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Online Persistent game infrastructure

Project description

Epic Server

Epic Server is a runtime/harness for running code that offers multi-process and multi-machine execution. It handles aspects such as auto-discovery and messaging and abstracts these in an easy to use for to allow you to focus on writing the code to run your services.

Note: This is alpha software. The below talks about features coming in the 0.1 release, the project is in a runnable state at the moment but does not include the multi machine/process execution at this time, this is expected to be finished shortly before official release


from epicserver.objects import Entity

class GameEntity(Entity): pass

class Player(GameEntity):
    score = 0
    async enter_level(self, barrels):
        for barrel in barrels:
            # shoot the barrel
            killed = await barrel.on_hit()
            if killed
                self.score += 1

    async get_score(self):
        return self.score

class Barrel(GameEntity):
    team = "Society for the prevention of cruelty to barrels'
    async on_hit(self):
        print("What, did you expect me to explode?")
        return False

async def setup(pool)
    barrels = [pool.Barrel[i] for i in range(30)]
    player = pool.Player['Bob: Slayer of armoured cylinders']

    await player.enter_level(barrels)
    score = await player.get_score()

    print(f"New High scrore for player: {score}")


  • Based on raw async/await
  • Inter object based messaging that looks like normal async method calls
  • Automatic ‘fabric’ creation, stiching togther multiple processes on the same or seperate machines.
  • Automatic discovery of machines
  • Virtual actors/objects, no explicit instantiation required
  • Isolated and pluggable systems allow swapping in diffrent implementations (eg Serialisation, networking)
  • Automatic object percistance to disk (aka ‘saving’)
  • Garbage colleciton of objects
  • Python >= 3.6 only support

Use Cases

  • Distributed Game Backend
  • Agent Simulation
  • Communciation {latform (instant messaging, twiiter like systems)
  • Graph based processing

Epic Server is ideal for anything that is messaging heavy and designed around objects that need to communicate

Epix Server is not a ‘drop in’ solution to python to gain concurrency. Applications seeking to exploit the parallelism of Epic Server will need to be written to use it. Code that uses fork or threads can be adapted to work (by making each fork/thread a separate object) however Epic Server is focused on high levels of parallelism (executing contexts is 10s to 100s of times the number of CPU’s) and short lived ‘calculations’


Epicserver draws fromconcepts from many diffrent languages and libraries, a short list is included below

  • Orleans from microsoft research (dotnet/C#). Used as the machinary behind Halo 5 and other games
  • Erlang
  • Stackless Python
  • AsyncIO
  • SansIO
  • Zope/ZODB

While the concepts of many of the above can be ported straight to python, modification of the concepts to make them feel fammilar to python programmers where undertaken in an attempt to make the resulting framework more pythonic and fit better with existing python ecosystems.


Epic Server consists of an event loop running in the main thread that is responsible for the execution of work, as well as several background IO threads responsible for sending and reciving IO requests as well as handling IO (both disk and network). Additional threads can be utilized for other blocking calls that may arrise such as Database access.

All access to thes IO threads is dont via message passing and mediated by the event loop

IO Loop

Epic Server is implemented as an IO Loop with a number of ‘syscalls’ that correspond to high level concepts. Rather than focusing on how to perform the IO, Epic Server presents primitives such as ‘persist to database’ or ‘Call remote object’ leaving the implementation up to the server and allowing you to avoid having IO routines embedded in your business logic that would need to be mocked out for testing.

By moving the IO out of the program and replacing it with a token based syscall system, one can simply and quickly test buisness logic without havign to mock out te IO routines or simulate services. Testing becomes a simple process of sending the code messages and checing the repsponses.

Moving the IO out of the code also allows high performance implemetnations such as TIPC or ininiband to be plugged in without modifcation of code, this applies to which database is in use or the searlisation library allowing futher updates to features and performance without having to rework (or test) code.


A number of the implementations this project was inspired by have very explict ideas in regards to the concurrency model and interleaving of requests, a short summary is below:

  • Orleans: Objects are single threaded, only one request may be performed on an object at a time
  • Erlang: Actors are lite-weight processes that communicate via mailboxes
  • Stackless Python: Follows CPython/GIL Semantics
  • AsyncIO: Cooperative multitasking, yields control flow at yield/await statements, Guaranteed exclusive execution between these two points (Providing threading is not used)

Epic Server is composed of multiple Python processes that may be running on the same or different machines. Each Python process has one ‘execution’ thread that corresponds to the main thread as well as a number of background threads for IO (DB access, sending requests and receiving requests)..

From the point of view of user code, a method call has exclusive access tot he CPU between ‘yield points’ (yield/await). At these points, the running context may be changed and other code may be executed. This is identical to AsyncIOs guarantees and a lot less restrictive (and dangerous) than other frameworks such as Orleans while being more in line with what python programmers expect.

In contrast to the Orleans approach the above means that multiple instances of the same method call can be executing and will be interleaved. Should method level or object level locking be required to introduce Orleans like semantics be required then a decorator will need to be used to ensure this.

The Use of IO threads should not interfere with the execution thread as these are interacted with via message passing and mediated by the event loop. Mutation of the messages after being ‘handed off’ however may cause undesirable effects and as such all ‘sent’ messages should not be further modified and references to them dropped.

As an object can only be running in a single process at a time, Parallelism can be achieved by executing multiple different objects in multiple processes at once. Spreading the work over multiple objects can be used to achieve speedups

File IO

File IO looks simmilar to normal file IO via open(). Under the hood however the implemented in a ‘completion ports’ fashion, rather than notifing when a socket or file is ready, a write or read is performed and the calling process is notified upon compleation of the entire event or an erorr is returned. This lower level interface is exposed to user code should the high level be insufficent.

By abstracting the IO model epicserver hopes to achive independence froma ny one IO loop allowing them to be plugged in with minimal effort.

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