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)
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")
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. Altering the databaseTo 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)
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)
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()
# ...
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()
# ...
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. Running a mutationTxn#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)
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)
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()
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)
Number of people named "Alice": 1
Alice
txn.do_request function to run the query.
request = txn.create_request(query=query)
txn.do_request(request)
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)
@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)
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()
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)
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 credentialsA CallCredentials object can be passed to the login, alter, query, and mutate methods using the credentials keyword argument. Authenticating to a reverse TLS proxyIf 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)
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)
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)
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.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4