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Version: 2.3.1

Implement a custom query module in Python

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Guide

This tutorial will give you a basic idea of how to develop a custom query module in Python with Memgraph Lab 2.0 and use it on a dataset.

In short, query modules allow you to expand the Cypher query module with various procedures. Procedures can be written in Python or C languages. Our MAGE library has various modules dealing with complex graph algorithms, but you can implement your own procedures gathered in query modules to optimize your queries. If you need more information about what query modules are, please read our reference guide on query modules.

Prerequisites

In order to start developing a custom query you will need:

Data model

For this tutorial, we will use the Europe backpacking data model with the data from The European Backpacker Index (2018). The data set contains information about 56 cities from 36 European countries, such as what cities are close, what countries border each other, various pricing and recommended accommodation.

Backpacking

A detailed explanation of the data model

Nodes:

  • Country - country with the following properties (example of the value):
    • id - country's id (5)
    • name - country's name ("Spain")
  • City - city with the following properties (example of the value):
    • name - city's name ("Barcelona")
    • country - country's name ("Spain")
    • cheapest_hostel - the cheapest hostel in the city ("Amistat Beach Hostel Barcelona")
    • hostel_url - URL of the cheapest hostel in the city ("https://www.priceoftravel.com/ABarcelonaHostel")
    • rank - the cheapest hostel's rank according to The European Backpacker Index (38)
    • local_currency - the name of the local currency ("Euro")
    • local_currency_code - ISO3 code of the local currency ("EUR")
    • total_USD - total daily cost including accommodation, attractions, meals/drinks and transportation in USD (80.104)
    • cost_per_night_USD - daily cost of the cheapest hostel per night in USD (23.684)
    • attractions_USD - daily cost of the attractions in USD (16.12)
    • meals_USD - daily cost of the meals in USD (23.808)
    • drinks_USD - daily cost of the drinks in USD (11.16)
    • transportation_USD - daily cost of the transportation in USD (5.332)

Relationships:

  • :Inside - connects City node to the Country node if the city is within the country
  • :CloseTo - connects two City nodes if cities are from the same or neighboring countries
    • eu_border - relationship property that indicates whether the EU border needs to be crossed to reach the other city (true)
  • :Borders - connects two Country nodes if they are neighboring countries.
    • eu_border - relationship property that indicates whether the EU border needs to be crossed to reach the other country (true)

In this tutorial, we will mostly focus on the two nodes, :City and :Country, and their :Inside relationship.

Preparing Memgraph

Let's open Memgraph Lab where we will import the dataset, as well as write and use the procedures from our query module.

If you have successfully installed Memgraph Platform, you should be able to open Memgraph Lab in a browser at http://localhost:3000/. Navigate to the Datasets menu item, click on the Europe backpacking dataset to import it into Memgraph. You can also check the details of the dataset by clicking on Quick View

Go to the Query Execution and try running a test query that will show the city Vienna and all its relationships:

MATCH p=(:City {name: "Vienna"})-[]-()
RETURN (p);

You can click on the :City nodes to check the nodes' properties and get better acquainted with the dataset. We will come back to this view every time we want to test our query modules in making.

Now navigate to Query Modules. Here you can see all the query modules available in Memgraph, such as utility modules or query modules from the MAGE library. To create a new custom query module, click on the New Module button, give the new module name backpacking and create the module. Memgraph Lab creates sample procedures to kick off your development. But before we start, let's decide how we will expand the query language.

Goals

Before we start to write a query module and procedures within, we need goals. How do we want to expand the query language?

Goal 1: We want to get a total cost of accommodation expenses for one night at the cheapest hostel in a given city, based on the number of adults and children that will be staying in it.

Goal 2: We also want to expand the data model by a given country and city. The new City node should get properties that it shares with the other cities in that country, such as country, local_currency and local_currency_code.

Python API

Python API is defined in the mgp module you can find in the Memgraph installation directory /usr/lib/memgraph/python_support. If you are using Docker, you can copy the file from the Docker container into your computer for faster access.

Copy the mgp module from a Docker container

1. If it's not running, start your Memgraph instance using Docker.

2. Open a new terminal and find the CONTAINER ID of the Memgraph Docker container:

docker ps

3. Position yourself in the local directory where you want to copy the file.

4. Copy a file from the container to your current directory with the following command:

docker cp <CONTAINER ID>:/usr/lib/memgraph/python_support/mgp.py mgp.py

Be sure to enter the correct CONTAINER ID.

Example of a command when copying the mgp.py file to the user's desktop:

C:\Users\Vlasta\Desktop>docker cp 63e35:/usr/lib/memgraph/python_support/mgp.py mgp.py

In essence, Python API is a wrapper around the C API. If you look at row 15 of the new module we've created in Memgraph Lab, you can see you need to import the mgp module at the beginning of every query module.

Below the import mgp, in line 17, you can see a @read_proc decorator. Python API defines @read_proc, @write_proc and @transformation_proc decorators. @read_proc decorator handles read-only procedures, the @write_proc decorator handles procedures that also write to the database, and the @transformation_proc decorator handles data coming from streams.

If you look at our two goals, to get the total cost of accommodation, Memgraph only needs to read from the database to get the value of the cost_per_night_USD, while to create new nodes it also needs to write in the database.

Feel free to examine the examples and tips available in this template, and when you are ready to continue with the tutorial, clear the file so we start writing our code from line 1.

We'll start with the @read_proc decorator to achieve the first goal, then we'll dive into a bit more complicated second goal and its @write_proc.

Read procedure

As we established in the previous chapter, first we need to import the mgp module and then use the @read_proc decorator. Then we will define the procedure by giving it a name and signature, that is, what arguments it needs to receive and what values it will return.

The goal of this procedure is to get a total cost of accommodation expenses for one night at the cheapest hostel in a given city, based on the number of adults and children that will be staying in it.

So, let's name the procedure total_cost. The procedure needs to receive the following arguments in order to calculate the total cost of accommodation:

  • the whole graph (all the nodes and relationships)
  • the name of the city we are interested in
  • the number of adults staying at the accommodation
  • the number of children.

The graph is passed to the procedure using the ProcCtx instance. The name of the city should be a string value, the number of adults an integer, and the number of children also an integer. Because customers can travel with or without children, we will define the children variable as optional by giving it a possibility to be NULL and setting it to a default value of None.

Output values are defined as arguments of the Record class. We want the function to return the total cost per night as a float value, and we'll enable the value to be NULL so that the procedure doesn't return an error if the city doesn't have the cost of accommodation as a property and thus can't calculate the total cost of accommodation.

After defining the name and signature, the code should look like this:

import mgp

@mgp.read_proc
def total_cost(context: mgp.ProcCtx,
city: mgp.Nullable[str],
adults: mgp.Nullable[int],
children: mgp.Nullable[int] = None
) -> mgp.Record(Total_cost_per_night = mgp.Nullable[float]):

Now we want to go through all the nodes (vertices) in our graph and find only those nodes that have both:

  1. the property name with the same value as the variable city
  2. the property cost_pre_night_USD with a float value.
import mgp

@mgp.read_proc
def total_cost(context: mgp.ProcCtx,
city: mgp.Any[str],
adults: mgp.Number[int],
children: mgp.Nullable[int] = None
) -> mgp.Record(Total_cost_per_night = mgp.Nullable[float]):

for vertex in context.graph.vertices:
if vertex.properties["name"] == city and isinstance(vertex.properties.get("cost_per_night_USD"),float):

When we find those nodes, we will save the cost of accommodation per night in a variable cost_per_night and multiply it with the number of adults to get the total_cost.

import mgp

@mgp.read_proc
def total_cost(context: mgp.ProcCtx,
city: mgp.Any[str],
adults: mgp.Number[int],
children: mgp.Nullable[int] = None
) -> mgp.Record(Total_cost_per_night = mgp.Nullable[float]):

for vertex in context.graph.vertices:
if vertex.properties["name"] == city and isinstance(vertex.properties.get("cost_per_night_USD"),float):
cost_per_night = vertex.properties.get("cost_per_night_USD")
total_cost = cost_per_night * adults

Then we need to check if the number of children was given as an argument when calling the procedure in query, and if it was, add half the cost of accommodation for each child. At the end, we return the total cost of accommodation per night.

import mgp

@mgp.read_proc
def total_cost(context: mgp.ProcCtx,
city: mgp.Any[str],
adults: mgp.Number[int],
children: mgp.Nullable[int] = None
) -> mgp.Record(Total_cost_per_night = mgp.Nullable[float]):

for vertex in context.graph.vertices:
if vertex.properties["name"] == city and isinstance(vertex.properties.get("cost_per_night_USD"),float):
cost_per_night = vertex.properties.get("cost_per_night_USD")
total_cost = cost_per_night * adults
if children is not None:
total_cost += cost_per_night / 2 * children
return mgp.Record(Total_cost_per_night = total_cost)

If none of the nodes have both the property name with the same value as the variable city nor the property cost_pre_night_USD with a float value, we will set the value of Total_cost_per_night to None (that is, NULL) in order to prevent the procedure from generating an error.

The finished procedure now looks like this:

import mgp

@mgp.read_proc
def total_cost(context: mgp.ProcCtx,
city: mgp.Any[str],
adults: mgp.Number[int],
children: mgp.Nullable[int] = None
) -> mgp.Record(
Total_cost_per_night = mgp.Nullable[float]):

for vertex in context.graph.vertices:
if vertex.properties["name"] == city and isinstance(vertex.properties.get("cost_per_night_USD"),float):
cost_per_night = vertex.properties.get("cost_per_night_USD")
total_cost = cost_per_night * adults
if children is not None:
total_cost += cost_per_night / 2 * children
return mgp.Record(Total_cost_per_night = total_cost)

return mgp.Record(Total_cost_per_night = None)

Save and close the query module. You will get an overview of the module that lists procedures and their signature.

Testing the read procedure

Switch to Query Execution and call the procedure using the clause CALL, then calling the right module and procedure within it (backpacking.total_cost). List all arguments except the whole graph inside brackets, and at the end YIELD all the results:

CALL backpacking.total_cost("Zagreb", 2, 3) YIELD *;

Result -> Total_cost_per_night = 32.129999999999995

CALL backpacking.total_cost("Vienna", 2) YIELD *;

Result -> Total_cost_per_night = 45.012

CALL backpacking.total_cost("Whatever", 2) YIELD *;

Result -> Total_cost_per_night = null

Detecting errors

Some errors will be written out as you are trying to call the procedure. Others can be viewed in the log file.

If you started your Memgraph Platform image by exposing the 7444 port, you can check the logs from Memgraph Lab. Otherwise, you need to access the logs in the Docker container.

But the rest of the errors in the code will result in the procedure not being detected. That means that if you go to the Query Modules menu item and check module details by clicking on the arrow on the right, the procedure with an error will not be listed.

Write procedure

You can continue writing the write procedure below the read procedure. To edit the current module go to Query Modules and find the backpacking module. Click on the arrow to view details about the module, such as the name of the procedures and their signatures. To continue editing the module, click on Edit code. If you are writing the write procedure in a new module, don't forget to import the mgp module. For the write procedure, we will use the @write_proc decorator.

The goal of this write procedure is to expand the data model by a given country and city. The new City node should get properties that it shares with the other cities in that country, such as country, local_currency and local_currency_code.

Let's name the procedure new_city. The procedure needs to receive the following arguments in order to create two new nodes and connect them:

  • the whole graph (all the nodes and relationships)
  • the name of the city
  • the name of the country.

The graph is passed to the procedure using the ProcCtx instance. The names of the city and country should be of string values.

Output values are defined as arguments of the Record class. We want the function to return the city and country nodes and their relationship.

After defining the name and signature, the code should look like this:

@mgp.write_proc
def new_city(context: mgp.ProcCtx,
in_city: mgp.Nullable[str],
in_country: mgp.Nullable[str]
) -> mgp.Record(City = mgp.Vertex,
Relationship = mgp.Edge,
Country = mgp.Vertex):

We will gradually expand our code to cover all three cases:

  1. the city and country nodes already exist
  2. the country node exists but the city node doesn't
  3. neither the country node nor the city node exist

The city and country nodes already exist

We want to check if the city and country passed as arguments already exist in the database because if they do, there is no need to create them. We can just return them as a result. Because the City nodes also include the country property, we can only check City nodes to find out if a certain city inside a certain country already exists.

So let's go through all the nodes (vertices) in the graph to check if there is a node with the country property of the same value as the in_country argument. If there is, let's check if the name property of that nodes is of the same value as the as the in_city argument. If there is, it means there are already City and Country nodes with the same name properties as the in_city and in_country arguments.

To return the relationship between them, we need to go through all the relationships from the City node and find the one with type Inside. Then we will save the destination node in the country variable, and return both nodes and the relationship.

@mgp.write_proc
def new_city(context: mgp.ProcCtx,
in_city: mgp.Nullable[str],
in_country: mgp.Nullable[str]
) -> mgp.Record(City = mgp.Vertex,
Relationship = mgp.Edge,
Country = mgp.Vertex):

for v in context.graph.vertices:
if v.properties.get("country") == in_country:
if v.properties.get("name") == in_city:
for r in v.out_edges:
if r.type == "Inside":
country = r.to_vertex
return mgp.Record(City=v, Relationship=r, Country=country)

At this point you can save the module and test the new procedure, by running the following query:

CALL backpacking.new_city("Zagreb","Croatia") YIELD *;

The country node exists but the city node doesn't

In the case that the Country node with that name exists, but the City node doesn't, we should create a new City node, and connect it with the existing Country node. Because the City nodes have properties about the country they are connected to, we will use the existing City nodes to copy property values to the new City node, such as local_currency and local_currency_code.

The new City node also has to get a new id number, that's why we will save the highest existing id among City nodes in the city_id and increase that number by 1 to get the ID of the new City node. Now that we have created a new City node, we need to create a relationship to connect it with the existing Country node and return both nodes and the relationship.

@mgp.write_proc
def new_city(context: mgp.ProcCtx,
in_city: mgp.Nullable[str],
in_country: mgp.Nullable[str]
) -> mgp.Record(City = mgp.Vertex,
Relationship = mgp.Edge,
Country = mgp.Vertex):

city_id = 0
currency = None
currency_code = None

for v in context.graph.vertices:
label, = v.labels # get node label
if (label == "City") and (v.properties.get("id") > city_id): # the following 2 lines are getting the highest ID
city_id = v.properties.get("id")
if v.properties.get("country") == in_country:
currency = v.properties.get("local_currency") # the following 2 lines are saving property values
currency_code = v.properties.get("local_currency_code")
if v.properties.get("name") == in_city:
for r in v.out_edges:
if r.type == "Inside":
country = r.to_vertex
return mgp.Record(City=v, Relationship=r, Country=country)

city = context.graph.create_vertex() # creating a new node with properties
city.add_label("City")
city.properties.set("id", city_id + 1)
city.properties.set("name", in_city)
city.properties.set("country", in_country)
city.properties.set("local_currency", currency)
city.properties.set("local_currency_code", currency_code)

for v in context.graph.vertices: # creating a new relationship to an existing country
if v.properties.get("name") == in_country:
context.graph.create_edge(city, v, mgp.EdgeType("Inside"))
for r in city.out_edges:
if r.type == "Inside":
return mgp.Record(City=city, Relationship=r, Country=v)

At this point you can save the module and test the new additions to the procedure, by running the following query:

CALL backpacking.new_city("Makarska","Croatia") YIELD *;

Neither the country node nor the city node exist

Lastly, in the case there is no City node nor Country node with the name properties the same as the provided arguments, we need to create both.

That is why we also need to find the largest ID among the Country nodes. Because we only need to create a new Country node if one doesn't exist, we will introduce a country_exists variable with a default value False. The value of that flag will change to True only if both the Country node with the name property the same as the in_country argument exists.

This is also the finished procedure:

@mgp.write_proc
def new_city(context: mgp.ProcCtx,
in_city: mgp.Nullable[str],
in_country: mgp.Nullable[str]
) -> mgp.Record(City = mgp.Vertex,
Relationship = mgp.Edge,
Country = mgp.Vertex):

in_country_exists = False
country_id = 0
city_id = 0
currency = None
currency_code = None

for v in context.graph.vertices:
label, = v.labels # get node label
if (label == "City") and (v.properties.get("id") > city_id): # the following 4 lines are getting the highest IDs
city_id = v.properties.get("id")
if (label == "Country") and (v.properties.get("id") > country_id):
country_id = v.properties.get("id")
if v.properties.get("country") == in_country:
country_exists = True # flag is changed to `True`
currency = v.properties.get("local_currency")
currency_code = v.properties.get("local_currency_code")
if v.properties.get("name") == in_city:
for r in v.out_edges:
if r.type == "Inside":
country = r.to_vertex
return mgp.Record(City=v, Relationship=r, Country=country)

city = context.graph.create_vertex() # creating a new node with properties
city.add_label("City")
city.properties.set("id", city_id + 1)
city.properties.set("name", in_city)
city.properties.set("country", in_country)
city.properties.set("local_currency", currency)
city.properties.set("local_currency_code", currency_code)

if in_country_exists == True: # creating a relationship if the country node exist
for v in context.graph.vertices: # creating a new relationship to an existing country
if v.properties.get("name") == in_country:
context.graph.create_edge(city, v, mgp.EdgeType("Inside"))
for r in city.out_edges:
if r.type == "Inside":
return mgp.Record(City=city, Relationship=r, Country=v)

if in_country_exists == False: # creating a node and relationship if the country node doesn't exist
new_country = context.graph.create_vertex()
new_country.add_label("Country")
new_country.properties.set("id", country_id + 1)
new_country.properties.set("name", in_country)
context.graph.create_edge(city, new_country, mgp.EdgeType("Inside"))
for r in city.out_edges:
if r.type == "Inside":
return mgp.Record(City=city, Relationship=r, Country=new_country)

Testing the write procedure

Save the query module, switch to Query Execution and call the procedure using the clause CALL, then calling the right module and procedure within it (backpacking.new_city). List all arguments except the whole graph inside brackets, and at the end YIELD all the results:

CALL backpacking.new_city("Zagreb", "Croatia") YIELD *;

The query returns existing City and Country nodes.

CALL backpacking.new_city("Vinkovci", "Croatia") YIELD *;

The query returns new City node connected to an existing Country node.

CALL backpacking.new_city("Vinkovci", "Makroland") YIELD *;

The query returns a new City node connected to a new Country node.

Where to next?

Congratulations! You've written your first custom query module! Feel free to play around with the Python API and let us know what you are working on through our Discord server.