Yurita
Yurita is an open source project for developing large scale anomaly detection models Site
Getting Started
Documentation
Documentation on Yurita's architecture, statistical models available, anomaly detection pipeline/data flow, etc can be found here: https://yurita.readthedocs.io/en/latest/
Build from source
foo@bar:~/yurita$ ./gradlew clean build
foo@bar:~/yurita$ ./gradlew publishToMavenLocal
Install from Maven Central
Please build the project from source at this time or try our dockerized Yurita demo application to build automatically as we make the project jar available on Maven Central in upcoming few days.
<dependency>
<groupId>io.github.paypal</groupId>
<artifactId>yurita</artifactId>
<version>1.0.0</version>
</dependency>
Other Required Dependencies:
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>2.4.1</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>2.4.1</version>
</dependency>
Running Dockerized Demo Application
YuritaSampleApp
directory in the Yurita project root path contains a standalone scala project for you to play around with. Run the demo through Docker inside YuritaSampleApp
directory as shown below.
Build Docker Image
foo@bar:~/YuritaSampleApp$ docker build -f Dockerfile -t yuritademo .
Run Docker Container
foo@bar:~/YuritaSampleApp$ docker run -p 8080:8080 -t yuritademo
Writing Your First App
Create SparkSession with your own configurations
val appName = "AnomalyDetectionAPI"
val sparkConf = new SparkConf().setAppName(appName).setMaster("local[*]")
val spark = SparkSession
.builder()
.config(sparkConf)
.getOrCreate()
Create dataframe of your data points/attributes with what time interval they occur on
//sample window timestamp
val window1 = (dateFormat.parse("2011-01-18 01:00:00.0"), dateFormat.parse("2011-01-18 01:00:10.0"))
val inputDF: DataFrame = Seq(
Person("Ned", "Stark", 40, 40.6, "M", Array(5.5), getTimestamp(window1)),
Person("Arya", "Stark", 9, 40.1, "F", Array(5.6), getTimestamp(window2)),
Person("Sansa", "Stark", 13, 46.3, "F", Array(5.6), getTimestamp(window3)),
Person("Jon Snow", "Stark", 17, 11.4, "M", Array(12.4), getTimestamp(window1),
...
).toDF()
Create a data pipe that will perform specified stastical methods on set columns of dataframe within the window size.
val categoricalPipe = PipelineBuilder()
.onColumns(Seq("surname", "gender"))
.setWindowing(Window.fixed("1 hour"))
.setWindowReferencing(windowRef)
.buildCategoricalModel(
Functions.Categorical.avgRef,
Functions.Categorical.entropy,
Functions.statResultThreshold(3.0))
Combine multiple pipelines
val workload = AnomalyWorkload.builder()
.addAllPipelines(categoricalPipe)
.addPartitioner("surname")
.buildWithWatermark("timestamp", "2 hours")
Dataset extended api
df.detectAnomalies(workload).map(_.toString).foreach(println(_))
Full demo application code can be viewed in our YuritaSampleApp project.
Contributing to Yurita
Thank you very much for contributing to Yurita. Please read the contribution guidelines for the process.
License
Yurita is licensed under the Apache License, v2.0