The Official Python Driver for Fauna.
This driver can only be used with FQL v10, and is not compatible with earlier versions of FQL. To query your databases with earlier API versions, see the faunadb package.
See the Fauna Documentation for additional information how to configure and query your databases.
pip install faunaThe following versions of Python are supported:
- Python 3.9
- Python 3.10
- Python 3.11
- Python 3.12
You can expect a Client instance to have reasonable defaults, like the Fauna endpoint https://db.fauna.com and a global HTTP client, but you will always need to configure a secret.
You can configure your secret by passing it directly to the client or by setting an environment variable.
Supported Environment Variables:
FAUNA_ENDPOINT: The Fauna endpoint to use. For example,http://localhost:8443FAUNA_SECRET: The Fauna secret to use.
from fauna import fql
from fauna.client import Client
from fauna.encoding import QuerySuccess
from fauna.errors import FaunaException
client = Client()
# The client defaults to using using the value stored FAUNA_SECRET for its secret.
# Either set the FAUNA_SECRET env variable or retrieve it from a secret store.
# As a best practice, don't store your secret directly in your code.
try:
# create a collection
q1 = fql('Collection.create({ name: "Dogs" })')
client.query(q1)
# create a document
q2 = fql('Dogs.create({ name: "Scout" })')
res: QuerySuccess = client.query(q2)
doc = res.data
print(doc)
except FaunaException as e:
# handle errors
print(e)This driver supports query composition with Python primitives, lists, dicts, and other FQL queries.
For FQL templates, denote variables with ${} and pass variables as kwargs to fql(). You can escape a variable by prepending an additional $.
from fauna import fql
from fauna.client import Client
client = Client()
def add_two(x):
return fql("${x} + 2", x=x)
q = fql("${y} + 4", y=add_two(2))
res = client.query(q)
print(res.data) # 8Serialization and deserialization with user-defined classes is not yet supported.
When building queries, adapt your classes into dicts or lists prior to using them in composition. When instantiating classes from the query result data, build them from the expected result.
class MyClass:
def __init__ (self, my_prop):
self.my_prop = my_prop
def to_dict(self):
return { 'my_prop': self.my_prop }
@static_method
def from_result(obj):
return MyClass(obj['my_prop'])The maximum number of times a query will be attempted if a retryable exception is thrown (ThrottlingError). Default 3, inclusive of the initial call. The retry strategy implemented is a simple exponential backoff.
To disable retries, pass max_attempts less than or equal to 1.
The maximum backoff in seconds to be observed between each retry. Default 20 seconds.
There are a few different timeout settings that can be configured; each comes with a default setting. We recommend that most applications use the defaults.
The query timeout is the time, as datetime.timedelta, that Fauna will spend executing your query before aborting with a QueryTimeoutError.
The query timeout can be set using the query_timeout option. The default value if you do not provide one is DefaultClientBufferTimeout (5 seconds).
from datetime import timedelta
from fauna.client import Client
client = Client(query_timeout=timedelta(seconds=20))The query timeout can also be set to a different value for each query using the QueryOptions.query_timeout option. Doing so overrides the client configuration when performing this query.
from datetime import timedelta
from fauna.client import Client, QueryOptions
response = client.query(myQuery, QueryOptions(query_timeout=timedelta(seconds=20)))The client timeout is the time, as datetime.timedelta, that the client will wait for a network response before canceling the request. If a client timeout occurs, the driver will throw an instance of NetworkError.
The client timeout is always the query timeout plus an additional buffer. This ensures that the client always waits for at least as long Fauna could work on your query and account for network latency.
The client timeout buffer is configured by setting the client_buffer_timeout option. The default value for the buffer if you do not provide on is DefaultClientBufferTimeout (5 seconds), therefore the default client timeout is 10 seconds when considering the default query timeout.
from datetime import timedelta
from fauna.client import Client
client = Client(client_buffer_timeout=timedelta(seconds=20))The idle timeout is the time, as datetime.timedelta, that a session will remain open after there is no more pending communication. Once the session idle time has elapsed the session is considered idle and the session is closed. Subsequent requests will create a new session; the session idle timeout does not result in an error.
Configure the idle timeout using the http_idle_timeout option. The default value if you do not provide one is DefaultIdleConnectionTimeout (5 seconds).
from datetime import timedelta
from fauna.client import Client
client = Client(http_idle_timeout=timedelta(seconds=6))> Note
> Your application process may continue executing after all requests are completed for the duration of the session idle timeout. To prevent this, it is recommended to call Client.close() once all requests are complete. It is not recommended to set http_idle_timeout to small values.
The connect timeout is the maximum amount of time, as datetime.timedelta, to wait until a connection to Fauna is established. If the client is unable to connect within this time frame, a ConnectTimeout exception is raised.
Configure the connect timeout using the http_connect_timeout option. The default value if you do not provide one is DefaultHttpConnectTimeout (5 seconds).
from datetime import timedelta
from fauna.client import Client
client = Client(http_connect_timeout=timedelta(seconds=6))The pool timeout specifies the maximum amount of time, as datetime.timedelta, to wait for acquiring a connection from the connection pool. If the client is unable to acquire a connection within this time frame, a PoolTimeout exception is raised. This timeout may fire if 20 connections are currently in use and one isn't released before the timeout is up.
Configure the pool timeout using the http_pool_timeout option. The default value if you do not provide one is DefaultHttpPoolTimeout (5 seconds).
from datetime import timedelta
from fauna.client import Client
client = Client(http_pool_timeout=timedelta(seconds=6))The read timeout specifies the maximum amount of time, as datetime.timedelta, to wait for a chunk of data to be received (for example, a chunk of the response body). If the client is unable to receive data within this time frame, a ReadTimeout exception is raised.
Configure the read timeout using the http_read_timeout option. The default value if you do not provide one is DefaultHttpReadTimeout (None).
from datetime import timedelta
from fauna.client import Client
client = Client(http_read_timeout=timedelta(seconds=6))The write timeout specifies the maximum amount of time, as datetime.timedelta, to wait for a chunk of data to be sent (for example, a chunk of the request body). If the client is unable to send data within this time frame, a WriteTimeout exception is raised.
Configure the write timeout using the http_write_timeout option. The default value if you do not provide one is DefaultHttpWriteTimeout (5 seconds).
from datetime import timedelta
from fauna.client import Client
client = Client(http_write_timeout=timedelta(seconds=6))Stats are returned on query responses and ServiceErrors.
from fauna import fql
from fauna.client import Client
from fauna.encoding import QuerySuccess, QueryStats
from fauna.errors import ServiceError
client = Client()
def emit_stats(stats: QueryStats):
print(f"Compute Ops: {stats.compute_ops}")
print(f"Read Ops: {stats.read_ops}")
print(f"Write Ops: {stats.write_ops}")
try:
q = fql('Collection.create({ name: "Dogs" })')
qs: QuerySuccess = client.query(q)
emit_stats(qs.stats)
except ServiceError as e:
if e.stats is not None:
emit_stats(e.stats)
# more error handling...Not implemented
$ virtualenv venv
$ source venv/bin/activate
$ pip install . .[test] .[lint]We use pytest. You can run tests directly or with docker. If you run integration tests directly, you must have fauna running locally.
If you want to run fauna, then run integration tests separately:
$ make run-fauna
$ source venv/bin/activate
$ make install
$ make integration-testTo run unit tests locally:
$ source venv/bin/activate
$ make install
$ make unit-testTo stand up a container and run all tests at the same time:
$ make docker-testSee the Makefile for more.
$ source venv/bin/activate
$ make coverageGitHub pull requests are very welcome.
Copyright 2023 Fauna, Inc.
Licensed under the Mozilla Public License, Version 2.0 (the "License"); you may not use this software except in compliance with the License. You can obtain a copy of the License at
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.