REST web service for the true real-time scoring (<1 ms) of R, Scikit-Learn and Apache Spark models
Java
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README.md

Openscoring Build Status

REST web service for scoring PMML models.

Table of Contents

Features

  • Full support for PMML specification versions 3.0 through 4.3. The evaluation is handled by the JPMML-Evaluator library.
  • Simple and powerful REST API:
    • Model deployment and undeployment.
    • Model evaluation in single prediction, batch prediction and CSV prediction modes.
    • Model metrics.
  • High performance and high throughput:
    • Sub-millisecond response times.
    • Request and response compression using gzip and deflate encodings.
    • Thread safe.
  • Open, extensible architecture for easy integration with proprietary systems and services:
    • User authentication and authorization.
    • Metrics dashboards.

Prerequisites

  • Java 1.8 or newer.

Installation

Binary release

Openscoring client and server uber-JAR files are distributed via the GitHub releases page, and the Openscoring webapp WAR file is distributed via the Maven Central repository.

This README file corresponds to latest source code snapshot. In order to follow its instructions as closely as possible, it's recommended to download the latest binary release.

The current version is 1.4.2 (2 July, 2018):

Source code snapshot

Enter the project root directory and build using Apache Maven:

mvn clean install

The build produces two uber-JAR files and a WAR file:

  • openscoring-client/target/openscoring-client-executable-1.4-SNAPSHOT.jar
  • openscoring-server/target/openscoring-server-executable-1.4-SNAPSHOT.jar
  • openscoring-webapp/target/openscoring-webapp-1.4-SNAPSHOT.war

Usage

The example PMML file DecisionTreeIris.pmml along with example JSON and CSV files can be found in the openscoring-service/src/etc directory.

Client side

Replay the life cycle of a sample DecisionTreeIris model (in "REST API", see below) by launching the following Java application classes from the uber-JAR file:

java -cp openscoring-client-executable-${version}.jar org.openscoring.client.Deployer --model http://localhost:8080/openscoring/model/DecisionTreeIris --file DecisionTreeIris.pmml

java -cp openscoring-client-executable-${version}.jar org.openscoring.client.Evaluator --model http://localhost:8080/openscoring/model/DecisionTreeIris -XSepal_Length=5.1 -XSepal_Width=3.5 -XPetal_Length=1.4 -XPetal_Width=0.2

java -cp openscoring-client-executable-${version}.jar org.openscoring.client.CsvEvaluator --model http://localhost:8080/openscoring/model/DecisionTreeIris --input input.csv --output output.csv

java -cp openscoring-client-executable-${version}.jar org.openscoring.client.Undeployer --model http://localhost:8080/openscoring/model/DecisionTreeIris

The deployment and undeployment of models can be automated by launching the org.openscoring.client.DirectoryDeployer Java application class from the uber-JAR file, which listens for PMML file addition and removal events on the specified directory ("PMML directory watchdog"):

java -cp openscoring-client-executable-${version}.jar org.openscoring.client.DirectoryDeployer --model-collection http://localhost:8080/openscoring/model --dir pmml

Server side

Launch the executable uber-JAR file:

java -jar openscoring-server-executable-${version}.jar

By default, the REST web service is started at http://localhost:8080/openscoring. The main class org.openscoring.server.Main accepts a number of configuration options for URI customization and other purposes. Please specify --help for more information.

java -jar openscoring-server-executable-${version}.jar --help
Advanced configuration

Copy the sample Typesafe's Config configuration file openscoring-server/application.conf.sample to a new file application.conf, and customize its content to current needs. Use the config.file system property to impose changes on the JVM:

java -Dconfig.file=application.conf -jar openscoring-server-executable-${version}.jar

The local configuration overrides the default configuration that is defined in the reference REST web service configuration file openscoring-service/src/main/reference.conf. For example, the following local configuration would selectively override the list-valued networkSecurityContextFilter.trustedAddresses property (treats any local or remote IP address as a trusted IP address):

networkSecurityContextFilter {
	trustedAddresses = ["*"]
}
Logging

Copy the sample Java Logging API configuration file openscoring-server/logging.properties.sample to a new file logging.properties, and customize its content to current needs. Use the java.util.logging.config.file system property to impose changes on the JVM:

java -Djava.util.logging.config.file=logging.properties -jar target/openscoring-server-executable-${version}.jar

REST API

Overview

Model REST API endpoints:

HTTP method Endpoint Required role(s) Description
GET /model - Get the summaries of all models
PUT /model/${id} admin Deploy a model
GET /model/${id} - Get the summary of a model
GET /model/${id}/pmml admin Download a model as a PMML document
POST /model/${id} - Evaluate data in "single prediction" mode
POST /model/${id}/batch - Evaluate data in "batch prediction" mode
POST /model/${id}/csv - Evaluate data in "CSV prediction" mode
DELETE /model/${id} admin Undeploy a model

Metric REST API endpoints:

HTTP method Endpoint Required role(s) Description
GET /metric/model admin Get the metric sets of all models
GET /metric/model/${id} admin Get the metric set of a model

By default, the "admin" role is granted to all HTTP requests that originate from the local network address.

In case of an error (ie. response status codes 4XX or 5XX), the response body is a JSON serialized form of an org.openscoring.common.SimpleResponse (source) object.

Java clients may use the following idiom to check if an operation succeeded or failed:

ModelResponse response = ...;

// The error condition is encoded by initializing the "message" field and leaving all other fields uninitialized
String message = response.getMessage();
if(message != null){
	throw new RuntimeException(message);
}

// Proceed as usual

Model deployment

PUT /model/${id}

Creates or updates a model.

The request body is a PMML document (indicated by content-type header text/xml or application/xml).

The response body is a JSON serialized form of an org.openscoring.common.ModelResponse (source) object.

Response status codes:

  • 200 OK. The model was updated.
  • 201 Created. A new model was created.
  • 400 Bad Request. The deployment failed permanently. The request body is not a valid and/or supported PMML document.
  • 403 Forbidden. The acting user does not have an "admin" role.
  • 500 Internal Server Error. The deployment failed temporarily.

Sample cURL invocation:

curl -X PUT --data-binary @DecisionTreeIris.pmml -H "Content-type: text/xml" http://localhost:8080/openscoring/model/DecisionTreeIris

The same, using the gzip encoding:

curl -X PUT --data-binary @DecisionTreeIris.pmml.gz -H "Content-encoding: gzip" -H "Content-type: text/xml" http://localhost:8080/openscoring/model/DecisionTreeIris

Model querying

GET /model

Gets the summaries of all models.

The response body is a JSON serialized form of an org.openscoring.common.BatchModelResponse (source) object.

Response status codes:

  • 200 OK. The model collection was queried.

Sample cURL invocation:

curl -X GET http://localhost:8080/openscoring/model
GET /model/${id}

Gets the summary of a model.

The response body is a JSON serialized form of an org.openscoring.common.ModelResponse (source) object.

Response status codes:

  • 200 OK. The model was queried.
  • 404 Not Found. The requested model was not found.

Sample cURL invocation:

curl -X GET http://localhost:8080/openscoring/model/DecisionTreeIris

Sample response:

{
	"id" : "DecisionTreeIris",
	"miningFunction" : "classification",
	"summary" : "Tree model",
	"properties" : {
		"created.timestamp" : "2015-03-17T12:41:35.933+0000",
		"accessed.timestamp" : "2015-03-21T09:35:58.582+0000",
		"file.size" : 4306,
		"file.md5sum" : "2d4698076ed807308c5ae40563b70345"
	},
	"schema" : {
		"inputFields" : [
			{
				"id" : "Sepal_Length",
				"name" : "Sepal length in cm",
				"dataType" : "double",
				"opType" : "continuous",
				"values" : [ "[4.3, 7.9]" ]
			},
			{
				"id" : "Sepal_Width",
				"name" : "Sepal width in cm",
				"dataType" : "double",
				"opType" : "continuous",
				"values" : [ "[2.0, 4.4]" ]
			},
			{
				"id" : "Petal_Length",
				"name" : "Petal length in cm",
				"dataType" : "double",
				"opType" : "continuous",
				"values" : [ "[1.0, 6.9]" ]
			},
			{
				"id" : "Petal_Width",
				"name" : "Petal width in cm",
				"dataType" : "double",
				"opType" : "continuous",
				"values" : [ "[0.1, 2.5]" ]
			}
		],
		"groupFields" : [],
		"targetFields" : [
			{
				"id" : "Species",
				"dataType" : "string",
				"opType" : "categorical",
				"values" : [ "setosa", "versicolor", "virginica" ]
			}
		],
		"outputFields" : [
			{
				"id" : "Probability_setosa",
				"dataType" : "double",
				"opType" : "continuous"
			},
			{
				"id" : "Probability_versicolor",
				"dataType" : "double",
				"opType" : "continuous"
			},
			{
				"id" : "Probability_virginica",
				"dataType" : "double",
				"opType" : "continuous"
			},
			{
				"id" : "Node_Id",
				"dataType" : "string",
				"opType" : "categorical"
			}
		]
	}
}

Field definitions are retrieved from the MiningSchema and Output elements of the PMML document. The input and group-by fields relate to the arguments attribute of the evaluation request, whereas the target and output fields relate to the result attribute of the evaluation response (see below).

GET /model/${id}/pmml

Downloads a model.

The response body is a PMML document.

Response status codes:

  • 200 OK. The model was downloaded.
  • 403 Forbidden. The acting user does not have an "admin" role.
  • 404 Not Found. The requested model was not found.

Sample cURL invocation:

curl -X GET http://localhost:8080/openscoring/model/DecisionTreeIris/pmml

Model evaluation

POST /model/${id}

Evaluates data in "single prediction" mode.

The request body is a JSON serialized form of an org.openscoring.common.EvaluationRequest (source) object.

The response body is a JSON serialized form of an org.openscoring.common.EvaluationResponse (source) object.

Response status codes:

  • 200 OK. The evaluation was successful.
  • 400 Bad Request. The evaluation failed permanently due to missing or invalid input data.
  • 404 Not Found. The requested model was not found.
  • 500 Internal Server Error. The evaluation failed temporarily.

Sample cURL invocation:

curl -X POST --data-binary @EvaluationRequest.json -H "Content-type: application/json" http://localhost:8080/openscoring/model/DecisionTreeIris

Sample request:

{
	"id" : "record-001",
	"arguments" : {
		"Sepal_Length" : 5.1,
		"Sepal_Width" : 3.5,
		"Petal_Length" : 1.4,
		"Petal_Width" : 0.2
	}
}

Sample response:

{
	"id" : "record-001",
	"result" : {
		"Species" : "setosa",
		"Probability_setosa" : 1.0,
		"Probability_versicolor" : 0.0,
		"Probability_virginica" : 0.0,
		"Node_Id" : "2"
	}
}
POST /model/${id}/batch

Evaluates data in "batch prediction" mode.

The request body is a JSON serialized form of an org.openscoring.common.BatchEvaluationRequest (source) object.

The response body is a JSON serialized form of an org.openscoring.common.BatchEvaluationResponse (source) object.

Response status codes:

  • 200 OK. The evaluation was successful.
  • 400 Bad Request. The evaluation failed permanently due to missing or invalid input data.
  • 404 Not Found. The requested model was not found.
  • 500 Internal Server Error. The evaluation failed temporarily.

Sample cURL invocation:

curl -X POST --data-binary @BatchEvaluationRequest.json -H "Content-type: application/json" http://localhost:8080/openscoring/model/DecisionTreeIris/batch

The evaluation is performed at "record" isolation level. If the evaluation of some org.openscoring.common.EvaluationRequest object fails, then the corresponding org.openscoring.common.EvaluationResponse object encodes the error condition (see above).

POST /model/${id}/csv

Evaluates data in "CSV prediction" mode.

The request body is a CSV document (indicated by content-type header text/plain). The data table must contain a data column for every input and group-by field. The ordering of data columns is not significant, because they are mapped to fields by name.

The CSV reader component detects the CSV dialect by probing ,, ; and \t as CSV delimiter characters. This detection functionality can be suppressed by supplying the value of the CSV delimiter character using the delimiterChar query parameter.

The response body is a CSV document. The data table contains a data column for every target and output field.

The first data column can be employed for row identification purposes. It will be copied over from the request data table to the response data table if its name equals to "Id" (the comparison is case insensitive) and the number of rows did not change during the evaluation.

Response status codes:

  • 200 OK. The evaluation was successful.
  • 400 Bad request. The evaluation failed permanently. The request body is not a valid and/or supported CSV document, or it contains cells with missing or invalid input data.
  • 404 Not Found. The requested model was not found.
  • 500 Internal Server Error. The evaluation failed temporarily.

Sample cURL invocation:

curl -X POST --data-binary @input.csv -H "Content-type: text/plain; charset=UTF-8" http://localhost:8080/openscoring/model/DecisionTreeIris/csv > output.csv

The same, using the gzip encoding:

curl -X POST --data-binary @input.csv.gz -H "Content-encoding: gzip" -H "Content-type: text/plain; charset=UTF-8" -H "Accept-encoding: gzip" http://localhost:8080/openscoring/model/DecisionTreeIris/csv > output.csv.gz

Sample request:

Id,Sepal_Length,Sepal_Width,Petal_Length,Petal_Width
record-001,5.1,3.5,1.4,0.2
record-002,7,3.2,4.7,1.4
record-003,6.3,3.3,6,2.5

Sample response:

Id,Species,Probability_setosa,Probability_versicolor,Probability_virginica,Node_Id
record-001,setosa,1.0,0.0,0.0,2
record-002,versicolor,0.0,0.9074074074074074,0.09259259259259259,6
record-003,virginica,0.0,0.021739130434782608,0.9782608695652174,7

The evaluation is performed at "all-records-or-nothing" isolation level. If the evaluation of some row fails, then the whole CSV document fails.

Model undeployment

DELETE /model/${id}

Deletes a model.

The response body is a JSON serialized form of an org.openscoring.common.SimpleResponse (source) object.

Response status codes:

  • 200 OK. The model was deleted.
  • 403 Forbidden. The acting user does not have an "admin" role.
  • 404 Not Found. The requested model was not found.
  • 500 Internal Server Error. The undeployment failed temporarily.

Sample cURL invocation:

curl -X DELETE http://localhost:8080/openscoring/model/DecisionTreeIris

An HTTP PUT or DELETE method can be masked as an HTTP POST method by using the HTTP method override mechanism.

Sample cURL invocation that employs the X-HTTP-Method-Override request header:

curl -X POST -H "X-HTTP-Method-Override: DELETE" http://localhost:8080/openscoring/model/DecisionTreeIris

Sample cURL invocation that employs the _method query parameter:

curl -X POST http://localhost:8080/openscoring/model/DecisionTreeIris?_method=DELETE

Metric querying

GET /metric/model/${id}

Gets the snapshot of the metric set of a model.

The response body is a JSON serialized form of an org.openscoring.common.MetricSetResponse (source) object.

Response status codes:

  • 200 OK. The evaluation was successful.
  • 403 Forbidden. The acting user does not have an "admin" role.
  • 404 Not Found. The requested model was not found.

Sample cURL invocation:

curl -X GET http://localhost:8080/openscoring/metric/model/DecisionTreeIris

Sample response:

{
	"version" : "3.0.0",
	"counters" : {
		"records" : {
			"count" : 1
		}
	},
	"gauges" : { },
	"histograms" : { },
	"meters" : { },
	"timers" : {
		"evaluate" : {
			"count" : 1,
			"max" : 0.008521913,
			"mean" : 0.008521913,
			"min" : 0.008521913,
			"p50" : 0.008521913,
			"p75" : 0.008521913,
			"p95" : 0.008521913,
			"p98" : 0.008521913,
			"p99" : 0.008521913,
			"p999" : 0.008521913,
			"stddev" : 0.0,
			"m15_rate" : 0.19237151525464488,
			"m1_rate" : 0.11160702915400945,
			"m5_rate" : 0.17797635419760474,
			"mean_rate" : 0.023793073545863026,
			"duration_units" : "seconds",
			"rate_units" : "calls/second"
		}
	}
}

License

Openscoring is dual-licensed under the GNU Affero General Public License (AGPL) version 3.0, and a commercial license.

Additional information

Openscoring is developed and maintained by Openscoring Ltd, Estonia.

Interested in using Openscoring software in your application? Please contact info@openscoring.io