NoSQL is really cool, but in this harsh world it is impossible to live without field validation.

WARNING: The last versions of pyArango are only compatible with ArangoDB 3.X. For the old version checkout the branch ArangoDBV2

pyArango is geared toward the developer. It's here to help to you develop really cool apps using ArangoDB, really fast.

• Light and simple interface
• Built-in validation of fields on setting or on saving
• Support for all index types
• Supports graphs, traversals and all types of queries
• Caching of documents with Insertions and Lookups in O(1)

Collections are treated as types that apply to the documents within. That means you can define a Collection and then create instances of this Collection in several databases. The same goes for graphs.

In other words, you can have two databases, cache_db and real_db, each of them with an instance of a Users Collection. You can then be assured that documents of both collections will be subjected to the same validation rules. Ain't that cool?

You can be 100% permissive or enforce schemas and validate fields on set, on save or both.

Supports python 2.7 and 3.5.

From PyPi:

pip install pyArango

For the latest version:

git clone https://github.com/tariqdaouda/pyArango.git
cd pyArango
python setup.py develop

This is the quickstart guide; you can find the full documentation here.

from pyArango.connection import *

conn = Connection()

conn.createDatabase(name="test_db")
db = conn["test_db"] # all databases are loaded automatically into the connection and are accessible in this fashion
collection = db.createCollection(name="users") # all collections are also loaded automatically

# collection.delete() # self explanatory

for i in xrange(100):
    doc = collection.createDocument()
    doc["name"] = "Tesla-%d" % i
    doc["number"] = i
    doc["species"] = "human"
    doc.save()

doc = collection.createDocument()
doc["name"] = "Tesla-101"
doc["number"] = 101
doc["species"] = "human"

doc["name"] = "Simba"
# doc.save() # overwrites the document
doc.patch() # updates the modified field
doc.delete()

aql = "FOR c IN users FILTER c.name == @name LIMIT 10 RETURN c"
bindVars = {'name': 'Tesla-3'}
# by setting rawResults to True you'll get dictionaries instead of Document objects, useful if you want to result to set of fields for example
queryResult = db.AQLQuery(aql, rawResults=False, batchSize=1, bindVars=bindVars)
document = queryResult[0]

PyArango supports all types of simple queries (see collection.py for the full list). Here's an example query:

example = {'species': "human"}
query = collection.fetchByExample(example, batchSize=20, count=True)
print query.count # print the total number or documents

for e in query :
    print e['name']

PyArango allows you to implement your own field validation. Validators are simple objects deriving from classes that inherit from Validator and implement a validate() method:

import pyArango.collection as COL
import pyArango.validation as VAL
from pyArango.theExceptions import ValidationError
import types

class String_val(VAL.Validator):
 def validate(self, value):
     if type(value) is not types.StringType :
         raise ValidationError("Field value must be a string")
     return True

class Humans(COL.Collection):

    _validation = {
        'on_save': False,
        'on_set': False,
        'allow_foreign_fields': True  # allow fields that are not part of the schema
    }

    _fields = {
        'name': COL.Field(validators=[VAL.NotNull(), String_val()]),
        'anything': COL.Field(),
        'species': COL.Field(validators=[VAL.NotNull(), VAL.Length(5, 15), String_val()])
    }

collection = db.createCollection('Humans')

In addition, you can also define collection properties (creation arguments for ArangoDB) right inside the definition:

class Humans(COL.Collection):

  _properties = {
      "keyOptions" : {
          "allowUserKeys": False,
          "type": "autoincrement",
          "increment": 1,
          "offset": 0,
      }
  }

    _validation = {
        'on_save': False,
        'on_set': False,
        'allow_foreign_fields': True  # allow fields that are not part of the schema
    }

    _fields = {
        'name': COL.Field(validators=[VAL.NotNull(), String_val()]),
        'anything': COL.Field(),
        'species': COL.Field(validators=[VAL.NotNull(), VAL.Length(5, 15), String_val()])
    }

There is no inheritance of the "_validation" and "_fields" dictionaries. If a class does not fully define its own, the defaults will be automatically assigned to any missing value.

from pyArango.collection import Edges

class Connections(Edges):

    _validation = {
        'on_save': False,
        'on_set': False,
        'allow_foreign_fields': True # allow fields that are not part of the schema
    }

    _fields = {
        'length': Field(NotNull=True),
    }

from pyArango.collection import *

class Things(Collection):
  ....

class Connections(Edges):
  ....

....
a = myThings.createDocument()
b = myThings.createDocument()

conn = myConnections.createEdge()

conn.links(a, b)
conn["someField"] = 35
conn.save() # once an edge links documents, save() and patch() can be used as with any other Document object

You can do it either from a Document or an Edges collection:

# in edges
myDocument.getInEdges(myConnections)
myConnections.getInEdges(myDocument)

# out edges
myDocument.getOutEdges(myConnections)
myConnections.getOutEdges(myDocument)

# both
myDocument.getEdges(myConnections)
myConnections.getEdges(myDocument)

# you can also of ask for the raw json with
myDocument.getInEdges(myConnections, rawResults=True)
# otherwise Document objects are retuned in a list

By using the graph interface you ensure for example that, whenever you delete a document, all the edges linking to that document are also deleted:

from pyArango.collection import Collection, Field
from pyArango.graph import Graph, EdgeDefinition

class Humans(Collection):
    _fields = {
        "name": Field()
    }

class Friend(Edges): # theGraphtheGraph
    _fields = {
        "lifetime": Field()
    }

# Here's how you define a graph
class MyGraph(Graph) :
    _edgeDefinitions = [EdgeDefinition("Friend", fromCollections=["Humans"], toCollections=["Humans"])]
    _orphanedCollections = []

# create the collections (do this only if they don't already exist in the database)
self.db.createCollection("Humans")
self.db.createCollection("Friend")
# same for the graph
theGraph = self.db.createGraph("MyGraph")

# creating some documents
h1 = theGraph.createVertex('Humans', {"name": "simba"})
h2 = theGraph.createVertex('Humans', {"name": "simba2"})

# linking them
theGraph.link('Friend', h1, h2, {"lifetime": "eternal"})

# deleting one of them along with the edge
theGraph.deleteVertex(h2)

Creating a Satellite Graph -----------------

If you want to benefit from the advantages of satellite graphs, you can also create them of course. Please read the official ArangoDB Documentation for further technical information.

from pyArango.connection import *
from pyArango.collection import Collection, Edges, Field
from pyArango.graph import Graph, EdgeDefinition

databaseName = "satellite_graph_db"

conn = Connection()

# Cleanup (if needed)
try:
    conn.createDatabase(name=databaseName)
except Exception:
    pass

# Select our "satellite_graph_db" database
db = conn[databaseName] # all databases are loaded automatically into the connection and are accessible in this fashion

# Define our vertex to use
class Humans(Collection):
    _fields = {
        "name": Field()
    }

# Define our edge to use
class Friend(Edges):
    _fields = {
        "lifetime": Field()
    }

# Here's how you define a Satellite Graph
class MySatelliteGraph(Graph) :
    _edgeDefinitions = [EdgeDefinition("Friend", fromCollections=["Humans"], toCollections=["Humans"])]
    _orphanedCollections = []

theSatelliteGraph = db.createSatelliteGraph("MySatelliteGraph")

pyArango collections have a caching system for documents that performs insertions and retrievals in O(1):

# create a cache a of 1500 documents for collection humans
humans.activateCache(1500)

# disable the cache
humans.deactivateCache()

pyArango can optionally report query times to a statsd server for statistical evaluation:

import statsd from pyArango.connection import Connection statsdclient = statsd.StatsClient(os.environ.get('STATSD_HOST'), int(os.environ.get('STATSD_PORT'))) conn = Connection('http://127.0.0.1:8529', 'root', 'opensesame', statsdClient = statsdclient, reportFileName = '/tmp/queries.log')

It's intended to be used in a two phase way: (we assume you're using bind values - right?)

• First run, which will trigger all usecases. You create the connection by specifying statsdHost, statsdPort and reportFileName. reportFilename will be filled with your queries paired with your hash identifiers. It's reported to statsd as 'pyArango<hash>'. Later on you can use this digest to identify your queries to the gauges.
• On subsequent runs you only specify statsdHost and statsdPort; only the request times are reported to statsd.

More examples can be found in the examples directory. To try them out change the connection strings according to your local setup.

If you are on a Debian / Ubuntu you can install packages with automatic dependency resolution. In the end this is a graph. This example parses Debian package files using the deb_pkg_tools, and will then create vertices and edges from packages and their relations.

Use examples/debiangraph.py to install it, or examples/fetchDebianDependencyGraph.py to browse it as an ascii tree.

You can create the ArangoDB SocialGraph using examples/createSocialGraph.py. It resemples The original ArangoDB Javascript implementation: in python.