Official Dgraph client implementation for Python (Python >= v2.7 and >= v3.5), using grpc.
This client follows the Dgraph Go client closely.
Before using this client, we highly recommend that you go through docs.dgraph.io, and understand how to run and work with Dgraph.
Install using pip:
pip install pydgraph
Build and run the 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.
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.
import pydgraph
client_stub = pydgraph.DgraphClientStub('localhost:9080')
client = pydgraph.DgraphClient(client_stub)
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)
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.
# 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)
To create a transaction, call DgraphClient#txn()
method, which returns a
new Txn
object. This operation incurs no network overhead.
It is good practise 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()
# ...
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.
# 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.
query1 = """query all($a: string)
{
all(func: eq(name, $a))
{
uid
}
}"""
variables1 = {'$a': 'Bob'}
res1 = client.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 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.
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 $a:
query all($a: string) {
all(func: eq(name, $a))
{
name
}
}
Run the query, deserialize the result from JSON and print it out:
# Run query.
query = """query all($a: string) {
all(func: eq(name, $a))
{
name
}
}"""
variables = {'$a': 'Alice'}
res = client.txn().query(query, variables=variables)
# If not doing a mutation in the same transaction, simply use:
# res = client.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
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.
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()
To cleanup 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
# ...
# Cleanup resources by closing all client stubs.
stub1.close()
stub2.close()
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 library and then run the following
command:
python scripts/protogen.py
Make sure you have a Dgraph server running on localhost before you run this task.
python setup.py test