The Python interface to the Redis key-value store.
redis-py 3.5.x will be the last version of redis-py that supports Python 2. The 3.5.x line will continue to get bug fixes and security patches that support Python 2 until August 1, 2020. redis-py 4.0 will be the next major version and will require Python 3.5+.
redis-py requires a running Redis server. See Redis's quickstart for installation instructions.
redis-py can be installed using pip similar to other Python packages. Do not use sudo with pip. It is usually good to work in a virtualenv or venv to avoid conflicts with other package managers and Python projects. For a quick introduction see Python Virtual Environments in Five Minutes.
To install redis-py, simply:
$ pip install redisor from source:
$ python setup.py installWant to contribute a feature, bug report, or report an issue? Check out our guide to contributing.
>>> import redis
>>> r = redis.Redis(host='localhost', port=6379, db=0)
>>> r.set('foo', 'bar')
True
>>> r.get('foo')
b'bar'By default, all responses are returned as bytes in Python 3 and str in Python 2. The user is responsible for decoding to Python 3 strings or Python 2 unicode objects.
If all string responses from a client should be decoded, the user can specify decode_responses=True to Redis.__init__. In this case, any Redis command that returns a string type will be decoded with the encoding specified.
redis-py 3.0 introduces many new features but required a number of backwards incompatible changes to be made in the process. This section attempts to provide an upgrade path for users migrating from 2.X to 3.0.
redis-py supports Python 3.5+.
redis-py 3.0 drops support for the legacy "Redis" client class. "StrictRedis" has been renamed to "Redis" and an alias named "StrictRedis" is provided so that users previously using "StrictRedis" can continue to run unchanged.
The 2.X "Redis" class provided alternative implementations of a few commands. This confused users (rightfully so) and caused a number of support issues. To make things easier going forward, it was decided to drop support for these alternate implementations and instead focus on a single client class.
2.X users that are already using StrictRedis don't have to change the class name. StrictRedis will continue to work for the foreseeable future.
2.X users that are using the Redis class will have to make changes if they use any of the following commands:
- SETEX: The argument order has changed. The new order is (name, time, value).
- LREM: The argument order has changed. The new order is (name, num, value).
- TTL and PTTL: The return value is now always an int and matches the official Redis command (>0 indicates the timeout, -1 indicates that the key exists but that it has no expire time set, -2 indicates that the key does not exist)
redis-py 3.0 changes the default value of the ssl_cert_reqs option from None to 'required'. See Issue 1016. This change enforces hostname validation when accepting a cert from a remote SSL terminator. If the terminator doesn't properly set the hostname on the cert this will cause redis-py 3.0 to raise a ConnectionError.
This check can be disabled by setting ssl_cert_reqs to None. Note that doing so removes the security check. Do so at your own risk.
Example with hostname verification using a local certificate bundle (linux):
>>> import redis
>>> r = redis.Redis(host='xxxxxx.cache.amazonaws.com', port=6379, db=0,
ssl=True,
ssl_ca_certs='/etc/ssl/certs/ca-certificates.crt')
>>> r.set('foo', 'bar')
True
>>> r.get('foo')
b'bar'Example with hostname verification using certifi:
>>> import redis, certifi
>>> r = redis.Redis(host='xxxxxx.cache.amazonaws.com', port=6379, db=0,
ssl=True, ssl_ca_certs=certifi.where())
>>> r.set('foo', 'bar')
True
>>> r.get('foo')
b'bar'Example turning off hostname verification (not recommended):
>>> import redis
>>> r = redis.Redis(host='xxxxxx.cache.amazonaws.com', port=6379, db=0,
ssl=True, ssl_cert_reqs=None)
>>> r.set('foo', 'bar')
True
>>> r.get('foo')
b'bar'These commands all accept a mapping of key/value pairs. In redis-py 2.X
this mapping could be specified as *args or as **kwargs. Both of these
styles caused issues when Redis introduced optional flags to ZADD. Relying on
*args caused issues with the optional argument order, especially in Python
2.7. Relying on **kwargs caused potential collision issues of user keys with
the argument names in the method signature.
To resolve this, redis-py 3.0 has changed these three commands to all accept a single positional argument named mapping that is expected to be a dict. For MSET and MSETNX, the dict is a mapping of key-names -> values. For ZADD, the dict is a mapping of element-names -> score.
MSET, MSETNX and ZADD now look like:
def mset(self, mapping):
def msetnx(self, mapping):
def zadd(self, name, mapping, nx=False, xx=False, ch=False, incr=False):All 2.X users that use these commands must modify their code to supply keys and values as a dict to these commands.
redis-py 2.X accidentally modified the argument order of ZINCRBY, swapping the order of value and amount. ZINCRBY now looks like:
def zincrby(self, name, amount, value):All 2.X users that rely on ZINCRBY must swap the order of amount and value for the command to continue to work as intended.
redis-py 3.0 only accepts user data as bytes, strings or numbers (ints, longs and floats). Attempting to specify a key or a value as any other type will raise a DataError exception.
redis-py 2.X attempted to coerce any type of input into a string. While occasionally convenient, this caused all sorts of hidden errors when users passed boolean values (which were coerced to 'True' or 'False'), a None value (which was coerced to 'None') or other values, such as user defined types.
All 2.X users should make sure that the keys and values they pass into redis-py are either bytes, strings or numbers.
redis-py 3.0 drops support for the pipeline-based Lock and now only supports the Lua-based lock. In doing so, LuaLock has been renamed to Lock. This also means that redis-py Lock objects require Redis server 2.6 or greater.
2.X users that were explicitly referring to "LuaLock" will have to now refer to "Lock" instead.
redis-py 3.0 now raises a LockError when using a lock as a context manager and the lock cannot be acquired within the specified timeout. This is more of a bug fix than a backwards incompatible change. However, given an error is now raised where none was before, this might alarm some users.
2.X users should make sure they're wrapping their lock code in a try/catch like this:
try:
with r.lock('my-lock-key', blocking_timeout=5) as lock:
# code you want executed only after the lock has been acquired
except LockError:
# the lock wasn't acquiredThe official Redis command documentation does a great job of explaining each command in detail. redis-py attempts to adhere to the official command syntax. There are a few exceptions:
- SELECT: Not implemented. See the explanation in the Thread Safety section below.
- DEL: 'del' is a reserved keyword in the Python syntax. Therefore redis-py uses 'delete' instead.
- MULTI/EXEC: These are implemented as part of the Pipeline class. The pipeline is wrapped with the MULTI and EXEC statements by default when it is executed, which can be disabled by specifying transaction=False. See more about Pipelines below.
- SUBSCRIBE/LISTEN: Similar to pipelines, PubSub is implemented as a separate class as it places the underlying connection in a state where it can't execute non-pubsub commands. Calling the pubsub method from the Redis client will return a PubSub instance where you can subscribe to channels and listen for messages. You can only call PUBLISH from the Redis client (see this comment on issue #151 for details).
- SCAN/SSCAN/HSCAN/ZSCAN: The *SCAN commands are implemented as they exist in the Redis documentation. In addition, each command has an equivalent iterator method. These are purely for convenience so the user doesn't have to keep track of the cursor while iterating. Use the scan_iter/sscan_iter/hscan_iter/zscan_iter methods for this behavior.
Behind the scenes, redis-py uses a connection pool to manage connections to a Redis server. By default, each Redis instance you create will in turn create its own connection pool. You can override this behavior and use an existing connection pool by passing an already created connection pool instance to the connection_pool argument of the Redis class. You may choose to do this in order to implement client side sharding or have fine-grain control of how connections are managed.
>>> pool = redis.ConnectionPool(host='localhost', port=6379, db=0)
>>> r = redis.Redis(connection_pool=pool)ConnectionPools manage a set of Connection instances. redis-py ships with two types of Connections. The default, Connection, is a normal TCP socket based connection. The UnixDomainSocketConnection allows for clients running on the same device as the server to connect via a unix domain socket. To use a UnixDomainSocketConnection connection, simply pass the unix_socket_path argument, which is a string to the unix domain socket file. Additionally, make sure the unixsocket parameter is defined in your redis.conf file. It's commented out by default.
>>> r = redis.Redis(unix_socket_path='/tmp/redis.sock')You can create your own Connection subclasses as well. This may be useful if you want to control the socket behavior within an async framework. To instantiate a client class using your own connection, you need to create a connection pool, passing your class to the connection_class argument. Other keyword parameters you pass to the pool will be passed to the class specified during initialization.
>>> pool = redis.ConnectionPool(connection_class=YourConnectionClass,
your_arg='...', ...)Connections maintain an open socket to the Redis server. Sometimes these sockets are interrupted or disconnected for a variety of reasons. For example, network appliances, load balancers and other services that sit between clients and servers are often configured to kill connections that remain idle for a given threshold.
When a connection becomes disconnected, the next command issued on that connection will fail and redis-py will raise a ConnectionError to the caller. This allows each application that uses redis-py to handle errors in a way that's fitting for that specific application. However, constant error handling can be verbose and cumbersome, especially when socket disconnections happen frequently in many production environments.
To combat this, redis-py can issue regular health checks to assess the
liveliness of a connection just before issuing a command. Users can pass
health_check_interval=N to the Redis or ConnectionPool classes or
as a query argument within a Redis URL. The value of health_check_interval
must be an integer. A value of 0, the default, disables health checks.
Any positive integer will enable health checks. Health checks are performed
just before a command is executed if the underlying connection has been idle
for more than health_check_interval seconds. For example,
health_check_interval=30 will ensure that a health check is run on any
connection that has been idle for 30 or more seconds just before a command
is executed on that connection.
If your application is running in an environment that disconnects idle
connections after 30 seconds you should set the health_check_interval
option to a value less than 30.
This option also works on any PubSub connection that is created from a
client with health_check_interval enabled. PubSub users need to ensure
that get_message() or listen() are called more frequently than
health_check_interval seconds. It is assumed that most workloads already
do this.
If your PubSub use case doesn't call get_message() or listen()
frequently, you should call pubsub.check_health() explicitly on a
regularly basis.
Parser classes provide a way to control how responses from the Redis server are parsed. redis-py ships with two parser classes, the PythonParser and the HiredisParser. By default, redis-py will attempt to use the HiredisParser if you have the hiredis module installed and will fallback to the PythonParser otherwise.
Hiredis is a C library maintained by the core Redis team. Pieter Noordhuis was kind enough to create Python bindings. Using Hiredis can provide up to a 10x speed improvement in parsing responses from the Redis server. The performance increase is most noticeable when retrieving many pieces of data, such as from LRANGE or SMEMBERS operations.
Hiredis is available on PyPI, and can be installed via pip just like redis-py.
$ pip install hiredisThe client class uses a set of callbacks to cast Redis responses to the appropriate Python type. There are a number of these callbacks defined on the Redis client class in a dictionary called RESPONSE_CALLBACKS.
Custom callbacks can be added on a per-instance basis using the set_response_callback method. This method accepts two arguments: a command name and the callback. Callbacks added in this manner are only valid on the instance the callback is added to. If you want to define or override a callback globally, you should make a subclass of the Redis client and add your callback to its RESPONSE_CALLBACKS class dictionary.
Response callbacks take at least one parameter: the response from the Redis server. Keyword arguments may also be accepted in order to further control how to interpret the response. These keyword arguments are specified during the command's call to execute_command. The ZRANGE implementation demonstrates the use of response callback keyword arguments with its "withscores" argument.
Redis client instances can safely be shared between threads. Internally, connection instances are only retrieved from the connection pool during command execution, and returned to the pool directly after. Command execution never modifies state on the client instance.
However, there is one caveat: the Redis SELECT command. The SELECT command allows you to switch the database currently in use by the connection. That database remains selected until another is selected or until the connection is closed. This creates an issue in that connections could be returned to the pool that are connected to a different database.
As a result, redis-py does not implement the SELECT command on client instances. If you use multiple Redis databases within the same application, you should create a separate client instance (and possibly a separate connection pool) for each database.
It is not safe to pass PubSub or Pipeline objects between threads.
Pipelines are a subclass of the base Redis class that provide support for buffering multiple commands to the server in a single request. They can be used to dramatically increase the performance of groups of commands by reducing the number of back-and-forth TCP packets between the client and server.
Pipelines are quite simple to use:
>>> r = redis.Redis(...)
>>> r.set('bing', 'baz')
>>> # Use the pipeline() method to create a pipeline instance
>>> pipe = r.pipeline()
>>> # The following SET commands are buffered
>>> pipe.set('foo', 'bar')
>>> pipe.get('bing')
>>> # the EXECUTE call sends all buffered commands to the server, returning
>>> # a list of responses, one for each command.
>>> pipe.execute()
[True, b'baz']For ease of use, all commands being buffered into the pipeline return the pipeline object itself. Therefore calls can be chained like:
>>> pipe.set('foo', 'bar').sadd('faz', 'baz').incr('auto_number').execute()
[True, True, 6]In addition, pipelines can also ensure the buffered commands are executed atomically as a group. This happens by default. If you want to disable the atomic nature of a pipeline but still want to buffer commands, you can turn off transactions.
>>> pipe = r.pipeline(transaction=False)A common issue occurs when requiring atomic transactions but needing to retrieve values in Redis prior for use within the transaction. For instance, let's assume that the INCR command didn't exist and we need to build an atomic version of INCR in Python.
The completely naive implementation could GET the value, increment it in Python, and SET the new value back. However, this is not atomic because multiple clients could be doing this at the same time, each getting the same value from GET.
Enter the WATCH command. WATCH provides the ability to monitor one or more keys prior to starting a transaction. If any of those keys change prior the execution of that transaction, the entire transaction will be canceled and a WatchError will be raised. To implement our own client-side INCR command, we could do something like this:
>>> with r.pipeline() as pipe:
... while True:
... try:
... # put a WATCH on the key that holds our sequence value
... pipe.watch('OUR-SEQUENCE-KEY')
... # after WATCHing, the pipeline is put into immediate execution
... # mode until we tell it to start buffering commands again.
... # this allows us to get the current value of our sequence
... current_value = pipe.get('OUR-SEQUENCE-KEY')
... next_value = int(current_value) + 1
... # now we can put the pipeline back into buffered mode with MULTI
... pipe.multi()
... pipe.set('OUR-SEQUENCE-KEY', next_value)
... # and finally, execute the pipeline (the set command)
... pipe.execute()
... # if a WatchError wasn't raised during execution, everything
... # we just did happened atomically.
... break
... except WatchError:
... # another client must have changed 'OUR-SEQUENCE-KEY' between
... # the time we started WATCHing it and the pipeline's execution.
... # our best bet is to just retry.
... continueNote that, because the Pipeline must bind to a single connection for the duration of a WATCH, care must be taken to ensure that the connection is returned to the connection pool by calling the reset() method. If the Pipeline is used as a context manager (as in the example above) reset() will be called automatically. Of course you can do this the manual way by explicitly calling reset():
>>> pipe = r.pipeline()
>>> while True:
... try:
... pipe.watch('OUR-SEQUENCE-KEY')
... ...
... pipe.execute()
... break
... except WatchError:
... continue
... finally:
... pipe.reset()A convenience method named "transaction" exists for handling all the boilerplate of handling and retrying watch errors. It takes a callable that should expect a single parameter, a pipeline object, and any number of keys to be WATCHed. Our client-side INCR command above can be written like this, which is much easier to read:
>>> def client_side_incr(pipe):
... current_value = pipe.get('OUR-SEQUENCE-KEY')
... next_value = int(current_value) + 1
... pipe.multi()
... pipe.set('OUR-SEQUENCE-KEY', next_value)
>>>
>>> r.transaction(client_side_incr, 'OUR-SEQUENCE-KEY')
[True]Be sure to call pipe.multi() in the callable passed to Redis.transaction prior to any write commands.
redis-py includes a PubSub object that subscribes to channels and listens for new messages. Creating a PubSub object is easy.
>>> r = redis.Redis(...)
>>> p = r.pubsub()Once a PubSub instance is created, channels and patterns can be subscribed to.
>>> p.subscribe('my-first-channel', 'my-second-channel', ...)
>>> p.psubscribe('my-*', ...)The PubSub instance is now subscribed to those channels/patterns. The subscription confirmations can be seen by reading messages from the PubSub instance.
>>> p.get_message()
{'pattern': None, 'type': 'subscribe', 'channel': b'my-second-channel', 'data': 1}
>>> p.get_message()
{'pattern': None, 'type': 'subscribe', 'channel': b'my-first-channel', 'data': 2}
>>> p.get_message()
{'pattern': None, 'type': 'psubscribe', 'channel': b'my-*', 'data': 3}Every message read from a PubSub instance will be a dictionary with the following keys.
- type: One of the following: 'subscribe', 'unsubscribe', 'psubscribe', 'punsubscribe', 'message', 'pmessage'
- channel: The channel [un]subscribed to or the channel a message was published to
- pattern: The pattern that matched a published message's channel. Will be None in all cases except for 'pmessage' types.
- data: The message data. With [un]subscribe messages, this value will be the number of channels and patterns the connection is currently subscribed to. With [p]message messages, this value will be the actual published message.
Let's send a message now.
# the publish method returns the number matching channel and pattern
# subscriptions. 'my-first-channel' matches both the 'my-first-channel'
# subscription and the 'my-*' pattern subscription, so this message will
# be delivered to 2 channels/patterns
>>> r.publish('my-first-channel', 'some data')
2
>>> p.get_message()
{'channel': b'my-first-channel', 'data': b'some data', 'pattern': None, 'type': 'message'}
>>> p.get_message()
{'channel': b'my-first-channel', 'data': b'some data', 'pattern': b'my-*', 'type': 'pmessage'}Unsubscribing works just like subscribing. If no arguments are passed to [p]unsubscribe, all channels or patterns will be unsubscribed from.
>>> p.unsubscribe()
>>> p.punsubscribe('my-*')
>>> p.get_message()
{'channel': b'my-second-channel', 'data': 2, 'pattern': None, 'type': 'unsubscribe'}
>>> p.get_message()
{'channel': b'my-first-channel', 'data': 1, 'pattern': None, 'type': 'unsubscribe'}
>>> p.get_message()
{'channel': b'my-*', 'data': 0, 'pattern': None, 'type': 'punsubscribe'}redis-py also allows you to register callback functions to handle published messages. Message handlers take a single argument, the message, which is a dictionary just like the examples above. To subscribe to a channel or pattern with a message handler, pass the channel or pattern name as a keyword argument with its value being the callback function.
When a message is read on a channel or pattern with a message handler, the message dictionary is created and passed to the message handler. In this case, a None value is returned from get_message() since the message was already handled.
>>> def my_handler(message):
... print('MY HANDLER: ', message['data'])
>>> p.subscribe(**{'my-channel': my_handler})
# read the subscribe confirmation message
>>> p.get_message()
{'pattern': None, 'type': 'subscribe', 'channel': b'my-channel', 'data': 1}
>>> r.publish('my-channel', 'awesome data')
1
# for the message handler to work, we need tell the instance to read data.
# this can be done in several ways (read more below). we'll just use
# the familiar get_message() function for now
>>> message = p.get_message()
MY HANDLER: awesome data
# note here that the my_handler callback printed the string above.
# `message` is None because the message was handled by our handler.
>>> print(message)
NoneIf your application is not interested in the (sometimes noisy) subscribe/unsubscribe confirmation messages, you can ignore them by passing ignore_subscribe_messages=True to r.pubsub(). This will cause all subscribe/unsubscribe messages to be read, but they won't bubble up to your application.
>>> p = r.pubsub(ignore_subscribe_messages=True)
>>> p.subscribe('my-channel')
>>> p.get_message() # hides the subscribe message and returns None
>>> r.publish('my-channel', 'my data')
1
>>> p.get_message()
{'channel': b'my-channel', 'data': b'my data', 'pattern': None, 'type': 'message'}There are three different strategies for reading messages.
The examples above have been using pubsub.get_message(). Behind the scenes, get_message() uses the system's 'select' module to quickly poll the connection's socket. If there's data available to be read, get_message() will read it, format the message and return it or pass it to a message handler. If there's no data to be read, get_message() will immediately return None. This makes it trivial to integrate into an existing event loop inside your application.
>>> while True:
>>> message = p.get_message()
>>> if message:
>>> # do something with the message
>>> time.sleep(0.001) # be nice to the system :)Older versions of redis-py only read messages with pubsub.listen(). listen() is a generator that blocks until a message is available. If your application doesn't need to do anything else but receive and act on messages received from redis, listen() is an easy way to get up an running.
>>> for message in p.listen():
... # do something with the messageThe third option runs an event loop in a separate thread. pubsub.run_in_thread() creates a new thread and starts the event loop. The thread object is returned to the caller of run_in_thread(). The caller can use the thread.stop() method to shut down the event loop and thread. Behind the scenes, this is simply a wrapper around get_message() that runs in a separate thread, essentially creating a tiny non-blocking event loop for you. run_in_thread() takes an optional sleep_time argument. If specified, the event loop will call time.sleep() with the value in each iteration of the loop.
Note: Since we're running in a separate thread, there's no way to handle messages that aren't automatically handled with registered message handlers. Therefore, redis-py prevents you from calling run_in_thread() if you're subscribed to patterns or channels that don't have message handlers attached.
>>> p.subscribe(**{'my-channel': my_handler})
>>> thread = p.run_in_thread(sleep_time=0.001)
# the event loop is now running in the background processing messages
# when it's time to shut it down...
>>> thread.stop()run_in_thread also supports an optional exception handler, which lets you catch exceptions that occur within the worker thread and handle them appropriately. The exception handler will take as arguments the exception itself, the pubsub object, and the worker thread returned by run_in_thread.
A PubSub object adheres to the same encoding semantics as the client instance it was created from. Any channel or pattern that's unicode will be encoded using the charset specified on the client before being sent to Redis. If the client's decode_responses flag is set the False (the default), the 'channel', 'pattern' and 'data' values in message dictionaries will be byte strings (str on Python 2, bytes on Python 3). If the client's decode_responses is True, then the 'channel', 'pattern' and 'data' values will be automatically decoded to unicode strings using the client's charset.
PubSub objects remember what channels and patterns they are subscribed to. In the event of a disconnection such as a network error or timeout, the PubSub object will re-subscribe to all prior channels and patterns when reconnecting. Messages that were published while the client was disconnected cannot be delivered. When you're finished with a PubSub object, call its .close() method to shutdown the connection.
>>> p = r.pubsub()
>>> ...
>>> p.close()The PUBSUB set of subcommands CHANNELS, NUMSUB and NUMPAT are also supported:
>>> r.pubsub_channels()
[b'foo', b'bar']
>>> r.pubsub_numsub('foo', 'bar')
[(b'foo', 9001), (b'bar', 42)]
>>> r.pubsub_numsub('baz')
[(b'baz', 0)]
>>> r.pubsub_numpat()
1204redis-py includes a Monitor object that streams every command processed by the Redis server. Use listen() on the Monitor object to block until a command is received.
>>> r = redis.Redis(...)
>>> with r.monitor() as m:
>>> for command in m.listen():
>>> print(command)redis-py supports the EVAL, EVALSHA, and SCRIPT commands. However, there are a number of edge cases that make these commands tedious to use in real world scenarios. Therefore, redis-py exposes a Script object that makes scripting much easier to use.
To create a Script instance, use the register_script function on a client instance passing the Lua code as the first argument. register_script returns a Script instance that you can use throughout your code.
The following trivial Lua script accepts two parameters: the name of a key and a multiplier value. The script fetches the value stored in the key, multiplies it with the multiplier value and returns the result.
>>> r = redis.Redis()
>>> lua = """
... local value = redis.call('GET', KEYS[1])
... value = tonumber(value)
... return value * ARGV[1]"""
>>> multiply = r.register_script(lua)multiply is now a Script instance that is invoked by calling it like a function. Script instances accept the following optional arguments:
- keys: A list of key names that the script will access. This becomes the KEYS list in Lua.
- args: A list of argument values. This becomes the ARGV list in Lua.
- client: A redis-py Client or Pipeline instance that will invoke the script. If client isn't specified, the client that initially created the Script instance (the one that register_script was invoked from) will be used.
Continuing the example from above:
>>> r.set('foo', 2)
>>> multiply(keys=['foo'], args=[5])
10The value of key 'foo' is set to 2. When multiply is invoked, the 'foo' key is passed to the script along with the multiplier value of 5. Lua executes the script and returns the result, 10.
Script instances can be executed using a different client instance, even one that points to a completely different Redis server.
>>> r2 = redis.Redis('redis2.example.com')
>>> r2.set('foo', 3)
>>> multiply(keys=['foo'], args=[5], client=r2)
15The Script object ensures that the Lua script is loaded into Redis's script cache. In the event of a NOSCRIPT error, it will load the script and retry executing it.
Script objects can also be used in pipelines. The pipeline instance should be passed as the client argument when calling the script. Care is taken to ensure that the script is registered in Redis's script cache just prior to pipeline execution.
>>> pipe = r.pipeline()
>>> pipe.set('foo', 5)
>>> multiply(keys=['foo'], args=[5], client=pipe)
>>> pipe.execute()
[True, 25]redis-py can be used together with Redis Sentinel to discover Redis nodes. You need to have at least one Sentinel daemon running in order to use redis-py's Sentinel support.
Connecting redis-py to the Sentinel instance(s) is easy. You can use a Sentinel connection to discover the master and slaves network addresses:
>>> from redis.sentinel import Sentinel
>>> sentinel = Sentinel([('localhost', 26379)], socket_timeout=0.1)
>>> sentinel.discover_master('mymaster')
('127.0.0.1', 6379)
>>> sentinel.discover_slaves('mymaster')
[('127.0.0.1', 6380)]You can also create Redis client connections from a Sentinel instance. You can connect to either the master (for write operations) or a slave (for read-only operations).
>>> master = sentinel.master_for('mymaster', socket_timeout=0.1)
>>> slave = sentinel.slave_for('mymaster', socket_timeout=0.1)
>>> master.set('foo', 'bar')
>>> slave.get('foo')
b'bar'The master and slave objects are normal Redis instances with their connection pool bound to the Sentinel instance. When a Sentinel backed client attempts to establish a connection, it first queries the Sentinel servers to determine an appropriate host to connect to. If no server is found, a MasterNotFoundError or SlaveNotFoundError is raised. Both exceptions are subclasses of ConnectionError.
When trying to connect to a slave client, the Sentinel connection pool will iterate over the list of slaves until it finds one that can be connected to. If no slaves can be connected to, a connection will be established with the master.
See Guidelines for Redis clients with support for Redis Sentinel to learn more about Redis Sentinel.
The *SCAN commands introduced in Redis 2.8 can be cumbersome to use. While these commands are fully supported, redis-py also exposes the following methods that return Python iterators for convenience: scan_iter, hscan_iter, sscan_iter and zscan_iter.
>>> for key, value in (('A', '1'), ('B', '2'), ('C', '3')):
... r.set(key, value)
>>> for key in r.scan_iter():
... print(key, r.get(key))
A 1
B 2
C 3redis-py does not currently support Cluster Mode.
redis-py is developed and maintained by Andy McCurdy ([email protected]). It can be found here: https://github.com/andymccurdy/redis-py
Special thanks to:
- Ludovico Magnocavallo, author of the original Python Redis client, from which some of the socket code is still used.
- Alexander Solovyov for ideas on the generic response callback system.
- Paul Hubbard for initial packaging support.