Python driver for Fauna v10 (current)
Official Python Driver for Fauna v10 (current)
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 on how to configure and query your databases.
Installation
Pre-release installations must specify the version you want to install. Find the version you want to install on PyPI.
pip install fauna==<version>
Compatibility
The following versions of Python are supported:
- Python 3.9
- Python 3.10
- Python 3.11
- Python 3.12
API reference
API reference documentation for the driver is available at
https://fauna.github.io/fauna-python/. The docs are generated using
pdoc.
Basic Usage
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 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)
Query Composition
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) # 8
Serialization / Deserialization
Serialization and deserialization with user-defined classes is not yet supported.
When building queries, adapt your classes into dicts or lists before 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'])
Client Configuration
Max Attempts
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.
Max Backoff
The maximum backoff in seconds to be observed between each retry. Default 20 seconds.
Timeouts
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.
Query Timeout
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)))
Client Timeout
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))
Idle Timeout
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 callclose()once all requests are complete. It is not recommended to sethttp_idle_timeoutto small values.
Connect Timeout
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))
Pool Timeout
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))
Read Timeout
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))
Write Timeout
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))
Query Stats
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...
Pagination
Use the paginate() method to iterate sets that contain more than one
page of results.
paginate() accepts the same query options as query().
Change the default items per page using FQL’s pageSize() method.
from datetime import timedelta
from fauna import fql
from fauna.client import Client, QueryOptions
# Adjust `pageSize()` size as needed.
query = fql(
"""
Product
.byName("limes")
.pageSize(60) { description }"""
)
client = Client()
options = QueryOptions(query_timeout=timedelta(seconds=20))
pages = client.paginate(query, options)
for products in pages:
for product in products:
print(products)
Event Feeds (beta)
The driver supports Event Feeds.
Request an Event Feed
An Event Feed asynchronously polls an event source
for paginated events.
To get an event source, append eventSource() or eventsOn() to a
supported Set.
To get paginated events, pass the event source to feed():
from fauna import fql
from fauna.client import Client
client = Client()
response = client.query(fql('''
let set = Product.all()
{
initialPage: set.pageSize(10),
eventSource: set.eventSource()
}
'''))
initial_page = response.data['initialPage']
event_source = response.data['eventSource']
client.feed(event_source)
You can also pass a query that produces an event source directly to feed():
query = fql('Product.all().eventsOn(.price, .stock)')
client.feed(query)
Iterate on an Event Feed
feed() returns an iterator that emits pages of events. You can use a
generator expression to iterate through the pages:
query = fql('Product.all().eventsOn(.price, .stock)')
feed = client.feed(query)
for page in feed:
print('Page stats: ', page.stats)
for event in page:
event_type = event['type']
if (event_type == 'add'):
print('Add event: ', event)
## ...
elif (event_type == 'update'):
print('Update event: ', event)
## ...
elif (event_type == 'remove'):
print('Remove event: ', event)
## ...
Alternatively, you can iterate through events instead of pages with
flatten():
query = fql('Product.all().eventsOn(.price, .stock)')
feed = client.feed(query)
for event in feed.flatten():
event_type = event['type']
## ...
The Event Feed iterator stops when there are no more events to poll.
Error handling
If a non-retryable error occurs when opening or processing an Event Feed, Fauna
raises a FaunaException:
from fauna import fql
from fauna.client import Client
from fauna.errors import FaunaException
client = Client()
try:
feed = client.feed(fql(
'Product.all().eventsOn(.price, .stock)'
))
for event in feed.flatten():
print(event)
# ...
except FaunaException as e:
print('error ocurred with event feed: ', e)
Errors can be raised at two different places:
- At the
feedmethod call; - At the page iteration.
This distinction allows for users to ignore errors originating from event
processing. For example:
from fauna import fql
from fauna.client import Client
from fauna.errors import FaunaException
client = Client()
# Imagine if there are some products with details = null.
# The ones without details will fail due to the toUpperCase call.
feed = client.feed(fql(
'Product.all().map(.details.toUpperCase()).eventSource()'
))
for page in feed:
try:
for event in page:
print(event)
# ...
except FaunaException as e:
# Pages will stop at the first error encountered.
# Therefore, its safe to handle an event failures
# and then pull more pages.
print('error ocurred with event processing: ', e)
Event Feed options
The client configuration sets default options for the feed() method.
You can pass a FeedOptions object to override these defaults:
options = FeedOptions(
max_attempts=3,
max_backoff=20,
query_timeout=timedelta(seconds=5),
page_size=None,
cursor=None,
start_ts=None,
)
client.feed(fql('Product.all().eventSource()'), options)
Event Streaming
The driver supports Event
Streaming.
Start a stream
An Event Stream lets you consume events from an event
source
as a real-time subscription.
To get an event source, append eventSource() or eventsOn() to a
supported Set.
To start and subscribe to the stream, pass the event source to stream():
from fauna import fql
from fauna.client import Client
client = Client()
response = client.query(fql('''
let set = Product.all()
{
initialPage: set.pageSize(10),
eventSource: set.eventSource()
}
'''))
initial_page = response.data['initialPage']
event_source = response.data['eventSource']
client.stream(event_source)
You can also pass a query that produces an event source directly to
stream():
query = fql('Product.all().eventsOn(.price, .stock)')
client.stream(query)
Iterate on a stream
stream() returns an iterator that emits events as they occur. You can
use a generator expression to iterate through the events:
query = fql('Product.all().eventsOn(.price, .stock)')
with client.stream(query) as stream:
for event in stream:
event_type = event['type']
if (event_type == 'add'):
print('Add event: ', event)
## ...
elif (event_type == 'update'):
print('Update event: ', event)
## ...
elif (event_type == 'remove'):
print('Remove event: ', event)
## ...
Close a stream
Use close() to close a stream:
query = fql('Product.all().eventsOn(.price, .stock)')
count = 0
with client.stream(query) as stream:
for event in stream:
print('Stream event', event)
# ...
count+=1
if (count == 2):
stream.close()
Error handling
If a non-retryable error occurs when opening or processing a stream, Fauna
raises a FaunaException:
from fauna import fql
from fauna.client import Client
from fauna.errors import FaunaException
client = Client()
try:
with client.stream(fql(
'Product.all().eventsOn(.price, .stock)'
)) as stream:
for event in stream:
print(event)
# ...
except FaunaException as e:
print('error ocurred with stream: ', e)
Stream options
The client configuration sets default options for the stream()
method.
You can pass a StreamOptions object to override these defaults:
options = StreamOptions(
max_attempts=3,
max_backoff=20,
start_ts=None,
cursor=None,
status_events=False,
)
client.stream(fql('Product.all().eventSource()'), options)
Logging
Logging is handled using Python’s standard logging package under the fauna namespace. Logs include the HTTP request with body (excluding the Authorization header) and the full HTTP response.
To enable logging:
import logging
from fauna.client import Client
from fauna import fql
logging.basicConfig(
level=logging.DEBUG
)
client = Client()
client.query(fql('42'))
For configuration options or to set specific log levels, see Python’s Logging HOWTO.
Setup
virtualenv venv
source venv/bin/activate
pip install . .[test] .[lint]
Testing
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-test
To run unit tests locally:
source venv/bin/activate
make install
make unit-test
To stand up a container and run all tests at the same time:
make docker-test
See the Makefile for more.
Coverage
source venv/bin/activate
make coverage
Contribute
GitHub pull requests are very welcome.
License
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.