streaming.dsl.mmlib.algs.SQLWaterMarkInPlace.scala Maven / Gradle / Ivy

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/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package streaming.dsl.mmlib.algs

import org.apache.spark.sql.expressions.UserDefinedFunction
import org.apache.spark.sql.{DataFrame, SparkSession}
import streaming.dsl.mmlib.SQLAlg

/**
  * Created by zhuml on 20/8/2018.
  */
class SQLWaterMarkInPlace extends SQLAlg with Functions {
  override def train(df: DataFrame, path: String, params: Map[String, String]): DataFrame = {
    emptyDataFrame()(df)
  }

  override def load(spark: SparkSession, _path: String, params: Map[String, String]): Any = {
    val inputTable = params.getOrElse("inputTable", _path)
    val eventTimeCol = params.getOrElse("eventTimeCol", "timestamp")
    val delayThreshold = params.getOrElse("delayThreshold", "10 seconds")
    val df = spark.table(inputTable)
    df.withWatermark(eventTimeCol, delayThreshold).createOrReplaceTempView(inputTable)
    null
  }

  override def predict(spark: SparkSession, _model: Any, name: String, params: Map[String, String]): UserDefinedFunction = {
    null
  }

  override def skipPathPrefix: Boolean = true
}