Never lose your graphs again with backups!
Create a backup of an ArcGIS Knowledge graph you have created to store your data model and data. The backup will be in the format of json files that can read to recreate your graph or be shared for others to be able to create the same graph on a different server.
Load a new graph easily from your backup files! Use the backup files with the load backup files portion of this notebook to create a graph with the same data model and data.
Setup
Import necessary libraries
Start by importing the libraries we need for connecting to portal, accessing knowledge graph, and manipulating data as needed
# imports
import os, json
from datetime import datetime
from uuid import UUID
from arcgis.gis import GIS
from arcgis.graph import KnowledgeGraphDefine folder and file names
Define all names for files so we can stay consistent for backing up and loading files
# output folder name
output_folder = "C:\backups\myknowledgegraph_backup"
# output backup json file names
dm_ent = "datamodel_entities.json"
dm_rel = "datamodel_relationships.json"
all_ent = "all_entities.json"
all_rel = "all_relationships.json"
prov_file = "provenance_entities.json" # this will only be used if you want to backup provenance recordsCreate backup files
Connect to portal and knowledge graph
Connect to the portal and knowledge graph on that portal, also ensure knowledge graph service exists (url is correct)
gis_backup = GIS("home") # connect to portal
# connect to knowledge graph service
knowledgegraph_backup = KnowledgeGraph(
"https://myportal.com/server/rest/services/Hosted/myknowledgegraph/KnowledgeGraphServer",
gis=gis_backup,
)
try:
knowledgegraph_backup.datamodel
except:
raise Exception("Knowledge graph to backup does not exist")Write data model entity types to backup json file
Iterate through the data model of the knowledge graph to write all entity type objects to a backup json file
# create list of formatted entity types
entity_types = []
for types in knowledgegraph_backup.datamodel["entity_types"]:
curr_entity_type = {
"name": knowledgegraph_backup.datamodel["entity_types"][types]["name"],
"properties": knowledgegraph_backup.datamodel["entity_types"][types][
"properties"
],
}
entity_types.append(curr_entity_type)# write entity types to json file
with open(os.path.join(output_folder, dm_ent), "w") as f:
json.dump(entity_types, f)Write data model relationship types to backup json file
Iterate through the data model of the knowledge graph to write all relationship type objects to a backup json file
# create list of formatted relationship types
relationship_types = []
for types in knowledgegraph_backup.datamodel["relationship_types"]:
curr_relationship_type = {
"name": knowledgegraph_backup.datamodel["relationship_types"][types]["name"],
"properties": knowledgegraph_backup.datamodel["relationship_types"][types][
"properties"
],
}
relationship_types.append(curr_relationship_type)# write relationship types to json file
with open(os.path.join(output_folder, dm_rel), "w") as f:
json.dump(relationship_types, f)Write entities to backup json file
Get all entities from the knowledge graph using a streaming query, clean them up for better writing when loading, and save the resulting entities to a json backup file
# query for all entities in graph
original_entities = knowledgegraph_backup.query_streaming(
"MATCH (n) RETURN distinct n"
)# create list of formatted entities to add to the graph
all_entities_fromquery = []
for entity in list(original_entities):
curr_entity = entity[0]
# convert UUID values to a string since json can't store UUIDs
curr_entity["_id"] = str(curr_entity["_id"])
for prop in curr_entity["_properties"]:
if type(curr_entity["_properties"][prop]) == UUID:
curr_entity["_properties"][prop] = str(curr_entity["_properties"][prop])
# delete objectid, the server will handle creating new ones when we load the backup
del curr_entity["_properties"][
"objectid"
]
all_entities_fromquery.append(curr_entity)# write entities list to json file
with open(os.path.join(output_folder, all_ent), "w") as f:
json.dump(all_entities_fromquery, f)Write relationships to backup json file
Get all relationships from the knowledge graph using a streaming query, clean them up for better writing when loading, and save the resulting relationships to a json backup file
# query for all relationships in graph
original_relationships = knowledgegraph_backup.query_streaming(
"MATCH ()-[rel]->() RETURN distinct rel"
)# create list of formatted entities to add to the graph
all_relationships_fromquery = []
for relationship in list(original_relationships):
curr_relationship = relationship[0]
# convert UUID values to a string since json can't store UUIDs
curr_relationship["_id"] = str(curr_relationship["_id"])
curr_relationship["_originEntityId"] = str(curr_relationship["_originEntityId"])
curr_relationship["_destinationEntityId"] = str(
curr_relationship["_destinationEntityId"]
)
for prop in curr_relationship["_properties"]:
if type(curr_relationship["_properties"][prop]) == UUID:
curr_relationship["_properties"][prop] = str(
curr_relationship["_properties"][prop]
)
# delete objectid, the server will handle creating new ones when we load the backup
del curr_relationship["_properties"][
"objectid"
]
all_relationships_fromquery.append(curr_relationship)# write relationships list to json file
with open(os.path.join(output_folder, all_rel), "w") as f:
json.dump(all_relationships_fromquery, f)OPTIONAL: Write provenance records to backup json file
If you have provenance records that you want to maintain in the backups, this will get all provenance records and save them to a json backup file
# query for all provenance records in the graph
provenance_entities = knowledgegraph_backup.query_streaming(
"MATCH (n:Provenance) RETURN distinct n", include_provenance=True
)# create list of formatted provenance records to the graph
all_provenance_fromquery = []
for entity in list(provenance_entities):
curr_provenance = entity[0]
# convert UUID values to a string since json can't store UUIDs
curr_provenance["_id"] = str(curr_provenance["_id"])
for prop in curr_provenance["_properties"]:
if type(curr_provenance["_properties"][prop]) == UUID:
curr_provenance["_properties"][prop] = str(
curr_provenance["_properties"][prop]
)
# delete objectid, the server will handle creating new ones when we load the backup
del curr_provenance["_properties"][
"objectid"
]
all_provenance_fromquery.append(curr_provenance)# write provenance list to json file
with open(os.path.join(output_folder, prov_file), "w") as f:
json.dump(all_provenance_fromquery, f)Load backup files
Connect to portal and create knowledge graph
Connect to the portal and create a new knowledge graph service to load data model and data into
# connect to portal via GIS
gis_load = GIS(
"https://myportal.com/portal", "username", "password"
)
# create a knowledge graph without provenance enabled
result = gis_load.content.create_service(
name="myknowledgegraph",
capabilities="Query,Editing,Create,Update,Delete",
service_type="KnowledgeGraph",
)
# OPTIONAL: switch line above with line below to create knowledge graph WITH provenance enabled
# result = gis_load.content.create_service(name="", service_type="KnowledgeGraph",create_params={"name": "myknowledgegraph", "capabilities": "Query", "jsonProperties": {"supportsProvenance": True}})
knowledgegraph_load = KnowledgeGraph(result.url, gis=gis_load)Populate data model from saved json files
Populate entity and relationship types from saved json files. This is using the variables defined in the 'Setup' section above.
# load data model json files into graph data model
with open(os.path.join(output_folder, dm_ent), "r") as file:
dm_ents = json.load(file)
with open(os.path.join(output_folder, dm_rel), "r") as file:
dm_rels = json.load(file)
knowledgegraph_load.named_object_type_adds(
entity_types=dm_ents, relationship_types=dm_rels
)Get original documents names to correctly load data
This section will get the names of the document entity and relationship types from the original graph data, using the above commands the document types get their default names of 'Document' and 'HasDocument'. Just in case they have been named differently in the original knowledge graph, this steps allows us to match them up in later loading steps.
# get document entity type name
doc_type_name = "Document"
for entity_type in dm_ents:
for prop in entity_type["properties"]:
if entity_type["properties"][prop]["role"] == "esriGraphNamedObjectDocument":
doc_type_name = entity_type["name"]# get document relationship type name
doc_rel_type_name = "HasDocument"
for relationship_type in dm_rels:
for prop in relationship_type["properties"]:
if (
relationship_type["properties"][prop]["role"]
== "esriGraphNamedObjectDocument"
):
doc_rel_type_name = relationship_type["name"]Add additional document entity and relationship type properties
In the case additional properties exist in the original graph on document entity or relationship types, since the type was created at the time of knowledge graph creation we need to additional properties using graph_property_adds.
# load any additional document entity type properties
origin_document_properties = None
for entity_type in dm_ents:
if entity_type["name"] == doc_type_name:
origin_document_properties = entity_type["properties"]
prop_list = []
for prop in origin_document_properties:
prop_list.append(origin_document_properties[prop])
knowledgegraph_load.graph_property_adds(
type_name="Document", graph_properties=prop_list
)# load any additional document relationship type properties
for relationship_type in dm_rels:
if relationship_type["name"] == doc_rel_type_name:
origin_document_rel_properties = relationship_type["properties"]
prop_list = []
for prop in origin_document_rel_properties:
prop_list.append(origin_document_rel_properties[prop])
knowledgegraph_load.graph_property_adds(
type_name="HasDocument", graph_properties=prop_list
)Get list of date properties from the data model json files
From the data model type json files, find and make a list of date files so we can correctly create datetime objects when loading the data into the knowledge graph.
date_properties = []
# add date property names for entity types
for types in dm_ents:
for prop in types["properties"]:
if types["properties"][prop]["fieldType"] == "esriFieldTypeDate":
date_properties.append(prop)# add date property names for relationship types
for types in dm_rels:
for prop in types["properties"]:
if types["properties"][prop]["fieldType"] == "esriFieldTypeDate":
date_properties.append(prop)Add all entities to the knowledge graph
Add all entities from json file to the knowledge graph, formatting UUIDs, dates, and documents before loading the values. This loads the data in batches of 20,000 entities.
# load entities json file
with open(os.path.join(output_folder, all_ent), "r") as file:
original_entities = json.load(file)
batch = []
for curr_entity in original_entities:
# if a batch reaches 20k records, apply that batch of edits to the knowledge graph
if len(batch) > 20000:
result = knowledgegraph_load.apply_edits(adds=batch)
batch = []
# print error if one occurs during edit operation
try:
print(result["error"])
except:
print("No error adding entities")
# in case original document type name is different, change name to Document
if curr_entity["_typeName"] == doc_type_name:
curr_entity["_typeName"] = "Document"
# format UUID and date properties
for prop in curr_entity["_properties"]:
if prop in date_properties:
try:
curr_entity["_properties"][prop] = datetime.fromtimestamp(
int(curr_entity["_properties"][prop] / 1000)
)
except:
curr_entity["_properties"][prop] = None
try:
curr_entity["_properties"][prop] = UUID(curr_entity["_properties"][prop])
except:
continue
# format id UUID
curr_entity["_id"] = UUID(curr_entity["_id"])
batch.append(curr_entity)
# apply final batch of edits to the knowledge graph
result = knowledgegraph_load.apply_edits(adds=batch)
# print error if one occurs during edit operation
try:
print(result["error"])
except:
print("No error adding entities")Add all relationships to the knowledge graph
Add all relationships from json file to the knowledge graph, formatting UUIDs, dates, and documents before loading the values. This loads the data in batches of 20,000 relationships.
# load relationships json file
with open(os.path.join(output_folder, all_rel), "r") as file:
original_rels = json.load(file)
batch = []
for curr_relationship in original_rels:
# if a batch reaches 20k records, apply that batch of edits to the knowledge graph
if len(batch) > 20000:
result = knowledgegraph_load.apply_edits(adds=batch)
batch = []
# print error if one occurs during edit operation
try:
print(result["error"])
except:
print("No error adding relationships")
# in case original document type name is different, change name to HasDocument
if curr_relationship["_typeName"] == doc_rel_type_name:
curr_relationship["_typeName"] = "HasDocument"
# format UUID and date properties
for prop in curr_relationship["_properties"]:
if prop in date_properties:
try:
curr_relationship["_properties"][prop] = datetime.fromtimestamp(
int(curr_relationship["_properties"][prop] / 1000)
)
except:
curr_relationship["_properties"][prop] = None
try:
curr_relationship["_properties"][prop] = UUID(
curr_relationship["_properties"][prop]
)
except:
continue
# format other relationship specific UUIDs
curr_relationship["_id"] = UUID(curr_relationship["_id"])
curr_relationship["_originEntityId"] = UUID(curr_relationship["_originEntityId"])
curr_relationship["_destinationEntityId"] = UUID(
curr_relationship["_destinationEntityId"]
)
batch.append(curr_relationship)
# apply final batch of edits to the knowledge graph
result = knowledgegraph_load.apply_edits(adds=batch)
# print error if one occurs during edit operation
try:
print(result["error"])
except:
print("No error adding relationships")Add search indexes to all text properties
Adding search indexes to all text properties will allow for the values in those properties to be searched for when using search in any client.
load_dm = knowledgegraph_load.datamodel
# add search indexes for all entity text properties
for entity_type in load_dm["entity_types"]:
prop_list = []
for prop in load_dm["entity_types"][entity_type]["properties"]:
if (
load_dm["entity_types"][entity_type]["properties"][prop]["fieldType"]
== "esriFieldTypeString"
):
prop_list.append(prop)
knowledgegraph_load.update_search_index(
adds={entity_type: {"property_names": prop_list}}
)# add search indexes for all relationship text properties
for entity_type in load_dm["relationship_types"]:
prop_list = []
for prop in load_dm["relationship_types"][entity_type]["properties"]:
if (
load_dm["relationship_types"][entity_type]["properties"][prop]["fieldType"]
== "esriFieldTypeString"
):
prop_list.append(prop)
knowledgegraph_load.update_search_index(
adds={entity_type: {"property_names": prop_list}}
)OPTIONAL: Add provenance records to the knowledge graph
This will only apply if you created a backup of provenance records and have enabled provenance on your knowledge graph service.
# load provenance records json file
with open(os.path.join(output_folder, prov_file), "r") as file:
prov_entities = json.load(file)# add all provenance records
for curr_prov in prov_entities:
# format UUID properties
for prop in curr_prov["_properties"]:
try:
curr_prov["_properties"][prop] = UUID(curr_prov["_properties"][prop])
except:
continue
# format id as UUID
curr_prov["_id"] = UUID(curr_prov["_id"])
# add provenance record
knowledgegraph_load.apply_edits(adds=[curr_prov])