Plugins

Arc has been designed so that it can be extended by simply building a JAR with logic that meets the interface specifications and placing it in the classpath. The rationalle for this is to allow teams to add custom functionality easily and not be reliant on a central team for development.

Arc can be exended in four ways by registering:

  • Dynamic Configuration Plugins which allow users to inject custom configuration parameters which will be processed before resolving the job configuration file.
  • Lifecycle Plugins which allow users to extend the base Arc framework with pipeline lifecycle hooks.
  • Pipeline Stage Plugins which allow users to extend the base Arc framework with custom stages which allow the full use of the Spark Scala API. All the Arc core stages are built by using this plugin interface.
  • User Defined Functions which extend the Spark SQL dialect.

Lifecycle Plugins

ChaosMonkey

Since: 2.10.0 - Supports Streaming: False

The ChaosMonkey plugin is intended to be used for testing your orchestration design. It will randomly execute a strategy after each stage such as to throw an exception based on a probability.

Parameters

Attribute Type Required Description
name String true Name of the stage for logging.
environments Array[String] true A list of environments under which this stage will be executed. See environments documentation.
strategy String true The strategy to apply. Supported values: exception.

Default: exception.
probability Double true The probability of this strategy being executed. Must be between 0.0 and 1.0.

Examples

{
  "type": "ai.tripl.arc.plugins.lifecycle.ChaosMonkey",
  "environments": ["test"],
  "strategy": "exception",
  "probability": 0.05
}

User Defined Functions

To help with common data tasks several additional functions have been added to Arc in addition to the inbuilt Spark SQL Functions.

get_json_double_array

Since: 1.0.9

Deprecated

Deprecated. Please use inbuilt Spark function get_json_object.

Similar to get_json_object - but extracts a json double array from path.

SELECT get_json_double_array('[0.1, 1.1]', '$')

get_json_integer_array

Since: 1.0.9

Deprecated

Deprecated. Please use inbuilt Spark function get_json_object.

Similar to get_json_object - but extracts a json integer array from path.

SELECT get_json_integer_array('[1, 2]', '$')

get_json_long_array

Since: 1.0.9

Deprecated

Deprecated. Please use inbuilt Spark function get_json_object.

Similar to get_json_object - but extracts a json long array from path.

SELECT get_json_long_array('[2147483648, 2147483649]', '$')

get_uri

Since: 2.10.1

get_uri returns the contents of a URI as an Array[Byte]. Prior to Arc 3.x this will not allow authentication to be modified from the standard inbuilt permissions (like AmazonIAM).

SELECT GET_URI('s3a://bucket/file.txt') AS content

If reading text this function can be wrapped with the inbuilt decode Spark SQL function to convert from Array[Byte] to string like:

SELECT DECODE(GET_URI('s3a://bucket/file.txt'), 'UTF-8')

get_uri_array

Since: 2.12.0

get_uri_array returns the contents of a Glob pattern as an Array of Array[Byte] which means the contents of multiple files can be returned. Prior to Arc 3.x this will not allow authentication to be modified from the standard inbuilt permissions (like AmazonIAM).

SELECT GET_URI_ARRAY('s3a://bucket/file.*') AS content

If reading text this function can be wrapped with the inbuilt decode and transform Spark SQL functions to convert from Array[Array[Byte]] to multiple records of type string like:

SELECT TRANSFORM(GET_URI_ARRAY('s3a://bucket/file*.txt'), bytes -> DECODE(bytes, 'UTF-8'))

or with the explode function to return multiple rows:

SELECT
  DECODE(uri_contents, 'UTF-8') AS string_value
FROM (
  SELECT EXPLODE(GET_URI_ARRAY('s3a://bucket/file*.txt')) AS uri_contents
) uri_array

get_uri_filename_array

Since: 2.12.1

get_uri_filename_array returns the contents of a Glob pattern as an Array of (Array[Byte], String) which means the contents of multiple files can be returned with their filenames. Prior to Arc 3.x this will not allow authentication to be modified from the standard inbuilt permissions (like AmazonIAM).

SELECT GET_URI_FILENAME_ARRAY('s3a://bucket/file.*') AS content

If reading text this function can be wrapped with the explode and decode functions to return multiple rows:

SELECT
  DECODE(col._1, 'UTF-8') AS value
  ,col._2 AS filename
FROM (
  SELECT EXPLODE(GET_URI_FILENAME_ARRAY('s3a://bucket/file.*'))
) files

to_xml

Since: 2.10.0

to_xml returns a XML string with a given struct value.

SELECT
  to_xml(
    NAMED_STRUCT(
      'Document', NAMED_STRUCT(
          '_VALUE', NAMED_STRUCT(
            'child0', 0,
            'child1', NAMED_STRUCT(
              'nested0', 0,
              'nested1', 'nestedvalue'
            )
          ),
      '_attribute', 'attribute'
      )
    )
  ) AS xml

Produces a the XML string:

<Document attribute="attribute">
  <child0>0</child0>
  <child1>
    <nested0>0</nested0>
    <nested1>nestedvalue</nested1>
  </child1>
</Document>

struct_keys

Since: 2.10.0

struct_keys returns an array with the names of the keys in the struct.

SELECT
  STRUCT_KEYS(
    NAMED_STRUCT(
      'key0', 'value0',
      'key1', 'value1'
    )
  )

Resolution

Plugins are resolved dynamically at runtime and are resolved by name and version.

Examples

Assuming we wanted to execute a KafkaExtract Pipeline Stage Plugin:

{
  "type": "KafkaExtract",
  "name": "load customers",
  "environments": [
    "production",
    "test"
  ],
  "outputView": "customer",
  "bootstrapServers": "kafka:29092",
  "topic": "customers",
  "groupID": "spark-customer-extract-job"
}

Arc will attempt to resolve the plugin by first looking in all the META-INF directories of all included JAR files (https://github.com/tripl-ai/arc-kafka-pipeline-plugin/blob/master/src/main/resources/META-INF/services/ai.tripl.arc.plugins.PipelineStagePlugin) for classes that extend PipelineStagePlugin which the KafkaExtract plugin does:

class KafkaExtract extends PipelineStagePlugin {

Arc is then able to resolve the plugin by matching on simpleName - in this case KafkaExtract - and then call the instantiate() method to create an instance of the plugin which is executed by Arc at the appropriate time depending on plugin type.

To allow more specitivity you can use either the full package name and/or include the version:

{
  "type": "ai.tripl.arc.extract.KafkaExtract",
  ...
{
  "type": "KafkaExtract:1.0.0",
  ...
{
  "type": "ai.tripl.arc.extract.KafkaExtract:1.0.0",
  ...

Dynamic Configuration Plugins

Since: 1.3.0

Dynamic vs Deterministic Configuration

Use of this functionality is discouraged as it goes against the principles of Arc specifically around statelessness/deterministic behaviour but is inlcuded here for users who have not yet committed to a job orchestrator such as Apache Airflow and have dynamic configuration requirements.

The Dynamic Configuration Plugin plugin allow users to inject custom configuration parameters which will be processed before resolving the job configuration file. The plugin must return a Typesafe Config object (which is easily created from a java.util.Map[String, Object] which will be included in the job configuration resolution step.

Examples

For example a custom runtime configuration plugin could be used calculate a formatted list of dates to be used with an Extract stage to read only a subset of documents:

package ai.tripl.arc.plugins.config

import java.util
import java.sql.Date
import java.time.LocalDate
import java.time.format.{DateTimeFormatter, DateTimeFormatterBuilder}
import java.time.format.ResolverStyle
import scala.collection.JavaConverters._

import com.typesafe.config._

import org.apache.spark.sql.SparkSession

import ai.tripl.arc.util.log.logger.Logger
import ai.tripl.arc.api.API.ARCContext
import ai.tripl.arc.util.EitherUtils._
import ai.tripl.arc.config.Error._
import ai.tripl.arc.plugins.DynamicConfigurationPlugin

class DeltaPeriodDynamicConfigurationPlugin extends DynamicConfigurationPlugin {

  val version = ai.tripl.arc.plugins.config.deltaperiod.BuildInfo.version

  def instantiate(index: Int, config: Config)(implicit spark: SparkSession, logger: ai.tripl.arc.util.log.logger.Logger, arcContext: ARCContext): Either[List[StageError], Config] = {
    import ai.tripl.arc.config.ConfigReader._
    import ai.tripl.arc.config.ConfigUtils._
    implicit val c = config

    val expectedKeys = "type" :: "environments" :: "returnName" :: "lagDays" :: "leadDays" :: "formatter" :: "currentDate" :: Nil
    val returnName = getValue[String]("returnName")
    val lagDays = getValue[Int]("lagDays")
    val leadDays = getValue[Int]("leadDays")
    val formatter = getValue[String]("formatter") |> parseFormatter("formatter") _
    val currentDate = formatter match {
      case Right(formatter) => {
        if (c.hasPath("currentDate")) getValue[String]("currentDate") |> parseCurrentDate("currentDate", formatter) _ else Right(java.time.LocalDate.now)
      }
      case _ => Right(java.time.LocalDate.now)
    }
    val invalidKeys = checkValidKeys(c)(expectedKeys)

    (returnName, lagDays, leadDays, formatter, currentDate, invalidKeys) match {
      case (Right(returnName), Right(lagDays), Right(leadDays), Right(formatter), Right(currentDate), Right(invalidKeys)) =>

        val res = (lagDays * -1 to leadDays).map { v =>
          formatter.format(currentDate.plusDays(v))
        }.mkString(",")

        val values = new java.util.HashMap[String, Object]()
        values.put(returnName, res)

        Right(ConfigFactory.parseMap(values))
      case _ =>
        val allErrors: Errors = List(returnName, lagDays, leadDays, formatter, currentDate, invalidKeys).collect{ case Left(errs) => errs }.flatten
        val err = StageError(index, this.getClass.getName, c.origin.lineNumber, allErrors)
        Left(err :: Nil)
    }
  }

  def parseFormatter(path: String)(formatter: String)(implicit c: Config): Either[Errors, DateTimeFormatter] = {
    def err(lineNumber: Option[Int], msg: String): Either[Errors, DateTimeFormatter] = Left(ConfigError(path, lineNumber, msg) :: Nil)

    try {
      Right(DateTimeFormatter.ofPattern(formatter).withResolverStyle(ResolverStyle.SMART))
    } catch {
      case e: Exception => err(Some(c.getValue(path).origin.lineNumber()), e.getMessage)
    }
  }

  def parseCurrentDate(path: String, formatter: DateTimeFormatter)(value: String)(implicit c: Config): Either[Errors, LocalDate] = {
    def err(lineNumber: Option[Int], msg: String): Either[Errors, LocalDate] = Left(ConfigError(path, lineNumber, msg) :: Nil)

    try {
      Right(LocalDate.parse(value, formatter))
    } catch {
      case e: Exception => err(Some(c.getValue(path).origin.lineNumber()), e.getMessage)
    }
  }
}

The plugin then needs to be registered in the plugins.config section of the job configuration and the full plugin name must be listed in your project’s /resources/META-INF/services/ai.tripl.arc.plugins.DynamicConfigurationPlugin file. See https://github.com/tripl-ai/arc-deltaperiod-config-plugin for a full example.

Note that the resolution order of these plugins is in descending order in that if the the ETL_CONF_DELTA_PERIOD was declared in multiple plugins the value set by the plugin with the lower index in the plugins.config array will take precedence.

The ETL_CONF_DELTA_PERIOD variable is then available to be resolved in a standard configuration:

{
  "plugins": {
    "config": [
      {
        "type": "ai.tripl.arc.plugins.config.DeltaPeriodDynamicConfigurationPlugin",
        "environments": [
          "production",
          "test"
        ],
        "returnName": "ETL_CONF_DELTA_PERIOD",
        "lagDays": "10",
        "leadDays": "1",
        "pattern": "yyyy-MM-dd"
      }
    ]
  },
  "stages": [
    {
      "type": "DelimitedExtract",
      "name": "load customer extract",
      "environments": [
        "production",
        "test"
      ],
      "inputURI": "hdfs://datalake/input/customer/customers_{"${ETL_CONF_DELTA_PERIOD}"}.csv",
      "outputView": "customer"
    }
  ]
}

Lifecycle Plugins

Since: 1.3.0

Custom Lifecycle Plugins allow users to extend the base Arc framework with logic which is executed before or after each Arc stage (lifecycle hooks). These stages are useful for implementing things like dataset logging after each stage execution for debugging.

Examples

package ai.tripl.arc.plugins.lifecycle

import org.apache.spark.sql.{DataFrame, SparkSession}

import ai.tripl.arc.api.API._
import ai.tripl.arc.plugins.LifecyclePlugin
import ai.tripl.arc.util.Utils
import ai.tripl.arc.config.Error._

class DataFramePrinter extends LifecyclePlugin {

  val version = Utils.getFrameworkVersion

  def instantiate(index: Int, config: com.typesafe.config.Config)(implicit spark: SparkSession, logger: ai.tripl.arc.util.log.logger.Logger, arcContext: ARCContext): Either[List[ai.tripl.arc.config.Error.StageError], LifecyclePluginInstance] = {
    import ai.tripl.arc.config.ConfigReader._
    import ai.tripl.arc.config.ConfigUtils._
    implicit val c = config

    val expectedKeys = "type" :: "environments" :: "numRows" :: "truncate" :: Nil
    val numRows = getValue[Int]("numRows", default = Some(20))
    val truncate = getValue[java.lang.Boolean]("truncate", default = Some(true))
    val invalidKeys = checkValidKeys(c)(expectedKeys)

    (numRows, truncate, invalidKeys) match {
      case (Right(numRows), Right(truncate), Right(invalidKeys)) =>
        Right(DataFramePrinterInstance(
          plugin=this,
          numRows=numRows,
          truncate=truncate
        ))
      case _ =>
        val allErrors: Errors = List(numRows, truncate, invalidKeys).collect{ case Left(errs) => errs }.flatten
        val err = StageError(index, this.getClass.getName, c.origin.lineNumber, allErrors)
        Left(err :: Nil)
    }
  }
}

case class DataFramePrinterInstance(
    plugin: LifecyclePlugin,
    numRows: Int,
    truncate: Boolean
  ) extends LifecyclePluginInstance {

  override def before(stage: PipelineStage, index: Int, stages: List[PipelineStage])(implicit spark: SparkSession, logger: ai.tripl.arc.util.log.logger.Logger, arcContext: ARCContext) {
    logger.trace()
      .field("event", "before")
      .field("stage", stage.name)
      .log()
  }

  override def after(result: Option[DataFrame], stage: PipelineStage, index: Int, stages: List[PipelineStage])(implicit spark: SparkSession, logger: ai.tripl.arc.util.log.logger.Logger, arcContext: ARCContext) {
    logger.trace()
      .field("event", "after")
      .field("stage", stage.name)
      .log()

    result match {
      case Some(df) => df.show(numRows, truncate)
      case None =>
    }
  }
}

The plugin then needs to be registered by adding the full plugin name must be listed in your project’s /resources/META-INF/services/ai.tripl.arc.plugins.LifecyclePlugin file.

To execute:

{
  "plugins": {
    "lifecycle": [
      {
        "type": "ai.tripl.arc.plugins.lifecycle.DataFramePrinterLifecyclePlugin",
        "environments": [
          "production",
          "test"
        ],
        "params": {
          "numRows": "100",
          "truncate": "false",
        }
      }
    ]
  },
  "stages": [
    ...
  ]
}

Pipeline Stage Plugins

Since: 1.3.0

Custom Pipeline Stage Plugins allow users to extend the base Arc framework with custom stages which allow the full use of the Spark Scala API. This means that private business logic or code which relies on libraries not included in the base Arc framework can be used - however it is strongly advised to use the inbuilt SQL stages where possible. If stages are general purpose enough for use outside your organisation consider contributing them to ai.tripl so that others can benefit.

Examples

class ConsoleLoad extends PipelineStagePlugin {

  val version = Utils.getFrameworkVersion

  def instantiate(index: Int, config: com.typesafe.config.Config)(implicit spark: SparkSession, logger: ai.tripl.arc.util.log.logger.Logger, arcContext: ARCContext): Either[List[ai.tripl.arc.config.Error.StageError], PipelineStage] = {
    import ai.tripl.arc.config.ConfigReader._
    import ai.tripl.arc.config.ConfigUtils._
    implicit val c = config

    val expectedKeys = "type" :: "name" :: "description" :: "environments" :: "inputView" :: "outputMode" :: "params" :: Nil
    val name = getValue[String]("name")
    val description = getOptionalValue[String]("description")
    val inputView = getValue[String]("inputView")
    val outputMode = getValue[String]("outputMode", default = Some("Append"), validValues = "Append" :: "Complete" :: "Update" :: Nil) |> parseOutputModeType("outputMode") _
    val params = readMap("params", c)
    val invalidKeys = checkValidKeys(c)(expectedKeys)

    (name, description, inputView, outputMode, invalidKeys) match {
      case (Right(name), Right(description), Right(inputView), Right(outputMode), Right(invalidKeys)) =>
        val stage = ConsoleLoadStage(
          plugin=this,
          name=name,
          description=description,
          inputView=inputView,
          outputMode=outputMode,
          params=params
        )

        stage.stageDetail.put("inputView", stage.inputView)
        stage.stageDetail.put("outputMode", stage.outputMode.sparkString)
        stage.stageDetail.put("params", params.asJava)

        Right(stage)
      case _ =>
        val allErrors: Errors = List(name, description, inputView, outputMode, invalidKeys).collect{ case Left(errs) => errs }.flatten
        val stageName = stringOrDefault(name, "unnamed stage")
        val err = StageError(index, stageName, c.origin.lineNumber, allErrors)
        Left(err :: Nil)
    }
  }

}

case class ConsoleLoadStage(
    plugin: ConsoleLoad,
    name: String,
    description: Option[String],
    inputView: String,
    outputMode: OutputModeType,
    params: Map[String, String]
  ) extends PipelineStage {

  override def execute()(implicit spark: SparkSession, logger: ai.tripl.arc.util.log.logger.Logger, arcContext: ARCContext): Option[DataFrame] = {
    ConsoleLoadStage.execute(this)
  }
}


object ConsoleLoadStage {

  def execute(stage: ConsoleLoadStage)(implicit spark: SparkSession, logger: ai.tripl.arc.util.log.logger.Logger, arcContext: ARCContext): Option[DataFrame] = {

    val df = spark.table(stage.inputView)

    if (!df.isStreaming) {
      throw new Exception("ConsoleLoad can only be executed in streaming mode.") with DetailException {
        override val detail = stage.stageDetail
      }
    }

    df.writeStream
        .format("console")
        .outputMode(stage.outputMode.sparkString)
        .start

    Option(df)
  }
}

The plugin then needs to be registered by adding the full plugin name must be listed in your project’s /resources/META-INF/services/ai.tripl.arc.plugins.PipelineStagePlugin file.

To execute:

{
  "stages": [
    {
      "type": "ConsoleLoad",
      "name": "load streaming data to console for testing",
      "environments": [
        "test"
      ],
      "inputView": "calculated_dataset",
      "outputMode": "Complete"
    }
  ]
}

Jupyter Code Completion

To allow the plugin to be registerd for code completion in Jupyter include the JupyterCompleter trait and define the snippet and documentationURI values:

class DelimitedExtract extends PipelineStagePlugin with JupyterCompleter {

  val snippet = """{
  |  "type": "DelimitedExtract",
  |  "name": "DelimitedExtract",
  |  "environments": [
  |    "production",
  |    "test"
  |  ],
  |  "inputURI": "hdfs://*.csv",
  |  "outputView": "outputView",
  |  "header": false
  |}""".stripMargin

  val documentationURI = new java.net.URI(s"${baseURI}/extract/#delimitedextract")

NotSerializableException

When writing plugins and you find Spark throwing NotSerializableException errors like:

Job aborted due to stage failure: Task not serializable: java.io.NotSerializableException: scala.collection.convert.Wrappers$MapWrapper

Ensure that any stage with a mapPartitions or map DataFrame does not require the PipelineStage instance to be passed into the map function. So instead of doing something like:

val transformedDF = try {
  df.mapPartitions[TransformedRow] { partition: Iterator[Row] =>
    val uri = stage.uri.toString

Declare the variables outside the map function so that stage does not have to be serialised and sent to all the executors (which fails if any of the PipelineStage contents are not serializable):

val stageUri = stage.uri

val transformedDF = try {
  df.mapPartitions[TransformedRow] { partition: Iterator[Row] =>
    val uri = stageUri.toString

User Defined Functions

Since: 1.3.0

User Defined Functions vs Spark SQL Functions

The inbuilt Spark SQL Functions are heavily optimised by the internal Spark code to a level which custom User Defined Functions cannot be (byte code) - so where possible it is better to use the inbuilt functions.

User Defined Functions allow users to extend the Spark SQL dialect.

Arc already includes some addtional functions which are not included in the base Spark SQL dialect so any useful generic functions can be included in the Arc repository so that others can benefit.

Examples

Write the code to define the custom User Defined Function:

package ai.tripl.arc.plugins
import java.util

import org.apache.spark.sql.SparkSession
import ai.tripl.arc.api.API.ARCContext

import ai.tripl.arc.util.log.logger.Logger

class TestUDFPlugin extends UDFPlugin {

  val version = "0.0.1"

  // one udf plugin can register multiple user defined functions
  override def register()(implicit spark: SparkSession, logger: ai.tripl.arc.util.log.logger.Logger, arcContext: ARCContext) = {

    // register the functions so they can be accessed via Spark SQL
    spark.sqlContext.udf.register("add_ten", TestUDFPlugin.addTen _ )           // SELECT add_ten(1) AS one_plus_ten
    spark.sqlContext.udf.register("add_twenty", TestUDFPlugin.addTwenty _ )     // SELECT add_twenty(1) AS one_plus_twenty

  }
}

object TestUDFPlugin {
  // add 10 to an incoming integer - DO NOT DO THIS IN PRODUCTION INSTEAD USE SPARK SQL DIRECTLY
  def addTen(input: Int): Int = {
    input + 10
  }

  // add 20 to an incoming integer  - DO NOT DO THIS IN PRODUCTION INSTEAD USE SPARK SQL DIRECTLY
  def addTwenty(input: Int): Int = {
    input + 20
  }
}

The plugin then needs to be registered by adding the full plugin name must be listed in your project’s /resources/META-INF/services/ai.tripl.arc.plugins.UDFPlugin file and would be executed like:

SELECT age, add_ten(age) FROM customer