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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 org.apache.spark.sql
import scala.language.implicitConversions
import scala.reflect.runtime.universe.TypeTag
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.analysis._
import org.apache.spark.sql.catalyst.dsl
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.optimizer.Optimizer
import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
import org.apache.spark.sql.catalyst.rules.RuleExecutor
import org.apache.spark.sql.execution._
/**
* <span class="badge" style="float: right; background-color: darkblue;">ALPHA COMPONENT</span>
*
* The entry point for running relational queries using Spark. Allows the creation of [[SchemaRDD]]
* objects and the execution of SQL queries.
*
* @groupname userf Spark SQL Functions
* @groupname Ungrouped Support functions for language integrated queries.
*/
class SQLContext(@transient val sparkContext: SparkContext)
extends Logging
with dsl.ExpressionConversions
with Serializable {
self =>
@transient
protected[sql] lazy val catalog: Catalog = new SimpleCatalog
@transient
protected[sql] lazy val analyzer: Analyzer =
new Analyzer(catalog, EmptyFunctionRegistry, caseSensitive = true)
@transient
protected[sql] val optimizer = Optimizer
@transient
protected[sql] val parser = new catalyst.SqlParser
protected[sql] def parseSql(sql: String): LogicalPlan = parser(sql)
protected[sql] def executeSql(sql: String): this.QueryExecution = executePlan(parseSql(sql))
protected[sql] def executePlan(plan: LogicalPlan): this.QueryExecution =
new this.QueryExecution { val logical = plan }
/**
* <span class="badge badge-red" style="float: right;">EXPERIMENTAL</span>
*
* Allows catalyst LogicalPlans to be executed as a SchemaRDD. Note that the LogicalPlan
* interface is considered internal, and thus not guranteed to be stable. As a result, using
* them directly is not reccomended.
*/
implicit def logicalPlanToSparkQuery(plan: LogicalPlan): SchemaRDD = new SchemaRDD(this, plan)
/**
* Creates a SchemaRDD from an RDD of case classes.
*
* @group userf
*/
implicit def createSchemaRDD[A <: Product: TypeTag](rdd: RDD[A]) =
new SchemaRDD(this, SparkLogicalPlan(ExistingRdd.fromProductRdd(rdd)))
/**
* Loads a parequet file, returning the result as a [[SchemaRDD]].
*
* @group userf
*/
def parquetFile(path: String): SchemaRDD =
new SchemaRDD(this, parquet.ParquetRelation("ParquetFile", path))
/**
* Registers the given RDD as a temporary table in the catalog. Temporary tables exist only
* during the lifetime of this instance of SQLContext.
*
* @group userf
*/
def registerRDDAsTable(rdd: SchemaRDD, tableName: String): Unit = {
catalog.registerTable(None, tableName, rdd.logicalPlan)
}
/**
* Executes a SQL query using Spark, returning the result as a SchemaRDD.
*
* @group userf
*/
def sql(sqlText: String): SchemaRDD = {
val result = new SchemaRDD(this, parseSql(sqlText))
// We force query optimization to happen right away instead of letting it happen lazily like
// when using the query DSL. This is so DDL commands behave as expected. This is only
// generates the RDD lineage for DML queries, but do not perform any execution.
result.queryExecution.toRdd
result
}
protected[sql] class SparkPlanner extends SparkStrategies {
val sparkContext = self.sparkContext
val strategies: Seq[Strategy] =
TopK ::
PartialAggregation ::
SparkEquiInnerJoin ::
BasicOperators ::
CartesianProduct ::
BroadcastNestedLoopJoin :: Nil
}
@transient
protected[sql] val planner = new SparkPlanner
/**
* Prepares a planned SparkPlan for execution by binding references to specific ordinals, and
* inserting shuffle operations as needed.
*/
@transient
protected[sql] val prepareForExecution = new RuleExecutor[SparkPlan] {
val batches =
Batch("Add exchange", Once, AddExchange) ::
Batch("Prepare Expressions", Once, new BindReferences[SparkPlan]) :: Nil
}
/**
* The primary workflow for executing relational queries using Spark. Designed to allow easy
* access to the intermediate phases of query execution for developers.
*/
protected abstract class QueryExecution {
def logical: LogicalPlan
lazy val analyzed = analyzer(logical)
lazy val optimizedPlan = optimizer(analyzed)
// TODO: Don't just pick the first one...
lazy val sparkPlan = planner(optimizedPlan).next()
lazy val executedPlan: SparkPlan = prepareForExecution(sparkPlan)
/** Internal version of the RDD. Avoids copies and has no schema */
lazy val toRdd: RDD[Row] = executedPlan.execute()
protected def stringOrError[A](f: => A): String =
try f.toString catch { case e: Throwable => e.toString }
override def toString: String =
s"""== Logical Plan ==
|${stringOrError(analyzed)}
|== Optimized Logical Plan
|${stringOrError(optimizedPlan)}
|== Physical Plan ==
|${stringOrError(executedPlan)}
""".stripMargin.trim
/**
* Runs the query after interposing operators that print the result of each intermediate step.
*/
def debugExec() = DebugQuery(executedPlan).execute().collect()
}
}
|
