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
The phase where the ExecutionPlan calls sequentially or parallel the underlying functions of all operations contained in execution steps, with inputs/outputs taken from the solution.
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
The ExecutionPlan.dag is a directed-acyclic-graph that contains the pruned nodes as build by Network._prune_graph(). This pruned subgraph is used to decide the execution steps.
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 eviction.

evict
eviction
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 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 last operation result 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 are 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 NetworkOperation.compute()
outputs

A dictionary of computed values returned by NetworkOperation.compute().

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

operation
Either the abstract notion of an action with specified needs and provides, or the concrete wraper FunctionalOperation for arbitrary callables.
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

Method Network._prune_graph() 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

Method network._unsatisfied_operations() collects all operations that fall into any of these two cases:

predicate
node predicate
A callable(op, node-data) that should return true for nodes not to be narrowed().
endurance

Keep executing as many operations as possible, even of some of them fail. If set_endure_execution() is se to true, you may interogate Solution properties to discover whether an operation may be:

  • executed successfully,
  • failed, or
  • canceled, if another operation, the sole provider of a compulsory needs, has failed upstreams.