ipython-ngql 0.2

iPython and Jupyter Plugin for Nebula Graph

ipython-ngql is a python package to extend the ability to connect Nebula Graph from your Jupyter Notebook or iPython. It's easier for data scientists to create, debug and share reusable and all-in-one Jupyter Notebooks with Nebula Graph interaction embedded.

ipython-ngql is inspired by 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 [e-mail]:wey-gu/ipython-ngql.git
cd ipython-ngql
python setup.py install

Load it in Jupyter Notebook or iPython

%load_ext ngql

Connect to Nebula Graph

Arguments as below are needed to connect a Nebula Graph DB instance:

Argument	Description
--address or -addr	IP address of the Nebula Graph Instance
--port or -P	Port number of the Nebula Graph 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 GO FROM "Tom" OVER owns_pokemon YIELD owns_pokemon._dst as pokemon_id;

Option 2: The multiple lines stype with %%ngql

%%ngql
USE pokemon_club;
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]: trainer = "Sue"

In [9]: %%ngql
   ...: GO FROM "{{ trainer }}" 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[9]:
  Trainer_Name
0        Jerry
1          Sue
2          Tom
3          Wey

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 comes from the nebula2-python itself.

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

%config IPythonNGQL.ngql_result_style="raw"

After above line being executed, the output will be like:

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'Jerry')]),
        Row(
            values=[Value(
                sVal=b'Sue')]),
        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 [10]: %config IPythonNGQL.ngql_result_style="raw"

In [11]: %%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[11]:
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'Jerry')]),
        Row(
            values=[Value(
                sVal=b'Sue')]),
        Row(
            values=[Value(
                sVal=b'Tom')]),
        Row(
            values=[Value(
                sVal=b'Wey')])]),
    space_name=b'pokemon_club'))

In [12]: r = _

In [13]: r.column_values(key='Trainer_Name')[0]._value.value
Out[13]: b'Tom'

Get Help

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

In [7]: %ngql help


        Supported Configurations:
        ------------------------

        > How to config ngql_result_style in "raw", "pandas"
        %config IPythonNGQL.ngql_result_style="raw"
        %config IPythonNGQL.ngql_result_style="pandas"

        > How to config ngql_verbose in True, False
        %config IPythonNGQL.ngql_verbose=True

        > How to config max_connection_pool_size
        %config IPythonNGQL.max_connection_pool_size=10

        Quick Start:
        -----------

        > Connect to Neubla Graph
        %ngql --address 127.0.0.1 --port 9669 --user user --password password

        > Use Space
        %ngql USE nba

        > Query
        %ngql SHOW TAGS;

        > Multile Queries
        %%ngql
        SHOW TAGS;
        SHOW HOSTS;

        Reload ngql Magic
        %reload_ext ngql

        > Variables in query, we are using Jinja2 here
        name = "nba"
        %ngql USE "{{ name }}"

Examples

Jupyter Notebook

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

iPython

venv ❯ ipython

In [1]: %load_ext ngql

In [2]: %ngql --address 127.0.0.1 --port 9669 --user user --password password
Connection Pool Created
Out[2]:
           Name
0  pokemon_club

In [3]: %ngql 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]:
  Trainer_Name
0          Tom
1        Jerry
2          Sue
3          Tom
4          Wey

In [4]: %%ngql
   ...: SHOW TAGS;
   ...: SHOW HOSTS;
   ...:
   ...:
Out[4]:
        Host    Port  Status  Leader count Leader distribution Partition distribution
0  storaged0  9779.0  ONLINE             0  No valid partition     No valid partition
1  storaged1  9779.0  ONLINE             1      pokemon_club:1         pokemon_club:1
2  storaged2  9779.0  ONLINE             0  No valid partition     No valid partition
3      Total     NaN    None             1      pokemon_club:1         pokemon_club:1

In [5]: trainer = "Sue"

In [6]: %%ngql
   ...: GO FROM "{{ trainer }}" 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[6]:
  Trainer_Name
0        Jerry
1          Sue
2          Tom
3          Wey

In [7]: %ngql help


        Supported Configurations:
        ------------------------

        > How to config ngql_result_style in "raw", "pandas"
        %config IPythonNGQL.ngql_result_style="raw"
        %config IPythonNGQL.ngql_result_style="pandas"

        > How to config ngql_verbose in True, False
        %config IPythonNGQL.ngql_verbose=True

        > How to config max_connection_pool_size
        %config IPythonNGQL.max_connection_pool_size=10

        Quick Start:
        -----------

        > Connect to Neubla Graph
        %ngql --address 127.0.0.1 --port 9669 --user user --password password

        > Use Space
        %ngql USE nba

        > Query
        %ngql SHOW TAGS;

        > Multile Queries
        %%ngql
        SHOW TAGS;
        SHOW HOSTS;

        Reload ngql Magic
        %reload_ext ngql

        > Variables in query, we are using Jinja2 here
        name = "nba"
        %ngql USE "{{ name }}"

In [8]: trainer = "Sue"

In [9]: %%ngql
   ...: GO FROM "{{ trainer }}" 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[9]:
  Trainer_Name
0        Jerry
1          Sue
2          Tom
3          Wey

In [10]: %config IPythonNGQL.ngql_result_style="raw"

In [11]: %%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[11]:
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'Jerry')]),
        Row(
            values=[Value(
                sVal=b'Sue')]),
        Row(
            values=[Value(
                sVal=b'Tom')]),
        Row(
            values=[Value(
                sVal=b'Wey')])]),
    space_name=b'pokemon_club'))

In [12]: r = _

In [13]: r.column_values(key='Trainer_Name')[0]._value.value
Out[13]: b'Tom'