def dump(start,end,backupdir,eshost):
conn = ES(eshost)
out = file('/tmp/out.json','w')
_type = 'habakkuk'
q = MatchAllQuery()
q = FilteredQuery(q, RangeFilter(qrange=ESRange('created_at_date',start,end,include_upper=False)))
q = q.search()
# print json.dumps(json.loads(q.to_search_json()),indent=2)
resultset = conn.search(query=q,indices=_type+"-*", doc_types=[_type], scan=True)
cnt=0
if not resultset.total:
sys.stderr.write("no data for %s - %s\n"%(start,end))
return
try:
sys.stderr.write("Will write %d lines to %s\n"%(resultset.total, out.name))
while True:
r = resultset.next()
cnt+=1
out.write(json.dumps(r)+'\n')
except StopIteration:
pass
out.close()
# gzip
ext = datetime.strftime(start,'%Y-%m-%d')
backup = os.path.join(backupdir,"habakkuk-%s.json.gz"%ext)
f_in = open(out.name,'rb')
f_out = gzip.open(backup,'wb')
f_out.writelines(f_in)
f_out.close()
f_out.close()
sys.stderr.write("Created %s\n"%backup)
def get_entries(self):
'''Get all entries for a team + their filter from ES/MozDef'''
teamfilter = self.config['teamsetup'][self.team]['filter']
es = ES((self.config['mozdef']['proto'], self.config['mozdef']['host'], self.config['mozdef']['port']))
# Default filter - time period
try:
td = self.config['es'][teamfilter]['_time_period']
except KeyError:
debug('No _time_period defined, defaulting to 24h')
td = 24
begindateUTC = toUTC(datetime.now() - timedelta(hours=td))
enddateUTC= toUTC(datetime.now())
print begindateUTC, enddateUTC
fDate = pyes.RangeQuery(qrange=pyes.ESRange('utctimestamp', from_value=begindateUTC, to_value=enddateUTC))
# Load team queries from our json config.
# Lists are "should" unless an item is negated with "!" then it's must_not
# Single items are "must"
query = pyes.query.BoolQuery()
query.add_must(pyes.QueryStringQuery('asset.autogroup: "{}"'.format(self.team)))
for item in self.config['es'][teamfilter]:
# items starting with '_' are internal/reserved, like _time_period
if (item.startswith('_')):
continue
val = self.config['es'][teamfilter][item]
if (type(val) == list):
for v in val:
if (v.startswith("!")):
query.add_must_not(pyes.MatchQuery(item, v[1:]))
else:
query.add_should(pyes.MatchQuery(item, v))
else:
if (val.startswith("!")):
query.add_must_not(pyes.MatchQuery(item, val))
else:
query.add_must(pyes.MatchQuery(item, val))
q = pyes.ConstantScoreQuery(query)
q = pyes.FilteredQuery(q, pyes.BoolFilter(must=[fDate]))
results = es.search(query=q, indices=self.config['es']['index'])
raw = results._search_raw(0, results.count())
# This doesn't do much, but pyes has no "close()" or similar functionality.
es.force_bulk()
if (raw._shards.failed != 0):
raise Exception("Some shards failed! {0}".format(raw._shards.__str__()))
# Nobody cares for the metadata past this point (all the goodies are in '_source')
data = []
for i in raw.hits.hits:
data += [i._source]
return data
def dump_topics(backupdir,
eshost,
_type,
indices="topics-all"):
conn = ES(eshost)
out = file('/tmp/out.json','w')
q = MatchAllQuery()
q = q.search()
resultset = conn.search(query=q,indices=indices, doc_types=[_type], scan=True)
cnt=0
if not resultset.total:
sys.stderr.write("no data\n")
return
try:
sys.stderr.write("Will write %d lines to %s\n"%(resultset.total, out.name))
while True:
r = resultset.next()
r['_id'] = r._meta.id
cnt+=1
out.write(json.dumps(r)+'\n')
except StopIteration:
pass
out.close()
# gzip
backup = os.path.join(backupdir,"topics.{}.json.gz".format(_type))
f_in = open(out.name,'rb')
f_out = gzip.open(backup,'wb')
f_out.writelines(f_in)
f_out.close()
f_out.close()
sys.stderr.write("Created %s\n"%backup)
class FullTextSearch(object):
def __init__(self, server, settings=None):
# These timeout and bulk_size parameters were determined through
# trial and error to be necessary to avoid timeout errors when
# generating indices on Sandbox. They should not be taken as gospel.
self.conn = ES(server, timeout=120.0) # Default timeout: 30.0
self.conn.bulker.bulk_size = 25 # Default: 400
if settings:
self.settings = settings
else:
self.settings = {
'index': {
'analysis': {
'analyzer': {
'ngram_analyzer': {
'tokenizer': 'keyword',
'filter': ['lowercase', 'filter_ngram'],
'type': 'custom'
}
},
'filter': {
'filter_ngram': {
'type': 'nGram',
'max_gram': 30,
'min_gram': 1
}
}
}
}
}
self.refresh_index_cache()
def search_index_text(self, query_string, fields="_all", **args):
q = query.MatchQuery(fields, query_string)
return self.search_index(q, **args)
def search_index(self, query, indices=None, num_results=None, node_type=None):
results = self.conn.search(
query=query, indices=indices, doc_types=node_type)
meta_list = [r.get_meta() for r in results[0:num_results]]
node_dict = {}
# fetch nodes grouped by type to reduce number of db calls
key = itemgetter('type')
for t, grouped_list in groupby(sorted(meta_list, key=key), key=key):
ids = [meta['id'] for meta in grouped_list]
for node in self.datastore.get_nodes(t, ids):
node_dict[(node.type, node.key)] = node
# return nodes in original order
nodelist = [node_dict[(meta['type'], meta['id'])]
for meta in meta_list]
return nodelist
def create_index(self, type, indexed_variables, index_name):
self.conn.indices.create_index_if_missing(index_name, self.settings)
mapping = {}
for arg in indexed_variables:
mapping[arg] = {'boost': 1.0,
'analyzer': 'ngram_analyzer',
'type': 'string',
'term_vector': 'with_positions_offsets'}
index_settings = {'index_analyzer': 'ngram_analyzer',
'search_analyzer': 'standard',
'properties': mapping}
self.conn.indices.put_mapping(str(type), index_settings, [index_name])
self.refresh_index_cache()
self.populate_index(type, index_name)
def refresh_index_cache(self):
try:
indices = self.conn.indices.get_mapping(raw=True)
except exceptions.IndexMissingException:
indices = {}
else:
indices = dict((k, v.get('mappings', {})) for k, v in indices.items())
self.indices = indices
def delete_index(self, index_name):
self.conn.indices.delete_index_if_exists(index_name)
self.refresh_index_cache()
def populate_index(self, type, index_name):
#add all the currently existing nodes into the index
ref_node = self.datastore.get_reference_node(type)
node_list = [rel.target_node for rel in ref_node.instance.outgoing]
for node in node_list:
key = node.key
index_dict = self.populate_index_document(node, index_name)
try:
self.conn.delete(index_name, type, key)
except ELASTIC_SEARCH_EXCEPTIONS as err:
log.exception(err)
pass
self.conn.index(index_dict, index_name, type, key, bulk=True)
self.conn.indices.refresh([index_name])
def on_create(self, node):
type_indices = self.get_indices_of_type(node.type)
for index_name in type_indices:
index_dict = self.populate_index_document(node, index_name)
try:
self.conn.index(index_dict, index_name, node.type, node.key, bulk=True)
self.conn.indices.refresh([index_name])
except ELASTIC_SEARCH_EXCEPTIONS as err:
log.exception(err)
pass
def on_delete(self, node):
type_indices = self.get_indices_of_type(node.type)
for index_name in type_indices:
try:
self.conn.delete(index_name, node.type, node.key, bulk=True)
self.conn.indices.refresh([index_name])
except ELASTIC_SEARCH_EXCEPTIONS as err:
log.exception(err)
pass
def on_modify(self, node):
type_indices = self.get_indices_of_type(node.type)
for index_name in type_indices:
index_dict = self.populate_index_document(node, index_name)
try:
self.conn.delete(index_name, node.type, node.key)
self.conn.index(index_dict, index_name, node.type, node.key, bulk=True)
self.conn.indices.refresh([index_name])
except ELASTIC_SEARCH_EXCEPTIONS as err:
log.exception(err)
pass
def get_indices_of_type(self, type):
type_indices = [
key for key, value in self.indices.items()
if type in value
]
return type_indices
def populate_index_document(self, node, index_name):
indexed_variables = self.indices[index_name][node.type]['properties'].keys()
index_dict = {
field: node[field] for field in indexed_variables
}
return index_dict
class ESDataSource(base.DataSource):
"""docstring for ClassName
"""
def __init__(self, document_type, database=None, host=None, port=None,
expand=False, **elasticsearch_args):
"""Creates a ElasticSearch data source stream.
:Attributes:
* document_type: elasticsearch document_type name
* database: database name
* host: elasticsearch database server host, default is ``localhost``
* port: elasticsearch port, default is ``27017``
* expand: expand dictionary values and treat children as top-level keys with dot '.'
separated key path to the child..
"""
self.document_type = document_type
self.database_name = database
self.host = host
self.port = port
self.elasticsearch_args = elasticsearch_args
self.expand = expand
self.connection = None
self._fields = None
def initialize(self):
"""Initialize ElasticSearch source stream:
"""
args = self.elasticsearch_args.copy()
server = ""
if self.host:
server = self.host
if self.port:
server += ":" + self.port
self.connection = ES(server, **args)
self.connection.default_indices = self.database_name
self.connection.default_types = self.document_type
def read_fields(self, limit=0):
keys = []
probes = {}
def probe_record(record, parent=None):
for key, value in record.items():
if parent:
full_key = parent + "." + key
else:
full_key = key
if self.expand and type(value) == dict:
probe_record(value, full_key)
continue
if not full_key in probes:
probe = dq.FieldTypeProbe(full_key)
probes[full_key] = probe
keys.append(full_key)
else:
probe = probes[full_key]
probe.probe(value)
for record in self.document_type.find(limit=limit):
probe_record(record)
fields = []
for key in keys:
probe = probes[key]
field = base.Field(probe.field)
storage_type = probe.unique_storage_type
if not storage_type:
field.storage_type = "unknown"
elif storage_type == "unicode":
field.storage_type = "string"
else:
field.storage_type = "unknown"
field.concrete_storage_type = storage_type
# FIXME: Set analytical type
fields.append(field)
self.fields = list(fields)
return self.fields
def rows(self):
if not self.connection:
raise RuntimeError("Stream is not initialized")
from pyes.query import MatchAllQuery
fields = self.fields.names()
results = self.connection.search(MatchAllQuery(), search_type="scan", timeout="5m", size="200")
return ESRowIterator(results, fields)
def records(self):
if not self.connection:
raise RuntimeError("Stream is not initialized")
from pyes.query import MatchAllQuery
results = self.connection.search(MatchAllQuery(), search_type="scan", timeout="5m", size="200")
return ESRecordIterator(results, self.expand)
'size': 2,
'explain': explain
}
return query
if __name__ == '__main__':
explain = True
# MongoDB
host = 'localhost'
port = 27017
mcm = MongoConnectionManager(host, port, MongoCodec())
database = 'processed'
resource_collection = mcm.get_collection(database, 'resources', Resource)
# ElasticSearch
es = ES('localhost:9200', timeout=60)
es_index = 'topic_tracking'
# find the same resource
resource = resource_collection.find_one_model()
query = build_query(resource, explain)
result = es.search(query, es_index, 'resource')
for r in result['hits']['hits']:
pprint(r)
print('Tested resource %s: %s' % (resource._id, resource.uri))
class FullTextSearch(object):
def __init__(self,server,settings = None ):
self.conn = ES(server)
self.indices = {}
if settings:
self.settings = settings
else:
self.settings = {
'index': {
'analysis' : {
'analyzer' : {
'ngram_analyzer' : {
'tokenizer' : 'keyword',
'filter' : ['lowercase', 'filter_ngram'],
'type' : 'custom'
}
},
'filter' : {
'filter_ngram' : {
'type' : 'nGram',
'max_gram' : 30,
'min_gram' : 1
}
}
}
}
}
def search_index(self, type, index_names, query_string, num_results=-1):
ns_index_names= [str(type) + "-_-" + index_name for index_name in index_names]
q = WildcardQuery('_all',lower(query_string))
results = self.conn.search(query=q, indices=ns_index_names, doc_types=type)
num_found = len(results)
if(num_results > num_found):
num_results = num_found
nodelist = [self.datastore.get_node(type,r['_id']) for r in results['hits']['hits']]
if(num_results!=-1):
return nodelist[0:num_results]
else:
return nodelist
def create_index(self, type, indexed_variables, index_name):
ns_index_name = str(type) + "-_-" + index_name
self.conn.delete_index_if_exists(ns_index_name)
self.conn.create_index(ns_index_name,self.settings)
mapping = {}
for arg in indexed_variables:
mapping[arg] = {'boost':1.0,
'analyzer' : 'ngram_analyzer',
'type': u'string',
'term_vector': 'with_positions_offsets'}
index_settings = {'index_analyzer':'ngram_analyzer',
'search_analyzer':'standard',
'properties':mapping}
self.conn.put_mapping(str(type),index_settings,[ns_index_name])
self.refresh_index_cache()
self.populate_index(type, index_name)
def refresh_index_cache(self):
self.indices = self.conn.get_indices()
def delete_index(self,type,index_name):
ns_index_name = str(type) + "-_-" + index_name
self.conn.delete_index_if_exists(ns_index_name)
self.refresh_index_cache()
def populate_index(self, type, index_name):
#add all the currently existing nodes into the index
ns_index_name = str(type) + "-_-" + index_name
ref_node = self.datastore.get_reference_node(type)
node_list = [rel.target_node for rel in ref_node.instance.outgoing]
mapping = self.conn.get_mapping(type,ns_index_name)
for node in node_list:
key = node.key
index_dict = self.populate_index_document(type,ns_index_name,node.attributes,mapping)
try:
self.conn.delete(ns_index_name,type,key)
except exceptions.NotFoundException:
pass
try:
self.conn.index(index_dict,ns_index_name,type,key)
except exceptions.ElasticSearchParseException:
pass
self.conn.refresh([ns_index_name])
def on_create(self,node):
type_indices = self.get_indices_of_type(node.type)
for ns_index_name in type_indices:
mapping = self.conn.get_mapping(node.type,ns_index_name)
index_dict = self.populate_index_document(node.type,ns_index_name,node.attributes,mapping)
self.conn.index(index_dict,ns_index_name,node.type,node.key)
self.conn.refresh([ns_index_name])
def on_delete(self, node):
type_indices = self.get_indices_of_type(node.type)
for ns_index_name in type_indices:
try:
self.conn.delete(ns_index_name,node.type,node.key)
self.conn.refresh([ns_index_name])
except exceptions.NotFoundException:
pass
def on_modify(self, node):
type_indices = self.get_indices_of_type(node.type)
for ns_index_name in type_indices:
mapping = self.conn.get_mapping(node.type,ns_index_name)
index_dict = self.populate_index_document(node.type,ns_index_name,node.attributes,mapping)
try:
self.conn.delete(ns_index_name,node.type,node.key)
self.conn.index(index_dict,ns_index_name,node.type,node.key)
self.conn.refresh([ns_index_name])
except exceptions.NotFoundException:
pass
def get_indices_of_type(self,type):
type_indices = []
for index in self.indices.keys():
if index.startswith(type+"-_-"):
type_indices.append(index)
return type_indices
def populate_index_document(self,type,ns_index_name,attributes,mapping):
indexed_variables = mapping[type]['properties'].keys()
index_dict = {}
for arg in indexed_variables:
try:
index_dict[arg] = attributes[arg]
except KeyError:
#if this attribute doesn't exist for this node, just pass
pass
return index_dict
class ESDataSource(DataSource):
"""
docstring for ClassName
"""
def __init__(self,
document_type,
index=None,
host=None,
port=None,
expand=False,
**elasticsearch_args):
"""Creates a ElasticSearch data source stream.
:Attributes:
* document_type: elasticsearch document_type name
* index: index name, default is test
* host: elasticsearch database server host, default is ``localhost``
* port: elasticsearch port, default is ``27017``
* expand: expand dictionary values and treat children as top-level keys with dot '.'
separated key path to the child..
"""
super(ESDataSource, self).__init__()
self.document_type = document_type
self.index = index or "test"
self.host = host or "127.0.0.1"
self.port = port or "9200"
self.elasticsearch_args = elasticsearch_args
self.expand = expand
self.connection = None
self._fields = None
def initialize(self):
"""Initialize ElasticSearch source stream:
"""
args = self.elasticsearch_args.copy()
server = ""
if self.host:
server = self.host
if self.port:
server += ":" + self.port
self.connection = ES(server, **args)
self.connection.default_indices = self.index
self.connection.default_types = self.document_type
def read_fields(self, limit=0, collapse=False):
keys = []
probes = {}
def probe_record(record, parent=None):
for key, value in record.items():
if parent:
full_key = parent + "." + key
else:
full_key = key
if self.expand and type(value) == dict:
probe_record(value, full_key)
continue
if not full_key in probes:
probe = FieldTypeProbe(full_key)
probes[full_key] = probe
keys.append(full_key)
else:
probe = probes[full_key]
probe.probe(value)
for record in self.document_type.find(limit=limit):
probe_record(record)
fields = []
for key in keys:
probe = probes[key]
field = Field(probe.field)
storage_type = probe.unique_storage_type
if not storage_type:
field.storage_type = "unknown"
elif storage_type == "unicode":
field.storage_type = "string"
else:
field.storage_type = "unknown"
field.concrete_storage_type = storage_type
# FIXME: Set analytical type
fields.append(field)
self._fields = list(fields)
return self._fields
def rows(self):
if not self.connection:
raise RuntimeError("Stream is not initialized")
from pyes.query import MatchAllQuery
fields = self.field_names
results = self.connection.search(MatchAllQuery(),
search_type="scan",
timeout="5m",
size="200")
return ESRowIterator(results, fields)
def records(self):
if not self.connection:
raise RuntimeError("Stream is not initialized")
from pyes.query import MatchAllQuery
results = self.connection.search(MatchAllQuery(),
search_type="scan",
timeout="5m",
size="200")
return ESRecordIterator(results, self.expand)
class FullTextSearch(object):
def __init__(self, server, settings=None):
self.conn = ES(server)
self.indices = {}
if settings:
self.settings = settings
else:
self.settings = {
'index': {
'analysis': {
'analyzer': {
'ngram_analyzer': {
'tokenizer': 'keyword',
'filter': ['lowercase', 'filter_ngram'],
'type': 'custom'
}
},
'filter': {
'filter_ngram': {
'type': 'nGram',
'max_gram': 30,
'min_gram': 1
}
}
}
}
}
def search_index(self, type, index_names, query_string, num_results=-1):
ns_index_names = [
str(type) + "-_-" + index_name for index_name in index_names
]
q = WildcardQuery('_all', lower(query_string))
results = self.conn.search(query=q,
indices=ns_index_names,
doc_types=type)
num_found = len(results)
if (num_results > num_found):
num_results = num_found
nodelist = [
self.datastore.get_node(type, r['_id'])
for r in results['hits']['hits']
]
if (num_results != -1):
return nodelist[0:num_results]
else:
return nodelist
def create_index(self, type, indexed_variables, index_name):
ns_index_name = str(type) + "-_-" + index_name
self.conn.delete_index_if_exists(ns_index_name)
self.conn.create_index(ns_index_name, self.settings)
mapping = {}
for arg in indexed_variables:
mapping[arg] = {
'boost': 1.0,
'analyzer': 'ngram_analyzer',
'type': u'string',
'term_vector': 'with_positions_offsets'
}
index_settings = {
'index_analyzer': 'ngram_analyzer',
'search_analyzer': 'standard',
'properties': mapping
}
self.conn.put_mapping(str(type), index_settings, [ns_index_name])
self.refresh_index_cache()
self.populate_index(type, index_name)
def refresh_index_cache(self):
self.indices = self.conn.get_indices()
def delete_index(self, type, index_name):
ns_index_name = str(type) + "-_-" + index_name
self.conn.delete_index_if_exists(ns_index_name)
self.refresh_index_cache()
def populate_index(self, type, index_name):
#add all the currently existing nodes into the index
ns_index_name = str(type) + "-_-" + index_name
ref_node = self.datastore.get_reference_node(type)
node_list = [rel.target_node for rel in ref_node.instance.outgoing]
mapping = self.conn.get_mapping(type, ns_index_name)
for node in node_list:
key = node.key
index_dict = self.populate_index_document(type, ns_index_name,
node.attributes, mapping)
try:
self.conn.delete(ns_index_name, type, key)
except exceptions.NotFoundException:
pass
try:
self.conn.index(index_dict, ns_index_name, type, key)
except exceptions.ElasticSearchParseException:
pass
self.conn.refresh([ns_index_name])
def on_create(self, node):
type_indices = self.get_indices_of_type(node.type)
for ns_index_name in type_indices:
mapping = self.conn.get_mapping(node.type, ns_index_name)
index_dict = self.populate_index_document(node.type, ns_index_name,
node.attributes, mapping)
self.conn.index(index_dict, ns_index_name, node.type, node.key)
self.conn.refresh([ns_index_name])
def on_delete(self, node):
type_indices = self.get_indices_of_type(node.type)
for ns_index_name in type_indices:
try:
self.conn.delete(ns_index_name, node.type, node.key)
self.conn.refresh([ns_index_name])
except exceptions.NotFoundException:
pass
def on_modify(self, node):
type_indices = self.get_indices_of_type(node.type)
for ns_index_name in type_indices:
mapping = self.conn.get_mapping(node.type, ns_index_name)
index_dict = self.populate_index_document(node.type, ns_index_name,
node.attributes, mapping)
try:
self.conn.delete(ns_index_name, node.type, node.key)
self.conn.index(index_dict, ns_index_name, node.type, node.key)
self.conn.refresh([ns_index_name])
except exceptions.NotFoundException:
pass
def get_indices_of_type(self, type):
type_indices = []
for index in self.indices.keys():
if index.startswith(type + "-_-"):
type_indices.append(index)
return type_indices
def populate_index_document(self, type, ns_index_name, attributes,
mapping):
indexed_variables = mapping[type]['properties'].keys()
index_dict = {}
for arg in indexed_variables:
try:
index_dict[arg] = attributes[arg]
except KeyError:
#if this attribute doesn't exist for this node, just pass
pass
return index_dict
class FullTextSearch(object):
def __init__(self, server, settings=None):
self.conn = ES(server)
if settings:
self.settings = settings
else:
self.settings = {
'index': {
'analysis': {
'analyzer': {
'ngram_analyzer': {
'tokenizer': 'keyword',
'filter': ['lowercase', 'filter_ngram'],
'type': 'custom'
}
},
'filter': {
'filter_ngram': {
'type': 'nGram',
'max_gram': 30,
'min_gram': 1
}
}
}
}
}
self.refresh_index_cache()
def search_index_text(self, query_string, fields="_all", **args):
q = query.TextQuery(fields, query_string)
return self.search_index(q, **args)
def search_index(self, query, indices=None, num_results=None, node_type=None):
results = self.conn.search(
query=query, indices=indices, doc_types=node_type)
meta_list = [r.get_meta() for r in results[0:num_results]]
node_dict = {}
# fetch nodes grouped by type to reduce number of db calls
key = itemgetter('type')
for t, grouped_list in groupby(sorted(meta_list, key=key), key=key):
ids = [meta['id'] for meta in grouped_list]
for node in self.datastore.get_nodes(t, ids):
node_dict[(node.type, node.key)] = node
# return nodes in original order
nodelist = [node_dict[(meta['type'], meta['id'])]
for meta in meta_list]
return nodelist
def create_index(self, type, indexed_variables, index_name):
self.conn.create_index_if_missing(index_name, self.settings)
mapping = {}
for arg in indexed_variables:
mapping[arg] = {'boost': 1.0,
'analyzer': 'ngram_analyzer',
'type': 'string',
'term_vector': 'with_positions_offsets'}
index_settings = {'index_analyzer': 'ngram_analyzer',
'search_analyzer': 'standard',
'properties': mapping}
self.conn.put_mapping(str(type), index_settings, [index_name])
self.refresh_index_cache()
self.populate_index(type, index_name)
def refresh_index_cache(self):
try:
self.indices = self.conn.get_mapping()
except exceptions.IndexMissingException:
self.indices = {}
def delete_index(self, index_name):
self.conn.delete_index_if_exists(index_name)
self.refresh_index_cache()
def populate_index(self, type, index_name):
#add all the currently existing nodes into the index
ref_node = self.datastore.get_reference_node(type)
node_list = [rel.target_node for rel in ref_node.instance.outgoing]
for node in node_list:
key = node.key
index_dict = self.populate_index_document(node, index_name)
try:
self.conn.delete(index_name, type, key)
except exceptions.NotFoundException:
pass
self.conn.index(index_dict, index_name, type, key)
self.conn.refresh([index_name])
def on_create(self, node):
type_indices = self.get_indices_of_type(node.type)
for index_name in type_indices:
index_dict = self.populate_index_document(node, index_name)
self.conn.index(index_dict, index_name, node.type, node.key)
self.conn.refresh([index_name])
def on_delete(self, node):
type_indices = self.get_indices_of_type(node.type)
for index_name in type_indices:
try:
self.conn.delete(index_name, node.type, node.key)
self.conn.refresh([index_name])
except exceptions.NotFoundException:
pass
def on_modify(self, node):
type_indices = self.get_indices_of_type(node.type)
for index_name in type_indices:
index_dict = self.populate_index_document(node, index_name)
try:
self.conn.delete(index_name, node.type, node.key)
self.conn.index(index_dict, index_name, node.type, node.key)
self.conn.refresh([index_name])
except exceptions.NotFoundException:
pass
def get_indices_of_type(self, type):
type_indices = [
key for key, value in self.indices.items()
if type in value
]
return type_indices
def populate_index_document(self, node, index_name):
indexed_variables = self.indices[index_name][node.type]['properties'].keys()
index_dict = {
field: node[field] for field in indexed_variables
}
return index_dict
