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  1. Apache Hudi
  2. HUDI-1722

hive beeline/spark-sql query specified field on mor table occur NPE

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      Description

      HUDI-892 introduce this problem。
      this pr skip adding projection columns if there are no log files in the hoodieRealtimeSplit。 but this pr donnot consider that multiple getRecordReaders share same jobConf。
      Consider the following questions:
      we have four getRecordReaders:
      reader1(its hoodieRealtimeSplit contains no log files)
      reader2 (its hoodieRealtimeSplit contains log files)
      reader3(its hoodieRealtimeSplit contains log files)
      reader4(its hoodieRealtimeSplit contains no log files)

      now reader1 run first, HoodieInputFormatUtils.HOODIE_READ_COLUMNS_PROP in jobConf will be set to be true, and no hoodie additional projection columns will be added to jobConf (see HoodieParquetRealtimeInputFormat.addProjectionToJobConf)

      reader2 run later, since HoodieInputFormatUtils.HOODIE_READ_COLUMNS_PROP in jobConf is set to be true, no hoodie additional projection columns will be added to jobConf. (see HoodieParquetRealtimeInputFormat.addProjectionToJobConf)
      which lead to the result that _hoodie_record_key would be missing and merge step would throw exceptions

      2021-03-25 20:23:14,014 | INFO  | AsyncDispatcher event handler | Diagnostics report from attempt_1615883368881_0038_m_000000_0: Error: java.lang.NullPointerException2021-03-25 20:23:14,014 | INFO  | AsyncDispatcher event handler | Diagnostics report from attempt_1615883368881_0038_m_000000_0: Error: java.lang.NullPointerException at org.apache.hudi.hadoop.realtime.RealtimeCompactedRecordReader.next(RealtimeCompactedRecordReader.java:101) at org.apache.hudi.hadoop.realtime.RealtimeCompactedRecordReader.next(RealtimeCompactedRecordReader.java:43) at org.apache.hudi.hadoop.realtime.HoodieRealtimeRecordReader.next(HoodieRealtimeRecordReader.java:79) at org.apache.hudi.hadoop.realtime.HoodieRealtimeRecordReader.next(HoodieRealtimeRecordReader.java:36) at org.apache.hudi.hadoop.realtime.RealtimeCompactedRecordReader.next(RealtimeCompactedRecordReader.java:92) at org.apache.hudi.hadoop.realtime.RealtimeCompactedRecordReader.next(RealtimeCompactedRecordReader.java:43) at org.apache.hudi.hadoop.realtime.HoodieRealtimeRecordReader.next(HoodieRealtimeRecordReader.java:79) at org.apache.hudi.hadoop.realtime.HoodieCombineRealtimeRecordReader.next(HoodieCombineRealtimeRecordReader.java:68) at org.apache.hudi.hadoop.realtime.HoodieCombineRealtimeRecordReader.next(HoodieCombineRealtimeRecordReader.java:77) at org.apache.hudi.hadoop.realtime.HoodieCombineRealtimeRecordReader.next(HoodieCombineRealtimeRecordReader.java:42) at org.apache.hadoop.mapred.MapTask$TrackedRecordReader.moveToNext(MapTask.java:205) at org.apache.hadoop.mapred.MapTask$TrackedRecordReader.next(MapTask.java:191) at org.apache.hadoop.mapred.MapRunner.run(MapRunner.java:52) at org.apache.hadoop.hive.ql.exec.mr.ExecMapRunner.run(ExecMapRunner.java:37) at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:465) at org.apache.hadoop.mapred.MapTask.run(MapTask.java:349) at org.apache.hadoop.mapred.YarnChild$1.run(YarnChild.java:183) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:422) at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1761) at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:177)

       

      Obviously, this is an occasional problem。 if reader2 run first, hoodie additional projection columns will be added to jobConf and in this case the query will be ok

      sparksql can avoid this problem by set  spark.hadoop.cloneConf=true which is not recommended in spark, however hive has no way to avoid this problem。

       test step:

      step1:

      val df = spark.range(0, 100000).toDF("keyid")
      .withColumn("col3", expr("keyid"))
      .withColumn("p", lit(0))
      .withColumn("p1", lit(0))
      .withColumn("p2", lit(7))
      .withColumn("a1", lit(Array[String] ("sb1", "rz")))
      .withColumn("a2", lit(Array[String] ("sb1", "rz")))

      // create hoodie table hive_14b

       merge(df, 4, "default", "hive_14b", DataSourceWriteOptions.MOR_TABLE_TYPE_OPT_VAL, op = "bulk_insert")

      notice:  bulk_insert will produce 4 files in hoodie table

      step2:

       val df = spark.range(99999, 100002).toDF("keyid")
      .withColumn("col3", expr("keyid"))
      .withColumn("p", lit(0))
      .withColumn("p1", lit(0))
      .withColumn("p2", lit(7))
      .withColumn("a1", lit(Array[String] ("sb1", "rz")))
      .withColumn("a2", lit(Array[String] ("sb1", "rz")))

      // upsert table 

      merge(df, 4, "default", "hive_14b", DataSourceWriteOptions.MOR_TABLE_TYPE_OPT_VAL, op = "upsert")

       now :  we have four base files and one log file in hoodie table

      step3: 

      spark-sql/beeline: 

       select count(col3) from hive_14b_rt;

      then the query failed.

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              • Assignee:
                xiaotaotao tao meng
                Reporter:
                xiaotaotao tao meng
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