4. Architecture

COMPUTE
computation

%3 graphtik-v4.1.0 flowchart cluster_compute compute operations operations compose compose operations->compose network network compose->network compile compile network->compile inputs input names inputs->compile outputs output names outputs->compile predicate node predicate predicate->compile plan execution plan compile->plan execute execute plan->execute solution solution execute->solution values input values values->execute The definition & execution of networked operation is splitted in 1+2 phases:

… it is constrained by these IO data-structures:

… populates these low-level data-structures:

… and utilizes these main classes:

graphtik.op.FunctionalOperation
graphtik.netop.NetworkOperation
graphtik.network.Network
graphtik.network.ExecutionPlan
graphtik.network.Solution
compose
COMPOSITION

The phase where operations are constructed and grouped into netops and corresponding networks.

Tip

compile
COMPILATION
The phase where the Network creates a new execution plan by pruning all graph nodes into a subgraph dag, and derriving the execution steps.
execute
EXECUTION
sequential

The phase where the ExecutionPlan calls the underlying functions of all operations contained in execution steps, with inputs/outputs taken from the solution.

Currently there are 2 ways to execute:

  • sequential
  • parallel, with a multiprocessing.ProcessPool

Plans may abort their execution by setting the abort run global flag.

parallel
parallel execution
execution pool
task

Execute operations in parallel, with a thread pool or process pool (instead of sequential). Operations and netop are marked as such on construction, or enabled globally from configurations.

Note that sideffects are not expected to function with process pools, certainly not when marshalling is enabled.

process pool

When the multiprocessing.Pool() class is used for parallel execution, the tasks must be communicated to/from the worker process, which requires pickling, and that may fail. With pickling failures you may try marshalling with dill library, and see if that helps.

Note that sideffects are not expected to function at all. certainly not when marshalling is enabled.

thread pool
When the multiprocessing.dummy.Pool() class for parallel execution, the tasks are run in process, so no marshalling is needed.
marshalling

Pickling parallel operations and their inputs/outputs using the dill module. It is configured either globally with set_marshal_tasks() or set with a flag on each operation / netop.

Note that sideffects do not work when this is enabled.

configurations

The functions controlling compile & execution globally are defined in config module; they undelying global data are stored in contextvars.ContextVar instances, to allow for nested control.

All boolean configuration flags are tri-state (None, False, True), allowing to “force” all operations, when they are not set to the None value. All of them default to None (false).

graph
network graph

The Network.graph (currently a DAG) contains all FunctionalOperation and _DataNode nodes of some netop.

They are layed out and connected by repeated calls of Network._append_operation() by Network constructor.

This graph is then pruned to extract the dag, and the execution steps are calculated, all ingridents for a new ExecutionPlan.

dag
execution dag
solution dag

There are 2 directed-acyclic-graphs instances used:

steps
execution steps

The ExecutionPlan.steps contains a list of the operation-nodes only from the dag, topologically sorted, and interspersed with instruction steps needed to compute the asked outputs from the given inputs.

It is built by Network._build_execution_steps() based on the subgraph dag.

The only instruction step is for performing evictions.

evictions
The _EvictInstruction steps erase items from solution as soon as they are not needed further down the dag, to reduce memory footprint while computing.
solution

A Solution instance created internally by NetworkOperation.compute() to hold the values both inputs & outputs, and the status of executed operations. It is based on a collections.ChainMap, to keep one dictionary for each operation executed +1 for inputs.

The results of the last operation executed “wins” in the final outputs produced, BUT while executing, the needs of each operation receive the solution values in reversed order, that is, the 1st operation result (or given input) wins for some needs name.

Rational:

During execution we want stability (the same input value used by all operations), and that is most important when consuming input values - otherwise, we would use (possibly overwritten and thus changing)) intermediate ones.

But at the end we want to affect the calculation results by adding operations into some netop - furthermore, it wouldn’t be very usefull to get back the given inputs in case of overwrites.

overwrites
Values in the solution that have been written by more than one operations, accessed by Solution.overwrites:
net
network
the Network contains a graph of operations and can compile an execution plan or prune a cloned network for given inputs/outputs/node predicate.
plan
execution plan

Class ExecutionPlan perform the execution phase which contains the dag and the steps.

Compileed execution plans are cached in Network._cached_plans across runs with (inputs, outputs, predicate) as key.

inputs
a dictionary of named input values given to a single operation, or to a netop, fed into Operation.compute() method.
outputs

A dictionary of computed values returned by a single operation or a netop when method Operation.compute() is called, or the actual (partial or complete) provides returned by some FunctionalOperation.

All computed values are retained in it when no specific outputs requested, to NetworkOperation.compute(), that is, no evictions happens.

A function may return partial outputs.

returns dictionary
When an operation is marked with this flag, the underlying function is not expected to treturn a sequence but a dictionary; hence, no “zipping” of outputs/provides takes place.
operation
Either the abstract notion of an action with specified needs and provides, or the concrete wraper FunctionalOperation for arbitrary functions (any callable).
netop
network operation
The NetworkOperation class holding a network of operations.
needs
A list of names of the compulsory/optional values or sideffects an operation’s underlying callable requires to execute.
provides
A list of names of the values produced when the operation’s underlying callable executes.
sideffects
Fictive needs or provides not consumed/produced by the underlying function of an operation, annotated with sideffect. A sideffect participates in the solution of the graph but is never given/asked to/from functions.
prune
pruning

A subphase of compilation performed by method Network._prune_graph(), which extracts a subgraph dag that does not contain any unsatisfied operations.

It topologically sorts the graph, and prunes based on given inputs, asked outputs, node predicate and operation needs & provides.

unsatisfied operation

The core of pruning & rescheduling, performed by network._unsatisfied_operations() function, which collects all operations that fall into any of these 2 cases:

  • they have needs that do not correspond to any of the given inputs or the intermediately computed outputs of the solution;
  • all threir provides are NOT needed by any other operation, nor are asked as outputs.
reschedule
rescheduling
partial outputs
partial operation
canceled operation

The partial pruning of the solution’s dag during execution. It happens when any of these 2 conditions apply:

  • an operation is marked with the FunctionalOperation.rescheduled attribute, which means that its underlying callable may produce only a subset of its provides (partial outputs);
  • endurance is enabled, either globally (in the configurations), or for a specific operation.

the solution must then reschedule the remaining operations downstreams, and possibly cancel some of those ( assigned in Solution.canceled).

endurance

Keep executing as many operations as possible, even if some of them fail. Endurance for an operation is enabled if set_endure_operations() is true globally in the configurations or if FunctionalOperation.endurance is true.

You may interrogate Solution.executed to discover the status of each executed operations or call scream_if_incomplete().

predicate
node predicate
A callable(op, node-data) that should return true for nodes to be included in graph during compilation.
abort run

A global configurations flag that when set with abort_run() function, it halts the execution of all currently or future plans.

It is reset automatically on every call of NetworkOperation.compute() (after a succesfull intermediate compilation), or manually, by calling reset_abort().