5.1. Main Features¶
The Python interface to the Trovares xGT graph analytics engine.
5.1.1. Data Loading¶
xGT is a strongly-typed graph system. Loading data is a three step process:
Create a namespace or use a pre-created namespace.
Frame names are represented using a two-level naming scheme. The first part represents the namespace in which the frame is stored. Users can create a namespace, or, alternatively, system administrators may want to create namespaces ahead of time. Creating a namespace can be done with
Connection.create_namespace()
and a list of existing namespaces can be obtained usingConnection.get_namespaces()
.
Describe the structure and data types of your graph.
Define the vertex and edge frame structure with
Connection.create_vertex_frame()
andConnection.create_edge_frame()
. Once the type structure is set,VertexFrame
andEdgeFrame
objects provide access to the server-side structures.
Load your edge and vertex data.
The
VertexFrame
andEdgeFrame
objects provide the high-performance, parallelload()
method to ingest data as well as a directinsert()
method to add small amounts of data piecewise.
5.1.2. Query Processing¶
Queries are expressed as strings written in TQL.
>>> query ='''
MATCH (emp:Employee)-[edge1:ReportsTo]->(boss:Employee)
RETURN emp.person_id AS employee_id,
boss.person_id AS boss_id
INTO ResultTable
'''
A query runs in the context of a Job
, which can be run, scheduled
and canceled. The Connection.run_job()
method runs the query and
blocks until it finishes successfully, terminates by an error, or is canceled.
5.1.3. Example Script¶
The following Python script shows some of the functions that can be used to create a graph, load data into it, run a query and access the results, and finally remove that graph from the system.
import xgt
# Connect to xgtd.
conn = xgt.Connection()
# Set a default namespace for this session.
conn.set_default_namespace('career')
# Define and create the graph in the default namespace.
employees = conn.create_vertex_frame(
name='Employees',
schema=[['person_id', xgt.INT],
['name', xgt.TEXT],
['postal_code', xgt.INT]],
key='person_id')
reports_to = conn.create_edge_frame(
name='ReportsTo',
schema=[['employee_id', xgt.INT],
['boss_id', xgt.INT],
['start_date', xgt.DATE],
['end_date', xgt.DATE]],
source=employees,
target=employees,
source_key='employee_id',
target_key='boss_id')
# Load data to the graph in xgtd.
# Use the insert() method for data of a few hundred rows or less;
# for bigger amounts of data, use the load() method with csv files.
employees.insert(
[[111111101, 'Manny', 98103],
[111111102, 'Trish', 98108],
[911111501, 'Frank', 98101],
[911111502, 'Alice', 98102]
])
reports_to.insert(
[[111111101, 911111501, '2015-01-03', '2017-04-14'],
[111111102, 911111501, '2016-04-02', '2017-04-14'],
[911111502, 911111501, '2016-07-07', '2017-04-14'],
[111111101, 911111502, '2017-04-15', '3000-12-31'],
[111111102, 911111502, '2017-04-15', '3000-12-31'],
[911111501, 911111502, '2017-04-15', '3000-12-31']
])
# Query data
cmd = '''
MATCH
(employee:Employees)-[edge1:ReportsTo]->
(boss:Employees)-[edge2:ReportsTo]->
(employee)
WHERE
edge1.end_date <= edge2.start_date
RETURN
employee.person_id AS employee1_id,
boss.person_id AS employee2_id,
edge1.start_date AS start1,
edge1.end_date AS end1,
edge2.start_date AS start2,
edge2.end_date AS end2
INTO
Results
'''
conn.run_job(cmd)
results = conn.get_table_frame('Results')
# Extract the results.
for frame in [employees, reports_to, results]:
print('{name}: {n_cols} columns, {n_rows} rows'.format(
name=frame.name,
n_cols=len(frame.schema),
n_rows=frame.num_rows))
print('\n--- Results ---')
for row in results.get_data(0,100):
print(', '.join([str(c) for c in row]))
# Drop all objects.
[conn.drop_frame(f) for f in [reports_to, employees, results]]