nebula-python
This repository holds the official Python API for NebulaGraph.
Before you start
Before you start, please read this section to choose the right branch for you. The compatibility between the API and NebulaGraph service can be found in How to choose nebula-python. The current master branch is compatible with NebulaGraph 3.x.
The directory structure
|--nebula-python
|
|-- nebula3 // client code
| |-- fbthrift // the fbthrift lib code
| |-- common
| |-- data
| |-- graph
| |-- meta
| |-- net // the net code for graph client
| |-- storage
| |-- Config.py // the pool config
| |__ Exception.py // the define exception
|
|-- examples
| |-- GraphClientMultiThreadExample.py // the multi thread example
| |-- GraphClientSimpleExample.py // the simple example
| |__ ScanVertexEdgeExample.py
|
|-- tests // the test code
|
|-- setup.py // used to install or package
|
|__ README.md // the introduction of nebula3-python
How to get nebula3-python
Option one: install with pip
# for v3.x
pip install nebula3-python==$version
# for v2.x
pip install nebula2-python==$versionOption two: install from the source code
- Clone from GitHub
git clone https://github.com/vesoft-inc/nebula-python.git
cd nebula-python- Install from source
For python version >= 3.7.0
pip install .For python version >= 3.6.2, < 3.7.0
python3 setup.py installQuick example to use graph-client to connect graphd
from nebula3.gclient.net import ConnectionPool
from nebula3.Config import Config
# define a config
config = Config()
config.max_connection_pool_size = 10
# init connection pool
connection_pool = ConnectionPool()
# if the given servers are ok, return true, else return false
ok = connection_pool.init([('127.0.0.1', 9669)], config)
# option 1 control the connection release yourself
# get session from the pool
session = connection_pool.get_session('root', 'nebula')
# select space
session.execute('USE nba')
# show tags
result = session.execute('SHOW TAGS')
print(result)
# release session
session.release()
# option 2 with session_context, session will be released automatically
with connection_pool.session_context('root', 'nebula') as session:
session.execute('USE nba')
result = session.execute('SHOW TAGS')
print(result)
# close the pool
connection_pool.close()Example of using session pool
There are some limitations while using the session pool:
1. There MUST be an existing space in the DB before initializing the session pool.
2. Each session pool is corresponding to a single USER and a single Space. This is to ensure that the user's access control is consistent. i.g. The same user may have different access privileges in different spaces. If you need to run queries in different spaces, you may have multiple session pools.
3. Every time when sessinPool.execute() is called, the session will execute the query in the space set in the session pool config.
4. Commands that alter passwords or drop users should NOT be executed via session pool.
see /example/SessinPoolExample.py
Quick example to fetch result to dataframe
from nebula3.gclient.net import ConnectionPool
from nebula3.Config import Config
import pandas as pd
from typing import Dict
from nebula3.data.ResultSet import ResultSet
def result_to_df(result: ResultSet) -> pd.DataFrame:
"""
build list for each column, and transform to dataframe
"""
assert result.is_succeeded()
columns = result.keys()
d: Dict[str, list] = {}
for col_num in range(result.col_size()):
col_name = columns[col_num]
col_list = result.column_values(col_name)
d[col_name] = [x.cast() for x in col_list]
return pd.DataFrame.from_dict(d, orient='columns')
# define a config
config = Config()
# init connection pool
connection_pool = ConnectionPool()
# if the given servers are ok, return true, else return false
ok = connection_pool.init([('127.0.0.1', 9669)], config)
# option 2 with session_context, session will be released automatically
with connection_pool.session_context('root', 'nebula') as session:
session.execute('USE <your graph space>')
result = session.execute('<your query>')
df = result_to_df(result)
print(df)
# close the pool
connection_pool.close()Quick example to use storage-client to scan vertex and edge
You should make sure the scan client can connect to the address of storage which see from SHOW HOSTS
from nebula3.mclient import MetaCache, HostAddr
from nebula3.sclient.GraphStorageClient import GraphStorageClient
# the metad servers's address
meta_cache = MetaCache([('172.28.1.1', 9559),
('172.28.1.2', 9559),
('172.28.1.3', 9559)],
50000)
# option 1 metad usually discover the storage address automatically
graph_storage_client = GraphStorageClient(meta_cache)
# option 2 manually specify the storage address
storage_addrs = [HostAddr(host='172.28.1.4', port=9779),
HostAddr(host='172.28.1.5', port=9779),
HostAddr(host='172.28.1.6', port=9779)]
graph_storage_client = GraphStorageClient(meta_cache, storage_addrs)
resp = graph_storage_client.scan_vertex(
space_name='ScanSpace',
tag_name='person')
while resp.has_next():
result = resp.next()
for vertex_data in result:
print(vertex_data)
resp = graph_storage_client.scan_edge(
space_name='ScanSpace',
edge_name='friend')
while resp.has_next():
result = resp.next()
for edge_data in result:
print(edge_data)How to choose nebula-python
| Nebula-Python Version | NebulaGraph Version |
|---|---|
| 1.0 | 1.x |
| 2.0.0 | 2.0.0/2.0.1 |
| 2.5.0 | 2.5.0 |
| 2.6.0 | 2.6.0/2.6.1 |
| 3.0.0 | 3.0.0 |
| 3.1.0 | 3.1.0 |
| 3.3.0 | 3.3.0 |
| 3.4.0 | >=3.4.0 |
| 3.5.0 | >=3.4.0 |
| master | master |
How to contribute to nebula-python
Fork this repo, then clone it locally
(be sure to replace the {username} in the repo URL below with your GitHub username):
git clone https://github.com/{username}/nebula-python.git
cd nebula-python
We use PMD to manage the package, install it first:
pipx install pdm
Visit the PDM documentation for alternative installation methods.
Install the package and all dev dependencies:
pdm install
Make sure the Nebula server in running, then run the tests with pytest:
pdm test
Using the default formatter with black.
Please run pdm fmt to format python code before submitting.
See How to contribute for the general process of contributing to Nebula projects.
