Python Slots

• Python Slots Vs Dict
• Python Slots Class Attribute

• Monty Python’s Spamalot proved to be an immensely popular online slots machine for Playtech when it was released several years ago.This progressive jackpot is now joined by a new Monty Python slot machine game and one which is based on the popular Holy Grail film.
• Just for completeness of my notes, note that there is a one-time cost per slot in the class's namespace of 64 bytes in Python 2, and 72 bytes in Python 3, because slots use data descriptors like properties, called 'members'.

The receivers of signals are called Slots in Qt terminology. A number of standard slots are provided on Qt classes to allow you to wire together different parts of your application. However, you can also use any Python function as a slot, and therefore receive the message yourself. slots Magic ¶ In Python every class can have instance attributes. By default Python uses a dict to store an object’s instance attributes. This is really helpful as it allows setting arbitrary new attributes at runtime.

The Question :

What is the purpose of __slots__ in Python — especially with respect to when I would want to use it, and when not?

The Answer 1

TLDR:

The special attribute __slots__ allows you to explicitly state which instance attributes you expect your object instances to have, with the expected results:

1. faster attribute access.
2. space savings in memory.

The space savings is from

1. Storing value references in slots instead of __dict__.
2. Denying __dict__ and __weakref__ creation if parent classes deny them and you declare __slots__.

Python Slots Vs Dict

Quick Caveats

Small caveat, you should only declare a particular slot one time in an inheritance tree. For example:

Python doesn’t object when you get this wrong (it probably should), problems might not otherwise manifest, but your objects will take up more space than they otherwise should. Python 3.8:

This is because the Base’s slot descriptor has a slot separate from the Wrong’s. This shouldn’t usually come up, but it could:

The biggest caveat is for multiple inheritance – multiple “parent classes with nonempty slots” cannot be combined.

To accommodate this restriction, follow best practices: Factor out all but one or all parents’ abstraction which their concrete class respectively and your new concrete class collectively will inherit from – giving the abstraction(s) empty slots (just like abstract base classes in the standard library).

See section on multiple inheritance below for an example.

Requirements:

• To have attributes named in __slots__ to actually be stored in slots instead of a __dict__, a class must inherit from object.

• To prevent the creation of a __dict__, you must inherit from object and all classes in the inheritance must declare __slots__ and none of them can have a '__dict__' entry.

There are a lot of details if you wish to keep reading.

Why use __slots__: Faster attribute access.

The creator of Python, Guido van Rossum, states that he actually created __slots__ for faster attribute access.

It is trivial to demonstrate measurably significant faster access:

and

The slotted access is almost 30% faster in Python 3.5 on Ubuntu.

In Python 2 on Windows I have measured it about 15% faster.

Why use __slots__: Memory Savings

Another purpose of __slots__ is to reduce the space in memory that each object instance takes up.

My own contribution to the documentation clearly states the reasons behind this:

The space saved over using __dict__ can be significant.

SQLAlchemy attributes a lot of memory savings to __slots__.

To verify this, using the Anaconda distribution of Python 2.7 on Ubuntu Linux, with guppy.hpy (aka heapy) and sys.getsizeof, the size of a class instance without __slots__ declared, and nothing else, is 64 bytes. That does not include the __dict__. Thank you Python for lazy evaluation again, the __dict__ is apparently not called into existence until it is referenced, but classes without data are usually useless. When called into existence, the __dict__ attribute is a minimum of 280 bytes additionally.

In contrast, a class instance with __slots__ declared to be () (no data) is only 16 bytes, and 56 total bytes with one item in slots, 64 with two.

For 64 bit Python, I illustrate the memory consumption in bytes in Python 2.7 and 3.6, for __slots__ and __dict__ (no slots defined) for each point where the dict grows in 3.6 (except for 0, 1, and 2 attributes):

So, in spite of smaller dicts in Python 3, we see how nicely __slots__ scale for instances to save us memory, and that is a major reason you would want to use __slots__.

Just for completeness of my notes, note that there is a one-time cost per slot in the class’s namespace of 64 bytes in Python 2, and 72 bytes in Python 3, because slots use data descriptors like properties, called “members”.

Demonstration of __slots__:

To deny the creation of a __dict__, you must subclass object:

now:

Or subclass another class that defines __slots__

and now:

but:

To allow __dict__ creation while subclassing slotted objects, just add '__dict__' to the __slots__ (note that slots are ordered, and you shouldn’t repeat slots that are already in parent classes):

and

Or you don’t even need to declare __slots__ in your subclass, and you will still use slots from the parents, but not restrict the creation of a __dict__:

And:

However, __slots__ may cause problems for multiple inheritance:

Because creating a child class from parents with both non-empty slots fails:

If you run into this problem, You could just remove __slots__ from the parents, or if you have control of the parents, give them empty slots, or refactor to abstractions:

Add '__dict__' to __slots__ to get dynamic assignment:

and now:

So with '__dict__' in slots we lose some of the size benefits with the upside of having dynamic assignment and still having slots for the names we do expect.

When you inherit from an object that isn’t slotted, you get the same sort of semantics when you use __slots__ – names that are in __slots__ point to slotted values, while any other values are put in the instance’s __dict__.

Avoiding __slots__ because you want to be able to add attributes on the fly is actually not a good reason – just add '__dict__' to your __slots__ if this is required.

You can similarly add __weakref__ to __slots__ explicitly if you need that feature.

Set to empty tuple when subclassing a namedtuple:

The namedtuple builtin make immutable instances that are very lightweight (essentially, the size of tuples) but to get the benefits, you need to do it yourself if you subclass them:

usage:

And trying to assign an unexpected attribute raises an AttributeError because we have prevented the creation of __dict__:

You can allow __dict__ creation by leaving off __slots__ = (), but you can’t use non-empty __slots__ with subtypes of tuple.

Biggest Caveat: Multiple inheritance

Even when non-empty slots are the same for multiple parents, they cannot be used together:

Using an empty __slots__ in the parent seems to provide the most flexibility, allowing the child to choose to prevent or allow (by adding '__dict__' to get dynamic assignment, see section above) the creation of a __dict__:

You don’t have to have slots – so if you add them, and remove them later, it shouldn’t cause any problems.

Going out on a limb here: If you’re composing mixins or using abstract base classes, which aren’t intended to be instantiated, an empty __slots__ in those parents seems to be the best way to go in terms of flexibility for subclassers.

To demonstrate, first, let’s create a class with code we’d like to use under multiple inheritance

We could use the above directly by inheriting and declaring the expected slots:

But we don’t care about that, that’s trivial single inheritance, we need another class we might also inherit from, maybe with a noisy attribute:

Now if both bases had nonempty slots, we couldn’t do the below. (In fact, if we wanted, we could have given AbstractBase nonempty slots a and b, and left them out of the below declaration – leaving them in would be wrong):

And now we have functionality from both via multiple inheritance, and can still deny __dict__ and __weakref__ instantiation:

Other cases to avoid slots:

• Avoid them when you want to perform __class__ assignment with another class that doesn’t have them (and you can’t add them) unless the slot layouts are identical. (I am very interested in learning who is doing this and why.)
• Avoid them if you want to subclass variable length builtins like long, tuple, or str, and you want to add attributes to them.
• Avoid them if you insist on providing default values via class attributes for instance variables.

You may be able to tease out further caveats from the rest of the __slots__documentation (the 3.7 dev docs are the most current), which I have made significant recent contributions to.

Critiques of other answers

The current top answers cite outdated information and are quite hand-wavy and miss the mark in some important ways.

Do not “only use __slots__ when instantiating lots of objects”

I quote:

“You would want to use __slots__ if you are going to instantiate a lot (hundreds, thousands) of objects of the same class.”

Abstract Base Classes, for example, from the collections module, are not instantiated, yet __slots__ are declared for them.

Why?

If a user wishes to deny __dict__ or __weakref__ creation, those things must not be available in the parent classes.

__slots__ contributes to reusability when creating interfaces or mixins.

It is true that many Python users aren’t writing for reusability, but when you are, having the option to deny unnecessary space usage is valuable.

__slots__ doesn’t break pickling

When pickling a slotted object, you may find it complains with a misleading TypeError:

This is actually incorrect. This message comes from the oldest protocol, which is the default. You can select the latest protocol with the -1 argument. In Python 2.7 this would be 2 (which was introduced in 2.3), and in 3.6 it is 4.

in Python 2.7:

in Python 3.6

So I would keep this in mind, as it is a solved problem.

Python Slots Class Attribute

Critique of the (until Oct 2, 2016) accepted answer

The first paragraph is half short explanation, half predictive. Here’s the only part that actually answers the question

The proper use of __slots__ is to save space in objects. Instead of having a dynamic dict that allows adding attributes to objects at anytime, there is a static structure which does not allow additions after creation. This saves the overhead of one dict for every object that uses slots

The second half is wishful thinking, and off the mark:

While this is sometimes a useful optimization, it would be completely unnecessary if the Python interpreter was dynamic enough so that it would only require the dict when there actually were additions to the object.

Python actually does something similar to this, only creating the __dict__ when it is accessed, but creating lots of objects with no data is fairly ridiculous.

The second paragraph oversimplifies and misses actual reasons to avoid __slots__. The below is not a real reason to avoid slots (for actual reasons, see the rest of my answer above.):

They change the behavior of the objects that have slots in a way that can be abused by control freaks and static typing weenies.

It then goes on to discuss other ways of accomplishing that perverse goal with Python, not discussing anything to do with __slots__.

The third paragraph is more wishful thinking. Together it is mostly off-the-mark content that the answerer didn’t even author and contributes to ammunition for critics of the site.

Create some normal objects and slotted objects:

Instantiate a million of them:

Inspect with guppy.hpy().heap():

Access the regular objects and their __dict__ and inspect again:

This is consistent with the history of Python, from Unifying types and classes in Python 2.2

If you subclass a built-in type, extra space is automatically added to the instances to accomodate __dict__ and __weakrefs__. (The __dict__ is not initialized until you use it though, so you shouldn’t worry about the space occupied by an empty dictionary for each instance you create.) If you don’t need this extra space, you can add the phrase “__slots__ = []” to your class.

The Answer 2

Quoting Jacob Hallen:

The proper use of __slots__ is to save space in objects. Instead of having a dynamic dict that allows adding attributes to objects at anytime, there is a static structure which does not allow additions after creation. [This use of __slots__ eliminates the overhead of one dict for every object.] While this is sometimes a useful optimization, it would be completely unnecessary if the Python interpreter was dynamic enough so that it would only require the dict when there actually were additions to the object.

Unfortunately there is a side effect to slots. They change the behavior of the objects that have slots in a way that can be abused by control freaks and static typing weenies. This is bad, because the control freaks should be abusing the metaclasses and the static typing weenies should be abusing decorators, since in Python, there should be only one obvious way of doing something.

Making CPython smart enough to handle saving space without __slots__ is a major undertaking, which is probably why it is not on the list of changes for P3k (yet).

The Answer 3

You would want to use __slots__ if you are going to instantiate a lot (hundreds, thousands) of objects of the same class. __slots__ only exists as a memory optimization tool.

It’s highly discouraged to use __slots__ for constraining attribute creation.

Pickling objects with __slots__ won’t work with the default (oldest) pickle protocol; it’s necessary to specify a later version.

Some other introspection features of python may also be adversely affected.

The Answer 4

Each python object has a __dict__ atttribute which is a dictionary containing all other attributes. e.g. when you type self.attr python is actually doing self.__dict__['attr']. As you can imagine using a dictionary to store attribute takes some extra space & time for accessing it.

However, when you use __slots__, any object created for that class won’t have a __dict__ attribute. Instead, all attribute access is done directly via pointers.

So if want a C style structure rather than a full fledged class you can use __slots__ for compacting size of the objects & reducing attribute access time. A good example is a Point class containing attributes x & y. If you are going to have a lot of points, you can try using __slots__ in order to conserve some memory.

The Answer 5

In addition to the other answers, here is an example of using __slots__:

So, to implement __slots__, it only takes an extra line (and making your class a new-style class if it isn’t already). This way you can reduce the memory footprint of those classes 5-fold, at the expense of having to write custom pickle code, if and when that becomes necessary.

The Answer 6

Slots are very useful for library calls to eliminate the “named method dispatch” when making function calls. This is mentioned in the SWIG documentation. For high performance libraries that want to reduce function overhead for commonly called functions using slots is much faster.

Now this may not be directly related to the OPs question. It is related more to building extensions than it does to using the slots syntax on an object. But it does help complete the picture for the usage of slots and some of the reasoning behind them.

The Answer 7

An attribute of a class instance has 3 properties: the instance, the name of the attribute, and the value of the attribute.

In regular attribute access, the instance acts as a dictionary and the name of the attribute acts as the key in that dictionary looking up value.

instance(attribute) –> value

In __slots__ access, the name of the attribute acts as the dictionary and the instance acts as the key in the dictionary looking up value.

attribute(instance) –> value

In flyweight pattern, the name of the attribute acts as the dictionary and the value acts as the key in that dictionary looking up the instance.

attribute(value) –> instance

The Answer 8

A very simple example of __slot__ attribute.

Problem: Without __slots__

If I don’t have __slot__ attribute in my class, I can add new attributes to my objects.

If you look at example above, you can see that obj1 and obj2 have their own x and y attributes and python has also created a dict attribute for each object (obj1 and obj2).

Suppose if my class Test has thousands of such objects? Creating an additional attribute dict for each object will cause lot of overhead (memory, computing power etc.) in my code.

Solution: With __slots__

Now in the following example my class Test contains __slots__ attribute. Now I can’t add new attributes to my objects (except attribute x) and python doesn’t create a dict attribute anymore. This eliminates overhead for each object, which can become significant if you have many objects.

The Answer 9

Another somewhat obscure use of __slots__ is to add attributes to an object proxy from the ProxyTypes package, formerly part of the PEAK project. Its ObjectWrapper allows you to proxy another object, but intercept all interactions with the proxied object. It is not very commonly used (and no Python 3 support), but we have used it to implement a thread-safe blocking wrapper around an async implementation based on tornado that bounces all access to the proxied object through the ioloop, using thread-safe concurrent.Future objects to synchronise and return results.

By default any attribute access to the proxy object will give you the result from the proxied object. If you need to add an attribute on the proxy object, __slots__ can be used.

The Answer 10

You have — essentially — no use for __slots__.

For the time when you think you might need __slots__, you actually want to use Lightweight or Flyweight design patterns. These are cases when you no longer want to use purely Python objects. Instead, you want a Python object-like wrapper around an array, struct, or numpy array.

The class-like wrapper has no attributes — it just provides methods that act on the underlying data. The methods can be reduced to class methods. Indeed, it could be reduced to just functions operating on the underlying array of data.

The Answer 11

The original question was about general use cases not only about memory.So it should be mentioned here that you also get better performance when instantiating large amounts of objects – interesting e.g. when parsing large documents into objects or from a database.

Here is a comparison of creating object trees with a million entries, using slots and without slots. As a reference also the performance when using plain dicts for the trees (Py2.7.10 on OSX):

Test classes (ident, appart from slots):

testcode, verbose mode:

Introduction

In some applications it is often necessary to perform long-running tasks, such as computations or network operations, that cannot be broken up into smaller pieces and processed alongside normal application events. In such cases, we would like to be able to perform these tasks in a way that does not interfere with the normal running of the application, and ensure that the user interface continues to be updated. One way of achieving this is to perform these tasks in a separate thread to the main user interface thread, and only interact with it when we have results we need to display.

This example shows how to create a separate thread to perform a task - in this case, drawing stars for a picture - while continuing to run the main user interface thread. The worker thread draws each star onto its own individual image, and it passes each image back to the example's window which resides in the main application thread.

The User Interface

We begin by importing the modules we require. We need the math and random modules to help us draw stars.

The main window in this example is just a QWidget. We create a single Worker instance that we can reuse as required.

The user interface consists of a label, spin box and a push button that the user interacts with to configure the number of stars that the thread wil draw. The output from the thread is presented in a QLabel instance, viewer.

We connect the standard finished() and terminated() signals from the thread to the same slot in the widget. This will reset the user interface when the thread stops running. The custom output(QRect, QImage) signal is connected to the addImage() slot so that we can update the viewer label every time a new star is drawn.

The start button's clicked() signal is connected to the makePicture() slot, which is responsible for starting the worker thread.

We place each of the widgets into a grid layout and set the window's title:

The makePicture() slot needs to do three things: disable the user interface widgets that are used to start a thread, clear the viewer label with a new pixmap, and start the thread with the appropriate parameters.

Since the start button is the only widget that can cause this slot to be invoked, we simply disable it before starting the thread, avoiding problems with re-entrancy.

We call a custom method in the Worker thread instance with the size of the viewer label and the number of stars, obtained from the spin box.

Whenever is star is drawn by the worker thread, it will emit a signal that is connected to the addImage() slot. This slot is called with a QRect value, indicating where the star should be placed in the pixmap held by the viewer label, and an image of the star itself:

We use a QPainter to draw the image at the appropriate place on the label's pixmap.

The updateUi() slot is called when a thread stops running. Since we usually want to let the user run the thread again, we reset the user interface to enable the start button to be pressed:

Now that we have seen how an instance of the Window class uses the worker thread, let us take a look at the thread's implementation.

The Worker Thread

The worker thread is implemented as a PyQt thread rather than a Python thread since we want to take advantage of the signals and slots mechanism to communicate with the main application.

We define size and stars attributes that store information about the work the thread is required to do, and we assign default values to them. The exiting attribute is used to tell the thread to stop processing.

Each star is drawn using a QPainterPath that we define in advance:

Before a Worker object is destroyed, we need to ensure that it stops processing. For this reason, we implement the following method in a way that indicates to the part of the object that performs the processing that it must stop, and waits until it does so.

For convenience, we define a method to set up the attributes required by the thread before starting it.

The start() method is a special method that sets up the thread and calls our implementation of the run() method. We provide the render() method instead of letting our own run() method take extra arguments because the run() method is called by PyQt itself with no arguments.

The run() method is where we perform the processing that occurs in the thread provided by the Worker instance:

Information stored as attributes in the instance determines the number of stars to be drawn and the area over which they will be distributed.

We draw the number of stars requested as long as the exiting attribute remains False. This additional check allows us to terminate the thread on demand by setting the exiting attribute to True at any time.

The drawing code is not particularly relevant to this example. We simply draw on an appropriately-sized transparent image.

For each star drawn, we send the main thread information about where it should be placed along with the star's image by emitting our custom output() signal:

Since QRect and QImage objects can be serialized for transmission via the signals and slots mechanism, they can be sent between threads in this way, making it convenient to use threads in a wide range of situations where built-in types are used.

Running the Example

We only need one more piece of code to complete the example: