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        "author": "Dgraph Labs",
        "author_email": "contact@dgraph.io",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Database",
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        "description": "pydgraph\n========\n\nOfficial Dgraph client implementation for Python (Python >= v2.7 and >=\nv3.5), using `grpc <https://grpc.io/>`__.\n\nThis client follows the `Dgraph Go\nclient <https://github.com/dgraph-io/dgo>`__ closely.\n\nBefore using this client, we highly recommend that you go through\n`docs.dgraph.io <https://docs.dgraph.io>`__, and understand how to run\nand work with Dgraph.\n\nTable of contents\n-----------------\n\n-  `Install <#install>`__\n-  `Quickstart <#quickstart>`__\n-  `Using a client <#using-a-client>`__\n\n   -  `Creating a client <#creating-a-client>`__\n   -  `Altering the database <#altering-the-database>`__\n   -  `Creating a transaction <#creating-a-transaction>`__\n   -  `Running a mutation <#running-a-mutation>`__\n   -  `Running a query <#running-a-query>`__\n   -  `Committing a transaction <#committing-a-transaction>`__\n   -  `Cleaning up Resources <#cleaning-up-resources>`__\n   -  `Setting Metadata Headers <#setting-metadata-headers>`__\n\n-  `Examples <#examples>`__\n-  `Development <#development>`__\n\n   -  `Building the source <#building-the-source>`__\n   -  `Running tests <#running-tests>`__\n\nInstall\n-------\n\nInstall using pip:\n\n.. code:: sh\n\n   pip install pydgraph\n\nQuickstart\n----------\n\nBuild and run the `simple <./examples/simple>`__ project in the\n``examples`` folder, which contains an end-to-end example of using the\nDgraph python client. Follow the instructions in the README of that\nproject.\n\nUsing a client\n--------------\n\nCreating a client\n~~~~~~~~~~~~~~~~~\n\nYou can initialize a ``DgraphClient`` object by passing it a list of\n``DgraphClientStub`` clients as variadic arguments. Connecting to\nmultiple Dgraph servers in the same cluster allows for better\ndistribution of workload.\n\nThe following code snippet shows just one connection.\n\n.. code:: python\n\n   import pydgraph\n\n   client_stub = pydgraph.DgraphClientStub('localhost:9080')\n   client = pydgraph.DgraphClient(client_stub)\n\nAltering the database\n~~~~~~~~~~~~~~~~~~~~~\n\nTo set the schema, create an ``Operation`` object, set the schema and\npass it to ``DgraphClient#alter(Operation)`` method.\n\n.. code:: python\n\n   schema = 'name: string @index(exact) .'\n   op = pydgraph.Operation(schema=schema)\n   client.alter(op)\n\n``Operation`` contains other fields as well, including drop predicate\nand drop all. Drop all is useful if you wish to discard all the data,\nand start from a clean slate, without bringing the instance down.\n\n.. code:: python\n\n   # Drop all data including schema from the Dgraph instance. This is a useful\n   # for small examples such as this since it puts Dgraph into a clean state.\n   op = pydgraph.Operation(drop_all=True)\n   client.alter(op)\n\nCreating a transaction\n~~~~~~~~~~~~~~~~~~~~~~\n\nTo create a transaction, call ``DgraphClient#txn()`` method, which\nreturns a new ``Txn`` object. This operation incurs no network overhead.\n\nIt is good practice to call ``Txn#discard()`` in a ``finally`` block\nafter running the transaction. Calling ``Txn#discard()`` after\n``Txn#commit()`` is a no-op and you can call ``Txn#discard()`` multiple\ntimes with no additional side-effects.\n\n.. code:: python\n\n   txn = client.txn()\n   try:\n     # Do something here\n     # ...\n   finally:\n     txn.discard()\n     # ...\n\nTo create a read-only transaction, call\n``DgraphClient#txn(read_only=True)``. Read-only transactions are ideal\nfor transactions which only involve queries. Mutations and commits are\nnot allowed.\n\n.. code:: python\n\n   txn = client.txn(read_only=True)\n   try:\n     # Do some queries here\n     # ...\n   finally:\n     txn.discard()\n     # ...\n\nTo create a read-only transaction that executes best-effort queries,\ncall ``DgraphClient#txn(read_only=True, best_effort=True)``. Best-effort\nqueries are faster than normal queries because they bypass the normal\nconsensus protocol. For this same reason, best-effort queries cannot\nguarantee to return the latest data. Best-effort queries are only\nsupported by read-only transactions.\n\nRunning a mutation\n~~~~~~~~~~~~~~~~~~\n\n``Txn#mutate(mu=Mutation)`` runs a mutation. It takes in a ``Mutation``\nobject, which provides two main ways to set data: JSON and RDF N-Quad.\nYou can choose whichever way is convenient.\n\n``Txn#mutate()`` provides convenience keyword arguments ``set_obj`` and\n``del_obj`` for setting JSON values and ``set_nquads`` and\n``del_nquads`` for setting N-Quad values. See examples below for usage.\n\nWe define a person object to represent a person and use it in a\ntransaction.\n\n.. code:: python\n\n   # Create data.\n   p = {\n       'name': 'Alice',\n   }\n\n   # Run mutation.\n   txn.mutate(set_obj=p)\n\n   # If you want to use a mutation object, use this instead:\n   # mu = pydgraph.Mutation(set_json=json.dumps(p).encode('utf8'))\n   # txn.mutate(mu)\n\n   # If you want to use N-Quads, use this instead:\n   # txn.mutate(set_nquads='_:alice <name> \"Alice\" .')\n\n.. code:: python\n\n   # Delete data.\n\n   query = \"\"\"query all($a: string)\n    {\n      all(func: eq(name, $a))\n       {\n         uid\n       }\n     }\"\"\"\n\n   variables = {'$a': 'Bob'}\n\n   res = txn.query(query, variables=variables)\n   ppl = json.loads(res.json)\n\n   # For a mutation to delete a node, use this:\n   txn.mutate(del_obj=person)\n\nFor a complete example with multiple fields and relationships, look at\nthe `simple <./examples/simple>`__ project in the ``examples`` folder.\n\nSometimes, you only want to commit a mutation, without querying anything\nfurther. In such cases, you can set the keyword argument\n``commit_now=True`` to indicate that the mutation must be immediately\ncommitted.\n\nA mutation can be executed using ``txn.do_request`` as well.\n\n.. code:: python\n\n   mutation = txn.create_mutation(set_nquads='_:alice <name> \"Alice\" .')\n   request = txn.create_request(mutations=[mutation], commit_now=True)\n   txn.do_request(request)\n\nRunning a query\n~~~~~~~~~~~~~~~\n\nYou can run a query by calling ``Txn#query(string)``. You will need to\npass in a GraphQL+- query string. If you want to pass an additional\ndictionary of any variables that you might want to set in the query,\ncall ``Txn#query(string, variables=d)`` with the variables dictionary\n``d``.\n\nThe response would contain the field ``json``, which returns the\nresponse JSON.\n\nLet\u2019s run a query with a variable ``$a``, deserialize the result from\nJSON and print it out:\n\n.. code:: python\n\n   # Run query.\n   query = \"\"\"query all($a: string) {\n     all(func: eq(name, $a))\n     {\n       name\n     }\n   }\"\"\"\n   variables = {'$a': 'Alice'}\n\n   res = txn.query(query, variables=variables)\n\n   # If not doing a mutation in the same transaction, simply use:\n   # res = client.txn(read_only=True).query(query, variables=variables)\n\n   ppl = json.loads(res.json)\n\n   # Print results.\n   print('Number of people named \"Alice\": {}'.format(len(ppl['all'])))\n   for person in ppl['all']:\n     print(person)\n\nThis should print:\n\n.. code:: console\n\n   Number of people named \"Alice\": 1\n   Alice\n\nYou can also use ``txn.do_request`` function to run the query.\n\n.. code:: python\n\n   request = txn.create_request(query=query)\n   txn.do_request(request)\n\nRunning an Upsert: Query + Mutation\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe ``txn.do_request`` function allows you to run upserts consisting of\none query and one mutation. Query variables could be defined and can\nthen be used in the mutation.\n\nTo know more about upsert, we highly recommend going through the docs at\nhttps://docs.dgraph.io/mutations/#upsert-block.\n\n.. code:: python\n\n   query = \"\"\"{\n     u as var(func: eq(name, \"Alice\"))\n   }\"\"\"\n   nquad = \"\"\"\n     uid(u) <age> \"25\" .\n   \"\"\"\n   cond = \"@if(eq(len(u), 1))\"\n   mutation = txn.create_mutation(set_nquads=nquad, cond=cond)\n   request = txn.create_request(query=query, mutations=[mutation], commit_now=True)\n   txn.do_request(request)\n\nCommitting a transaction\n~~~~~~~~~~~~~~~~~~~~~~~~\n\nA transaction can be committed using the ``Txn#commit()`` method. If\nyour transaction consisted solely of calls to ``Txn#query`` or\n``Txn#queryWithVars``, and no calls to ``Txn#mutate``, then calling\n``Txn#commit()`` is not necessary.\n\nAn error is raised if another transaction(s) modify the same data\nconcurrently that was modified in the current transaction. It is up to\nthe user to retry transactions when they fail.\n\n.. code:: python\n\n   txn = client.txn()\n   try:\n     # ...\n     # Perform any number of queries and mutations\n     # ...\n     # and finally...\n     txn.commit()\n   except Exception as e:\n     if isinstance(e, pydgraph.AbortedError):\n       # Retry or handle exception.\n     else:\n       raise e\n   finally:\n     # Clean up. Calling this after txn.commit() is a no-op\n     # and hence safe.\n     txn.discard()\n\nCleaning Up Resources\n~~~~~~~~~~~~~~~~~~~~~\n\nTo clean up resources, you have to call ``DgraphClientStub#close()``\nindividually for all the instances of ``DgraphClientStub``.\n\n.. code:: python\n\n   SERVER_ADDR = \"localhost:9080\"\n\n   # Create instances of DgraphClientStub.\n   stub1 = pydgraph.DgraphClientStub(SERVER_ADDR)\n   stub2 = pydgraph.DgraphClientStub(SERVER_ADDR)\n\n   # Create an instance of DgraphClient.\n   client = pydgraph.DgraphClient(stub1, stub2)\n\n   # ...\n   # Use client\n   # ...\n\n   # Clean up resources by closing all client stubs.\n   stub1.close()\n   stub2.close()\n\nSetting Metadata Headers\n~~~~~~~~~~~~~~~~~~~~~~~~\n\nMetadata headers such as authentication tokens can be set through the\nmetadata of gRPC methods. Below is an example of how to set a header\nnamed \u201cauth-token\u201d.\n\n.. code:: python\n\n   # The following piece of code shows how one can set metadata with\n   # auth-token, to allow Alter operation, if the server requires it.\n   # metadata is a list of arbitrary key-value pairs.\n   metadata = [(\"auth-token\", \"the-auth-token-value\")]\n   dg.alter(op, metadata=metadata)\n\nExamples\n--------\n\n-  `simple <./examples/simple>`__: Quickstart example of using pydgraph.\n\nDevelopment\n-----------\n\nBuilding the source\n~~~~~~~~~~~~~~~~~~~\n\n.. code:: sh\n\n   python setup.py install\n   # To install for the current user, use this instead:\n   # python setup.py install --user\n\nIf you have made changes to the ``pydgraph/proto/api.proto`` file, you\nneed need to regenerate the source files generated by Protocol Buffer\ntools. To do that, install the\n`grpcio-tools <https://pypi.python.org/pypi/grpcio-tools>`__ library and\nthen run the following command:\n\n.. code:: sh\n\n   python scripts/protogen.py\n\nRunning tests\n~~~~~~~~~~~~~\n\nTo run the tests in your local machine, you can run the script\n``scripts/local-tests.sh``. This script assumes Dgraph and dgo (Go\nclient) are already built on the local machine. The script will take\ncare of bringing up a Dgraph cluster and bringing it down after the\ntests are executed. The script uses the port 9180 by default to prevent\ninterference with clusters running on the default port. Docker and\ndocker-compose need to be installed before running the script. Refer to\nthe official Docker documentation for instructions on how to install\nthose packages.\n\nThe ``test.sh`` script downloads and installs Dgraph. It is meant for\nuse by our CI systems and using it for local development is not\nrecommended.\n\n\n",
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