UltraDict 0.0.2

Sychronized, streaming dictionary that uses shared memory as a backend

UltraDict

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

Warning: This is an early hack. There 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 data 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.

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

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:

• multiprocessing.Manager
• shared-memory-dict
• Redis
• Memcached

How to use?

In one process:

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'}
>>> orig = dict({ 1:1 }, some_key='some_value')
>>> orig
{1: 1, 'some_key': 'some_value'}
>>>
>>> # We need the shared memory name in the other process.
>>> ultra.name
'psm_ad73da69'

In another process:

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

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 to 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 changed 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.

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 user of the dict will automatically load the full dump and continue with the reset streaming buffer.

serializer: Use a different serialized from the default pickle, e. g. marshal, dill, json. The callable 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 synchornize and not overwrite each other's changes. Shared locks are slow. They rely on the atomics package for atomic locks. Alternatively, 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 creator process goes away. Thus you must plan ahead which processes will be writing and 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)
>>> # Lets use a value with 100.000 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 when you normally access the UltraDict,
>>> # but can be useful to call after a forced load.
>>> ultra.apply_update()

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

>>> # Use any serialize 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()