Keymaker

Keymaker, Neo4j’s Applied Analytics Framework, is a data model agnostic tool designed to help organizations operationalize their graph based analytical queries.

Keymaker includes an admin console where users can build out their analytical or recommendation pipelines, and exposes a GraphQL api where results can be accessed.

image

Important Concepts

Recommendation

A recommendation set is a list of score item pairs, and an additional optional details field.

Both the item and detail fields can be any type supported by Node.js and Neo4j and a score must be a numerical type.

The recommendation set is accumulated and scored using a cypher based analytical engine, built on top of Neo4j.

Analytical Engine

An analytical engine is a pipeline composed of different steps, called phases, which takes as input an arbitrary json object and produces a list of scored results or recommendations.

An engine must have a connection to a Neo4j database.

However, Keymaker is data model agnostic and can connect to any number of existing Neo4j Databases simply by providing the database url and credentials.

Each phase in the engine pipeline is defined using Neo4j’s Cypher query language.

There are two varying execution modes; server-side execution, where all phases are compiled into a single cypher statement and executed at once, and sequential execution, where each phase is executed independently one after another.

High Level Architecture

Keymaker has 3 components: the admin dashboard, admin API, and recommendation API. The admin dashboard and API facilitate user interaction with the framework (i.e. database connection management, engine creation, and writing Cypher). The recommendation API connects with your Neo4j database and exposes an endpoint which executes the engine. The following diagram demonstrates this architecture.

image

Database Prerequisites

Before connecting your Neo4j database to Keymaker ensure that APOC is installed. If you would like to leverage the Graph Data Science Library within your Cypher pipelines ensure that is installed as well.

Engine Phases

There are different types of phases, each with a specific purpose in the pipeline. Each phase has access to all of the engine inputs as cypher parameters.

Discovery Phase

A discovery phase is used to locate potential results or recommendations, giving each item an initial score. The first phase in each engine must be a discovery phase, but additional discovery phases may be included later in the list. A discovery phase can be any arbitrary cypher query which returns a list of records containing a recommendation composed of an item, score, and details field.

Boost Phase

A boost phase is used to modify the score of items previously discovered. A pipeline can have any number of boost phases. A boost phase is any arbitrary query which returns a single score value, which is in turn added to the existing score. Note that unlike Discover phase queries which are each run once, a Boost phase is run against each result or recommendation in the current list. The item in the current result or recommendation can be accessed from cypher using the keyword ‘this’.

Exclude Phase

An exclude phase is used to filter down the list of results or recommendations either by specifying which items to remove, or by specifying which items to keep and removing the rest. Like a boost phase, an Exclude phase is also executed against each recommendation in the list and returns a single corresponding value for each. If the value is null then the recommendation is removed from the list. Exclude phases can also be inverted where only the null values remain.

Diversity Phase

A Diversity phase is used to ensure that the recommendations in the list are heterogeneous over a specified attribute. A limit is chosen for the maximum number of items included for each attribute, prioritizing items with the highest score. The phase then executes a query which returns a single attribute value for each recommendation in the list and then returns the value of the attribute for each item, removing any items beyond the set limit.

Collection Phase

A Collection Phase runs before your pipeline and allows you to match on nodes or patterns in your graph and return them as a map. The values returned in this map will be accessible later at any point in your pipeline using the $ syntax. This becomes especially helpful if there are expensive traversals/computations which would otherwise need to be performed in multiple phases. This phase must end with something that looks like: RETURN \{key: value} AS map.

Write Phase

A Write Phase allows you to perform analysis on your graph and write back results. This phase should have no RETURN statement.

GDS Create Phase

A GDS (Graph Data Science) Create Phase allows you to use gds.graph.create or gds.graph.create.cypher to create an in memory graph. This phase should have no RETURN statement but you will need to YIELD something after the create procedure.

GDS Write Phase

A GDS Write Phase allows you to use gds.<algo>.write to write back to Neo4j or gds.<algo>.mutate to update your in memory graph. You may optionally use gds.<algo>.stream to write results back manually with Cypher. This phase should have no RETURN statement but you will need to YIELD something after the create, mutate or stream procedure.

GDS Drop Phase

A GDS Drop Phase allows you to use gds.graph.drop to remove an in memory graph. This phase should have no RETURN statement but you will need to YIELD something after the drop procedure.

Admin Console

There are different types of phases, each with a specific purpose in the pipeline. Each phase has access to all of the engine inputs as cypher parameters.

Database Connection Monitoring

Keymaker tracks which databases are up and running and can be reached by the framework. It provides relevant information such as the database version and licenses, and ensures that all necessary plugins and procedures are present.

Active and Inactive Phases

Keymaker allows the user to toggle the phases of an engine on and off in order to analyze the impact that each has on the results and performance of the engine.#

Engine Monitoring

Keymaker monitors all engines to indicate when an engine cannot connect to it’s designated database.

Performance Testing

Keymaker’s recommendation API provides lightweight performance testing capabilities where users can execute asynchronous calls to an engine and track there execution times.

Admin API

Keymaker utilizes two GraphQL APIs\. /nThe first is called the admin API.

This API is used to perform all the CRUD operations on your engines and database connections.

It also handles user management and role based access control. The API can also be used to bypass the admin console and interact with the Keymaker database directly.#

Recommendation API

The second API is called the recommendation API. The sole purpose of this API is to handle client requests. When a request is received the API will build the specified engine and execute it against its linked database connection (selected upon engine creation).

Recommendations can be computed in two modes, either incrementally where phases are run independently, or they can be compiled into a single query and executed all at once. Each call to this API will return a list of json objects. Each json record contains the recommended item (some entity in your Neo4j database), a score associated with that item, and another json object which may contain other arbitrary information about that particular recommendation.

The recommendation API can be accessed via any http client. The primary endpoint is called recommendations and has 1 required argument and 5 optional arguments.

The schema definition is as follows:

recommendations(engineID: ID!, params: JSON = {}, first: Int = 20, skip: Int = 0,
  parallel: Boolean = true, concurrency: Int = 5)

As you can see engineID is required and the subsequent arguments are optional and have default values. Executing this inside a GraphQL query would look something like:

query {
  recommendations(engineID: <your_engine_id>, params: {someParam: "value"},
    first: 25, skip: 0, parallel: true, concurrency: 10) {
      item       score       details   }
}

The second endpoint is called recommendationsForPhases and will allow you to run phases either individually or in a custom order of your choosing. It has the same type definition as the recommendations endpoint except it uses a list of phase IDs instead of a single engine ID. Executing this inside a graphql query would look something like:

query {
  recommendationsForPhases(phaseID: [<your_phase_id>, <your_phase_id>], params: {},
    first: 100, skip: 0, parallel: false) {
      item       score       details   }
}

Note: the values given to the optional arguments in these examples are just to demonstrate usage and are completely arbitrary.#

You may provide parameters to your engine via the params object and access them from your cypher phases by prefacing the name of the parameter with a dollar sign (aka $). You may also use the first and skip arguments for pagination. The parallel and concurrency arguments determine whether queries will be run concurrently and to what extent. Keymaker will identify which queries to run concurrently based on the phase type. In addition to the recommendations and recommendationsForPhases endpoints you also have access to the recommendationPerformance endpoint. This can be used to simulate multiple users running your engine.

The schema definition is as follows:

recommendationPerformance(engineID: <your_engine_id>, params: {}, first: 25, skip: 0,
  numUsers: 10, sleep: 500)

The recommendation API also allows you to run an engine as a scheduled database job. The associated endpoints are as follows:

runBatchEngine(engineID: ID!, timeIntervalSeconds: Int!,
  params: JSON = {}, delaySeconds: Int = 0): Boolean
isBatchEngineRunning(engineID: ID!): Boolean
cancelBatchEngine(engineID: ID!): Boolean

Finally, here’s an example of a request to the recommendations API from a client application (javascript) using the http client Axios.

export const getSomeRecommendations = async () => {
  const res = await axios     .post("https://<IP or domain name>/graphql", {
      query: "query { <your graphql query> }",
    })
    .catch((err) => console.log(err));
  return res.data ? res.data.data.recommendations : [];
};

Scaling and Clustering

You can achieve greater throughput with Keymaker by using it with a Neo4j cluster and setting up multiple Keymaker instances pointing to the cluster. The Neo4j database and Keymaker can be scaled independently as needed. A load balancer in the deployment environment would need to load balance between all configured Keymaker installations.

During Engine execution Keymaker is stateless, therefore providing the ability to scale horizontally by installing on additional machines. Keymaker consists of an Administration UI, Adminstration API, internal Neo4j database, and a Recommendations API. Only the Recommendations API would need to be installed and configured separately on each additional machine, the other pieces can remain on the main Keymaker machine and be shared by each additional Recommendations API install.

The image below shows 2 installations of Keymaker - pictured here as the Neo4j Intelligent Recommender Framework - behind a load balancer. Each of the Keymaker installations points to the same Neo4j cluster.

image

Solutions Workbench Integration

You may also leverage the Solutions Workbench visual Cypher Builder to create Keymaker pipelines.

To use this integration first attach a data model to your Keymaker engine (either during engine creation or by clicking the edit button on the engine page). You will only have access to data models you also have access to in Solutions Workbench. Next, click the 'edit visually' button in the phase card drop down menu. This will launch the Solutions Workbench visual Cypher Builder. Build your cypher query (for more info on the Visual Cypher Builder click here) and click the 'Update Keymaker' button in the top right corner of the screen.

This will update the Keymaker phase from which you launched the Cypher Builder.