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Official Dgraph client implementation for Python

Project description


This is the official Dgraph database client implementation for Python (Python >= v3.7), using gRPC.

This client follows the Dgraph Go client closely.

Before using this client, we highly recommend that you read the the product documentation at

Table of contents


Install using pip:

pip install pydgraph

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
20.3.X 20.3.0
20.7.X 20.7.0
20.11.X 20.7.0
21.03.X 21.3.0
22.0.X 21.3.0
23.0.X 23.0.0


Build and run the simple project 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.

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.

import pydgraph

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

Login into a Namespace

If your server has Access Control Lists enabled (Dgraph v1.1 or above), the client must be logged in for accessing data. Use login endpoint:

Calling login will obtain and remember the access and refresh JWT tokens. All subsequent operations via the logged in client will send along the stored access token.

client.login("groot", "password")

If your server additionally has namespaces (Dgraph v21.03 or above), use the login_into_namespace API.

client.login_into_namespace("groot", "password", "123")

Connecting To Dgraph Cloud

If you want to connect to Dgraph running on Dgraph Cloud instance, then get the gRPC endpoint of your cluster that you can find in the Settings section of Dgraph Cloud console and obtain a Client or Admin API key (created in the API key tab of the Setting section). Create the client_stub using the gRPC endpoint and the API key:

client_stub = pydgraph.DgraphClientStub.from_cloud(
    "", "<api-key>")
client = pydgraph.DgraphClient(client_stub)

The DgraphClientStub.from_slash_endpoint() method has been removed v23.0. Please use DgraphClientStub.from_cloud() instead.

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)

Operation contains other fields as well, including DropAttr and DropAll. DropAll is useful if you wish to discard all the data, and start from a clean slate, without bringing the instance down. DropAttr is used to drop all the data related to a predicate.

# 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)

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)

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()
  # Do something here
  # ...
  # ...

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.

txn = client.txn(read_only=True)
  # Do some queries here
  # ...
  # ...

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.

# Create data.
p = { 'name': 'Alice' }

# Run mutation.

# 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, $a))
variables = {'$a': 'Bob'}

res = txn.query(query, variables=variables)
ppl = json.loads(res.json)

# For a mutation to delete a node, use this:

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)

Running a Query

You can run a query by calling Txn#query(string). You will 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, $a))
variables = {'$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']:

This should print:

Number of people named "Alice": 1

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

request = txn.create_request(query=query)

Query with RDF response

You can get query result as a RDF response by calling Txn#query(string) with resp_format set to RDF. The response would contain a rdf field, which has the RDF encoded result.

Note: If you are querying only for uid values, use a JSON format response.

res = txn.query(query, variables=variables, resp_format="RDF")

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)

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, ""))

cond = "@if(eq(len(user), 1))"
nquads = """
  uid(user) <email> "" .

mutation = txn.create_mutation(cond=cond, set_nquads=nquads)
request = txn.create_request(mutations=[mutation], query=query, commit_now=True)

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.

txn = client.txn()
  # ...
  # Perform any number of queries and mutations
  # ...
  # and finally...
except pydgraph.AbortedError:
  # Retry or handle exception.
  # Clean up. Calling this after txn.commit() is a no-op
  # and hence safe.

Cleaning Up Resources

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

SERVER_ADDR1 = "localhost:9080"
SERVER_ADDR2 = "localhost:9080"

# Create instances of DgraphClientStub.
stub1 = pydgraph.DgraphClientStub(SERVER_ADDR1)
stub2 = pydgraph.DgraphClientStub(SERVER_ADDR2)

# Create an instance of DgraphClient.
client = pydgraph.DgraphClient(stub1, stub2)

# Use client

# Clean up resources by closing all client stubs.

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)

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)

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)

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()
    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.


  • simple: Quickstart example of using pydgraph.
  • tls: Quickstart example that uses TLS.
  • parse_datetime: Demonstration of converting Dgraph's DateTime strings to native python datetime.


Setting up environment

There are many ways to set up your local Python environment. We suggest some sane defaults here.

  • Use pyenv to manage your Python installations.
  • Most recent versions of Python should work, but the version of Python officially supported is located in .python-version
  • Create a Python virtual environment using python -m venv .venv
  • Activate virtual environment via source .venv/bin/activate

Build from source

To build and install pydgraph locally, run

pip install -e ".[dev]"

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 library and then run the following command:

python scripts/

Running tests

To run the tests in your local machine, run:

bash scripts/

This script assumes dgraph is located on your path. Dgraph release binaries can be found here. The test script also requires that docker and docker compose are installed on 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 for instructions on how to install those packages.

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