ngdi 0.1.8

NebulaGraph Data Intelligence Suite

NebulaGraph Data Intelligence(ngdi) Suite

NebulaGraph Data Intelligence Suite for Python (ngdi) is a powerful Python library that offers a range of APIs for data scientists to effectively read, write, analyze, and compute data in NebulaGraph. This library allows data scientists to perform these operations on a single machine using NetworkX, or in a distributed computing environment using Spark, in unified and intuitive API. With ngdi, data scientists can easily access and process data in NebulaGraph, enabling them to perform advanced analytics and gain valuable insights.

        ┌───────────────────────────────────────────────────┐            
        │   Spark Cluster                                   │            
        │    .─────.    .─────.    .─────.    .─────.       │            
     ┌─▶│   :       ;  :       ;  :       ;  :       ;      │            
     │  │     `───'      `───'      `───'      `───'        │            
Algorithm                                                   │            
  Spark └───────────────────────────────────────────────────┘            
 Engine ┌────────────────────────────────────────────────────────────────┐
     └──┤                                                                │
        │   NebulaGraph Data Intelligence Suite(ngdi)                    │
        │     ┌────────┐    ┌──────┐    ┌────────┐   ┌─────┐             │
        │     │ Reader │    │ Algo │    │ Writer │   │ GNN │             │
        │     └────────┘    └──────┘    └────────┘   └─────┘             │
        │          ├────────────┴───┬────────┴─────┐    └──────┐         │
        │          ▼                ▼              ▼           ▼         │
        │   ┌─────────────┐ ┌──────────────┐ ┌──────────┐┌───────────┐   │
     ┌──┤   │ SparkEngine │ │ NebulaEngine │ │ NetworkX ││ DGLEngine │   │
     │  │   └─────────────┘ └──────────────┘ └──────────┘└───────────┘   │
     │  └──────────┬─────────────────────────────────────────────────────┘
     │             │        Spark                                        
     │             └────────Reader ────────────┐                         
Spark Reader              Query Mode           │                         
Scan Mode                                      ▼                         
     │  ┌───────────────────────────────────────────────────┐            
     │  │  NebulaGraph Graph Engine         Nebula-GraphD   │            
     │  ├──────────────────────────────┬────────────────────┤            
     │  │  NebulaGraph Storage Engine  │                    │            
     └─▶│  Nebula-StorageD             │    Nebula-Metad    │            
        └──────────────────────────────┴────────────────────┘            

Installation

pip install ngdi

Spark Engine Prerequisites

• Spark 2.4, 3.0(not yet tested)
• NebulaGraph 3.4+
• NebulaGraph Spark Connector 3.4+
• NebulaGraph Algorithm 3.1+

NebulaGraph Engine Prerequisites

• NebulaGraph 3.4+
• NebulaGraph Python Client 3.4+
• NetworkX

Run on PySpark Jupyter Notebook(Spark Engine)

Assuming we have put the nebula-spark-connector.jar and nebula-algo.jar in /opt/nebulagraph/ngdi/package/.

export PYSPARK_PYTHON=python3
export PYSPARK_DRIVER_PYTHON=jupyter
export PYSPARK_DRIVER_PYTHON_OPTS="notebook --ip=0.0.0.0 --port=8888 --no-browser"

pyspark --driver-class-path /opt/nebulagraph/ngdi/package/nebula-spark-connector.jar \
    --driver-class-path /opt/nebulagraph/ngdi/package/nebula-algo.jar \
    --jars /opt/nebulagraph/ngdi/package/nebula-spark-connector.jar \
    --jars /opt/nebulagraph/ngdi/package/nebula-algo.jar

Then we could access Jupyter Notebook with PySpark and refer to examples/spark_engine.ipynb

Submit Algorithm job to Spark Cluster(Spark Engine)

Assuming we have put the nebula-spark-connector.jar and nebula-algo.jar in /opt/nebulagraph/ngdi/package/; We have put the ngdi-py3-env.zip in /opt/nebulagraph/ngdi/package/. And we have the following Algorithm job in pagerank.py:

from ngdi import NebulaGraphConfig
from ngdi import NebulaReader

# set NebulaGraph config
config_dict = {
    "graphd_hosts": "graphd:9669",
    "metad_hosts": "metad0:9669,metad1:9669,metad2:9669",
    "user": "root",
    "password": "nebula",
    "space": "basketballplayer",
}
config = NebulaGraphConfig(**config_dict)

# read data with spark engine, query mode
reader = NebulaReader(engine="spark")
query = """
    MATCH ()-[e:follow]->()
    RETURN e LIMIT 100000
"""
reader.query(query=query, edge="follow", props="degree")
df = reader.read()

# run pagerank algorithm
pr_result = df.algo.pagerank(reset_prob=0.15, max_iter=10)

Note, this could be done by Airflow, or other job scheduler in production.

Then we can submit the job to Spark cluster:

spark-submit --master spark://master:7077 \
    --driver-class-path /opt/nebulagraph/ngdi/package/nebula-spark-connector.jar \
    --driver-class-path /opt/nebulagraph/ngdi/package/nebula-algo.jar \
    --jars /opt/nebulagraph/ngdi/package/nebula-spark-connector.jar \
    --jars /opt/nebulagraph/ngdi/package/nebula-algo.jar \
    --py-files /opt/nebulagraph/ngdi/package/ngdi-py3-env.zip \
    pagerank.py

Run ngdi algorithm job from python script(Spark Engine)

We have everything ready as above, including the pagerank.py.

import subprocess

subprocess.run(["spark-submit", "--master", "spark://master:7077",
                "--driver-class-path", "/opt/nebulagraph/ngdi/package/nebula-spark-connector.jar",
                "--driver-class-path", "/opt/nebulagraph/ngdi/package/nebula-algo.jar",
                "--jars", "/opt/nebulagraph/ngdi/package/nebula-spark-connector.jar",
                "--jars", "/opt/nebulagraph/ngdi/package/nebula-algo.jar",
                "--py-files", "/opt/nebulagraph/ngdi/package/ngdi-py3-env.zip",
                "pagerank.py"])

Run on single machine(NebulaGraph Engine)

Assuming we have NebulaGraph cluster up and running, and we have the following Algorithm job in pagerank_nebula_engine.py:

This file is the same as pagerank.py except for the following line:

- reader = NebulaReader(engine="spark")
+ reader = NebulaReader(engine="nebula")

Then we can run the job on single machine:

python3 pagerank.py

Documentation

API Reference

Usage

Spark Engine Examples

See also: examples/spark_engine.ipynb

from ngdi import NebulaReader

# read data with spark engine, query mode
reader = NebulaReader(engine="spark")
query = """
    MATCH ()-[e:follow]->()
    RETURN e LIMIT 100000
"""
reader.query(query=query, edge="follow", props="degree")
df = reader.read() # this will take some time
df.show(10)

# read data with spark engine, scan mode
reader = NebulaReader(engine="spark")
reader.scan(edge="follow", props="degree")
df = reader.read() # this will take some time
df.show(10)

# read data with spark engine, load mode (not yet implemented)
reader = NebulaReader(engine="spark")
reader.load(source="hdfs://path/to/edge.csv", format="csv", header=True, schema="src: string, dst: string, rank: int")
df = reader.read() # this will take some time
df.show(10)

# run pagerank algorithm
pr_result = df.algo.pagerank(reset_prob=0.15, max_iter=10) # this will take some time

# convert dataframe to NebulaGraphObject
graph = reader.to_graphx() # not yet implemented

NebulaGraph Engine Examples(not yet implemented)

from ngdi import NebulaReader

# read data with nebula engine, query mode
reader = NebulaReader(engine="nebula")
reader.query("""
    MATCH ()-[e:follow]->()
    RETURN e.src, e.dst, e.degree LIMIT 100000
""")
df = reader.read() # this will take some time
df.show(10)

# read data with nebula engine, scan mode
reader = NebulaReader(engine="nebula")
reader.scan(edge_types=["follow"])
df = reader.read() # this will take some time
df.show(10)

# convert dataframe to NebulaGraphObject
graph = reader.to_graph() # this will take some time
graph.nodes.show(10)
graph.edges.show(10)

# run pagerank algorithm
pr_result = graph.algo.pagerank(reset_prob=0.15, max_iter=10) # this will take some time