CogDB - Micro Graph Database for Python Applications

Documents and examples at cogdb.io

New release!: 3.0.1

Ability to use lambda in graph queries.

Adjustable height and width for views.

Installing Cog

pip install cogdb

CogDB is a persistent graph database implemented purely in Python. Torque is CogDB's graph query language. CogDB is an ideal choice if you need a database that is very easy to use and that has no setup overhead. All you need to do is to import it into your Python application. CogDB can be used interactively in an IPython environment like Jupyter notebooks.

CogDB can load a graph stored as N-Triples, a serialization format for RDF. See Wikipedia, W3C for details.

In short, an N-Triple is sequence of subject, predicate and object in a single line that defines a connection between two vertices:

vertex <predicate> vertex

Learn more about RDF triples

Creating a graph

from cog.torque import Graph
g = Graph("people")
g.put("alice","follows","bob")
g.put("bob","follows","fred")
g.put("bob","status","cool_person")
g.put("charlie","follows","bob")
g.put("charlie","follows","dani")
g.put("dani","follows","bob")
g.put("dani","follows","greg")
g.put("dani","status","cool_person")
g.put("emily","follows","fred")
g.put("fred","follows","greg")
g.put("greg","status","cool_person")
g.put("bob","score","5")
g.put("greg","score","10")
g.put("alice","score","7")
g.put("dani","score","100")

Torque query examples

Scan vertices

g.scan(3)

{'result': [{'id': 'bob'}, {'id': 'emily'}, {'id': 'charlie'}]}

Scan edges

g.scan(3, 'e')

{'result': [{'id': 'status'}, {'id': 'follows'}]}

Starting from a vertex, follow all outgoing edges and list all vertices

g.v("bob").out().all()

{'result': [{'id': '5'}, {'id': 'fred'}, {'id': 'cool_person'}]}

Everyone with status 'cool_person'

g.v().has("status", 'cool_person').all()

{'result': [{'id': 'bob'}, {'id': 'dani'}, {'id': 'greg'}]}

Include edges in the results

g.v().has("follows", "fred").inc().all('e')

{'result': [{'id': 'dani', 'edges': ['follows']}, {'id': 'charlie', 'edges': ['follows']}, {'id': 'alice', 'edges': ['follows']}]}

starting from a vertex, follow all outgoing edges and count vertices

g.v("bob").out().count()

'3'

See who is following who and create a view of that network

Note: `render()` is supported only in IPython environment like Jupyter notebook otherwise use view(..).url.

By tagging the vertices 'from' and 'to', the resulting graph can be visualized.

g.v().tag("from").out("follows").tag("to").view("follows").render()

g.v().tag("from").out("follows").tag("to").view("follows").url

file:///Path/to/your/cog_home/views/follows.html

List all views

g.lsv()

['follows']

Load existing visualization

g.getv('follows').render()

starting from a vertex, follow all out going edges and tag them

g.v("bob").out().tag("from").out().tag("to").all()

{'result': [{'from': 'fred', 'id': 'greg', 'to': 'greg'}]}

starting from a vertex, follow all incoming edges and list all vertices

g.v("bob").inc().all()

{'result': [{'id': 'alice'}, {'id': 'charlie'}, {'id': 'dani'}]}

Using lambda to chose vertices while traversing the graph.

g.v(func=lambda x: x.startswith("d")).all()

{'result': [{'id': 'dani'}]}

g.v().out("score", func=lambda x: int(x) > 5).inc().all()

{'result': [{'id': 'alice'}, {'id': 'dani'}, {'id': 'greg'}]}

g.v("emily").out("follows", func=lambda x: x.startswith("f")).all()

{'result': [{'id': 'fred'}]}

Loading data from a file

Create a graph from CSV file

from cog.torque import Graph
g = Graph("books")
g.load_csv('test/test-data/books.csv', "book_id")

Get the names of the books that have an average rating greater than 4.0

g.v().out("average_rating", func=lambda x: float(x) > 4.0).inc().out("title").all()

Triples file

from cog.torque import Graph
g = Graph(graph_name="people")
g.load_triples("/path/to/triples.nt", "people")

Edgelist file

from cog.torque import Graph
g = Graph(graph_name="people")
g.load_edgelist("/path/to/edgelist", "people")

Low level key-value store API:

Every record inserted into Cog's key-value store is directly persisted on to disk. It stores and retrieves data based on hash values of the keys, it can perform fast look ups (O(1) avg) and fast (O(1) avg) inserts.

from cog.database import Cog

cogdb = Cog('path/to/dbdir')

# create a namespace
cogdb.create_or_load_namespace("my_namespace")

# create new table
cogdb.create_table("new_db", "my_namespace")

# put some data
cogdb.put(('key', 'val'))

# retrieve data 
cogdb.get('key')

# put some more data
cogdb.put(('key2', 'val2'))

# scan
scanner = cogdb.scanner()
for r in scanner:
 print
 r

# delete data
cogdb.delete('key1')

Config

If no config is provided when creating a Cog instance, it will use the defaults:

COG_PATH_PREFIX = "/tmp"
COG_HOME = "cog-test"

Example updating config

from cog import config

config.COG_HOME = "app1_home"
data = ('user_data:id=1', '{"firstname":"Hari","lastname":"seldon"}')
cog = Cog(config)
cog.create_or_load_namespace("test")
cog.create_table("db_test", "test")
cog.put(data)
scanner = cog.scanner()
for r in scanner:
 print
 r

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