org.apache.spark.sql.execution.CacheManager.scala Maven / Gradle / Ivy
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* The ASF licenses this file to You under the Apache License, Version 2.0
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*
* http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.spark.sql.execution
import java.util.concurrent.locks.ReentrantReadWriteLock
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.spark.internal.Logging
import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
import org.apache.spark.sql.Dataset
import org.apache.spark.sql.execution.columnar.InMemoryRelation
import org.apache.spark.sql.execution.datasources.{HadoopFsRelation, LogicalRelation}
import org.apache.spark.sql.SparkSession
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 scala.collection.mutable.ArrayBuffer[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.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.queryExecution.analyzed
if (lookupCachedData(planToCache).nonEmpty) {
logWarning("Asked to cache already cached data.")
} else {
val sparkSession = query.sparkSession
cachedData +=
CachedData(
planToCache,
InMemoryRelation(
sparkSession.sessionState.conf.useCompression,
sparkSession.sessionState.conf.columnBatchSize,
storageLevel,
sparkSession.sessionState.executePlan(planToCache).executedPlan,
tableName))
}
}
/**
* Tries to remove the data for the given [[Dataset]] from the cache.
* No operation, if it's already uncached.
*/
def uncacheQuery(query: Dataset[_], blocking: Boolean = true): Boolean = writeLock {
val planToCache = query.queryExecution.analyzed
val dataIndex = cachedData.indexWhere(cd => planToCache.sameResult(cd.plan))
val found = dataIndex >= 0
if (found) {
cachedData(dataIndex).cachedRepresentation.cachedColumnBuffers.unpersist(blocking)
cachedData.remove(dataIndex)
}
found
}
/** Optionally returns cached data for the given [[Dataset]] */
def lookupCachedData(query: Dataset[_]): Option[CachedData] = readLock {
lookupCachedData(query.queryExecution.analyzed)
}
/** Optionally returns cached data for the given [[LogicalPlan]]. */
def lookupCachedData(plan: LogicalPlan): Option[CachedData] = readLock {
cachedData.find(cd => plan.sameResult(cd.plan))
}
/** Replaces segments of the given logical plan with cached versions where possible. */
def useCachedData(plan: LogicalPlan): LogicalPlan = {
plan transformDown {
case currentFragment =>
lookupCachedData(currentFragment)
.map(_.cachedRepresentation.withOutput(currentFragment.output))
.getOrElse(currentFragment)
}
}
/**
* Invalidates the cache of any data that contains `plan`. Note that it is possible that this
* function will over invalidate.
*/
def invalidateCache(plan: LogicalPlan): Unit = writeLock {
cachedData.foreach {
case data if data.plan.collect { case p if p.sameResult(plan) => p }.nonEmpty =>
data.cachedRepresentation.recache()
case _ =>
}
}
/**
* Invalidates the cache of any data that contains `resourcePath` in one or more
* `HadoopFsRelation` node(s) as part of its logical plan.
*/
def invalidateCachedPath(
sparkSession: SparkSession, resourcePath: String): Unit = writeLock {
val (fs, qualifiedPath) = {
val path = new Path(resourcePath)
val fs = path.getFileSystem(sparkSession.sessionState.newHadoopConf())
(fs, path.makeQualified(fs.getUri, fs.getWorkingDirectory))
}
cachedData.foreach {
case data if data.plan.find(lookupAndRefresh(_, fs, qualifiedPath)).isDefined =>
val dataIndex = cachedData.indexWhere(cd => data.plan.sameResult(cd.plan))
if (dataIndex >= 0) {
data.cachedRepresentation.cachedColumnBuffers.unpersist(blocking = true)
cachedData.remove(dataIndex)
}
sparkSession.sharedState.cacheManager.cacheQuery(Dataset.ofRows(sparkSession, data.plan))
case _ => // Do Nothing
}
}
/**
* Traverses a given `plan` and searches for the occurrences of `qualifiedPath` in the
* [[org.apache.spark.sql.execution.datasources.FileCatalog]] 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 invalidate = hr.location.paths
.map(_.makeQualified(fs.getUri, fs.getWorkingDirectory))
.contains(qualifiedPath)
if (invalidate) hr.location.refresh()
invalidate
case _ => false
}
case _ => false
}
}
}