org.apache.spark.sql.hive.HiveContext.scala Maven / Gradle / Ivy
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* 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.hive
import java.io.File
import java.net.{URL, URLClassLoader}
import java.sql.Timestamp
import java.util.concurrent.TimeUnit
import java.util.regex.Pattern
import scala.collection.JavaConverters._
import scala.collection.mutable.HashMap
import scala.language.implicitConversions
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.hadoop.hive.common.StatsSetupConst
import org.apache.hadoop.hive.common.`type`.HiveDecimal
import org.apache.hadoop.hive.conf.HiveConf
import org.apache.hadoop.hive.conf.HiveConf.ConfVars
import org.apache.hadoop.hive.ql.metadata.Table
import org.apache.hadoop.hive.ql.parse.VariableSubstitution
import org.apache.hadoop.hive.serde2.io.{DateWritable, TimestampWritable}
import org.apache.hadoop.util.VersionInfo
import org.apache.spark.api.java.JavaSparkContext
import org.apache.spark.sql.SQLConf.SQLConfEntry
import org.apache.spark.sql.SQLConf.SQLConfEntry._
import org.apache.spark.sql._
import org.apache.spark.sql.catalyst.analysis._
import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
import org.apache.spark.sql.catalyst.expressions.{Expression, LeafExpression}
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.{InternalRow, ParserDialect, SqlParser}
import org.apache.spark.sql.execution.datasources.{ResolveDataSource, DataSourceStrategy, PreInsertCastAndRename, PreWriteCheck}
import org.apache.spark.sql.execution.ui.SQLListener
import org.apache.spark.sql.execution.{CacheManager, ExecutedCommand, ExtractPythonUDFs, SetCommand}
import org.apache.spark.sql.hive.client._
import org.apache.spark.sql.hive.execution.{DescribeHiveTableCommand, HiveNativeCommand}
import org.apache.spark.sql.types._
import org.apache.spark.unsafe.types.UTF8String
import org.apache.spark.util.Utils
import org.apache.spark.{Logging, SparkContext}
/**
* This is the HiveQL Dialect, this dialect is strongly bind with HiveContext
*/
private[hive] class HiveQLDialect(sqlContext: HiveContext) extends ParserDialect {
override def parse(sqlText: String): LogicalPlan = {
sqlContext.executionHive.withHiveState {
HiveQl.parseSql(sqlText)
}
}
}
/**
* Returns the current database of metadataHive.
*/
private[hive] case class CurrentDatabase(ctx: HiveContext)
extends LeafExpression with CodegenFallback {
override def dataType: DataType = StringType
override def foldable: Boolean = true
override def nullable: Boolean = false
override def eval(input: InternalRow): Any = {
UTF8String.fromString(ctx.metadataHive.currentDatabase)
}
}
/**
* An instance of the Spark SQL execution engine that integrates with data stored in Hive.
* Configuration for Hive is read from hive-site.xml on the classpath.
*
* @since 1.0.0
*/
class HiveContext private[hive](
sc: SparkContext,
cacheManager: CacheManager,
listener: SQLListener,
@transient private val execHive: ClientWrapper,
@transient private val metaHive: ClientInterface,
isRootContext: Boolean)
extends SQLContext(sc, cacheManager, listener, isRootContext) with Logging {
self =>
def this(sc: SparkContext) = {
this(sc, new CacheManager, SQLContext.createListenerAndUI(sc), null, null, true)
}
def this(sc: JavaSparkContext) = this(sc.sc)
import org.apache.spark.sql.hive.HiveContext._
logDebug("create HiveContext")
/**
* Returns a new HiveContext as new session, which will have separated SQLConf, UDF/UDAF,
* temporary tables and SessionState, but sharing the same CacheManager, IsolatedClientLoader
* and Hive client (both of execution and metadata) with existing HiveContext.
*/
override def newSession(): HiveContext = {
new HiveContext(
sc = sc,
cacheManager = cacheManager,
listener = listener,
execHive = executionHive.newSession(),
metaHive = metadataHive.newSession(),
isRootContext = false)
}
/**
* When true, enables an experimental feature where metastore tables that use the parquet SerDe
* are automatically converted to use the Spark SQL parquet table scan, instead of the Hive
* SerDe.
*/
protected[sql] def convertMetastoreParquet: Boolean = getConf(CONVERT_METASTORE_PARQUET)
/**
* When true, also tries to merge possibly different but compatible Parquet schemas in different
* Parquet data files.
*
* This configuration is only effective when "spark.sql.hive.convertMetastoreParquet" is true.
*/
protected[sql] def convertMetastoreParquetWithSchemaMerging: Boolean =
getConf(CONVERT_METASTORE_PARQUET_WITH_SCHEMA_MERGING)
/**
* When true, a table created by a Hive CTAS statement (no USING clause) will be
* converted to a data source table, using the data source set by spark.sql.sources.default.
* The table in CTAS statement will be converted when it meets any of the following conditions:
* - The CTAS does not specify any of a SerDe (ROW FORMAT SERDE), a File Format (STORED AS), or
* a Storage Hanlder (STORED BY), and the value of hive.default.fileformat in hive-site.xml
* is either TextFile or SequenceFile.
* - The CTAS statement specifies TextFile (STORED AS TEXTFILE) as the file format and no SerDe
* is specified (no ROW FORMAT SERDE clause).
* - The CTAS statement specifies SequenceFile (STORED AS SEQUENCEFILE) as the file format
* and no SerDe is specified (no ROW FORMAT SERDE clause).
*/
protected[sql] def convertCTAS: Boolean = getConf(CONVERT_CTAS)
/**
* The version of the hive client that will be used to communicate with the metastore. Note that
* this does not necessarily need to be the same version of Hive that is used internally by
* Spark SQL for execution.
*/
protected[hive] def hiveMetastoreVersion: String = getConf(HIVE_METASTORE_VERSION)
/**
* The location of the jars that should be used to instantiate the HiveMetastoreClient. This
* property can be one of three options:
* - a classpath in the standard format for both hive and hadoop.
* - builtin - attempt to discover the jars that were used to load Spark SQL and use those. This
* option is only valid when using the execution version of Hive.
* - maven - download the correct version of hive on demand from maven.
*/
protected[hive] def hiveMetastoreJars: String = getConf(HIVE_METASTORE_JARS)
/**
* A comma separated list of class prefixes that should be loaded using the classloader that
* is shared between Spark SQL and a specific version of Hive. An example of classes that should
* be shared is JDBC drivers that are needed to talk to the metastore. Other classes that need
* to be shared are those that interact with classes that are already shared. For example,
* custom appenders that are used by log4j.
*/
protected[hive] def hiveMetastoreSharedPrefixes: Seq[String] =
getConf(HIVE_METASTORE_SHARED_PREFIXES).filterNot(_ == "")
/**
* A comma separated list of class prefixes that should explicitly be reloaded for each version
* of Hive that Spark SQL is communicating with. For example, Hive UDFs that are declared in a
* prefix that typically would be shared (i.e. org.apache.spark.*)
*/
protected[hive] def hiveMetastoreBarrierPrefixes: Seq[String] =
getConf(HIVE_METASTORE_BARRIER_PREFIXES).filterNot(_ == "")
/*
* hive thrift server use background spark sql thread pool to execute sql queries
*/
protected[hive] def hiveThriftServerAsync: Boolean = getConf(HIVE_THRIFT_SERVER_ASYNC)
protected[hive] def hiveThriftServerSingleSession: Boolean =
sc.conf.get("spark.sql.hive.thriftServer.singleSession", "false").toBoolean
@transient
protected[sql] lazy val substitutor = new VariableSubstitution()
/**
* The copy of the hive client that is used for execution. Currently this must always be
* Hive 13 as this is the version of Hive that is packaged with Spark SQL. This copy of the
* client is used for execution related tasks like registering temporary functions or ensuring
* that the ThreadLocal SessionState is correctly populated. This copy of Hive is *not* used
* for storing persistent metadata, and only point to a dummy metastore in a temporary directory.
*/
@transient
protected[hive] lazy val executionHive: ClientWrapper = if (execHive != null) {
execHive
} else {
logInfo(s"Initializing execution hive, version $hiveExecutionVersion")
val loader = new IsolatedClientLoader(
version = IsolatedClientLoader.hiveVersion(hiveExecutionVersion),
execJars = Seq(),
config = newTemporaryConfiguration(),
isolationOn = false,
baseClassLoader = Utils.getContextOrSparkClassLoader)
loader.createClient().asInstanceOf[ClientWrapper]
}
/**
* Overrides default Hive configurations to avoid breaking changes to Spark SQL users.
* - allow SQL11 keywords to be used as identifiers
*/
private[sql] def defaultOverrides() = {
setConf(ConfVars.HIVE_SUPPORT_SQL11_RESERVED_KEYWORDS.varname, "false")
}
defaultOverrides()
/**
* The copy of the Hive client that is used to retrieve metadata from the Hive MetaStore.
* The version of the Hive client that is used here must match the metastore that is configured
* in the hive-site.xml file.
*/
@transient
protected[hive] lazy val metadataHive: ClientInterface = if (metaHive != null) {
metaHive
} else {
val metaVersion = IsolatedClientLoader.hiveVersion(hiveMetastoreVersion)
// We instantiate a HiveConf here to read in the hive-site.xml file and then pass the options
// into the isolated client loader
val metadataConf = new HiveConf()
val defaultWarehouseLocation = metadataConf.get("hive.metastore.warehouse.dir")
logInfo("default warehouse location is " + defaultWarehouseLocation)
// `configure` goes second to override other settings.
val allConfig = metadataConf.asScala.map(e => e.getKey -> e.getValue).toMap ++ configure
val isolatedLoader = if (hiveMetastoreJars == "builtin") {
if (hiveExecutionVersion != hiveMetastoreVersion) {
throw new IllegalArgumentException(
"Builtin jars can only be used when hive execution version == hive metastore version. " +
s"Execution: ${hiveExecutionVersion} != Metastore: ${hiveMetastoreVersion}. " +
"Specify a vaild path to the correct hive jars using $HIVE_METASTORE_JARS " +
s"or change ${HIVE_METASTORE_VERSION.key} to $hiveExecutionVersion.")
}
// We recursively find all jars in the class loader chain,
// starting from the given classLoader.
def allJars(classLoader: ClassLoader): Array[URL] = classLoader match {
case null => Array.empty[URL]
case urlClassLoader: URLClassLoader =>
urlClassLoader.getURLs ++ allJars(urlClassLoader.getParent)
case other => allJars(other.getParent)
}
val classLoader = Utils.getContextOrSparkClassLoader
val jars = allJars(classLoader)
if (jars.length == 0) {
throw new IllegalArgumentException(
"Unable to locate hive jars to connect to metastore. " +
"Please set spark.sql.hive.metastore.jars.")
}
logInfo(
s"Initializing HiveMetastoreConnection version $hiveMetastoreVersion using Spark classes.")
new IsolatedClientLoader(
version = metaVersion,
execJars = jars.toSeq,
config = allConfig,
isolationOn = true,
barrierPrefixes = hiveMetastoreBarrierPrefixes,
sharedPrefixes = hiveMetastoreSharedPrefixes)
} else if (hiveMetastoreJars == "maven") {
// TODO: Support for loading the jars from an already downloaded location.
logInfo(
s"Initializing HiveMetastoreConnection version $hiveMetastoreVersion using maven.")
IsolatedClientLoader.forVersion(
hiveMetastoreVersion = hiveMetastoreVersion,
hadoopVersion = VersionInfo.getVersion,
config = allConfig,
barrierPrefixes = hiveMetastoreBarrierPrefixes,
sharedPrefixes = hiveMetastoreSharedPrefixes)
} else {
// Convert to files and expand any directories.
val jars =
hiveMetastoreJars
.split(File.pathSeparator)
.flatMap {
case path if new File(path).getName() == "*" =>
val files = new File(path).getParentFile().listFiles()
if (files == null) {
logWarning(s"Hive jar path '$path' does not exist.")
Nil
} else {
files.filter(_.getName().toLowerCase().endsWith(".jar"))
}
case path =>
new File(path) :: Nil
}
.map(_.toURI.toURL)
logInfo(
s"Initializing HiveMetastoreConnection version $hiveMetastoreVersion " +
s"using ${jars.mkString(":")}")
new IsolatedClientLoader(
version = metaVersion,
execJars = jars.toSeq,
config = allConfig,
isolationOn = true,
barrierPrefixes = hiveMetastoreBarrierPrefixes,
sharedPrefixes = hiveMetastoreSharedPrefixes)
}
isolatedLoader.createClient()
}
protected[sql] override def parseSql(sql: String): LogicalPlan = {
super.parseSql(substitutor.substitute(hiveconf, sql))
}
override protected[sql] def executePlan(plan: LogicalPlan): this.QueryExecution =
new this.QueryExecution(plan)
/**
* Invalidate and refresh all the cached the metadata of the given table. For performance reasons,
* Spark SQL or the external data source library it uses might cache certain metadata about a
* table, such as the location of blocks. When those change outside of Spark SQL, users should
* call this function to invalidate the cache.
*
* @since 1.3.0
*/
def refreshTable(tableName: String): Unit = {
val tableIdent = SqlParser.parseTableIdentifier(tableName)
catalog.refreshTable(tableIdent)
}
protected[hive] def invalidateTable(tableName: String): Unit = {
val tableIdent = SqlParser.parseTableIdentifier(tableName)
catalog.invalidateTable(tableIdent)
}
/**
* Analyzes the given table in the current database to generate statistics, which will be
* used in query optimizations.
*
* Right now, it only supports Hive tables and it only updates the size of a Hive table
* in the Hive metastore.
*
* @since 1.2.0
*/
def analyze(tableName: String) {
val tableIdent = SqlParser.parseTableIdentifier(tableName)
val relation = EliminateSubQueries(catalog.lookupRelation(tableIdent))
relation match {
case relation: MetastoreRelation =>
// This method is mainly based on
// org.apache.hadoop.hive.ql.stats.StatsUtils.getFileSizeForTable(HiveConf, Table)
// in Hive 0.13 (except that we do not use fs.getContentSummary).
// TODO: Generalize statistics collection.
// TODO: Why fs.getContentSummary returns wrong size on Jenkins?
// Can we use fs.getContentSummary in future?
// Seems fs.getContentSummary returns wrong table size on Jenkins. So we use
// countFileSize to count the table size.
val stagingDir = metadataHive.getConf(HiveConf.ConfVars.STAGINGDIR.varname,
HiveConf.ConfVars.STAGINGDIR.defaultStrVal)
def calculateTableSize(fs: FileSystem, path: Path): Long = {
val fileStatus = fs.getFileStatus(path)
val size = if (fileStatus.isDir) {
fs.listStatus(path)
.map { status =>
if (!status.getPath().getName().startsWith(stagingDir)) {
calculateTableSize(fs, status.getPath)
} else {
0L
}
}
.sum
} else {
fileStatus.getLen
}
size
}
def getFileSizeForTable(conf: HiveConf, table: Table): Long = {
val path = table.getPath
var size: Long = 0L
try {
val fs = path.getFileSystem(conf)
size = calculateTableSize(fs, path)
} catch {
case e: Exception =>
logWarning(
s"Failed to get the size of table ${table.getTableName} in the " +
s"database ${table.getDbName} because of ${e.toString}", e)
size = 0L
}
size
}
val tableParameters = relation.hiveQlTable.getParameters
val oldTotalSize =
Option(tableParameters.get(StatsSetupConst.TOTAL_SIZE))
.map(_.toLong)
.getOrElse(0L)
val newTotalSize = getFileSizeForTable(hiveconf, relation.hiveQlTable)
// Update the Hive metastore if the total size of the table is different than the size
// recorded in the Hive metastore.
// This logic is based on org.apache.hadoop.hive.ql.exec.StatsTask.aggregateStats().
if (newTotalSize > 0 && newTotalSize != oldTotalSize) {
catalog.client.alterTable(
relation.table.copy(
properties = relation.table.properties +
(StatsSetupConst.TOTAL_SIZE -> newTotalSize.toString)))
}
case otherRelation =>
throw new UnsupportedOperationException(
s"Analyze only works for Hive tables, but $tableName is a ${otherRelation.nodeName}")
}
}
override def setConf(key: String, value: String): Unit = {
super.setConf(key, value)
executionHive.runSqlHive(s"SET $key=$value")
metadataHive.runSqlHive(s"SET $key=$value")
// If users put any Spark SQL setting in the spark conf (e.g. spark-defaults.conf),
// this setConf will be called in the constructor of the SQLContext.
// Also, calling hiveconf will create a default session containing a HiveConf, which
// will interfer with the creation of executionHive (which is a lazy val). So,
// we put hiveconf.set at the end of this method.
hiveconf.set(key, value)
}
override private[sql] def setConf[T](entry: SQLConfEntry[T], value: T): Unit = {
setConf(entry.key, entry.stringConverter(value))
}
/* A catalyst metadata catalog that points to the Hive Metastore. */
@transient
override protected[sql] lazy val catalog =
new HiveMetastoreCatalog(metadataHive, this) with OverrideCatalog
// Note that HiveUDFs will be overridden by functions registered in this context.
@transient
override protected[sql] lazy val functionRegistry: FunctionRegistry =
new HiveFunctionRegistry(FunctionRegistry.builtin.copy(), this.executionHive)
// The Hive UDF current_database() is foldable, will be evaluated by optimizer, but the optimizer
// can't access the SessionState of metadataHive.
functionRegistry.registerFunction(
"current_database",
(expressions: Seq[Expression]) => new CurrentDatabase(this))
/* An analyzer that uses the Hive metastore. */
@transient
override protected[sql] lazy val analyzer: Analyzer =
new Analyzer(catalog, functionRegistry, conf) {
override val extendedResolutionRules =
catalog.ParquetConversions ::
catalog.CreateTables ::
catalog.PreInsertionCasts ::
ExtractPythonUDFs ::
ResolveHiveWindowFunction ::
PreInsertCastAndRename ::
(if (conf.runSQLOnFile) new ResolveDataSource(self) :: Nil else Nil)
override val extendedCheckRules = Seq(
PreWriteCheck(catalog)
)
}
/** Overridden by child classes that need to set configuration before the client init. */
protected def configure(): Map[String, String] = {
// Hive 0.14.0 introduces timeout operations in HiveConf, and changes default values of a bunch
// of time `ConfVar`s by adding time suffixes (`s`, `ms`, and `d` etc.). This breaks backwards-
// compatibility when users are trying to connecting to a Hive metastore of lower version,
// because these options are expected to be integral values in lower versions of Hive.
//
// Here we enumerate all time `ConfVar`s and convert their values to numeric strings according
// to their output time units.
Seq(
ConfVars.METASTORE_CLIENT_CONNECT_RETRY_DELAY -> TimeUnit.SECONDS,
ConfVars.METASTORE_CLIENT_SOCKET_TIMEOUT -> TimeUnit.SECONDS,
ConfVars.METASTORE_CLIENT_SOCKET_LIFETIME -> TimeUnit.SECONDS,
ConfVars.HMSHANDLERINTERVAL -> TimeUnit.MILLISECONDS,
ConfVars.METASTORE_EVENT_DB_LISTENER_TTL -> TimeUnit.SECONDS,
ConfVars.METASTORE_EVENT_CLEAN_FREQ -> TimeUnit.SECONDS,
ConfVars.METASTORE_EVENT_EXPIRY_DURATION -> TimeUnit.SECONDS,
ConfVars.METASTORE_AGGREGATE_STATS_CACHE_TTL -> TimeUnit.SECONDS,
ConfVars.METASTORE_AGGREGATE_STATS_CACHE_MAX_WRITER_WAIT -> TimeUnit.MILLISECONDS,
ConfVars.METASTORE_AGGREGATE_STATS_CACHE_MAX_READER_WAIT -> TimeUnit.MILLISECONDS,
ConfVars.HIVES_AUTO_PROGRESS_TIMEOUT -> TimeUnit.SECONDS,
ConfVars.HIVE_LOG_INCREMENTAL_PLAN_PROGRESS_INTERVAL -> TimeUnit.MILLISECONDS,
ConfVars.HIVE_STATS_JDBC_TIMEOUT -> TimeUnit.SECONDS,
ConfVars.HIVE_STATS_RETRIES_WAIT -> TimeUnit.MILLISECONDS,
ConfVars.HIVE_LOCK_SLEEP_BETWEEN_RETRIES -> TimeUnit.SECONDS,
ConfVars.HIVE_ZOOKEEPER_SESSION_TIMEOUT -> TimeUnit.MILLISECONDS,
ConfVars.HIVE_ZOOKEEPER_CONNECTION_BASESLEEPTIME -> TimeUnit.MILLISECONDS,
ConfVars.HIVE_TXN_TIMEOUT -> TimeUnit.SECONDS,
ConfVars.HIVE_COMPACTOR_WORKER_TIMEOUT -> TimeUnit.SECONDS,
ConfVars.HIVE_COMPACTOR_CHECK_INTERVAL -> TimeUnit.SECONDS,
ConfVars.HIVE_COMPACTOR_CLEANER_RUN_INTERVAL -> TimeUnit.MILLISECONDS,
ConfVars.HIVE_SERVER2_THRIFT_HTTP_MAX_IDLE_TIME -> TimeUnit.MILLISECONDS,
ConfVars.HIVE_SERVER2_THRIFT_HTTP_WORKER_KEEPALIVE_TIME -> TimeUnit.SECONDS,
ConfVars.HIVE_SERVER2_THRIFT_HTTP_COOKIE_MAX_AGE -> TimeUnit.SECONDS,
ConfVars.HIVE_SERVER2_THRIFT_LOGIN_BEBACKOFF_SLOT_LENGTH -> TimeUnit.MILLISECONDS,
ConfVars.HIVE_SERVER2_THRIFT_LOGIN_TIMEOUT -> TimeUnit.SECONDS,
ConfVars.HIVE_SERVER2_THRIFT_WORKER_KEEPALIVE_TIME -> TimeUnit.SECONDS,
ConfVars.HIVE_SERVER2_ASYNC_EXEC_SHUTDOWN_TIMEOUT -> TimeUnit.SECONDS,
ConfVars.HIVE_SERVER2_ASYNC_EXEC_KEEPALIVE_TIME -> TimeUnit.SECONDS,
ConfVars.HIVE_SERVER2_LONG_POLLING_TIMEOUT -> TimeUnit.MILLISECONDS,
ConfVars.HIVE_SERVER2_SESSION_CHECK_INTERVAL -> TimeUnit.MILLISECONDS,
ConfVars.HIVE_SERVER2_IDLE_SESSION_TIMEOUT -> TimeUnit.MILLISECONDS,
ConfVars.HIVE_SERVER2_IDLE_OPERATION_TIMEOUT -> TimeUnit.MILLISECONDS,
ConfVars.SERVER_READ_SOCKET_TIMEOUT -> TimeUnit.SECONDS,
ConfVars.HIVE_LOCALIZE_RESOURCE_WAIT_INTERVAL -> TimeUnit.MILLISECONDS,
ConfVars.SPARK_CLIENT_FUTURE_TIMEOUT -> TimeUnit.SECONDS,
ConfVars.SPARK_JOB_MONITOR_TIMEOUT -> TimeUnit.SECONDS,
ConfVars.SPARK_RPC_CLIENT_CONNECT_TIMEOUT -> TimeUnit.MILLISECONDS,
ConfVars.SPARK_RPC_CLIENT_HANDSHAKE_TIMEOUT -> TimeUnit.MILLISECONDS
).map { case (confVar, unit) =>
confVar.varname -> hiveconf.getTimeVar(confVar, unit).toString
}.toMap
}
/**
* SQLConf and HiveConf contracts:
*
* 1. create a new SessionState for each HiveContext
* 2. when the Hive session is first initialized, params in HiveConf will get picked up by the
* SQLConf. Additionally, any properties set by set() or a SET command inside sql() will be
* set in the SQLConf *as well as* in the HiveConf.
*/
@transient
protected[hive] lazy val hiveconf: HiveConf = {
val c = executionHive.conf
setConf(c.getAllProperties)
c
}
protected[sql] override lazy val conf: SQLConf = new SQLConf {
override def dialect: String = getConf(SQLConf.DIALECT, "hiveql")
override def caseSensitiveAnalysis: Boolean = getConf(SQLConf.CASE_SENSITIVE, false)
}
protected[sql] override def getSQLDialect(): ParserDialect = {
if (conf.dialect == "hiveql") {
new HiveQLDialect(this)
} else {
super.getSQLDialect()
}
}
@transient
private val hivePlanner = new SparkPlanner with HiveStrategies {
val hiveContext = self
override def strategies: Seq[Strategy] = experimental.extraStrategies ++ Seq(
DataSourceStrategy,
HiveCommandStrategy(self),
HiveDDLStrategy,
DDLStrategy,
TakeOrderedAndProject,
InMemoryScans,
HiveTableScans,
DataSinks,
Scripts,
Aggregation,
LeftSemiJoin,
EquiJoinSelection,
BasicOperators,
BroadcastNestedLoop,
CartesianProduct,
DefaultJoin
)
}
private def functionOrMacroDDLPattern(command: String) = Pattern.compile(
".*(create|drop)\\s+(temporary\\s+)?(function|macro).+", Pattern.DOTALL).matcher(command)
protected[hive] def runSqlHive(sql: String): Seq[String] = {
val command = sql.trim.toLowerCase
if (functionOrMacroDDLPattern(command).matches()) {
executionHive.runSqlHive(sql)
} else if (command.startsWith("set")) {
metadataHive.runSqlHive(sql)
executionHive.runSqlHive(sql)
} else {
metadataHive.runSqlHive(sql)
}
}
@transient
override protected[sql] val planner = hivePlanner
/** Extends QueryExecution with hive specific features. */
protected[sql] class QueryExecution(logicalPlan: LogicalPlan)
extends org.apache.spark.sql.execution.QueryExecution(this, logicalPlan) {
/**
* Returns the result as a hive compatible sequence of strings. For native commands, the
* execution is simply passed back to Hive.
*/
def stringResult(): Seq[String] = executedPlan match {
case ExecutedCommand(desc: DescribeHiveTableCommand) =>
// If it is a describe command for a Hive table, we want to have the output format
// be similar with Hive.
desc.run(self).map {
case Row(name: String, dataType: String, comment) =>
Seq(name, dataType,
Option(comment.asInstanceOf[String]).getOrElse(""))
.map(s => String.format(s"%-20s", s))
.mkString("\t")
}
case command: ExecutedCommand =>
command.executeCollect().map(_.getString(0))
case other =>
val result: Seq[Seq[Any]] = other.executeCollectPublic().map(_.toSeq).toSeq
// We need the types so we can output struct field names
val types = analyzed.output.map(_.dataType)
// Reformat to match hive tab delimited output.
result.map(_.zip(types).map(HiveContext.toHiveString)).map(_.mkString("\t")).toSeq
}
override def simpleString: String =
logical match {
case _: HiveNativeCommand => ""
case _: SetCommand => ""
case _ => super.simpleString
}
}
protected[sql] override def addJar(path: String): Unit = {
// Add jar to Hive and classloader
executionHive.addJar(path)
metadataHive.addJar(path)
Thread.currentThread().setContextClassLoader(executionHive.clientLoader.classLoader)
super.addJar(path)
}
}
private[hive] object HiveContext {
/** The version of hive used internally by Spark SQL. */
val hiveExecutionVersion: String = "1.2.1"
val HIVE_METASTORE_VERSION = stringConf("spark.sql.hive.metastore.version",
defaultValue = Some(hiveExecutionVersion),
doc = "Version of the Hive metastore. Available options are " +
s"0.12.0 through $hiveExecutionVersion.")
val HIVE_EXECUTION_VERSION = stringConf(
key = "spark.sql.hive.version",
defaultValue = Some(hiveExecutionVersion),
doc = "Version of Hive used internally by Spark SQL.")
val HIVE_METASTORE_JARS = stringConf("spark.sql.hive.metastore.jars",
defaultValue = Some("builtin"),
doc = s"""
| Location of the jars that should be used to instantiate the HiveMetastoreClient.
| This property can be one of three options: "
| 1. "builtin"
| Use Hive ${hiveExecutionVersion}, which is bundled with the Spark assembly jar when
| -Phive is enabled. When this option is chosen,
| spark.sql.hive.metastore.version must be either
| ${hiveExecutionVersion} or not defined.
| 2. "maven"
| Use Hive jars of specified version downloaded from Maven repositories.
| 3. A classpath in the standard format for both Hive and Hadoop.
""".stripMargin)
val CONVERT_METASTORE_PARQUET = booleanConf("spark.sql.hive.convertMetastoreParquet",
defaultValue = Some(true),
doc = "When set to false, Spark SQL will use the Hive SerDe for parquet tables instead of " +
"the built in support.")
val CONVERT_METASTORE_PARQUET_WITH_SCHEMA_MERGING = booleanConf(
"spark.sql.hive.convertMetastoreParquet.mergeSchema",
defaultValue = Some(false),
doc = "TODO")
val CONVERT_CTAS = booleanConf("spark.sql.hive.convertCTAS",
defaultValue = Some(false),
doc = "TODO")
val HIVE_METASTORE_SHARED_PREFIXES = stringSeqConf("spark.sql.hive.metastore.sharedPrefixes",
defaultValue = Some(jdbcPrefixes),
doc = "A comma separated list of class prefixes that should be loaded using the classloader " +
"that is shared between Spark SQL and a specific version of Hive. An example of classes " +
"that should be shared is JDBC drivers that are needed to talk to the metastore. Other " +
"classes that need to be shared are those that interact with classes that are already " +
"shared. For example, custom appenders that are used by log4j.")
private def jdbcPrefixes = Seq(
"com.mysql.jdbc", "org.postgresql", "com.microsoft.sqlserver", "oracle.jdbc")
val HIVE_METASTORE_BARRIER_PREFIXES = stringSeqConf("spark.sql.hive.metastore.barrierPrefixes",
defaultValue = Some(Seq()),
doc = "A comma separated list of class prefixes that should explicitly be reloaded for each " +
"version of Hive that Spark SQL is communicating with. For example, Hive UDFs that are " +
"declared in a prefix that typically would be shared (i.e. org.apache.spark.*).")
val HIVE_THRIFT_SERVER_ASYNC = booleanConf("spark.sql.hive.thriftServer.async",
defaultValue = Some(true),
doc = "TODO")
/** Constructs a configuration for hive, where the metastore is located in a temp directory. */
def newTemporaryConfiguration(): Map[String, String] = {
val tempDir = Utils.createTempDir()
val localMetastore = new File(tempDir, "metastore")
val propMap: HashMap[String, String] = HashMap()
// We have to mask all properties in hive-site.xml that relates to metastore data source
// as we used a local metastore here.
HiveConf.ConfVars.values().foreach { confvar =>
if (confvar.varname.contains("datanucleus") || confvar.varname.contains("jdo")
|| confvar.varname.contains("hive.metastore.rawstore.impl")) {
propMap.put(confvar.varname, confvar.getDefaultExpr())
}
}
propMap.put(HiveConf.ConfVars.METASTOREWAREHOUSE.varname, localMetastore.toURI.toString)
propMap.put(HiveConf.ConfVars.METASTORECONNECTURLKEY.varname,
s"jdbc:derby:;databaseName=${localMetastore.getAbsolutePath};create=true")
propMap.put("datanucleus.rdbms.datastoreAdapterClassName",
"org.datanucleus.store.rdbms.adapter.DerbyAdapter")
// SPARK-11783: When "hive.metastore.uris" is set, the metastore connection mode will be
// remote (https://cwiki.apache.org/confluence/display/Hive/AdminManual+MetastoreAdmin
// mentions that "If hive.metastore.uris is empty local mode is assumed, remote otherwise").
// Remote means that the metastore server is running in its own process.
// When the mode is remote, configurations like "javax.jdo.option.ConnectionURL" will not be
// used (because they are used by remote metastore server that talks to the database).
// Because execution Hive should always connects to a embedded derby metastore.
// We have to remove the value of hive.metastore.uris. So, the execution Hive client connects
// to the actual embedded derby metastore instead of the remote metastore.
// You can search HiveConf.ConfVars.METASTOREURIS in the code of HiveConf (in Hive's repo).
// Then, you will find that the local metastore mode is only set to true when
// hive.metastore.uris is not set.
propMap.put(ConfVars.METASTOREURIS.varname, "")
propMap.toMap
}
protected val primitiveTypes =
Seq(StringType, IntegerType, LongType, DoubleType, FloatType, BooleanType, ByteType,
ShortType, DateType, TimestampType, BinaryType)
protected[sql] def toHiveString(a: (Any, DataType)): String = a match {
case (struct: Row, StructType(fields)) =>
struct.toSeq.zip(fields).map {
case (v, t) => s""""${t.name}":${toHiveStructString(v, t.dataType)}"""
}.mkString("{", ",", "}")
case (seq: Seq[_], ArrayType(typ, _)) =>
seq.map(v => (v, typ)).map(toHiveStructString).mkString("[", ",", "]")
case (map: Map[_, _], MapType(kType, vType, _)) =>
map.map {
case (key, value) =>
toHiveStructString((key, kType)) + ":" + toHiveStructString((value, vType))
}.toSeq.sorted.mkString("{", ",", "}")
case (null, _) => "NULL"
case (d: Int, DateType) => new DateWritable(d).toString
case (t: Timestamp, TimestampType) => new TimestampWritable(t).toString
case (bin: Array[Byte], BinaryType) => new String(bin, "UTF-8")
case (decimal: java.math.BigDecimal, DecimalType()) =>
// Hive strips trailing zeros so use its toString
HiveDecimal.create(decimal).toString
case (other, tpe) if primitiveTypes contains tpe => other.toString
}
/** Hive outputs fields of structs slightly differently than top level attributes. */
protected def toHiveStructString(a: (Any, DataType)): String = a match {
case (struct: Row, StructType(fields)) =>
struct.toSeq.zip(fields).map {
case (v, t) => s""""${t.name}":${toHiveStructString(v, t.dataType)}"""
}.mkString("{", ",", "}")
case (seq: Seq[_], ArrayType(typ, _)) =>
seq.map(v => (v, typ)).map(toHiveStructString).mkString("[", ",", "]")
case (map: Map[_, _], MapType(kType, vType, _)) =>
map.map {
case (key, value) =>
toHiveStructString((key, kType)) + ":" + toHiveStructString((value, vType))
}.toSeq.sorted.mkString("{", ",", "}")
case (null, _) => "null"
case (s: String, StringType) => "\"" + s + "\""
case (decimal, DecimalType()) => decimal.toString
case (other, tpe) if primitiveTypes contains tpe => other.toString
}
}
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