0.5.4 • Published 5 years ago

flow-calc v0.5.4

Weekly downloads
43
License
MIT
Repository
github
Last release
5 years ago

flow-calc

Serialize and run complex business logic or calculations using a dependency graph/flow programming. Very work-in-progress, used internally on a project. Uses mobx and graphlib.

Very work-in-progress. Use at your own peril, no support is currently offered, etc.

Overview

Motivation is to:

  • Express business logic in a serializable format that can be attached to objects in an application as a kind of contract. Like "here's the numbers we came up with, and here's how we came up with them."

  • Allow for visibility into the calculation flow. Graphs are visualizable, editable, etc. Lots of projects use flow programming.

  • Allow for decoupling of transaction records from the app code that generated them. That code inevitably evolves, making the old calculations difficult to re-generate if needed.

  • Allow for easier composability in situations with complicated dependencies. For example: integrating various tax laws from different locales into a cost calculation. Settle on a convention of inputs and outputs and you could compose the tax calculations in code in any number of sound ways—functions, hierarchies, etc. The issue with using actual coded functions is that sometimes the taxes affect different parts of the overall calculation spreadsheet, and so getting the I/O conventions and dependencies right can lead to spaghetti. Particularly as the code evolves, and support for old ways must continue as new ways are built. If on the other hand we use a graph, a given node should have access to any dependencies it needs just by building an edge, and subsequent parts of the calculation will likewise have access to the tax results, as long as there is no logical circular dependency. (If there is, the calculation is not possible to begin with.)

Yeah so the overall idea idea here is to let the machine figure out the dependencies! And serialize the calculation. As long as the resulting graph, no matter how complicated, is logically acyclic, there should be a topological sort, allowing for a solution.

TODO: readme is out of date. Shocking!

Example

const DGraph = require('flow-calc')

const graphDefinition = [
	{ name: 'staticNode', type: 'static', value: 'hello, ' },
	{ name: 'aliasNode', type: 'alias', mirror: 'inputs.stringValue' },
	{ name: 'concatExample', type: 'transform', fn: 'concat', params: ['staticNode', 'inputs.stringValue'] },
	{ name: 'multiplyExample', type: 'transform', fn: 'mult', params: { amt: 'inputs.numberValue', factor: 3 } }
]

const inputs = {
	stringValue: new Promise(r => setTimeout(() => r('world'), 500)),
	numberValue: 4
}

const dGraph = new DGraph(graphDefinition)
dGraph.run(inputs).then(result => {
	console.log(JSON.stringify(result, null, 4))
})

Output:

{
    "staticNode": "hello, ",
    "aliasNode": "world",
    "concatExample": "hello, world",
    "multiplyExample": 12,
    "inputs": {
        "numberValue": 4,
        "stringValue": "world"
    }
}

Usage

TODO: fill this out.

Some nomenclature. A graph definition, often shortened to graphDef is a JSON array describing nodes and values, transforms on values, and dependencies among nodes/node values. A graph is an in-memory representation of a graph definition, with:

  • live dependency tracking and updating, courtesy of mobx
  • nodes and edges in an instance of a graphlib Graph

Nodes have a name. And/or an id. The code is currently prettly sloppy about this. They're the same thing and the name/id must be unique within the graph.

Nodes also have a value. The value is undefined until all of its dependencies have been resolved. Once resolved, the value can be anything: an atomic built-in, an object, or an array.

All graphs accept inputs. All transform nodes accept params. Other note types will have specific properties, depending. For example an alias node expects a mirror property. For now the best documentation is the source.

Every graph has a special inputs node wherein you can find the inputs passed to the graph, either from the first argument to the run function or as pass-throughs or nodes from supergraphs.

Paths are dot-separated paths to nodes and/or node values (once the node has resolved to, for example, an object).

The wildcard * character can be used in a path for simple property mapping on a collection. For example, given:

    {
        things: [
            { name: 'foo', amount: 4 },
            { name: 'bar', amount: 2 }
        ]
    }

The path things.*.amount would resolve to the array [4, 2]. Only one wildcard per path is supported. Also see caveat below re: mapping over collections in the inputs node (you can't, at the moment).

Features

  • Inputs can be promises (or any then-able).
  • Nodes in a graph can be graphs themselves.
  • Nodes can find their inputs (for graphs) or params (for transforms) by name implicitly when the supplied paths resolve to nodes in the current graph, nodes in the supergraph, or inputs to the supergraph. If you supply a string value as a param or input and it does not resolve to a node name, the graph will interpret it as a literal value.
  • A template subgraph (isTemplate: true) can be used multiple times by explicitly supplying different inputs to each instance.
  • Set collectionMode on a subgraph and pass a path to a collection that resolves to an array. Currently only map is supported: the subgraph will be applied to every item in the collection and the node's value will be the resulting mapped array.
  • Set isHidden: true on a node to hide its value from the output of DGraph.run and DGraph.getState.

Caveats

  • Diagnostics are really pretty bad right now. There is no checking for cycles. Passing the option { logUndefinedPaths: true } to the run function will at least log which nodes remain unresolved as the graph runs. But the dependencies among those nodes is not apparent, so you have to either figure it out in your head or do some trial and error debugging.
  • Currently subgraphs resolve as a whole unit with respect to the containing graph. Individual nodes within the subgraph will not be visible until the entire subgraph resolves. This means that a subgraph A's nodes can depend on a sibling subgraph B's nodes as long as there is not any dependency back from B.someNode to A.someOtherNode. This is true even if there is no logical circular dependency among those nodes. You can use an alias node in the shared parent supergraph to get around this limitation.
  • Nodes currently can't map over arrays (with the wildcard *) in the inputs node of a graph. In other words a path like inputs.someCollection.*.property will fail. You can make an alias of the array path and then map over that. In this case the alias node's mirror value would be inputs.someCollection and the original node could then refer to the alias: aliasOfInputsCollection.*.property.
  • Paths to array indices (eg some.collection.45) probably works but hasn't been tested.
  • This package was put together using nod and, as of this writing, minimal effort to follow best practices or testing. The commands in the Commands section may or may not do the right thing ...

run command line util

Enables running graph compositions from command line.

Node Types

  • static: A hard-coded value. Can be atomic or object, array, etc.
  • comments: Comments node; a no-op in the graph. Every node type also supports a comments property.
  • alias: Aliases any path as a different path.
  • echo: Echos an input node to the output.
  • dereference: Dereference a property of one node based on the value of another node. Like variable[propName] in straight JS.
  • transform: Apply any of several functions on the passed params paths. Similar to stream operators in stream/Rx libraries.
  • inputs: Generated automatically in each graph.
  • async: A node that resolves asynchronously ... no particular use case for it really.
  • branch: Similar to a switch statement, this node resolves to the value of one of several other nodes, depending on the result of a test value as compared to a list of cases.
  • graph: A subgraph. Inputs to the graph can be implicit unless using isTemplate: true.

Transform Functions

Refer to src/transform-fns.js

TODO

So much! Better diagnostics are a big one. Correctly converting mobx edges into graphlib edges is another.

Commands

$ npm test # run tests with Jest
$ npm run coverage # run tests with coverage and open it on browser
$ npm run lint # lint code
$ npm run docs # generate docs
$ npm run build # generate docs and transpile code

API Warning

Note that API below is auto-generated and at this time is not checked for accuracy or usefulness.

API

Table of Contents

getValueAtPathWithArraySupport

Supports using "*" to return an array of items.

"path.to.array." => "path.to.array..path.in.item" =>

Parameters

  • obj any
  • path any

DNode

Base class for DNodes. Construct with the graph in which the node participates, and the node's definition.

Parameters

  • dGraph
  • nodeDef

getPathProps

Properties on the nodeDef that should be treated like paths to values in the graph. Allows checking for the existence of dependent nodes and inferring whether a property value is a path to a node or a literal value.

Return an object with keys that are property names and values that describe how the property names should be handled/interpreted. Currently the only such option is hasSubproperties, which is used to help describe edge i/o. This is a wee messy.

StaticDNode

Extends DNode

Initial value is its forever value.

Usage:

{ name: , type: "static", value: }

Parameters

  • dGraph
  • nodeDef

AliasDNode

Extends DNode

Provide an alias name for a path to a value. Usage:

{ name: , type: "alias", mirror: "path.to.other.value" }

Parameters

  • dGraph
  • nodeDef

EchoDNode

Extends DNode

Echos to output state an input node with the same name as this node. Normally, inputs cannot have names that conflict with node names. The echo node is an exception to this.

An inputName prop is optional but if the input name is different you could probably use an alias node instead.

Parameters

  • dGraph
  • nodeDef

DereferenceDNode

Extends DNode

Dereference a property using a dynamic value path.

Usage:

{ name: , type: "dereference", objectPath: <path to object value to dereference>, propNamePath: <path to propName, a string value> }

The value of the node will be the value of objectpropName.

Parameters

  • dGraph
  • nodeDef

TransformDNode

Extends DNode

Take the values of n input nodes and output a value based on one of several predefined functions.

Usage:

{ name: , type: "transform", fn: <fn name, a function exported from transform-fns.js> params: { <...list of params, depending on fn> } }

Parameters

  • dGraph
  • nodeDef

InputsDNode

Extends DNode

Used internally to automatically create an inputs node.

Parameters

  • dGraph
  • nodeDef

AsyncDNode

Extends DNode

Node with an async value. Really only used for testing, since this node would not be serializable.

Parameters

  • dGraph
  • nodeDef

BranchDNode

Extends DNode

Acts like a switch statement for other graph nodes, depending on the value of passed test value as compared to elements of the passed cases array.

Note at this time only the test value can be dynamic (ie, be a path to a node the value of which is resolved at runtime).

Expects a one-to-one mapping from cases to nodeNames.

A _default_ case can be included (& hopefully no one would ever need a legit value to be called "default").

Parameters

  • dGraph
  • nodeDef

GraphDNode

Extends DNode

Create a subgraph node. The value of this node can depend on some of its supergraph's nodes and its supergraph's nodes can depend on the value of this node. Just be sure those are two separate sets of nodes: circular dependencies will prevent the graph from ever fulfilling.

You can supply explicit inputs with an inputs property. Otherwise, the subgraph will attempt to find its required inputs automatically from its supergraph's nodes OR, barring that, from properties on its supergraph's inputs node.

Usage:

{ name: , type: "graph", graphDef: <graph definition, aka an array of node definitions>

}

Parameters

  • dGraph
  • nodeDef

waitForFulfillment

Don't we all ... don't we all.

_runAsMap

Conventions for mapping a template graph over a collection of items:

  • The mapping node must provide an collection property in the graph's inputs.
  • Each item in the collection will be passed to the graph that is applied to each item as item.
  • Remaining properties in inputs will be available as named.

So in the supergraph definition:

{
  "name": "mappingNodeName",
  "type": "graph",
  "collectionMode": "map",
  "graphDef": "graphToBeAppliedToEachItem"
  "inputs": {
    "collection": "nodeThatResolvesToArrayOfObjects",
    "otherArg": "someOtherArgsGoHere"
  }
}

Let's say the nodeThatResolvesToArrayOfObjects resolves to [{ value: 5 }, { value: 20 }] and someOtherArgsGoHere resolves to the number 3.

Then graphToBeAppliedToEachItem could be defined as, for example:

[{
  "name": "result",
  "type": "transform",
  "fn": "mult",
  "params": {
    "amt": "inputs.item.value",
    "factor": "inputs.otherArg"
  }
}]

Then mappingNodeName should resolve to an array like [{ result: 15 }, { result: 60 }].

Parameters
  • args any
  • dispose any

DGraph

Extends EventEmitter

DGraph: Dependency Graph

DGraph allows you to calculate values using dependency graphs (https://en.wikipedia.org/wiki/Dependency_graph), using a few built-in node types and built-in operations.

All operations and nodes available can be found in dNodes.js, but the basic operations (called "transforms" in the code) are things like AND, OR, ADD, MULTIPLY, and so on. From a handful of primitive operations and topologically-sorted evalutation trees, you can build up complicated business logic which can be serialized and composed.

It uses reactive programming and works more or less like a spreadsheet, where dependent cells are automatically re-evaluated when their inputs change.

Basic use:

  1. define a graph, graphDef, using JSON
  2. build an in-memory graph via const g = new DGraph(graphDef)
  3. run the graph by passing in your inputs: g.run({ vehicle: {...}, user: {...} })
  4. run returns a promise which will fulfill with the calculated values, derived from the inputs

For now refer to the /scripts/d-graph/tests files to see examples of building and running a DGraph. One note: when you run a DGraph, it automatically creates an inputs node that will contain all the values you provide. Refer to those values via a path, just like you would to any other node: inputs.user.name.first, etc. This obviously means that you shouldn't name a node inputs in the graph definition itself.

Two features make DGraph particularly useful for us:

  1. Inputs can be promises. When the promise fulfills, the graph updates all dependent values.
  2. There is a graph node type, with which you can define subgraphs. The subgraph can run asynchronously and its evaluated results can be referred to just like any other value in the graph.

#1 means that we can use queries or other asynchronous operations as direct inputs to the graph. For example:

g.run({
  user: User.findOne({ _id: userId }).exec()
})

#2 means that we can compose graphs and business logic. If, for example, the only difference between two state's cost calculations is whether mileage is taxed or not, we should be able to build a mostly re-usable cost graph and just plug in the tax calculation subgraph for each state.

NB Current limitations:

  • Diagnostics are not very helpful. When things go wrong the graph can often just stall in an unresolved state with little clue as to what didn't work out.

  • Subgraph nodes can depend on interior nodes of other subgraphs, BUT ONLY if there no resulting cycle between the subgraph nodes themselves. In other words if A and B are subgraphs, A.nodeInA can depend on B.nodeInB, or vice-versa, but if you have dependencies in both directions, the graph will never resolve, even if there is no logical cycle among individual nodes (that is, if they were all together in a single big graph). You can work around for now this by defining an alias of one of the values in the root graph and then referring to that.

Parameters

  • graphDefinition Array A list of nodes describing this graph.
  • name String? The name of the graph.
  • supergraph DGraph? This graph's supergraph (used by graph nodes).
  • options Object? Options object.

getDNode

Return the DNode identified by name in this graph.

If searchAncestors is true, also search in supergraphs.

Parameters

getDNodes

A list of all DNodes in this graph.

getDEdges

A list of all edges in this graph. An edge is shaped like:

{
  srcNodeId: <source node name>,
  srcPropName: <dependent property name in source>,
  dstNodeId: <dest node name>,
  dstValuePath: <path to depended-upon value in dest>
}

dstValuePath may be undefined if the dest node value is atomic.

setInputs

Parameters
  • inputs any a plain object. values can be either promises or plain values.

srcFromPath

Returns a pair with the nodeId split out from the rest of the path:

{ nodeId: , valuePath: <rest of the path, if any> }

Parameters
  • srcPath

normalizePathDef

Accept a few ways to specify paths to other values in the graph. In all cases return an object with keys that are property names and values that are paths.

Parameters
  • pathDef

collectExpectedInputNames

Traverse nodes and if any node depends on the inputs node, collect the top-level property name required.

Parameters
  • graphDef

collectExpectedInputPaths

Traverse nodes and if any node depends on the inputs node, collect the full path of that dependency, except for inputs. at the beginning of the path (that part is assumed).

Relies on DNode class advertising their property names that will refer to other nodes in getPathProps.

Pass recursive to include subgraph inputs in result. This will not currently include template subgraph inputs.

Parameters
  • graphDef
  • recursive (optional, default false)

collectEdgeDefs

Collect edges, v -> w, read v depends upon w. Resulting edges are shaped:

{ srcNodeId, srcPropName, dstNodeId, dstValuePath }

Precondition for running this is that the graph and all subgraphs are constructed.

Parameters
  • dNode

collectObjectPaths

Traverses passed object depth-first and collects all object's own paths recursively.

Parameters

  • obj Object The object to traverse.
  • parent String Parent property name, if any.

Returns Array<String> Array of paths.

flattenObject

Flattens a deep object tree (arrays and objects) into a single non-tree object whose keys are the paths of nested properties with matching values.

Properties with dots in their names should be preserved.

Not super-speedy. :)

Parameters

  • obj Object Object to flatten.
  • filterFn

expandObject

Expands an object flattened by flattenObject.

Parameters

  • obj Object Object to expand
  • filterFn

pathValueToObject

Create minimal nested objects/arrays following path all the way down to the last item in path, then set the value of that property.

Not particularly well-tested.

Parameters

  • path String A path like "path.to.3.property".
  • value Any Value to set.
  • oObj (optional, default null)
  • obj Object If defined, create the path in this object or use object's existing path.

extractNItems

Try to extract an array of values from arguments to the xxxN functions.

Bit cheesy to accept all these variants.

Parameters

  • items any

License

MIT © Diego Haz

generator-nod
graphliblodashmobxmobx-utilsyargs
@everything-registry/sub-chunk-1683@zalastax/nolb-flow@infinitebrahmanuniverse/nolb-flow
0.5.4

5 years ago

0.5.3

5 years ago

0.5.2

5 years ago

0.5.1

5 years ago

0.5.0

5 years ago

0.4.1

5 years ago

0.4.0

5 years ago

0.3.0

5 years ago

0.2.1

5 years ago

0.2.0

5 years ago

0.1.0

5 years ago

0.0.1

5 years ago