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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.execution
import java.util.concurrent.locks.ReentrantReadWriteLock
import scala.collection.JavaConverters._
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.spark.internal.Logging
import org.apache.spark.sql.{Dataset, SparkSession}
import org.apache.spark.sql.catalyst.expressions.SubqueryExpression
import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, ResolvedHint}
import org.apache.spark.sql.execution.columnar.InMemoryRelation
import org.apache.spark.sql.execution.datasources.{HadoopFsRelation, LogicalRelation}
import org.apache.spark.storage.StorageLevel
import org.apache.spark.storage.StorageLevel.MEMORY_AND_DISK
/** Holds a cached logical plan and its data */
case class CachedData(plan: LogicalPlan, cachedRepresentation: InMemoryRelation)
/**
* Provides support in a SQLContext for caching query results and automatically using these cached
* results when subsequent queries are executed. Data is cached using byte buffers stored in an
* InMemoryRelation. This relation is automatically substituted query plans that return the
* `sameResult` as the originally cached query.
*
* Internal to Spark SQL.
*/
class CacheManager extends Logging {
@transient
private val cachedData = new java.util.LinkedList[CachedData]
@transient
private val cacheLock = new ReentrantReadWriteLock
/** Acquires a read lock on the cache for the duration of `f`. */
private def readLock[A](f: => A): A = {
val lock = cacheLock.readLock()
lock.lock()
try f finally {
lock.unlock()
}
}
/** Acquires a write lock on the cache for the duration of `f`. */
private def writeLock[A](f: => A): A = {
val lock = cacheLock.writeLock()
lock.lock()
try f finally {
lock.unlock()
}
}
/** Clears all cached tables. */
def clearCache(): Unit = writeLock {
cachedData.asScala.foreach(_.cachedRepresentation.cachedColumnBuffers.unpersist())
cachedData.clear()
}
/** Checks if the cache is empty. */
def isEmpty: Boolean = readLock {
cachedData.isEmpty
}
/**
* Caches the data produced by the logical representation of the given [[Dataset]].
* Unlike `RDD.cache()`, the default storage level is set to be `MEMORY_AND_DISK` because
* recomputing the in-memory columnar representation of the underlying table is expensive.
*/
def cacheQuery(
query: Dataset[_],
tableName: Option[String] = None,
storageLevel: StorageLevel = MEMORY_AND_DISK): Unit = writeLock {
val planToCache = query.logicalPlan
if (lookupCachedData(planToCache).nonEmpty) {
logWarning("Asked to cache already cached data.")
} else {
val sparkSession = query.sparkSession
val inMemoryRelation = InMemoryRelation(
sparkSession.sessionState.conf.useCompression,
sparkSession.sessionState.conf.columnBatchSize, storageLevel,
sparkSession.sessionState.executePlan(planToCache).executedPlan,
tableName,
planToCache.stats)
cachedData.add(CachedData(planToCache, inMemoryRelation))
}
}
/**
* Un-cache all the cache entries that refer to the given plan.
*/
def uncacheQuery(query: Dataset[_], blocking: Boolean = true): Unit = writeLock {
uncacheQuery(query.sparkSession, query.logicalPlan, blocking)
}
/**
* Un-cache all the cache entries that refer to the given plan.
*/
def uncacheQuery(spark: SparkSession, plan: LogicalPlan, blocking: Boolean): Unit = writeLock {
val it = cachedData.iterator()
while (it.hasNext) {
val cd = it.next()
if (cd.plan.find(_.sameResult(plan)).isDefined) {
cd.cachedRepresentation.cachedColumnBuffers.unpersist(blocking)
it.remove()
}
}
}
/**
* Tries to re-cache all the cache entries that refer to the given plan.
*/
def recacheByPlan(spark: SparkSession, plan: LogicalPlan): Unit = writeLock {
recacheByCondition(spark, _.find(_.sameResult(plan)).isDefined)
}
private def recacheByCondition(spark: SparkSession, condition: LogicalPlan => Boolean): Unit = {
val it = cachedData.iterator()
val needToRecache = scala.collection.mutable.ArrayBuffer.empty[CachedData]
while (it.hasNext) {
val cd = it.next()
if (condition(cd.plan)) {
cd.cachedRepresentation.cachedColumnBuffers.unpersist()
// Remove the cache entry before we create a new one, so that we can have a different
// physical plan.
it.remove()
val newCache = InMemoryRelation(
useCompression = cd.cachedRepresentation.useCompression,
batchSize = cd.cachedRepresentation.batchSize,
storageLevel = cd.cachedRepresentation.storageLevel,
child = spark.sessionState.executePlan(cd.plan).executedPlan,
tableName = cd.cachedRepresentation.tableName,
statsOfPlanToCache = cd.plan.stats)
needToRecache += cd.copy(cachedRepresentation = newCache)
}
}
needToRecache.foreach(cachedData.add)
}
/** Optionally returns cached data for the given [[Dataset]] */
def lookupCachedData(query: Dataset[_]): Option[CachedData] = readLock {
lookupCachedData(query.logicalPlan)
}
/** Optionally returns cached data for the given [[LogicalPlan]]. */
def lookupCachedData(plan: LogicalPlan): Option[CachedData] = readLock {
cachedData.asScala.find(cd => plan.sameResult(cd.plan))
}
/** Replaces segments of the given logical plan with cached versions where possible. */
def useCachedData(plan: LogicalPlan): LogicalPlan = {
val newPlan = plan transformDown {
// Do not lookup the cache by hint node. Hint node is special, we should ignore it when
// canonicalizing plans, so that plans which are same except hint can hit the same cache.
// However, we also want to keep the hint info after cache lookup. Here we skip the hint
// node, so that the returned caching plan won't replace the hint node and drop the hint info
// from the original plan.
case hint: ResolvedHint => hint
case currentFragment =>
lookupCachedData(currentFragment)
.map(_.cachedRepresentation.withOutput(currentFragment.output))
.getOrElse(currentFragment)
}
newPlan transformAllExpressions {
case s: SubqueryExpression => s.withNewPlan(useCachedData(s.plan))
}
}
/**
* Tries to re-cache all the cache entries that contain `resourcePath` in one or more
* `HadoopFsRelation` node(s) as part of its logical plan.
*/
def recacheByPath(spark: SparkSession, resourcePath: String): Unit = writeLock {
val (fs, qualifiedPath) = {
val path = new Path(resourcePath)
val fs = path.getFileSystem(spark.sessionState.newHadoopConf())
(fs, fs.makeQualified(path))
}
recacheByCondition(spark, _.find(lookupAndRefresh(_, fs, qualifiedPath)).isDefined)
}
/**
* Traverses a given `plan` and searches for the occurrences of `qualifiedPath` in the
* [[org.apache.spark.sql.execution.datasources.FileIndex]] of any [[HadoopFsRelation]] nodes
* in the plan. If found, we refresh the metadata and return true. Otherwise, this method returns
* false.
*/
private def lookupAndRefresh(plan: LogicalPlan, fs: FileSystem, qualifiedPath: Path): Boolean = {
plan match {
case lr: LogicalRelation => lr.relation match {
case hr: HadoopFsRelation =>
val prefixToInvalidate = qualifiedPath.toString
val invalidate = hr.location.rootPaths
.map(_.makeQualified(fs.getUri, fs.getWorkingDirectory).toString)
.exists(_.startsWith(prefixToInvalidate))
if (invalidate) hr.location.refresh()
invalidate
case _ => false
}
case _ => false
}
}
}
