UltraDict

Sychronized, streaming Python dictionary that uses shared memory as a backend

Warning: This is an early hack. There are only few unit tests and so on. Maybe not stable!

Features:

Fast (compared to other shareing solutions)
No running manager processes
Works in spawn and fork context
Safe locking between independent processes
Tested with Python >= v3.9 on Linux and Windows
Optional recursion for nested dicts

General Concept

UltraDict uses multiprocessing.shared_memory to synchronize a dict between multiple processes.

It does so by using a stream of updates in a shared memory buffer. This is efficient because only changes have to be serialized and transferred.

If the buffer is full, UltraDict will automatically do a full dump to a new shared memory space, reset the streaming buffer and continue to stream further updates. All users of the UltraDict will automatically load full dumps and continue using streaming updates afterwards.

Issues

On Windows, if no process has any handles on the shared memory, the OS will gc all of the shared memory making it inaccessible for future processes. To work around this issue you can currently set full_dump_size which will cause the creator of the dict to set a static full dump memory of the requested size. This full dump memory will live as long as the creator lives. This approach has the downside that you need to plan ahead for your data size and if it does not fit into the full dump memory, it will break.

Alternatives

There are many alternatives:

How to use?

Simple

In one Python REPL:

Python 3.9.2 on linux
>>> 
>>> from UltraDict import UltraDict
>>> ultra = UltraDict({ 1:1 }, some_key='some_value')
>>> ultra
{1: 1, 'some_key': 'some_value'}
>>>
>>> # We need the shared memory name in the other process.
>>> ultra.name
'psm_ad73da69'

In another Python REPL:

Python 3.9.2 on linux
>>> 
>>> from UltraDict import UltraDict
>>> # Connect to the shared memory with the name above
>>> other = UltraDict(name='psm_ad73da69')
>>> other
{1: 1, 'some_key': 'some_value'}
>>> other[2] = 2

Back in the first Python REPL:

>>> ultra[2]
2

Nested

In one Python REPL:

Python 3.9.2 on linux
>>> 
>>> from UltraDict import UltraDict
>>> ultra = UltraDict(recurse=True)
>>> ultra['nested'] = { 'counter': 0 }
>>> type(ultra['nested'])
<class 'UltraDict.UltraDict'>
>>> ultra.name
'psm_0a2713e4'

In another Python REPL:

Python 3.9.2 on linux
>>> 
>>> from UltraDict import UltraDict
>>> other = UltraDict(name='psm_0a2713e4')
>>> other['nested']['counter'] += 1

Back in the first Python REPL:

>>> ultra['nested']['counter']
1

Performance comparison

Python 3.9.2 on linux
>>> 
>>> from UltraDict import UltraDict
>>> ultra = UltraDict()
>>> for i in range(10_000): ultra[i] = i
... 
>>> len(ultra)
10000
>>> ultra[500]
500
>>> # Now let's do some performance testing
>>> import multiprocessing, timeit
>>> orig = dict(ultra)
>>> len(orig)
10000
>>> orig[500]
500
>>> managed = multiprocessing.Manager().dict(orig)
>>> len(managed)
10000

Read performance

>>> timeit.timeit('orig[1]', globals=globals())
0.03503723500762135
>>>
>>> timeit.timeit('ultra[1]', globals=globals())
0.380401570990216
>>>
>>> timeit.timeit('managed[1]', globals=globals())
15.848494678968564
>>>
>>> # We are factor 10 slower than a real, local dict,
>>> # but way faster than using a Manager
>>>
>>> # If you need full read performance, you can access the underlying
>>> # cache directly and get almost original dict performance,
>>> # of course at the cost of not having real-time updates anymore.
>>>
>>> timeit.timeit('ultra.data[1]', globals=globals())
0.047667117964010686

Write performance

>>> timeit.timeit('orig[1] = 1', globals=globals())
0.02869905502302572
>>>
>>> timeit.timeit('ultra[1] = 1', globals=globals())
2.259694856009446
>>>
>>> timeit.timeit('managed[1] = 1', globals=globals())
16.352361536002718
>>>
>>> # We are factor 100 slower than a real, local dict,
>>> # but still way faster than using a Manager

Parameters

Ultradict(*arg, name=None, buffer_size=10000, serializer=pickle, shared_lock=False, full_dump_size=None, auto_unlink=True, recurse=False, **kwargs)

name: Name of the shared memory. A random name will be chosen if not set. If a name is given a new shared memory space is created if it does not exist yet. Otherwise the existing shared memory space is attached.

buffer_size: Size of the shared memory buffer used for streaming changes of the dict.

The buffer size limits the biggest change that can be streamed, so when you use large values or deeply nested dicts you might need a bigger buffer. Otherwise, if the buffer is too small, it will fall back to a full dump. Creating full dumps can be slow, depending on the size of your dict.

Whenever the buffer is full, a full dump will be created. A new shared memory is allocated just big enough for the full dump. Afterwards the streaming buffer is reset. All other users of the dict will automatically load the full dump and continue streaming updates.

serializer: Use a different serialized from the default pickle, e. g. marshal, dill, json. The module or object provided must support the methods loads() and dumps()

shared_lock: When writing to the same dict at the same time from multiple, independent processes, they need a shared lock to synchronize and not overwrite each other's changes. Shared locks are slow. They rely on the atomics package for atomic locks. By default, UltraDict will use a multiprocessing.RLock() instead which works well in fork context and is much faster.

full_dump_size: If set, uses a static full dump memory instead of dynamically creating it. This might be necessary on Windows depending on your write behaviour. On Windows, the full dump memory goes away if the process goes away that had created the full dump. Thus you must plan ahead which processes might be writing to the dict and therefore creating full dumps.

auto_unlink: If True, the creator of the shared memory will automatically unlink the handle at exit so it is not visible or accessible to new processes. All existing, still connected processes can continue to use the dict.

recurse: If True, any nested dict objects will be automaticall wrapped in an UltraDict allowing transparent nested updates.

Advanced usage

See examples folder

>>> ultra = UltraDict({ 'init': 'some initial data' }, name='my-name', buffer_size=100_000)
>>> # Let's use a value with 100k bytes length.
>>> # This will not fit into our 100k bytes buffer due to the serialization overhead.
>>> ultra[0] = ' ' * 100_000
>>> ultra.print_status()
{'buffer': SharedMemory('my-name_memory', size=100000),
 'control': SharedMemory('my-name', size=300),
 'full_dump_counter': 1,
 'full_dump_counter_remote': 1,
 'full_dump_lock_pid_remote': 0,
 'full_dump_lock_remote': 0,
 'full_dump_memory_name_remote': 'psm_a99c3a83',
 'lock': <Lock(owner=None)>,
 'lock_pid_remote': 0,
 'lock_remote': 0,
 'name': 'my-name',
 'shared_lock': SharedLock @0x7f0828864040 lock_name='full_dump_lock_remote', has_lock=0, pid=455581),
 'update_stream_position': 0,
 'update_stream_position_remote': 0}

Other things you can do:

>>> # Load latest full dump if one is available
>>> ultra.load()

>>> # Show statistics
>>> ultra.print_status()

>>> # Force load of latest full dump, even if we had already processed it.
>>> # There might also be streaming updates available after loading the full dump.
>>> ultra.load(force=True)

>>> # Apply full dump and stream updates to
>>> # underlying local dict, this is automatically
>>> # called by accessing the UltraDict in any usual way,
>>> # but can be useful to call after a forced load.
>>> ultra.apply_update()

>>> # Access underlying local dict directly
>>> ultra.data

>>> # Use any serializer you like, given it supports the loads() and dumps() methods
>>> import pickle 
>>> ultra = UltraDict(serializer=pickle)

>>> # Unlink all shared memory, it will not be visible to new processes afterwards
>>> ultra.unlink()

Contributing

Contributions are always welcome!

OmerShubi/UltraDict