streaming.dsl.mmlib.algs.SQLCacheExt.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.ml.param.{BooleanParam, Param}
import org.apache.spark.sql.expressions.UserDefinedFunction
import org.apache.spark.sql.mlsql.session.MLSQLException
import org.apache.spark.sql.{DataFrame, SparkSession}
import streaming.dsl.mmlib._
import streaming.dsl.mmlib.algs.param.{BaseParams, WowParams}


class SQLCacheExt(override val uid: String) extends SQLAlg with WowParams {

  override def train(df: DataFrame, path: String, params: Map[String, String]): DataFrame = {

    val exe = params.get(execute.name).getOrElse {
      "cache"
    }

    val _isEager = params.get(isEager.name).map(f => f.toBoolean).getOrElse(false)

    if (!execute.isValid(exe)) {
      throw new MLSQLException(s"${execute.name} should be cache or uncache")
    }

    if (exe == "cache") {
      df.persist()
    } else {
      df.unpersist()
    }

    if (_isEager) {
      df.count()
    }
    df
  }

  override def load(sparkSession: SparkSession, path: String, params: Map[String, String]): Any = {
    throw new RuntimeException("register is not support")
  }

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

  final val execute: Param[String] = new Param[String](this, "execute", "cache|uncache", isValid = (m: String) => {
    m == "cache" || m == "uncache"
  })

  final val isEager: BooleanParam = new BooleanParam(this, "isEager", "if set true, execute computing right now, and cache the table")


  override def doc: Doc = Doc(MarkDownDoc,
    """
      |SQLCacheExt is used to cache/uncache table.
      |
      |```sql
      |run table as CacheExt.`` where execute="cache" and isEager="true";
      |```
      |
      |If you execute the upper command, then table will be cached immediately, othersise only the second time
      |to use the table you will fetch the table from cache.
      |
      |To release the table , do like this:
      |
      |```sql
      |run table as CacheExt.`` where execute="uncache";
      |```
    """.stripMargin)

  override def modelType: ModelType = ProcessType

  def this() = this(BaseParams.randomUID())
  override def explainParams(sparkSession: SparkSession): DataFrame = _explainParams(sparkSession)

}