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Algorithm execution engine.


The Side-effect-Process-Loop, or SePL in short, is a simple-yet-powerful algorithm execution framework.
It requires the user, you, to express logic to be executed via simple, concise, declarative building blocks. In return, the very execution of the algorithm gets converted into data, literally. Since data is easy to control and manipulate, so too is the execution of the algorithm itself.
Pragmatically, processes which require elaborate integration with the world outside, or which are long-running (and so are naturally more fragile), stand to gain the most from using this library.


Easy maintenance
Plug and play with existing or new integrations effortlessly.

Execution-style agnostic
Run async or blocking, locally or remotely, or fuse different styles together.

** The above two are largely thanks to the seamless-async library

Space-time agnostic
Execution is data, and data can be sent anywhere (ok, anywhere but backwards in time, which is doable, yet can be tricky).

Execution is data, and data can be persisted.

Disaster recovery
See above.

Advanced safety measures
Automatically break execution by the amount of steps taken, overall running time, or by depth of the unfolding algorithm computation tree.

User optimizations
The user may interject optimization logic, that's capable of mutating the execution running state, by observing the unfolding algorithm computation tree.

Similar to code debugging in concept, but at the algorithm terminology level - you debug your own business logic, rather than code lines.


This approach may be applied to existing code, or while designing new code. There is no need to structure your code differently. The approach does generally encourage 'clean' code, so for example, code that already maintains proper separation of concerns (e.g. side effects divorced from pure business logic) would fit more naturally with this.

To use it, SePL requires 3 inputs:

  1. Logic declaration - a collection of logic segments called flows.
    A flow expresses a segment of your logic, which starts with some interaction with the world, and moves on to further computations based on the outcome of that interaction. Such interactions are called side effects, and they're used to either enrich the internal state of the execution, or to mutate the external state of the world. The computation part of the flow is generically called process, the outcome of which are followup steps (see step below) to be taken next.
    Flows aim to uniquely identify naturally fragile points of execution, or to segment your logic to the most high-level building blocks. For example, Ginfer - the graph inference library that's built ontop of SePL, only consists of 3 flows: update, notify, and eval.

  2. State - a user-defined, stateful context, to be preserved/mutated throughout the execution.
    This state could be anything, it is simply passed as argument to user logic throughout the execution. It can be mutated by declaring a flow which only has its side effect declared but lacks process - the outcome of the side effect would effectively become the mutated state for subsequent execution steps.

  3. Initial step - data model referencing some flow, and holding the input arguments needed for the execution of that flow.
    The execution of flows creates further steps, so this is simply how the SePL is triggered initially. Steps serve as the 'glue' between flows, and when put together in a sequence, they express the algorithm execution as computations tree, in pure data terms.

Let's see what all this means through an example.
We will show how SePL may be used to execute the Towers of Hanoi solution.

user=> (def towers-of-hanoi {"peg-A" [3 2 1]
  #_=>                       "peg-B" []
  #_=>                       "peg-C" []})

user=> (def num-disks (count (get towers-of-hanoi "peg-A")))

user=> (defn move-disk
  #_=>   "Move top-most disk from the 'from' peg to the 'to' peg."
  #_=>   [state [from to]]
  #_=>   (-> state
  #_=>       (update from pop)
  #_=>       (update to conj (peek (get state from)))))

user=> (defn get-auxiliary-peg
  #_=>   "Given current state and from/to pegs, identify and return auxiliary peg."
  #_=>   [state [from to]]
  #_=>   (first (keys (dissoc state from to))))

So above, we're given the initial state of the problem to solve, and we also have some fns which allow us to interact with that state.
So far, nothing out of the ordinary, this is just exposition - non Sepl yet.

Next, enter SePL.
We would require 2 flows:

  • 'move-disk' would be given from/to pegs as arguments, and it would do just that, with the help of the move-disk side effect fn. This would be a state mutating flow so it would have no further computations declared.
  • 'solve' would be given the same arguments. For side effect, it would first realize which peg can be used as auxiliary. Then it would call itself to move all disks, but the bottom one, to the auxiliary peg. Next it would call 'move-disk' to move the single remaining disk to its final resting place. Finally, it would call itself once again to move all disks from the auxiliary peg, on top of the single disk.

We define the solve fn to carry out all these computations of the 'solve' flow.
To indicate which flows should be next, the ->step api is used, with flow id and arguments.

After all that, we're ready to properly declare our flows as a mapping from flow ids to our logic.

user=> (require '[sepl.core :refer [->step sepl lazy-sepl]])

user=> (defn solve
  #_=>   "Move all but bottom disk to auxiliary peg, then move it to target peg,"
  #_=>   "and finally move all disks from auxiliary peg to target peg."
  #_=>   [[from to num-disks]                                      ;args
  #_=>    aux                                                      ;side-effect outcome
  #_=>    & [metacontext]]                                         ;ignore -- not in use
  #_=>   (if (= 1 num-disks)
  #_=>     [(->step "move-disk" [from to])]
  #_=>     (-> '()
  #_=>         (->step "solve" [from aux (- num-disks 1)])
  #_=>         (->step "move-disk" [from to])
  #_=>         (->step "solve" [aux to (- num-disks 1)]))))

user=> (def flows {"move-disk" {:side-effect-fn move-disk}
  #_=>             "solve"     {:side-effect-fn get-auxiliary-peg
  #_=>                          :process-fn     solve}})

So -
Our flows are properly defined above.
Our state is the very exposition of the Towers of Hanoi problem above.
For initial step we trigger 'solve' to move all disks from peg-A to peg-B.
Calling the sepl api with these would eagerly solve the Towers of Hanoi:

user=> (def initial-step (->step "solve" ["peg-A" "peg-B" num-disks]))

user=> (sepl flows towers-of-hanoi [initial-step])
{"peg-A" [], "peg-B" [3 2 1], "peg-C" []}

Many words, very little code. Simple?

Lets have a look at a slightly different execution api.
lazy-sepl is triggered in much the same fashion, but returns a lazy sequence of execution steps. Steps are being executed as they're being pulled from the sequence. The following specifically filters those state-mutating 'move-disk' steps, and gives us the state at those positions:

user=> (->> (lazy-sepl flows towers-of-hanoi [initial-step])
  #_=>      (filter (comp #{"move-disk"} :type :step))
  #_=>      (map :state))
[{"peg-A" [3 2] "peg-B" [1] "peg-C" []}
 {"peg-A" [3] "peg-B" [1] "peg-C" [2]}
 {"peg-A" [3] "peg-B" [] "peg-C" [2 1]}
 {"peg-A" [] "peg-B" [3] "peg-C" [2 1]}
 {"peg-A" [1] "peg-B" [3] "peg-C" [2]}
 {"peg-A" [1] "peg-B" [3 2] "peg-C" []}
 {"peg-A" [] "peg-B" [3 2 1] "peg-C" []}]

This is cool!
It gives a sense of what the logic was 'thinking' at each step of the way.

It is important to understand - where 'state' is used in code above, it is really an analogue for db/cloud/remote storage access; and the Towers of Hanoi is logic analogue of some long-running computation, hiding dozens of fns, and hundreds of code lines, under a flow construct. Framing these in SePL terms did not require us to do anything much different than we normally would, it simply gave us an unparalleled level of control over the execution of the logic.

SePL is merely a simple iteration over data maps, and nothing more.
Each of those maps, a step, is referencing a single building-block of our logic (a flow), and carrying the arguments for its invocation.
During each iteration, the flow's side effect is executed over the global state and using given arguments. The outcome is then passed along with those same arguments as input for the flow's process, to determine next steps. And so the loop keeps feeding itself, until all steps have exhausted. Steps are added at the head, which gives a DFS style execution, rather than at the tail (which would have produced a BFS walk). When all is set and done, the sequence of steps that was produced, is essentially an expression of the execution computations tree that was unfolded. This is how SePL is successfully converting execution of logic into data.

If all this is a bit much, but you are curious, perhaps the baby-sepl library is just what you need. It demonstrates the essence of the concept expressed here, but does so as a gist, in less than 20 lines of code..

It is worth mentioning one more execution variation - async-sepl is somewhat similar to lazy-sepl; it pushes interim states onto a channel, out of which they are pulled one by one. All SePL execution variations in essence do exactly the same thing, these are just different implementation flavours.

Further examples are expressed as tests:


Copyright © 2023 @s-doti

This program and the accompanying materials are made available under the terms of the Eclipse Public License 2.0 which is available at

This Source Code may also be made available under the following Secondary Licenses when the conditions for such availability set forth in the Eclipse Public License, v. 2.0 are satisfied: GNU General Public License as published by the Free Software Foundation, either version 2 of the License, or (at your option) any later version, with the GNU Classpath Exception which is available at

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