Data and Training Analytics Simulated Input Modeler (DATASIM)

What is DATASIM?

DATASIM is an open source R&D project designed to provide specifications and a reference model application for the purpose of generating simulated xAPI data at scale.

DATASIM provides DoD distributed learning stakeholders and the broader xAPI community with the ability to simulate learning activities and generate the resulting xAPI statements at scale both in order to benchmark and stress-test the design of applications with the Total Learning Architecture and to provide stakeholders a way to evaluate the implementation of xAPI data design using the xAPI Profile specification. Ultimately, DATASIM can be used to support conformance testing of applications across the future learning ecosystem.

Early work on DATASIM was originally funded by the Advanced Distributed Learning Initiative.

Installation

To use the core DATASIM library in your project, use the following dependency in your deps.edn file:

com.yetanalytics/datasim {:mvn/version "0.4.4"}

If you wish to install DATASIM as an application with features such as CLI or the webserver, perform the following steps:

Clone the DATASIM GitHub repo
Execute the make bundle command

See Deployment Models for more information about the differences between using DATASIM as a library and as an app.

Usage

Simulation Inputs

The inputs to DATASIM consist of four parts, each represented by JSON. They are as follows:

xAPI Profile(s)

One or more valid xAPI Profiles are required for DATASIM to generate xAPI Statements. You can learn more about the xAPI Profile Specification here. This input can either be a single Profile JSON-LD document or an array of JSON-LD format profiles. At this time all referenced concepts in a Profile must be included in the input. For instance if in "Profile A" I have a Pattern that references a Statement Template found in "Profile B", both Profiles must be included in an array as the Profile input.

Note that by default, any patterns with a primary property set to true in the provided profiles will be used for generation. You can control which profiles these primary patterns are sourced from with the genProfiles option by supplying one or more profile IDs. You can further control which specific primary patterns are used with the genPatterns option by supplying one or more pattern IDs.

Personae

Predefined xAPI Actors (upon whom the simulation will be based) are required to run a DATASIM simulation. This takes the form of a JSON array of xAPI Groups, each object containing an array of conformant Actor members, an example of which is below:

    [
        {
            "name": "trainees1",
            "objectType": "Group",
            "member": [
                {
                    "name": "Bob Fakename",
                    "mbox": "mailto:bob@example.org"
                },
                {
                    "name": "Alice Faux",
                    "mbox": "mailto:alice@example.org"
                }
            ]
        },
        {
            "name": "trainees2",
            "objectType": "Group",
            "member": [
                {
                    "name": "Fred Ersatz",
                    "mbox": "mailto:fred@example.org"
                }
            ]
        }
    ]

Models

Models represents user-provided influences on xAPI simulation. Each model is a JSON object that consists of the following properties:

personae: An array of Actors, Groups, or Role objects that define who the model applies to. If this is missing, then the model serves as the default model for the simulation. Each personae array must be unique, though Actors, Groups, or Roles may repeat across different models.
verbs: An array of objects with Verb id and weight values. Valid weight values range from 0 to 1, where 0 denotes that that component will not be chosen (unless all other weights are also 0). If not present, a default weight of 0.5 will be used.
activities: An array of objects with Activity id and weight values (as described under verbs).
activityTypes: An array of objects with Activity Type id and weight values (as described under verbs).
patterns: An array of objects with Pattern id and the following additional optional values:
- weights: An array of child Pattern/Template id and weight values. Each weight affects how likely each of the Pattern's child patterns are chosen (for alternates) or how likely the child Pattern will be selected at all (for optional, for these null is also a valid option). This has no effect on sequence, zeroOrMore, or oneOrMore Patterns.
- repeat-max: A positive integer representing the maximum number of times (exclusive) the child pattern can be generated. Only affects zeroOrMore and oneOrMore patterns.
- bounds: An array of objects containing key-value pairs where each value is an array of singular values (e.g. "January") or arrays of start, end, and optional step values (e.g. ["January", "October"]). For example {"years": [2023], "months": [[1, 5]]} describes an inclusive bound from January to May 2023; making the months bound [[1, 5, 2]] would have restricted it to only January, March, and May 2023. If not present, bounds indicates an infinite bound, such that any timestamp is valid. The following are valid bound values:
  - years: Any positive integer
  - months: 1 to 12, or their name equivalents, i.e. "January" to "December"
  - daysOfMonth: 1 to 31 (though 29 or 30 are skipped at runtime for months that do not include these days)
  - daysOfWeek: 0 to 6, or their name equivalents, i.e. "Sunday" to "Saturday"
  - hours: 0 to 23
  - minutes: 0 to 59
  - seconds: 0 to 59
- boundRestarts: An array of Pattern IDs to retry if the timestamp violates bounds. The top-most Pattern in boundRestarts will be tried, e.g. if Pattern A is a parent of Pattern B and both are listed in boundRestarts, it will be Pattern A that is retried. If boundRestarts is empty or not present, or if none of the ancestor Patterns are included, then Statement generation will continue at its current point.
- periods: An array of objects that specify the amount of time between generated Statements. Only the first valid period in the array will be applied to generate the next Statement (see bounds property). Each period object has the following optional properties:
  - min: a minimum amount of time between Statements; default is 0
  - mean the average amount of time between Statements (added on top of min); default is 1
  - fixed: a fixed amount of time between Statements; overrides min and mean
  - unit: the time unit for all temporal values. Valid values are millis, seconds, minutes, hours, days, and weeks; the default is minutes
  - bounds: an array of the temporal bounds the period can apply in. During generation, the current Statement timestamp is checked against each period's bounds, and the first period whose bound satisfies the timestamp will be used to generate the next Statement timestamp. A nonexisting bounds value indicates an infinite bound, i.e. any timestamp is always valid. The syntax is the same as the top-level bounds array. At least one period must not have a bounds value, so it can act as the default period.
templates: An array of objects with Statement Template id and optional bounds, boundRestarts, and period properties, as explained above in patterns. Note that weights and repeat-max do not apply here.
objectOverrides: An array of objects containing (xAPI) object and weight. If present, these objects will overwrite any that would have been set by the Profile.

An example of a model array with valid personae, verbs, and templates is shown below:

[
    {
        "personae": [
            {
                "id": "mbox::mailto:bob@example.org",
                "type": "Agent"
            }
        ],
        "verbs": [
            {
                "component": "https://example.org/verb/did",
                "weight": 0.8
            }
        ],
        "templates": [
            {
                "component": "https://w3id.org/xapi/cmi5#satisfied",
                "bounds": [
                    {
                        "years": [2023],
                        "months": [["January", "May"]]
                    }
                ],
                "boundRestarts": [
                    "https://w3id.org/xapi/cmi5#toplevel"
                ],
                "period": {
                    "min": 1,
                    "mean": 2.0,
                    "unit": "second"
                }
            }
        ]
    }
]

Simulation Parameters

The simulation parameters input covers the details of the simulation not covered by other pieces. This includes Start Time, End Time, Timezone, Max (number of statements) and seed. When run, the simulation will create a time sequence from the Start Time to the End Time and generated xAPI statements will have corresponding dates and times. The seed is important as it controls the inputs to all random value generation and corresponds to repeatability. A simulation run with the same inputs and the same seed will deterministically create the same xAPI Statements, but changing the seed value will create an entirely different simulation. An example of simulation parameters is below:

    {
        "start": "2019-11-18T11:38:39.219768Z",
        "end": "2019-11-19T11:38:39.219768Z",
        "max": 200,
        "timezone": "America/New_York",
        "seed": 42,
        "maxRestarts": 10
    }

Note the maxRestarts parameter; this is to limit the amount of times a particular Pattern is restarted when a bounds is violated.

Additional parameters include genPatterns and genProfiles, which are explained in more detail under xAPI Profiles.

(Alternatively) Simulation Specification

The simulation specification is a single object containing of all of the above. This is exported during a simulation run and can serve as the sole input to another simulation.

    {
        "profiles": [ ... ],
        "parameters": ...,
        "personae-array": [ ... ],
        "models": [...]
    }

System Requirements

Java (JDK 8+, OpenJDK or Oracle)

Clojure CLI (1.10.2+)

Deployment Models

This reference implementation of DATASIM can either be used as a CLI tool, or as a library embedded in another JVM application.

CLI

In the form of a CLI application, DATASIM takes the inputs listed above as JSON files as command line arguments and runs a simulation based on them. It also outputs the Simulation Specification during this process.

Local CLI

For the CLI the first step is to build the project so that it can be run on a JVM.

    make bundle

Now that we have this, navigate to target/bundle and run

    bin/run.sh

With no commands or --help it will give you the list of subcommands:

Subcommand	Description
`validate-input`	Validate the input and create an input JSON file.
`generate`	Generate statements from input and print to stdout.
`generate-post`	Generate statements from input and POST them to an LRS.

The validate-input subcommand is used to validate and combine input files. These are its arguments:

Argument	Description
`-p, --profile URI`	The location of an xAPI profile, can be used multiple times.
`-a, --actor-personae URI`	The location of an Actor Personae document indicating the actors in the sim.
`-m, --models URI`	The location of an Persona Model document, to describe alignments and overrides for the personae.
`-o, -parameters URI`	The location of simulation parameters document. Uses the current time and timezone as defaults if they are not present. (The "o" stands for "options.")
`-i, --input URI`	The location of a JSON file containing a combined simulation input spec.
`-v, --validated-input URI`	The location of the validated input to be produced.

The generate subcommand is used to generate statements from an input and print them to standard output. The inputs can be a combined --input location or a combination of -p, -a, -m, and -o inputs. The additional arguments are as follows:

Argument	Description
`--seed SEED`	An integer seed to override the one in the input spec. Use -1 for a random seed.
`--actor AGENT_ID`	Pass an agent id in the format 'mbox::mailto:[email]' to select actor(s)
`--gen-profile IRI`	Only generate based on primary patterns in the given profile. May be given multiple times to include multiple profiles.
`--gen-pattern IRI`	Only generate based on the given primary pattern. May be given multiple times to include multiple patterns.

The generate-post subcommand is used to generate statements from an input and POST them to an LRS. In addition to the generate arguments, this subcommands has these additional arguments:

Argument	Description	Default
`-E, --endpoint URI`	The xAPI endpoint of an LRS to POST to, ex: `https://lrs.example.org/xapi`	N/A
`-U, --username URI`	The Basic Auth username for the LRS.	N/A
`-P, --password URI`	The Basic Auth password for the LRS.	N/A
`-B, --batch-size SIZE`	The batch size, i.e. how many statements to send at a time, for POSTing.	`25`
`-C, --concurrency CONC`	The max concurrency of the LRS POST pipeline.	`4`
`-L, --post-limit LIMIT`	The total number of statements that will be sent to the LRS before termination. Overrides sim params. Set to -1 for no limit.	`999`
`-A, --[no-]async`	Async operation. Use `--no-async` if statements must be sent to server in timestamp order.	`true`

The following is an example of a simple run. We first create a combined input file using validate-input:

bin/run.sh validate-input \
    -p dev-resources/profile/cmi5/fixed.json \
    -a dev-resources/personae/simple.json \
    -m dev-resources/models/simple.json \
    -o dev-resources/parameters/simple.json \ 
    -v dev-resources/input/simple.json

Once we have that sim specification, we can run the simulation using the generate:

bin/run.sh generate -i dev-resources/input/simple.json

If we have an endpoint and credentials for an LRS we can directly POST the simulated statements using generate-post:

bin/run.sh generate-post \
    -i dev-resources/input/simple.json \
    -E http://localhost:8080/xapi \
    -U username \
    -P password \
    -B 20 \
    -L 1000 \

As statements are successfully sent to the LRS their IDs will be sent to stdout.

NOTE: If the input specification doesn't have an end parameter and we set the option -L -1, DATASIM will continue posting to the LRS indefinitely.

Docker

Build:

make clean bundle && docker build -t yetanalytics/datasim:latest .

Run (CLI):

docker run -v "$(pwd)"/dev-resources:/dev-resources  \
           -i yetanalytics/datasim:latest \
           -i /dev-resources/input/simple.json \
           generate

Run (API):

docker run -it --entrypoint bin/server.sh yetanalytics/datasim:latest

Library

As a library, this reference model can be integrated with any JVM application and its algorithms can be passed inputs and executed from code. It can be imported as a dep in Clojure, or compiled class files can be referenced from Java.

API

Starting the API

To start the API, run the following command from this directory:

make server

By default the server starts at http://localhost:9090

API Config

The API is configurable with the following runtime environment variables:

Variable	Default	Notes	Example
CREDENTIALS	`username:password`	Basic Authentication credentials required to call the API endpoints in the form of `username:password`	`datasim:datasim`
API_ROOT_PATH		Root path to prefix API routes. Must begin with a `/`, cannot end with a `/`.	`/foo`
API_HOST	`0.0.0.0`	Host on which to bind the API server.	`localhost`
API_PORT	`9090`	Port on which to bind the API server.	`8080`
API_ALLOWED_ORIGINS	`https://yetanalytics.github.io,http://localhost:9091` (URLs)	CORS allowed origins for the API server, separated by commas.	`*`

Currently defaults are configured to work with the default settings in the DATASIM-UI project locally.

API Endpoints

When launched as a REST API webapp, it has a few endpoints to allow dataset generation. The API is secured by Basic Authentication headers at this time (see API Config). The application has the following endpoints:

GET /health

This endpoint is simply a health check for the API. It should return a 200-OK if the app is up and running.

POST /api/v1/generate

This endpoint takes a set of simulation inputs, returns a file with the output dataset and optionally pushes the data to an LRS. It accepts the inputs in the Content Type multipart/form-data of the following fields:

profiles: Array of json-ld xAPI Profiles

personae-array: Array of JSON Objects containing Actors formatted as above

models: Array of JSON Objects containing Models formatted as above

parameters: Simulation Parameters JSON Object

lrs-endpoint: String with a valid LRS endpoint

api-key: String with LRS API Key

api-secret-key: String with LRS API Secret Key

send-to-lrs: Boolean indicating whether or not to send data to the LRS if applicable

Distributed Generation

DATASIM deterministically generates streams of statements on a per-actor basis making it possible to distribute the generation of simulation data across multiple processes or physical servers.

Onyx Peer Cluster

DATASIM uses Onyx and ZooKeeper to coordinate distributed generation. One or more DATASIM peers can be launched in a cluster.

The cluster accepts DATASIM combined input files and LRS target information as input. The cluster peers will coordinate to generate data and post it to the target LRS.

Capacity Planning

In order to generate and send the data the cluster must contain enough peers to generate and execute the specified input.

The user specifies desired concurrency by use of the -c option. This option must be a positive integer not greater than the number of actors in the simulation.

DATASIM will evenly partition the data into as many "buckets" as specified and attempt to send them simultaneously.

For each partition of simulation actors, two peers are required. Therefore:

total-required-peers = concurrency * 2

For example, the DATASIM "simple" example input found at dev-resources/input/simple.json contains 3 actors. If we choose the maximum concurrency of 3 then:

total-required-peers = 3 * 2 = 6

If we wanted to sacrifice throughput we could run it with the minimum concurrency of 1:

total-required-peers = 1 * 2 = 2

Note that if a cluster does not have sufficient peers to execute a job it will wait until it does and complete it. Each physical instance in a cluster can run as many "virtual" peers as it has processors.

Utility CLI

DATASIM has a separate CLI for distributed operation:

bin/onyx.sh --help ## in dev, do: clojure -Monyx:onyx-dev -m com.yetanalytics.datasim.onyx.main --help

DATASIM Cluster CLI

Usage: bin/onyx.sh [options] action

Options:
  -n, --n-vpeers N_VPEERS               Number of VPEERS to launch. Overrides config value.
  -t, --tenancy-id TENANCY_ID           Onyx Tenancy ID
  -i, --input-loc INPUT_LOC             DATASIM input location
  -c, --concurrency                     Desired concurrency of job.
  -e, --endpoint ENDPOINT               xAPI LRS Endpoint like https://lrs.example.org/xapi
  -u, --username USERNAME               xAPI LRS BASIC Auth username
  -p, --password PASSWORD               xAPI LRS BASIC Auth password
      --x-api-key X_API_KEY             API Gateway API key
      --[no-]strip-ids                  Strip IDs from generated statements
      --[no-]remove-refs                Filter out statement references
  -b, --[no-]block                      Block until the job is done
      --nrepl-bind NREPL_BIND  0.0.0.0  If provided on peer launch will start an nrepl server bound to this address
      --nrepl-port NREPL_PORT           If provided on peer launch will start an nrepl server on this port
  -h, --help

Actions:
  start-peer    Start an onyx peer
  start-driver  Start an aeron media driver
  submit-job    Submit a datasim input for submission to an LRS
  repl          Start a local repl

AWS Deployment

A set of AWS CloudFormation templates capable of deploying the cluster is included for demonstration purposes. Note that these templates should not be used for production systems.

VPC (Optional) - `template/0_vpc.yml`

To deploy the cluster you'll need an AWS VPC with at least 1 subnet. The included template will create a VPC with 4 subnets, 2 public and 2 private.

ZooKeeper (Optional) - `template/1_zk.yml`

The cluster requires a working Apache Zookeeper Ensemble version 3.5. This template creates a simple static-ip based ensemble of 3 nodes. Make sure to choose a private subnet and ensure that the chosen IPs fall within its CIDR range.

Cluster - `template/2_cluster.yml`

Make sure you've done the following (refer to the template params referenced):

Compile the project with make clean bundle
Zip the target/bundle directory to a file called <ArtifactId>-<ArtifactVersion>
Upload the zip to an s3 bucket with an enclosing path of your choosing like: s3://<ArtifactBucketName>/<ArtifactBasePath>/<ArtifactId>-<ArtifactVersion>

Deploy the template to the same VPC as ZooKeeper to a subnet that can reach the ZooKeeper instances. Make sure to choose the correct security group for the ZooKeeper ensemble for ZooKeeperGroupId.

For documentation on other parameters, see the template.

Submitting a Job

You can submit a job as follows:

SSH in to a cluster node:

sudo su        # be root
cd /datasim    # correct working dir

# optionally get input first
curl https://raw.githubusercontent.com/yetanalytics/datasim/master/dev-resources/input/simple.json -o simple.json
# note the CloudFormation Stack params -> env

TENANCY_ID=<TenancyId> \ # optional if -t or --tenancy-id is provided below
ONYX_PROFILE=prod \
ZK_ADDRESS=<ZooKeeperAddress> \
ZK_SERVER_PORT=<ZooKeeperPort> \
ZK_TIMEOUT=<ZooKeeperTimeout> \
PEER_PORT=<PeerPort> \
N_VPEERS=<VPeersCount> \
LANG=en_US.UTF-8 \
AWS_REGION=<AWS::Region> \
X_RAY_ENABLED=true \
AWS_XRAY_CONTEXT_MISSING=LOG_ERROR \
AWS_XRAY_TRACING_NAME=datasim-cluster:us-east-1 \
BIND_ADDR=<IP of Instance> \
./bin/submit_job.sh \
  -t <override tenancy (optional)> \
  --concurrency 3 \
  -i simple.json \
  -e https://lrs.example.org/xapi \
  -u <LRS BASIC Auth Username> \
  -p <LRS BASIC Auth Password>

License

DATASIM is licensed under the Apache License, Version 2.0. See LICENSE for the full license text

THE DATASIM SOFTWARE (“SOFTWARE”) IS PUBLISHED AS OPEN SOURCE SOFTWARE TO ENABLE USERS TO TEST CERTAIN CAPABILITIES OF THEIR SYSTEMS INCLUDING THE LEVEL OR CAPACITY OF xAPI DATA THAT CAN BE HANDLED BY A USER’S SYSTEM. THE SOFTWARE IS EXPRESSLY INTENDED TO TEST CAPACITY AND SYSTEM LIMITS AND CAN CAUSE SYSTEM OUTAGES WHEN A SYSTEM’S CAPACITY IS EXCEEDED. IT MUST BE USED WITH CAUTION.

THE PROVIDER AND PUBLISHER OF THE SOFTWARE (“PROVIDER”) PROVIDES NO WARRANTY, EXPRESS OR IMPLIED, WITH RESPECT TO THE SOFTWARE, ITS RELATED DOCUMENTATION OR OTHERWISE. THE SOFTWARE AND DOCUMENTATION ARE PROVIDED ON AN “AS IS” BASIS WITH ALL FAULTS. THE PROVIDER HEREBY DISCLAIMS ALL WARRANTIES AND CONDITIONS, EXPRESS OR IMPLIED, WRITTEN OR ORAL, INCLUDING, BUT NOT LIMITED TO, WARRANTIES OF MERCHANTABLE QUALITY, MERCHANTABILITY AND FITNESS FOR A PARTICULAR USE OR PURPOSE, NON-INFRINGEMENT AND THOSE ARISING BY STATUTE OR FROM A COURSE OF DEALING OR USAGE OF TRADE WITH RESPECT TO THE SOFTWARE, DOCUMENTATION AND ANY SUPPORT.

IN NO EVENT WILL PROVIDER OR ITS SUBSIDIARIES, OR AFFILIATES, NOR ANY OF THEIR RESPECTIVE SHAREHOLDERS, OFFICERS, DIRECTORS, EMPLOYEES, AGENTS OR REPRESENTATIVES HAVE ANY LIABILITY TO ANY USER OR TO ANY THIRD PARTY FOR ANY LOST PROFITS OR REVENUES OR FOR ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, CONSEQUENTIAL, COVER OR PUNITIVE DAMAGES HOWEVER CAUSED, WHETHER IN CONTRACT, TORT OR UNDER ANY OTHER THEORY OF LIABILITY, AND WHETHER OR NOT THE PROVIDER HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. THE FOREGOING DISCLAIMER WILL NOT APPLY ONLY TO THE EXTENT PROHIBITED BY APPLICABLE LAW. BY MAKING USE OF THE SOFTWARE AND DOCUMENTATION, EACH USER HEREBY AGREES TO THE FORGOING DISCLAIMERS AND LIMITATIONS, AND HEREBY AGREES TO (I) RELEASE AND FOREVER DISCHARGE PROVIDER AND EACH OF ITS SUBSIDIARIES AND AFFILIATES, AND EACH OF THEIR RESPECTIVE SHAREHOLDERS, OFFICERS, DIRECTORS, EMPLOYEES, AGENTS OR REPRESENTATIVES (COLLECTIVELY, THE “RELEASED PARTIES”) FROM ANY CLAIM, DEMAND, CAUSE, ACTION, OR DAMAGE ARISING OUT OF OR IN CONNECTION WITH ANY USE OF THE SOFTWARE OR DOCUMENTATION (EACH, A “CLAIM”), AND (II) INDEMNIFY, DEFEND AND SAVE EACH RELEASED PARTY FROM ANY CLAIM AND ANY LOSS, DAMAGE, COST OR EXPENSE ARISING OUT OF OR IN CONNECTION WITH ANY CLAIM INCLUDING CLAIMS OF ANY THIRD PARTY RESULTING FROM USER’S USE OF THE SOFTWARE OR DOCUMENTATION. IF YOU, AS THE USER, DO NOT AGREE TO THE FORGOING, THEN YOU ARE NOT AUTHORIZED TO USE THE SOFTWARE OR DOCUMENTATION AND ANY SUCH USE IS STRICTLY PROHIBITED.

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doc		doc
onyx-resources		onyx-resources
scripts		scripts
src		src
template		template
.dir-locals.el		.dir-locals.el
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
Dockerfile		Dockerfile
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
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Data and Training Analytics Simulated Input Modeler (DATASIM)

What is DATASIM?

Installation

Usage

Simulation Inputs

xAPI Profile(s)

Personae

Models

Simulation Parameters

(Alternatively) Simulation Specification

System Requirements

Deployment Models

CLI

Local CLI

Docker

Library

API

Starting the API

API Config

API Endpoints

GET /health

POST /api/v1/generate

Distributed Generation

Onyx Peer Cluster

Capacity Planning

Utility CLI

AWS Deployment

VPC (Optional) - template/0_vpc.yml

ZooKeeper (Optional) - template/1_zk.yml

Cluster - template/2_cluster.yml

Submitting a Job

License

About

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VPC (Optional) - `template/0_vpc.yml`

ZooKeeper (Optional) - `template/1_zk.yml`

Cluster - `template/2_cluster.yml`

Packages