scoro

Serializable COROutines in a Crystal
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scoro

Serializable COROutines in a Crystal (white-box state machine approach)

Works by recursively unrolling AST to one state machine

This would be impossible without

Installation

  1. Add the dependency to your shard.yml:

    dependencies:
      scoro:
        github: konovod/scoro
    
  2. Run shards install

Usage

Simple example

require "scoro"

LIST = ["a", "b", "c"]

# This will create Serializable COROutine (SCORO)
fib = scoro do
  10.times do |i|
    LIST.each do |item|
      puts "#{item}, #{@_i1}"
      yield
    end
  end
end

# it can be `#run` sequentially (will execute until next yield)
2.times do
  fib.run
end # will print "a, 0" and "b, 0"

# its state can be saved and restored
puts fib
fib2 = fib.dup

# and then it can be resumed from point where it stopped
while !fib2.complete
  fib2.run
end # will print "c, 0","a, 1","b, 1","c, 1",...

implement_scoro # must be placed at end of file to actually implement all scoro classes

Named scoros

require "scoro"

LIST = ["a", "b", "c"]

# This will declare class Fiber1 with a Serializable COROutine (SCORO)
scoro(Fiber1) do
  10.times do |i|
    LIST.each do |item|
      puts "#{item}, #{i}"
      yield
    end
  end
end

# it can be instantiated and then used:
fib = Fiber1.new
while !fib.complete
  fib.run
end

implement_scoro # must be placed at end of file to actually implement all scoro classes

Local vars

local vars won't be saved in a scoro. So they can be only inside one state (between yields).

scoro(Fiber1) do
  loop do
    a = 1 # var usage is limited to one state
    10.times do |i|
      a += 1
    end
    puts a # this will compile, because `a` is defined in same state as used
    yield
    # puts a <- compilation error, because `a` isn't serialized and can be undefined at this point
  end
end

To solve it, use serialized vars (instance vars of scoro class). They can be defined as following:

scoro(Fiber1) do
  loop do
    @a : Int32 = 1 # instance var, can be used in any states
    10.times do |i|
      @a += 1
      yield
    end
    yield
    puts @a
  end
end

Passing arguments to scoros

Note first example used constant LIST instead of local var list because scoros do not capture local vars. To pass arguments to scoro you can use serialized vars without initial value:

  fib = scoro(list: ["a", "b", "c"]) do
    @list : Array(String) # declares serialized var without initial value. Note list is passed as named argument when creating fiber
    2.times do |i|
      @list.each do |item|
        puts "#{item}"
        yield
      end
    end
  end

  fib.run  

or for the named scoros:

scoro FiberWithList do
  @list : Array(String)
  @list.each do |item|
    puts item
    yield
  end
end

# fib = FiberWithList.new <- this won't compile
fib = FiberWithList.new(list: ["a","b","c"])

Blocks

In general, yields inside blocks can't be serialized:

scoro MyFiber do
  thrice do |item| # what is contained inside function `thrice` is unknown to a scoro library, so it's state can't be serialized
    puts item
    yield
  end
end

All above examples used blocks though and compile. Why? Because calls of times, each, loop are hardcoded in a library.

loop: simplest, equivalent to while true..., no serialized state

n.times: equivalent to i=0; while i<n..., has internal state of one Int32 var

list.each do |item|: rewritten to i=0; while i<n; item=list[i]..., has internal state of one Int32 var (index)

Actual serialization

ok, all of this is nice, but how to actually serialize coroutine state! All generated scoro classes are inherited from SerializableCoroutine You can include JSON::Serializable or include YAML::Serializable or add other serialization method of your choice to it.

If you use named scoro, you can also reopen its class to add needed methods.

require "scoro"
require "json"

class SerializableCoroutine
  include JSON::Serializable
end

fib = scoro(list: ["a", "b", "c"]) do
  @list : Array(String)
  2.times do |i|
    @list.each do |item|
      puts "#{item}"
      yield
    end
  end
end

fib.run
puts fib.to_json # prints {"state":7,"complete":false,"list":["a","b","c"],"_i1":0,"_i2":0}

fib2 = typeof(fib).from_json(fib.to_json)
fib2.run

implement_scoro

Limitations

Note that most limitations only apply to code that contain yield. AST nodes without yield are mostly copy-pasted to a generated state-machine, so everything should work there.

  • currently, type of serialized vars must be always specified explicitly
  • currently, serialized vars can be declared only on top level. This example won't compile:
  while some_action_required
   @i : Int32 = 5
   while @i > 0 
     @i -= 1
     yield
   end
  else  
  end

Fix it so:

  @i : Int32 = 0
  while some_action_required
   @i = 5
   while @i > 0 
     @i -= 1
     yield
   end
  end
  • ensure\rescue blocks are not supported. I don't think they are needed for most use cases. Any exception inside scoro will just propagate to calling code.
  • yield inside general blocks is not supported. Hardcoded support added for times, loop, each blocks. Support for each_with_index and sleep is planned.
  • each caller is evaluated on every iteration, so [1,2,3].each inside scoro isn't a good idea (will allocate new array every time scoro is resumed).
  • each only work with Indexable, not Enumerable, because state of Indexable iteration can be easily serialized - it's just index
  • for now, all control constructs (next`break\return) unrolls involved loops to state machine even if there is no yield` inside loop.
  • currently, x = if..., x = case... are not supported

Development

  • BUG: update all variables for inner loops
  • BUG: case is still not implemented
  • FEATURE: pass arguments to anonymous scoros
  • OPTIMIZATION: merge loop start with loop control state
  • OPTIMIZATION: don't mark all ControlExpressions as dirty (separate pass?)
  • OPTIMIZATION: tail call of next state
  • FEATURE: sleep
  • FEATURE: channel.send\receive?
  • FEATURE: each_with_index
  • FEATURE: if, case with assignment
  • FEATURE: named yields, to make serialization more readable (and changeproof)

Contributing

  1. Fork it (https://github.com/konovod/fork)
  2. Create your feature branch (git checkout -b my-new-feature)
  3. Commit your changes (git commit -am 'Add some feature')
  4. Push to the branch (git push origin my-new-feature)
  5. Create a new Pull Request

Contributors

Repository

scoro

Owner
Statistic
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  • 11 months ago
  • January 15, 2024
License

MIT License

Links
Synced at

Sun, 22 Dec 2024 18:01:55 GMT

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