Metadata-Version: 2.1
Name: pydgraph
Version: 21.3.1
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
        
        This is the official Dgraph database client implementation for Python (Python >= v2.7 and >= v3.5), using [gRPC][grpc].
        
        [grpc]: https://grpc.io/
        
        This client follows the [Dgraph Go client][goclient] closely.
        
        **Use [Discuss Issues](https://discuss.dgraph.io/c/issues/35/clients/46) to report issues about this repository.**
        
        [goclient]: https://github.com/dgraph-io/dgo
        
        Before using this client, we highly recommend that you read the [Dgraph Python
        Client docs](https://dgraph.io/docs/clients/python/), as well as reviewing
        the product documentation at [docs.dgraph.io].
        
        [docs.dgraph.io]:https://docs.dgraph.io
        
        ## Table of contents
        
        - [Install](#install)
        - [Supported Versions](#supported-versions)
        - [Quickstart](#quickstart)
        - [Using a Client](#using-a-client)
          - [Creating a Client](#creating-a-client)
          - [Altering the Database](#altering-the-database)
          - [Creating a Transaction](#creating-a-transaction)
          - [Running a Mutation](#running-a-mutation)
          - [Committing a Transaction](#committing-a-transaction)
          * [Running a Query](#running-a-query)
          * [Running an Upsert: Query + Mutation](#running-an-upsert-query--mutation)
          * [Running a Conditional Upsert](#running-a-conditional-upsert)
          - [Cleaning up Resources](#cleaning-up-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:
        
        ```sh
        pip install pydgraph
        ```
        
        ### Install Notes
        
        To avoid issues when adding composite credentials or when using client authorization, please install gRPC version 1.19.0:
        
        ```sh
        pip install grpcio==1.19.0
        ```
        
        ## Supported Versions
        
        Depending on the version of Dgraph that you are connecting to, you will have to
        use a different version of this client.
        
        | Dgraph version |   pydgraph version   |
        |:--------------:|:--------------------:|
        |     1.0.X      |      <= *1.2.0*      |
        |     1.1.X      |      >= *2.0.0*      |
        |     1.2.X      |      >= *2.0.0*      |
        
        ## Quickstart
        
        Build and run the [simple project][simple] in the `examples` folder, which
        contains an end-to-end example of using the Dgraph python client. For additional details, follow the
        instructions in the project's [README](./examples/simple/README.md).
        
        [simple]: ./examples/simple
        
        ## Using a client
        
        ### Creating a Client
        
        You can initialize a `DgraphClient` object 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.
        
        ```python
        import pydgraph
        
        client_stub = pydgraph.DgraphClientStub('localhost:9080')
        client = pydgraph.DgraphClient(client_stub)
        ```
        
        ### Altering the Database
        
        To set the schema, create an `Operation` object, set the schema and pass it to
        `DgraphClient#alter(Operation)` method.
        
        ```python
        schema = 'name: string @index(exact) .'
        op = pydgraph.Operation(schema=schema)
        client.alter(op)
        ```
        
        Starting with Dgraph version 20.03.0, indexes can be computed in the background.
        You can set the `run_in_background` field of `pydgraph.Operation` to `True`
        before passing it to the `Alter` function. You can find more details
        [here](https://docs.dgraph.io/master/query-language/#indexes-in-background).
        
        ```python
        schema = 'name: string @index(exact) .'
        op = pydgraph.Operation(schema=schema, run_in_background=True)
        client.alter(op)
        ```
        
        `Operation` contains other fields as well, including the `drop` predicate and `drop all`.
        Drop all is useful if you wish to discard all the data, and start with a clean
        slate, without bringing the instance down.
        
        ```python
        # Drop all data including schema from the Dgraph instance. This is a useful
        # for small examples such as this since it puts Dgraph into a clean state.
        op = pydgraph.Operation(drop_all=True)
        client.alter(op)
        ```
        
        ### Creating a Transaction
        
        To create a transaction, call the `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.
        
        ```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.
        
        ```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.
        
        ### Running 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.
        
        `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.
        
        ```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" .')
        ```
        
        ```python
        # Delete data.
        
        query = """query all($a: string)
         {
           all(func: eq(name, $a))
            {
              uid
            }
          }"""
        
        variables = {'$a': 'Bob'}
        
        res = txn.query(query, variables=variables)
        ppl = json.loads(res.json)
        
        # For a mutation to delete a node, use this:
        txn.mutate(del_obj=person)
        ```
        
        For a complete example with multiple fields and relationships, look at the
        [simple project][simple] 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.
        
        A mutation can be executed using `txn.do_request` as well.
        
        ```python
        mutation = txn.create_mutation(set_nquads='_:alice <name> "Alice" .')
        request = txn.create_request(mutations=[mutation], commit_now=True)
        txn.do_request(request)
        ```
        
        ### Committing a Transaction
        
        A transaction can be committed using the `Txn#commit()` method. If your transaction
        consist solely of `Txn#query` or `Txn#queryWithVars` calls, and no calls to
        `Txn#mutate`, then calling `Txn#commit()` is not necessary.
        
        An error is raised if another transaction(s) modify the same data concurrently that was
        modified in the current transaction. It is up to the user to retry transactions
        when they fail.
        
        ```python
        txn = client.txn()
        try:
          # ...
          # Perform any number of queries and mutations
          # ...
          # and finally...
          txn.commit()
        except pydgraph.AbortedError:
          # Retry or handle exception.
        finally:
          # Clean up. Calling this after txn.commit() is a no-op
          # and hence safe.
          txn.discard()
        ```
        
        ### Running a Query
        
        You can run a query by calling `Txn#query(string)`. You will need to pass in a
        [DQL](https://dgraph.io/docs/query-language/) 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 query response contains the `json` field, which returns the JSON response.
        
        Let’s run a query with a variable `$a`, deserialize the result from JSON and
        print it out:
        
        ```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:
        
        ```console
        Number of people named "Alice": 1
        Alice
        ```
        
        You can also use `txn.do_request` function to run the query.
        
        ```python
        request = txn.create_request(query=query)
        txn.do_request(request)
        ```
        
        ### Running an Upsert: Query + Mutation
        
        The `txn.do_request` function allows you to use upsert blocks. An upsert block
        contains one query block and one or more mutation blocks, so it lets you perform
        queries and mutations in a single request. Variables defined in the query block
        can be used in the mutation blocks using the `uid` and `val` functions
        implemented by DQL.
        
        To learn more about upsert blocks, see the
        [Upsert Block documentation](https://dgraph.io/docs/mutations/upsert-block/).
        
        ```python
        query = """{
          u as var(func: eq(name, "Alice"))
        }"""
        nquad = """
          uid(u) <name> "Alice" .
          uid(u) <age> "25" .
        """
        mutation = txn.create_mutation(set_nquads=nquad)
        request = txn.create_request(query=query, mutations=[mutation], commit_now=True)
        txn.do_request(request)
        ```
        
        ### Running a Conditional Upsert
        
        The upsert block also allows specifying a conditional mutation block using an `@if` directive. The mutation is executed
        only when the specified condition is true. If the condition is false, the mutation is silently ignored.
        
        See more about Conditional Upserts [here](https://docs.dgraph.io/mutations/#conditional-upsert).
        
        ```python
        query = """
          {
            user as var(func: eq(email, "wrong_email@dgraph.io"))
          }
        """
        cond = "@if(eq(len(user), 1))"
        nquads = """
          uid(user) <email> "correct_email@dgraph.io" .
        """
        mutation = txn.create_mutation(cond=cond, set_nquads=nquads)
        request = txn.create_request(mutations=[mutation], query=query, commit_now=True)
        txn.do_request(request)
        ```
        
        ### Cleaning Up Resources
        
        To clean up resources, you have to call `DgraphClientStub#close()` individually for
        all the instances of `DgraphClientStub`.
        
        ```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
        # ...
        
        # Clean up 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".
        
        ```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 arbitrary key-value pairs.
        metadata = [("auth-token", "the-auth-token-value")]
        dg.alter(op, metadata=metadata)
        ```
        
        ### Setting a timeout
        
        A timeout value representing the number of seconds can be passed to the `login`,
        `alter`, `query`, and `mutate` methods using the `timeout` keyword argument.
        
        For example, the following alters the schema with a timeout of ten seconds:
        `dg.alter(op, timeout=10)`
        
        ### Passing credentials
        
        A `CallCredentials` object can be passed to the `login`, `alter`, `query`, and
        `mutate` methods using the `credentials` keyword argument.
        
        ### Authenticating to a reverse TLS proxy
        
        If the Dgraph instance is behind a reverse TLS proxy, credentials can also be
        passed through the methods available in the gRPC library. Note that in this case
        every request will need to include the credentials. In the example below, we are
        trying to add authentication to a proxy that requires an API key. This value is
        expected to be included in the metadata using the key "authorization".
        
        ```python
        creds = grpc.ssl_channel_credentials()
        call_credentials = grpc.metadata_call_credentials(
            lambda context, callback: callback((("authorization", "<api-key>"),), None))
        composite_credentials = grpc.composite_channel_credentials(creds, call_credentials)
        client_stub = pydgraph.DgraphClientStub(
            '{host}:{port}'.format(host=GRPC_HOST, port=GRPC_PORT), composite_credentials)
        client = pydgraph.DgraphClient(client_stub)
        ```
        
        ### Async methods
        
        The `alter` method in the client has an asynchronous version called
        `async_alter`. The async methods return a future. You can directly call the
        `result` method on the future. However. The DgraphClient class provides a static
        method `handle_alter_future` to handle any possible exception.
        
        ```python
        alter_future = self.client.async_alter(pydgraph.Operation(
        	schema="name: string @index(term) ."))
        response = pydgraph.DgraphClient.handle_alter_future(alter_future)
        ```
        
        The `query` and `mutate` methods int the `Txn` class also have async versions
        called `async_query` and `async_mutation` respectively. These functions work
        just like `async_alter`.
        
        You can use the `handle_query_future` and `handle_mutate_future` static methods
        in the `Txn` class to retrieve the result. A short example is given below:
        
        ```python
        txn = client.txn()
        query = "query body here"
        future = txn.async_query()
        response = pydgraph.Txn.handle_query_future(future)
        ```
        
        A working example can be found in the `test_asycn.py` test file.
        
        Keep in mind that due to the nature of async calls, the async functions cannot
        retry the request if the login is invalid. You will have to check for this error
        and retry the login (with the function `retry_login` in both the `Txn` and
        `Client` classes). A short example is given below:
        
        ```python
        client = DgraphClient(client_stubs) # client_stubs is a list of gRPC stubs.
        alter_future = client.async_alter()
        try:
            response = alter_future.result()
        except Exception as e:
        	# You can use this function in the util package to check for JWT
            # expired errors.
            if pydgraph.util.is_jwt_expired(e):
                # retry your request here.
        ```
        
        ## Examples
        
        - [simple][]: Quickstart example of using pydgraph.
        
        ## Development
        
        ### Building the source
        
        ```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][grpcio-tools] library and then run the following
        command:
        
        [grpcio-tools]: https://pypi.python.org/pypi/grpcio-tools
        
        ```sh
        python scripts/protogen.py
        ```
        
        The generated file `api_pb2_grpc.py` needs to be changed in recent versions of python.
        The required change is outlined below as a diff.
        
        ```diff
        -import api_pb2 as api__pb2
        +from . import api_pb2 as api__pb2
        ```
        
        ### Running tests
        
        To run the tests in your local machine, install and run `tox`:
        
        ```bash
        pip install tox
        tox
        ```
        
        This script assumes Dgraph and [dgo](https://github.com/dgraph-io/dgo) (Go
        client) are already built on the local machine and that their code is in
        `$GOPATH/src`. It also requires that docker and docker-compose are installed in
        your machine.
        
        The script will take care of bringing up a Dgraph cluster and bringing it down
        after the tests are executed. The script connects to randomly selected ports for
        HTTP and gRPC requests 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](https://docs.docker.com/)
        for instructions on how to install those packages.
        
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
Description-Content-Type: text/markdown
