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Executor Timed Out


I am running a spark application, where I am loading two tables as a dataframe, doing a left join, and generating a row number on records missing from right table.

I have my code below and my spark submit command as well.

spark-submit --master yarn --deploy-mode client --num-executors 16 --driver-memory 10g --executor-memory 7g --executor-cores 5 --class CLASS_NAME PATH_TO_JAR


public static void main(String[] args){
SparkConf conf = new SparkConf().setAppName("Spark Sequence Test");
JavaSparkContext jsc = new JavaSparkContext(conf);
HiveContext hc = new HiveContext(;
 DataFrame cvxDataFrame = hc.sql("select * from Table_A");
  DataFrame skDataFrame = hc.sql("select * from Table_B");
    DataFrame new_df =  cvxDataFrame.join(skDataFrame,   cvxDataFrame.col("cvx_code").equalTo(skDataFrame.col("xref_code_id")), "left_outer");
       DataFrame fordf ="cvx_code").as("xref_code_id"),new_df.col("xref_code_sk")).filter(new_df.col("xref_code_sk").isNull());      
        Column rowNum = functions.row_number().over(Window.orderBy(fordf.col("xref_code_id")));
        DataFrame df ="xref_code_id"),"xref_code_sk"));
      hc.sql("INSERT INTO TABLE TABLE_C SELECT xref_code_id, xref_code_sk, 'CVX' as xref_code_tp_cd from final_result" );


This works when both Table A and Table B has 50 million records, but It is failing when Table A has 50 million records and Table B has 0 records. The error I am getting is “Executor heartbeat timed out…”


ERROR cluster.YarnScheduler: Lost executor 7 on sas-hdp-d03.devapp.domain: Executor heartbeat timed out after 161445 ms

16/09/14 11:23:58 WARN scheduler.TaskSetManager: Lost task 0.0 in stage 3.0 (TID 232, sas-hdp-d03.devapp.domain): ExecutorLostFailure (executor 7 exited caused by one of the running tasks) Reason: Executor heartbeat timed out after 161445 ms




I would really appreciate if anyone has any suggestion on how I can get around this.






Expert Contributor

A lost task often means the task had an OOM or YARN killed the task because it was using more memory than  it had requested.  Check the task logs and the application master logs, you can pull the logs from yarn with:


yarn logs -applicationId <application ID>


If yarn killed the task, it will say so within the application master.  If this is the case, you can increase the overhead spark requests beyond executor memory with spark.yarn.executor.memoryOverhead, it defaults to requesting 10% of the executor memory.

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