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Posted to commits@hudi.apache.org by GitBox <gi...@apache.org> on 2020/12/08 01:53:03 UTC

[GitHub] [hudi] bithw1 opened a new issue #2304: [SUPPORT]Hoodie clean operation result explanation

bithw1 opened a new issue #2304:
URL: https://github.com/apache/hudi/issues/2304


   Hi,
   I have following simple code that do upsert 100 times(The code is at the end of the question description), and I disable the auto clean during writes. When the writes is done, there are about 100 parquets in the hoodie table folder.
   
   After I run `cleans run` in the hudi-cli, there are only 11 parquets file  left in the hoodie table folder, that means, old version files has been cleaned up after clean.
   
   Then I run the `select count(1) from hudi_hive_read_write_cow_disable_cleaner_1' and the result is still 101, looks like no data is lost after clean.**I would like to make sure whether there is no data lost after cleaning old version files. In my case, I have written 101 letters, and still get back 101 letters after clean, which works as my expectation** 
   
   Another obervation is: 
   
   When I run `select `_hoodie_file_name` from hudi_hive_read_write_cow_disable_cleaner_1;`  the result shows the parquest file name, but some of them have been deleted after cleaning, not sure whether it is a bug that it shows the file name that is deleted(I think it should show the file name where the data resides), and also this leads to another question, since the corresponding file has been deleted, where does the data in hive query result come from
   
   
   ```
   import org.apache.hudi.DataSourceWriteOptions
   import org.apache.hudi.config.{HoodieCompactionConfig, HoodieIndexConfig, HoodieWriteConfig}
   import org.apache.hudi.index.HoodieIndex
   import org.apache.spark.sql.{DataFrame, SaveMode, SparkSession}
   
   object COWDisableCleanerTest {
     val spark = SparkSession.builder.appName("COWDisableCleanerTest")
       .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
       .config("spark.sql.warehouse.dir", "hdfs:///user/hive/warehouse")
       .enableHiveSupport().getOrCreate()
   
     val hudi_table = "hudi_hive_read_write_cow_disable_cleaner_1"
   
     val base_path = s"/data/hudi_demo/$hudi_table"
   
     def run(df: DataFrame, round: Int) = {
       val saveMode = round match {
         case 0 => SaveMode.Overwrite
         case _ => SaveMode.Append
       }
   
       df.write.format("hudi")
         .option(DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY, "name")
         .option(DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY, "creation_date")
         .option(DataSourceWriteOptions.HIVE_DATABASE_OPT_KEY, "xyz")
         .option(DataSourceWriteOptions.HIVE_TABLE_OPT_KEY, hudi_table)
         .option(DataSourceWriteOptions.HIVE_SYNC_ENABLED_OPT_KEY, "true")
         .option(HoodieIndexConfig.BLOOM_INDEX_UPDATE_PARTITION_PATH, "true")
         .option(DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY, "dt")
         .option(DataSourceWriteOptions.HIVE_URL_OPT_KEY, "jdbc:hive2://10.41.90.208:10000")
         .option(HoodieWriteConfig.TABLE_NAME, hudi_table)
         .option(DataSourceWriteOptions.HIVE_PARTITION_EXTRACTOR_CLASS_OPT_KEY, "org.apache.hudi.hive.MultiPartKeysValueExtractor")
         .option(HoodieIndexConfig.INDEX_TYPE_PROP, HoodieIndex.IndexType.GLOBAL_BLOOM.name())
         .option(DataSourceWriteOptions.HIVE_PARTITION_FIELDS_OPT_KEY, "dt")
         .option(HoodieWriteConfig.HOODIE_AUTO_COMMIT_PROP, "false")
   
         //在100次commit过程中不做clean
         .option(HoodieCompactionConfig.AUTO_CLEAN_PROP, "false")
         .option("hoodie.insert.shuffle.parallelism", "2")
         .option("hoodie.upsert.shuffle.parallelism", "2")
         .mode(saveMode)
         .save(base_path);
     }
   
   
     def main(args: Array[String]): Unit = {
       import spark.implicits._
       val order = MyOrder("Key-0", "Price-0", "2020-11-18 14:43:00", "2020-11-19")
   
       //Create table and insert 1 row
       run(spark.createDataset(Seq(order)).toDF(), 1)
   
       //Run 100 times and insert 100 rows ,one row per commit
       (1 to 100).foreach {
         i =>
           val order = MyOrder("Key-" + i, "Price-" + i, "2020-11-18 14:43:" + i, "2020-11-19")
           val insertData = spark.createDataset(Seq(order)).toDF()
           run(insertData, i)
       }
   
     }
   }
   ```
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   


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[GitHub] [hudi] bithw1 closed issue #2304: [SUPPORT]Hoodie clean operation result explanation

Posted by GitBox <gi...@apache.org>.
bithw1 closed issue #2304:
URL: https://github.com/apache/hudi/issues/2304


   


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[GitHub] [hudi] bvaradar commented on issue #2304: [SUPPORT]Hoodie clean operation result explanation

Posted by GitBox <gi...@apache.org>.
bvaradar commented on issue #2304:
URL: https://github.com/apache/hudi/issues/2304#issuecomment-740518409


   Yeah, cleans are supposed to only delete old versions of the files safely. No data loss is expected as you have noticed.
   
   The observation regarding hudi_file_name is expected. We usually use it to see the file-id and not the physical file name. For records that are untouched, the row retains the last file name where the record got changed. 


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[GitHub] [hudi] bithw1 commented on issue #2304: [SUPPORT]Hoodie clean operation result explanation

Posted by GitBox <gi...@apache.org>.
bithw1 commented on issue #2304:
URL: https://github.com/apache/hudi/issues/2304#issuecomment-740544780


   Thanks @bvaradar  for the great answer. I am closing this.


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