def _get_all_fields (app, app_type=None):
"""
Retrieve all possible fields in an application
:param app: [string] application name (e.g. xdata_v3)
:param app_type: [string] application type (e.g. logs)
:return: [list] list of strings representing the fields names
"""
d = list ()
query = { "aggs" : {
"fields" : {
"terms" : {
"field" : "_field_names",
"size" : 100
}
}
}
}
try:
response = es.search (index=app, doc_type=app_type, body=query)
for tag in response['aggregations']['fields']['buckets']:
d.append (tag ['key'])
except TransportError as e:
d.append (str (e.info))
except Exception as e:
d.append (str (e))
return d
def segment(app, app_type=None, params=''):
"""
Just support match all for now.
"""
q = params.get("q") if params.get("q") else {}
fields = params.get("fields") if params.get("fields") else []
size = params.get("size") if params.get("size") else 10
scroll = params.get("scroll") if params.get("scroll") else False
fl = params.get("fl") if params.get("fl") else []
# filters = params.get ("filter") if params.get ("filter") else {}
# 'q': args.get('q', '{}'),
# 'fields': args.get('fl', '{}'),
# 'size': args.get ('size', 100),
# 'scroll': args.get ('scroll', False),
# 'filters': request_args.getlist ('fq')
query = {}
query['size'] = size
if q:
res = q.split(":")
key = res[0]
val = res[1]
query['query'] = {"match": {key: val}}
else:
query['query'] = {"match_all": {}}
if len(fields) > 0:
ex = {"include": fields.split(",")}
query['_source'] = ex
response = es.search(index=app, doc_type=app_type, body=query)
return jsonify(response)
def _get_all_fields(app, app_type=None):
"""
Retrieve all possible fields in an application
:param app: [string] application name (e.g. xdata_v3)
:param app_type: [string] application type (e.g. logs)
:return: [list] list of strings representing the fields names
"""
d = list()
query = {
"aggs": {
"fields": {
"terms": {
"field": "_field_names",
"size": 100
}
}
}
}
try:
response = es.search(index=app, doc_type=app_type, body=query)
for tag in response['aggregations']['fields']['buckets']:
d.append(tag['key'])
except TransportError as e:
d.append(str(e.info))
except Exception as e:
d.append(str(e))
return d
def search(app,
app_type=None,
filters=list(),
size=100,
include="*",
scroll=None,
sort_field=None):
"""
Perform a search query.
:param app: [string] application id (e.g. "xdata_v3")
:param app_type: [string] name of the application type.
If None all application types are searched.
:param filters: [list of strings] list of filters for a query.
:param size: [int] maximum number of hits that should be returned
:param sort_field: [string] sorting field.
Currently supported fields: "timestamp", "date"
:return: [dict] dictionary with processed results.
If STOUT is enabled, STOUT data will be merged with final result.
"""
# Need some query builder...
query = {}
log_result = es.search(index=app,
doc_type=app_type,
body=query,
fields=filters,
size=size)
# stout_result = Stout.getSessions()
#
# data = merged_results(log_result, stout_result)
return log_result
def search (app, app_type=None, filters=list (), size=100, include="*", scroll=None, sort_field=None): """ Perform a search query. :param app: [string] application id (e.g. "xdata_v3") :param app_type: [string] name of the application type. If None all application types are searched. :param filters: [list of strings] list of filters for a query. :param size: [int] maximum number of hits that should be returned :param sort_field: [string] sorting field. Currently supported fields: "timestamp", "date" :return: [dict] dictionary with processed results. If STOUT is enabled, STOUT data will be merged with final result. """ # Need some query builder... log_result = es.search (index=app, doc_type=app_type, body=query, fields=filters, size=size) stout_result = Stout.getSessions () data = merged_results (log_result, stout_result) return data
def histogram(app, app_type=None, q=""):
"""
Only works on numerical data.
"""
field = q.get("field") if q.get("field") else ""
interval = 50
query = {
"aggs": {
"hist_agg": {
"histogram": {
"field": field,
"interval": interval
}
}
}
}
d = {}
try:
response = es.search(index=app, doc_type=app_type, body=query)
for tag in response['aggregations']['hist_agg']['buckets']:
d[tag['key']] = tag['doc_count']
except TransportError as e:
d['error'] = e.info
except Exception as e:
d['error'] = str(e)
return jsonify(d)
def histogram (app, app_type=None, q=""):
"""
Only works on numerical data.
"""
field = q.get ("field") if q.get ("field") else ""
interval = 50
query = { "aggs" : {
"hist_agg" : {
"histogram" : {
"field" : field,
"interval" : interval
}
}
}
}
d = {}
try:
response = es.search (index=app, doc_type=app_type, body=query)
for tag in response['aggregations']['hist_agg']['buckets']:
d [tag ['key']] = tag ['doc_count']
except TransportError as e:
d ['error'] = e.info
except Exception as e:
d ['error'] = str (e)
return jsonify (d)
def unique_terms(app, app_type=None, q=""):
"""
Aggregate the number of unique terms in a field.
Missing values are counted and marked as "N/A".
.. todo::
Need to incorporate QueryBuilder library instead of
manually generating queries.
:param app: [string] application name
:param app_type: [string] application type
:param field: [string] field to search against for unique values
:param size: [int] the top size terms returned in the result.
Default value is 10.
:param min_hits: [int] return tags which have been found
in min_hits or more. Default value is 1.
:return: [dict] dictionary of results
"""
field = q.get("field") if q.get("field") else ""
size = q.get("size") if q.get("size") else 10000
min_hits = q.get("min_hits") if q.get("min_hits") else 0
print field
query = {
"aggs": {
"terms_agg": {
"terms": {
"field": field,
"size": size,
"min_doc_count": min_hits,
"missing": "N/A"
}
}
}
}
d = {}
try:
response = es.search(index=app, doc_type=app_type, body=query)
for tag in response['aggregations']['terms_agg']['buckets']:
d[tag['key']] = tag['doc_count']
except TransportError as e:
d['error'] = e.info
except Exception as e:
d['error'] = str(e)
return jsonify(d)
def unique_terms (app, app_type=None, q=""):
"""
Aggregate the number of unique terms in a field. Missing values are counted and marked as "N/A".
.. todo::
Need to incorporate QueryBuilder library instead of manually generating queries.
:param app: [string] application name
:param app_type: [string] application type
:param field: [string] field to search against for unique values
:param size: [int] the top size terms returned in the result. Default value is 10.
:param min_hits: [int] return tags which have been found in min_hits or more. Default value is 1.
:return: [dict] dictionary of results
"""
field = q.get ("field") if q.get ("field") else ""
size = q.get ("size") if q.get ("size") else 10000
min_hits = q.get ("min_hits") if q.get ("min_hits") else 0
print field
query = { "aggs" : {
"terms_agg" : {
"terms" : {
"field" : field,
"size" : size,
"min_doc_count" : min_hits,
"missing" : "N/A"
}
}
}
}
d = {}
try:
response = es.search (index=app, doc_type=app_type, body=query)
for tag in response['aggregations']['terms_agg']['buckets']:
d [tag ['key']] = tag ['doc_count']
except TransportError as e:
d ['error'] = e.info
except Exception as e:
d ['error'] = str (e)
return jsonify (d)
def terms(app, app_type=None, q=''):
"""
Group by field (find all elements )
"""
field = q.get("field") if q.get("field") else ""
segment = q.get("seg") if q.get("seg") else "*"
size = q.get("size") if q.get("size") else 10000
numhits = q.get("numhits") if q.get("numhits") else 10
query = {
"aggs": {
"count_by_type": {
"terms": {
"field": field,
"size": size # maximum number of keys (unique fields)
},
"aggs": {
"top": { # arbitrary name
"top_hits": {
"size": numhits, # number of logs in subgroup
"_source":
{ # segment on fields - return only subgroup based on field
"include": [segment]
}
}
}
}
}
}
}
d = {}
# try:
response = es.search(index=app, doc_type=app_type, body=query)
# for tag in response['aggregations']['count_by_type']['buckets']:
# d [tag ['key']] = tag ['doc_count']
# except TransportError as e:
# d ['error'] = e.info
# except Exception as e:
# d ['error'] = str (e)
# return jsonify (d)
return jsonify(response)
def terms (app, app_type=None, q=''):
"""
Group by field (find all elements )
"""
field = q.get ("field") if q.get ("field") else ""
segment = q.get ("seg") if q.get ("seg") else "*"
size = q.get ("size") if q.get ("size") else 10000
numhits = q.get ("numhits") if q.get ("numhits") else 10
query = { "aggs" : {
"count_by_type" : {
"terms" : {
"field" : field,
"size" : size # maximum number of keys (unique fields)
},
"aggs" : {
"top" : { # arbitrary name
"top_hits" : {
"size" : numhits, # number of logs in subgroup
"_source" : { # segment on fields - return only subgroup based on field
"include" : [
segment
]
}
}
}
}
}
}
}
d = {}
# try:
response = es.search (index=app, doc_type=app_type, body=query)
# for tag in response['aggregations']['count_by_type']['buckets']:
# d [tag ['key']] = tag ['doc_count']
# except TransportError as e:
# d ['error'] = e.info
# except Exception as e:
# d ['error'] = str (e)
# return jsonify (d)
return jsonify (response)
def get_applications():
"""
Fetch all the registered applications in Distill.
.. note:: Private indexes starting with a period are not included
in the result set
:return: [dict] dictionary of all registered applications and meta info
"""
doc = {}
query = {
"aggs": {
"count_by_type": {
"terms": {
"field": "_type",
"size": 100
}
}
}
}
try:
cluster_status = es.cat.indices(h=["index"], pri=False)
x = cluster_status.splitlines()
for idx in x:
idx = idx.rstrip()
# Ignore private indexes (like .kibana or .stout)
if idx[:1] != '.':
response = es.search(index=idx, body=query)
d = {}
for tag in response["aggregations"]["count_by_type"][
"buckets"]:
d[tag['key']] = tag['doc_count']
doc[idx] = d
except TransportError as e:
doc['error'] = e.info
except Exception as e:
doc['error'] = str(e)
return doc
def get_applications ():
"""
Fetch all the registered applications in Distill.
.. note:: Private indexes starting with a period are not included in the result set
:return: [dict] dictionary of all registered applications and meta information
"""
doc = {}
query = { "aggs" : {
"count_by_type" : {
"terms" : {
"field" : "_type",
"size" : 100
}
}
}
}
try:
cluster_status = es.cat.indices (h=["index"], pri=False)
x = cluster_status.splitlines()
for idx in x:
idx = idx.rstrip ()
# Ignore private indexes (like .kibana or .stout)
if idx [:1] != '.':
response = es.search (index=idx, body=query)
d = {}
for tag in response["aggregations"]["count_by_type"]["buckets"]:
d [tag ['key']] = tag ['doc_count']
doc [idx] = d
except TransportError as e:
doc ['error'] = e.info
except Exception as e:
doc ['error'] = str (e)
return doc
def segment (app, app_type=None, params=''):
"""
Just support match all for now.
"""
q = params.get ("q") if params.get ("q") else {}
fields = params.get ("fields") if params.get ("fields") else []
size = params.get ("size") if params.get ("size") else 10
scroll = params.get ("scroll") if params.get ("scroll") else False
fl = params.get ("fl") if params.get ("fl") else []
# filters = params.get ("filter") if params.get ("filter") else {}
# 'q': args.get('q', '{}'),
# 'fields': args.get('fl', '{}'),
# 'size': args.get ('size', 100),
# 'scroll': args.get ('scroll', False),
# 'filters': request_args.getlist ('fq')
query = {}
query ['size'] = size
if q:
res = q.split(":")
key = res [0]
val = res [1]
query ['query'] = {"match" : { key : val } }
else:
query ['query'] = {"match_all" : {}}
if len (fields) > 0:
ex = {
"include" : fields.split(",")
}
query ['_source'] = ex
response = es.search (index=app, doc_type=app_type, body=query)
return jsonify (response)
def generate_graph(app,
app_type='logs',
log_type='raw',
targets=[],
events=[],
time_range=['now-1h', 'now'],
size=20):
"""
Return all elements from an application, possible matching against
a specific event type (e.g. click, mouseover, etc)
"""
# @TODO ref_url filter
must_not_query = [{
"term": {
"type": "mousedown"
}
}, {
"term": {
"type": "mouseup"
}
}]
filter_query = [
{
"term": {
"logType": log_type
},
},
]
# Filtering
should_query = []
must_query = []
# Include these events in the request
if events:
include_events = {"terms": {"type": events}}
filter_query.append(include_events)
target_in = targets[0]
target_out = targets[1]
if target_in:
include_targets = {"terms": {"target": target_in}}
filter_query.append(include_targets)
# Remove these elementids from result set
for target in target_out:
res = {"term": {"target": target}}
must_not_query.append(res)
# Finish off should query
# must_query.append({"bool": {"should": should_query}})
# Sort By Time
sort_query = [{"clientTime": {"order": "asc"}}]
# Timestamp range - date math
timestamp_query = {
"range": {
"@timestamp": {
"gte": time_range[0],
"lte": time_range[1]
}
}
}
filter_query.append(timestamp_query)
agg_query = dict()
# Get all unique sessions
session_query = {"terms": {"field": "sessionID", "min_doc_count": 1}}
agg_query['sessions'] = session_query
# Generating all top targets and breakdowns by type, including path_length
target_query = {
"terms": {
"field": "target",
"min_doc_count": 1,
"size": size
},
"aggs": {
"events": {
"terms": {
"field": "type",
"min_doc_count": 1,
"size": size
}
},
"top_target": {
"top_hits": {
"script_fields": {
"path_length": {
"script": {
"lang": "painless",
"inline": "doc['path'].length;"
}
}
},
"size": 1
}
}
}
}
agg_query['targets'] = target_query
# Main query
query = {
"sort": sort_query,
"query": {
"bool": {
# "must": must_query,
# "should": should_query,
"filter": filter_query,
"must_not": must_not_query,
# "minimum_should_match": len(should_query) - 1
}
},
"_source": {
"includes": ['*'],
},
"script_fields": {
"path_length": {
"script": {
"lang": "painless",
"inline": "doc['path'].length;"
}
}
},
"aggregations": agg_query
}
# return query
# Process Aggregate Results
response = es.search(app, doc_type=app_type, body=query, size=0)
# Only want to look at aggregations
sessions = response['aggregations']['sessions']['buckets']
# allSessions = { x['key']: [] for x in sessions }
# intervalSessions = { x['key']: [] for x in sessions }
# Deal with bar chart
allTargets = response['aggregations']['targets']['buckets']
# Re-execute query to get all hits
iter = helpers.scan(es,
query=query,
index=app,
doc_type=app_type,
preserve_order=True)
allSessions = dict()
# Store all hits in the user's bucket.
for elem in iter:
data = elem['_source']
data['pathLength'] = elem['fields']['path_length'][0]
if 'sessionID' in data:
sessionID = data['sessionID']
if sessionID in allSessions:
allSessions[sessionID].append(data)
else:
allSessions[sessionID] = [data]
# This fixed sequence/interval logging that what was produced in
# UserALE.js v 0.2.0
# Possible to remove self-loops here as well (html->html->html->window) := (html->window)
intervalSessions = dict()
for sessionID in allSessions:
data = allSessions[sessionID]
newData = []
intervalLog = []
pairs = zip(data, data[1:])
for curr, next in pairs:
target1 = curr['target']
event1 = curr['type']
target2 = next['target']
event2 = next['type']
if target1 != target2: # ignore self-loops
targetChange = int(True)
eventChange = int(False)
if event1 != event2:
eventChange = int(True)
# Starting over no matter what
# Based off of curr, update the log
curr['targetChange'] = targetChange
curr['typeChange'] = eventChange
curr['intervalCount'] = len(
intervalLog) # some number maybe 0
if len(intervalLog) >= 2:
# Calculate duration
curr['duration'] = intervalLog[-1:]['clientTime'] - \
intervalLog[0]['clientTime']
else:
curr['duration'] = 0
newData.append(curr)
intervalLog = []
# else:
# # They are the same
# targetChange = int(False)
# eventChange = int(False)
# if event1 != event2:
# eventChange = int(True)
# # starting over
# curr['targetChange'] = targetChange
# curr['typeChange'] = eventChange
# curr['intervalCount'] = len(intervalLog)
# # if len(intervalLog) >= 2:
# # # Calculate duration
# # curr['duration'] = intervalLog[-1:]['clientTime'] - \
# # intervalLog[0]['clientTime']
# # else:
# # curr['duration'] = 0
# newData.append(curr)
# intervalLog = []
# else:
# # increase counter
# intervalLog.append(curr)
intervalSessions[sessionID] = newData
# return intervalSessions
newSessions = []
# Generate all edges tied to a user
# [ edge list, edge list, ... ]
for k, v in intervalSessions.items():
pairs = pairwise(v) # list of edges for a user
newSessions.append(pairs)
# Node Map
node_list = [] # Need to keep 0-based index for sankey diagram
links = [] # Aggregate sequence list
node_map = [] # Final node map {"name": "foo", "id": 0"}
# Align the sequences
alignment = itertools.izip_longest(*newSessions)
src_ids = {}
target_ids = {}
for i, step in enumerate(alignment):
# print(i)
c = collections.Counter()
visitedLinks = []
# visitedLinksUnique = set([])
nodenames = set([])
for edge in step: # for a single step look at all links
if edge:
node1 = edge[0]
node2 = edge[1]
session = node1['sessionID']
nodename1 = node1['target']
nodename2 = node2['target']
seqID = '%s->%s' % (nodename1, nodename2)
#print(seqID)
if nodename1 != nodename2: #double check again for self-loops
#print(node1)
link = {
'sequenceID':
seqID,
'sourceName':
nodename1,
'targetName':
nodename2,
'type':
node1['type'],
'duration':
node1['duration'],
'pathLength':
len(node1['path'])
if node1['path'] is not None else 0,
'targetChange':
node1['targetChange'],
'typeChange':
node1['typeChange']
}
visitedLinks.append(link)
# Done with visits in a step. Now calculate counts
counts = collections.Counter(k['sequenceID'] for k in visitedLinks
if k.get('sequenceID'))
# print(counts)
visitedLinksUnique = {v['sequenceID']: v
for v in visitedLinks}.values()
# print(visitedLinksUnique)
# Visit unique links and generate src/targetid
if len(node_map) == 0:
for link in visitedLinksUnique:
# Add all sources
if link['sourceName'] not in src_ids:
node_map.append({"name": link['sourceName']})
src_ids[link['sourceName']] = len(node_map) - 1
# Add all targets
if link['targetName'] not in target_ids:
node_map.append({"name": link['targetName']})
target_ids[link['targetName']] = len(node_map) - 1
else:
src_ids = target_ids # sources were previous targets
target_ids = {}
for link in visitedLinksUnique:
# Add all sources
# if link['sourceName'] not in src_ids.values():
# node_map.append(link['sourceName'])
# src_ids[len(node_map)-1] = link['sourceName']
# Add all targets
if link['targetName'] not in target_ids:
node_map.append({"name": link['targetName']})
target_ids[link['targetName']] = len(node_map) - 1
for link in visitedLinksUnique:
# Perform lookup for ids
# Perform lookup for counts
link['source'] = src_ids[link['sourceName']]
link['target'] = target_ids[link['targetName']]
link['value'] = counts[link['sequenceID']]
links.append(link)
# for step in alignment:
# # step through every users sequence
# c = collections.Counter()
# visitedLinks = []
# nodenames = set([])
#
# # Process all the edges
# for edge in step:
# if edge:
# node1 = edge[0]
# node2 = edge[1]
#
# nodename1 = node1['target']
# nodename2 = node2['target']
#
# # Add src and targetids
# nodenames.add(nodename1)
# nodenames.add(nodename2)
#
# # Generate sequence ID
# seqID = '%s->%s' % (nodename1, nodename2)
#
# # @todo Ensure src and target are not the same (self-loop)
# if nodename1 != nodename2:
# link = {
# 'sequenceID': seqID,
# 'sourceName': nodename1,
# 'targetName': nodename2,
# 'type': node1['type'],
# # 'duration': node1['duration'],
# 'pathLength': len(node1['path']),
# 'targetChange': node1['targetChange'],
# 'typeChange': node1['typeChange']
# }
# visitedLinks.append(link)
#
# # How many users visited a sequence at this step
# counts = collections.Counter(k['sequenceID'] for k in visitedLinks if k.get('sequenceID'))
# # print(counts)
# # Append into growing node_list
# map(lambda x: node_list.append(x), nodenames)
#
# # map(lambda x: node_map.append({ "name": x}
# # "id": len(node_list) - 1 - node_list[::-1].index(x)}), nodenames)
#
# map(lambda x: node_map.append({ "name": x}), nodenames)
# # "id": len(node_list) - 1 - node_list[::-1].index(x)}), nodenames)
# for v in visitedLinks:
# # Pass through and update count, also generate src and target id
# v['value'] = counts[v['sequenceID']]
# # Last occurence is the src and target id
# v['source'] = len(node_list) -1 - node_list[::-1].index(v['sourceName'])
# v['target'] = len(node_list) -1 - node_list[::-1].index(v['targetName'])
# links.append(v)
# Save everything
res = dict()
res['histogram'] = generate_bargraph(allTargets)
# res['sankey'] = {
# # 'sessions': sessions,
# 'links': links,
# 'nodes': node_map
# }
res['nodes'] = node_map
res['links'] = links
res['sessions'] = sessions
# with open('sankey.json', 'w') as outfile:
# json.dump(res, outfile, sort_keys=False, indent=4)
# with open('data.txt', 'w') as outfile:
# json.dump(intervalSessions, outfile, indent=4, sort_keys=False)
#
# with open('query.json', 'w') as outfile:
# json.dump(query, outfile, indent=4, sort_keys=False)
# Iterate first to get nodes
# pairs = pairwise(iter)
#
# nodes = []
# links = []
# for p in pairs:
# node1 = p[0]['_source']
# node2 = p[1]['_source']
# # Append nodes to list
# nodes.append(node1['target'])
# nodes.append(node2['target'])
# Iterate again to get edges
# pairs = pairwise(iter2)
# srcID = targetID = None
# for p in pairs:
# node1 = p[0]['_source']
# node2 = p[1]['_source']
#
# # Append nodes to list
# nodes.append(node1['target'])
# # nodes.append(node2['target'])
#
# srcID = len(nodes) - 1
# targetID = len(nodes)
#
# # if (node1['target'] != node2['target']):
# # Append links to list (remove self-loops)
# link = {
# 'sourceID': srcID,
# 'targetID': targetID,
# 'sourceName': node1['target'],
# 'targetName': node2['target'],
# 'type': node1['type'],
# 'duration': node1['duration'],
# 'value': node1['count'],
# 'pathLength': len(node1['path']),
# 'targetChange': int(node1['targetChange']),
# 'typeChange': int(node1['typeChange'])
# }
# links.append(link)
#
# # Get all unique nodes
# # node_names = np.unique(nodes).tolist()
# node_list = []
#
# for indx, name in enumerate(nodes):
# n = {'id': indx, 'name': name}
# node_list.append(n)
#
# # Remove self-loops
# newLinks = []
# for indx, elem in enumerate(links):
# srcID = elem['sourceID']
# targetID = elem['targetID']
#
# if srcID != targetID:
# newLinks.append(elem)
#
#
return res
