Metadata-Version: 1.1
Name: pydgraph
Version: 1.1.2
Summary: Official Dgraph client implementation for Python
Home-page: https://github.com/dgraph-io/pydgraph
Author: Dgraph Labs
Author-email: contact@dgraph.io
License: Apache License, Version 2.0
Description: pydgraph |Build Status| |Coverage Status|
        =========================================
        
        Official Dgraph client implementation for Python (Python >= v2.7 and >=
        v3.5), using `grpc <https://grpc.io/>`__.
        
        This client follows the `Dgraph Go
        client <https://github.com/dgraph-io/dgo>`__ closely.
        
        Before using this client, we highly recommend that you go through
        `docs.dgraph.io <https://docs.dgraph.io>`__, and understand how to run
        and work with Dgraph.
        
        Table of contents
        -----------------
        
        -  `Install <#install>`__
        -  `Quickstart <#quickstart>`__
        -  `Using a client <#using-a-client>`__
        
           -  `Create a client <#create-a-client>`__
           -  `Alter the database <#alter-the-database>`__
           -  `Create a transaction <#create-a-transaction>`__
           -  `Run a mutation <#run-a-mutation>`__
           -  `Run a query <#run-a-query>`__
           -  `Commit a transaction <#commit-a-transaction>`__
           -  `Cleanup Resources <#cleanup-resources>`__
           -  `Setting Metadata Headers <#setting-metadata-headers>`__
        
        -  `Examples <#examples>`__
        -  `Development <#development>`__
        
           -  `Building the source <#building-the-source>`__
           -  `Running tests <#running-tests>`__
        
        Install
        -------
        
        Install using pip:
        
        .. code:: sh
        
           pip install pydgraph
        
        Quickstart
        ----------
        
        Build and run the `simple <./examples/simple>`__ project in the
        ``examples`` folder, which contains an end-to-end example of using the
        Dgraph python client. Follow the instructions in the README of that
        project.
        
        Using a client
        --------------
        
        Create a client
        ~~~~~~~~~~~~~~~
        
        A ``DgraphClient`` object can be initialised by passing it a list of
        ``DgraphClientStub`` clients as variadic arguments. Connecting to
        multiple Dgraph servers in the same cluster allows for better
        distribution of workload.
        
        The following code snippet shows just one connection.
        
        .. code:: python
        
           import pydgraph
        
           client_stub = pydgraph.DgraphClientStub('localhost:9080')
           client = pydgraph.DgraphClient(client_stub)
        
        Alter the database
        ~~~~~~~~~~~~~~~~~~
        
        To set the schema, create an ``Operation`` object, set the schema and
        pass it to ``DgraphClient#alter(Operation)`` method.
        
        .. code:: python
        
           schema = 'name: string @index(exact) .'
           op = pydgraph.Operation(schema=schema)
           client.alter(op)
        
        ``Operation`` contains other fields as well, including drop predicate
        and drop all. Drop all is useful if you wish to discard all the data,
        and start from a clean slate, without bringing the instance down.
        
        .. code:: python
        
           # Drop all data including schema from the Dgraph instance. This is useful
           # for small examples such as this, since it puts Dgraph into a clean
           # state.
           op = pydgraph.Operation(drop_all=True)
           client.alter(op)
        
        Create a transaction
        ~~~~~~~~~~~~~~~~~~~~
        
        To create a transaction, call ``DgraphClient#txn()`` method, which
        returns a new ``Txn`` object. This operation incurs no network overhead.
        
        It is good practice to call ``Txn#discard()`` in a ``finally`` block
        after running the transaction. Calling ``Txn#discard()`` after
        ``Txn#commit()`` is a no-op and you can call ``Txn#discard()`` multiple
        times with no additional side-effects.
        
        .. code:: python
        
           txn = client.txn()
           try:
             # Do something here
             # ...
           finally:
             txn.discard()
             # ...
        
        To create a read-only transaction, call
        ``DgraphClient#txn(read_only=True)``. Read-only transactions are ideal
        for transactions which only involve queries. Mutations and commits are
        not allowed.
        
        .. code:: python
        
           txn = client.txn(read_only=True)
           try:
             # Do some queries here
             # ...
           finally:
             txn.discard()
             # ...
        
        To create a read-only transaction that executes best-effort queries,
        call ``DgraphClient#txn(read_only=True, best_effort=True)``. Best-effort
        queries are faster than normal queries because they bypass the normal
        consensus protocol. For this same reason, best-effort queries cannot
        guarantee to return the latest data. Best-effort queries are only
        supported by read-only transactions.
        
        Run a mutation
        ~~~~~~~~~~~~~~
        
        ``Txn#mutate(mu=Mutation)`` runs a mutation. It takes in a ``Mutation``
        object, which provides two main ways to set data: JSON and RDF N-Quad.
        You can choose whichever way is convenient. Most users won’t need to
        create a ``Mutation`` object themselves.
        
        ``Txn#mutate()`` provides convenience keyword arguments ``set_obj`` and
        ``del_obj`` for setting JSON values and ``set_nquads`` and
        ``del_nquads`` for setting N-Quad values. See examples below for usage.
        
        We define a person object to represent a person and use it in a
        transaction.
        
        .. code:: python
        
           # Create data.
           p = {
               'name': 'Alice',
           }
        
           # Run mutation.
           txn.mutate(set_obj=p)
        
           # If you want to use a mutation object, use this instead:
           # mu = pydgraph.Mutation(set_json=json.dumps(p).encode('utf8'))
           # txn.mutate(mu)
        
           # If you want to use N-Quads, use this instead:
           # txn.mutate(set_nquads='_:alice <name> "Alice" .')
        
        .. code:: python
        
           # Delete data.
        
           query1 = """query all($a: string)
            {
              all(func: eq(name, $a)) 
               {
                 uid
               }   
             }"""
             
           variables1 = {'$a': 'Bob'}
        
           res1 = txn.query(query1, variables=variables1)
        
           ppl1 = json.loads(res1.json)
        
           #For mutation to delete node, use this: 
           txn.mutate(del_obj= person)
        
        For a more complete example with multiple fields and relationships, look
        at the `simple <./examples/simple>`__ project in the ``examples``
        folder.
        
        Sometimes, you only want to commit a mutation, without querying anything
        further. In such cases, you can set the keyword argument
        ``commit_now=True`` to indicate that the mutation must be immediately
        committed.
        
        Keyword argument ``ignore_index_conflict=True`` can be used to not run
        conflict detection over the index, which would decrease the number of
        transaction conflicts and aborts. However, this would come at the cost
        of potentially inconsistent upsert operations.
        
        Run a query
        ~~~~~~~~~~~
        
        You can run a query by calling ``Txn#query(string)``. You will need to
        pass in a GraphQL+- query string. If you want to pass an additional
        dictionary of any variables that you might want to set in the query,
        call ``Txn#query(string, variables=d)`` with the variables dictionary
        ``d``.
        
        The response would contain the field ``json``, which returns the
        response JSON.
        
        Let’s run the following query with a variable $a:
        
        .. code:: console
        
           query all($a: string) {
             all(func: eq(name, $a))
             {
               name
             }
           }
        
        Run the query, deserialize the result from JSON and print it out:
        
        .. code:: python
        
           # Run query.
           query = """query all($a: string) {
             all(func: eq(name, $a))
             {
               name
             }
           }"""
           variables = {'$a': 'Alice'}
        
           res = txn.query(query, variables=variables)
           # If not doing a mutation in the same transaction, simply use:
           # res = client.txn(read_only=True).query(query, variables=variables)
        
           ppl = json.loads(res.json);
        
           # Print results.
           print('Number of people named "Alice": {}'.format(len(ppl['all'])))
           for person in ppl['all']:
             print(person)
        
        This should print:
        
        .. code:: console
        
           Number of people named "Alice": 1
           Alice
        
        Commit a transaction
        ~~~~~~~~~~~~~~~~~~~~
        
        A transaction can be committed using the ``Txn#commit()`` method. If
        your transaction consisted solely of calls to ``Txn#query`` or
        ``Txn#queryWithVars``, and no calls to ``Txn#mutate``, then calling
        ``Txn#commit()`` is not necessary.
        
        An error will be raised if other transactions running concurrently
        modify the same data that was modified in this transaction. It is up to
        the user to retry transactions when they fail.
        
        .. code:: python
        
           txn = client.txn();
           try:
             # ...
             # Perform any number of queries and mutations
             # ...
             # and finally...
             txn.commit()
           except Exception as e:
             if isinstance(e, pydgraph.AbortedError):
               # Retry or handle exception.
             else:
               raise e
           finally:
             # Clean up. Calling this after txn.commit() is a no-op
             # and hence safe.
             txn.discard()
        
        Cleanup Resources
        ~~~~~~~~~~~~~~~~~
        
        To cleanup resources, you have to call ``DgraphClientStub#close()``
        individually for all the instances of ``DgraphClientStub``.
        
        .. code:: python
        
           SERVER_ADDR = "localhost:9080"
        
           # Create instances of DgraphClientStub.
           stub1 = pydgraph.DgraphClientStub(SERVER_ADDR)
           stub2 = pydgraph.DgraphClientStub(SERVER_ADDR)
        
           # Create an instance of DgraphClient.
           client = pydgraph.DgraphClient(stub1, stub2)
        
           # ...
           # Use client
           # ...
        
           # Cleanup resources by closing all client stubs.
           stub1.close()
           stub2.close()
        
        Setting Metadata Headers
        ~~~~~~~~~~~~~~~~~~~~~~~~
        
        Metadata headers such as authentication tokens can be set through the
        metadata of gRPC methods. Below is an example of how to set a header
        named “auth-token”.
        
        .. code:: python
        
           # The following piece of code shows how one can set metadata with
           # auth-token, to allow Alter operation, if the server requires it.
           # metadata is a list of arbritary key-value pairs.
           metadata = [("auth-token", "the-auth-token-value")]
           dg.alter(op, metadata=metadata)
        
        Examples
        --------
        
        -  `simple <./examples/simple>`__: Quickstart example of using pydgraph.
        -  `tls <./examples/tls>`__: Example of using pydgraph with a Dgraph
           cluster secured with TLS.
        
        Development
        -----------
        
        Building the source
        ~~~~~~~~~~~~~~~~~~~
        
        .. code:: sh
        
           python setup.py install
           # To install for the current user, use this instead:
           # python setup.py install --user
        
        If you have made changes to the ``pydgraph/proto/api.proto`` file, you
        need need to regenerate the source files generated by Protocol Buffer
        tools. To do that, install the
        `grpcio-tools <https://pypi.python.org/pypi/grpcio-tools>`__ library and
        then run the following command:
        
        .. code:: sh
        
           python scripts/protogen.py
        
        Running tests
        ~~~~~~~~~~~~~
        
        To run the tests in your local machine you can run the script
        ``scripts/local-tests.sh``. This script assumes Dgraph and dgo (Go
        client) are already built on the local machine. The script will take
        care of bringing up a Dgraph cluster and bringing it down after the
        tests are executed. The script uses the port 9180 by default to prevent
        interference with clusters running on the default port. Docker and
        docker-compose need to be installed before running the script. Refer to
        the official Docker documentation for instructions on how to install
        those packages.
        
        The ``test.sh`` script downloads and installs Dgraph. It is meant for
        use by our CI systems and using it for local development is not
        recommended.
        
        .. |Build Status| image:: https://img.shields.io/travis/dgraph-io/pydgraph/master.svg?style=flat
           :target: https://travis-ci.org/dgraph-io/pydgraph
        .. |Coverage Status| image:: https://img.shields.io/coveralls/github/dgraph-io/pydgraph/master.svg?style=flat
           :target: https://coveralls.io/github/dgraph-io/pydgraph?branch=master
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Database
Classifier: Topic :: Software Development
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
