Python

Official Dgraph client implementation for Python (Python >= v2.7 and >= v3.5), using gRPC. This client follows the Dgraph Go client closely.

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Supported versions

More details on the supported versions can be found at this link.

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Using a client

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Creating a client

You can initialize a DgraphClient object by passing it a list of DgraphClientStub clients as arguments. Connecting to multiple Dgraph servers in the same cluster allows for better distribution of workload.

The following code snippet shows just one connection.

import pydgraph

client_stub = pydgraph.DgraphClientStub('localhost:9080')
client = pydgraph.DgraphClient(client_stub)

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Multi-tenancy

In multi-tenancy environments, PyDgraph provides a new method login_into_namespace(), which allows the users to login to a specific namespace.

In order to create a python client, and make the client login into namespace 123:

client_stub = pydgraph.DgraphClientStub('localhost:9080')
client = pydgraph.DgraphClient(client_stub)
// Login to namespace groot user of namespace 123
client.login_into_namespace("groot", "password", "123")

In the example above, the client logs into namespace 123 using username groot and password password. Once logged in, the client can perform all the operations allowed to the groot user of namespace 123.

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Altering the database

To set the schema, create an Operation object, set the schema and pass it to DgraphClient#alter(Operation) method.

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.

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.

# 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)

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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.

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 aren’t allowed.

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 can’t guarantee to return the latest data. Best-effort queries are only supported by read-only transactions.

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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.

# 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" .')

# 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 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.

mutation = txn.create_mutation(set_nquads='_:alice <name> "Alice" .')
request = txn.create_request(mutations=[mutation], commit_now=True)
txn.do_request(request)

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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() isn’t necessary.

An error is raised if another transaction modifies the same data concurrently that was modified in the current transaction. It is up to the user to retry transactions when they fail.

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()

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Running a query

You can run a query by calling Txn#query(string). You need to pass in a DQL 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:

# 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:

Number of people named "Alice": 1
Alice

You can also use txn.do_request function to run the query.

request = txn.create_request(query=query)
txn.do_request(request)

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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.

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)

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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.

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)

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Cleaning up resources

To clean up resources, you have to call DgraphClientStub#close() individually for all the instances of DgraphClientStub.

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()

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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.

# 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)

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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)

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Passing credentials

A CallCredentials object can be passed to the login, alter, query, and mutate methods using the credentials keyword argument.

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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 needs to include the credentials. In the example below, we’re 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.

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)

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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.

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:

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:

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.