NeoAccess library - Tutorial 2 : identical to tutorial 1, but with the DEBUG option turned on.¶
As a result, the Cypher queries and the data binding managed by NeoAccess will become visible.¶
NOTE: only meant for people who are knowledgeable about Cypher, and want an under-the-hood look at the NeoAccess library! General users should use the other tutorials.¶
(debug mode ON)¶
Overview and Intro article to accompany these tutorials¶
CAUTION: running this tutorial will clear out the database¶
In [1]:
import set_path # Importing this module will add the project's home directory to sys.path
Added 'D:\Docs\- MY CODE\Brain Annex\BA-Win7' to sys.path
In [2]:
import os
import sys
import getpass
from neoaccess import NeoAccess
Connect to the database¶
You can use a free local install of the Neo4j database, or a remote one on a virtual machine under your control, or a hosted solution, or simply the FREE "Sandbox" : instructions here¶
NOTE: This tutorial is tested on version 4 of the Neo4j database, but will probably also work on the new version 5
In [3]:
# Save your credentials here - or use the prompts given by the next cell
host = "" # EXAMPLES: bolt://123.456.789.012 OR neo4j://localhost
password = ""
In [3]:
print("To create a database connection, enter the host IP, but leave out the port number: (EXAMPLES: bolt://1.2.3.4 OR neo4j://localhost )\n")
host = input("Enter host IP WITHOUT the port number. EXAMPLE: bolt://123.456.789.012 ")
host += ":7687" # EXAMPLE of host value: "bolt://123.456.789.012:7687"
password = getpass.getpass("Enter the database password:")
print(f"\n=> Will be using: host='{host}', username='neo4j', password=**********")
To create a database connection, enter the host IP, but leave out the port number: (EXAMPLES: bolt://1.2.3.4 OR neo4j://localhost )
=> Will be using: host='bolt://123.456.789.012:7687', username='neo4j', password=**********
In [4]:
db = NeoAccess(host=host,
credentials=("neo4j", password), debug=True) # Notice the debug option being ON
~~~~~~~~~ Initializing NeoAccess object ~~~~~~~~~ Attempting to connect to Neo4j host 'bolt://155.248.202.124:7687', with username 'neo4j' Connection to host 'bolt://155.248.202.124:7687' established
In [5]:
print("Version of the Neo4j driver: ", db.version())
Version of the Neo4j driver: 4.4.11
Examples of basic NeoAccess library operations¶
In [6]:
db.empty_dbase() # WARNING: USE WITH CAUTION!!!
In [7]:
# Create a "Car" node and a "Person" node
neo_car = db.create_node("Car", {'color': 'white', 'make': 'Toyota'}) # create_node returns the internal database ID of the new node
neo_person = db.create_node("Person", {'name': 'Julian'})
In create_node(). Query:
CREATE (n :`Car` {`color`: $par_1, `make`: $par_2}) RETURN n
Data binding:
{'par_1': 'white', 'par_2': 'Toyota'}
In create_node(). Query:
CREATE (n :`Person` {`name`: $par_1}) RETURN n
Data binding:
{'par_1': 'Julian'}
In [8]:
# Link the "Car" node to the "Person" node (using internal database ID's to refer to existing nodes)
number_added = db.add_links(match_from=neo_car, match_to=neo_person, rel_name="OWNED_BY")
number_added
In add_links()
cypher_match_from: CYPHER-PROCESSED match structure:
node: (from) where: id(from) = 31 data_binding: {} dummy_node_name: from
cypher_match_to: CYPHER-PROCESSED match structure:
node: (to) where: id(to) = 1 data_binding: {} dummy_node_name: to
In add_links(). Query:
MATCH (from), (to)
WHERE (id(from) = 31 AND id(to) = 1)
MERGE (from) -[:`OWNED_BY`]-> (to)
In update_query(). Attributes of ResultSummary object:
metadata -> {'query': '\n MATCH (from), (to)\n WHERE (id(from) = 31 AND id(to) = 1)\n MERGE (from) -[:`OWNED_BY`]-> (to) \n ', 'parameters': {}, 'server': <neo4j.api.ServerInfo object at 0x000000001A060880>, 't_first': 18, 'fields': [], 'bookmark': 'FB:kcwQxosFA855RdybwjuMf2O2J8oAAPzukA==', 'stats': {'relationships-created': 1}, 'type': 'w', 't_last': 0, 'db': 'neo4j', 'notifications': [{'severity': 'WARNING', 'description': 'If a part of a query contains multiple disconnected patterns, this will build a cartesian product between all those parts. This may produce a large amount of data and slow down query processing. While occasionally intended, it may often be possible to reformulate the query that avoids the use of this cross product, perhaps by adding a relationship between the different parts or by using OPTIONAL MATCH (identifier is: (to))', 'code': 'Neo.ClientNotification.Statement.CartesianProductWarning', 'position': {'column': 1, 'offset': 13, 'line': 2}, 'title': 'This query builds a cartesian product between disconnected patterns.'}]}
server -> <neo4j.api.ServerInfo object at 0x000000001A060880>
database -> neo4j
query ->
MATCH (from), (to)
WHERE (id(from) = 31 AND id(to) = 1)
MERGE (from) -[:`OWNED_BY`]-> (to)
parameters -> {}
query_type -> w
plan -> None
profile -> None
notifications -> [{'severity': 'WARNING', 'description': 'If a part of a query contains multiple disconnected patterns, this will build a cartesian product between all those parts. This may produce a large amount of data and slow down query processing. While occasionally intended, it may often be possible to reformulate the query that avoids the use of this cross product, perhaps by adding a relationship between the different parts or by using OPTIONAL MATCH (identifier is: (to))', 'code': 'Neo.ClientNotification.Statement.CartesianProductWarning', 'position': {'column': 1, 'offset': 13, 'line': 2}, 'title': 'This query builds a cartesian product between disconnected patterns.'}]
counters -> {'relationships_created': 1}
result_available_after -> 18
result_consumed_after -> 0
RESULT of update_query in add_links(): {'relationships_created': 1, 'returned_data': []}
Out[8]:
1
In [9]:
# Retrieve the car node (in the most straightforward way, using an internal database ID)
db.get_nodes(neo_car)
In get_nodes()
match_structure: CYPHER-PROCESSED match structure:
node: (n) where: id(n) = 31 data_binding: {} dummy_node_name: n
In get_nodes(). Query:
MATCH (n) WHERE (id(n) = 31) RETURN n
Out[9]:
[{'color': 'white', 'make': 'Toyota'}]
In [10]:
# Retrieve a single property of the car node (to be used when only 1 node is present)
db.get_nodes(neo_car, single_cell="color")
In get_nodes()
match_structure: CYPHER-PROCESSED match structure:
node: (n) where: id(n) = 31 data_binding: {} dummy_node_name: n
In get_nodes(). Query:
MATCH (n) WHERE (id(n) = 31) RETURN n
Out[10]:
'white'
In [11]:
# How many owners does the car have?
db.count_links(neo_car, rel_name="OWNED_BY", rel_dir="OUT")
In count_links()
match_structure: CYPHER-PROCESSED match structure:
node: (n) where: id(n) = 31 data_binding: {} dummy_node_name: n
In count_links(). Query:
MATCH (n) - [:OWNED_BY] -> (neighbor )WHERE (id(n) = 31) RETURN count(neighbor) AS link_count
Out[11]:
1
In [12]:
# Look up information about the car owner(s)
db.follow_links(neo_car, rel_name="OWNED_BY", rel_dir="OUT")
In follow_links()
match_structure: CYPHER-PROCESSED match structure:
node: (n) where: id(n) = 31 data_binding: {} dummy_node_name: n
In follow_links(). Query:
MATCH (n) - [:OWNED_BY] -> (neighbor )WHERE (id(n) = 31) RETURN neighbor
Out[12]:
[{'name': 'Julian'}]
In [ ]:
Let's pretend we didn't save the internal database ID's of our 2 nodes; let's specify those nodes, for the purpose of LATER retrieving them from the database¶
In [13]:
# Lets provide a way to later look up the "Car" node, using the match() method.
# IMPORTANT: NO DATABASE OPERATION IS ACTUALLY PERFORMED HERE! We're just saving up all the specs
# (to indentify a node, OR GROUP OF NODES) into an object of class "NodeSpecs"
car_match = db.match(labels="Car", properties={'color': 'white', 'make': 'Toyota'})
car_match
Out[13]:
<neoaccess.cypher_utils.NodeSpecs at 0x1a0a1160>
In [14]:
print(car_match) # Let's look at the specs we saved up; they will be used LATER in actual database operations
RAW match structure:
internal_id: None labels: Car key_name: None key_value: None properties: {'color': 'white', 'make': 'Toyota'} clause: None clause_dummy_name: None
In [15]:
# A lot of parameters can be passed to match(). Some examples of alternative ways to specify the same node as above:
car_match_alt = db.match(labels="Car", clause="n.color = 'white' AND n.make = 'Toyota'", dummy_node_name="n")
print(car_match_alt)
RAW match structure:
internal_id: None labels: Car key_name: None key_value: None properties: None clause: n.color = 'white' AND n.make = 'Toyota' clause_dummy_name: n
In [ ]:
In [16]:
# Various ways to specify our Person node (again, NO DATABASE OPERATION IS ACTUALLY PERFORMED HERE!)
person_match = db.match(labels="Person", properties={'name': 'Julian'})
person_match_alt_1 = db.match(labels="Person", clause="n.name = 'Julian'", dummy_node_name="n")
person_match_alt_2 = db.match(labels="Person", key_name='name', key_value='Julian')
Armed with the "NodeSpecs" objects, we can do all sort of operations - passing those objects (serving as "handles") in lieu of the internal database ID's that we lack (and which would require extra database operations to retrieve)¶
Note: NO EXTRA DATABASE OPERATIONS ARE WASTED ON LOCATING THE NODES! Efficient, 1-step, database queries are created and executed at the very LAST stage; for example to create the following link
In [17]:
# Link the "Person" node to the "Car" node (a reverse link of the one we created before)
# HERE'S WHERE THE (SINGLE) DATABASE OPERATION ACTUALLY GETS PERFORMED
number_added = db.add_links(match_from=person_match, match_to=car_match, rel_name="OWNS")
number_added
In add_links()
cypher_match_from: CYPHER-PROCESSED match structure:
node: (from :`Person` {`name`: $from_par_1}) where: data_binding: {'from_par_1': 'Julian'} dummy_node_name: from
cypher_match_to: CYPHER-PROCESSED match structure:
node: (to :`Car` {`color`: $to_par_1, `make`: $to_par_2}) where: data_binding: {'to_par_1': 'white', 'to_par_2': 'Toyota'} dummy_node_name: to
In add_links(). Query:
MATCH (from :`Person` {`name`: $from_par_1}), (to :`Car` {`color`: $to_par_1, `make`: $to_par_2})
MERGE (from) -[:`OWNS`]-> (to)
Data binding:
{'from_par_1': 'Julian', 'to_par_1': 'white', 'to_par_2': 'Toyota'}
In update_query(). Attributes of ResultSummary object:
metadata -> {'query': '\n MATCH (from :`Person` {`name`: $from_par_1}), (to :`Car` {`color`: $to_par_1, `make`: $to_par_2})\n \n MERGE (from) -[:`OWNS`]-> (to) \n ', 'parameters': {'from_par_1': 'Julian', 'to_par_1': 'white', 'to_par_2': 'Toyota'}, 'server': <neo4j.api.ServerInfo object at 0x000000001A060880>, 't_first': 1, 'fields': [], 'bookmark': 'FB:kcwQxosFA855RdybwjuMf2O2J8oAAPzvkA==', 'stats': {'relationships-created': 1}, 'type': 'w', 't_last': 0, 'db': 'neo4j', 'notifications': [{'severity': 'WARNING', 'description': 'If a part of a query contains multiple disconnected patterns, this will build a cartesian product between all those parts. This may produce a large amount of data and slow down query processing. While occasionally intended, it may often be possible to reformulate the query that avoids the use of this cross product, perhaps by adding a relationship between the different parts or by using OPTIONAL MATCH (identifier is: (to))', 'code': 'Neo.ClientNotification.Statement.CartesianProductWarning', 'position': {'column': 1, 'offset': 13, 'line': 2}, 'title': 'This query builds a cartesian product between disconnected patterns.'}]}
server -> <neo4j.api.ServerInfo object at 0x000000001A060880>
database -> neo4j
query ->
MATCH (from :`Person` {`name`: $from_par_1}), (to :`Car` {`color`: $to_par_1, `make`: $to_par_2})
MERGE (from) -[:`OWNS`]-> (to)
parameters -> {'from_par_1': 'Julian', 'to_par_1': 'white', 'to_par_2': 'Toyota'}
query_type -> w
plan -> None
profile -> None
notifications -> [{'severity': 'WARNING', 'description': 'If a part of a query contains multiple disconnected patterns, this will build a cartesian product between all those parts. This may produce a large amount of data and slow down query processing. While occasionally intended, it may often be possible to reformulate the query that avoids the use of this cross product, perhaps by adding a relationship between the different parts or by using OPTIONAL MATCH (identifier is: (to))', 'code': 'Neo.ClientNotification.Statement.CartesianProductWarning', 'position': {'column': 1, 'offset': 13, 'line': 2}, 'title': 'This query builds a cartesian product between disconnected patterns.'}]
counters -> {'relationships_created': 1}
result_available_after -> 1
result_consumed_after -> 0
RESULT of update_query in add_links(): {'relationships_created': 1, 'returned_data': []}
Out[17]:
1
In [ ]:
Some verifications¶
The "Car" node can be found and extracted (performing a DATABASE OPERATION), using EITHER its Internal Database ID (which we had saved at the very beginning, though we we were acting like we didn't) OR any of the alternative ways we created to specify it
In [18]:
db.get_nodes(neo_car) # Fetch by the internal database ID
In get_nodes()
match_structure: CYPHER-PROCESSED match structure:
node: (n) where: id(n) = 31 data_binding: {} dummy_node_name: n
In get_nodes(). Query:
MATCH (n) WHERE (id(n) = 31) RETURN n
Out[18]:
[{'color': 'white', 'make': 'Toyota'}]
In [19]:
db.get_nodes(car_match) # Fetch by "NodeSpecs" object returned by the match() method
In get_nodes()
match_structure: CYPHER-PROCESSED match structure:
node: (n :`Car` {`color`: $n_par_1, `make`: $n_par_2}) where: data_binding: {'n_par_1': 'white', 'n_par_2': 'Toyota'} dummy_node_name: n
In get_nodes(). Query:
MATCH (n :`Car` {`color`: $n_par_1, `make`: $n_par_2}) RETURN n
Data binding:
{'n_par_1': 'white', 'n_par_2': 'Toyota'}
Out[19]:
[{'color': 'white', 'make': 'Toyota'}]
In [20]:
db.get_nodes(car_match_alt) # Fetch by an alternate version of the "NodeSpecs" object
In get_nodes()
match_structure: CYPHER-PROCESSED match structure:
node: (n :`Car` ) where: n.color = 'white' AND n.make = 'Toyota' data_binding: {} dummy_node_name: n
In get_nodes(). Query:
MATCH (n :`Car` ) WHERE (n.color = 'white' AND n.make = 'Toyota') RETURN n
Out[20]:
[{'color': 'white', 'make': 'Toyota'}]
Likewise for the "Person" node:
In [21]:
db.get_nodes(neo_person) # Fetch by the internal database ID
In get_nodes()
match_structure: CYPHER-PROCESSED match structure:
node: (n) where: id(n) = 1 data_binding: {} dummy_node_name: n
In get_nodes(). Query:
MATCH (n) WHERE (id(n) = 1) RETURN n
Out[21]:
[{'name': 'Julian'}]
In [22]:
db.get_nodes(person_match) # Fetch by "NodeSpecs" object returned by the match() method
In get_nodes()
match_structure: CYPHER-PROCESSED match structure:
node: (n :`Person` {`name`: $n_par_1}) where: data_binding: {'n_par_1': 'Julian'} dummy_node_name: n
In get_nodes(). Query:
MATCH (n :`Person` {`name`: $n_par_1}) RETURN n
Data binding:
{'n_par_1': 'Julian'}
Out[22]:
[{'name': 'Julian'}]
In [23]:
db.get_nodes(person_match_alt_1)
In get_nodes()
match_structure: CYPHER-PROCESSED match structure:
node: (n :`Person` ) where: n.name = 'Julian' data_binding: {} dummy_node_name: n
In get_nodes(). Query:
MATCH (n :`Person` ) WHERE (n.name = 'Julian') RETURN n
Out[23]:
[{'name': 'Julian'}]
In [24]:
db.get_nodes(person_match_alt_2)
In get_nodes()
match_structure: CYPHER-PROCESSED match structure:
node: (n :`Person` {`name`: $n_par_1}) where: data_binding: {'n_par_1': 'Julian'} dummy_node_name: n
In get_nodes(). Query:
MATCH (n :`Person` {`name`: $n_par_1}) RETURN n
Data binding:
{'n_par_1': 'Julian'}
Out[24]:
[{'name': 'Julian'}]
In [ ]:
If you know the Cypher query language, and simply want to run a generic query, no problem!¶
In [25]:
q = '''MATCH (p :Person) -[:OWNS] -> (c :Car) -[OWNED_BY] -> (p)
RETURN p.name, c.color, c.make
''' # This query will verify the forward and reverse links that we created earlier
In [26]:
db.query(q) # Run the query; by default, it will return a list of records (each record is a dict)
Out[26]:
[{'p.name': 'Julian', 'c.color': 'white', 'c.make': 'Toyota'}]
In [27]:
q_paint_car_red = '''MATCH (c :Car) -[OWNED_BY] -> (p :Person {name: 'Julian'})
SET c.color = 'red'
''' # Paint all of Julian's cars red!
result = db.update_query(q_paint_car_red) # It returns a dict of info about what it did
result
In update_query(). Attributes of ResultSummary object:
metadata -> {'query': "MATCH (c :Car) -[OWNED_BY] -> (p :Person {name: 'Julian'})\n SET c.color = 'red'\n ", 'parameters': {}, 'server': <neo4j.api.ServerInfo object at 0x000000001A060880>, 't_first': 0, 'fields': [], 'bookmark': 'FB:kcwQxosFA855RdybwjuMf2O2J8oAAPzwkA==', 'stats': {'properties-set': 1}, 'type': 'w', 't_last': 0, 'db': 'neo4j'}
server -> <neo4j.api.ServerInfo object at 0x000000001A060880>
database -> neo4j
query -> MATCH (c :Car) -[OWNED_BY] -> (p :Person {name: 'Julian'})
SET c.color = 'red'
parameters -> {}
query_type -> w
plan -> None
profile -> None
notifications -> None
counters -> {'properties_set': 1}
result_available_after -> 0
result_consumed_after -> 0
Out[27]:
{'properties_set': 1, 'returned_data': []}
In [28]:
db.query(q) # Re-run the earlier query (to verify the forward and reverse links); notice how the car is now red
Out[28]:
[{'p.name': 'Julian', 'c.color': 'red', 'c.make': 'Toyota'}]
In [ ]: