def delete(self, *args, **kwargs):
batch = WriteBatch(graph_db)
node = self.get_node()
for rel in node.get_relationships():
batch.delete_relationship(rel)
batch.delete_node(node)
super(Campaign, self).delete(*args, **kwargs)
def set_contacts(self, profiles):
"""
*profiles* -- registration.models.Userprofile objects.
"""
me = self.get_node()
batch = WriteBatch(graph_db)
for profile in profiles:
batch.get_or_create_relationship(me, self.contact_relationship, profile.__node__)
r = batch.submit()
logger.warn(r)
def set_contacts(self, profiles):
"""
*profiles* -- registration.models.Userprofile objects.
"""
me = self.get_node()
batch = WriteBatch(graph_db)
for profile in profiles:
batch.get_or_create_relationship(me, self.contact_relationship,
profile.__node__)
r = batch.submit()
logger.warn(r)
def create_directory_children(dirs, parent):
'''Create the following relationship: (p:Directory)<-[:PARENT]-(d:Directory)
where dirs is a list of strings and parent is a py2neo node.'''
batch = WriteBatch(graph_db)
for d in dirs:
dir_node = batch.create({'name': d, '_id': uuid.uuid4().hex})
batch.add_labels(dir_node, "Directory")
batch.create(rel(dir_node, "PARENT", parent))
batch.run()
def create_file_children(files, parent, root_path):
'''Create (p:Directory)<-{:PARENT]-(f:File)
for all files to the given parent. Also stores the file's contents in
the content property.'''
batch = WriteBatch(graph_db)
for f in files:
file_content = get_file_content(f, root_path)
file_node = batch.create({'name': f, '_id': uuid.uuid4().hex,
'content': file_content})
batch.add_labels(file_node, 'File')
batch.create(rel(file_node, "PARENT", parent))
batch.run()
def delete(self, *args, **kwargs):
batch = WriteBatch(graph_db)
node = self.get_node()
for rel in node.get_relationships():
batch.delete_relationship(rel)
batch.delete_node(node)
super(Campaign, self).delete(*args, **kwargs)
def set_is_client(self, is_client):
if not is_client:
self.is_client = False
self.save()
return
batch = WriteBatch(neomodel.core.connection())
for rel in self.known.all():
batch.delete(rel)
batch.set_node_property(self.__node__, 'is_client', True)
self.get_set_client_lock().acquire()
try:
batch.submit()
finally:
self.get_set_client_lock().release()
def set_is_client(self, is_client):
if not is_client:
self.is_client = False
self.save()
return
batch = WriteBatch(neomodel.core.connection())
for rel in self.known.all():
batch.delete(rel)
batch.set_node_property(self.__node__, 'is_client', True)
self.get_set_client_lock().acquire()
try:
batch.submit()
finally:
self.get_set_client_lock().release()
create_business_query = '''
// MERGE ON categories
CREATE (b:Business {id: {business_id}, name: {name}, lat:{latitude}, lon:{longitude},
stars: {stars}, review_count: {review_count}})
'''
merge_category_query = '''
MATCH (b:Business {id: {business_id}})
MERGE (c:Category {name: {category}})
CREATE UNIQUE (c)<-[:IS_IN]-(b)
'''
print "Beginning business batch"
with open('data/yelp_academic_dataset_business.json', 'r') as f:
business_batch = WriteBatch(db)
count = 0
for b in (json.loads(l) for l in f):
business_batch.append_cypher(create_business_query, b)
count += 1
if count >= 10000:
business_batch.run()
business_batch.clear()
count = 0
if count > 0:
business_batch.run()
print "Beginning category batch"
with open('data/yelp_academic_dataset_business.json', 'r') as f:
category_batch = WriteBatch(db)
count = 0
create_business_query = '''
// MERGE ON categories
CREATE (b:Business {id: {business_id}, name: {name}, lat:{latitude}, lon:{longitude},
stars: {stars}, review_count: {review_count}})
'''
merge_category_query = '''
MATCH (b:Business {id: {business_id}})
MERGE (c:Category {name: {category}})
CREATE UNIQUE (c)<-[:IS_IN]-(b)
'''
print "Beginning business batch"
with open('data/yelp_academic_dataset_business.json', 'r') as f:
business_batch = WriteBatch(db)
count = 0
for b in (json.loads(l) for l in f):
business_batch.append_cypher(create_business_query, b)
count += 1
if count >= 10000:
business_batch.run()
business_batch.clear()
count = 0
if count > 0:
business_batch.run()
print "Beginning category batch"
with open('data/yelp_academic_dataset_business.json', 'r') as f:
category_batch = WriteBatch(db)
count = 0
def handle(self, *args, **options):
try:
verses_by_unique = collate_verses_by_unique()
names_by_unique = collate_names_by_unique()
verse_uniques_by_name_unique = collate_verse_uniques_by_name_unique(names_by_unique, verses_by_unique)
graph_db = GraphDatabaseService("http://localhost:7474/db/data/")
self.stdout.write("Clearing the old graph...")
#graph_db.clear()
self.stdout.write("Writing name nodes...")
names = graph_db.get_or_create_index(Node, 'names')
print("Created index ",names)
batch = WriteBatch(graph_db)
name_nodes = list()
for count, name in enumerate(names_by_unique.values()):
abstract = Node.abstract(**name.to_dict())
batch.create_in_index_or_fail(Node,names,'by_unique',name.unique,abstract)
if count % 500 is 0:
name_nodes.extend(batch.submit())
batch = WriteBatch(graph_db)
name_nodes.extend(batch.submit())
verses = graph_db.get_or_create_index(Node,'verses')
batch = WriteBatch(graph_db)
self.stdout.write("Writing verse nodes...")
for count, verse in enumerate(verses_by_unique.values()):
abstract = Node.abstract(**verse.to_dict())
#batch.add_labels(abstract, 'Test')
batch.create_in_index_or_fail(Node,verses,'by_unique',verse.unique, abstract)
if count % 250 is 0:
batch.submit()
batch = WriteBatch(graph_db)
batch.submit()
self.stdout.write("Relating nodes...")
for name_node in name_nodes:
name = name_node['name'].lower()
verse_uniques = verse_uniques_by_name_unique.get(name)
if not verse_uniques:
continue
read_batch = ReadBatch(graph_db)
for unique in verse_uniques:
read_batch.get_indexed_nodes(verses,'by_unique',unique)
verse_nodes = read_batch.submit()
batch = WriteBatch(graph_db)
for verse_node in verse_nodes:
abstract = Relationship.abstract(verse_node[0],'REFERENCES_NAME', name_node)
batch.add_indexed_relationship(abstract)
batch.submit()
except:
traceback.print_exc()