Inrupt ETL Tutorial

This repository contains code demonstrating how to Extract, Transform, and Load (ETL) into user Pods from various data sources, including publicly accessible 3rd-party data sources, local files, JSON Objects, etc.

Developed by Inrupt, inc.

Prerequisites

This repository is intended for use by experienced TypeScript developers who are also familiar with running Node.JS applications from the command-line, and who are also somewhat familiar with the principles of Linked Data and using Solid Pods.

Background

To aid in the understanding of Linked Data, which is the foundation for everything in Solid, we first recommend reading this High-level overview of how Solid stores data.

Quick-start Worksheet

If you want a complete but quick, start-to-finish run-through of the entire process of installing, configuring, testing, and running the ETL Tutorial, then you can follow our detailed Worksheet instructions here.

The following instructions provide more background, and go into greater detail.

Install and Run

Since you may not yet wish to publicly publish any of the vocabularies we develop during this tutorial (namely the vocabularies you create on behalf of 3rd-party data sources that don't yet provide RDF vocabularies themselves), we recommend first generating a local npm package that bundles together all the JavaScript classes representing all the terms from all those vocabularies.

To do this, we run Inrupt's open-source Artifact Generator, pointing it at our local configuration YAML file that references all the local vocabularies we wish to use terms from, and that bundles together the generated JavaScript classes that contain constants for all the terms from all of those vocabularies (which are all located in the ./resources/Vocab directory):

npx @inrupt/artifact-generator generate --vocabListFile "resources/Vocab/vocab-etl-tutorial-bundle-all.yml" --outputDirectory "./src/InruptTooling/Vocab/EtlTutorial" --noPrompt --force --publish npmInstallAndBuild

Note: If you have the Artifact Generator installed locally (e.g., for faster execution), then you can run it directly:

node ../SDK/artifact-generator/src/index.js generate --vocabListFile "resources/Vocab/vocab-etl-tutorial-bundle-all.yml" --outputDirectory "./src/InruptTooling/Vocab/EtlTutorial" --noPrompt --force --publish npmInstallAndBuild

Note: During ETL development generally it's common to re-run the Artifact Generator regularly, for example after any local vocabulary changes or updates. We can also keep it running constantly in 'file watcher' mode so that it re-runs automatically on any local vocab file changes, which can be really convenient.

So since it can be run so regularly, it can generally be a good idea to clone and run the open-source Artifact Generator locally (as that's much faster than using npx).

Now install our ETL Tutorial:

npm install

Finally, execute the units tests to ensure everything is configured correctly:

Note: You can expect to see error output in the console, as we have 100% branch code coverage, meaning our tests deliberately force lots of error situations so that we can test that our code handles all those situations correctly. What we expect to see is a completely green final Jest report, with 100% coverage across the board.

npm test

End-2-End tests

We provide multiple forms of End-2-End tests to demonstrate and test different aspects of the overall ETL process in isolation. This clear separation also allows you to understand the various different credentials we generally need for an overall ETL process flow.

For all these tests, and indeed when running the actual ETL process 'for real', we only need to create and edit a single local environment file to provide the various credentials needed.

Note however, that all these End-2-End tests, and the ETL process itself, will also all 'pass' without any such environment file at all, as the ETL code actually treats the loading of data into Pods or a triplestore as completely optional (to allow easy testing of just the Extract phase, or just the Transformation phase). So even if no credentials are provided at all, everything will still pass, but you'll see lots of console output saying the Extraction and/or the Loading phases are being ignored!

Overview of our End-2-End test suites

The End-2-End test suites we provide are described below, and you can safely run them now without first creating the local environment file. But without credentials, you should see the console output telling you various steps are being 'ignored':

1. Extract, Transform, and display to console:

   npm run e2e-test-node-ExtractTransform-display
   

   (Without any credentials, we'll see this test successfully Extract data from local copies of 3rd-party data, successfully transform that data into Linked Data, and then display that data to the console, but we'll see it ignore Extraction from any 3rd-parties that require credentials.)
2. Load locally, Transform, and Load to Pods (and/or a triplestore, if configured):

   npm run e2e-test-node-localExtract-TransformLoad
   

   (Without any credentials, we'll see this test successfully Extract data from local copies of 3rd-party data, successfully transform that data into Linked Data, but then ignore all attempts to Load those resources into any Pod or triplestore.)

Overview of test suites

Here we describe our test suites in a bit more detail...

1. Extract, Transform, and display to console.

Tests that connect to each of our 3rd-party data sources to Extract data, Transform that extracted data into Linked Data, and then just outputs some of that data to the console (rather than Loading it anywhere!). This is to demonstrate and test only the Extract and Transform stages of the ETL process, and so for these tests we don't need to configure or setup anything to do with Solid Pods or triplestores (since we deliberately don't attempt to 'Load' this Extracted and Transformed data anywhere yet).

2. Load locally, Transform, and Load to Pods (and/or triplestore).

Tests that read local copies of 3rd-party data (so in this case, we are deliberately avoiding the need for any credentials to connect to any of our 3rd-party data sources). These tests Transform that local data into Linked Data, and attempt to Load it into a Solid Pod (and optionally, a triplestore). In other words, this is for demonstrating and testing only the Transformation and Loading phases of the ETL process.

Create a local-only environment file

To run our ETL Tutorial or execute our End-2-End tests for 'real' (i.e., where we attempt to Extract real data from actual 3rd-parties, and/or Load data into real Solid Pods or a triplestore), we need to provide real, valid credentials, i.e., to allow our application to authenticate with the real APIs of our 3rd-party data sources, and/or to allow our application to write Linked Data to real user's Solid Pods (and/or, optionally, to a triplestore).

To allow us do all of this, we simply need to create and configure a single local environment file, as follows:

1. Make a copy of the ./e2e/node/.env.example file, naming your copy ./e2e/node/.env.test.local.
2. Now we need to replace the example placeholder values with valid credential values, depending on what we wish to do (i.e., run one or more of the End-2-End tests, and/or the full ETL process itself).

We can now configure this local environment file in various ways, and re-run our End-2-End test suites to understand all the possible mix-and-match variations of ETL.

Loading into a triplestore

If you are already familiar with triplestores, then perhaps the easiest option initially is to simply create a new repository in your favorite triplestore and provide that repository's SPARQL update endpoint in your local environment file.

If you are not already familiar with triplestores, you can safely ignore this entire section (although registering, downloading, and running a free triplestore can literally take less than 10 minutes - see detailed instructions here).

Configuring your triplestore

For example, if your favored triplestore was Ontotext's GraphDB, and you created a default repository named inrupt-etl-tutorial, then you'd simply need to add the following value to your .env.test.local environment file:

INRUPT_TRIPLESTORE_ENDPOINT_UPDATE="http://localhost:7200/repositories/inrupt-etl-tutorial/statements"

Now when you run the End-2-End tests to Load (there's little point in re-running the 'only-display-to-console' test suite, since it deliberately never attempts to Load any data anywhere anyway)...

npm run e2e-test-node-localExtract-TransformLoad

...you should see console output informing you that our ETL application successfully Loaded resources into your triplestore repository. If your triplestore is not running, or you provided an incorrect SPARQL Update endpoint, then this test suite should fail (for example, with connect ECONNREFUSED errors).

Assuming the test suite passes, you should now be able to see and query the Loaded data using your familiar triplestore tools (for example, simply running a select * where { ?s ?p ?o } SPARQL query should show you all our ETL'ed data - (if you like, you can turn off the 'Include inferred data in the results' option (i.e., using the '>>' icon on the right-hand side of the 'SPARQL Query & Update' user interface) - this will reduce the noise of the default inferred triples in all search results)).

Note: At this stage (i.e., by only configuring our triplestore via the ETL environment file), all triples will be added directly to the default Named Graph. For more information on how to populate separate Named Graphs per user, see later in this documentation.

If your triplestore supports visualizing triples (such as GraphDB), then our data can already be intuitively inspected and navigated by starting at the following test node, generated by default by our test suite if no Solid Pod storage root was configured: https://different.domain.example.com/testStorageRoot/private/inrupt/etl-tutorial/etl-run-1/

Note: One thing you might notice is that the triplestore does not contain any Linked Data Platform (LDP) containment triples (e.g., no ldp:contains or ldp:BasicContainer, etc., triples). This is because this test specifically Loaded data into a raw triplestore, which has no inherent notion of LDP containment. We'll see later that Loading the same resources into a Solid Pod does result in these containment triples, since they'll have been created by virtue of Solid servers (currently) also being LDP servers.

Running just Extract and Transform

The test suite e2e/node/ExtractTransform-display.test.ts tests the Extraction of data from each of our 3rd-party data sources, Transforms that Extracted data into Linked Data, and then displays it to the console for manual, visual verification (i.e., it deliberately does not attempt to Load this Transformed data anywhere, such as a Solid Pod or a triplestore).

To execute this test suite, run this script from the root directory:

npm run e2e-test-node-ExtractTransform-display

Note: We can still run these tests without any environment file at all, but the code simply won't attempt any Extraction or Loading.

If the credentials you supplied were all valid, you should see data displayed on-screen, with colorful console output (via the debug library) from all data sources that have configured credentials. Data sources without credentials are simply ignored, so these tests are convenient for testing individual data sources in isolation (i.e., simply comment out the credentials for the other data sources), or collectively.

Running the ETL application 'for real'

To execute the entire ETL process 'for real' (i.e., hitting the 3rd-party APIs and populating real Solid Pods (and optionally also a triplestore)), we run the application from the command-line, and drive it via credential resources stored as Linked Data.

For example, we can have user credential resources as local Turtle files (or we could extend this to store these credentials as resources in user Pods), one per user, configured with that user's API credentials for each of the 3rd-party data sources that that user has access to (each individual user may have credentials for none, some, or all, of the data sources), and also providing their Solid Pod credentials (such as their WebID and storage root, and also the ETL application registration credentials (see below)).

Note: We can also provide within these user-specific credential resources a SPARQL Update endpoint URL for a triplestore, and also a Named Graph IRI to represent that user's separate 'Pod' within that triplestore repository. This allows us to populate multiple user's data in a single triplestore instance, with each user's Pod isolated by having its data in its own Named Graph. If no Named Graph value is provided, then that user's data will be loaded into the 'default' graph of the triplestore, which is really only useful if running the ETL for a single user (as loading multiple users would just result in each user's data overwriting the data of the previously ETL'ed user).

ETL Application

Our ETL process runs as an automated application - one that individual end users need to specifically grant access to, to allow that application Load data into their Pods on their behalf. (Note: if an Enterprise provides their users with Pods, then that Pod provisioning phase can automate the granting of that permission, so the actual end users themselves may never need to take any specific action here at all).

To allow the ETL application to be granted access to any Pod, it needs to have an identifier (i.e., it needs a WebID). The easiest way to do this is simply to create a new Pod for your ETL Tutorial application.

Note: YopMail is a very convenient, easy-to-use tool that can be used to create 'burner' email addresses for creating development or test accounts.

Note: Secure Password Generator is a very convenient, easy-to-use tool that can be used to create secure 'burner' passwords for creating development or test accounts.

Registering our ETL application for each user

In order for our ETL application to populate any user's Pod, the application itself must first be registered. This simple registration process will generate standard OAuth Client ID and Client Secret values that our application will use to authenticate itself to allow it access individual user Pod's to Load their respective data.

1. Go to the registration endpoint of the user's Identity Provider. For example, for Pods registered with Inrupt's PodSpaces, that would be:

   https://login.inrupt.com/registration.html
   

2. Login as the ETL user.

3. After successful login, the "Inrupt Application Registration" is redisplayed.

4. In the "Register an App" field, enter a descriptive name for our ETL application, and click the "REGISTER" button.

5. After registration, record the displayed Client ID and Client Secret values, which we'll need in the next step.

Providing a Turtle credential file for the ETL application

Our ETL application needs credentials with which it can connect to user Pods, so that (once authorized by each user) it can then Load user-specific data into those Pods.

The easiest way to provide these credentials is to use a local Turtle file. An example Turtle file is provided here: resources/CredentialResource/RegisteredApp/example-registered-app-credential.ttl.

For more detailed instructions, see the README.md file in that directory.

Providing a Turtle credential file per user

We can drive our ETL application using a credential resource per user, and the easiest way to provide these resources is to use local Turtle files - one per user (these could also be stored as resources within each user's individual Pod!).

1. Make a copy of the example user credentials Turtle file resources/CredentialResource/User/example-user-credential.ttl in the same directory.
2. Name the copied file using a simple naming convention such as user-credential-<USER-NAME>.ttl.
3. Repeat this process, once for each user, filling in that user's 3rd-party API and Solid Pod credentials as appropriate for each user (if a user doesn't have credentials for a particular data source simply leave out those credentials, or provide empty string values - our ETL application will skip that data source for that user).

Executing the ETL application

Make sure the project is successfully compiled to JavaScript:

npm run build

...and then run it from the command line:

node dist/index.js runEtl --etlCredentialResource <resources/CredentialResource/RegisteredApp/MY-REGISTERED-APP-CREDENTIAL-FILE.ttl> --localUserCredentialResourceGlob "resources/CredentialResource/User/user-credential-*.ttl"

Note: Make sure to enclose the directory and glob pattern matching the naming convention you used to name your user credential files in double quote characters, so that any wildcard characters (like asterisks or question marks) will be correctly interpreted.

Using ts-node to speed up running the ETL application repeatedly

Since this project is TypeScript, it can be very convenient to use ts-node so that we don't have to repeatedly re-run the TypeScript compilation step. We install ts-node globally, but of course you don't have to do this, you can install it locally too, or you could choose to use npx:

npm install -g ts-node typescript '@types/node'

Now the entire ETL application can be run for multiple users with just a single command:

ts-node src/index.ts runEtl --etlCredentialResource <resources/CredentialResource/RegisteredApp/MY-REGISTERED-APP-CREDENTIAL-FILE.ttl> --localUserCredentialResourceGlob "resources/UserCredentialResource/user*.ttl"

Note: Make sure to enclose the directory and glob pattern matching the naming convention you used to name your user credential files in double quote characters, so that any wildcard characters (like asterisks or question marks) will be correctly interpreted.

Running Extract, Transform, and Load (from local data only)

The next set of End-2-End tests run the full ETL process, and can populate both real Solid Pods and a triplestore, but are fed from local copies of data source API responses. These are convenient if we don't wish to continually be hitting the 3rd-party data sources to retrieve information (e.g., some data sources can have rate limits, or actually charge per API invocation).

Being able to run the entire ETL process from local copies of API responses is very convenient during active development, or to run purely locally without any need for an internet connection (e.g., to populate a locally running triplestore, or Pods hosted on a locally running Solid server).

For these tests to run though, we need local copies of real API responses. Currently, the tests are hard-coded to look for specifically named JSON files in the directory resources/test/RealData/RealApiResponse/ (see the imports at the top of the test file e2e/node/localExtract-TransformLoad.test.ts for the expected filenames).

To run these tests, execute this script from the root directory:

npm run e2e-test-node-localExtract-TransformLoad

If the credentials you supplied are all valid, you should see data displayed on-screen (with colorful console output via the debug library) from all data sources that have configured credentials.

Publishing simulating events to a user Pod

The ETL application can publish simulated events to any user Pod, using the command-line command publishEvent. Events are represented as JSON files, with the format of the event being determined by the data source that produces the event (e.g., Flume for water-usage or leak-related events, or Sense for device or electricity-usage events like devices turning on or off).

There are a number of example event files provided in the directory ./resources/test/DummyData/DummyEvent.

We need to provide command-line switches with references to the ETL application's credential resource, the targeted user's credential resource, and the event filename:

ts-node src/index.ts publishEvent --etlCredentialResource <MY-REGISTERED-APP-CREDENTIAL-FILE.ttl> --userCredentialResource <USER-CREDENTIALS.ttl> --eventResource ../../resources/test/DummyData/DummyEvent/Flume/dummy-event-flume-leak.json

Generating HTML documentation

The Inrupt Artifact Generator integrates closely with a sophisticated open-source documentation-generating tool named Widoco.

Widoco automatically generates a detailed website describing all the information contained in a vocabulary (it can actually be configured to generate a website per human-language, meaning if our vocabularies have descriptive meta-data in English, French, Spanish, etc., Widoco can generate websites in each of those languages, with very convenient links between them all).

Note: Widoco is a Java application, and requires Java version 8 or higher to be installed on your machine. See here for installation and setup guidance.

To tell the Artifact Generator to generate Widoco documentation, simply add the following command-line switch:

--runWidoco

Websites will automatically be generated in the <OUTPUT DIRECTORY>/Generated/Widoco directory (e.g., in ./src/InruptTooling/Vocab/EtlTutorial/Generated/Widoco).

Documentation can be generated in multiple human-languages, configured via the Artifact Generator configuration file using the widocoLanguages field, and providing 2-character languages codes with hyphen separators (e.g., en-es-fr for English, Spanish, and French documentation).

For example usage, see our local configuration file here: vocab-etl-tutorial-bundle-all.yml).

If you successfully ran the Artifact Generator locally with the --runWidoco switch, you should see the documentation for our local vocabularies in Spanish here.

Postman collections to invoke APIs

Postman provides a very convenient means to quickly and easily make API calls, and we've provided example Postman collections (version 2.1 format) in the directory ./resources/Postman. Each collection provides a number of sample API calls.

Credentials provided by environment variables

We've been careful not to include any credentials in our Postman collections, instead relying on environment variables. To see which environment variables are required for the various data sources, see the example environment file we provide for running our End-2-End tests here.

Making Postman calls

The general approach for invoking 3rd-party APIs is to first request a fresh access token, which generally requires providing identifying credentials, such as a username and password. This access token is then generally provided as the value of the HTTP Authorization: header in all subsequent API calls.

Our collections automatically store the data-source-specific access token in internal environment variables using the 'Tests' feature of Postman. For example, for EagleView, open the https://webservices-integrations.eagleview.com/Token request in Postman and look at the 'Tests' tab to see the code that copies the access token from a successful authentication request into the eagleViewAuthToken environment variable. This environment variable is then used in subsequent EagleView calls, such as the https://webservices-integrations.eagleview.com/v2/Product/GetAvailableProducts request, where it is specified as the Authorization: header value (look in the 'Headers' tab) .

Advanced Vocabulary Management

For details on how to efficiently update even remotely published vocabularies from 3rd-parties, see Advanced Vocabulary management.

Contents

"People think [Linked Data] is a pain because it is complicated.") - Dan Brickley (Google and Schema.org) and Libby Miller (BBC).

Vocabularies:

• Schema.org (from Google, Microsoft and Yahoo): RDF
• Inrupt: Common Terms
• 3rd-parties: Vocabulary per data source

Generated artifacts:

• After running the Artifact Generator as described in the install instructions above, a series of artifacts will be generated in ./src/InruptTooling/Vocab/EtlTutorial.
• You'll notice multiple 'forms' of generated artifacts here, for both Java and JavaScript (see https://github.com/inrupt/artifact-generator/blob/main/documentation/multiple-forms-of-artifact.md for a detailed description of these various forms of artifact).
• Initially we'll use the simplest form (just term identifiers as simple strings), but we'll build up to demonstrate also using RDF library IRI forms, and finally to using the form that provides full programmatic access to all term meta-data (e.g., each term's labels and descriptions (available in multiple human languages), 'seeAlso' links, etc.).

Generated documentation:

• If you installed Widoco, and ran the Artifact Generator command from the install instructions above with the optional --runWidoco switch, the generator will also generate really nice HTML documentation for each of the vocabularies we use in this project (and do so in both English and Spanish).
• This documentation is in the form of an entire website per vocabulary, with each website generated under ./src/InruptTooling/Vocab/EtlTutorial/Generated/Widoco.
• To open any of these websites, browse to the ./index-en.html file in the root of each directory. Note: Notice that the documentation is generated in both English and Spanish (language selection is available in the top-right-hand corner of the vocabulary webpage), as all our vocabularies describe themselves and the terms they contain in both of those languages (see our Companies House vocabulary for instance).

Postman Collections for API calls:

• Postman collections
• We've been careful not to include any credentials in our Postman collections, instead relying on environment variables to provide these.

Extract, Transform, and Load (ETL):

• TypeScript modules for external data sources (all under ./src/dataSource):

  • Companies House UK.
  • Local Passport data as JSON.

• Note: We have 100% branch code coverage, which we plan to maintain throughout this project.

Real-time data (example using Sense electricity monitor):

• Note: Currently NOT included - but we expect to add it soon!

• WebSocket client for Sense: ./src/websocket

• To run, you need to provide valid credentials for Sense in the End-2-End environment file (i.e., in your local ./e2e/node/.env.test.local file, within which you provide credentials for all data sources), and then run:

  cd ./src/websocket/Sense
  npm i
  node index.js
  

  You should see a successful authentication to Sense, and then live updates every 500 milliseconds from the Sense monitor.

References

Vocabularies

• Inrupt's vocabulary repository (public): inrupt/solid-common-vocab-rdf
• Inrupt's Artifact Generator (public) : inrupt/artifact-generator
• Inrupt's published artifacts:
  • Java: Cloudsmith SDK Development (private - we intend to open-source, based on demand)
  • JavaScript: npmjs.org (public)

Utilities

• Bash scripts to manage Artifact Generator configuration files (YAMLs) (public): inrupt/solid-common-vocab-script
• Widoco documentation-generation (integrated with Inrupt's Artifact Generator) (public): Widoco

Libraries

• JavaScript:
  • Linked Data (RDF) library: solid-client-js
  • Solid Authentication Library: solid-client-authn-js
• Java:
  • Linked Data (RDF) library: (private - we intend to open-source, based on demand)

Potential Future Work

• We should probably only delete Containers for data sources that are being re-loaded (so if I want to re-run just for InsideMaps and leave all other creds empty). But then, how would I delete or remove entire data source Containers?

• Error handling - should the ETL tool exit when it encounters an error reading from a data source? Should it just fail for the current user, or the entire process?

• Investigate getting local government public data, e.g., perhaps local areas publish their local water charge rates (so we could query that data in real-time to provide water charges based on the user's "census_tract" field).

• User preferences, for things like gallons vs liters, or feet vs meters. Ideally these should be accessible by any app accessing the Pod, but if not yet defined in the Pod, the user could stipulate they wish to create these Pod-wide preferences based on current preferences they may have set.

• Drive the ETL process based on direct user input via a WebApp, to do things like:

  • Manually enter credentials for pre-defined data sources.
  • Detect discrepancies in preferences between data sources (e.g., user has set imperial as their Pod-wide choice, but the preference set within a specific data source is metric). Alert the user to this, and allow them to update their Pod-wide preference if they choose.

Knowledge Transfer

• Runs as a new CLI option (i.e., --runEod (for 'Run End-of-Day')).
  • Mandatory param: --etlCredentialResource
  • Mandatory param: --localUserCredentialResourceGlob
  • Optional param: --localUserCredentialResourceGlobIgnore
  • Optional param: --fromDate (defaults to yesterday)
  • Optional param: --toDate (defaults to yesterday)
• Code is also called as part of the normal --runEtl process too for each user, and defaults to yesterday.

Frequently Asked Questions

• Is it ok to create the same Thing or Container multiple times?

  • A: Yes.

• Should I extract API responses from multiple data sources and then do a single transform?

  • A: Its just personal choice really. Personally I don't think I would, as generally all data sources should be isolated from each other, and know nothing of each other. So I would perform the ETL for each source independently, and Load each data source's usage data into their own resources in the Pod.

• Where should we put partner credential secrets?

  • A: It may be easiest (and perhaps most appropriate) to simply add these credentials to the existing registered application credentials resource (e.g., here) Alternatively, I already added a placeholder partner-credential resource here: resources/CredentialResource/Partner/example-partner-credential.ttl, but the ETL tool would need to be extended to look for, read from, and use, any values from this resource.

• What is owl:Property?

  • A: It's a fairly common vocabulary practice to include simple metadata from OWL for things like Classes, Properties and NamedIndividuals, but it's certainly not required. I only included them in the vocabs for this project because the documentation tool Widoco looks for them when generating its HTML documentation for vocabularies.

• How much code should be shared between one-off jobs and runEtl?

  • A: Just in general, I'd try and reuse code as much as possible. Commonly, EOD jobs run either reconciliation or aggregation functions, and so conceptually it can be very useful to run directly after all ETL jobs. It certainly makes sense in our case to run the EOD process directly after the initial ETL so that the user can immediately see yesterday's usage statistics as soon as their Pod is created (as opposed to having to wait until the following day to allow the EOD job to run that night).

• What are the relationships between resources, datasets, containers, and things?

  • A: 'Resource' is the most general term we use to refer to any 'entity' in a Solid Pod, so it could be a Container, an RDF resource or a binary blob.

    A 'Dataset' is the formal name for a collection of RDF quads, and really Solid Pods are made up of only either RDF Datasets, or binary Blobs.

    'Containers' are simply RDF Datasets that logically 'contain' other Resources. They themselves are always RDF Datasets, and the name basically comes from the W3C standard of Linked Data Platform (but that standard should just be seen as a guide - i.e., Solid took initial inspiration from that standard, but may move away from formally requiring compliance with that standard).

    A 'Thing' is simply a collection of RDF triples where all the triples have the exact same RDF Subject IRI. It can be a convenient conceptual model when working with RDF, as we often wish to read or write a number of properties of a single 'thing' or entity at once.

• When should we call getThing as opposed to getSolidDataset?

  • A: We use getThing to extract a very specific collection of triples from an RDF Dataset (i.e., from a SolidDataset), where all the triples in that collection have the exact same RDF Subject IRI value (which is the IRI value we pass as the 2nd parameter to the getThing function). This is useful because a single Dataset can contain any number of triples with differing RDF Subject values (i.e., a single Dataset can have triples describing multiple 'things').

Changelog

See the release notes.

License

MIT © Inrupt