def alanize(request):
response = {'status': -1}
cluster = json.loads(request.POST.get('cluster', '{}'))
query_id = json.loads(request.POST.get('query_id'))
api = _get_api(request.user, cluster=cluster)
if query_id:
LOG.debug("Attempting to get Impala query profile for query ID: %s" % (query_id))
doc = Document2.objects.get(id=query_id)
snippets = doc.data_dict.get('snippets', [])
secret = snippets[0]['result']['handle']['secret']
impala_query_id = unpack_guid(base64.decodestring(secret))
query_profile = api.get_query_profile_encoded(impala_query_id)
profile = analyzer.analyze(analyzer.parse_data(query_profile))
ANALYZER.pre_process(profile)
result = ANALYZER.run(profile)
heatmap = {}
summary = analyzer.summary(profile)
heatmapMetrics = ['AverageThreadTokens', 'BloomFilterBytes', 'PeakMemoryUsage', 'PerHostPeakMemUsage', 'PrepareTime', 'RowsProduced', 'TotalCpuTime', 'TotalNetworkReceiveTime', 'TotalNetworkSendTime', 'TotalStorageWaitTime', 'TotalTime']
for key in heatmapMetrics:
metrics = analyzer.heatmap_by_host(profile, key)
if metrics['data']:
heatmap[key] = metrics
response['data'] = { 'query': { 'healthChecks' : result[0]['result'], 'summary': summary, 'heatmap': heatmap, 'heatmapMetrics': sorted(list(heatmap.iterkeys())) } }
response['status'] = 0
return JsonResponse(response)
def alanize(request):
response = {'status': -1}
cluster = json.loads(request.POST.get('cluster', '{}'))
query_id = json.loads(request.POST.get('query_id'))
api = _get_api(request.user, cluster=cluster)
if query_id:
LOG.debug("Attempting to get Impala query profile for query ID: %s" % (query_id))
doc = Document2.objects.get(id=query_id)
snippets = doc.data_dict.get('snippets', [])
secret = snippets[0]['result']['handle']['secret']
impala_query_id = unpack_guid(base64.decodestring(secret))
query_profile = api.get_query_profile_encoded(impala_query_id)
profile = analyzer.analyze(analyzer.parse_data(query_profile))
ANALYZER.pre_process(profile)
result = ANALYZER.run(profile)
heatmap = {}
summary = analyzer.summary(profile)
heatmapMetrics = ['AverageThreadTokens', 'BloomFilterBytes', 'PeakMemoryUsage', 'PerHostPeakMemUsage', 'PrepareTime', 'RowsProduced', 'TotalCpuTime', 'TotalNetworkReceiveTime', 'TotalNetworkSendTime', 'TotalStorageWaitTime', 'TotalTime']
for key in heatmapMetrics:
metrics = analyzer.heatmap_by_host(profile, key)
if metrics['data']:
heatmap[key] = metrics
response['data'] = { 'query': { 'healthChecks' : result[0]['result'], 'summary': summary, 'heatmap': heatmap, 'heatmapMetrics': sorted(list(heatmap.keys())) } }
response['status'] = 0
return JsonResponse(response)
def setUp(self):
self.profile = a.analyze(
a.parse(
os.path.join(
os.path.dirname(__file__),
"../..",
"testdata",
"profile.thrift")))
def _metrics(self, appid):
query_profile = self.api.get_query_profile_encoded(appid)
profile = analyzer.analyze(analyzer.parse_data(query_profile))
ANALYZER.pre_process(profile)
metrics = analyzer.metrics(profile)
result = ANALYZER.run(profile)
if result and result[0]:
for factor in result[0]['result']:
if factor['reason'] and factor['result_id'] and metrics['nodes'].get(factor['result_id']):
metrics['nodes'][factor['result_id']]['health'] = factor['reason']
return metrics
def alanize(request):
response = {'status': -1}
cluster = json.loads(request.POST.get('cluster', '{}'))
query_id = json.loads(request.POST.get('query_id'))
application = _get_server_name(cluster)
query_server = dbms.get_query_server_config()
session = Session.objects.get_session(request.user,
query_server['server_name'])
server_url = _get_impala_server_url(session)
if query_id:
LOG.debug(
"Attempting to get Impala query profile at server_url %s for query ID: %s"
% (server_url, query_id))
doc = Document2.objects.get(id=query_id)
snippets = doc.data_dict.get('snippets', [])
secret = snippets[0]['result']['handle']['secret']
api = get_impalad_api(user=request.user, url=server_url)
impala_query_id = "%x:%x" % struct.unpack(b"QQ",
base64.decodestring(secret))
api.kill(
impala_query_id
) # There are many statistics that are not present when the query is open. Close it first.
query_profile = api.get_query_profile_encoded(impala_query_id)
profile = analyzer.analyze(analyzer.parse_data(query_profile))
result = ANALYZER.run(profile)
heatmap = {}
summary = analyzer.summary(profile)
heatmapMetrics = [
'AverageThreadTokens', 'BloomFilterBytes', 'PeakMemoryUsage',
'PerHostPeakMemUsage', 'PrepareTime', 'RowsProduced',
'TotalCpuTime', 'TotalNetworkReceiveTime', 'TotalNetworkSendTime',
'TotalStorageWaitTime', 'TotalTime'
]
for key in heatmapMetrics:
metrics = analyzer.heatmap_by_host(profile, key)
if metrics['data']:
heatmap[key] = metrics
response['data'] = {
'query': {
'healthChecks': result[0]['result'],
'summary': summary,
'heatmap': heatmap,
'heatmapMetrics': sorted(list(heatmap.iterkeys()))
}
}
response['status'] = 0
return JsonResponse(response)
def alanize_metrics(request):
response = {'status': -1}
cluster = json.loads(request.POST.get('cluster', '{}'))
query_id = json.loads(request.POST.get('query_id'))
api = _get_api(request.user, cluster=cluster)
if query_id:
LOG.debug("Attempting to get Impala query profile for query ID: %s" % (query_id))
query_profile = api.get_query_profile_encoded(query_id)
profile = analyzer.analyze(analyzer.parse_data(query_profile))
ANALYZER.pre_process(profile)
metrics = analyzer.metrics(profile)
response['data'] = metrics
response['status'] = 0
return JsonResponse(response)
def alanize_metrics(request):
response = {'status': -1}
cluster = json.loads(request.POST.get('cluster', '{}'))
query_id = json.loads(request.POST.get('query_id'))
api = _get_api(request.user, cluster=cluster)
if query_id:
LOG.debug("Attempting to get Impala query profile for query ID: %s" % (query_id))
query_profile = api.get_query_profile_encoded(query_id)
profile = analyzer.analyze(analyzer.parse_data(query_profile))
ANALYZER.pre_process(profile)
metrics = analyzer.metrics(profile)
response['data'] = metrics
response['status'] = 0
return JsonResponse(response)
def _metrics(self, appid):
query_profile = self.api.get_query_profile_encoded(appid)
profile = analyzer.analyze(analyzer.parse_data(query_profile))
ANALYZER.pre_process(profile)
return analyzer.metrics(profile)
