原创问题定位分享（18）beeline连接spark thrift有时会卡住

Posted 2021-01-28 barneywill

spark 2.1.1

 

beeline连接spark thrift之后，执行use database有时会卡住，而use database 在server端对应的是 setCurrentDatabase，

经过排查发现当时spark thrift正在执行insert操作，

 

org.apache.spark.sql.hive.execution.InsertIntoHiveTable

  protected override def doExecute(): RDD[InternalRow] = {
    sqlContext.sparkContext.parallelize(sideEffectResult.asInstanceOf[Seq[InternalRow]], 1)
  }
...
  @transient private val externalCatalog = sqlContext.sharedState.externalCatalog

  protected[sql] lazy val sideEffectResult: Seq[InternalRow] = {
  ...
        externalCatalog.loadDynamicPartitions(
          externalCatalog.getPartitionOption(
          externalCatalog.loadPartition(
      externalCatalog.loadTable(

可见insert操作中可能会调用loadDynamicPartitions、getPartitionOption、loadPartition、loadTable等方法，

 

org.apache.spark.sql.hive.client.HiveClientImpl

  def loadTable(
      loadPath: String, // TODO URI
      tableName: String,
      replace: Boolean,
      holdDDLTime: Boolean): Unit = withHiveState {
...
  def loadPartition(
      loadPath: String,
      dbName: String,
      tableName: String,
      partSpec: java.util.LinkedHashMap[String, String],
      replace: Boolean,
      holdDDLTime: Boolean,
      inheritTableSpecs: Boolean): Unit = withHiveState {
...
  override def setCurrentDatabase(databaseName: String): Unit = withHiveState {

而HiveClientImpl中对应的方法都会执行withHiveState，而withHiveState有synchronized，所以insert操作中的部分代码（比如loadPartition）和use database操作会被同步执行，当insert执行很慢时就会卡住所有的其他操作；

 

spark thrift中实现原理详见 https://www.cnblogs.com/barneywill/p/10137672.html