[SUPPORT]failed to read timestamp column in version 0.7.0 even when HIVE_SUPPORT_TIMESTAMP is enabled

Failed to read timestamp column after the hive sync is enabled

Here is the testing version list

hive = 3.1.2
hadoop = 3.2.2
spark = 3.0.1
hudi = 0.7.0

Here is the test application code snippet

import org.apache.spark.sql._
import org.apache.hudi.QuickstartUtils._
import scala.collection.JavaConversions._
import org.apache.spark.sql.SaveMode._
import org.apache.hudi.DataSourceReadOptions._
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.config.HoodieWriteConfig._
import org.apache.spark.sql.functions._
import org.apache.hudi.QuickstartUtils._
import scala.collection.JavaConversions._
import org.apache.spark.sql.SaveMode._
import org.apache.hudi.DataSourceReadOptions
import org.apache.hudi.DataSourceWriteOptions
import org.apache.hudi.config.HoodieWriteConfig
import org.apache.hudi.hive.MultiPartKeysValueExtractor
import org.apache.spark.sql.functions._
import org.apache.hudi.keygen._
import org.apache.spark.sql.streaming._

case class Person(firstname:String, age:Int, gender:Int)
val personDF = List(Person("tom",45,1), Person("iris",44,0)).toDF.withColumn("ts",unix_timestamp).withColumn("insert_time",current_timestamp)
//val personDF2 = List(Person("peng",56,1), Person("iris",51,0),Person("jacky",25,1)).toDF.withColumn("ts",unix_timestamp).withColumn("insert_time",current_timestamp)

//personDF.write.mode(SaveMode.Overwrite).format("hudi").saveAsTable("employee")

val tableName = "employee"
val hudiCommonOptions = Map(
  "hoodie.compact.inline" -> "true",
  "hoodie.compact.inline.max.delta.commits" ->"5",
  "hoodie.base.path" -> s"/tmp/$tableName",
  "hoodie.table.name" -> tableName,
  "hoodie.datasource.write.table.type"->"MERGE_ON_READ",
  "hoodie.datasource.write.operation" -> "upsert",
  "hoodie.clean.async" -> "true"
)

val hudiHiveOptions = Map(
    DataSourceWriteOptions.HIVE_SYNC_ENABLED_OPT_KEY -> "true",
    DataSourceWriteOptions.HIVE_URL_OPT_KEY -> "jdbc:hive2://localhost:10000",
    DataSourceWriteOptions.HIVE_PARTITION_FIELDS_OPT_KEY -> "gender",
    DataSourceWriteOptions.HIVE_STYLE_PARTITIONING_OPT_KEY -> "true",
    "hoodie.datasource.hive_sync.support_timestamp"->"true",
    DataSourceWriteOptions.HIVE_TABLE_OPT_KEY -> tableName,
    DataSourceWriteOptions.HIVE_PARTITION_EXTRACTOR_CLASS_OPT_KEY -> classOf[MultiPartKeysValueExtractor].getName
)

val basePath = s"/tmp/$tableName"
personDF.write.format("hudi").
  option(PRECOMBINE_FIELD_OPT_KEY, "ts").
  option(RECORDKEY_FIELD_OPT_KEY, "firstname").
  option(PARTITIONPATH_FIELD_OPT_KEY, "gender").
  options(hudiCommonOptions).
  options(hudiHiveOptions).
  mode(SaveMode.Overwrite).
  save(basePath)

sql("select * from employee_rt").show(false)

The final query got failed and the following is the error message

174262 [Executor task launch worker for task 12017] ERROR org.apache.spark.executor.Executor  - Exception in task 0.0 in stage 31.0 (TID 12017)
java.lang.ClassCastException: org.apache.hadoop.io.LongWritable cannot be cast to org.apache.hadoop.hive.serde2.io.TimestampWritable
	at org.apache.hadoop.hive.serde2.objectinspector.primitive.WritableTimestampObjectInspector.getPrimitiveJavaObject(WritableTimestampObjectInspector.java:39)
	at org.apache.spark.sql.hive.HadoopTableReader$.$anonfun$fillObject$14(TableReader.scala:468)
	at org.apache.spark.sql.hive.HadoopTableReader$.$anonfun$fillObject$14$adapted(TableReader.scala:467)
	at org.apache.spark.sql.hive.HadoopTableReader$.$anonfun$fillObject$18(TableReader.scala:493)
	at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
	at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
	at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729)
	at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:127)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:446)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:449)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

If read the hudi files directly, the result is correct as expected.

val employeeDF = spark.read.format("hudi").load("/tmp/employee")
employeeDF.show(false)

the result looks like this

+-------------------+--------------------+------------------+----------------------+------------------------------------------------------------------------+---------+---+------+----------+-----------------------+
|_hoodie_commit_time|_hoodie_commit_seqno|_hoodie_record_key|_hoodie_partition_path|_hoodie_file_name                                                       |firstname|age|gender|ts        |insert_time            |
+-------------------+--------------------+------------------+----------------------+------------------------------------------------------------------------+---------+---+------+----------+-----------------------+
|20210206160718     |20210206160718_0_1  |iris              |gender=0              |4fd7d48f-7828-4e77-97a5-a5202e32ad08-0_0-21-12008_20210206160718.parquet|iris     |44 |0     |1612598839|2021-02-06 16:07:19.251|
|20210206160718     |20210206160718_1_2  |tom               |gender=1              |c0014e5c-66d2-49fa-8af2-9a0b3df9bcf7-0_1-21-12009_20210206160718.parquet|tom      |45 |1     |1612598839|2021-02-06 16:07:19.251|
+-------------------+--------------------+------------------+----------------------+------------------------------------------------------------------------+---------+---+------+----------+-----------------------+

[BenjMaq]

Hey everyone, I’m also facing this issue. I see some of you guys already worked on some type of fix/workaround. How would you advise dealing with this?

I tried to add "hoodie.datasource.hive_sync.support_timestamp", true as an option but it does not look like it’s working.

Also, I’ve seen multiple Github issues raised about this issue, and in most of them there’s a link to @li36909’s comment above as a workaround. Unfortunately, as mentioned by @cdmikechen “TimestampWritableV2 is a hive3 class”, and unfortunately we are also relying on Hive2.

Is there a workaround for Hive2? I’ll be happy to help with anything to move this forward (given my relatively low familiarity with Hudi…). Thanks a mil!

[Gatsby-Lee]

@codope

Can you tell me where I can find the commit for this fix? And, do you know if there is any downside of setting this config? “hoodie.datasource.hive_sync.support_timestamp”: “true”,