low code – Data Commons Scotland

“Might GraphQL be an easy means to query Linked Open Data? Moreover, might it even be a handy means for building it?”

We use a product called JUXT Site, in exploring these questions.

JUXT Site

JUXT Site is a software product which offers a low-code approach for building HTTP-accessible databases. It has an add-on which allows for those databases (data models with their read & write operations) to be defined using GraphQL schemas.

JUXT Site is built on top of a database engine called XTDB which natively supports (temporal) graph queries (in the Datalog language). It has an add-on which supports (a subset of) SPARQL.

JUXT Site is Open-source software. It is in the pre-alpha phase of development (although its XTDB substrate is production ready).

So, JUXT Site’s has components which make it a promising platform on which to explore the questions at the head of this article. Here’s a diagram which summaries these components:

(We made a small modification site-mod-sparql to JUXT Site, to surface XTDB’s SPARQL support.)

Next, we use JUXT Site’s GraphQL to build then query our linked data model.

Using GraphQL to build our linked data model

We took a subset of the linked data model that we defined for carbon savings and defined it using a GraphQL schema. The following snippets provide a flavour of that GraphQL definition.

Defining a record type

A StcmfRedistributedFood record says that a batch (weight batchKg) of food material was (time period from → to) redistributed to a destination. In our linked data model, further information about this – such as how the food material gets repurposed at the destination, and the lookup tables to calculate carbon savings – may be found by following links to other nodes in the data graph.

Here is how a StcmfRedistributedFood record type, is defined in GraphQL (on JUXT Site):

""" A batch of redistributed food material """
type StcmfRedistributedFood {

  id: ID!

  " The start of the period, inclusive "
  from: Date! @site(a: "pasi:pred/from") (1)

  " The end of the period, exclusive "
  to: Date! @site(a: "pasi:pred/to")

  " How the food material got used "
  destination: StcmfDestination
    @site( (2)
      q: { find: [e]
           where: [[e {keyword: "pasi:pred/type"} "StcmfDestination"]
                   [object {keyword: "pasi:pred/destination"} e]]
         }
    )

  " The weight in kilograms of this batch of food material "
  batchKg: Float! @site(a: "pasi:pred/batchKg")
}

In GraphQL, a directive (@(…)) can be used to say how a field should be mapped to/from to the underlying system.

In JUXT Site, @site(…) directives are used to map to/from structures in the underlying XTDB database. On this specific line, a: says that the field named from at the GraphQL level, should be mapped from the field named pasi:pred/from at the XTDB level.

At the XTDB level, we use names like pasi:pred/from for our fields because such names are IRI-compliant, which means that they can be used as RDF predicates and queried using SPARQL.
In this directive, we use the Datalog language to code how to find the appropriate StcmfDestination record in the underlying XTDB database.

Defining a query

Here is how a query operation to return all StcmfRedistributedFood records, is defined in GraphQL (on JUXT Site):

type Query {

  """ Return all records about batches of redistributed food material """
  stcmfRedistributedFood: [StcmfRedistributedFood]! (1)
}

Simply declare that this returns a list ([…]) of StcmfRedistributedFood records, and JUXT Site will take care of the implementation details.

Defining a mutation

Here is how a mutation operation to create or update a StcmfRedistributedFood record, is defined in GraphQL (on JUXT Site):

type Mutation {

  """ Create or update a record about a batch of redistributed food material """
  upsertStcmfRedistributedFood(

    id: ID
      @site(
        a: "xt/id"
        gen: {
          type: TEMPLATE
          template: "pasi:ent/StcmfRedistributedFood/{{from}}/{{to}}/{{destination}}" (1)
        }
      )

      " The start of the period, inclusive "
      from: Date! @site(a: "pasi:pred/from")

      " The end of the period, exclusive "
      to: Date! @site(a: "pasi:pred/to")

      " How the food material got used "
      destination: String! (2)
      destinationRef: ID
        @site(
          a: "pasi:pred/destination"
          gen: {
            type: TEMPLATE
            template: "pasi:ent/StcmfDestination/{{destination}}"
          }
        )

      " The weight in kilograms of this batch of food material "
      batchKg: Float! @site(a: "pasi:pred/batchKg")

  ): StcmfRedistributedFood @site(mutation: "update")
}

We specify that a StcmfRedistributedFood record is identified by an IRI-compliant, natural key, composed from the from, to and destination values. Uniqueness is enforced over ID values therefore that combination of from, to and destination values will identity one or zero existing record(s).
On invocation, this mutation will be supplied with a String value for the destination parameter. The destination String value is used to construct the ID of the targeted StcmfDestination record, and this ID is stored in a field named pasi:pred/destinationin the underlying XTDB database.

Querying our linked data model

We used JUXT Site’s GraphQL to build our linked data model (in terms of data structures and operations). Now let’s see what querying our data model looks like – firstly using GraphQL, then using SPARQL.

We will query not only for our StcmfRedistributedFood records but also for the associated information that we would need to create a waste reduction report which includes estimates of carbon savings. (Although we haven’t discussed this associated information in this article, showing the queries for it will make this exploration more informative.)

Querying using GraphQL

The query:

query PASI {
  stcmfRedistributedFood {
    batchKg
    from
    to
    destination {
      name
      refDataConnectors { (1)
        fraction
        refMaterial {
          carbonWeighting
          wasteStream
        }
        refProcess {
          name
        }
        enabler {
          name
        }
      }
    }
  }
}

We haven’t discussed it in this article but we introduced an artificial direct connection, called refDataConnectors, into our data model to allow a query to walk easily to the reference data records that are needed to report on carbon savings.

The query’s raw result (truncated):

{
  "data": {
    "stcmfRedistributedFood": [
      {
        "batchKg": 87.61,
        "from": "2021-01-28",
        "to": "2021-01-29",
        "destination": {
          "name": "Used for human-food, bio-etc & sanctuary",
          "refDataConnectors": [
            {
              "fraction": 0.2,
              "refMaterial": {
                "carbonWeighting": "2.7",
                "wasteStream": "Mixed Food and Garden Waste (dry AD)"
              },
              "refProcess": {
                "name": "recycling"
              },
              "enabler": {
                "name": "Stirling Community Food"
              }
            },
            {
              "fraction": 0.8,
              "refMaterial": {
                "carbonWeighting": "4.35",
                "wasteStream": "Food and Drink Waste (wet AD)"
              },
              "refProcess": {
                "name": "reusing"
              },
              "enabler": {
                "name": "Stirling Community Food"
              }
            }
          ]
        }
      },
      {
        "batchKg": 0.48,
        "from": "2021-01-28",
        "to": "2021-01-29",
        "destination": {
          "name": "Used for compost-indiv",
          "refDataConnectors": [
            {
              "fraction": 1,
              "refMaterial": {
                "carbonWeighting": "3.48",
                "wasteStream": "Food and Drink Waste (Composting)"
              },
              "refProcess": {
[TRUNCATED]

The query’s result after formatting into a tabular report and calculating the carbonSaving column:

:enabler	:from	:to	:batchKg	:foodDestination	:ref_process	:ref_wasteStream	:ref_carbonSavingCo2eKg
Stirling Community Food	2021-01-28	2021-01-29	0.48	Used for compost-indiv	recycling	Food and Drink Waste (Composting)	1.67
Stirling Community Food	2021-01-28	2021-01-29	17.52	Used for human-food, bio-etc & sanctuary	recycling	Mixed Food and Garden Waste (dry AD)	47.31
Stirling Community Food	2021-01-28	2021-01-29	70.09	Used for human-food, bio-etc & sanctuary	reusing	Food and Drink Waste (wet AD)	304.88
Stirling Community Food	2021-01-29	2021-01-30	8.00	Used for compost-indiv	recycling	Food and Drink Waste (Composting)	27.84
Stirling Community Food	2021-01-29	2021-01-30	56.02	Used for human-food, bio-etc & sanctuary	recycling	Mixed Food and Garden Waste (dry AD)	151.26
Stirling Community Food	2021-01-29	2021-01-30	224.10	Used for human-food, bio-etc & sanctuary	reusing	Food and Drink Waste (wet AD)	974.82

Querying using SPARQL

SPARQL is used extensively by the Open Data community to query RDF datasets/graph databases.

Our chosen platform, JUXT Site (with XTDB), supports (a subset of) SPARQL. And we have defined our GraphQL-built data model to include RDF/SPARQL compliant names (i.e. IRI names for records and predicates/fields). So we can use SPARQL to query our data.

Here’s the SPARQL (almost) equivalent of the above GraphQL query:

PREFIX pasi: <pasi:pred/> (1)
SELECT ?enabler ?from ?to ?batchKg ?foodDestination ?ref_process ?ref_wasteStream ?ref_carbonSavingCo2eKg
WHERE {
  ?stcmfRedistributedFood pasi:type "StcmfRedistributedFood" ; (2)
                          pasi:from ?from ;
                          pasi:to ?to ;
                          pasi:batchKg ?origBatchKg ;
                          pasi:destination ?destination .
  ?destination pasi:name ?foodDestination .
  ?opsAceToRefData pasi:type "OpsStcmfToRefData" ; (2)
                   pasi:destination ?destination ;
                   pasi:fraction ?fraction ;
                   pasi:refMaterial/pasi:wasteStream ?ref_wasteStream ;
                   pasi:refMaterial/pasi:carbonWeighting ?carbonWeighting ;
                   pasi:refProcess/pasi:name ?ref_process ;
                   pasi:enabler/pasi:name ?enabler .
  BIND((?origBatchKg * ?fraction) AS ?batchKg) (3)
  BIND((?batchKg * ?carbonWeighting) AS ?ref_carbonSavingCo2eKg) (3)
}
ORDER BY ?enabler ?from ?to"

We use pasi as the scheme part of all our IRIs. PASI is our an abbreviation for the (waste reduction) case study whose data model we’ve sampled in this article. It’s kind-of our root-level namespace.
This SPARQL query uses two graph entry points StcmfRedistributedFood and OpsStcmfToRefData in order to walk to all the required graph nodes. Whereas, in GraphQL, we introduced an artificial direct connection, (refDataConnectors) which allowed the query to seamlessly walk to all the required graph nodes from a single graph entry point.
The carbonSavings calculation is performed in SPARQL query. Whereas, with GraphQL, we performed the calculation outside of the query. Although, we could add an explicit carbonSavings field into data model with a GraphQL directive which specifies how to perform the calculation.

This SPARQL query can support the same tabular report as that supported by the GraphQL query, so we won’t bother (re)displaying that tabular report here.

Conclusions

JUXT Site offers an appealing low-code, GraphQL based approach for defining transactional, linked data systems. It’s a pre-alpha. Its sweet spot will probably be to back websites where humans drive query and transaction volumes.
With its ability to support RDF data models and SPARQL, it is a promising platform for Open Data. Currently, it supports only a subset of SPARQL but (again) it is only a pre-alpha.
So, “might GraphQL be an easy means to query Linked Open Data?“.

Well, GraphQL was designed to describe the services that apps use. But, its query syntax is easier to understand that SPARQL’s (compare the above GraphQL and SPARQL queries) – so there is something to be said for providing a GraphQL interface as a means to explore an open dataset. With the proviso that GraphQL is more abstract/less exact than SPARQL, and it doesn’t directly support federated queries.

They are, of course, different beasts. But a platform which is capable of supporting both over the same data might be a great way of servicing the audience for both.
Also – and we’ve not addressed these in this article but – the XTDB database (used by JUXT Site) has a number of other features that are important for transacting Linked Open Data: immutable records, temporal queries, and upcoming data-level authorisation scheme.
We see JUXT Site as a candidate platform on which to prototype our ‘PASI‘ system which will allow organisations to: upload their social impact data (including waste reduction data); validate it; assure security and track provenance; compose and accumulate it; and publish it as open linked data.

Tag: low code

Linked Open Data using a low-code, GraphQL based approach

JUXT Site

Using GraphQL to build our linked data model

Defining a record type

Defining a query

Defining a mutation

Querying our linked data model

Querying using GraphQL

Querying using SPARQL

Conclusions

JUXT Site

Using GraphQL to build our linked data model

Defining a record type

Defining a query

Defining a mutation

Querying our linked data model

Querying using GraphQL

Querying using SPARQL

Conclusions

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