Skip to main content

Jupyter and iPython extension for NebulaGraph

Project description

ipython-ngql is a Python package that enhances your ability to connect to NebulaGraph from your Jupyter Notebook or iPython. It simplifies the process for data scientists to create, debug, and share all-in-one Jupyter Notebooks with NebulaGraph interaction embedded to facilitate easier collaboration.

ipython-ngql draws inspiration from ipython-sql, created by Catherine Devlin

Get Started

Installation

ipython-ngql could be installed either via pip or from this git repo itself.

Install via pip

pip install ipython-ngql

Install inside the repo

git clone git@github.com:wey-gu/ipython-ngql.git
cd ipython-ngql
python setup.py install

Load it in Jupyter Notebook or iPython

%load_ext ngql

Connect to NebulaGraph

Arguments as below are needed to connect a NebulaGraph DB instance:

Argument Description
--address or -addr IP address of the NebulaGraph Instance
--port or -P Port number of the NebulaGraph Instance
--user or -u User name
--password or -p Password

Below is an exmple on connecting to 127.0.0.1:9669 with username: "user" and password: "password".

%ngql --address 127.0.0.1 --port 9669 --user user --password password

Make Queries

Now two kind of iPtython Magics are supported:

Option 1: The one line stype with %ngql:

%ngql USE basketballplayer;
%ngql MATCH (v:player{name:"Tim Duncan"})-->(v2:player) RETURN v2.player.name AS Name;

Option 2: The multiple lines stype with %%ngql

%%ngql
SHOW TAGS;
SHOW HOSTS;

There will be other options in future, i.e. from a .ngql file.

Query String with Variables

ipython-ngql supports taking variables from the local namespace, with the help of Jinja2 template framework, it's supported to have queries like the below example.

The actual query string should be GO FROM "Sue" OVER owns_pokemon ..., and "{{ trainer }}" was renderred as "Sue" by consuming the local variable trainer:

In [8]: vid = "player100"

In [9]: %%ngql
   ...: MATCH (v)<-[e:follow]- (v2)-[e2:serve]->(v3)
   ...:   WHERE id(v) == "{{ vid }}"
   ...: RETURN v2.player.name AS FriendOf, v3.team.name AS Team LIMIT 3;
Out[9]:   RETURN v2.player.name AS FriendOf, v3.team.name AS Team LIMIT 3;
FriendOf	Team
0	LaMarcus Aldridge	Trail Blazers
1	LaMarcus Aldridge	Spurs
2	Marco Belinelli	Warriors

Draw query results

Just call %ng_draw after queries with graph data.

# one query
%ngql GET SUBGRAPH 2 STEPS FROM "player101" YIELD VERTICES AS nodes, EDGES AS relationships;
%ng_draw

# another query
%ngql match p=(:player)-[]->() return p LIMIT 5
%ng_draw

Configure ngql_result_style

By default, ipython-ngql will use pandas dataframe as output style to enable more human-readable output, while it's supported to use the raw thrift data format that comes from the nebula3-python itself.

This can be done ad-hoc with below one line:

%config IPythonNGQL.ngql_result_style="raw"

After the above line is executed, the output will be like this:

ResultSet(ExecutionResponse(
    error_code=0,
    latency_in_us=2844,
    data=DataSet(
        column_names=[b'Trainer_Name'],
        rows=[Row(
            values=[Value(
                sVal=b'Tom')]),
...
        Row(
            values=[Value(
                sVal=b'Wey')])]),
    space_name=b'pokemon_club'))

The result are always stored in variable _ in Jupyter Notebook, thus, to tweak the result, just refer a new var to it like:

In [1] : %config IPythonNGQL.ngql_result_style="raw"

In [2] : %%ngql USE pokemon_club;
    ...: GO FROM "Tom" OVER owns_pokemon YIELD owns_pokemon._dst as pokemon_id
    ...: | GO FROM $-.pokemon_id OVER owns_pokemon REVERSELY YIELD owns_pokemon._dst AS Trainer_Name;
    ...:
    ...:
Out[3]:
ResultSet(ExecutionResponse(
    error_code=0,
    latency_in_us=3270,
    data=DataSet(
        column_names=[b'Trainer_Name'],
        rows=[Row(
            values=[Value(
                sVal=b'Tom')]),
...
        Row(
            values=[Value(
                sVal=b'Wey')])]),
    space_name=b'pokemon_club'))

In [4]: r = _

In [5]: r.column_values(key='Trainer_Name')[0].cast()
Out[5]: 'Tom'

Get Help

Don't remember anything or even relying on the cheatsheet here, oen takeaway for you: the help!

In [1]: %ngql help

Examples

Jupyter Notebook

Please refer here:https://github.com/wey-gu/ipython-ngql/blob/main/examples/get_started.ipynb

iPython

In [1]: %load_ext ngql

In [2]: %ngql --address 192.168.8.128 --port 9669 --user root --password nebula
Connection Pool Created
Out[2]: 
                        Name
0           basketballplayer
1  demo_movie_recommendation
2                        k8s
3                       test

In [3]: %ngql USE basketballplayer;
   ...: %ngql MATCH (v:player{name:"Tim Duncan"})-->(v2:player) RETURN v2.player.name AS Name;
Out[3]: 
            Name
0    Tony Parker
1  Manu Ginobili

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ipython-ngql-0.7.4.tar.gz (8.3 kB view details)

Uploaded Source

Built Distribution

ipython_ngql-0.7.4-py3-none-any.whl (8.7 kB view details)

Uploaded Python 3

File details

Details for the file ipython-ngql-0.7.4.tar.gz.

File metadata

  • Download URL: ipython-ngql-0.7.4.tar.gz
  • Upload date:
  • Size: 8.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for ipython-ngql-0.7.4.tar.gz
Algorithm Hash digest
SHA256 ad3346bb414261f16e8d9f9b284deb8f377ae88e91d7738ea92eff1c901e1dab
MD5 0184bd97147602c1c7185d517ddf9c84
BLAKE2b-256 dacc4377eafe22b1c7026e11fef83bf7628383c5d05fdb66ac05ef9e02cd2ffc

See more details on using hashes here.

File details

Details for the file ipython_ngql-0.7.4-py3-none-any.whl.

File metadata

File hashes

Hashes for ipython_ngql-0.7.4-py3-none-any.whl
Algorithm Hash digest
SHA256 60b113d7cb4912de580f995751379e95cedd112199acac704dd35a67362ec621
MD5 fed3c280faf753dee66456fa0fe5de5c
BLAKE2b-256 316281d6c5717dad9666dab672220ba4cb36300316653b568d2c7846b244bcbe

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page