerun_coord_popup.mako",
request,
{
"rerun_form": rerun_form,
"params_form": params_form,
"action": reverse("oozie:rerun_oozie_coord", kwargs={"job_id": job_id, "app_path": app_path}),
},
force_template=True,
).content
return JsonResponse(popup, safe=False)
def escape_rows(rows):
data = []
for row in rows:
escaped_row = []
for field in row:
if isinstance(field, numbers.Number):
if math.isnan(field) or math.isinf(field):
escaped_field = json.dumps(field)
else:
escaped_field = field
elif field is None:
escaped_field = 'NULL'
else:
field = smart_unicode(field, errors='replace') # Prevent error when getting back non utf8 like charset=iso-8859-1
escaped_field = escape(field).replace(' ', ' ')
escaped_row.append(escaped_field)
data.append(escaped_row)
return data
def __unicode__(self): return smart_unicode(self.message)
response['status'] = -3
except AuthenticationRequired, e:
response['status'] = 401
except ValidationError, e:
LOG.exception('Error validation %s' % func)
response['status'] = -1
response['message'] = e.message
except OperationTimeout, e:
response['status'] = -4
except FilesystemException, e:
response['status'] = 2
response['message'] = e.message
except QueryError, e:
LOG.exception('Error running %s' % func)
response['status'] = 1
response['message'] = smart_unicode(e)
if e.handle:
response['handle'] = e.handle
if e.extra:
response.update(e.extra)
except OperationNotSupported, e:
response['status'] = 5
response['message'] = e.message
except Exception, e:
LOG.exception('Error running %s' % func)
response['status'] = -1
response['message'] = smart_unicode(e)
finally:
if response:
return JsonResponse(response)
def augment_response(collection, query, response):
# HTML escaping
if not query.get('download'):
id_field = collection.get('idField', '')
for doc in response['response']['docs']:
link = None
if 'link-meta' in doc:
meta = json.loads(doc['link-meta'])
link = get_data_link(meta)
elif 'link' in doc:
meta = {'type': 'link', 'link': doc['link']}
link = get_data_link(meta)
for field, value in doc.iteritems():
if isinstance(value, numbers.Number):
escaped_value = value
elif field == '_childDocuments_': # Nested documents
escaped_value = value
elif isinstance(value, list): # Multivalue field
escaped_value = [
smart_unicode(escape(val), errors='replace')
for val in value
]
else:
value = smart_unicode(value, errors='replace')
escaped_value = escape(value)
doc[field] = escaped_value
doc['externalLink'] = link
doc['details'] = []
doc['hueId'] = smart_unicode(doc.get(id_field, ''))
if 'moreLikeThis' in response and response['moreLikeThis'][
doc['hueId']].get('numFound'):
_doc = response['moreLikeThis'][doc['hueId']]
doc['_childDocuments_'] = _doc['docs']
doc['numFound'] = _doc['numFound']
del response['moreLikeThis'][doc['hueId']]
highlighted_fields = response.get('highlighting', {}).keys()
if highlighted_fields and not query.get('download'):
id_field = collection.get('idField')
if id_field:
for doc in response['response']['docs']:
if id_field in doc and smart_unicode(
doc[id_field]) in highlighted_fields:
highlighting = response['highlighting'][smart_unicode(
doc[id_field])]
if highlighting:
escaped_highlighting = {}
for field, hls in highlighting.iteritems():
_hls = [
escape(smart_unicode(
hl, errors='replace')).replace(
'<em>', '<em>').replace(
'</em>', '</em>')
for hl in hls
]
escaped_highlighting[field] = _hls[0] if len(
_hls) == 1 else _hls
doc.update(escaped_highlighting)
else:
response['warning'] = _(
"The Solr schema requires an id field for performing the result highlighting"
)
def wrapper(*args, **kwargs):
response = {}
try:
return f(*args, **kwargs)
except SessionExpired as e:
response['status'] = -2
except QueryExpired as e:
response['status'] = -3
if e.message and isinstance(e.message, basestring):
response['message'] = e.message
except AuthenticationRequired as e:
response['status'] = 401
if e.message and isinstance(e.message, basestring):
response['message'] = e.message
except ValidationError as e:
LOG.exception('Error validation %s' % f)
response['status'] = -1
response['message'] = e.message
except OperationTimeout as e:
response['status'] = -4
except FilesystemException as e:
response['status'] = 2
response['message'] = e.message or 'Query history not found'
except QueryError as e:
LOG.exception('Error running %s' % f.__name__)
response['status'] = 1
response['message'] = smart_unicode(e)
if response['message'].index("max_row_size"):
size = re.search(r"(\d+.?\d*) (.B)", response['message'])
if size and size.group(1):
response['help'] = {
'setting': {
'name':
'max_row_size',
'value':
str(
int(
_closest_power_of_2(
_to_size_in_bytes(
size.group(1), size.group(2)))))
}
}
if e.handle:
response['handle'] = e.handle
if e.extra:
response.update(e.extra)
except OperationNotSupported as e:
response['status'] = 5
response['message'] = e.message
except RestException as e:
message = extract_solr_exception_message(e)
response['status'] = 1
response['message'] = message.get('error')
except Exception as e:
LOG.exception('Error running %s' % f.__name__)
response['status'] = -1
response['message'] = smart_unicode(e)
finally:
if response:
return JsonResponse(response)
def augment_solr_response(response, collection, query):
augmented = response
augmented['normalized_facets'] = []
normalized_facets = []
selected_values = dict([((fq['id'], fq['field'], fq['type']), fq['filter']) for fq in query['fqs']])
if response and response.get('facet_counts'):
# e.g. [{u'field': u'sun', u'type': u'query', u'id': u'67b43a63-ed22-747b-47e8-b31aad1431ea', u'label': u'sun'}
for facet in collection['facets']:
category = facet['type']
if category == 'field' and response['facet_counts']['facet_fields']:
name = facet['field']
collection_facet = get_facet_field(category, name, collection['facets'])
counts = pairwise2(name, selected_values.get((facet['id'], name, category), []), response['facet_counts']['facet_fields'][name])
if collection_facet['properties']['sort'] == 'asc':
counts.reverse()
facet = {
'id': collection_facet['id'],
'field': name,
'type': category,
'label': collection_facet['label'],
'counts': counts,
# add total result count?
}
normalized_facets.append(facet)
elif category == 'range' and response['facet_counts']['facet_ranges']:
name = facet['field']
collection_facet = get_facet_field(category, name, collection['facets'])
counts = response['facet_counts']['facet_ranges'][name]['counts']
end = response['facet_counts']['facet_ranges'][name]['end']
counts = range_pair(name, selected_values.get((facet['id'], name, 'range'), []), counts, end)
if collection_facet['properties']['sort'] == 'asc':
counts.reverse()
facet = {
'id': collection_facet['id'],
'field': name,
'type': category,
'label': collection_facet['label'],
'counts': counts,
'extraSeries': []
}
normalized_facets.append(facet)
elif category == 'query' and response['facet_counts']['facet_queries']:
for name, value in response['facet_counts']['facet_queries'].iteritems():
collection_facet = get_facet_field(category, name, collection['facets'])
facet = {
'id': collection_facet['id'],
'query': name,
'type': category,
'label': name,
'count': value,
}
normalized_facets.append(facet)
elif category == 'pivot':
name = ','.join([facet['field']] + [f['field'] for f in facet['properties']['facets']])
if 'facet_pivot' in response['facet_counts'] and name in response['facet_counts']['facet_pivot']:
if facet['properties']['scope'] == 'stack':
count = _augment_pivot_2d(facet['id'], response['facet_counts']['facet_pivot'][name], selected_values)
else:
count = response['facet_counts']['facet_pivot'][name]
_augment_pivot_nd(facet['id'], count, selected_values)
else:
count = []
facet = {
'id': facet['id'],
'query': name,
'type': category,
'label': name,
'count': count,
}
normalized_facets.append(facet)
# HTML escaping
for doc in response['response']['docs']:
for field, value in doc.iteritems():
if isinstance(value, numbers.Number):
escaped_value = value
else:
value = smart_unicode(value, errors='replace')
escaped_value = escape(value)
doc[field] = escaped_value
if not query['download']:
doc['showDetails'] = False
doc['details'] = []
highlighted_fields = response.get('highlighting', {}).keys()
if highlighted_fields and not query.get('download'):
id_field = collection.get('idField')
if id_field:
for doc in response['response']['docs']:
if id_field in doc and str(doc[id_field]) in highlighted_fields:
highlighting = response['highlighting'][str(doc[id_field])]
if highlighting:
escaped_highlighting = {}
for field, hls in highlighting.iteritems():
_hls = [escape(smart_unicode(hl, errors='replace')).replace('<em>', '<em>').replace('</em>', '</em>') for hl in hls]
escaped_highlighting[field] = _hls
doc.update(escaped_highlighting)
else:
response['warning'] = _("The Solr schema requires an id field for performing the result highlighting")
if normalized_facets:
augmented['normalized_facets'].extend(normalized_facets)
return augmented
def augment_solr_response(response, collection, query):
augmented = response
augmented["normalized_facets"] = []
NAME = "%(field)s-%(id)s"
normalized_facets = []
selected_values = dict([(fq["id"], fq["filter"]) for fq in query["fqs"]])
if response and response.get("facet_counts"):
for facet in collection["facets"]:
category = facet["type"]
if category == "field" and response["facet_counts"]["facet_fields"]:
name = NAME % facet
collection_facet = get_facet_field(category, name, collection["facets"])
counts = pairwise2(
facet["field"], selected_values.get(facet["id"], []), response["facet_counts"]["facet_fields"][name]
)
if collection_facet["properties"]["sort"] == "asc":
counts.reverse()
facet = {
"id": collection_facet["id"],
"field": facet["field"],
"type": category,
"label": collection_facet["label"],
"counts": counts,
}
normalized_facets.append(facet)
elif (category == "range" or category == "range-up") and response["facet_counts"]["facet_ranges"]:
name = NAME % facet
collection_facet = get_facet_field(category, name, collection["facets"])
counts = response["facet_counts"]["facet_ranges"][name]["counts"]
end = response["facet_counts"]["facet_ranges"][name]["end"]
counts = range_pair(
facet["field"], name, selected_values.get(facet["id"], []), counts, end, collection_facet
)
facet = {
"id": collection_facet["id"],
"field": facet["field"],
"type": category,
"label": collection_facet["label"],
"counts": counts,
"extraSeries": [],
}
normalized_facets.append(facet)
elif category == "query" and response["facet_counts"]["facet_queries"]:
for name, value in response["facet_counts"]["facet_queries"].iteritems():
collection_facet = get_facet_field(category, name, collection["facets"])
facet = {
"id": collection_facet["id"],
"query": name,
"type": category,
"label": name,
"counts": value,
}
normalized_facets.append(facet)
elif category == "pivot":
name = NAME % facet
if "facet_pivot" in response["facet_counts"] and name in response["facet_counts"]["facet_pivot"]:
if facet["properties"]["scope"] == "stack":
count = _augment_pivot_2d(
name, facet["id"], response["facet_counts"]["facet_pivot"][name], selected_values
)
else:
count = response["facet_counts"]["facet_pivot"][name]
_augment_pivot_nd(facet["id"], count, selected_values)
else:
count = []
facet = {"id": facet["id"], "field": name, "type": category, "label": name, "counts": count}
normalized_facets.append(facet)
if response and response.get("facets"):
for facet in collection["facets"]:
category = facet["type"]
name = facet["id"] # Nested facets can only have one name
if category == "function" and name in response["facets"]:
value = response["facets"][name]
collection_facet = get_facet_field(category, name, collection["facets"])
facet = {"id": collection_facet["id"], "query": name, "type": category, "label": name, "counts": value}
normalized_facets.append(facet)
elif category == "nested" and name in response["facets"]:
value = response["facets"][name]
collection_facet = get_facet_field(category, name, collection["facets"])
extraSeries = []
counts = response["facets"][name]["buckets"]
# Date range
if collection_facet["properties"]["isDate"]:
dimension = 3
# Single dimension or dimension 2 with analytics
if not collection_facet["properties"]["facets"] or collection_facet["properties"]["facets"][0][
"aggregate"
] not in ("count", "unique"):
counts = [_v for _f in counts for _v in (_f["val"], _f["d2"] if "d2" in _f else _f["count"])]
counts = range_pair(
facet["field"], name, selected_values.get(facet["id"], []), counts, 1, collection_facet
)
else:
# Dimension 1 with counts and 2 with analytics
_series = collections.defaultdict(list)
for f in counts:
for bucket in f["d2"]["buckets"] if "d2" in f else []:
_series[bucket["val"]].append(f["val"])
_series[bucket["val"]].append(bucket["d2"] if "d2" in bucket else bucket["count"])
for name, val in _series.iteritems():
_c = range_pair(
facet["field"], name, selected_values.get(facet["id"], []), val, 1, collection_facet
)
extraSeries.append({"counts": _c, "label": name})
counts = []
elif not collection_facet["properties"]["facets"] or collection_facet["properties"]["facets"][0][
"aggregate"
] not in ("count", "unique"):
# Single dimension or dimension 2 with analytics
dimension = 1
counts = [_v for _f in counts for _v in (_f["val"], _f["d2"] if "d2" in _f else _f["count"])]
counts = pairwise2(facet["field"], selected_values.get(facet["id"], []), counts)
else:
# Dimension 1 with counts and 2 with analytics
dimension = 2
counts = _augment_stats_2d(name, facet, counts, selected_values)
if collection_facet["properties"]["sort"] == "asc":
counts.reverse()
facet = {
"id": collection_facet["id"],
"field": facet["field"],
"type": category,
"label": collection_facet["label"],
"counts": counts,
"extraSeries": extraSeries,
"dimension": dimension,
}
normalized_facets.append(facet)
# Remove unnecessary facet data
if response:
response.pop("facet_counts")
response.pop("facets")
# HTML escaping
for doc in response["response"]["docs"]:
for field, value in doc.iteritems():
if isinstance(value, numbers.Number):
escaped_value = value
elif isinstance(value, list): # Multivalue field
escaped_value = [smart_unicode(val, errors="replace") for val in value]
else:
value = smart_unicode(value, errors="replace")
escaped_value = escape(value)
doc[field] = escaped_value
if not query.get("download"):
link = None
if "link-meta" in doc:
meta = json.loads(doc["link-meta"])
link = get_data_link(meta)
doc["externalLink"] = link
doc["details"] = []
highlighted_fields = response.get("highlighting", {}).keys()
if highlighted_fields and not query.get("download"):
id_field = collection.get("idField")
if id_field:
for doc in response["response"]["docs"]:
if id_field in doc and smart_unicode(doc[id_field]) in highlighted_fields:
highlighting = response["highlighting"][smart_unicode(doc[id_field])]
if highlighting:
escaped_highlighting = {}
for field, hls in highlighting.iteritems():
_hls = [
escape(smart_unicode(hl, errors="replace"))
.replace("<em>", "<em>")
.replace("</em>", "</em>")
for hl in hls
]
escaped_highlighting[field] = _hls
doc.update(escaped_highlighting)
else:
response["warning"] = _("The Solr schema requires an id field for performing the result highlighting")
if normalized_facets:
augmented["normalized_facets"].extend(normalized_facets)
return augmented
def view_results(request, id, first_row=0):
"""
Returns the view for the results of the QueryHistory with the given id.
The query results MUST be ready.
To display query results, one should always go through the execute_query view.
If the result set has has_result_set=False, display an empty result.
If ``first_row`` is 0, restarts (if necessary) the query read. Otherwise, just
spits out a warning if first_row doesn't match the servers conception.
Multiple readers will produce a confusing interaction here, and that's known.
It understands the ``context`` GET parameter. (See execute_query().)
"""
first_row = long(first_row)
start_over = (first_row == 0)
results = type('Result', (object, ), {
'rows': 0,
'columns': [],
'has_more': False,
'start_row': 0,
})
data = []
fetch_error = False
error_message = ''
log = ''
columns = []
app_name = get_app_name(request)
query_history = authorized_get_query_history(request, id, must_exist=True)
query_server = query_history.get_query_server_config()
db = dbms.get(request.user, query_server)
handle, state = _get_query_handle_and_state(query_history)
context_param = request.GET.get('context', '')
query_context = parse_query_context(context_param)
# Update the status as expired should not be accessible
# Impala does not support startover for now
expired = state == models.QueryHistory.STATE.expired
# Retrieve query results or use empty result if no result set
try:
if query_server[
'server_name'] == 'impala' and not handle.has_result_set:
downloadable = False
else:
results = db.fetch(handle, start_over, 100)
data = []
# Materialize and HTML escape results
# TODO: use Number + list comprehension
for row in results.rows():
escaped_row = []
for field in row:
if isinstance(field, (int, long, float, complex, bool)):
escaped_field = field
elif field is None:
escaped_field = 'NULL'
else:
field = smart_unicode(
field, errors='replace'
) # Prevent error when getting back non utf8 like charset=iso-8859-1
escaped_field = escape(field).replace(' ', ' ')
escaped_row.append(escaped_field)
data.append(escaped_row)
# We display the "Download" button only when we know that there are results:
downloadable = first_row > 0 or data
log = db.get_log(handle)
columns = results.data_table.cols()
except Exception, ex:
fetch_error = True
error_message, log = expand_exception(ex, db, handle)
def augment_solr_response(response, collection, query):
augmented = response
augmented['normalized_facets'] = []
NAME = '%(field)s-%(id)s'
normalized_facets = []
selected_values = dict([(fq['id'], fq['filter']) for fq in query['fqs']])
if response and response.get('facet_counts'):
for facet in collection['facets']:
category = facet['type']
if category == 'field' and response['facet_counts']['facet_fields']:
name = NAME % facet
collection_facet = get_facet_field(category, name, collection['facets'])
counts = pairwise2(facet['field'], selected_values.get(facet['id'], []), response['facet_counts']['facet_fields'][name])
if collection_facet['properties']['sort'] == 'asc':
counts.reverse()
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
}
normalized_facets.append(facet)
elif (category == 'range' or category == 'range-up') and response['facet_counts']['facet_ranges']:
name = NAME % facet
collection_facet = get_facet_field(category, name, collection['facets'])
counts = response['facet_counts']['facet_ranges'][name]['counts']
end = response['facet_counts']['facet_ranges'][name]['end']
counts = range_pair(facet['field'], name, selected_values.get(facet['id'], []), counts, end, collection_facet)
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
'extraSeries': []
}
normalized_facets.append(facet)
elif category == 'query' and response['facet_counts']['facet_queries']:
for name, value in response['facet_counts']['facet_queries'].iteritems():
collection_facet = get_facet_field(category, name, collection['facets'])
facet = {
'id': collection_facet['id'],
'query': name,
'type': category,
'label': name,
'counts': value,
}
normalized_facets.append(facet)
elif category == 'pivot':
name = NAME % facet
if 'facet_pivot' in response['facet_counts'] and name in response['facet_counts']['facet_pivot']:
if facet['properties']['scope'] == 'stack':
count = _augment_pivot_2d(name, facet['id'], response['facet_counts']['facet_pivot'][name], selected_values)
else:
count = response['facet_counts']['facet_pivot'][name]
_augment_pivot_nd(facet['id'], count, selected_values)
else:
count = []
facet = {
'id': facet['id'],
'field': name,
'type': category,
'label': name,
'counts': count,
}
normalized_facets.append(facet)
if response and response.get('facets'):
for facet in collection['facets']:
category = facet['type']
name = facet['id'] # Nested facets can only have one name
if category == 'function' and name in response['facets']:
value = response['facets'][name]
collection_facet = get_facet_field(category, name, collection['facets'])
facet = {
'id': collection_facet['id'],
'query': name,
'type': category,
'label': name,
'counts': value,
}
normalized_facets.append(facet)
elif category == 'nested' and name in response['facets']:
value = response['facets'][name]
collection_facet = get_facet_field(category, name, collection['facets'])
extraSeries = []
counts = response['facets'][name]['buckets']
cols = ['%(field)s' % facet, 'count(%(field)s)' % facet]
last_x_col = 0
last_xx_col = 0
for i, f in enumerate(facet['properties']['facets']):
if f['aggregate']['function'] == 'count':
cols.append(f['field'])
last_xx_col = last_x_col
last_x_col = i + 2
cols.append(SolrApi._get_aggregate_function(f))
rows = []
# For dim in dimensions
# Number or Date range
if collection_facet['properties']['canRange'] and not facet['properties'].get('type') == 'field':
dimension = 3 if collection_facet['properties']['isDate'] else 1
# Single dimension or dimension 2 with analytics
if not collection_facet['properties']['facets'] or collection_facet['properties']['facets'][0]['aggregate']['function'] != 'count' and len(collection_facet['properties']['facets']) == 1:
column = 'count'
if len(collection_facet['properties']['facets']) == 1:
agg_keys = [key for key, value in counts[0].items() if key.lower().startswith('agg_')]
legend = agg_keys[0].split(':', 2)[1]
column = agg_keys[0]
else:
legend = facet['field'] # 'count(%s)' % legend
agg_keys = [column]
_augment_stats_2d(name, facet, counts, selected_values, agg_keys, rows)
counts = [_v for _f in counts for _v in (_f['val'], _f[column])]
counts = range_pair(facet['field'], name, selected_values.get(facet['id'], []), counts, 1, collection_facet)
else:
# Dimension 1 with counts and 2 with analytics
agg_keys = [key for key, value in counts[0].items() if key.lower().startswith('agg_') or key.lower().startswith('dim_')]
agg_keys.sort(key=lambda a: a[4:])
if len(agg_keys) == 1 and agg_keys[0].lower().startswith('dim_'):
agg_keys.insert(0, 'count')
counts = _augment_stats_2d(name, facet, counts, selected_values, agg_keys, rows)
_series = collections.defaultdict(list)
for row in rows:
for i, cell in enumerate(row):
if i > last_x_col:
legend = cols[i]
if last_xx_col != last_x_col:
legend = '%s %s' % (cols[i], row[last_x_col])
_series[legend].append(row[last_xx_col])
_series[legend].append(cell)
for name, val in _series.iteritems():
_c = range_pair(facet['field'], name, selected_values.get(facet['id'], []), val, 1, collection_facet)
extraSeries.append({'counts': _c, 'label': name})
counts = []
elif collection_facet['properties'].get('isOldPivot'):
facet_fields = [collection_facet['field']] + [f['field'] for f in collection_facet['properties'].get('facets', []) if f['aggregate']['function'] == 'count']
column = 'count'
agg_keys = [key for key, value in counts[0].items() if key.lower().startswith('agg_') or key.lower().startswith('dim_')]
agg_keys.sort(key=lambda a: a[4:])
if len(agg_keys) == 1 and agg_keys[0].lower().startswith('dim_'):
agg_keys.insert(0, 'count')
counts = _augment_stats_2d(name, facet, counts, selected_values, agg_keys, rows)
#_convert_nested_to_augmented_pivot_nd(facet_fields, facet['id'], count, selected_values, dimension=2)
dimension = len(facet_fields)
elif not collection_facet['properties']['facets'] or (collection_facet['properties']['facets'][0]['aggregate']['function'] != 'count' and len(collection_facet['properties']['facets']) == 1):
# Dimension 1 with 1 count or agg
dimension = 1
column = 'count'
if len(collection_facet['properties']['facets']) == 1:
agg_keys = [key for key, value in counts[0].items() if key.lower().startswith('agg_')]
legend = agg_keys[0].split(':', 2)[1]
column = agg_keys[0]
else:
legend = facet['field']
agg_keys = [column]
_augment_stats_2d(name, facet, counts, selected_values, agg_keys, rows)
counts = [_v for _f in counts for _v in (_f['val'], _f[column])]
counts = pairwise2(legend, selected_values.get(facet['id'], []), counts)
else:
# Dimension 2 with analytics or 1 with N aggregates
dimension = 2
agg_keys = [key for key, value in counts[0].items() if key.lower().startswith('agg_') or key.lower().startswith('dim_')]
agg_keys.sort(key=lambda a: a[4:])
if len(agg_keys) == 1 and agg_keys[0].lower().startswith('dim_'):
agg_keys.insert(0, 'count')
counts = _augment_stats_2d(name, facet, counts, selected_values, agg_keys, rows)
actual_dimension = 1 + sum([_f['aggregate']['function'] == 'count' for _f in collection_facet['properties']['facets']])
counts = filter(lambda a: len(a['fq_fields']) == actual_dimension, counts)
num_bucket = response['facets'][name]['numBuckets'] if 'numBuckets' in response['facets'][name] else len(response['facets'][name])
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
'extraSeries': extraSeries,
'dimension': dimension,
'response': {'response': {'start': 0, 'numFound': num_bucket}}, # Todo * nested buckets + offsets
'docs': [dict(zip(cols, row)) for row in rows],
'fieldsAttributes': [Collection2._make_gridlayout_header_field({'name': col, 'type': 'aggr' if '(' in col else 'string'}) for col in cols]
}
normalized_facets.append(facet)
# Remove unnecessary facet data
if response:
response.pop('facet_counts')
response.pop('facets')
# HTML escaping
if not query.get('download'):
id_field = collection.get('idField', '')
for doc in response['response']['docs']:
for field, value in doc.iteritems():
if isinstance(value, numbers.Number):
escaped_value = value
elif field == '_childDocuments_': # Nested documents
escaped_value = value
elif isinstance(value, list): # Multivalue field
escaped_value = [smart_unicode(escape(val), errors='replace') for val in value]
else:
value = smart_unicode(value, errors='replace')
escaped_value = escape(value)
doc[field] = escaped_value
link = None
if 'link-meta' in doc:
meta = json.loads(doc['link-meta'])
link = get_data_link(meta)
doc['externalLink'] = link
doc['details'] = []
doc['hueId'] = smart_unicode(doc.get(id_field, ''))
highlighted_fields = response.get('highlighting', {}).keys()
if highlighted_fields and not query.get('download'):
id_field = collection.get('idField')
if id_field:
for doc in response['response']['docs']:
if id_field in doc and smart_unicode(doc[id_field]) in highlighted_fields:
highlighting = response['highlighting'][smart_unicode(doc[id_field])]
if highlighting:
escaped_highlighting = {}
for field, hls in highlighting.iteritems():
_hls = [escape(smart_unicode(hl, errors='replace')).replace('<em>', '<em>').replace('</em>', '</em>') for hl in hls]
escaped_highlighting[field] = _hls[0] if len(_hls) == 1 else _hls
doc.update(escaped_highlighting)
else:
response['warning'] = _("The Solr schema requires an id field for performing the result highlighting")
if normalized_facets:
augmented['normalized_facets'].extend(normalized_facets)
return augmented
def _invoke(self,
method,
relpath=None,
params=None,
data=None,
headers=None,
files=None,
allow_redirects=False,
clear_cookies=False,
log_response=True):
"""
Invoke an API method.
Print trace even when there is an exception.
@return: Raw body or JSON dictionary (if response content type is JSON).
"""
path = self._join_uri(relpath)
start_time = time.time()
resp = None
try:
resp = self._client.execute(method,
path,
params=params,
data=data,
headers=headers,
files=files,
allow_redirects=allow_redirects,
urlencode=self._urlencode,
clear_cookies=clear_cookies)
finally:
# Output duration without content
log_length = conf.REST_RESPONSE_SIZE.get(
) != -1 and conf.REST_RESPONSE_SIZE.get() if log_response else 0
duration = time.time() - start_time
try:
req_data = smart_unicode(data, errors='replace')
resp_content = smart_unicode(
resp.content,
errors='replace') if resp and resp.content else ''
message = u'%s %s %s%s%s %s%s returned in %dms %s %s %s%s' % (
method, type(self._client._session.auth)
if self._client._session and self._client._session.auth
else None, self._client._base_url,
smart_unicode(path, errors='replace'),
iri_to_uri('?' + urlencode(params)) if params else '',
req_data[:log_length] if data else '', log_length and
len(req_data) > log_length and '...' or '' if data else '',
(duration * 1000), resp.status_code if resp else 0,
len(resp_content) if resp else 0,
resp_content[:log_length] if resp else '',
log_length and len(resp_content) > log_length and '...'
or '' if resp else '')
except:
short_call_name = '%s %s' % (method, self._client._base_url)
LOG.exception('Error logging return call %s' % short_call_name)
message = '%s returned in %dms' % (short_call_name, duration)
self._client.logger.disabled = 0
log_if_slow_call(duration=duration,
message=message,
logger=self._client.logger)
return resp
def guess_field_types(request):
file_format = json.loads(request.POST.get('fileFormat', '{}'))
if file_format['inputFormat'] == 'file':
indexer = MorphlineIndexer(request.user, request.fs)
path = urllib_unquote(file_format["path"])
stream = request.fs.open(path)
encoding = check_encoding(stream.read(10000))
stream.seek(0)
_convert_format(file_format["format"], inverse=True)
format_ = indexer.guess_field_types({
"file": {
"stream": stream,
"name": path
},
"format": file_format['format']
})
# Note: Would also need to set charset to table (only supported in Hive)
if 'sample' in format_ and format_['sample']:
format_['sample'] = escape_rows(format_['sample'],
nulls_only=True,
encoding=encoding)
for col in format_['columns']:
col['name'] = smart_unicode(col['name'],
errors='replace',
encoding=encoding)
elif file_format['inputFormat'] == 'table':
sample = get_api(request, {
'type': 'hive'
}).get_sample_data({'type': 'hive'},
database=file_format['databaseName'],
table=file_format['tableName'])
db = dbms.get(request.user)
table_metadata = db.get_table(database=file_format['databaseName'],
table_name=file_format['tableName'])
format_ = {
"sample":
sample['rows'][:4],
"columns": [
Field(col.name,
HiveFormat.FIELD_TYPE_TRANSLATE.get(col.type,
'string')).to_dict()
for col in table_metadata.cols
]
}
elif file_format['inputFormat'] == 'query':
query_id = file_format['query']['id'] if file_format['query'].get(
'id') else file_format['query']
notebook = Notebook(document=Document2.objects.document(
user=request.user, doc_id=query_id)).get_data()
snippet = notebook['snippets'][0]
db = get_api(request, snippet)
if file_format.get('sampleCols'):
columns = file_format.get('sampleCols')
sample = file_format.get('sample')
else:
snippet['query'] = snippet['statement']
try:
sample = db.fetch_result(notebook, snippet, 4,
start_over=True)['rows'][:4]
except Exception as e:
LOG.warn(
'Skipping sample data as query handle might be expired: %s'
% e)
sample = [[], [], [], [], []]
columns = db.autocomplete(snippet=snippet, database='', table='')
columns = [
Field(
col['name'],
HiveFormat.FIELD_TYPE_TRANSLATE.get(col['type'],
'string')).to_dict()
for col in columns['extended_columns']
]
format_ = {
"sample": sample,
"columns": columns,
}
elif file_format['inputFormat'] == 'rdbms':
api = _get_api(request)
sample = api.get_sample_data(None,
database=file_format['rdbmsDatabaseName'],
table=file_format['tableName'])
format_ = {
"sample":
list(sample['rows'])[:4],
"columns": [
Field(col['name'], col['type']).to_dict()
for col in sample['full_headers']
]
}
elif file_format['inputFormat'] == 'stream':
if file_format['streamSelection'] == 'kafka':
if file_format.get(
'kafkaSelectedTopics') == 'NavigatorAuditEvents':
kafkaFieldNames = [
'id', 'additionalInfo', 'allowed', 'collectionName',
'databaseName', 'db', 'DELEGATION_TOKEN_ID', 'dst',
'entityId', 'family', 'impersonator', 'ip', 'name',
'objectType', 'objType', 'objUsageType', 'operationParams',
'operationText', 'op', 'opText', 'path', 'perms',
'privilege', 'qualifier', 'QUERY_ID', 'resourcePath',
'service', 'SESSION_ID', 'solrVersion', 'src', 'status',
'subOperation', 'tableName', 'table', 'time', 'type',
'url', 'user'
]
kafkaFieldTypes = ['string'] * len(kafkaFieldNames)
kafkaFieldNames.append('timeDate')
kafkaFieldTypes.append('date')
else:
# Note: mocked here, should come from SFDC or Kafka API or sampling job
kafkaFieldNames = file_format.get('kafkaFieldNames',
'').split(',')
kafkaFieldTypes = file_format.get('kafkaFieldTypes',
'').split(',')
data = """%(kafkaFieldNames)s
%(data)s""" % {
'kafkaFieldNames': ','.join(kafkaFieldNames),
'data': '\n'.join(
[','.join(['...'] * len(kafkaFieldTypes))] * 5)
}
stream = string_io()
stream.write(data)
_convert_format(file_format["format"], inverse=True)
indexer = MorphlineIndexer(request.user, request.fs)
format_ = indexer.guess_field_types({
"file": {
"stream": stream,
"name": file_format['path']
},
"format": file_format['format']
})
type_mapping = dict(list(zip(kafkaFieldNames, kafkaFieldTypes)))
for col in format_['columns']:
col['keyType'] = type_mapping[col['name']]
col['type'] = type_mapping[col['name']]
elif file_format['streamSelection'] == 'flume':
if 'hue-httpd/access_log' in file_format['channelSourcePath']:
columns = [{
'name': 'id',
'type': 'string',
'unique': True
}, {
'name': 'client_ip',
'type': 'string'
}, {
'name': 'time',
'type': 'date'
}, {
'name': 'request',
'type': 'string'
}, {
'name': 'code',
'type': 'plong'
}, {
'name': 'bytes',
'type': 'plong'
}, {
'name': 'method',
'type': 'string'
}, {
'name': 'url',
'type': 'string'
}, {
'name': 'protocol',
'type': 'string'
}, {
'name': 'app',
'type': 'string'
}, {
'name': 'subapp',
'type': 'string'
}]
else:
columns = [{'name': 'message', 'type': 'string'}]
format_ = {
"sample": [['...'] * len(columns)] * 4,
"columns": [
Field(col['name'],
HiveFormat.FIELD_TYPE_TRANSLATE.get(
col['type'], 'string'),
unique=col.get('unique')).to_dict()
for col in columns
]
}
elif file_format['inputFormat'] == 'connector':
if file_format['connectorSelection'] == 'sfdc':
sf = Salesforce(username=file_format['streamUsername'],
password=file_format['streamPassword'],
security_token=file_format['streamToken'])
table_metadata = [{
'name': column['name'],
'type': column['type']
} for column in sf.restful('sobjects/%(streamObject)s/describe/' %
file_format)['fields']]
query = 'SELECT %s FROM %s LIMIT 4' % (', '.join(
[col['name']
for col in table_metadata]), file_format['streamObject'])
print(query)
try:
records = sf.query_all(query)
except SalesforceRefusedRequest as e:
raise PopupException(message=str(e))
format_ = {
"sample":
[list(row.values())[1:] for row in records['records']],
"columns": [
Field(
col['name'],
HiveFormat.FIELD_TYPE_TRANSLATE.get(
col['type'], 'string')).to_dict()
for col in table_metadata
]
}
else:
raise PopupException(
_('Connector format not recognized: %(connectorSelection)s') %
file_format)
else:
raise PopupException(
_('Input format not recognized: %(inputFormat)s') % file_format)
return JsonResponse(format_)
def guess_field_types(request):
file_format = json.loads(request.POST.get('fileFormat', '{}'))
if file_format['inputFormat'] == 'file':
indexer = MorphlineIndexer(request.user, request.fs)
path = urllib.unquote(file_format["path"])
stream = request.fs.open(path)
encoding = chardet.detect(stream.read(10000)).get('encoding')
stream.seek(0)
_convert_format(file_format["format"], inverse=True)
format_ = indexer.guess_field_types({
"file": {
"stream": stream,
"name": path
},
"format": file_format['format']
})
# Note: Would also need to set charset to table (only supported in Hive)
if 'sample' in format_:
format_['sample'] = escape_rows(format_['sample'],
nulls_only=True,
encoding=encoding)
for col in format_['columns']:
col['name'] = smart_unicode(col['name'],
errors='replace',
encoding=encoding)
elif file_format['inputFormat'] == 'table':
sample = get_api(request, {
'type': 'hive'
}).get_sample_data({'type': 'hive'},
database=file_format['databaseName'],
table=file_format['tableName'])
db = dbms.get(request.user)
table_metadata = db.get_table(database=file_format['databaseName'],
table_name=file_format['tableName'])
format_ = {
"sample":
sample['rows'][:4],
"columns": [
Field(col.name,
HiveFormat.FIELD_TYPE_TRANSLATE.get(col.type,
'string')).to_dict()
for col in table_metadata.cols
]
}
elif file_format['inputFormat'] == 'query':
query_id = file_format['query']['id'] if file_format['query'].get(
'id') else file_format['query']
notebook = Notebook(document=Document2.objects.document(
user=request.user, doc_id=query_id)).get_data()
snippet = notebook['snippets'][0]
db = get_api(request, snippet)
if file_format.get('sampleCols'):
columns = file_format.get('sampleCols')
sample = file_format.get('sample')
else:
snippet['query'] = snippet['statement']
try:
sample = db.fetch_result(notebook, snippet, 4,
start_over=True)['rows'][:4]
except Exception, e:
LOG.warn(
'Skipping sample data as query handle might be expired: %s'
% e)
sample = [[], [], [], [], []]
columns = db.autocomplete(snippet=snippet, database='', table='')
columns = [
Field(
col['name'],
HiveFormat.FIELD_TYPE_TRANSLATE.get(col['type'],
'string')).to_dict()
for col in columns['extended_columns']
]
format_ = {
"sample": sample,
"columns": columns,
}
def guess_field_types(request):
file_format = json.loads(request.POST.get('fileFormat', '{}'))
if file_format['inputFormat'] == 'localfile':
path = urllib_unquote(file_format['path'])
with open(path, 'r') as local_file:
reader = csv.reader(local_file)
csv_data = list(reader)
if file_format['format']['hasHeader']:
sample = csv_data[1:5]
column_row = [
re.sub('[^0-9a-zA-Z]+', '_', col) for col in csv_data[0]
]
else:
sample = csv_data[:4]
column_row = [
'field_' + str(count + 1)
for count, col in enumerate(sample[0])
]
field_type_guesses = []
for count, col in enumerate(column_row):
column_samples = [
sample_row[count] for sample_row in sample
if len(sample_row) > count
]
field_type_guess = guess_field_type_from_samples(
column_samples)
field_type_guesses.append(field_type_guess)
columns = [
Field(column_row[count], field_type_guesses[count]).to_dict()
for count, col in enumerate(column_row)
]
format_ = {'columns': columns, 'sample': sample}
elif file_format['inputFormat'] == 'file':
indexer = MorphlineIndexer(request.user, request.fs)
path = urllib_unquote(file_format["path"])
if path[-3:] == 'xls' or path[-4:] == 'xlsx':
path = excel_to_csv_file_name_change(path)
stream = request.fs.open(path)
encoding = check_encoding(stream.read(10000))
LOG.debug('File %s encoding is %s' % (path, encoding))
stream.seek(0)
_convert_format(file_format["format"], inverse=True)
format_ = indexer.guess_field_types({
"file": {
"stream": stream,
"name": path
},
"format": file_format['format']
})
# Note: Would also need to set charset to table (only supported in Hive)
if 'sample' in format_ and format_['sample']:
format_['sample'] = escape_rows(format_['sample'],
nulls_only=True,
encoding=encoding)
for col in format_['columns']:
col['name'] = smart_unicode(col['name'],
errors='replace',
encoding=encoding)
elif file_format['inputFormat'] == 'table':
sample = get_api(request, {
'type': 'hive'
}).get_sample_data({'type': 'hive'},
database=file_format['databaseName'],
table=file_format['tableName'])
db = dbms.get(request.user)
table_metadata = db.get_table(database=file_format['databaseName'],
table_name=file_format['tableName'])
format_ = {
"sample":
sample['rows'][:4],
"columns": [
Field(col.name,
HiveFormat.FIELD_TYPE_TRANSLATE.get(col.type,
'string')).to_dict()
for col in table_metadata.cols
]
}
elif file_format['inputFormat'] == 'query':
query_id = file_format['query']['id'] if file_format['query'].get(
'id') else file_format['query']
notebook = Notebook(document=Document2.objects.document(
user=request.user, doc_id=query_id)).get_data()
snippet = notebook['snippets'][0]
db = get_api(request, snippet)
if file_format.get('sampleCols'):
columns = file_format.get('sampleCols')
sample = file_format.get('sample')
else:
snippet['query'] = snippet['statement']
try:
sample = db.fetch_result(notebook, snippet, 4,
start_over=True)['rows'][:4]
except Exception as e:
LOG.warning(
'Skipping sample data as query handle might be expired: %s'
% e)
sample = [[], [], [], [], []]
columns = db.autocomplete(snippet=snippet, database='', table='')
columns = [
Field(
col['name'],
HiveFormat.FIELD_TYPE_TRANSLATE.get(col['type'],
'string')).to_dict()
for col in columns['extended_columns']
]
format_ = {
"sample": sample,
"columns": columns,
}
elif file_format['inputFormat'] == 'rdbms':
api = _get_api(request)
sample = api.get_sample_data(None,
database=file_format['rdbmsDatabaseName'],
table=file_format['tableName'])
format_ = {
"sample":
list(sample['rows'])[:4],
"columns": [
Field(col['name'], col['type']).to_dict()
for col in sample['full_headers']
]
}
elif file_format['inputFormat'] == 'stream':
if file_format['streamSelection'] == 'kafka':
data = get_topic_data(request.user,
file_format.get('kafkaSelectedTopics'))
kafkaFieldNames = [col['name'] for col in data['full_headers']]
kafkaFieldTypes = [col['type'] for col in data['full_headers']]
topics_data = data['rows']
format_ = {
"sample":
topics_data,
"columns": [
Field(col, 'string', unique=False).to_dict()
for col in kafkaFieldNames
]
}
elif file_format['streamSelection'] == 'flume':
if 'hue-httpd/access_log' in file_format['channelSourcePath']:
columns = [{
'name': 'id',
'type': 'string',
'unique': True
}, {
'name': 'client_ip',
'type': 'string'
}, {
'name': 'time',
'type': 'date'
}, {
'name': 'request',
'type': 'string'
}, {
'name': 'code',
'type': 'plong'
}, {
'name': 'bytes',
'type': 'plong'
}, {
'name': 'method',
'type': 'string'
}, {
'name': 'url',
'type': 'string'
}, {
'name': 'protocol',
'type': 'string'
}, {
'name': 'app',
'type': 'string'
}, {
'name': 'subapp',
'type': 'string'
}]
else:
columns = [{'name': 'message', 'type': 'string'}]
format_ = {
"sample": [['...'] * len(columns)] * 4,
"columns": [
Field(col['name'],
HiveFormat.FIELD_TYPE_TRANSLATE.get(
col['type'], 'string'),
unique=col.get('unique')).to_dict()
for col in columns
]
}
elif file_format['inputFormat'] == 'connector':
if file_format['connectorSelection'] == 'sfdc':
sf = Salesforce(username=file_format['streamUsername'],
password=file_format['streamPassword'],
security_token=file_format['streamToken'])
table_metadata = [{
'name': column['name'],
'type': column['type']
} for column in sf.restful('sobjects/%(streamObject)s/describe/' %
file_format)['fields']]
query = 'SELECT %s FROM %s LIMIT 4' % (', '.join(
[col['name']
for col in table_metadata]), file_format['streamObject'])
print(query)
try:
records = sf.query_all(query)
except SalesforceRefusedRequest as e:
raise PopupException(message=str(e))
format_ = {
"sample":
[list(row.values())[1:] for row in records['records']],
"columns": [
Field(
col['name'],
HiveFormat.FIELD_TYPE_TRANSLATE.get(
col['type'], 'string')).to_dict()
for col in table_metadata
]
}
else:
raise PopupException(
_('Connector format not recognized: %(connectorSelection)s') %
file_format)
else:
raise PopupException(
_('Input format not recognized: %(inputFormat)s') % file_format)
return JsonResponse(format_)
def augment_solr_response(response, collection, query):
augmented = response
augmented['normalized_facets'] = []
normalized_facets = []
selected_values = dict([((fq['id'], fq['field'], fq['type']), fq['filter'])
for fq in query['fqs']])
if response and response.get('facet_counts'):
# e.g. [{u'field': u'sun', u'type': u'query', u'id': u'67b43a63-ed22-747b-47e8-b31aad1431ea', u'label': u'sun'}
for facet in collection['facets']:
category = facet['type']
if category == 'field' and response['facet_counts']['facet_fields']:
name = facet['field']
collection_facet = get_facet_field(category, name,
collection['facets'])
counts = pairwise2(
name, selected_values.get((facet['id'], name, category),
[]),
response['facet_counts']['facet_fields'][name])
if collection_facet['properties']['sort'] == 'asc':
counts.reverse()
facet = {
'id': collection_facet['id'],
'field': name,
'type': category,
'label': collection_facet['label'],
'counts': counts,
# add total result count?
}
normalized_facets.append(facet)
elif category == 'range' and response['facet_counts'][
'facet_ranges']:
name = facet['field']
collection_facet = get_facet_field(category, name,
collection['facets'])
counts = response['facet_counts']['facet_ranges'][name][
'counts']
end = response['facet_counts']['facet_ranges'][name]['end']
counts = range_pair(
name, selected_values.get((facet['id'], name, 'range'),
[]), counts, end)
if collection_facet['properties']['sort'] == 'asc':
counts.reverse()
facet = {
'id': collection_facet['id'],
'field': name,
'type': category,
'label': collection_facet['label'],
'counts': counts,
'extraSeries': []
}
normalized_facets.append(facet)
elif category == 'query' and response['facet_counts'][
'facet_queries']:
for name, value in response['facet_counts'][
'facet_queries'].iteritems():
collection_facet = get_facet_field(category, name,
collection['facets'])
facet = {
'id': collection_facet['id'],
'query': name,
'type': category,
'label': name,
'count': value,
}
normalized_facets.append(facet)
# pivot_facet
# HTML escaping
for doc in response['response']['docs']:
for field, value in doc.iteritems():
if isinstance(value, numbers.Number):
escaped_value = value
else:
value = smart_unicode(value, errors='replace')
escaped_value = escape(value)
doc[field] = escaped_value
doc['showDetails'] = False
doc['details'] = []
highlighted_fields = response.get('highlighting', {}).keys()
if highlighted_fields:
id_field = collection.get('idField')
if id_field:
for doc in response['response']['docs']:
if id_field in doc and doc[id_field] in highlighted_fields:
doc.update(response['highlighting'][doc[id_field]])
else:
response['warning'] = _(
"The Solr schema requires an id field for performing the result highlighting"
)
if normalized_facets:
augmented['normalized_facets'].extend(normalized_facets)
return augmented
try:
return func(*args, **kwargs)
except SessionExpired, e:
response['status'] = -2
except QueryExpired, e:
response['status'] = -3
except AuthenticationRequired, e:
response['status'] = 401
except ValidationError, e:
LOG.exception('Error validation %s' % func)
response['status'] = -1
response['message'] = e.message
except QueryError, e:
LOG.exception('Error running %s' % func)
response['status'] = 1
response['message'] = smart_unicode(e)
if e.handle:
response['handle'] = e.handle
if e.extra:
response.update(e.extra)
except Exception, e:
LOG.exception('Error running %s' % func)
response['status'] = -1
response['message'] = smart_unicode(e)
finally:
if response:
return JsonResponse(response)
return decorator
def augment_solr_response(response, collection, query):
augmented = response
augmented['normalized_facets'] = []
NAME = '%(field)s-%(id)s'
normalized_facets = []
selected_values = dict([(fq['id'], fq['filter']) for fq in query['fqs']])
if response and response.get('facet_counts'):
for facet in collection['facets']:
category = facet['type']
if category == 'field' and response['facet_counts']['facet_fields']:
name = NAME % facet
collection_facet = get_facet_field(category, name, collection['facets'])
counts = pairwise2(facet['field'], selected_values.get(facet['id'], []), response['facet_counts']['facet_fields'][name])
if collection_facet['properties']['sort'] == 'asc':
counts.reverse()
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
}
normalized_facets.append(facet)
elif (category == 'range' or category == 'range-up') and response['facet_counts']['facet_ranges']:
name = NAME % facet
collection_facet = get_facet_field(category, name, collection['facets'])
counts = response['facet_counts']['facet_ranges'][name]['counts']
end = response['facet_counts']['facet_ranges'][name]['end']
counts = range_pair(facet['field'], name, selected_values.get(facet['id'], []), counts, end, collection_facet)
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
'extraSeries': []
}
normalized_facets.append(facet)
elif category == 'query' and response['facet_counts']['facet_queries']:
for name, value in response['facet_counts']['facet_queries'].iteritems():
collection_facet = get_facet_field(category, name, collection['facets'])
facet = {
'id': collection_facet['id'],
'query': name,
'type': category,
'label': name,
'counts': value,
}
normalized_facets.append(facet)
elif category == 'pivot':
name = NAME % facet
if 'facet_pivot' in response['facet_counts'] and name in response['facet_counts']['facet_pivot']:
if facet['properties']['scope'] == 'stack':
count = _augment_pivot_2d(name, facet['id'], response['facet_counts']['facet_pivot'][name], selected_values)
else:
count = response['facet_counts']['facet_pivot'][name]
_augment_pivot_nd(facet['id'], count, selected_values)
else:
count = []
facet = {
'id': facet['id'],
'field': name,
'type': category,
'label': name,
'counts': count,
}
normalized_facets.append(facet)
if response and response.get('facets'):
for facet in collection['facets']:
category = facet['type']
name = facet['id'] # Nested facets can only have one name
if category == 'function' and name in response['facets']:
value = response['facets'][name]
collection_facet = get_facet_field(category, name, collection['facets'])
facet = {
'id': collection_facet['id'],
'query': name,
'type': category,
'label': name,
'counts': value,
}
normalized_facets.append(facet)
elif category == 'nested' and name in response['facets']:
value = response['facets'][name]
collection_facet = get_facet_field(category, name, collection['facets'])
extraSeries = []
counts = response['facets'][name]['buckets']
cols = ['%(field)s' % facet, 'count(%(field)s)' % facet]
last_x_col = 0
last_xx_col = 0
for i, f in enumerate(facet['properties']['facets']):
if f['aggregate']['function'] == 'count':
cols.append(f['field'])
last_xx_col = last_x_col
last_x_col = i + 2
cols.append(SolrApi._get_aggregate_function(f))
rows = []
# For dim in dimensions
# Number or Date range
if collection_facet['properties']['canRange'] and not facet['properties'].get('type') == 'field':
dimension = 3
# Single dimension or dimension 2 with analytics
if not collection_facet['properties']['facets'] or collection_facet['properties']['facets'][0]['aggregate']['function'] != 'count' and len(collection_facet['properties']['facets']) == 1:
column = 'count'
if len(collection_facet['properties']['facets']) == 1:
agg_keys = [key for key, value in counts[0].items() if key.lower().startswith('agg_')]
legend = agg_keys[0].split(':', 2)[1]
column = agg_keys[0]
else:
legend = facet['field'] # 'count(%s)' % legend
agg_keys = [column]
_augment_stats_2d(name, facet, counts, selected_values, agg_keys, rows)
counts = [_v for _f in counts for _v in (_f['val'], _f[column])]
counts = range_pair(facet['field'], name, selected_values.get(facet['id'], []), counts, 1, collection_facet)
else:
# Dimension 1 with counts and 2 with analytics
agg_keys = [key for key, value in counts[0].items() if key.lower().startswith('agg_') or key.lower().startswith('dim_')]
agg_keys.sort(key=lambda a: a[4:])
if len(agg_keys) == 1 and agg_keys[0].lower().startswith('dim_'):
agg_keys.insert(0, 'count')
counts = _augment_stats_2d(name, facet, counts, selected_values, agg_keys, rows)
_series = collections.defaultdict(list)
for row in rows:
for i, cell in enumerate(row):
if i > last_x_col:
legend = cols[i]
if last_xx_col != last_x_col:
legend = '%s %s' % (cols[i], row[last_x_col])
_series[legend].append(row[last_xx_col])
_series[legend].append(cell)
for name, val in _series.iteritems():
_c = range_pair(facet['field'], name, selected_values.get(facet['id'], []), val, 1, collection_facet)
extraSeries.append({'counts': _c, 'label': name})
counts = []
elif collection_facet['properties'].get('isOldPivot'):
facet_fields = [collection_facet['field']] + [f['field'] for f in collection_facet['properties'].get('facets', []) if f['aggregate']['function'] == 'count']
column = 'count'
agg_keys = [key for key, value in counts[0].items() if key.lower().startswith('agg_') or key.lower().startswith('dim_')]
agg_keys.sort(key=lambda a: a[4:])
if len(agg_keys) == 1 and agg_keys[0].lower().startswith('dim_'):
agg_keys.insert(0, 'count')
counts = _augment_stats_2d(name, facet, counts, selected_values, agg_keys, rows)
# _augment_stats_2d(name, facet, counts, selected_values, agg_keys, rows)
print counts
# count = response['facets'][name]
# _convert_nested_to_augmented_pivot_nd(facet_fields, facet['id'], count, selected_values, dimension=2)
dimension = len(facet_fields)
elif not collection_facet['properties']['facets'] or (collection_facet['properties']['facets'][0]['aggregate']['function'] != 'count' and len(collection_facet['properties']['facets']) == 1):
# Dimension 1 with 1 count or agg
dimension = 1
column = 'count'
if len(collection_facet['properties']['facets']) == 1:
agg_keys = [key for key, value in counts[0].items() if key.lower().startswith('agg_')]
legend = agg_keys[0].split(':', 2)[1]
column = agg_keys[0]
else:
legend = facet['field']
agg_keys = [column]
_augment_stats_2d(name, facet, counts, selected_values, agg_keys, rows)
counts = [_v for _f in counts for _v in (_f['val'], _f[column])]
counts = pairwise2(legend, selected_values.get(facet['id'], []), counts)
else:
# Dimension 2 with analytics or 1 with N aggregates
dimension = 2
agg_keys = [key for key, value in counts[0].items() if key.lower().startswith('agg_') or key.lower().startswith('dim_')]
agg_keys.sort(key=lambda a: a[4:])
if len(agg_keys) == 1 and agg_keys[0].lower().startswith('dim_'):
agg_keys.insert(0, 'count')
counts = _augment_stats_2d(name, facet, counts, selected_values, agg_keys, rows)
actual_dimension = 1 + sum([_f['aggregate']['function'] == 'count' for _f in collection_facet['properties']['facets']])
counts = filter(lambda a: len(a['fq_fields']) == actual_dimension, counts)
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
'extraSeries': extraSeries,
'dimension': dimension,
'response': {'response': {'start': 0, 'numFound': response['facets'][name]['numBuckets']}}, # Todo * nested buckets + offsets
'docs': [dict(zip(cols, row)) for row in rows],
'fieldsAttributes': [Collection2._make_gridlayout_header_field({'name': col, 'type': 'aggr' if '(' in col else 'string'}) for col in cols]
}
normalized_facets.append(facet)
# Remove unnecessary facet data
if response:
response.pop('facet_counts')
response.pop('facets')
# HTML escaping
if not query.get('download'):
id_field = collection.get('idField', '')
for doc in response['response']['docs']:
for field, value in doc.iteritems():
if isinstance(value, numbers.Number):
escaped_value = value
elif field == '_childDocuments_': # Nested documents
escaped_value = value
elif isinstance(value, list): # Multivalue field
escaped_value = [smart_unicode(escape(val), errors='replace') for val in value]
else:
value = smart_unicode(value, errors='replace')
escaped_value = escape(value)
doc[field] = escaped_value
link = None
if 'link-meta' in doc:
meta = json.loads(doc['link-meta'])
link = get_data_link(meta)
doc['externalLink'] = link
doc['details'] = []
doc['hueId'] = smart_unicode(doc.get(id_field, ''))
highlighted_fields = response.get('highlighting', {}).keys()
if highlighted_fields and not query.get('download'):
id_field = collection.get('idField')
if id_field:
for doc in response['response']['docs']:
if id_field in doc and smart_unicode(doc[id_field]) in highlighted_fields:
highlighting = response['highlighting'][smart_unicode(doc[id_field])]
if highlighting:
escaped_highlighting = {}
for field, hls in highlighting.iteritems():
_hls = [escape(smart_unicode(hl, errors='replace')).replace('<em>', '<em>').replace('</em>', '</em>') for hl in hls]
escaped_highlighting[field] = _hls[0] if len(_hls) == 1 else _hls
doc.update(escaped_highlighting)
else:
response['warning'] = _("The Solr schema requires an id field for performing the result highlighting")
if normalized_facets:
augmented['normalized_facets'].extend(normalized_facets)
return augmented
def __unicode__(self):
return smart_unicode(self.message)
def augment_solr_response(response, collection, query):
augmented = response
augmented['normalized_facets'] = []
NAME = '%(field)s-%(id)s'
normalized_facets = []
selected_values = dict([(fq['id'], fq['filter']) for fq in query['fqs']])
if response and response.get('facet_counts'):
for facet in collection['facets']:
category = facet['type']
if category == 'field' and response['facet_counts']['facet_fields']:
name = NAME % facet
collection_facet = get_facet_field(category, name, collection['facets'])
counts = pairwise2(facet['field'], selected_values.get(facet['id'], []), response['facet_counts']['facet_fields'][name])
if collection_facet['properties']['sort'] == 'asc':
counts.reverse()
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
}
normalized_facets.append(facet)
elif (category == 'range' or category == 'range-up') and response['facet_counts']['facet_ranges']:
name = NAME % facet
collection_facet = get_facet_field(category, name, collection['facets'])
counts = response['facet_counts']['facet_ranges'][name]['counts']
end = response['facet_counts']['facet_ranges'][name]['end']
counts = range_pair(facet['field'], name, selected_values.get(facet['id'], []), counts, end, collection_facet)
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
'extraSeries': []
}
normalized_facets.append(facet)
elif category == 'query' and response['facet_counts']['facet_queries']:
for name, value in response['facet_counts']['facet_queries'].iteritems():
collection_facet = get_facet_field(category, name, collection['facets'])
facet = {
'id': collection_facet['id'],
'query': name,
'type': category,
'label': name,
'counts': value,
}
normalized_facets.append(facet)
elif category == 'pivot':
name = NAME % facet
if 'facet_pivot' in response['facet_counts'] and name in response['facet_counts']['facet_pivot']:
if facet['properties']['scope'] == 'stack':
count = _augment_pivot_2d(name, facet['id'], response['facet_counts']['facet_pivot'][name], selected_values)
else:
count = response['facet_counts']['facet_pivot'][name]
_augment_pivot_nd(facet['id'], count, selected_values)
else:
count = []
facet = {
'id': facet['id'],
'field': name,
'type': category,
'label': name,
'counts': count,
}
normalized_facets.append(facet)
if response and response.get('facets'):
for facet in collection['facets']:
category = facet['type']
name = facet['id'] # Nested facets can only have one name
if category == 'function' and name in response['facets']:
value = response['facets'][name]
collection_facet = get_facet_field(category, name, collection['facets'])
facet = {
'id': collection_facet['id'],
'query': name,
'type': category,
'label': name,
'counts': value,
}
normalized_facets.append(facet)
elif category == 'nested' and name in response['facets']:
value = response['facets'][name]
collection_facet = get_facet_field(category, name, collection['facets'])
counts = _augment_stats_2d(name, facet, response['facets'][name]['buckets'], selected_values)
if collection_facet['properties']['sort'] == 'asc':
counts.reverse()
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
}
normalized_facets.append(facet)
# Remove unnecessary facet data
if response:
response.pop('facet_counts')
response.pop('facets')
# HTML escaping
for doc in response['response']['docs']:
for field, value in doc.iteritems():
if isinstance(value, numbers.Number):
escaped_value = value
else:
value = smart_unicode(value, errors='replace')
escaped_value = escape(value)
doc[field] = escaped_value
if not query.get('download'):
doc['showDetails'] = False
doc['details'] = []
highlighted_fields = response.get('highlighting', {}).keys()
if highlighted_fields and not query.get('download'):
id_field = collection.get('idField')
if id_field:
for doc in response['response']['docs']:
if id_field in doc and str(doc[id_field]) in highlighted_fields:
highlighting = response['highlighting'][str(doc[id_field])]
if highlighting:
escaped_highlighting = {}
for field, hls in highlighting.iteritems():
_hls = [escape(smart_unicode(hl, errors='replace')).replace('<em>', '<em>').replace('</em>', '</em>') for hl in hls]
escaped_highlighting[field] = _hls
doc.update(escaped_highlighting)
else:
response['warning'] = _("The Solr schema requires an id field for performing the result highlighting")
if normalized_facets:
augmented['normalized_facets'].extend(normalized_facets)
return augmented
def rerun_oozie_coordinator(request, job_id, app_path):
oozie_coordinator = check_job_access_permission(request, job_id)
check_job_edition_permission(oozie_coordinator, request.user)
ParametersFormSet = formset_factory(ParameterForm, extra=0)
if request.method == 'POST':
params_form = ParametersFormSet(request.POST)
rerun_form = RerunCoordForm(request.POST,
oozie_coordinator=oozie_coordinator)
if sum([rerun_form.is_valid(), params_form.is_valid()]) == 2:
args = {}
args['deployment_dir'] = app_path
params = {
'type':
'action',
'scope':
','.join(
oozie_coordinator.aggreate(
rerun_form.cleaned_data['actions'])),
'refresh':
rerun_form.cleaned_data['refresh'],
'nocleanup':
rerun_form.cleaned_data['nocleanup'],
}
properties = dict([(param['name'], param['value'])
for param in params_form.cleaned_data])
_rerun_coordinator(request, job_id, args, params, properties)
request.info(_('Coordinator re-running.'))
return redirect(
reverse('oozie:list_oozie_coordinator',
kwargs={'job_id': job_id}))
else:
request.error(
_('Invalid submission form: %s') %
smart_unicode(rerun_form.errors))
return list_oozie_coordinator(request, job_id)
else:
rerun_form = RerunCoordForm(oozie_coordinator=oozie_coordinator)
initial_params = ParameterForm.get_initial_params(
oozie_coordinator.conf_dict)
params_form = ParametersFormSet(initial=initial_params)
popup = render('dashboard/rerun_coord_popup.mako',
request, {
'rerun_form':
rerun_form,
'params_form':
params_form,
'action':
reverse('oozie:rerun_oozie_coord',
kwargs={
'job_id': job_id,
'app_path': app_path
}),
},
force_template=True).content
return HttpResponse(json.dumps(popup), mimetype="application/json")
def view_results(request, id, first_row=0):
"""
Returns the view for the results of the QueryHistory with the given id.
The query results MUST be ready.
To display query results, one should always go through the execute_query view.
If the result set has has_result_set=False, display an empty result.
If ``first_row`` is 0, restarts (if necessary) the query read. Otherwise, just
spits out a warning if first_row doesn't match the servers conception.
Multiple readers will produce a confusing interaction here, and that's known.
It understands the ``context`` GET parameter. (See execute_query().)
"""
first_row = long(first_row)
start_over = (first_row == 0)
results = type('Result', (object,), {
'rows': 0,
'columns': [],
'has_more': False,
'start_row': 0,
})
data = []
fetch_error = False
error_message = ''
log = ''
columns = []
app_name = get_app_name(request)
query_history = authorized_get_query_history(request, id, must_exist=True)
query_server = query_history.get_query_server_config()
db = dbms.get(request.user, query_server)
handle, state = _get_query_handle_and_state(query_history)
context_param = request.GET.get('context', '')
query_context = parse_query_context(context_param)
# Update the status as expired should not be accessible
expired = state == models.QueryHistory.STATE.expired
# Retrieve query results or use empty result if no result set
try:
if query_server['server_name'] == 'impala' and not handle.has_result_set:
downloadable = False
else:
results = db.fetch(handle, start_over, 100)
data = []
# Materialize and HTML escape results
# TODO: use Number + list comprehension
for row in results.rows():
escaped_row = []
for field in row:
if isinstance(field, (int, long, float, complex, bool)):
if math.isnan(field) or math.isinf(field):
escaped_field = json.dumps(field)
else:
escaped_field = field
elif field is None:
escaped_field = 'NULL'
else:
field = smart_unicode(field, errors='replace') # Prevent error when getting back non utf8 like charset=iso-8859-1
escaped_field = escape(field).replace(' ', ' ')
escaped_row.append(escaped_field)
data.append(escaped_row)
# We display the "Download" button only when we know that there are results:
downloadable = first_row > 0 or data
log = db.get_log(handle)
columns = results.data_table.cols()
except Exception, ex:
fetch_error = True
error_message, log = expand_exception(ex, db, handle)
def augment_solr_response(response, collection, query):
augmented = response
augmented['normalized_facets'] = []
NAME = '%(field)s-%(id)s'
normalized_facets = []
selected_values = dict([(fq['id'], fq['filter']) for fq in query['fqs']])
if response and response.get('facet_counts'):
for facet in collection['facets']:
category = facet['type']
if category == 'field' and response['facet_counts']['facet_fields']:
name = NAME % facet
collection_facet = get_facet_field(category, name,
collection['facets'])
counts = pairwise2(
facet['field'], selected_values.get(facet['id'], []),
response['facet_counts']['facet_fields'][name])
if collection_facet['properties']['sort'] == 'asc':
counts.reverse()
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
}
normalized_facets.append(facet)
elif (category == 'range' or category
== 'range-up') and response['facet_counts']['facet_ranges']:
name = NAME % facet
collection_facet = get_facet_field(category, name,
collection['facets'])
counts = response['facet_counts']['facet_ranges'][name][
'counts']
end = response['facet_counts']['facet_ranges'][name]['end']
counts = range_pair(facet['field'], name,
selected_values.get(facet['id'], []),
counts, end, collection_facet)
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
'extraSeries': []
}
normalized_facets.append(facet)
elif category == 'query' and response['facet_counts'][
'facet_queries']:
for name, value in response['facet_counts'][
'facet_queries'].iteritems():
collection_facet = get_facet_field(category, name,
collection['facets'])
facet = {
'id': collection_facet['id'],
'query': name,
'type': category,
'label': name,
'counts': value,
}
normalized_facets.append(facet)
elif category == 'pivot':
name = NAME % facet
if 'facet_pivot' in response[
'facet_counts'] and name in response['facet_counts'][
'facet_pivot']:
if facet['properties']['scope'] == 'stack':
count = _augment_pivot_2d(
name, facet['id'],
response['facet_counts']['facet_pivot'][name],
selected_values)
else:
count = response['facet_counts']['facet_pivot'][name]
_augment_pivot_nd(facet['id'], count, selected_values)
else:
count = []
facet = {
'id': facet['id'],
'field': name,
'type': category,
'label': name,
'counts': count,
}
normalized_facets.append(facet)
# Remove unnecessary facet data
if response:
response.pop('facet_counts')
# HTML escaping
for doc in response['response']['docs']:
for field, value in doc.iteritems():
if isinstance(value, numbers.Number):
escaped_value = value
else:
value = smart_unicode(value, errors='replace')
escaped_value = escape(value)
doc[field] = escaped_value
if not query.get('download'):
doc['showDetails'] = False
doc['details'] = []
highlighted_fields = response.get('highlighting', {}).keys()
if highlighted_fields and not query.get('download'):
id_field = collection.get('idField')
if id_field:
for doc in response['response']['docs']:
if id_field in doc and str(
doc[id_field]) in highlighted_fields:
highlighting = response['highlighting'][str(doc[id_field])]
if highlighting:
escaped_highlighting = {}
for field, hls in highlighting.iteritems():
_hls = [
escape(smart_unicode(
hl, errors='replace')).replace(
'<em>', '<em>').replace(
'</em>', '</em>')
for hl in hls
]
escaped_highlighting[field] = _hls
doc.update(escaped_highlighting)
else:
response['warning'] = _(
"The Solr schema requires an id field for performing the result highlighting"
)
if normalized_facets:
augmented['normalized_facets'].extend(normalized_facets)
return augmented
def augment_solr_response(response, collection, query):
augmented = response
augmented['normalized_facets'] = []
NAME = '%(field)s-%(id)s'
normalized_facets = []
selected_values = dict([(fq['id'], fq['filter']) for fq in query['fqs']])
if response and response.get('facet_counts'):
for facet in collection['facets']:
category = facet['type']
if category == 'field' and response['facet_counts']['facet_fields']:
name = NAME % facet
collection_facet = get_facet_field(category, name, collection['facets'])
counts = pairwise2(facet['field'], selected_values.get(facet['id'], []), response['facet_counts']['facet_fields'][name])
if collection_facet['properties']['sort'] == 'asc':
counts.reverse()
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
}
normalized_facets.append(facet)
elif (category == 'range' or category == 'range-up') and response['facet_counts']['facet_ranges']:
name = NAME % facet
collection_facet = get_facet_field(category, name, collection['facets'])
counts = response['facet_counts']['facet_ranges'][name]['counts']
end = response['facet_counts']['facet_ranges'][name]['end']
counts = range_pair(facet['field'], name, selected_values.get(facet['id'], []), counts, end, collection_facet)
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
'extraSeries': []
}
normalized_facets.append(facet)
elif category == 'query' and response['facet_counts']['facet_queries']:
for name, value in response['facet_counts']['facet_queries'].iteritems():
collection_facet = get_facet_field(category, name, collection['facets'])
facet = {
'id': collection_facet['id'],
'query': name,
'type': category,
'label': name,
'counts': value,
}
normalized_facets.append(facet)
elif category == 'pivot':
name = NAME % facet
if 'facet_pivot' in response['facet_counts'] and name in response['facet_counts']['facet_pivot']:
if facet['properties']['scope'] == 'stack':
count = _augment_pivot_2d(name, facet['id'], response['facet_counts']['facet_pivot'][name], selected_values)
else:
count = response['facet_counts']['facet_pivot'][name]
_augment_pivot_nd(facet['id'], count, selected_values)
else:
count = []
facet = {
'id': facet['id'],
'field': name,
'type': category,
'label': name,
'counts': count,
}
normalized_facets.append(facet)
if response and response.get('facets'):
for facet in collection['facets']:
category = facet['type']
name = facet['id'] # Nested facets can only have one name
if category == 'function' and name in response['facets']:
value = response['facets'][name]
collection_facet = get_facet_field(category, name, collection['facets'])
facet = {
'id': collection_facet['id'],
'query': name,
'type': category,
'label': name,
'counts': value,
}
normalized_facets.append(facet)
elif category == 'nested' and name in response['facets']:
value = response['facets'][name]
collection_facet = get_facet_field(category, name, collection['facets'])
extraSeries = []
counts = response['facets'][name]['buckets']
# Date range
if collection_facet['properties']['isDate']:
dimension = 3
# Single dimension or dimension 2 with analytics
if not collection_facet['properties']['facets'] or collection_facet['properties']['facets'][0]['aggregate'] not in ('count', 'unique'):
counts = [_v for _f in counts for _v in (_f['val'], _f['d2'] if 'd2' in _f else _f['count'])]
counts = range_pair(facet['field'], name, selected_values.get(facet['id'], []), counts, 1, collection_facet)
else:
# Dimension 1 with counts and 2 with analytics
_series = collections.defaultdict(list)
for f in counts:
for bucket in (f['d2']['buckets'] if 'd2' in f else []):
_series[bucket['val']].append(f['val'])
_series[bucket['val']].append(bucket['d2'] if 'd2' in bucket else bucket['count'])
for name, val in _series.iteritems():
_c = range_pair(facet['field'], name, selected_values.get(facet['id'], []), val, 1, collection_facet)
extraSeries.append({'counts': _c, 'label': name})
counts = []
elif not collection_facet['properties']['facets'] or collection_facet['properties']['facets'][0]['aggregate'] not in ('count', 'unique'):
# Single dimension or dimension 2 with analytics
dimension = 1
counts = [_v for _f in counts for _v in (_f['val'], _f['d2'] if 'd2' in _f else _f['count'])]
counts = pairwise2(facet['field'], selected_values.get(facet['id'], []), counts)
else:
# Dimension 1 with counts and 2 with analytics
dimension = 2
counts = _augment_stats_2d(name, facet, counts, selected_values)
if collection_facet['properties']['sort'] == 'asc':
counts.reverse()
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
'extraSeries': extraSeries,
'dimension': dimension
}
normalized_facets.append(facet)
# Remove unnecessary facet data
if response:
response.pop('facet_counts')
response.pop('facets')
# HTML escaping
for doc in response['response']['docs']:
for field, value in doc.iteritems():
if isinstance(value, numbers.Number):
escaped_value = value
else:
value = smart_unicode(value, errors='replace')
escaped_value = escape(value)
doc[field] = escaped_value
if not query.get('download'):
link = None
if 'link-meta' in doc:
meta = json.loads(doc['link-meta'])
link = get_data_link(meta)
doc['externalLink'] = link
doc['details'] = []
highlighted_fields = response.get('highlighting', {}).keys()
if highlighted_fields and not query.get('download'):
id_field = collection.get('idField')
if id_field:
for doc in response['response']['docs']:
if id_field in doc and smart_unicode(doc[id_field]) in highlighted_fields:
highlighting = response['highlighting'][smart_unicode(doc[id_field])]
if highlighting:
escaped_highlighting = {}
for field, hls in highlighting.iteritems():
_hls = [escape(smart_unicode(hl, errors='replace')).replace('<em>', '<em>').replace('</em>', '</em>') for hl in hls]
escaped_highlighting[field] = _hls
doc.update(escaped_highlighting)
else:
response['warning'] = _("The Solr schema requires an id field for performing the result highlighting")
if normalized_facets:
augmented['normalized_facets'].extend(normalized_facets)
return augmented
def augment_solr_response(response, collection, query):
augmented = response
augmented['normalized_facets'] = []
NAME = '%(field)s-%(id)s'
normalized_facets = []
selected_values = dict([(fq['id'], fq['filter']) for fq in query['fqs']])
if response and response.get('facet_counts'):
for facet in collection['facets']:
category = facet['type']
if category == 'field' and response['facet_counts']['facet_fields']:
name = NAME % facet
collection_facet = get_facet_field(category, name,
collection['facets'])
counts = pairwise2(
facet['field'], selected_values.get(facet['id'], []),
response['facet_counts']['facet_fields'][name])
if collection_facet['properties']['sort'] == 'asc':
counts.reverse()
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
}
normalized_facets.append(facet)
elif (category == 'range' or category
== 'range-up') and response['facet_counts']['facet_ranges']:
name = NAME % facet
collection_facet = get_facet_field(category, name,
collection['facets'])
counts = response['facet_counts']['facet_ranges'][name][
'counts']
end = response['facet_counts']['facet_ranges'][name]['end']
counts = range_pair(facet['field'], name,
selected_values.get(facet['id'], []),
counts, end, collection_facet)
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
'extraSeries': []
}
normalized_facets.append(facet)
elif category == 'query' and response['facet_counts'][
'facet_queries']:
for name, value in response['facet_counts'][
'facet_queries'].iteritems():
collection_facet = get_facet_field(category, name,
collection['facets'])
facet = {
'id': collection_facet['id'],
'query': name,
'type': category,
'label': name,
'counts': value,
}
normalized_facets.append(facet)
elif category == 'pivot':
name = NAME % facet
if 'facet_pivot' in response[
'facet_counts'] and name in response['facet_counts'][
'facet_pivot']:
if facet['properties']['scope'] == 'stack':
count = _augment_pivot_2d(
name, facet['id'],
response['facet_counts']['facet_pivot'][name],
selected_values)
else:
count = response['facet_counts']['facet_pivot'][name]
_augment_pivot_nd(facet['id'], count, selected_values)
else:
count = []
facet = {
'id': facet['id'],
'field': name,
'type': category,
'label': name,
'counts': count,
}
normalized_facets.append(facet)
if response and response.get('facets'):
for facet in collection['facets']:
category = facet['type']
name = facet['id'] # Nested facets can only have one name
if category == 'function' and name in response['facets']:
value = response['facets'][name]
collection_facet = get_facet_field(category, name,
collection['facets'])
facet = {
'id': collection_facet['id'],
'query': name,
'type': category,
'label': name,
'counts': value,
}
normalized_facets.append(facet)
elif category == 'nested' and name in response['facets']:
value = response['facets'][name]
collection_facet = get_facet_field(category, name,
collection['facets'])
extraSeries = []
counts = response['facets'][name]['buckets']
# Date range
if collection_facet['properties']['isDate']:
dimension = 3
# Single dimension or dimension 2 with analytics
if not collection_facet['properties'][
'facets'] or collection_facet['properties'][
'facets'][0]['aggregate'] not in ('count',
'unique'):
counts = [
_v for _f in counts
for _v in (_f['val'],
_f['d2'] if 'd2' in _f else _f['count'])
]
counts = range_pair(
facet['field'], name,
selected_values.get(facet['id'], []), counts, 1,
collection_facet)
else:
# Dimension 1 with counts and 2 with analytics
_series = collections.defaultdict(list)
for f in counts:
for bucket in (f['d2']['buckets']
if 'd2' in f else []):
_series[bucket['val']].append(f['val'])
_series[bucket['val']].append(
bucket['d2'] if 'd2' in
bucket else bucket['count'])
for name, val in _series.iteritems():
_c = range_pair(
facet['field'], name,
selected_values.get(facet['id'], []), val, 1,
collection_facet)
extraSeries.append({'counts': _c, 'label': name})
counts = []
elif not collection_facet['properties'][
'facets'] or collection_facet['properties']['facets'][
0]['aggregate'] not in ('count', 'unique'):
# Single dimension or dimension 2 with analytics
dimension = 1
counts = [
_v for _f in counts
for _v in (_f['val'],
_f['d2'] if 'd2' in _f else _f['count'])
]
counts = pairwise2(facet['field'],
selected_values.get(facet['id'], []),
counts)
else:
# Dimension 1 with counts and 2 with analytics
dimension = 2
counts = _augment_stats_2d(name, facet, counts,
selected_values)
if collection_facet['properties']['sort'] == 'asc':
counts.reverse()
facet = {
'id': collection_facet['id'],
'field': facet['field'],
'type': category,
'label': collection_facet['label'],
'counts': counts,
'extraSeries': extraSeries,
'dimension': dimension
}
normalized_facets.append(facet)
# Remove unnecessary facet data
if response:
response.pop('facet_counts')
response.pop('facets')
# HTML escaping
if not query.get('download'):
for doc in response['response']['docs']:
for field, value in doc.iteritems():
if isinstance(value, numbers.Number):
escaped_value = value
elif isinstance(value, list): # Multivalue field
escaped_value = [
smart_unicode(val, errors='replace') for val in value
]
else:
value = smart_unicode(value, errors='replace')
escaped_value = escape(value)
doc[field] = escaped_value
link = None
if 'link-meta' in doc:
meta = json.loads(doc['link-meta'])
link = get_data_link(meta)
doc['externalLink'] = link
doc['details'] = []
highlighted_fields = response.get('highlighting', {}).keys()
if highlighted_fields and not query.get('download'):
id_field = collection.get('idField')
if id_field:
for doc in response['response']['docs']:
if id_field in doc and smart_unicode(
doc[id_field]) in highlighted_fields:
highlighting = response['highlighting'][smart_unicode(
doc[id_field])]
if highlighting:
escaped_highlighting = {}
for field, hls in highlighting.iteritems():
_hls = [
escape(smart_unicode(
hl, errors='replace')).replace(
'<em>', '<em>').replace(
'</em>', '</em>')
for hl in hls
]
escaped_highlighting[field] = _hls
doc.update(escaped_highlighting)
else:
response['warning'] = _(
"The Solr schema requires an id field for performing the result highlighting"
)
if normalized_facets:
augmented['normalized_facets'].extend(normalized_facets)
return augmented
